Multi-factor coupling-based intelligent accounting and prediction method and system for carbon sequestration and emission reduction of biochar

Abstract

The invention discloses an intelligent accounting and prediction method and system for carbon sequestration and emission reduction of biochar based on multi-factor coupling, and the method comprises the steps: S1, carrying out the calculation of the carbon sequestration and emission reduction of biochar under the conditions of the same environment parameter matrix, the same period and the same detection frequency; respectively collecting reference data of blank soil samples without biochar and detection data of multiple groups of mixed soil samples with different biochar addition proportions, and collecting corresponding biochar background data; s2, calculating an environmental parameter coupling correction coefficient and a soil physical and chemical parameter correction coefficient, calculating the carbon sequestration emission reduction amount of a single group of mixed soil samples in combination with charcoal background data, and performing statistical verification on multiple groups of samples; s3, constructing a time sequence-multiple regression coupling prediction model based on the accounting carbon sequestration emission reduction and the environmental parameter matrix, and predicting future carbon sequestration emission reduction; according to the invention, dynamic accounting and long-term trend prediction of carbon sequestration emission reduction based on multi-source real-time detection data are realized.

Description

A Smart Accounting and Prediction Method and System for Biochar Carbon Sequestration and Emission Reduction Based on Multi-Factor Coupling Technical Field

[0001] This invention relates to the field of biochar carbon sequestration and emission reduction technology, and more specifically to a method and system for intelligent accounting and prediction of biochar carbon sequestration and emission reduction based on multi-factor coupling. Background Technology

[0002] In existing technologies, biochar carbon sequestration and emission reduction accounting often relies on static calculations of a single factor or focuses only on local parameters such as the proportion of biochar addition. It does not fully consider the coupled and interactive effects of environmental parameters such as temperature, humidity, and light with physicochemical parameters such as soil compaction and porosity. Furthermore, it lacks a unified correction mechanism and statistical verification process, resulting in insufficient accuracy of the accounting results.

[0003] In addition, existing methods are mostly limited to simple calculations of short-term emission reductions, making it difficult to accurately predict long-term carbon sequestration trends. They also cannot dynamically adjust detection strategies based on environmental changes or fluctuations in carbon sequestration effects. These methods suffer from prominent defects such as "emphasizing calculation over prediction and emphasizing single parameters over multi-factor coupling," which restricts the promotion and precise application of biochar carbon sequestration and emission reduction technologies.

[0004] Therefore, how to achieve dynamic accounting and long-term trend prediction of carbon sequestration and emission reduction based on multi-source real-time detection data is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of the above problems, the present invention aims to provide a method and system for intelligent accounting and prediction of biochar carbon sequestration and emission reduction based on multi-factor coupling to overcome or at least partially solve the above problems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a smart accounting and prediction method for biochar carbon sequestration and emission reduction based on multi-factor coupling, comprising: S1. Under the conditions of the same environmental parameter matrix, the same period, and the same detection frequency, collecting baseline data of blank soil samples without added biochar and detection data of multiple groups of mixed soil samples with different biochar addition ratios, and collecting corresponding biochar background data; S2. Calculating the environmental parameter coupling correction coefficient and the soil physicochemical parameter correction coefficient, and combining the biochar background data to calculate the carbon sequestration and emission reduction of a single group of mixed soil samples and performing statistical verification of multiple groups of samples; S3. Based on the calculated carbon sequestration and emission reduction and the environmental parameter matrix, constructing a time series-multivariate regression coupled prediction model to predict future carbon sequestration and emission reduction.

[0007] Preferably, the baseline data for blank soil and the test data for biochar mixed soil both include: equivalent CO2 emissions, leachate carbon and nitrogen content, soil compaction and porosity; the background data for biochar includes the background carbon content and specific surface area of ​​biochar.

[0008] Preferably, the environmental parameter matrix is ​​E=[T,H,L,D,P], where T is temperature, H is humidity, L is light intensity, D is diurnal temperature range, and P is precipitation frequency.

[0009] Preferably, step S2 includes the following: S21. Calculating the environmental parameter coupling correction coefficient based on the nonlinear effects of temperature, humidity, and light intensity on soil respiration in the simulated target area; S22. Calculating the soil physicochemical parameter correction coefficient based on soil compaction and porosity of blank soil samples and biochar mixed soil samples, quantifying the impact of physical structure on carbon emissions; S23. Calculating the carbon dioxide emission reduction from biochar application by combining the environmental parameter coupling correction coefficient and the soil physicochemical parameter correction coefficient, and then superimposing the background carbon content of biochar to obtain the total carbon sequestration reduction of a single sample group; S24. Performing variance analysis on the total carbon sequestration reduction of n mixed soil samples with different biochar addition ratios to verify the reliability of the data.

[0010] Preferably, the environmental parameter coupling correction coefficient KE is: KE=1+α×(T) T0) / T0+β×(H H0) / H0+γ×(L L0) / L0 where T, H, and L represent the actual temperature, relative humidity, and light intensity of the target area during the detection period, respectively; T0, H0, and L0 represent the annual average temperature, humidity, and light intensity of the target area, respectively; and α, β, and γ represent the influence weighting coefficients of temperature, humidity, and light intensity, respectively. The soil physicochemical parameter correction coefficient KS is: KS=(ρ n ×θ0) / (ρ0×θ n Where: ρ0 and θ0 are the compaction degree and porosity of the blank soil, respectively. n θ n The compaction and porosity of the soil mixed with biochar; the carbon dioxide emission reduction ΔC of biochar application. n For: ΔC n =(C0 C n )×KE×KS×(1+0.03×ω n Among them, 0.03×ω n The proportion of biochar added ω n Synergistic effect item, C0 is the equivalent CO2 emission of blank soil, C n The equivalent CO2 emissions of the biochar mixed soil in group n; the total carbon sequestration reduction for a single sample is: Q n =Cb×ω n ×(Sb / Sb0)×(1+0.01×|pHb-7 |)+ΔC nWherein, Cb is the background carbon content of the detected biochar, Sb is the specific surface area of ​​the detected biochar, Sb0 is the specific surface area of ​​the standard biochar, and 0.01×|pHb-7| is the pH correction term for the biochar.

[0011] Preferably, step S3 specifically includes: S31. Using time t as the independent variable, and environmental parameters such as temperature T, humidity H, light intensity L, and the calculated total carbon sequestration and emission reduction Q. n S32. A multiple linear regression prediction model is established with the variable as the dependent variable; S33. A moving average error correction term is introduced to correct the prediction error, and the corrected predicted carbon sequestration emission reduction is obtained; S34. The predicted carbon sequestration emission reduction change curve is displayed in real time, and the detection parameters, including the gas sampling frequency and the leachate component analysis frequency, are automatically adjusted according to the prediction results.

[0012] The preferred multiple linear regression prediction model is: Q(t) = a0 + a1×t + a2×T(t) + a3×H(t) + a4×L(t) + a5×Q n (t 1) Where Q(t) is the predicted carbon sequestration reduction on day t, a0, a1, a2, a3, a4, a5 are model coefficients, and T(t), H(t), L(t) are the predicted environmental parameters on day t. n (t 1) is the actual carbon sequestration reduction on day t-1; the corrected predicted carbon sequestration reduction is:

[0013] Where m is the sliding window size, and Q(ti) is the actual calculated carbon sequestration reduction on day ti. The value is the model prediction for day ti.

[0014] A smart accounting and prediction system for biochar carbon sequestration and emission reduction based on multi-factor coupling, based on the aforementioned smart accounting and prediction method for biochar carbon sequestration and emission reduction, includes: a smart environment simulation module, a soil sample control module, a multi-channel gas acquisition and detection module, a data processing and feedback control module, and a cloud platform smart environment simulation module for simulating environmental parameters; the soil sample control module and the multi-channel gas acquisition and detection module are used to collect baseline data of blank soil samples without added biochar and detection data of multiple groups of mixed soil samples with different biochar addition ratios under the same environmental parameter matrix, the same period, and the same detection frequency, and to collect corresponding biochar background data; the data processing and feedback control module is used to calculate the environmental parameter coupling correction coefficient and the soil physicochemical parameter correction coefficient, and combine the biochar background data to calculate the carbon sequestration and emission reduction of a single group of mixed soil samples and perform statistical verification of multiple groups of samples; the cloud platform is used to construct a time series-multivariate regression coupled prediction model based on the calculated carbon sequestration and emission reduction and the environmental parameter matrix to predict future carbon sequestration and emission reduction.

[0015] Preferably, the intelligent environment simulation module includes a light regulation component, a temperature and humidity dynamic control component, and a diurnal rhythm simulation unit; the soil sample control module includes a sample containment component, a soil pretreatment unit, and a precise replenishment component; the multi-channel gas acquisition and detection module includes a gas capture component, a multi-component detection unit, and a gas circulation component; and the data processing and feedback control module includes a control unit, a data storage and transmission component, and an intelligent feedback regulation unit.

[0016] Preferably, the aforementioned intelligent accounting and prediction system for biochar carbon sequestration and emission reduction based on multi-factor coupling also includes a mobile terminal APP, which supports remote control of equipment operation, viewing of real-time data, and generation of test reports.

[0017] As can be seen from the above technical solution, compared with the prior art, this invention discloses a method and system for intelligent accounting and prediction of biochar carbon sequestration and emission reduction based on multi-factor coupling. For the first time, it incorporates controllable environmental parameters and soil physicochemical parameters into the accounting in a formulaic form, solving the defect of traditional methods that ignore the interaction of parameters. On the basis of the original accounting logic, it adds environmental parameter coupling correction terms and soil physicochemical parameter weight coefficients to construct a closed-loop system of detection data-precise accounting-trend prediction-parameter self-adjustment. By introducing a multiple regression model and time series prediction algorithm, it realizes dynamic accounting and long-term trend prediction of carbon sequestration and emission reduction based on real-time detection data. At the same time, it quantifies the impact of each factor on the carbon sequestration effect in a formulaic form, breaking through the limitation of existing methods that only calculate and do not predict, and improving the scientific nature of the method. Statistical methods such as variance analysis and moving average correction are introduced to ensure the reliability of the accounting and prediction results. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0019] Figure 1 is a schematic diagram of an intelligent accounting and prediction method for biochar carbon sequestration and emission reduction based on multi-factor coupling provided in an embodiment of the present invention; Figure 2 is a schematic diagram of the accounting process for carbon sequestration and emission reduction provided in an embodiment of the present invention; Figure 3 is a schematic diagram of the prediction process for changes in carbon sequestration and emission reduction provided in an embodiment of the present invention. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] This invention discloses an intelligent accounting and prediction method for biochar carbon sequestration and emission reduction based on multi-factor coupling, as shown in Figure 1, comprising: S1. Under the conditions of the same environmental parameter matrix, the same period, and the same detection frequency, collecting baseline data of blank soil samples without added biochar and detection data of multiple groups of mixed soil samples with different biochar addition ratios, and collecting corresponding biochar background data; S2. Calculating the environmental parameter coupling correction coefficient and the soil physicochemical parameter correction coefficient, and combining the biochar background data to calculate the carbon sequestration and emission reduction of a single group of mixed soil samples and performing statistical verification of multiple groups of samples; S3. Based on the calculated carbon sequestration and emission reduction and the environmental parameter matrix, constructing a time series-multivariate regression coupled prediction model to predict future carbon sequestration and emission reduction.

[0022] To further implement the above technical solutions, the baseline data for blank soil and the test data for biochar mixed soil both include: equivalent CO2 emissions, leachate carbon and nitrogen content, soil compaction and porosity; the background data for biochar includes the background carbon content and specific surface area of ​​biochar.

[0023] In this embodiment, the actual emissions of CO2, CH4, and N2O from the soil are detected and converted into equivalent CO2 emissions based on the IPCC greenhouse gas warming potential. Equivalent CO2 emissions = CO2 emissions × 1 + CH4 emissions × 28 + N2O emissions × 265.

[0024] To further implement the above technical solution, the environmental parameter matrix is ​​E=[T,H,L,D,P], where T is temperature, H is humidity, L is light intensity, D is diurnal temperature range, and P is precipitation frequency.

[0025] To further implement the above technical solution, as shown in Figure 2, step S2 includes the following: S21. Calculate the environmental parameter coupling correction coefficient based on the nonlinear effects of temperature, humidity, and light intensity on soil respiration in the simulated target area; S22. Calculate the soil physicochemical parameter correction coefficient based on the soil compaction and porosity of blank soil samples and biochar mixed soil samples, quantifying the impact of physical structure on carbon emissions; S23. Combine the environmental parameter coupling correction coefficient and the soil physicochemical parameter correction coefficient to calculate the carbon dioxide emission reduction from biochar application, and then add the background carbon content of biochar to obtain the total carbon sequestration reduction of a single sample; S24. Perform variance analysis on the total carbon sequestration reduction of n mixed soil samples with different biochar addition ratios to verify the reliability of the data.

[0026] To further implement the above technical solution, the environmental parameter coupling correction coefficient KE is: KE=1+α×(T) T0) / T0+β×(H H0) / H0+γ×(L L0) / L0, where T, H, and L are the actual temperature, relative humidity, and light intensity of the target area during the detection period, respectively; T0, H0, and L0 are the annual average temperature, humidity, and light intensity of the target area, respectively, retrieved from the cloud database to ensure matching with the actual application scenario; α, β, and γ are the influence weight coefficients of temperature, humidity, and light intensity, respectively, obtained by fitting historical detection data using the device's built-in multivariate analysis algorithm, α∈[0.2,0.4], β∈[0.1,0.3], γ∈[0.05,0.2]; the soil physicochemical parameter correction coefficient KS is: KS=(ρ n ×θ0) / (ρ0×θ n Where: ρ0 and θ0 are the compaction degree and porosity of the blank soil, respectively. n θ n The compaction and porosity of the biochar-mixed soil are considered. In this embodiment, when KS > 1.2, KS = 1.2 is used to avoid interference from extreme values; when KS < 0.8, KS = 0.8 is used. The carbon dioxide emission reduction ΔC from biochar application is also considered. n For: ΔC n =(C0 C n )×KE×KS×(1+0.03×ω n Among them, 0.03×ω n The proportion of biochar added ω nThe synergistic effect of the additives, verified by long-term device monitoring data, shows that for every 1% increase in the addition ratio, the emission reduction effect increases by approximately 3%; C0 represents the equivalent CO2 emissions from blank soil, C... n The equivalent CO2 emissions of the biochar mixed soil in group n; the total carbon sequestration reduction for a single sample is: Q n =Cb×ω n ×(Sb / Sb0)×(1+0.01×|pHb-7 |)+ΔC n Where Cb represents the background carbon content of the detected biochar, and Sb represents the specific surface area of ​​the detected biochar, taken as 500 m². 2 g -1 , is the industry-standard benchmark value; Sb0 is the specific surface area of ​​standard biochar, 0.01×|pHb-7| is the pH correction term for biochar, neutral biochar (pH≈7) has the best carbon sequestration effect, and the correction coefficient is adjusted proportionally when it deviates from neutral.

[0027] In this embodiment, Q is used for n groups with different biochar addition ratios. n Perform analysis of variance (ANOVA) to verify the reliability of the data: F = MSB / MSW

[0028]

[0029] Where MSB is the between-group mean square, MSW is the within-group mean square, n is the number of biochar addition gradients, and k is the number of replicates per group. When F > F 0.05 When the range is (n-1, nk-n), it indicates that the differences between the data groups are significant and the calculation results are valid.

[0030] To further implement the above technical solution, as shown in Figure 3, the specific content of step S3 is as follows: S31. Taking time t as the independent variable, and using environmental parameters such as temperature T, humidity H, light intensity L, and the calculated total carbon sequestration and emission reduction Q... n S32. A multiple linear regression prediction model is established with the variable as the dependent variable; S33. A moving average error correction term is introduced to correct the prediction error, and the corrected predicted carbon sequestration emission reduction is obtained; S34. The predicted carbon sequestration emission reduction change curve is displayed in real time, and the detection parameters, including the gas sampling frequency and the leachate component analysis frequency, are automatically adjusted according to the prediction results.

[0031] To further implement the above technical solution, the specific multiple linear regression prediction model is: Q(t) = a0 + a1×t + a2×T(t) + a3×H(t) + a4×L(t) + a5×Q n (t 1) Where Q(t) is the predicted carbon sequestration reduction on day t, a0, a1, a2, a3, a4, and a5 are model coefficients, obtained by training based on historical detection data using the gradient descent algorithm built into the device; T(t), H(t), and L(t) are the predicted environmental parameters on day t, obtained by connecting to a meteorological data platform to obtain short-term forecast data; Q n (t 1) This represents the actual carbon sequestration reduction on day t-1, which is a lagged term to improve prediction accuracy. A moving average error correction term is introduced to reduce the impact of short-term fluctuations on the prediction results. The corrected predicted carbon sequestration reduction is:

[0032] Where m is the sliding window size, which is taken as 7 days in practical applications to match the weekly variation pattern of soil respiration; Q(ti) is the actual calculated carbon sequestration reduction on day ti. The value is the model prediction for day ti.

[0033] In this embodiment, when predicting When the decrease exceeds 10%, the gas sampling frequency is automatically increased from 1 time / hour to 3 times / hour, and the frequency of leachate component analysis is also increased to ensure that abnormal data is captured in a timely manner.

[0034] A smart accounting and prediction system for biochar carbon sequestration and emission reduction based on multi-factor coupling is disclosed. Based on this method, the system comprises: a smart environment simulation module, a soil sample control module, a multi-channel gas acquisition and detection module, a data processing and feedback control module, and a cloud platform. The smart environment simulation module simulates environmental parameters. The soil sample control module and the multi-channel gas acquisition and detection module collect baseline data from blank soil samples without added biochar and detection data from multiple mixed soil samples with different biochar addition ratios under the same environmental parameter matrix, period, and detection frequency, and collect corresponding biochar background data. The data processing and feedback control module calculates the environmental parameter coupling correction coefficient and the soil physicochemical parameter correction coefficient, combines the biochar background data to calculate the carbon sequestration and emission reduction of a single mixed soil sample, and performs statistical verification on multiple samples. The cloud platform constructs a time series-multivariate regression coupled prediction model based on the calculated carbon sequestration and emission reduction and the environmental parameter matrix to predict future carbon sequestration and emission reduction.

[0035] To further implement the above technical solution, the intelligent environment simulation module includes a lighting adjustment component, a temperature and humidity dynamic control component, and a circadian rhythm simulation unit. In this embodiment, the lighting adjustment component includes an adjustable spectrum LED light group, a light intensity sensor, and an angle adjustment motor. The LED light group is mounted on the top of the detection cavity via a slide rail, and the angle adjustment motor drives the LED light group. The light assembly allows for 0-90° angle adjustment to simulate the angle of sunlight at different times of day. The dynamic temperature and humidity control component includes a high-precision temperature and humidity sensor, a frequency conversion heating element, an ultrasonic humidifier, and an intelligent ventilation valve. The temperature and humidity sensors are evenly distributed inside the detection chamber to collect environmental data in real time. The circadian rhythm simulation unit has a built-in time controller that can preset the day-night temperature difference and illumination duration to accurately replicate the natural environmental rhythm. The soil sample control module includes a sample containment component, a soil pretreatment unit, and a precision replenishment component. In this embodiment, the sample containment component has 4-6 independent containment chambers. Each containment chamber has a pressure sensor and a porosity adjustment plate on its inner wall. The pressure sensor monitors the soil compaction in real time, and the porosity adjustment plate adjusts the soil porosity level by a manual knob. The soil pretreatment unit has a permeable layer and a leachate collection tank at the bottom of each containment chamber. The leachate collection tank is connected to the component analysis module in the equipment chamber through a conduit to detect the carbon and nitrogen content in the soil leachate. Precision supply component: Each containment chamber has an independent drip irrigation head on top, which is connected to the integrated water and fertilizer unit via branch pipes to achieve differentiated water and nutrient supply for different samples; Multi-channel gas acquisition and detection module includes a gas capture component, a multi-component detection unit, and a gas circulation component; In this embodiment, the gas capture component: Each containment chamber has a sealed gas collection hood on top, with a miniature air pump and a gas filter membrane inside the hood. The air pump controls the gas collection frequency via a solenoid valve; Multi-component detection unit: Includes a gas mixing chamber and a high-precision detection sensor group, covering the detection modules for three core greenhouse gases: carbon dioxide, methane, and nitrous oxide; Gas circulation component: After detection, the gas is filtered by a purification filter and partially returned to the detection chamber to maintain the stability of the gas environment in the detection chamber; Data processing and feedback control module includes a control unit, a data storage and transmission component, and an intelligent feedback adjustment unit; In this embodiment, the control unit: Uses an STM32 microcontroller. The microcontroller serves as the main control chip, with a built-in multivariate analysis algorithm that can process environmental parameters, soil parameters, and gas detection data in real time. Data storage and transmission components include a local storage module and a wireless transmission module, enabling real-time data uploads to the cloud platform. An intelligent feedback adjustment unit automatically adjusts the operating parameters of the environmental simulation module and sample replenishment component based on the deviation between the detection data and preset thresholds, achieving adaptive optimization of the detection process.

[0036] To further implement the above technical solutions, a biochar carbon sequestration and emission reduction intelligent accounting and prediction system based on multi-factor coupling also includes a mobile terminal APP, which supports remote control of equipment operation, viewing of real-time data and generation of test reports.

[0037] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0038] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart accounting and prediction method for biochar carbon sequestration and emission reduction based on multi-factor coupling, characterized in that, include: S1. Under the same environmental parameter matrix, the same period, and the same detection frequency, baseline data of blank soil samples without added biochar and detection data of multiple groups of mixed soil samples with different biochar addition ratios were collected, and corresponding biochar background data were collected. S2. Calculate the environmental parameter coupling correction coefficient and the soil physicochemical parameter correction coefficient, and combine the biochar background data to calculate the carbon sequestration and emission reduction of a single mixed soil sample and perform statistical verification of multiple samples; S3. Based on the calculated carbon sequestration and emission reduction and the environmental parameter matrix, construct a time series-multivariate regression coupled prediction model to predict future carbon sequestration and emission reduction.

2. The intelligent accounting and prediction method for biochar carbon sequestration and emission reduction based on multi-factor coupling as described in claim 1, characterized in that, The baseline data for blank soil and the test data for biochar mixed soil both include: equivalent CO2 emissions, leachate carbon and nitrogen content, soil compaction and porosity; the background data for biochar include the background carbon content and specific surface area of ​​biochar.

3. The intelligent accounting and prediction method for biochar carbon sequestration and emission reduction based on multi-factor coupling as described in claim 1, characterized in that, The environmental parameter matrix E=[T,H,L,D,P], where T is temperature, H is humidity, L is light intensity, D is diurnal temperature range, and P is precipitation frequency.

4. The intelligent accounting and prediction method for biochar carbon sequestration and emission reduction based on multi-factor coupling as described in claim 1, characterized in that, Step S2 includes the following steps: S21. Calculate the environmental parameter coupling correction coefficient based on the nonlinear effects of temperature, humidity, and light intensity on soil respiration in the simulated target area; S22. Calculate the soil physicochemical parameter correction coefficient based on soil compaction and porosity of blank soil samples and biochar mixed soil samples, quantifying the impact of physical structure on carbon emissions; S23. Combine the environmental parameter coupling correction coefficient and the soil physicochemical parameter correction coefficient to calculate the carbon dioxide emission reduction from biochar application, and then add the background carbon content of biochar to obtain the total carbon sequestration reduction for a single sample group; S24. Perform variance analysis on the total carbon sequestration reduction of n mixed soil samples with different biochar addition ratios to verify the reliability of the data.

5. The intelligent accounting and prediction method for biochar carbon sequestration and emission reduction based on multi-factor coupling as described in claim 4, characterized in that, The environmental parameter coupling correction coefficient KE is: KE=1+α×(T) T0) / T0+β×(H H0) / H0+γ×(L L0) / L0 where T, H, and L represent the actual temperature, relative humidity, and light intensity of the target area during the detection period, respectively; T0, H0, and L0 represent the annual average temperature, humidity, and light intensity of the target area, respectively; and α, β, and γ represent the influence weighting coefficients of temperature, humidity, and light intensity, respectively. The soil physicochemical parameter correction coefficient KS is: KS=(ρ n ×θ0) / (ρ0×θ n Where: ρ0 and θ0 are the compaction degree and porosity of the blank soil, respectively. n θ n The compaction and porosity of the soil mixed with biochar; the carbon dioxide emission reduction ΔC of biochar application. n For: ΔC n =(C0 C n )×KE×KS×(1+0.03×ω n Among them, 0.03×ω n The proportion of biochar added ω n Synergistic effect item, C0 is the equivalent CO2 emission of blank soil, C n The equivalent CO2 emissions of the biochar mixed soil in group n; the total carbon sequestration reduction for a single sample is: Q n =Cb×ω n ×(Sb / Sb0)×(1+0.01×|pHb-7 |)+ΔC n Wherein, Cb is the background carbon content of the detected biochar, Sb is the specific surface area of ​​the detected biochar, Sb0 is the specific surface area of ​​the standard biochar, and 0.01×|pHb-7| is the pH correction term for the biochar.

6. The intelligent accounting and prediction method for biochar carbon sequestration and emission reduction based on multi-factor coupling as described in claim 1, characterized in that, The specific content of step S3 is as follows: S31. Taking time t as the independent variable, and using environmental parameters such as temperature T, humidity H, light intensity L, and the calculated total carbon sequestration and emission reduction Q... n S32. A multiple linear regression prediction model is established with the variable as the dependent variable; S33. A moving average error correction term is introduced to correct the prediction error, and the corrected predicted carbon sequestration emission reduction is obtained; S34. The predicted carbon sequestration emission reduction change curve is displayed in real time, and the detection parameters, including the gas sampling frequency and the leachate component analysis frequency, are automatically adjusted according to the prediction results.

7. The intelligent accounting and prediction method for biochar carbon sequestration and emission reduction based on multi-factor coupling as described in claim 6, characterized in that, The specific multiple linear regression prediction model is: Q(t) = a0 + a1×t + a2×T(t) + a3×H(t) + a4×L(t) + a5×Q n (t 1) Where Q(t) is the predicted carbon sequestration reduction on day t, a0, a1, a2, a3, a4, a5 are model coefficients, and T(t), H(t), L(t) are the predicted environmental parameters on day t. n (t 1) is the actual carbon sequestration reduction on day t-1; the corrected predicted carbon sequestration reduction is: Where m is the sliding window size, and Q(ti) is the actual calculated carbon sequestration reduction on day ti. The value is the model prediction for day ti.

8. A smart accounting and prediction system for biochar carbon sequestration and emission reduction based on multi-factor coupling, characterized in that, A method for intelligent accounting and prediction of biochar carbon sequestration and emission reduction based on multi-factor coupling, according to any one of claims 1-7, comprises: an intelligent environment simulation module, a soil sample control module, a multi-channel gas acquisition and detection module, a data processing and feedback control module, and a cloud platform; the intelligent environment simulation module is used to simulate environmental parameters; the soil sample control module and the multi-channel gas acquisition and detection module are used to collect baseline data of blank soil samples without added biochar and detection data of multiple mixed soil samples with different biochar addition ratios under the same environmental parameter matrix, the same period, and the same detection frequency, and to collect corresponding biochar background data; the data processing and feedback control module is used to calculate the environmental parameter coupling correction coefficient and the soil physicochemical parameter correction coefficient, and combine the biochar background data to calculate the carbon sequestration and emission reduction of a single mixed soil sample and perform statistical verification of multiple samples; the cloud platform is used to construct a time series-multivariate regression coupled prediction model based on the calculated carbon sequestration and emission reduction and the environmental parameter matrix to predict future carbon sequestration and emission reduction.

9. The intelligent accounting and prediction system for biochar carbon sequestration and emission reduction based on multi-factor coupling as described in claim 8, characterized in that, The intelligent environment simulation module includes a light regulation component, a temperature and humidity dynamic control component, and a diurnal rhythm simulation unit; the soil sample control module includes a sample containment component, a soil pretreatment unit, and a precise replenishment component; the multi-channel gas acquisition and detection module includes a gas capture component, a multi-component detection unit, and a gas circulation component; and the data processing and feedback control module includes a control unit, a data storage and transmission component, and an intelligent feedback regulation unit.

10. The intelligent accounting and prediction system for biochar carbon sequestration and emission reduction based on multi-factor coupling as described in claim 8, characterized in that, It also includes a mobile app that supports remote control of equipment operation, viewing of real-time data, and generation of test reports.

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