Carbon sink intelligent monitoring and dynamic evaluation system based on internet of things and cloud computing

Abstract

The present application relates to the cross technical field of livestock breeding carbon sink measurement and environmental monitoring, and discloses a carbon sink intelligent monitoring and dynamic evaluation system based on internet of things and cloud computing, wherein a kind of carbon sink intelligent monitoring and dynamic evaluation method based on internet of things and cloud computing includes: collecting the operation data of each link in pig farm manure biogas recovery system by internet of things equipment to generate original data set;The original data set is subjected to outlier rejection, time synchronization, unit unification to generate a standardized data set;Based on the standardized data set, the cumulative carbon sink is calculated using the carbon sink calculation model, and the carbon sink evaluation result is output;The present application solves the technical problems of fragmented monitoring system and insufficient dynamic response capability, and realizes accurate monitoring and dynamic evaluation of carbon sink.

Description

Technical Field

[0001] This invention relates to the interdisciplinary field of carbon sequestration measurement and environmental monitoring in livestock farming, and more specifically, to an intelligent monitoring and dynamic assessment system for carbon sequestration based on the Internet of Things and cloud computing. Background Technology

[0002] Driven by the "dual carbon" strategy, pig farm manure biogas recovery is a core carbon emission reduction pathway in the agricultural sector. The accurate measurement and dynamic assessment of its carbon sink volume are key prerequisites for carbon sink project certification, policy subsidy distribution, and technology optimization.

[0003] In existing technologies, carbon sequestration monitoring of pig farm manure biogas recovery mainly uses decentralized hardware devices (such as independent biogas flow meters and electricity meters) for data collection, and carbon sequestration assessment relies on static emission factors such as the IPCC fixed methane yield coefficient for calculation.

[0004] However, existing technologies suffer from the following major technical problems: First, the monitoring systems are fragmented and lack integrated design, leading to issues such as asynchronous data collection, incompatible interfaces, and data incompatibility, resulting in distorted basic data for carbon sink calculations. Second, dynamic response capabilities are insufficient; traditional assessments rely on static emission factors and cannot adapt to dynamic scenarios such as fluctuations in pig farm manure composition, ambient temperature fluctuations, and changes in equipment operating conditions, resulting in a carbon sink measurement deviation rate exceeding 20%. Third, data reliability is low, lacking data traceability and verification mechanisms, with a high proportion of manually entered data, making it prone to data falsification or error accumulation. Furthermore, the entire process of data collection, transmission, and calculation cannot be traced, failing to meet the data filing requirements for carbon sink projects. Therefore, existing technologies are insufficient to meet the core requirements of carbon sink projects for "monitorable data, traceable processes, and verifiable results." Summary of the Invention

[0005] This invention provides an intelligent monitoring and dynamic assessment system for carbon sequestration based on the Internet of Things and cloud computing, which solves the technical problems of fragmented monitoring systems, insufficient dynamic response capabilities, and low data reliability in related technologies.

[0006] This invention provides a method for intelligent monitoring and dynamic assessment of carbon sequestration based on the Internet of Things and cloud computing, comprising the following steps:

[0007] The system collects operational data from each stage of the pig farm manure biogas recovery system using IoT devices to generate a raw dataset. The operational data includes farm data, biogas purification system data, flare combustion system data, biogas output system data, biomethane production and output system data, power generation system data, and system energy consumption data.

[0008] The original dataset is preprocessed, including outlier removal, time synchronization of monitoring data with different collection frequencies, and unit standardization of the monitoring data to generate a standardized dataset.

[0009] Based on the standardized dataset, the cumulative carbon sink for the current month is calculated using the carbon sink calculation model, and the carbon sink assessment results are output.

[0010] The cumulative carbon sink is obtained by subtracting the sum of project emissions for each month from the sum of baseline emissions for each month. The baseline emissions include greenhouse gas emissions from liquid manure storage and baseline emissions replaced by project energy output. The project emissions include greenhouse gas emissions from anaerobic digester and pipelines, greenhouse gas emissions from biogas residue and biogas slurry treatment systems, emissions from biogas combustion in flares, and emissions from purchased electricity consumed by the project.

[0011] Furthermore, the farm data includes data on the number of pigs of different types and their weight; the biogas purification system data includes data on the volume concentration of methane in the biogas and the biogas flow rate at the outlet of the biogas purification system; the flare combustion system data includes data on the biogas flow rate entering the flare combustion system; the biogas output system data includes data on the biogas flow rate delivered to users; the biogas production and output system data includes data on the natural gas flow rate delivered to users; the power generation system data includes data on the amount of electricity supplied to the project; and the system energy consumption data includes data on the purchased electricity consumed by the pig farm manure biogas recovery system.

[0012] Furthermore, the outlier removal process for the original dataset includes: determining whether the absolute value of the difference between the monitoring data and the historical average value of the monitoring indicator exceeds three times the historical standard deviation; if it does, the monitoring data is determined to be an outlier and removed. The time synchronization of monitoring data with different collection frequencies includes: accumulating the instantaneous flow data collected by the flow meter to generate daily cumulative flow data, and arithmetically averaging the methane volume concentration data over days to generate daily average methane volume concentration data, so that the time granularity of all monitoring data is unified to days.

[0013] Furthermore, the greenhouse gas emissions generated from the liquid manure storage include methane emissions and nitrous oxide emissions generated from the liquid manure storage. The baseline emissions replaced by the project's energy output include the baseline emissions replaced by the project's external power supply, the baseline emissions replaced by the project's biogas output, and the baseline emissions replaced by the project's natural gas output.

[0014] Furthermore, the methane emissions from the liquid manure storage are calculated based on the total amount of volatile solids entering the liquid manure storage, the methane production potential of pig manure, the methane conversion factor of the liquid manure storage, the methane density at room temperature and pressure, and the global warming potential of methane; the nitrous oxide emissions from the liquid manure storage are calculated based on the total nitrogen content of the manure in the liquid manure storage, the direct nitrous oxide emission factor, the indirect nitrous oxide emission factor, and the global warming potential of nitrous oxide.

[0015] Furthermore, the total amount of volatile solids entering the liquid manure storage is calculated in two ways. The first method is a forward calculation based on the daily stocking volume of different pig types, the average weight of pigs, and the default value of volatile solids excretion. The second method is a reverse calculation based on the biogas volume at the outlet of the biogas purification system, the average methane volume concentration of biogas, the methane production potential of pig manure, and the volatile solids removal rate of the anaerobic digester. The smaller value of the two calculation methods is taken as the total amount of volatile solids entering the liquid manure storage.

[0016] Furthermore, the greenhouse gas emissions from the anaerobic digester and pipelines are calculated based on the biogas volume at the outlet of the biogas purification system, the average methane volume concentration in the biogas, the methane emission factor of the anaerobic digester and pipelines, the methane density at room temperature and pressure, and the global warming potential of methane. The greenhouse gas emissions from the biogas residue and slurry treatment system include the methane emissions and nitrous oxide emissions generated by the system. The emissions from the flare combustion of biogas are calculated based on the biogas volume entering the flare, the average methane volume concentration in the biogas, the flare combustion efficiency, the methane density at room temperature and pressure, and the global warming potential of methane. The emissions from the purchased electricity consumed by the project are calculated based on the purchased electricity consumed by the project and the carbon emission factor of the power grid in the region.

[0017] Furthermore, during the preprocessing of the original dataset, a unique traceability code is assigned to each piece of monitoring data. The traceability code records the acquisition device number, acquisition timestamp, transmission node identifier, and preprocessing operation log. The generation rule for the traceability code is a combination of device type code, device number, timestamp, and data sequence number.

[0018] Furthermore, after outputting the carbon sink assessment results, the method also includes: visualizing the carbon sink assessment results and generating a carbon sink trend curve; setting an abnormal warning threshold, triggering an warning when the methane volume concentration in biogas is lower than a preset concentration threshold, and triggering a data abnormality warning when the monthly carbon sink amount is lower than a preset proportion of the historical monthly average.

[0019] This invention provides a smart carbon sink monitoring and dynamic assessment system based on the Internet of Things and cloud computing, used to execute the aforementioned method, including:

[0020] The data acquisition module is used to collect operational data from each stage of the pig farm manure biogas recovery system through IoT devices and generate raw datasets.

[0021] The data preprocessing module is used to remove outliers, synchronize time, and unify units in the original dataset to generate a standardized dataset.

[0022] The carbon sink calculation module is used to calculate the cumulative carbon sink for the current month based on the standardized dataset and using the carbon sink calculation model.

[0023] The results output module is used to output the carbon sink assessment results.

[0024] The beneficial effects of this invention are as follows: By integrating a monitoring system, a data preprocessing mechanism, and a dynamic evaluation model, this invention solves the technical problems of fragmented monitoring systems, insufficient dynamic response capabilities, and low data reliability, and achieves the technical effect of accurate monitoring and dynamic evaluation of carbon sinks. Attached Figure Description

[0025] Figure 1 This is a flowchart of the intelligent monitoring and dynamic assessment method for carbon sequestration based on the Internet of Things and cloud computing of the present invention;

[0026] Figure 2 This is the system model boundary diagram of the present invention;

[0027] Figure 3 This is a system flowchart of the present invention. Detailed Implementation

[0028] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0029] Example 1

[0030] This embodiment provides a method for intelligent monitoring and dynamic assessment of carbon sequestration based on the Internet of Things and cloud computing, including the following steps:

[0031] Step 1, Integrated Monitoring System:

[0032]

[0033] The monitoring system utilizes IoT devices to automate the entire process of data acquisition, transmission, processing, and traceability. It adopts a modular design, supporting on-demand deployment. The core units include:

[0034] Step 2, Software Architecture Development:

[0035] Developed based on cloud computing architecture, it includes four main functional modules:

[0036] Step 2.1, Data Acquisition and Cleaning Module: Automatically receives hardware data, removes outliers (such as out-of-range data caused by sensor failure) using the "3σ criterion", and performs time synchronization interpolation on data of different frequencies;

[0037] Step 2.2, Data Traceability Module: Assign a unique traceability code to each piece of data, record the collection device number, collection time, transmission node, and processing flow, and support blockchain notarization (optional configuration) to ensure that the data cannot be tampered with;

[0038] Step 2.3, Data Visualization Module: Real-time display of core indicators such as carbon sequestration, biogas production, and energy consumption; generation of daily / weekly / monthly trend curves; and support for abnormal data alerts.

[0039] Step 2.4, Access Control Module: Set up three levels of permissions for administrators, operators, and auditors, and restrict data modification and export permissions to meet the audit requirements of carbon sink projects.

[0040] Step 3, Dynamic Evaluation Model Construction:

[0041] Based on real-time data collected by the monitoring system, and building upon the existing architecture, a closed-loop assessment process of "data input - dynamic correction - carbon sequestration calculation" is constructed. The specific steps are as follows:

[0042] Step 3.1, Real-time Acquisition and Preprocessing of Multi-Source Data:

[0043] Data from various systems is collected in real time through the hardware modules of the monitoring system.

[0044] A1, Farm data (A1.1 Smart scale, A1.2 Smart ear tag, A1.3 Electronic access control, three types of equipment collect data on the number of breeding pigs, the number of pigs, the number of sows, and the weight of each pig).

[0045] A2, Biogas purification system data (A2.1 Infrared methane sensor, collecting methane volume percentage; A2.2 Flow meter, collecting biogas flow rate; A2.3 Temperature sensor, collecting local temperature).

[0046] A3, Flare Combustion System (A3.1 Flow Meter, which collects the biogas flow rate entering the flare combustion system).

[0047] A4, Biogas Output System (A4.1 Flow Meter, for collecting and outputting biogas flow rate).

[0048] A5, Biogas Production and Output System (A5.1 Flow Meter, for collecting natural gas output flow).

[0049] A6, Power Generation System (A6.1 Energy Meter, which collects external power supply).

[0050] A7, the entire pig farm manure biogas recovery system (A7.1 electricity meter, which collects the purchased electricity consumed by the entire system).

[0051] After being cleaned and synchronized by the software platform, a standardized dataset is generated. The main cleaning process involves unifying the data, accumulating the data from each flow meter, and calculating the daily flow rate. All data was collected from 0:00 to 24:00.

[0052] Step 3.2, Model Construction and Parameter Setting:

[0053] Build as Figure 1 The model shown. Configure parameters according to the actual situation.

[0054] Step 3.3, Dynamic Calculation of Carbon Sequestration:

[0055] Step 3.3.1, Carbon Sequestration Calculation:

[0056] The formula for calculating the cumulative carbon sequestration in the current month y is:

[0057]

[0058] in, The current cumulative carbon sequestration for month y, expressed in tons of carbon dioxide equivalent (t CO2e). This represents the baseline emission reduction for month i, expressed in tons of carbon dioxide equivalent (tCO2e). The project emissions for month i are expressed in tons of carbon dioxide equivalent (t CO2e).

[0059] Step 3.3.2, Baseline Emission Calculation:

[0060] The formula for calculating the baseline emissions for month i is:

[0061]

[0062] in, The baseline for liquid sewage storage in month i. Emissions, expressed in tons of carbon dioxide equivalent (t CO2e). The baseline for liquid sewage storage in month i. Emissions, expressed in tons of carbon dioxide equivalent (t CO2e). The baseline emissions replaced by the external power supply of the project in month i, expressed in tons of carbon dioxide (t CO2). The baseline emissions replaced by biogas output from the project in month i, expressed in tons of carbon dioxide (t CO2). The baseline emissions replaced by biogas output from the project in month i, expressed in tons of carbon dioxide (t CO2).

[0063] Step 3.3.2.1, baseline for liquid sewage storage Emissions calculation:

[0064] The baseline for liquid sewage storage in month i Emissions The calculation formula is:

[0065]

[0066] in, This represents the total amount of volatile solids entering the liquid sewage storage in month i, expressed in kilograms of volatile solids (kg VS). The methane production potential of pig manure is expressed in cubic meters of methane per kilogram of volatile solids (m3CH4 / kg VS). The methane conversion factor of liquid sewage storage in month i is expressed as a percentage (%). The value is the density of methane at room temperature and pressure, expressed in tons of methane per cubic meter (t CH4 / m3), and is 0.00067. On a 100-year timescale The global warming potential is expressed in tons of carbon dioxide equivalent per ton of methane (t CO2e / t CH4).

[0067] Calculate the total amount of volatile solids entering the liquid sewage storage in month i. Considering the rationality of the methodology, The smaller value is used to calculate the result of the following two formulas;

[0068] Calculate the daily inventory of different pig types based on actual monitoring using the following formula. :

[0069]

[0070] in, This represents the number of pigs of different types on day d of month i. The default values ​​for daily volatile solids excretion for different pig types are expressed in kilograms of volatile solids per 1000 kg body weight per day (kg VS / 1000 kg body weight / day). The average weight of different types of pigs is expressed in kilograms (kg). Let i be the number of days in the i-th month, in days. It is a type of pig.

[0071] The biogas production in month i is used to estimate the total volatile solids entering liquid manure storage for that month, calculated using the following formula: :

[0072]

[0073] in, The biogas volume at the outlet of the biogas purification system on day d of month i is expressed in cubic meters (m3). The average methane volume concentration in biogas is expressed as a percentage (%). The methane production potential of pig manure is expressed in cubic meters of methane per kilogram of volatile solids (m3 CH4 / kg VS). The volatile solids removal rate of the anaerobic digester is expressed as a percentage (%). Let be the number of days in the i-th month, expressed in days.

[0074] Step 3.3.2.2, baseline for liquid sewage storage Emissions calculation:

[0075] The baseline for liquid sewage storage in month i Emissions The calculation formula is:

[0076]

[0077] in, The baseline for liquid sewage storage in month i. Direct emissions, expressed in kilograms of nitrogen in nitrous oxide (kg N2O-N). The baseline for liquid sewage storage in month i. Indirect emissions, expressed in kilograms of nitrogen in nitrous oxide (kg N2O-N). To convert N2O-N into coefficient, For unit conversion, On a 100-year timescale The global warming potential is expressed in tons of carbon dioxide equivalent per ton of nitrous oxide (t CO2e / t N2O).

[0078] The baseline for liquid sewage storage in month i direct emissions The calculation formula is:

[0079]

[0080] in, The total nitrogen content of liquid fecal waste stored in month i is expressed in kilograms of nitrogen (kg N). For storage of liquid sewage Direct emission factor, expressed in units of nitrogen per kilogram of nitrogen in kilograms of nitrous oxide (kg N2O-N / kg N).

[0081] The baseline for liquid sewage storage in month i Indirect emissions The calculation formula is:

[0082]

[0083] in, The total nitrogen content of liquid fecal waste stored in month i is expressed in kilograms of nitrogen (kg N). The proportion of nitrogen loss due to the volatilization of NH3 and NOx from the storage of liquid sewage, expressed in kilograms of ammonia and nitrogen oxides per kilogram of excreted nitrogen (kg NH3-N and NOx-N / kg N excretion). The indirect emission factor of N2O is the nitrogen in kilograms of nitrous oxide and nitrogen in kilograms of ammonia and nitrogen oxides, which volatilize into the atmosphere and then settle into the soil or water. The unit is nitrogen in kilograms of nitrous oxide per kilogram of ammonia and nitrogen in kilograms of nitrogen oxides (kg N2O-N / kg NH3-N and NOx-N).

[0084] Total nitrogen content of liquid fecal waste in month i The calculation formula is:

[0085]

[0086] in, This represents the number of pigs of different types on day d of month i. The default values ​​for daily nitrogen excretion are for different types of pigs, expressed in kilograms of nitrogen per 1000 kg body weight per day (kg N / 1000 kg body weight / day). The average weight of different types of pigs is expressed in kilograms (kg). Let i be the number of days in the i-th month, in days. It is a type of pig.

[0087] Step 3.3.2.3, Calculation of the baseline emissions replaced by the project's external power supply:

[0088] The baseline emissions replaced by the external power supply of the project in month i The calculation formula is:

[0089]

[0090] in, This represents the external power supply for the project in month i, expressed in megawatt-hours (MW·h). is the combined marginal emission factor of the power grid in the area where the project is located in month i, expressed in tons of carbon dioxide per megawatt-hour (t CO2 / MW·h).

[0091] The combined marginal emission factor of the power grid in the project area in month i The calculation formula is:

[0092]

[0093] in, represents the marginal emission factor of electricity in the power grid of the project area in month i, expressed in tons of carbon dioxide per megawatt-hour (t CO2 / MW·h). The weights of the marginal emission factor for electricity. , where represents the capacity marginal emission factor of the power grid in the area where the project is located in month i, expressed in tons of carbon dioxide per megawatt-hour (t CO2 / MW·h). The weights of the capacity marginal emission factor.

[0094] Step 3.3.2.4, Calculation of the baseline emissions replaced by the biogas output from the project:

[0095] The baseline emissions replaced by biogas output from the project in month i The calculation formula is:

[0096]

[0097] in, This represents the amount of biogas delivered to users by the project in month i, expressed in cubic meters (m3). The average methane volume concentration in biogas is expressed as a volume percentage (%). The lower heating value of methane is expressed in trillion joules per 10,000 cubic meters (TJ / 104m3). This represents the carbon oxidation rate of methane combustion, expressed as a percentage (%). The emission factor for methane combustion is expressed in tons of carbon dioxide per trillion joules (tCO2 / TJ).

[0098] Step 3.3.2.5, Calculation of baseline emissions replaced by the natural gas output from the project:

[0099] The baseline emissions replaced by the project's natural gas output in month i The calculation formula is:

[0100]

[0101] in, The natural gas delivered to users for the project in month i is expressed in cubic meters (m3). The average methane volume concentration in natural gas is expressed as a volume percentage (%). The lower heating value of methane is expressed in trillion joules per 10,000 cubic meters (TJ / 104m3). This represents the carbon oxidation rate of methane combustion, expressed as a percentage (%). The emission factor for methane combustion is expressed in tons of carbon dioxide per trillion joules (tCO2 / TJ).

[0102] Step 3.3.3, Calculation of project emissions:

[0103] Project emissions in month i The calculation formula is:

[0104]

[0105] in, The figure represents the CH4 emissions generated by the anaerobic digester and pipelines of the project in month i, expressed in tons of carbon dioxide equivalent (t CO2e). The amount of CH4 emitted by the biogas residue and biogas slurry treatment system in the i-th month is expressed in tons of carbon dioxide equivalent (t CO2e). The N2O emissions generated by the biogas residue and biogas slurry treatment system in the i-th month are expressed in tons of carbon dioxide equivalent (t CO2e). The emission amount is the amount of biogas produced by the project flare combustion in the i-th month, expressed in tons of carbon dioxide equivalent (t CO2e). Emissions generated from purchased electricity consumed by the project in month i, expressed in tons of carbon dioxide (tCO2).

[0106] Step 3.3.3.1, Calculation of CH4 emissions from the oxygen digester and pipelines:

[0107] CH4 emissions from the anaerobic digester and pipelines of the project in month i The calculation formula is:

[0108]

[0109] in, The biogas volume at the outlet of the biogas purification system on day d of month i is expressed in cubic meters (m3). The average methane volume concentration in biogas is expressed as a percentage (%). CH4 emission factor in anaerobic digester and pipeline, expressed as a percentage (%). The value is the density of methane at room temperature and pressure, expressed in tons of methane per cubic meter (t CH4 / m3), and is 0.00067. The global warming potential of CH4 on a 100-year timescale is expressed in tons of carbon dioxide equivalent per ton of methane (t CO2e / t CH4). Let be the number of days in the i-th month, expressed in days.

[0110] Step 3.3.3.2, Calculation of CH4 emissions generated by the biogas residue and biogas slurry treatment system:

[0111] CH4 emissions from the biogas residue and biogas slurry treatment system in month i The calculation formula is:

[0112]

[0113] in, This represents the total volatile solids entering liquid manure storage in month i, expressed in kilograms of volatile solids (kg VS). Data is derived from the daily inventory of different pig types monitored in actual operations, calculated using the formula. The total amount of volatile solids entering liquid manure storage in the current month is calculated by inversely calculating the biogas production in month i and the total amount of volatile solids according to the formula. The result is a larger value. The methane production potential of pig manure is expressed in cubic meters of methane per kilogram of volatile solids (m3 CH4 / kg VS). The methane conversion factor of liquid sewage storage in month i is expressed as a percentage (%). The volatile solids removal rate of the anaerobic digester is expressed as a percentage (%). The value is the density of methane at room temperature and pressure, expressed in tons of methane per cubic meter (t CH4 / m3), and is 0.000673. The global warming potential of CH4 on a 100-year timescale is expressed in tons of carbon dioxide equivalent per ton of methane (t CO2e / t CH4).

[0114] Step 3.3.3.3, Calculation of N2O emissions from the biogas residue and biogas slurry treatment system:

[0115] N2O emissions from the biogas residue and biogas slurry treatment system in month i The calculation formula is:

[0116]

[0117] in, The direct emission of N2O from the biogas residue and biogas slurry treatment system in the i-th month is expressed in kilograms of nitrogen in nitrous oxide (kg N2O-N). The indirect N2O emissions generated by the biogas residue and biogas slurry treatment system in the i-th month are expressed in units of nitrogen in kilograms of nitrous oxide (kg N2O-N). The coefficient for converting N2O-N to N2O, For unit conversion, The global warming potential of N2O over a 100-year timescale is expressed in tons of carbon dioxide equivalent per ton of nitrous oxide (t CO2e / t N2O).

[0118] The direct N2O emissions from the biogas residue and biogas slurry treatment system of the project in month i The calculation formula is:

[0119]

[0120] in, The total nitrogen content of the fecal waste entering the anaerobic digester in month i, expressed in kilograms of nitrogen (kg N), is the total nitrogen content of the fecal waste stored using the liquid fecal waste from month i. Calculation formula ;

[0121] The direct emission factor of N2O generated by the biogas residue and biogas slurry treatment system is expressed as nitrogen per kilogram of nitrogen in kilograms of nitrous oxide (kg N2O-N / kg N).

[0122] Baseline N2O indirect emissions from liquid sewage storage in month i The calculation formula is:

[0123]

[0124] in, The total nitrogen content of liquid fecal waste stored in month i is expressed in kilograms of nitrogen (kg N). The proportion of nitrogen loss due to the volatilization of NH3 and NOx from the storage of liquid sewage, expressed in kilograms of ammonia and nitrogen oxides per kilogram of excreted nitrogen (kg NH3-N and NOx-N / kg N excretion). The indirect emission factor of N2O is the nitrogen in kilograms of nitrous oxide and nitrogen in kilograms of ammonia and nitrogen oxides, which volatilize into the atmosphere and then settle into the soil or water. The unit is nitrogen in kilograms of nitrous oxide per kilogram of ammonia and nitrogen in kilograms of nitrogen oxides (kg N2O-N / kg NH3-N and NOx-N).

[0125] Step 3.3.3.4, Calculation of emissions from biogas combustion in a flare:

[0126] Emissions from biogas combustion in the project in month i The calculation formula is:

[0127]

[0128] in, The amount of biogas entering the flare for combustion in the i-th month is expressed in cubic meters (m3). The average methane volume concentration in biogas is expressed as a volume percentage (%). The value represents the biogas combustion efficiency of the flare, expressed as a percentage (%). The value is the density of methane at room temperature and pressure, expressed in tons of methane per cubic meter (t CH4 / m3), and is 0.00067. The global warming potential of CH4 on a 100-year timescale is expressed in tons of carbon dioxide equivalent per ton of methane (t CO2e / t CH4).

[0129] Step 3.3.3.5, Calculation of emissions from purchased electricity consumed by the project:

[0130] Emissions generated from purchased electricity consumed by the project in month i The calculation formula is:

[0131]

[0132] in, The electricity consumed by the project in the region during the i-th month is measured in megawatt-hours (MW·h). denoted as the carbon emission factor of the power grid in the region where the i-th month is located, expressed in tons of carbon dioxide per megawatt-hour (t CO2 / MW·h).

[0133] Example 2

[0134] This embodiment provides a method for monitoring and dynamically assessing carbon sink data from pig farm manure biogas recovery. Based on IoT and cloud computing technologies, it achieves accurate monitoring and dynamic assessment of carbon sink. The method is based on a deployed integrated monitoring system. This system adopts a modular design of IoT devices, with core hardware modules including: a farm monitoring module (smart scale, smart ear tag, electronic access control), a biogas purification system monitoring module (infrared methane sensor, eddy flow meter), a flare combustion system monitoring module (eddy flow meter), a biogas output system monitoring module (eddy flow meter), a biogas production and output system monitoring module (eddy flow meter), a power generation system monitoring module (electricity meter), and a system energy consumption monitoring module (electricity meter). Each monitoring module connects to the cloud computing platform through a unified data acquisition interface, such as... Figure 1 As shown, the method includes the following steps:

[0135] Step 100: Collect multi-source monitoring data and generate the raw dataset.

[0136] Through the various hardware modules of the integrated monitoring system, operational data of each link in the pig farm manure biogas recovery system are collected in real time, including farm data, biogas purification system data, flare combustion system data, biogas output system data, biogas production and output system data, power generation system data, and system energy consumption data, generating a raw dataset containing multi-source heterogeneous monitoring data.

[0137] It should be noted that the data collection for the aforementioned farms involved collecting the weight of each pig using a smart scale (unit: kg / head, collection frequency: 1 time / day), and collecting the number of different pig types (including breeding pigs, finishing pigs, and sows, unit: head, collection frequency: 1 time / day) using smart ear tags and electronic access control. The data collection for the aforementioned biogas purification system involved collecting the volumetric concentration of methane in the biogas using an infrared methane sensor (unit: %), collecting the biogas flow rate at the outlet of the biogas purification system using an eddy current flow meter (unit: cubic meters / second, collection frequency: 1 time / second), and collecting the local ambient temperature using a temperature sensor (collection frequency: 1 time / second). The data collection for the aforementioned flare combustion system involved collecting the biogas flow rate entering the flare combustion system using an eddy current flow meter (unit: cubic meters / second, collection frequency: 1 time / second). The data collection for the aforementioned biogas output system is conducted by using an eddy current flow meter to collect biogas flow rate data delivered to users (unit: cubic meters / second, collection frequency: 1 time / second). The data collection for the aforementioned biogas production and output system is conducted by using an eddy current flow meter to collect natural gas flow rate data delivered to users (unit: cubic meters / second, collection frequency: 1 time / second). The data collection for the aforementioned power generation system is conducted by using an electricity meter to collect external power supply data for the project (unit: megawatt-hours, collection frequency: 1 time / day). The data collection for the aforementioned system energy consumption is conducted by using an electricity meter to collect purchased electricity consumption data for the entire pig farm manure biogas recovery system (unit: megawatt-hours, collection frequency: 1 time / day).

[0138] Step 200: Preprocess the raw data to generate a standardized dataset.

[0139] The multi-source heterogeneous data in the original dataset are preprocessed, including data cleaning, data time synchronization, and unit standardization, to generate a standardized dataset.

[0140] It should be noted that the above data cleaning uses the "3σ criterion" to remove outliers from the original dataset. This criterion's judgment rule is: when the absolute value of the difference between a monitoring data point and its historical average exceeds three times the historical standard deviation, the monitoring data is considered an outlier and removed. Outliers include over-range data caused by sensor malfunctions and null data caused by communication interruptions. Regarding data time synchronization, for monitoring data with different acquisition frequencies, the instantaneous flow data collected by all flowmeters (acquisition frequency: 1 time / second) are cumulatively calculated to generate daily cumulative flow data (unit: cubic meters / day). The methane volume concentration data (acquisition frequency: 1 time / second) is arithmetically averaged over days to generate daily average methane volume concentration data (unit: %). This unifies the time granularity of all monitoring data to "days" and the data acquisition period to 0:00 to 24:00 of the current day. Regarding unit unification, the daily cumulative flow data of each flowmeter is unified to cubic meters per day (unit: ...). The electricity meter readings will be standardized to megawatt-hours (unit: The number of pigs in stock will be kept as heads, and the weight of pigs will be standardized to kilograms (unit: ).

[0141] In this embodiment of the application, to ensure data traceability, a unique traceability code is assigned to each piece of monitoring data during the preprocessing process. This traceability code records the acquisition device number, acquisition timestamp, transmission node identifier, and preprocessing operation log, ensuring that the entire process from data acquisition to calculation is traceable. The generation rule for the traceability code is: "Device type code (2 digits)" - "Device number (4 digits)" - "Timestamp (year, month, day, hour, minute, second, 14 digits)" - "Data sequence number (6 digits)". For example, the traceability code "FL-0001-20260107120000-000123" indicates that this data is the 123rd piece of data collected by flow meter (FL) device number 0001 at 12:00:00 on January 7, 2026.

[0142] Step 300: Calculate the carbon sink and output the carbon sink assessment results.

[0143] Based on monitoring data and preset calculation parameters in a standardized dataset, the current carbon sink calculation model is used to calculate the current [number]. Monthly cumulative carbon sequestration It also outputs carbon sink assessment results.

[0144] It should be noted that the above carbon sink calculation model includes three calculation modules: cumulative carbon sink calculation module, baseline emission calculation module, and project emission calculation module.

[0145] The aforementioned cumulative carbon sequestration calculation module calculates the current cumulative carbon sequestration according to the formula. Monthly cumulative carbon sequestration :

[0146]

[0147] In the formula: For the current number Monthly cumulative carbon sequestration, in tons of carbon dioxide equivalent (tc / v). ); For the first The baseline emissions for the month, in tons of carbon dioxide equivalent (TCO). ); For the first Monthly project emissions, in tons of carbon dioxide equivalent (tc / month). ); This is the current month number; Indexed by month.

[0148] The above baseline emission calculation module calculates the first emission according to the formula. Monthly baseline emissions :

[0149]

[0150] In the formula: For the first The baseline for monthly liquid fecal waste storage Emissions, in tons of carbon dioxide equivalent (tCO2) ); For the first The baseline for monthly liquid fecal waste storage Emissions, in tons of carbon dioxide equivalent (tCO2) ); For the first The baseline emissions replaced by the monthly external power supply of the project, in tons of carbon dioxide (CO2). ); For the first The baseline emissions replaced by biogas produced by the monthly project, expressed in tons of carbon dioxide (CCO2). ); For the first The baseline emissions replaced by monthly biogas output from the project, expressed in tons of carbon dioxide (CCO2). ).

[0151] Among them, the The baseline for monthly liquid fecal waste storage Emissions The calculation formula is:

[0152]

[0153] In the formula: For the first The total amount of volatile solids entering liquid sewage storage per month, expressed in kilograms. ); The methane production potential of pig manure is expressed in cubic meters of methane per kilogram of volatile solids. The value is 0.45. For the first The methane conversion factor of monthly liquid sewage storage, in percentage (%), is the measured value; The density of methane at room temperature and pressure is expressed in tons of methane per cubic meter. The value is 0.00067. On a 100-year timescale The global warming potential, expressed in tons of carbon dioxide equivalent per ton of methane ( ), with a value of 28.

[0154] Calculate the first Total volatile solids entering liquid sewage storage per month In this case, considering the rationality of the methodology, the smaller value of the calculation results from the following two formulas is used:

[0155] Calculate using the actual daily inventory of different pig types based on the formula. :

[0156]

[0157] In the formula: For the first Month The number of pigs in stock for different types of pigs per day, in heads; The default values ​​for daily volatile solids excretion are for different pig types, expressed in kilograms per 1000 kg body weight per day. weight The value for piglets is 4.3, for fattening pigs it is 5.1, and for breeding pigs it is 2.3. The average weight of different pig types is expressed in kilograms. (), which is the measured value; For the first The number of days in a month, expressed in days; It refers to the type of pig (piglets, fattening pigs, breeding pigs).

[0158] No. The monthly biogas production is used to estimate the total amount of volatile solids entering liquid manure storage that month. The total amount of volatile solids is then calculated using the formula. :

[0159]

[0160] In the formula: For the first Month The biogas volume at the outlet of the biogas purification system of the Tian project is expressed in cubic meters. (), which is the measured value; The average methane volume concentration in biogas is expressed as a percentage (%), and is a measured value. The methane production potential of pig manure is expressed in cubic meters of methane per kilogram of volatile solids. The value is 0.45. The volatile solids removal rate of the anaerobic digester is expressed as a percentage (%), with a value of 80%. For the first The number of days in a month, expressed in days.

[0161] Among them, the The baseline for monthly liquid fecal waste storage Emissions The calculation formula is:

[0162]

[0163] In the formula: For the first The baseline for monthly liquid fecal waste storage Direct emissions, expressed in kilograms of nitrogen in nitrous oxide (N). ); For the first The baseline for monthly liquid fecal waste storage Indirect emissions, expressed in kilograms of nitrogen in nitrous oxide (N). ); To be Convert to The coefficient; Unit conversion; On a 100-year timescale The global warming potential, expressed in tons of carbon dioxide equivalent per ton of nitrous oxide (TCO). ), with a value of 265.

[0164] No. The baseline for monthly liquid fecal waste storage direct emissions The calculation formula is:

[0165]

[0166] In the formula: For the first The total nitrogen content of liquid fecal waste stored monthly, expressed in kilograms of nitrogen (kg nitrogen). ); For storage of liquid sewage Direct emission factor, expressed as nitrogen per kilogram of nitrogen in kilograms of nitrous oxide (N). The value is 0.005.

[0167] No. The baseline for monthly liquid fecal waste storage Indirect emissions The calculation formula is:

[0168]

[0169] In the formula: For the first The total nitrogen content of liquid fecal waste stored monthly, expressed in kilograms of nitrogen (kg nitrogen). ); For the storage of liquid sewage and The proportion of nitrogen loss due to volatilization, expressed as nitrogen per kilogram of ammonia and nitrogen oxides (nitrogen excreted per kilogram). and excretion); for and It evaporates into the atmosphere and then settles onto the soil or into water. Indirect emission factors, expressed in units of nitrogen per kilogram of nitrous oxide and nitrogen per kilogram of ammonia and nitrogen oxides ( and ), with a value of 0.01.

[0170] No. Total nitrogen content of liquid fecal waste stored monthly The calculation formula is:

[0171]

[0172] In the formula: For the first Month The number of pigs in stock for different types of pigs per day, in heads; The default values ​​for daily nitrogen excretion are for different pig types, expressed in kilograms of nitrogen per 1000 kg body weight per day. weight (The value is 0.54 for piglets, 0.63 for fattening pigs, and 0.32 for breeding pigs.) The average weight of different pig types is expressed in kilograms. (), which is the measured value; For the first The number of days in a month, expressed in days; It refers to the type of pig (piglets, fattening pigs, breeding pigs).

[0173] Among them, the The baseline emissions replaced by the monthly external power supply of the project The calculation formula is:

[0174]

[0175] In the formula: For the first Monthly external power supply for the project, in megawatt-hours (MWH) (), which is the measured value; For the first The combined marginal emission factor of the power grid in the project area is expressed in tons of carbon dioxide per megawatt-hour. ).

[0176] No. The combined marginal emission factor of the power grid in the area where the project is located. The calculation formula is:

[0177]

[0178] In the formula: For the first The marginal emission factor of electricity in the power grid of the project area, expressed in tons of carbon dioxide per megawatt-hour (MWh). ); The weight of the marginal emission factor for electricity is 0.5; For the first The marginal emission factor of the power grid capacity in the area where the project is located, expressed in tons of carbon dioxide per megawatt-hour (MWh). ); This is the weight of the capacity marginal emission factor, with a value of 0.5.

[0179] Among them, the The baseline emissions replaced by biogas produced by the monthly project The calculation formula is:

[0180]

[0181] In the formula: For the first The monthly biogas volume delivered to users by the project, in cubic meters ( (), which is the measured value; The average methane volume concentration in biogas is expressed as a volume percentage (%), and is a measured value. The lower heating value of methane is expressed in trillion joules per 10,000 cubic meters. The value is 0.359. The carbon oxidation rate of methane combustion is expressed as a percentage (%), with a value of 0.99. The methane combustion emission factor is expressed in tons of carbon dioxide per trillion joules (TCO). The value is 54.3.

[0182] Among them, the The baseline emissions replaced by natural gas output from the monthly project The calculation formula is:

[0183]

[0184] In the formula: For the first The natural gas delivered to users by the project each month is measured in cubic meters. (), which is the measured value; The average methane volume concentration in natural gas is expressed as a volume percentage (%), with a value of 96%. The lower heating value of methane is expressed in trillion joules per 10,000 cubic meters. The value is 0.359. The carbon oxidation rate of methane combustion is expressed as a percentage (%), with a value of 0.99. The methane combustion emission factor is expressed in tons of carbon dioxide per trillion joules (TCO). The value is 54.3.

[0185] The above-mentioned project emission calculation module calculates the emissions according to the formula. Monthly project emissions :

[0186]

[0187] In the formula: For the first The monthly project generated from the anaerobic digester and pipelines Emissions, expressed in tons of carbon dioxide equivalent (TCO). ); For the first The monthly project's biogas residue and biogas slurry treatment system generates Emissions, in tons of carbon dioxide equivalent (tCO2) ); For the first The monthly project's biogas residue and biogas slurry treatment system generates Emissions, in tons of carbon dioxide equivalent (tCO2) ); For the first The monthly emissions of biogas produced by the project's flare combustion are expressed in tons of carbon dioxide equivalent. ); For the first Emissions generated from purchased electricity consumed by the project each month, expressed in tons of carbon dioxide (CO2). ).

[0188] Among them, the The monthly project generated from the anaerobic digester and pipelines Emissions The calculation formula is:

[0189]

[0190] In the formula: For the first Month The biogas volume at the outlet of the biogas purification system of the Tian project is expressed in cubic meters. (), which is the measured value; The average methane volume concentration in biogas is expressed as a percentage (%), and is a measured value. Anaerobic digester and pipeline Dissipation factor, expressed as a percentage (%), is 1%; The density of methane at room temperature and pressure is expressed in tons of methane per cubic meter. The value is 0.00067. On a 100-year timescale The global warming potential, expressed in tons of carbon dioxide equivalent per ton of methane ( The value is 28; For the first The number of days in a month, expressed in days.

[0191] Among them, the The monthly project's biogas residue and biogas slurry treatment system generates Emissions The calculation formula is:

[0192]

[0193] In the formula: For the first The total amount of volatile solids entering liquid sewage storage per month, expressed in kilograms. The data is taken as the larger value of the formula result; The methane production potential of pig manure is expressed in cubic meters of methane per kilogram of volatile solids. The value is 0.45. For the first The methane conversion factor of monthly liquid sewage storage, in percentage (%), is the measured value; The volatile solids removal rate of the anaerobic digester is expressed as a percentage (%), with a value of 80%. The density of methane at room temperature and pressure is expressed in tons of methane per cubic meter. The value is 0.00067. On a 100-year timescale The global warming potential, expressed in tons of carbon dioxide equivalent per ton of methane ( ), with a value of 28.

[0194] Among them, the The monthly project's biogas residue and biogas slurry treatment system generates Emissions The calculation formula is:

[0195]

[0196] In the formula: For the first The Moon Sludge and Biogas Slurry Treatment System produces Direct emissions, expressed in kilograms of nitrogen in nitrous oxide (N). ); For the first The Moon Sludge and Biogas Slurry Treatment System produces Indirect emissions, expressed in kilograms of nitrogen in nitrous oxide (N). ); To be Convert to The coefficient; Unit conversion; On a 100-year timescale The global warming potential, expressed in tons of carbon dioxide equivalent per ton of nitrous oxide (TCO). ), with a value of 265.

[0197] No. The monthly project's biogas residue and biogas slurry treatment system generates direct emissions The calculation formula is:

[0198]

[0199] In the formula: For the first The total nitrogen content of fecal waste entering the anaerobic digester per month, expressed in kilograms of nitrogen (kg nitrogen). ), calculate using formula ; For biogas residue and biogas slurry treatment systems Direct emission factor, expressed as nitrogen per kilogram of nitrogen in kilograms of nitrous oxide (N). The value is 0.005.

[0200] No. The baseline for monthly liquid fecal waste storage Indirect emissions The calculation formula is:

[0201]

[0202] In the formula: For the first The total nitrogen content of liquid fecal waste stored monthly, expressed in kilograms of nitrogen (kg nitrogen). ); For the storage of liquid sewage and The proportion of nitrogen loss due to volatilization, expressed as nitrogen per kilogram of ammonia and nitrogen oxides (nitrogen excreted per kilogram). and excretion); for and It evaporates into the atmosphere and then settles onto the soil or into water. Indirect emission factors, expressed in units of nitrogen per kilogram of nitrous oxide and nitrogen per kilogram of ammonia and nitrogen oxides ( and ), with a value of 0.01.

[0203] Among them, the Emissions from biogas combustion in the monthly project The calculation formula is:

[0204]

[0205] In the formula: For the first The amount of biogas fed into the flare for combustion in a monthly project, in cubic meters ( (), which is the measured value; The average methane volume concentration in biogas is expressed as a volume percentage (%), and is a measured value. The value represents the biogas combustion efficiency of the flare, expressed as a percentage (%), with a value of 50%. The density of methane at room temperature and pressure is expressed in tons of methane per cubic meter. The value is 0.00067. On a 100-year timescale The global warming potential, expressed in tons of carbon dioxide equivalent per ton of methane ( ), with a value of 28.

[0206] Among them, the Emissions generated from the purchased electricity consumed by the monthly project The calculation formula is:

[0207]

[0208] In the formula: For the first The monthly electricity consumption of the local power grid for the project, in megawatt-hours (MWH). (), which is the measured value; For the first The carbon emission factor of the power grid in the region where the month is located is expressed in tons of carbon dioxide per megawatt-hour. ).

[0209] The above carbon sink assessment results include: the current [number]th Monthly cumulative carbon sequestration (unit: ), No. Monthly carbon sequestration (unit: ), No. Monthly baseline emissions (unit: ), No. Monthly project emissions (unit: ).

[0210] In this embodiment of the application, to improve the visualization and anomaly detection capabilities of carbon sink data, after outputting the carbon sink assessment results, the following steps are also included: visualizing the carbon sink assessment results and generating a carbon sink trend curve and anomaly warning information. Specifically, the cumulative carbon sink... Monthly carbon sequestration baseline emissions Project emissions A line chart is generated based on the time series to show the dynamic trend of carbon sink; at the same time, an abnormal early warning threshold is set, which is triggered when the volume concentration of methane in biogas... When the level falls below 50%, an SMS alert is triggered, indicating a potential malfunction in the biogas purification system; this is the monthly carbon sequestration level. When the value is below 80% of the historical monthly average, a data anomaly warning is triggered, indicating that there may be a deviation in the calculation of the carbon sink for the month, and the accuracy of the monitoring data needs to be verified.

[0211] It is understood that data preprocessing methods known to those skilled in the art include data cleaning, data transformation, and data reduction. Data transformation includes type conversion and normalization and standardization. Although the dimensions and types of data were omitted in the description of the preceding embodiments, data preprocessing is a technical knowledge known to those skilled in the art and a prerequisite step in data processing. Therefore, the previously described well-known data preprocessing steps were not described independently.

[0212] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. A carbon sink intelligent monitoring and dynamic evaluation method based on Internet of Things and cloud computing, characterized in that, Includes the following steps: The system collects operational data from each stage of the pig farm manure biogas recovery system using IoT devices to generate a raw dataset. The operational data includes farm data, biogas purification system data, flare combustion system data, biogas output system data, biomethane production and output system data, power generation system data, and system energy consumption data. The original dataset is preprocessed, including outlier removal, time synchronization of monitoring data with different collection frequencies, and unit standardization of the monitoring data to generate a standardized dataset. Based on the standardized dataset, the cumulative carbon sink for the current month is calculated using the carbon sink calculation model, and the carbon sink assessment results are output. The cumulative carbon sink is obtained by subtracting the sum of project emissions for each month from the sum of baseline emissions for each month. The baseline emissions include greenhouse gas emissions from liquid manure storage and baseline emissions replaced by project energy output. The project emissions include greenhouse gas emissions from anaerobic digester and pipelines, greenhouse gas emissions from biogas residue and biogas slurry treatment systems, emissions from biogas combustion in flares, and emissions from purchased electricity consumed by the project.

2. The method of claim 1, wherein, The farm data includes data on the number of pigs of different types and their weight; the biogas purification system data includes data on the volume concentration of methane in the biogas and the biogas flow rate at the outlet of the biogas purification system; the flare combustion system data includes data on the biogas flow rate entering the flare combustion system; the biogas output system data includes data on the biogas flow rate delivered to users; the biogas production and output system data includes data on the natural gas flow rate delivered to users; the power generation system data includes data on the amount of electricity supplied to the project; and the system energy consumption data includes data on the purchased electricity consumed by the pig farm manure biogas recovery system.

3. The method of claim 1, wherein, The outlier removal process for the original dataset includes: determining whether the absolute value of the difference between the monitoring data and the historical average value of the monitoring indicator exceeds three times the historical standard deviation; if it does, the monitoring data is determined to be an outlier and removed. The time synchronization of monitoring data with different collection frequencies includes: calculating the instantaneous flow data collected by the flow meter to generate daily cumulative flow data, and arithmetically averaging the methane volume concentration data over days to generate daily average methane volume concentration data, so that the time granularity of all monitoring data is unified to days.

4. The method of claim 1, wherein, The greenhouse gas emissions from the liquid manure storage include methane emissions and nitrous oxide emissions. The baseline emissions replaced by the project's energy output include the baseline emissions replaced by the project's external power supply, the baseline emissions replaced by the project's biogas output, and the baseline emissions replaced by the project's natural gas output.

5. The method of claim 4, wherein, The methane emissions from the liquid manure storage are calculated based on the total volatile solids entering the liquid manure storage, the methane production potential of pig manure, the methane conversion factor of the liquid manure storage, the methane density at room temperature and pressure, and the global warming potential of methane; the nitrous oxide emissions from the liquid manure storage are calculated based on the total nitrogen content of the manure in the liquid manure storage, the direct nitrous oxide emission factor, the indirect nitrous oxide emission factor, and the global warming potential of nitrous oxide.

6. The method of claim 5, wherein, The total amount of volatile solids entering the liquid manure storage is calculated in two ways. The first method is a forward calculation based on the daily stock of different types of pigs, the average weight of pigs, and the default value of volatile solid excretion. The second method is a reverse calculation based on the biogas volume at the outlet of the biogas purification system, the average methane volume concentration of biogas, the methane production potential of pig manure, and the volatile solids removal rate of the anaerobic digester. The smaller value of the two calculation methods is taken as the total amount of volatile solids entering the liquid manure storage.

7. The method of claim 1, wherein, The greenhouse gas emissions from the anaerobic digester and pipelines are calculated based on the biogas volume at the outlet of the biogas purification system, the average methane volume concentration in the biogas, the methane emission factor of the anaerobic digester and pipelines, the methane density at room temperature and pressure, and the global warming potential of methane. The greenhouse gas emissions from the biogas residue and slurry treatment system include the methane emissions and nitrous oxide emissions from the system. The emissions from the flare combustion of biogas are calculated based on the biogas volume entering the flare, the average methane volume concentration in the biogas, the flare combustion efficiency, the methane density at room temperature and pressure, and the global warming potential of methane. The emissions from the purchased electricity consumed by the project are calculated based on the purchased electricity consumed by the project and the carbon emission factor of the power grid in the region.

8. The method of claim 1, wherein, During the preprocessing of the original dataset, a unique traceability code is assigned to each piece of monitoring data. The traceability code records the acquisition device number, acquisition timestamp, transmission node identifier, and preprocessing operation log. The generation rule of the traceability code is a combination of device type code, device number, timestamp, and data sequence number.

9. The method of claim 1, wherein, After outputting the carbon sink assessment results, the method also includes: visualizing the carbon sink assessment results and generating a carbon sink trend curve; setting an abnormal warning threshold, triggering an warning when the methane volume concentration in biogas is lower than a preset concentration threshold, and triggering a data abnormality warning when the monthly carbon sink is lower than a preset proportion of the historical monthly average.

10. A smart monitoring and dynamic assessment system for carbon sequestration based on the Internet of Things and cloud computing, used to execute the method described in any one of claims 1 to 9, characterized in that, include: The data acquisition module is used to collect operational data from each stage of the pig farm manure biogas recovery system through IoT devices and generate raw datasets. The data preprocessing module is used to remove outliers, synchronize time, and unify units in the original dataset to generate a standardized dataset. The carbon sink calculation module is used to calculate the cumulative carbon sink for the current month based on the standardized dataset and using the carbon sink calculation model. The results output module is used to output the carbon sink assessment results.

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