Agricultural and pastoral park carbon emission monitoring system and method based on energy flow-carbon flow coupling characteristic and readable storage medium

Abstract

The embodiment of the invention discloses an agricultural and pastoral park carbon emission monitoring system and method based on energy flow-carbon flow coupling characteristics and a readable storage medium, and relates to the technical field of carbon emission measurement and calculation. The system obtains agricultural production data, animal husbandry production data, biomass power generation data, photovoltaic power generation data and park load data through an agricultural Internet of Things data terminal; the carbon emission calculation engine obtains park carbon emission data by calling the calculation model in the model library based on data sent by the agricultural Internet of Things data terminal. According to the method, the energy flow of the modern agricultural and pastoral park is taken as an entry point, the park carbon flow is determined based on the coupling characteristic between the park energy flow and the carbon flow, the carbon accounting boundary is determined according to the carbon flow, the operation data of each area is correspondingly collected, and the carbon emission is calculated by using a corresponding calculation model. Accurate and high-pertinence carbon emission measurement and calculation of the modern agricultural and pastoral park are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission monitoring, and in particular to a carbon emission monitoring system, method and readable storage medium for an agricultural and pastoral park based on energy flow-carbon flow coupling characteristics. Background Art

[0002] Under the current background of global climate change and energy crisis, clean energy power generation and low-carbon operation have become important development directions. As the concentration of agriculture and animal husbandry, the energy consumption and emission issues of modern agricultural and animal husbandry parks have also attracted attention.

[0003] As for the carbon emission measurement methods related to modern agricultural and animal husbandry parks, the most common methods in agricultural carbon emission measurement research are emission coefficient method, model simulation method and field measurement method. However, these methods usually focus on the carbon emission accounting of a single specific area of ​​modern agricultural and animal husbandry parks. At the same time, due to the extremely scattered sources of carbon emissions in modern agricultural and animal husbandry parks with a high proportion of renewable energy access, the carbon emission accounting of modern agricultural and animal husbandry parks is very complicated, and the data boundaries are difficult to determine, making it impossible to accurately measure the carbon emissions of modern agricultural and animal husbandry parks. Therefore, it is impossible to carry out further net zero carbon transformation of modern agricultural and animal husbandry parks based on carbon flow and carbon source in a targeted manner. Summary of the invention

[0004] The purpose of the present invention is to provide a carbon emission monitoring system, method and readable storage medium for agricultural and pastoral areas based on the energy flow-carbon flow coupling characteristics, so as to accurately measure the carbon emission data of agricultural and pastoral areas.

[0005] In the first aspect, a carbon emission monitoring system for an agricultural and pastoral park based on the energy flow-carbon flow coupling characteristics is provided, comprising:

[0006] Agricultural area data collection device, deployed in agricultural areas to obtain agricultural production data;

[0007] Pastoral area data collection device, which is deployed in the pastoral area to obtain pastoral production data;

[0008] Energy zone sensor nodes, deployed in the energy zone to obtain biomass power generation data and photovoltaic power generation data;

[0009] Smart meters are used to obtain park load data and purchased electricity data;

[0010] The agricultural IoT data terminal is connected to the agricultural area data collection device, the pastoral area data collection device, the energy area sensor node and the smart meter through the corresponding edge gateway to obtain agricultural production data, pastoral production data, biomass power generation data, photovoltaic power generation data, park load data and purchased power data;

[0011] The Internet of Things platform is connected to the agricultural Internet of Things data terminal through a data transmission network, and includes a carbon emission calculation engine. The carbon emission calculation engine obtains the carbon emission data of the park by calling the calculation models in the model library based on the agricultural production data, livestock production data, biomass power generation data, photovoltaic power generation data, and park load data sent by the agricultural Internet of Things data terminal. Among them, the calculation models include: an agricultural project carbon emission calculation model for calculating crop carbon absorption data; a livestock project carbon emission calculation model for calculating livestock and poultry carbon emissions during a predetermined growth cycle; a biomass power generation project carbon emission calculation model for calculating the carbon emissions of biomass power generation and biogas composting according to the biomass power generation data; and a park electricity consumption carbon emission calculation model for calculating the carbon emissions of park electricity consumption according to the purchased electricity data or biomass power generation data, photovoltaic power generation data, and park load data.

[0012] In a second aspect, a method for monitoring the carbon emissions of a farming and pastoral park based on the coupling characteristics of energy flow and carbon flow is provided, including:

[0013] Obtaining agricultural production data through the agricultural area data collection device configured in the agricultural area;

[0014] Obtaining livestock production data through the livestock area data collection device configured in the livestock area;

[0015] Obtaining biomass power generation data and photovoltaic power generation data through the energy area sensor nodes configured in the energy area;

[0016] Obtaining park load data and purchased electricity data through an intelligent electricity meter;

[0017] Connecting the agricultural Internet of Things data terminal to the agricultural area data collection device, livestock area data collection device, energy area sensor nodes, and intelligent electricity meter respectively through the corresponding edge gateways to obtain agricultural production data, livestock production data, biomass power generation data, photovoltaic power generation data, park load data, and purchased electricity data;

[0018] Configure the Internet of Things platform to connect it to the agricultural Internet of Things data terminal through a data transmission network, so as to use the carbon emission calculation engine of the Internet of Things platform to obtain the park carbon emission data based on the agricultural production data, livestock production data, biomass power generation data, photovoltaic power generation data and park load data sent by the agricultural Internet of Things data terminal; wherein, the calculation models include: an agricultural project carbon emission calculation model for calculating crop carbon absorption data; a livestock project carbon emission calculation model for calculating livestock carbon emission data within a predetermined growth cycle; a biomass power generation project carbon emission calculation model for calculating the carbon emissions of biomass power generation and biogas composting according to the biomass power generation data; and a park electricity consumption carbon emission calculation model for calculating the park electricity consumption carbon emissions according to the purchased electricity data or biomass power generation data, photovoltaic power generation data and park load data.

[0019] In the system or method of the above aspect, exemplarily, the agricultural project carbon emission calculation model calculates the carbon absorption amount during crop growth according to the dry matter weight per unit area of the crop, the carbon absorption rate of the crop for synthesizing unit organic dry matter, the amount of carbon absorbed and fixed by the crop, and the adjustment coefficient corresponding to the crop type.

[0020] In the system or method of the above aspect, exemplarily, the livestock project carbon emission calculation model calculates the carbon emissions according to the feeding amount of different types of livestock and the CH4 carbon emission coefficient.

[0021] In the system or method of the above aspect, exemplarily, when the purchased electricity is greater than 0, the first biomass power generation project carbon emission calculation model is used to calculate the carbon emissions according to the purchased electricity from the power grid and the carbon emission factor; otherwise, the second biomass power generation project carbon emission calculation model is used to calculate the carbon emissions according to the biomass power generation data, photovoltaic power generation data and park load data, wherein the park production electricity consumption is calculated based on the park load data, and the carbon emissions are calculated according to the biomass power generation data, photovoltaic power generation data and park production electricity consumption data.

[0022] In a third aspect, a readable storage medium is provided, on which a computer program is stored; when the computer program is executed, the monitoring method as described in the above aspect is implemented.

[0023] Aiming at the problems of unclear carbon accounting boundaries and unclear carbon emission accounting items in modern agricultural and pastoral parks, the present invention takes the energy flow of modern agricultural and pastoral parks as the starting point, determines the carbon flow of the park based on the coupling characteristics between the energy flow and carbon flow in the park, determines the carbon accounting boundary according to the carbon flow, and correspondingly collects the operation data of each region, and uses the corresponding calculation models to calculate the carbon emissions, realizing accurate and highly targeted carbon emission measurement for modern agricultural and pastoral parks.

[0024] In addition, in response to the problem that traditional data collection methods are difficult to support the refined carbon emission calculation of modern agricultural and pastoral parks, the present invention uses Internet of Things technology to collect and transmit relevant data of agricultural and pastoral parks to Internet of Things terminals in real time through multiple gateways and nodes, providing data support for the carbon emission calculation of the park.

[0025] Further features and advantages of the present invention will become apparent from the following detailed description of exemplary embodiments of the present invention with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0027] Figure 1 This is a schematic diagram of carbon flow in modern agricultural and animal husbandry areas;

[0028] Figure 2 The figure is a schematic diagram of information flow of a carbon emission monitoring system for an agricultural and pastoral park based on the energy flow-carbon flow coupling characteristics according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0030] In modern agricultural and animal husbandry parks, the energy flow conversion mechanism of each link involved in the entire chain of bio-derivatives is complex. When the biomass power generation system is integrated with other power generation systems in the park, such as photovoltaics, energy storage, and flexible loads, the response characteristics in energy flow are diversified and differentiated. This makes it difficult to evaluate the available power generation capacity and response characteristics of the low-carbon energy system in the agricultural and animal husbandry zero-carbon park in real time. The complexity of energy flow conversion also leads to a large carbon conversion range and unclear conversion mechanism in each production link of modern agricultural and animal husbandry parks, making it difficult to quantify the low-carbon emission reduction effect of modern agricultural and animal husbandry parks, which ultimately leads to difficulty in modeling the multi-energy synergistic system of agricultural and animal husbandry zero-carbon parks.

[0031] 1. Analysis of energy flow-carbon flow coupling characteristics in modern agricultural and animal husbandry parks

[0032] Taking the low-carbon, energy-saving and circular agricultural model of a typical modern agricultural and animal husbandry park as an example, in the agricultural production link, agricultural products are sent to the processing process and sold to the market after processing. The remaining biomass can be used as feed and sent to the animal husbandry production link as feed, and the rest is sent to the biogas digester as waste biomass for fermentation and composting. In addition, fertilizers and other production materials need to be purchased from the market.

[0033] In the livestock production process, in addition to receiving the feed provided by the agricultural production process, a certain amount of feed also needs to be purchased from the market to ensure the efficient economic production of the livestock industry. The biological waste such as manure generated by the livestock industry can be sent to the biogas digester as waste biomass for fermentation composting. The main products such as meat and milk in the livestock industry are processed and sold to the market. Finally, after the waste biomass in the biogas digester undergoes fermentation and the renewable energy manufacturing process, the remaining residue is supplied to the agricultural production process as biogas fertilizer.

[0034] Thus, based on the material flow in the modern agricultural and livestock park, the present invention analyzes the energy flow relying on the material flow in the park, and also takes into account the energy flows that do not rely on the material flow, such as biomass power generation, photovoltaic power generation, and the electricity consumption in the modern agricultural and livestock park, into the model to obtain the energy flow situation in the modern agricultural and livestock park.

[0035] In addition, in the agricultural production process, solar energy is converted into chemical energy through photosynthesis of crops. The purchase of fertilizers from the market brings the input of chemical energy. The decomposition of fertilizers in the soil will cause the dissipation of chemical energy. Agricultural products will carry the chemical energy contained in them to the market. Part of the agricultural waste such as straw and fallen leaves can be used as feed to transfer chemical energy to the livestock production process, and part can be used as raw materials for biomass power generation to transfer chemical energy to the biomass power generation process. In the livestock production process, the feed purchased from the market or made from straw and other materials brings the input of chemical energy. Livestock products will carry the chemical energy contained in them to the market, and livestock waste will transfer chemical energy to the biomass power generation process. In the biomass power generation process, part of the chemical energy in agricultural and livestock waste is converted into electrical energy in the biogas digester, and the remaining chemical energy is used as fertilizer to input the agricultural production process with the biogas residue. In the energy production and use process, the power consumption of the park production equipment comes from biomass power generation, photovoltaic power generation and purchasing electricity from the external power grid. It is assumed here that all park production equipment uses electricity as energy.

[0036] Different from the material flow and energy flow, the carbon flow is a virtual flow constructed to specify the flow of carbon in the system. Therefore, to analyze the carbon flow characteristics of the modern agricultural and livestock park, first of all, it is necessary to study the correlation law and mapping relationship between the biological derivation state of each link and the carbon flow - energy flow. The existing carbon flow calculation methods start from macro data and are statistically based on energy consumption. The energy consumption generates greenhouse gas emissions into the atmosphere, forming carbon emissions. In the new modern agricultural and livestock park, due to the high degree of agricultural electrification transformation, the carbon footprint mainly comes from the generation and use of energy in the park and the activities of organisms in the park. In addition, previous studies on the carbon flow in the park mainly focused on carbon emissions, that is, positive carbon flow, and to a certain extent ignored that in the modern agricultural and livestock park, in the processes of plant photosynthesis, photovoltaic and biogas power generation, etc., there are often carbon sequestration behaviors or the generation of green electricity that can offset carbon emissions, and thus generate negative carbon flow.

[0037] In summary, there are complex and close correlation laws and mapping relationships between the biological derivative state of the low-carbon circular modern agricultural and pastoral park and the carbon flow - energy flow. According to the analysis of the coupling characteristics of energy flow - carbon flow in the previous text, comprehensively considering the carbon flow characteristics of the energy flow and the energy flow - carbon flow mapping relationship in the modern agricultural and pastoral park as Figure 1 shown.

[0038] 2. Carbon Emission Monitoring System for Agricultural and Pastoral Parks

[0039] In modern agricultural and pastoral parks, due to the large number of measurement items and the complexity of the measurement work caused by the existence of the biological derivative chain, it is difficult for traditional data collection methods to achieve refined carbon emission measurement. The present invention combines related technologies such as agricultural Internet of Things, edge computing, and data stream transmission, so that the data collection coverage in the production links of modern agricultural parks is wider and the delay is lower. Based on the agricultural Internet of Things information flow system, relevant data related to the energy flow and carbon flow in the park can be obtained more comprehensively, accurately, and timely.

[0040] According to the analysis of the coupling characteristics of energy flow - carbon flow in the previous text, comprehensively considering the carbon flow characteristics of the energy flow and the energy flow - carbon flow mapping relationship in the modern agricultural and pastoral park, the present invention proposes a carbon emission monitoring system for modern agricultural and pastoral parks based on the coupling characteristics of energy flow - carbon flow, as Figure 2 shown, the monitoring system 100 includes:

[0041] Agricultural area data collection device 101, configured in the agricultural area to obtain agricultural production data;

[0042] Pastoral area data collection device 102, configured in the pastoral area to obtain pastoral production data;

[0043] Energy area sensor nodes, configured in the energy area to obtain biomass power generation data and photovoltaic power generation data;

[0044] Smart meter, used to obtain park load data;

[0045] Agricultural Internet of Things data terminal 103, connected to the agricultural area data collection device, pastoral area data collection device, energy area sensor nodes, and smart meter respectively through the corresponding edge gateway 104 to obtain agricultural production data, pastoral production data, biomass power generation data, photovoltaic power generation data, and park load data;

[0046] The Internet of Things platform is connected to the agricultural Internet of Things data terminal through a data transmission network, including a carbon emission calculation engine. The carbon emission calculation engine obtains park carbon emission data by calling the calculation models in the model library based on the agricultural production data, pastoral production data, biomass power generation data, photovoltaic power generation data, and park load data sent by the agricultural Internet of Things data terminal.

[0047] In Figure 2In the system shown, by setting multiple gateways 104 and data acquisition nodes (such as sensor nodes, input devices, etc.) in the corresponding project areas, agricultural production data, livestock production data, biomass power generation data, photovoltaic power generation data, park load data, and external grid power purchase data are collected and transmitted in real time to the agricultural Internet of Things data terminal 103. Based on the data sent by the agricultural Internet of Things data terminal, the Internet of Things platform obtains the park carbon emission data by calling the preset calculation models in the model library.

[0048] As an example, the system network architecture adopts a hierarchical design, including: an edge perception layer (data acquisition layer), a network transmission layer, and a platform layer (data processing and analysis layer).

[0049] The devices in the edge perception layer mainly include sensor nodes. For example, in the agricultural area, soil temperature and humidity sensors, light intensity sensors, CO2 concentration sensors, and nitrogen, phosphorus, and potassium sensors are set; in the livestock area, livestock body temperature monitoring collars, feed consumption flow meters, and methane gas sensors are set; in the energy area, fuel consumption flow meters and power generation power sensors for biomass power generation, etc., and photovoltaic panel current / voltage sensors and irradiance sensors for photovoltaic power generation are included. In addition, smart meters are also included to monitor the park load (monitor electricity consumption by region / device) and collect power purchase power, electricity, etc.

[0050] The communication protocol of the edge perception layer devices can, for example, according to the specific device situation, use a low-power wide area network (LPWAN), such as LoRaWAN / NB-IoT (wide coverage, low power consumption, suitable for scattered agricultural / livestock areas), short-range communication, such as Zigbee 3.0 (high-density sensor clusters, such as greenhouses), Wi-Fi 6 (high-bandwidth scenarios, such as video monitoring).

[0051] The edge gateways are deployed by region, including agricultural area gateways, livestock area gateways, energy area gateways, etc., which are used to achieve multi-protocol aggregation (supporting LoRa / Zigbee / Modbus, etc.), edge computing (data cleaning, compression, local caching), and device management (OTA upgrade, status monitoring). As an example, a lightweight AI model (such as TensorFlow Lite) can be deployed on the gateway to achieve local filtering of abnormal data.

[0052] In the network transmission layer, the communication link can adopt hybrid networking, such as SD-WAN technology, to dynamically select the optimal transmission path (5G / fiber complementarity).

[0053] The platform layer is configured with databases, such as time series databases and relational databases, to store the data sent by the agricultural Internet of Things data terminal.

[0054] A variety of calculation models are preset in the model library. By calling the calculation models preset in the model library and based on the data sent by the agricultural Internet of Things data terminal, the carbon emission data of the park is obtained.

[0055] As an example, the model library also includes a machine learning model to predict the carbon emission trend based on historical data.

[0056] As an example, the model library also includes a control model for optimizing the energy consumption of the park according to the carbon emission data to achieve net-zero carbon operation of the park.

[0057] As an example, the key emission data is deployed on the blockchain to improve data security and credibility.

[0058] 3. Carbon Emission Calculation Model

[0059] According to the analysis of the energy flow-carbon flow coupling characteristics in the previous text, and comprehensively considering the energy flow and the carbon flow characteristics of the energy flow-carbon flow mapping relationship in modern agricultural and pastoral parks, the carbon emission calculation model constructed in the embodiments of the present invention includes:

[0060] An agricultural project carbon emission calculation model for calculating crop carbon absorption data;

[0061] A livestock project carbon emission calculation model for calculating livestock and poultry carbon emission data within a predetermined growth cycle;

[0062] A biomass power generation project carbon emission calculation model for calculating the carbon emissions of biomass power generation and biogas composting according to biomass power generation data;

[0063] A park electricity consumption carbon emission calculation model for calculating park electricity consumption carbon emission data according to biomass power generation data, photovoltaic power generation data and park load data.

[0064] (1) Agricultural Project Carbon Emission Calculation Model

[0065] According to the analysis of the carbon flow in typical modern agricultural and pastoral parks in the previous text, the carbon emissions in the agricultural production link should consider both the carbon emissions and carbon sinks of agriculture. At the same time, according to the carbon emission accounting boundary determined by the energy flow-carbon flow coupling analysis, the formula for calculating agricultural production carbon emissions is obtained as:

[0066] C = ∑C(i) = ∑T(i) × Q(i) (1)

[0067] Among them, C represents the total agricultural carbon emissions; C i represents the emissions of the i-th type of carbon source or the carbon absorption of the carbon sink; T i represents the scale of the i-th type of carbon source or carbon sink; Q i represents the carbon emission or absorption coefficient of the i-th type of carbon source. Among them, C and Q are positive for carbon emissions and negative for carbon absorption.

[0068] Accounting for the energy consumption of agricultural production mechanization operations in the energy production and use links, in the agricultural production process, as mentioned in the above carbon flow analysis, agricultural carbon emissions mainly involve agricultural fertilizers, crop respiration, and crop photosynthesis. Among them, the carbon emissions of agricultural fertilizers are measured by the carbon emission factor method. For example, according to the data released by the IPCC, the carbon emission coefficient is taken as 0.8956 kg(C)·kg -1 . And due to the accumulation of carbon-containing substances in the growth process of crops, the overall intensity of photosynthesis is greater than that of respiration. Generally speaking, the growth cycle of crops belongs to a carbon sequestration behavior. Therefore, the carbon absorption of plant photosynthesis and the carbon emissions of respiration are combined for accounting. As an example, the carbon amount absorbed by the above-ground parts of various crops is estimated according to the dry matter weight of various crops and their carbon absorption rates. Therefore, the estimation formula for carbon absorption during crop growth is:

[0069] C t =∑(F i ×T i )×C i (2)

[0070] In the formula, C t is the amount of carbon absorbed and fixed by the crop; F i is the carbon absorption rate of a certain crop for synthesizing unit organic dry matter; T i is the dry matter weight per unit area of the crop.

[0071] Exemplarily, the carbon absorption rates of common cash crops are shown in the following table:

[0072] Crop type Carbon absorption rate Rice 0.414 Wheat 0.485 Corn 0.471 Cotton 0.450 Oil crops 0.450 Vegetables 0.450 Beans 0.450 Tubers 0.423 Beet 0.407

[0073] At the same time, after the above-ground parts of the crop fix carbon, they will transport part of the carbon assimilation substances to the underground to supply root growth. While growing, the roots of the crop will secrete organic substances into the soil. For example, the carbon content of the roots and root exudates of food crops accounts for 20% - 30% of the total assimilation of the crop, and that of forage crops accounts for 30% - 40%. Therefore, the estimation formula for carbon absorption of food crops is:

[0074] C t =1.25×∑(F i ×T i )×C i (3)

[0075] The estimation formula for carbon absorption of forage crops is:

[0076] C t =1.35×∑(F i ×T i )×C i (4)

[0077] (2) Carbon emission calculation model for livestock projects

[0078] According to the carbon flow analysis of modern agricultural and pastoral parks in the previous text, the main carbon emissions in the livestock industry are CH4 and N2O generated during livestock intestinal fermentation and manure treatment. According to the heating effect, CH4 and N2O are uniformly converted into CO2 equivalent, that is, 1 ton of CH4 is equivalent to 25 tons of CO2, and 1 ton of N2O is equivalent to 298 tons of CO2. Due to the existence of the biomass power generation link, the carbon emissions from livestock manure treatment are included in the biomass power generation link.

[0079] The carbon emission calculation formula for the livestock industry is shown in Equation 5:

[0080] C = C CH4 = ∑N i ·α i (5)

[0081] Where N i is the feeding amount of the i-th type of livestock; α i is the CH4 carbon emission coefficient of the i-th type of livestock.

[0082] When the production cycle of livestock and poultry is greater than or equal to one year, the year-end inventory quantity of this type of livestock and poultry is used as the average annual feeding amount. When the production cycle is less than one year, the annual slaughter data is used to calculate the average annual feeding amount.

[0083] Exemplarily, the carbon emission coefficients of the main livestock breeds can be referred to the following table (unit: kg / head):

[0084]

[0085] (3) Carbon emission calculation model for biomass power generation projects

[0086] When calculating the carbon emissions of biomass power generation and biogas composting, the average carbon emission intensity (g CO2 / kWh) report of various power sources released by the United Nations Intergovernmental Panel on Climate Change is adopted. The report shows that if biomass energy is used for power generation, its carbon emission intensity is 18 g CO2 / kWh. Therefore, when calculating the carbon emissions of biomass power generation, the specific formula is as follows:

[0087] C s = 0.018 × Q s (6)

[0088] Where C s represents the carbon emissions of biomass power generation, and Q s represents the power generation of biomass power generation.

[0089] (4) Carbon emission calculation model for park electricity consumption

[0090] According to the previous analysis of the energy flow in the park's production, the electricity consumption for the park's production mainly comes from three parts: biomass power generation, photovoltaic power generation, and power purchased from the external power grid. Generally speaking, the electric energy generated by renewable energy power generation in the park is preferentially supplied for use in the park. Without considering participating in the sale of electricity to the grid, the carbon responsibility converted from the electricity consumption for the park's production can be offset against the carbon responsibility of biomass power generation, and the carbon responsibilities offset by photovoltaic power generation are combined for calculation.

[0091] Therefore, the carbon emissions calculation for the electricity consumption in the park is as follows:

[0092] C e = C h - C pv - C s (7)

[0093] Among them, C e is the net converted carbon emissions of the electricity consumption for the park's production, C h is the total converted carbon emission responsibility of the electricity consumption for the park's production, C pv is the carbon emission responsibility offset by the green electricity generated by photovoltaic, and C s is the carbon emission responsibility offset by the green electricity generated by biomass.

[0094] When the electric energy generated by renewable energy power generation in the park cannot meet the park's demand, power needs to be purchased from the grid. Then, the carbon emission responsibility C p converted from the power purchased from the grid by the park is calculated by multiplying the power purchased from the grid by the carbon emission factor:

[0095] C p = Q p × β p (8)

[0096] Among them, Q p is the power purchased from the grid, and β p is the carbon emission factor of that area in that year. For example, the carbon emission factor in Beijing in 2020 is 0.615 (kgCO2 / kWh).

[0097] Example:

[0098] With the data support provided by the information flow of the agricultural Internet of Things, and based on the analysis of the material flow - energy flow - carbon flow in the modern agricultural and pastoral park above, and the carbon accounting boundary and accounting items determined based on the carbon flow, the following gives a specific positive and negative carbon monitoring case for each production and operation link in the modern agricultural and pastoral park.

[0099] The clean energy power generation access of a modern agricultural industrial park in the northwest region was taken as the research object for analysis. The total planting area of ​​the park is 48.97 hectares, of which corn planting and wheat planting account for 31.04% and 16.95% respectively, and the oilseed planting area is 2.3 hectares; the main breeding industry is cattle, sheep, pigs and chickens, including 650 dairy cows, 480 sheep, 2850 pigs and 11560 chickens. The data collected based on the information flow of agricultural Internet of Things technology and traditional statistical methods are as follows:

[0100] Table 1 Planting scale of the park

[0101] Category Corn Corn Vegetables Beet Orchard Area / hectare 15.2 8,3 5,4 1,57 15.4

[0102] Table 2 Scale of animal husbandry

[0103] Category Cattle Pigs Goats Chickens Quantity / thousand heads 650 2850 480 11560

[0104] Table 3 Manure production data

[0105]

[0106]

[0107] Table 4 Biomass production in the park

[0108] Raw material Solid fraction / % Organic matter percentage / % Biogas production / (m3 / t) Sheep manure 38 46 122 Goat manure 21 79 75 Pig manure 83 83 358 Chicken manure 18 76 52 Corn straw 87 69 558

[0109] Table 5 Park energy load

[0110]

[0111] The park uses anaerobic power generation technology, with a power generation efficiency of 2.0-2.6 degrees per cubic meter of biogas. This article takes the median of 2.3 degrees per cubic meter. The photovoltaic installation area of ​​the park's warehouses, buildings, and open spaces is 500m2, and the annual power generation is 150 watts per square meter of photovoltaic power. Based on the information flow of the agricultural park, the calculation results of the park's carbon emissions for one cycle (365 days) are as follows:

[0112] Table 6 Carbon emission accounting results of a farming and animal husbandry park in Northwest China

[0113] Project Carbon emissions / ton Agriculture section -128.2 Animal husbandry section 267.9 Biomass power generation section 23.1 Park energy consumption 175.5 Total 338.3

[0114] From the above calculation data, it can be seen that the main carbon emission projects of agricultural and pastoral parks are animal husbandry and park energy consumption, and agricultural production can produce larger carbon sinks when less pesticides and fertilizers are used. When the scale of agricultural production is large enough and animal husbandry production is controlled, the energy consumption of the park can be optimized and there is hope to achieve net zero carbon operation of the park.

[0115] In view of the problems of unclear carbon accounting boundaries and unclear carbon emission accounting items in modern agricultural and pastoral parks, the present invention takes the energy flow in modern agricultural and pastoral parks as the starting point. Based on the coupling characteristics between the energy flow and carbon flow in the park, the carbon flow in the park is determined. The carbon accounting boundary is determined according to the carbon flow, and the operation data of each region is collected correspondingly. The carbon emission is calculated by using the corresponding calculation model, realizing accurate and targeted carbon emission calculation for modern agricultural and pastoral parks.

[0116] In addition, in view of the problem that the traditional data collection method is difficult to support the refined carbon emission calculation of modern agricultural and pastoral parks, the present invention uses the Internet of Things technology to collect and transmit the relevant data of the agricultural and pastoral park to the Internet of Things terminal in real time through multiple gateways and nodes, providing data support for the carbon emission calculation of the park.

[0117] The above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them; although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that: the specific implementation manners of the present invention can still be modified or some technical features can be equivalently replaced; without departing from the spirit of the technical solutions of the present invention, they should all be covered within the scope of the technical solutions claimed by the present invention.

Claims

1. A carbon emission monitoring system for agricultural and pastoral areas based on energy flow-carbon flow coupling characteristics, characterized in that: include: Agricultural area data collection device, deployed in agricultural areas to obtain agricultural production data; Pastoral area data collection device, which is deployed in the pastoral area to obtain pastoral production data; Energy zone sensor nodes, deployed in the energy zone to obtain biomass power generation data and photovoltaic power generation data; Smart meters are used to obtain park load data and purchased electricity data; The agricultural IoT data terminal is connected to the agricultural area data collection device, the pastoral area data collection device, the energy area sensor node and the smart meter through the corresponding edge gateway to obtain agricultural production data, pastoral production data, biomass power generation data, photovoltaic power generation data, park load data and purchased power data; The Internet of Things platform is connected to the agricultural Internet of Things data terminal through a data transmission network, and includes a carbon emission calculation engine. The carbon emission calculation engine obtains the park carbon emission data by calling the calculation model in the model library based on the agricultural production data, animal husbandry production data, biomass power generation data, photovoltaic power generation data and park load data sent by the agricultural Internet of Things data terminal; wherein the calculation model includes: an agricultural project carbon emission calculation model, which is used to calculate crop carbon absorption data; an animal husbandry project carbon emission calculation model, which is used to calculate the carbon emission data of livestock and poultry within a predetermined growth cycle; a biomass power generation project carbon emission calculation model, which is used to calculate the carbon emission data of biomass power generation and biogas composting based on biomass power generation data; a park electricity carbon emission calculation model, which is used to calculate the park electricity carbon emission data based on purchased electricity data or biomass power generation data, photovoltaic power generation data and park load data.

2. The monitoring system according to claim 1, characterized in that: The agricultural project carbon emission calculation model calculates the carbon absorption during crop growth based on the dry matter weight per unit area of ​​the crop, the carbon absorption rate per unit organic dry matter synthesized by the crop, the amount of carbon absorbed and fixed by the crop, and the adjustment coefficient of the corresponding crop type.

3. The monitoring system according to claim 1, characterized in that: The carbon emission calculation model of the animal husbandry project calculates carbon emissions based on the feeding amount of different types of livestock and the CH4 carbon emission coefficient.

4. The monitoring system according to claim 1, characterized in that: When the amount of purchased electricity is greater than 0, the first biomass power generation project carbon emission calculation model is used to calculate the carbon emissions based on the amount of electricity purchased from the power grid and the carbon emission factor; otherwise, the second biomass power generation project carbon emission calculation model is used to calculate the carbon emissions based on the biomass power generation data, photovoltaic power generation data and park load data, wherein the park production electricity consumption is calculated based on the park load data, and the carbon emissions are calculated based on the biomass power generation data, photovoltaic power generation data and park production electricity consumption data.

5. A method for monitoring carbon emissions in agricultural and pastoral areas based on the energy flow-carbon flow coupling characteristics, characterized in that: include: Acquiring agricultural production data through agricultural area data collection devices arranged in agricultural areas; Acquire animal husbandry production data through animal husbandry data collection devices configured in animal husbandry areas; Obtain biomass power generation data and photovoltaic power generation data through energy zone sensor nodes configured in the energy zone; Obtain park load data and purchased electricity data through smart meters; The agricultural IoT data terminal is connected to the agricultural area data collection device, the pastoral area data collection device, the energy area sensor node and the smart meter through the corresponding edge gateway to obtain agricultural production data, pastoral production data, biomass power generation data, photovoltaic power generation data, park load data and purchased power data; The Internet of Things platform is configured to be connected to the agricultural Internet of Things data terminal through a data transmission network, so as to utilize the carbon emission calculation engine of the Internet of Things platform to obtain the park carbon emission data by calling the calculation model in the model library based on the agricultural production data, animal husbandry production data, biomass power generation data, photovoltaic power generation data and park load data sent by the agricultural Internet of Things data terminal; wherein the calculation model includes: an agricultural project carbon emission calculation model, which is used to calculate crop carbon absorption data; an animal husbandry project carbon emission calculation model, which is used to calculate the carbon emission data of livestock and poultry within a predetermined growth cycle; a biomass power generation project carbon emission calculation model, which is used to calculate the carbon emission data of biomass power generation and biogas composting according to the biomass power generation data; and a park electricity carbon emission calculation model, which is used to calculate the park electricity carbon emission data according to the purchased electricity data or the biomass power generation data, the photovoltaic power generation data and the park load data.

6. The monitoring method according to claim 5, characterized in that: The agricultural project carbon emission calculation model calculates the carbon absorption during crop growth based on the dry matter weight per unit area of ​​the crop, the carbon absorption rate per unit organic dry matter synthesized by the crop, the amount of carbon absorbed and fixed by the crop, and the adjustment coefficient of the corresponding crop type.

7. The monitoring method according to claim 5, characterized in that: The carbon emission calculation model of the animal husbandry project calculates carbon emissions based on the feeding amount of different types of livestock and the CH4 carbon emission coefficient.

8. The monitoring method according to claim 5, characterized in that: When the amount of purchased electricity is greater than 0, the first biomass power generation project carbon emission calculation model is used to calculate the carbon emissions based on the amount of electricity purchased from the power grid and the carbon emission factor; otherwise, the second biomass power generation project carbon emission calculation model is used to calculate the carbon emissions based on the biomass power generation data, photovoltaic power generation data and park load data, wherein the park production electricity consumption is calculated based on the park load data, and the carbon emissions are calculated based on the biomass power generation data, photovoltaic power generation data and park production electricity consumption data.

9. A readable storage medium, characterized in that: The readable storage medium stores a computer program; when the computer program is executed, the monitoring method according to any one of claims 5 to 8 is implemented.

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