Method and device for calculating carbon footprint of whole life cycle of agri-photovoltaic system

Abstract

The application discloses a kind of carbon footprint accounting method and device of the whole life cycle of agricultural light complementary system, the method includes: obtaining the system performance parameter of agricultural light complementary system, according to the system performance parameter of agricultural light complementary system to determine the whole life cycle of agricultural light complementary system, according to the carbon emission of photovoltaic subsystem whole life cycle, the carbon emission of agricultural subsystem whole life cycle, the carbon sink of agricultural subsystem whole life cycle and the carbon emission reduction amount of photovoltaic subsystem power generation replaces agricultural subsystem production electricity generation, determine the total carbon footprint of the whole life cycle of agricultural light complementary system.The application can truly reflect the synergistic carbon reduction effect between photovoltaic subsystem and agricultural subsystem by taking the carbon emission reduction amount of photovoltaic subsystem power generation replaces agricultural subsystem production electricity generation as one of the basis for determining the total carbon footprint of the whole life cycle of agricultural light complementary system, so that the evaluation result is closer to the actual carbon footprint level of agricultural light complementary system.

Description

Technical Field

[0001] This application belongs to the field of carbon neutrality and renewable energy, specifically relating to a method and apparatus for calculating the carbon footprint of an agricultural-solar hybrid system throughout its entire life cycle. Background Technology

[0002] Solar-agricultural hybrid systems combine the functions of clean energy production and agricultural planting, enabling simultaneous photovoltaic power generation and crop production on the same land. This is a crucial approach to alleviating the land use conflict between renewable energy development and arable land protection. With the deepening implementation of the "dual carbon" goals, the carbon footprint of solar-agricultural hybrid systems throughout their entire lifecycle is receiving increasing attention. Accurately assessing the carbon emissions and carbon sink effects of solar-agricultural hybrid systems is of great significance for optimizing system design, guiding carbon trading, and formulating industrial policies. Therefore, comprehensively and accurately assessing the carbon footprint of the entire solar-agricultural hybrid system is particularly important. Summary of the Invention

[0003] The purpose of this application is to provide a method and apparatus for calculating the carbon footprint of an agricultural-solar hybrid system throughout its entire life cycle, which can solve the problem of how to comprehensively and accurately assess the carbon footprint of the entire agricultural-solar hybrid system.

[0004] To solve the above-mentioned technical problems, this application is implemented as follows: In a first aspect, embodiments of this application provide a method for calculating the carbon footprint of an agricultural-photovoltaic complementary system throughout its entire lifecycle. The agricultural-photovoltaic complementary system includes a photovoltaic subsystem and an agricultural subsystem. The method includes: Obtain the system performance parameters of the agricultural-solar hybrid system; The full life cycle of the agricultural-solar hybrid system is determined based on the system performance parameters of the agricultural-solar hybrid system. Obtain the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, the carbon emissions of the agricultural subsystem throughout its entire life cycle, the carbon sink of the agricultural subsystem throughout its entire life cycle, and the carbon emission reduction generated by the photovoltaic subsystem replacing the electricity used for production in the agricultural subsystem. The total carbon footprint of the agricultural-photovoltaic complementary system is determined based on the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, the carbon emissions of the agricultural subsystem throughout its entire life cycle, the carbon sink of the agricultural subsystem throughout its entire life cycle, and the carbon emission reduction generated by the photovoltaic subsystem replacing the electricity used for production in the agricultural subsystem.

[0005] Optionally, obtaining the carbon emissions of the photovoltaic subsystem throughout its entire lifecycle includes: Acquire activity level data for the entire lifecycle of the photovoltaic subsystem and the corresponding carbon emission factor; wherein, the activity level data for the entire lifecycle of the photovoltaic subsystem includes quantitative data for at least one stage of raw material acquisition and production, transportation, construction, operation and maintenance, and waste recycling; The carbon emissions of the photovoltaic subsystem throughout its entire life cycle are determined based on the activity level data of the photovoltaic subsystem and the carbon emission factor corresponding to the activity level data.

[0006] Optionally, obtaining the carbon emissions of the agricultural subsystem throughout its entire life cycle includes: Acquire activity level data of the entire life cycle of the agricultural subsystem and the carbon emission factor corresponding to the activity level data; wherein, the activity level data of the entire life cycle of the agricultural subsystem includes quantitative data of at least one stage of agricultural input production and transportation, agricultural production activities, and agricultural product processing and transportation; The carbon emissions of the agricultural subsystem throughout its entire life cycle are determined based on the activity level data of the agricultural subsystem and the carbon emission factor corresponding to the activity level data.

[0007] Optionally, obtaining the carbon sequestration of the agricultural subsystem throughout its entire life cycle includes: Obtain carbon sink accounting data for the entire life cycle of the agricultural subsystem and the carbon sink factor corresponding to the carbon sink accounting data; wherein, the carbon sink accounting data includes at least one of the aboveground biomass data of crops, the underground biomass data of crops, and the sampling and detection data of soil organic carbon content; The carbon sink amount of the agricultural subsystem throughout its entire life cycle is determined based on the carbon sink accounting data of the agricultural subsystem and the carbon sink factor corresponding to the carbon sink accounting data.

[0008] Optionally, obtaining the carbon emission reduction generated by replacing the electricity used in agricultural production by the photovoltaic subsystem includes: Obtain the average carbon emission factor of the agricultural power grid and the agricultural production electricity consumption of the agricultural subsystem. The carbon emission reduction generated by replacing the electricity used for agricultural production in the agricultural subsystem is determined based on the electricity consumption of the agricultural subsystem and the average carbon emission factor of the power grid.

[0009] Optionally, the method further includes: Obtain several sets of decision variable parameters for the photovoltaic subsystem and the agricultural subsystem; Under the preset optimization objective and preset constraints, a set of decision variable parameters of photovoltaic subsystem and agricultural subsystem that satisfy the optimization objective and the constraints is determined from the several sets of decision variable parameters of photovoltaic subsystem and agricultural subsystem, and the set of decision variable parameters of photovoltaic subsystem and agricultural subsystem that satisfy the optimization objective and the constraints is used as the optimization parameters of the agricultural-photovoltaic complementary system. The agricultural-solar complementary system is optimized based on the optimization parameters of the agricultural-solar complementary system to obtain the optimized agricultural-solar complementary system.

[0010] Optionally, the photovoltaic subsystem includes photovoltaic panels, and the agricultural subsystem includes crops; The decision variable parameters of the photovoltaic subsystem include at least one of the following: the spacing of the photovoltaic panels, the tilt angle of the photovoltaic panels, the installation height of the photovoltaic panels, and the type of photovoltaic panels. The decision variable parameters of the agricultural subsystem include at least one of the following: the planting structure of the crop, the irrigation method of the crop, the fertilization plan of the crop, and the farming system of the crop.

[0011] Optionally, the optimization objective includes at least one of minimizing the total carbon footprint of the agricultural-photovoltaic complementary system throughout its entire life cycle, maximizing photovoltaic power generation, and maximizing total economic benefits; The constraints include at least one of the following: the spacing between the photovoltaic panels is greater than or equal to a preset spacing; the yield of the crops is greater than or equal to a preset yield; the soil erosion modulus is less than or equal to a preset soil erosion modulus; the initial investment is less than or equal to a preset budget; the groundwater extraction volume is less than or equal to a preset groundwater extraction volume; and the biodiversity index is greater than or equal to a preset biodiversity index.

[0012] Secondly, embodiments of this application provide a carbon footprint accounting device for the entire life cycle of an agricultural-photovoltaic complementary system. The agricultural-photovoltaic complementary system includes a photovoltaic subsystem and an agricultural subsystem. The carbon footprint accounting device for the entire life cycle of the agricultural-photovoltaic complementary system includes: The first data acquisition module is used to acquire the system performance parameters of the agricultural-photovoltaic complementary system; The full life cycle determination module is used to determine the full life cycle of the agricultural-solar hybrid system based on the system performance parameters of the agricultural-solar hybrid system. The second data acquisition module is used to acquire the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, the carbon emissions of the agricultural subsystem throughout its entire life cycle, the carbon sink of the agricultural subsystem throughout its entire life cycle, and the carbon emission reduction generated by the photovoltaic subsystem generating electricity to replace the electricity used for production in the agricultural subsystem. The carbon footprint accounting module is used to determine the total carbon footprint of the agricultural-photovoltaic complementary system throughout its entire life cycle based on the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, the carbon emissions of the agricultural subsystem throughout its entire life cycle, the carbon sink of the agricultural subsystem throughout its entire life cycle, and the carbon emission reduction generated by the photovoltaic subsystem replacing the electricity used for production in the agricultural subsystem.

[0013] Optionally, the second data acquisition module includes: A photovoltaic subsystem-related data acquisition submodule is used to acquire activity level data of the photovoltaic subsystem throughout its entire life cycle and the carbon emission factor corresponding to the activity level data; wherein, the activity level data of the photovoltaic subsystem throughout its entire life cycle includes quantitative data of at least one stage of raw material acquisition and production, transportation, construction, operation and maintenance, and waste recycling; The photovoltaic subsystem carbon emission calculation submodule is used to determine the carbon emissions of the photovoltaic subsystem throughout its entire life cycle based on the activity level data of the photovoltaic subsystem throughout its entire life cycle and the carbon emission factor corresponding to the activity level data.

[0014] Optionally, the second data acquisition module includes: The first agricultural subsystem related data acquisition submodule is used to acquire the activity level data of the agricultural subsystem throughout its entire life cycle and the carbon emission factor corresponding to the activity level data; wherein, the activity level data of the agricultural subsystem throughout its entire life cycle includes quantitative data of at least one stage of agricultural input production and transportation, agricultural production activities, and agricultural product processing and transportation; The agricultural subsystem carbon emission calculation submodule is used to determine the carbon emissions of the agricultural subsystem throughout its entire life cycle based on the activity level data of the agricultural subsystem and the carbon emission factor corresponding to the activity level data.

[0015] Optionally, the second data acquisition module includes: The second agricultural subsystem related data acquisition submodule is used to acquire carbon sink accounting data for the entire life cycle of the agricultural subsystem and the carbon sink factor corresponding to the carbon sink accounting data; wherein, the carbon sink accounting data includes at least one of the aboveground biomass data of crops, the underground biomass data of crops, and the sampling and detection data of soil organic carbon content. The agricultural subsystem carbon sink calculation submodule is used to determine the carbon sink amount of the agricultural subsystem throughout its entire life cycle based on the carbon sink accounting data of the agricultural subsystem and the carbon sink factor corresponding to the carbon sink accounting data.

[0016] Optionally, the second data acquisition module includes: The agricultural production electricity consumption acquisition submodule is used to acquire the agricultural production electricity consumption of the agricultural subsystem and the average carbon emission factor of the power grid. The carbon emission reduction calculation submodule is used to determine the carbon emission reduction generated by the photovoltaic subsystem replacing the electricity used for agricultural production in the agricultural subsystem, based on the electricity consumption of the agricultural subsystem and the average carbon emission factor of the power grid.

[0017] Optionally, the device further includes: The decision variable parameter acquisition module is used to acquire several sets of decision variable parameters for the photovoltaic subsystem and the agricultural subsystem. A multi-objective optimization module is used to determine, from the several sets of decision variable parameters of photovoltaic subsystems and agricultural subsystems that satisfy the optimization objective and the constraints, a set of decision variable parameters of photovoltaic subsystems and agricultural subsystems that satisfy the optimization objective and the constraints, and to use the set of decision variable parameters of photovoltaic subsystems and agricultural subsystems that satisfy the optimization objective and the constraints as the optimization parameters of the agricultural-photovoltaic complementary system. The agricultural-solar complementary system optimization module is used to optimize the agricultural-solar complementary system according to the optimization parameters of the agricultural-solar complementary system, so as to obtain the optimized agricultural-solar complementary system.

[0018] Optionally, the photovoltaic subsystem includes photovoltaic panels, and the agricultural subsystem includes crops; The decision variable parameters of the photovoltaic subsystem include at least one of the following: the spacing of the photovoltaic panels, the tilt angle of the photovoltaic panels, the installation height of the photovoltaic panels, and the type of photovoltaic panels. The decision variable parameters of the agricultural subsystem include at least one of the following: the planting structure of the crop, the irrigation method of the crop, the fertilization plan of the crop, and the farming system of the crop.

[0019] Optionally, the optimization objective includes at least one of minimizing the total carbon footprint of the agricultural-photovoltaic complementary system throughout its entire life cycle, maximizing photovoltaic power generation, and maximizing total economic benefits; The constraints include at least one of the following: the spacing between the photovoltaic panels is greater than or equal to a preset spacing; the yield of the crops is greater than or equal to a preset yield; the soil erosion modulus is less than or equal to a preset soil erosion modulus; the initial investment is less than or equal to a preset budget; the groundwater extraction volume is less than or equal to a preset groundwater extraction volume; and the biodiversity index is greater than or equal to a preset biodiversity index.

[0020] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein the program or instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0021] Fourthly, embodiments of this application provide a readable storage medium on which a program or instructions are stored, which, when executed by a processor, implement the steps of the method described in the first aspect.

[0022] Fifthly, embodiments of this application provide a chip, the chip including a processor and a communication interface, the communication interface being coupled to the processor, the processor being used to run programs or instructions to implement the method as described in the first aspect.

[0023] In this embodiment, system performance parameters of the agricultural-solar complementary system are obtained. Based on these parameters, the entire lifecycle of the system is determined. The carbon emissions of the photovoltaic subsystem, the carbon emissions of the agricultural subsystem, the carbon sink of the agricultural subsystem, and the carbon emission reductions resulting from the photovoltaic subsystem replacing agricultural subsystem electricity generation are obtained. Based on these figures, the total carbon footprint of the agricultural-solar complementary system is determined. This application, by obtaining data across four dimensions—carbon emissions of the photovoltaic subsystem, carbon emissions of the agricultural subsystem, carbon sink of the agricultural subsystem, and carbon emission reductions resulting from the photovoltaic subsystem replacing agricultural subsystem electricity generation—and determining the total carbon footprint of the agricultural-solar complementary system, integrates the photovoltaic and agricultural subsystems into a unified carbon footprint accounting framework, enabling a comprehensive assessment of the overall carbon footprint level of the agricultural-solar complementary system. In addition, this application uses the carbon emission reduction generated by replacing the electricity used in the agricultural subsystem with the photovoltaic subsystem as one of the bases for determining the total carbon footprint of the agricultural-photovoltaic complementary system throughout its entire life cycle. This can truly reflect the synergistic carbon reduction effect between the photovoltaic subsystem and the agricultural subsystem, making the assessment results closer to the actual carbon footprint level of the agricultural-photovoltaic complementary system. Attached Figure Description

[0024] Figure 1 This is a schematic diagram of the architecture of an agricultural-solar complementary system provided in an embodiment of this application; Figure 2 This is a flowchart illustrating the steps of a method for calculating the carbon footprint of an agricultural-solar hybrid system throughout its entire lifecycle, as provided in an embodiment of this application. Figure 3This is a schematic diagram illustrating the carbon-energy coupling relationship between a photovoltaic subsystem and an agricultural subsystem, as provided in an embodiment of this application. Figure 4 This is a device block diagram of a carbon footprint accounting device for the entire life cycle of an agricultural-solar hybrid system provided in an embodiment of this application; Figure 5 This is a structural diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0025] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0026] The terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and not to describe a specific order or sequence. It should be understood that such use of data can be interchanged where appropriate so that embodiments of this application can be implemented in orders other than those illustrated or described herein. Furthermore, in the specification and claims, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0027] The following description, in conjunction with the accompanying drawings, details a vehicle power replenishment control method, apparatus, equipment, and medium provided in this application through specific embodiments and application scenarios.

[0028] Please see Figure 1 , Figure 1 This is a schematic diagram of the architecture of an agricultural-solar complementary system provided in an embodiment of this application.

[0029] like Figure 1 As shown, the agricultural-solar complementary system includes a photovoltaic subsystem, an agricultural subsystem, and a shared infrastructure system. The photovoltaic subsystem includes equipment such as photovoltaic panel arrays, support systems, inverters, combiner boxes, cables, and transformers, as well as supporting infrastructure such as roads, fences, and monitoring systems. The agricultural subsystem includes arable land, crops, irrigation systems, fertilization systems, pest and disease control systems, and agricultural product harvesting and primary processing equipment. The shared infrastructure system includes shared infrastructure, which refers to facilities shared between the photovoltaic and agricultural subsystems.

[0030] Figure 2 This is a flowchart illustrating the steps of a method for calculating the carbon footprint of an agricultural-solar hybrid system throughout its entire lifecycle, as provided in an embodiment of this application. Figure 2 As shown, the method includes: Step 201: Obtain the system performance parameters of the agricultural-solar hybrid system.

[0031] Step 202: Determine the entire life cycle of the agricultural-solar complementary system based on the system performance parameters of the agricultural-solar complementary system.

[0032] Step 203: Obtain the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, the carbon emissions of the agricultural subsystem throughout its entire life cycle, the carbon sink of the agricultural subsystem throughout its entire life cycle, and the carbon emission reduction generated by the photovoltaic subsystem replacing the electricity used for agricultural production.

[0033] Step 204: Determine the total carbon footprint of the agricultural-photovoltaic complementary system throughout its entire life cycle based on the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, the carbon emissions of the agricultural subsystem throughout its entire life cycle, the carbon sink of the agricultural subsystem throughout its entire life cycle, and the carbon emission reduction generated by the photovoltaic subsystem replacing the electricity used for agricultural production.

[0034] In some embodiments of this application, system performance parameters of an agricultural-photovoltaic complementary system are obtained. These system performance parameters include the technical parameters of the photovoltaic subsystem and the production parameters of the agricultural subsystem. The technical parameters of the photovoltaic subsystem include the total installed capacity, the technical route of the photovoltaic panels, the support structure, the installation parameters of the support structure, and the design service life. The production parameters of the agricultural subsystem include the main crop variety, the planting system (monoculture / crop rotation / intercropping), and the planting years.

[0035] The entire lifecycle of an agro-solar hybrid system is determined based on its system performance parameters. This lifecycle encompasses six stages for both the photovoltaic and agricultural subsystems: raw material acquisition, manufacturing, transportation, construction, operation and maintenance, and waste recycling. Furthermore, the comprehensive annual benefits generated per unit area of ​​the agro-solar hybrid system can be determined based on its system performance parameters.

[0036] The carbon emissions of the photovoltaic subsystem throughout its entire life cycle, the carbon emissions of the agricultural subsystem throughout its entire life cycle, the carbon sink of the agricultural subsystem throughout its entire life cycle, and the carbon emission reduction generated by the photovoltaic subsystem replacing the agricultural subsystem's electricity production are obtained. Substituting the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, the carbon emissions of the agricultural subsystem throughout its entire life cycle, the carbon sink of the agricultural subsystem throughout its entire life cycle, and the carbon emission reduction generated by the photovoltaic subsystem replacing the agricultural subsystem's electricity production are into formula (1) for calculation, the total carbon footprint of the agricultural-photovoltaic complementary system throughout its entire life cycle can be obtained.

[0037] CF_total = CF_pv + CF_agri - CS_agri - C_elec (Formula 1) Wherein, CF_total is the total carbon footprint of the agricultural-solar hybrid system throughout its entire life cycle, CF_pv is the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, CF_agri is the carbon emissions of the agricultural subsystem throughout its entire life cycle, CS_agri is the carbon sink of the agricultural subsystem throughout its entire life cycle, and C_elec is the carbon emission reduction generated by the photovoltaic subsystem replacing the electricity used for agricultural production.

[0038] In addition, the Monte Carlo simulation method can be used to conduct uncertainty analysis on the total carbon footprint of several agricultural-photovoltaic complementary systems throughout their entire life cycle. This can yield probability distribution curves of several total carbon footprints. Through these probability distribution curves, the mean, median, and 95% confidence interval of several total carbon footprints can be obtained, thus providing a clear view of the uncertainty range of several total carbon footprints.

[0039] The Spearman rank correlation coefficient method can also be used to conduct sensitivity analysis on the total carbon footprint of the agricultural-photovoltaic complementary system throughout its entire life cycle. This involves calculating the correlation coefficient between each system performance parameter and the total carbon footprint based on Monte Carlo simulation. A larger absolute value of the correlation coefficient indicates a stronger impact of the system performance parameter on the total carbon footprint. A positive correlation coefficient means that increasing the system performance parameter increases the total carbon footprint; a negative correlation coefficient means that increasing the system performance parameter decreases the total carbon footprint. Finally, the top 5-10 system performance parameters with the greatest impact on the total carbon footprint are output, providing direction for data acquisition optimization and result calibration.

[0040] Furthermore, the agricultural-solar hybrid system can also include an Internet of Things (IoT) data access module for real-time collection of photovoltaic (PV) power generation data, meteorological data, soil moisture data, and agricultural production activity data. This allows for dynamic monitoring and real-time updates of the total carbon footprint of the agricultural-solar hybrid system throughout its entire lifecycle. Meteorological data directly determines the power generation and efficiency of the PV panels, thus directly affecting the calculation of carbon emission reductions. Soil moisture data affects irrigation energy consumption and carbon emissions. Meteorological data (e.g., temperature, precipitation, humidity) influences the probability of pest and disease occurrence and fertilizer utilization, indirectly altering the application of pesticides and fertilizers, thereby affecting the calculation of implicit carbon emissions from agricultural inputs.

[0041] This application obtains data from four dimensions: the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, the carbon emissions of the agricultural subsystem throughout its entire life cycle, the carbon sink of the agricultural subsystem throughout its entire life cycle, and the carbon emission reduction generated by the photovoltaic subsystem replacing the agricultural subsystem's electricity production. Based on this data, the total carbon footprint of the agro-photovoltaic complementary system throughout its entire life cycle is determined. This allows both the photovoltaic and agricultural subsystems to be included in a unified carbon footprint accounting framework, enabling a comprehensive assessment of the overall carbon footprint level of the agro-photovoltaic complementary system. Furthermore, by using the carbon emission reduction generated by the photovoltaic subsystem replacing the agricultural subsystem's electricity production as one of the bases for determining the total carbon footprint of the agro-photovoltaic complementary system throughout its entire life cycle, this application can accurately reflect the synergistic carbon reduction effect between the photovoltaic and agricultural subsystems, making the assessment results closer to the actual carbon footprint level of the agro-photovoltaic complementary system.

[0042] Furthermore, in some embodiments of this application, step 203 may also include the following steps: Step 2031: Obtain activity level data for the entire life cycle of the photovoltaic subsystem and the corresponding carbon emission factors; wherein, the activity level data for the entire life cycle of the photovoltaic subsystem includes quantitative data for at least one stage of raw material acquisition and production, transportation, construction, operation and maintenance, and waste recycling.

[0043] Step 2032: Determine the carbon emissions of the photovoltaic subsystem throughout its entire life cycle based on the activity level data of the photovoltaic subsystem and the carbon emission factor corresponding to the activity level data.

[0044] In some embodiments of this application, activity level data of the photovoltaic subsystem throughout its entire life cycle can be obtained from a preset carbon emission inventory of the photovoltaic subsystem throughout its entire life cycle.

[0045] The carbon emission inventory of the photovoltaic subsystem throughout its entire lifecycle records activity level data for the entire lifecycle of the photovoltaic subsystem. This activity level data includes quantitative data for at least one stage: raw material acquisition and production, transportation, construction, operation and maintenance, and waste recycling. Raw material acquisition and production includes photovoltaic panels (silicon, silicon wafers, solar cells, module packaging), support structures (steel, aluminum), inverters (electronic components, casings), and cables (copper, plastic), etc.; transportation refers to the transport of raw materials to the production plant and equipment to the project site; construction includes land leveling, foundation construction, equipment installation, and commissioning; operation and maintenance includes module cleaning, regular equipment inspection, and replacement of faulty equipment; and waste recycling includes equipment dismantling, material recycling, and hazardous waste disposal.

[0046] It can also obtain the carbon emission factors corresponding to the activity level data of the photovoltaic subsystem throughout its entire life cycle from a preset carbon factor library.

[0047] The carbon factor database is a structured database that maps regions, crops, and photovoltaic (PV) panel technology routes in three dimensions. It contains multiple sets of correspondences: 1) Regions: 31 provinces (autonomous regions and municipalities) in China, corresponding to different grid carbon emission factors, energy structure factors, and agricultural production background emission factors; 2) Crops: More than 15 major crops (e.g., rice, wheat, corn, cotton, soybeans, vegetables, etc.), corresponding to different carbon emission factors per unit yield and carbon sink factors per unit area; 3) PV panel technology routes: More than 6 mainstream PV panel technology routes, corresponding to different carbon emission factors per unit installed capacity / unit power generation. These more than 6 mainstream PV panel technology routes include monocrystalline silicon, polycrystalline silicon, thin film, TOPCon, HJT, PERC, and other PV panel technologies. The energy consumption and carbon emissions of PV panels using different technology routes vary significantly, thus requiring different carbon emission factors. Furthermore, a time dimension can be introduced to consider the impact of technological progress and changes in energy structure on carbon emission factors, establishing a dynamic update mechanism for the carbon factor database.

[0048] Based on the activity level data of the photovoltaic subsystem throughout its entire life cycle and the corresponding carbon emission factor, the carbon emissions of the photovoltaic subsystem throughout its entire life cycle are determined. Specifically, the carbon emissions of the photovoltaic subsystem throughout its entire life cycle = the activity level data of the photovoltaic subsystem throughout its entire life cycle × the carbon emission factor corresponding to the activity level data of the photovoltaic subsystem throughout its entire life cycle.

[0049] This application achieves complete and accurate quantification of the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, from "phased accounting" to "total aggregation," by collecting activity level data of the photovoltaic subsystem in stages according to different stages of its entire life cycle and matching them with the corresponding carbon emission factors. At the same time, it provides a locationable data foundation for subsequent refined carbon emission management and system optimization.

[0050] Furthermore, in some embodiments of this application, step 203 may also include the following steps: Step 2033: Obtain activity level data for the entire life cycle of the agricultural subsystem and the corresponding carbon emission factors; wherein, the activity level data for the entire life cycle of the agricultural subsystem includes quantitative data for at least one stage of agricultural input production and transportation, agricultural production activities, and agricultural product processing and transportation.

[0051] Step 2034: Determine the carbon emissions of the agricultural subsystem throughout its entire life cycle based on the activity level data of the agricultural subsystem and the corresponding carbon emission factors.

[0052] In some embodiments of this application, activity level data for the entire lifecycle of an agricultural subsystem can be obtained from a pre-defined carbon emission inventory of the entire lifecycle of the agricultural subsystem. Carbon emission factors corresponding to the activity level data for the entire lifecycle of the agricultural subsystem can also be obtained from a pre-defined carbon factor library. The activity level data for the entire lifecycle of the agricultural subsystem includes quantitative data for at least one stage of agricultural input production and transportation, agricultural production activities, and agricultural product processing and transportation. Agricultural input production and transportation includes the production and transportation of agricultural inputs such as seeds, fertilizers (nitrogen, phosphate, and potash), pesticides, agricultural films, and diesel fuel. Agricultural production activities include land preparation, sowing, irrigation, fertilization, pest and disease control, and harvesting. Agricultural product processing and transportation includes primary processing of agricultural products and transportation to the market.

[0053] Based on the activity level data of the agricultural subsystem throughout its entire life cycle and the corresponding carbon emission factor, the carbon emissions of the agricultural subsystem throughout its entire life cycle are determined. Specifically, the carbon emissions of the agricultural subsystem throughout its entire life cycle = the activity level data of the agricultural subsystem throughout its entire life cycle × the carbon emission factor of the agricultural subsystem throughout its entire life cycle.

[0054] This application collects activity level data of the agricultural subsystem in stages according to three phases: agricultural input production and transportation, agricultural production activities, and agricultural product processing and transportation, and matches each phase with its corresponding carbon emission factors. This enables a complete and accurate quantification of the carbon emissions of the agricultural subsystem throughout its entire life cycle, from "phased accounting" to "total aggregation". At the same time, it provides a locationable data foundation for subsequent refined management of agricultural carbon emissions and optimization of agronomic measures.

[0055] Furthermore, in some embodiments of this application, step 203 may also include the following steps: Step 2035: Obtain carbon sink accounting data for the entire life cycle of the agricultural subsystem and the corresponding carbon sink factor; wherein, the carbon sink accounting data includes at least one of the aboveground biomass data of crops, the underground biomass data of crops, and the sampling and detection data of soil organic carbon content.

[0056] Step 2036: Determine the carbon sink amount of the agricultural subsystem throughout its entire life cycle based on the carbon sink accounting data and the corresponding carbon sink factor.

[0057] In some embodiments of this application, carbon sequestration data for the entire lifecycle of an agricultural subsystem can be obtained from a pre-defined carbon sequestration inventory of the agricultural subsystem. This carbon sequestration data includes at least one of the following: aboveground crop biomass data, underground crop biomass data, and soil organic carbon content sampling and testing data. The aboveground crop biomass data includes stems, leaves, fruits, etc., while the underground crop biomass data includes roots, etc.

[0058] It can also obtain the carbon sink factors corresponding to the carbon sink accounting data of the entire life cycle of the agricultural subsystem from the preset carbon factor library.

[0059] Based on the carbon sink accounting data and the corresponding carbon sink factor of the agricultural subsystem throughout its entire life cycle, the carbon sink amount of the agricultural subsystem throughout its entire life cycle is determined. Specifically, the carbon sink amount of the agricultural subsystem throughout its entire life cycle = carbon sink accounting data of the agricultural subsystem throughout its entire life cycle × carbon sink factor corresponding to the carbon sink accounting data of the agricultural subsystem throughout its entire life cycle.

[0060] This application divides the carbon sink accounting data of agricultural subsystems into three categories: aboveground biomass data of crops, underground biomass data, and soil organic carbon content sampling and detection data. It then calculates the aboveground biomass carbon sink, underground root carbon sink, and soil organic carbon sequestration respectively, thus achieving a comprehensive, complete, and refined accounting of the carbon sink of agricultural subsystems.

[0061] Furthermore, in some embodiments of this application, step 203 may also include the following steps: Step 2037: Obtain the agricultural production electricity consumption of the agricultural subsystem and the average carbon emission factor of the power grid.

[0062] Step 2038: Determine the carbon emission reduction generated by the photovoltaic subsystem replacing the electricity used for agricultural production based on the electricity consumption of the agricultural subsystem and the average carbon emission factor of the power grid.

[0063] In some embodiments of this application, the average carbon emission factor of the agricultural subsystem and the power grid are obtained. Substituting these two factors into formula (2) yields the carbon emission reduction resulting from the photovoltaic subsystem replacing the agricultural subsystem's electricity consumption. Specifically, the carbon emission reduction from the photovoltaic subsystem replacing the agricultural subsystem's electricity consumption refers to the electricity used for agricultural production within the agricultural subsystem being supplied locally by the photovoltaic subsystem, without the need to purchase electricity from the grid—essentially self-generated and self-consumed electricity. This portion of the agricultural production electricity, which originally needed to be obtained from the grid (corresponding to carbon emissions from fossil fuel power generation), is now provided by the photovoltaic subsystem, thus resulting in carbon emission reduction. C_elec = E_agri × EF_grid formula (2) Where C_elec represents the carbon emission reduction generated by the photovoltaic subsystem replacing the electricity used for agricultural production in the agricultural subsystem, E_agri represents the electricity used for agricultural production in the agricultural subsystem, and EF_grid represents the average carbon emission factor of the power grid.

[0064] This application identifies and quantifies the carbon emission reduction generated by replacing agricultural production electricity consumption with the average carbon emission factor of the power grid by obtaining agricultural production electricity consumption. This alternative carbon emission reduction is included as an independent accounting item in the total carbon footprint accounting framework of the agricultural-photovoltaic complementary system, which truly reflects the carbon-energy synergistic carbon reduction effect between the photovoltaic subsystem and the agricultural subsystem. This avoids the carbon footprint assessment distortion caused by the separate accounting of the photovoltaic subsystem and the agricultural subsystem in related schemes.

[0065] Furthermore, in some embodiments of this application, the method may further include the following steps: Step 205: Obtain several sets of decision variable parameters for the photovoltaic subsystem and the agricultural subsystem.

[0066] Step 206: Under the preset optimization objective and preset constraints, determine a set of decision variable parameters of the photovoltaic subsystem and the agricultural subsystem that satisfy the optimization objective and constraints from several sets of decision variable parameters of the photovoltaic subsystem and the agricultural subsystem, and use the set of decision variable parameters of the photovoltaic subsystem and the agricultural subsystem that satisfy the optimization objective and constraints as the optimization parameters of the agricultural-photovoltaic complementary system.

[0067] Step 207: Optimize the agricultural-solar complementary system according to the optimization parameters of the agricultural-solar complementary system to obtain the optimized agricultural-solar complementary system.

[0068] In some embodiments of this application, the photovoltaic subsystem includes a photovoltaic panel array, which is composed of a plurality of photovoltaic panels, and the agricultural subsystem includes crops. The decision variable parameters of the photovoltaic subsystem include at least one of the following: the spacing between photovoltaic panels, the tilt angle of the photovoltaic panels, the installation height of the photovoltaic panels, and the type of photovoltaic panels. The decision variable parameters of the agricultural subsystem include at least one of the following: the crop planting structure, the crop irrigation method, the crop fertilization scheme, and the cropping system.

[0069] A multi-objective optimization model can be used to select at least one set of decision variable parameters for both the photovoltaic (PV) and agricultural subsystems from several sets of such parameters, ensuring that the selected parameters meet the preset optimization objectives and constraints. Furthermore, the decision-maker can choose a separate set of decision variable parameters from this selection based on preference, and use this set as the optimization parameters for the photovoltaic-agricultural complementary system. Subsequently, the photovoltaic-agricultural complementary system can be further optimized using these optimization parameters to obtain the optimized system.

[0070] The specific implementation process of selecting at least one set of decision variable parameters for the photovoltaic subsystem and the agricultural subsystem from several sets of decision variable parameters for the agricultural subsystem using a multi-objective optimization model is as follows: Optimization objectives are established, including at least one of the following: minimizing the total carbon footprint of the agro-photovoltaic complementary system throughout its entire lifecycle, maximizing photovoltaic power generation, and maximizing total economic benefits. Constraints are imposed, including at least one of the following: photovoltaic panel spacing is greater than or equal to a preset spacing (ensuring agricultural machinery can travel alongside); crop yield is greater than or equal to a preset yield; soil erosion modulus is less than or equal to a preset soil erosion modulus; initial investment is less than or equal to a preset budget; groundwater extraction is less than or equal to a preset groundwater extraction volume; and biodiversity index is greater than or equal to a preset biodiversity index.

[0071] The multi-objective optimization model integrates NSGA-II (Non-dominated sorting genetic algorithm). The non-dominated sorting genetic algorithm is used to solve for the decision variable parameters of several sets of photovoltaic subsystems and agricultural subsystems, obtaining at least one set of decision variable parameters for both the photovoltaic and agricultural subsystems that satisfy the preset optimization objectives and constraints.

[0072] Furthermore, after obtaining the total carbon footprint of the agricultural-solar complementary system throughout its entire life cycle and its optimized parameters, the total carbon footprint of the agricultural-solar complementary system throughout its entire life cycle and its optimized parameters can be displayed in chart form, and a carbon footprint accounting report conforming to national standards can be automatically generated.

[0073] This application, through systematic comparison and screening of decision variable parameters of multiple sets of photovoltaic subsystems and agricultural subsystems, ensures that the final determined parameter combination takes into account the mutual constraints between the photovoltaic and agricultural subsystems and meets the requirements of engineering feasibility under the combined effect of the guidance of the optimization objective and the constraints of the conditions. This avoids the overall performance imbalance of the agricultural-photovoltaic complementary system caused by optimizing a single subsystem independently, and enables the optimized agricultural-photovoltaic complementary system to achieve substantial synergistic optimization effects in terms of carbon footprint, energy output and economic benefits throughout the entire life cycle.

[0074] Furthermore, in some embodiments of this application, the carbon-energy coupling interface between the photovoltaic subsystem and the agricultural subsystem can be quantified by a preset carbon-energy coupling relationship quantification model.

[0075] The pre-defined quantitative models for carbon-energy coupling include a photovoltaic shading effect model, an energy substitution model, an agricultural activity impact model, and a shared infrastructure carbon emission allocation model. The carbon-energy coupling interface includes the impact of photovoltaic panel shading on crop photosynthetic rate, crop yield, and carbon sequestration capacity; the substitution of agricultural electricity consumption (e.g., irrigation, fertilization, harvesting) and carbon emission reduction by photovoltaic power generation; the impact of agricultural irrigation on photovoltaic panel surface temperature and power generation efficiency; the impact of agricultural fertilization / tillage dust on photovoltaic panel surface cleanliness and power generation efficiency; and the carbon emission allocation of shared infrastructure (e.g., roads, fences, power facilities).

[0076] Among them, such as Figure 3 As shown, the impact of photovoltaic (PV) panel shading on crop photosynthetic rate, crop yield, and carbon sequestration capacity specifically refers to PV panel shading → change in light intensity → crop photosynthesis → change in crop yield and carbon sequestration capacity. The substitution of agricultural electricity consumption and carbon emission reduction by PV power generation specifically refers to PV power generation → substitution of agricultural electricity consumption → reduction in fossil fuel consumption → reduction in carbon emissions. The impact of agricultural irrigation on PV panel surface temperature and power generation efficiency specifically refers to agricultural irrigation → increased air humidity → decreased PV panel surface temperature → increased power generation efficiency. The impact of agricultural fertilization / tillage dust on PV panel surface cleanliness and power generation efficiency specifically refers to agricultural fertilization → dust generation → PV panel surface contamination → decreased power generation efficiency. Carbon emission sharing of shared infrastructure specifically refers to shared infrastructure → carbon emissions being shared between the PV subsystem and the agricultural subsystem.

[0077] 1. The specific implementation process for quantifying the impact of photovoltaic panel shading on crop photosynthetic rate, crop yield, and carbon sequestration capacity using a photovoltaic shading effect model is as follows: 1) Calculation of the spatiotemporal distribution of sunlight under photovoltaic panels: The photovoltaic shading effect model integrates a ray tracing algorithm. The arrangement parameters of the photovoltaic panel array are input into the model, and the integrated ray tracing algorithm can determine the ray projection and calculate the shading boundary based on the arrangement parameters of the photovoltaic panel array, simulating the shadow movement trajectory formed by the photovoltaic panels on the farmland canopy. Therefore, the photovoltaic shading effect model can output the hourly effective photosynthetically active radiation intensity of the canopy at any coordinate point within the farmland plane. The calculation formula for the effective photosynthetically active radiation intensity of any canopy at any coordinate point within the farmland plane is shown in formula (3).

[0078] Formula (3) in, for Time coordinates The photosynthetically active radiation intensity of the canopy at that location, in units of , for The intensity of light and effective radiation in an open, unobstructed environment at any given time can be obtained using local typical meteorological year data or real-time meteorological observation data. for Time coordinates The shading attenuation coefficient at a given location ranges from 0 to 1 and is calculated by a ray tracing algorithm based on the arrangement parameters of the photovoltaic panel array.

[0079] 2) Field experimental calibration of the light-crop response relationship Multiple field control plots with different shading gradients were set up within the project area. The crop varieties, basic soil fertility, water and fertilizer management, and temperature environment within each plot were kept consistent. Only the canopy light intensity was changed, and in-situ observation experiments were conducted throughout the entire crop growth period. The experimental indicators covered the entire crop growth chain: crop plant height, leaf area index (LAI), net photosynthetic rate of leaves, aboveground / belowground biomass at maturity, economic yield, plant carbon content, and stratified soil organic carbon content under different shading gradients. Based on the measured sample data, a quantitative regression relationship between light intensity (i.e., canopy photosynthetically active radiation intensity) and various crop indicators (e.g., crop plant height, leaf area index, net photosynthetic rate of leaves, etc.) was established through statistical fitting methods. For example, the quantitative regression relationship between light intensity and net photosynthetic rate of leaves is shown in formula (4).

[0080] Formula (4) in, The net photosynthetic rate of the leaf. Initial light energy utilization rate, For the maximum net photosynthetic rate, This refers to the rate of dark respiration. Light intensity.

[0081] 3) Multi-factor coupled crop yield and carbon sink calculation model Based on the light-crop response relationship, correction coefficients for three core environmental factors—temperature, soil moisture, and fertilizer application—are introduced. A multi-factor coupled crop growth and carbon sink calculation model is pre-constructed for accurate calculation of crop yield and carbon sink in real-world scenarios. The calculation formula for the multi-factor coupled crop growth and carbon sink calculation model is shown in formula (5).

[0082] Formula (5) in, This refers to crop yield per unit area or carbon sequestration per unit area. Light intensity, The ambient temperature of the canopy. This refers to the soil volumetric water content. This refers to the amount of nitrogen fertilizer applied per unit area.

[0083] By inputting daily sunlight intensity, real-time temperature, soil moisture, and fertilizer application rate into a multi-factor coupled crop growth and carbon sink calculation model, the model can output the crop yield and carbon sink amount under photovoltaic shading scenarios.

[0084] 2. The specific implementation process of quantifying the substitution of agricultural production electricity consumption in the agricultural subsystem and carbon emission reduction by the photovoltaic subsystem power generation through the energy substitution model is as follows: input the agricultural production electricity consumption of the agricultural subsystem and the average carbon emission factor of the power grid into the energy substitution model, and the energy substitution model can automatically output the carbon emission reduction generated by the photovoltaic subsystem power generation replacing the agricultural production electricity consumption.

[0085] 3. The specific implementation process of quantifying the impact of agricultural production activities on the power generation efficiency of photovoltaic panels through an agricultural activity impact model is as follows: The power generation efficiency, surface temperature, and dust deposition thickness of the photovoltaic panel under standard conditions are obtained. These parameters are then input into the agricultural activity impact model, which automatically outputs the actual power generation efficiency of the photovoltaic panel. The calculation formula corresponding to the agricultural activity impact model is shown in formula (6).

[0086] η = η0 × (l - k1 × T - k2 × D) Formula (6) Where η is the actual power generation efficiency of the photovoltaic panel, η0 is the power generation efficiency of the photovoltaic panel under standard conditions, k1 and k2 are preset empirical coefficients, T is the temperature of the photovoltaic panel surface, and D is the thickness of dust deposition on the photovoltaic panel surface.

[0087] The dust deposition thickness is dynamically calculated. The calculation logic for dust deposition thickness is as follows: based on the dust deposition rate driven by agricultural activities, the natural cleaning effects of rainfall, wind, etc. are superimposed, and dynamic accumulation is carried out in the time dimension. It is also constrained by the reset of the photovoltaic panel manual cleaning cycle, and finally the actual dust deposition thickness on the surface of the module at a certain moment is obtained.

[0088] The calculation process for dust deposition thickness is as follows: 1) Calculate the dust deposition rate driven by agricultural activities. Obtain the fertilizer application rate per unit area, tillage intensity parameters, and average near-surface wind speed. Substitute the fertilizer application rate per unit area, tillage intensity parameters, and average near-surface wind speed into formula (7) for calculation to obtain the dust deposition rate.

[0089] Formula (7) in, Dust deposition rate, in g / (m²) d) or mm / d, This refers to the amount of fertilizer applied per unit area. For tillage intensity parameters, denoted as the near-surface average wind speed, and a, b, c, and d are empirical coefficients calibrated through experiments.

[0090] The physical meaning is that agricultural activities such as tilling and fertilizing generate near-ground dust. Some of this dust settles onto the surface of photovoltaic panels, resulting in a continuous increase in dust accumulation. The stronger the agricultural activities, the greater the amount of dust and the higher the rate of dust deposition.

[0091] 2) Calculate the natural cleaning rate In real-world scenarios, rain and wind continuously remove dust from the surface of photovoltaic panels, offsetting some of the increase in dust accumulation. Therefore, it is necessary to calculate the natural cleaning rate simultaneously.

[0092] Wind-driven cleaning: When the wind speed exceeds the threshold, the purging effect is positively correlated with the wind speed, removing a small amount of floating dust.

[0093] Rainfall cleaning: When the daily rainfall reaches the critical value (usually ≥5mm), most of the dust accumulated on the surface of the photovoltaic panel can be washed away. The cleaning effect is positively correlated with the rainfall. After heavy rainfall, the thickness of dust deposition can be approximately zero.

[0094] Obtain the daily rainfall and average daily wind speed. Substitute the daily rainfall and average daily wind speed into formula (8) for calculation to obtain the natural cleaning rate.

[0095] Formula (8) in, For natural cleaning rate, This refers to the daily rainfall. The average daily wind speed, These are preset empirical coefficients.

[0096] 3) The dust deposition thickness is obtained by dynamically accumulating over time. Under the combined effect of deposition and cleaning, the thickness of dust deposition changes dynamically over time. Simultaneously, it is affected by the reset effect of the manual cleaning cycle. Accumulation calculation on a daily basis better reflects actual operational data. The daily increase in dust deposition caused by agricultural dust, the daily amount of dust removal by natural processes, and the dust deposition thickness at the end of day n-1 are obtained. Substituting these factors into formula (9), the dust deposition thickness at the end of day n can be calculated.

[0097] Formula (9) in, For the first The thickness of dust accumulation at the end of the day, if manual component cleaning is performed on that day, will be [affected by the dust accumulation at the end of the day]. , For the first Dust deposition thickness at the end of the day The increase in dust deposition due to agricultural dust on that day is obtained by multiplying the dust deposition rate on that day by the daily step size. This represents the amount of dust deposits removed by natural processes on that day.

[0098] 4) The specific implementation process of quantifying the allocation of carbon emissions between the photovoltaic subsystem and the agricultural subsystem using a shared infrastructure carbon emission allocation model is as follows: Obtain the total carbon emissions over the entire lifecycle of the shared infrastructure and the usage intensity data of the agricultural-photovoltaic complementary system. The usage intensity data includes annual transport volume (tons). (km), annual electricity consumption (kWh), land area ( The data includes the total carbon emissions of shared infrastructure throughout its entire lifecycle and the usage intensity of the agricultural-solar hybrid system. These data are then input into the shared infrastructure carbon emission allocation model, which automatically outputs the carbon emissions allocated to the photovoltaic subsystem and the shared infrastructure carbon emissions allocated to the agricultural subsystem.

[0099] Corresponding to the method provided in the above-described embodiment of the carbon footprint accounting method for the entire life cycle of an agro-solar hybrid system in this application, see [link to relevant documentation]. Figure 4 This application also provides a device block diagram for calculating the carbon footprint of an agricultural-solar hybrid system throughout its entire life cycle. In this embodiment, the device includes: The first data acquisition module 401 is used to acquire the system performance parameters of the agricultural-photovoltaic complementary system. The full life cycle determination module 402 is used to determine the full life cycle of the agricultural-solar hybrid system based on the system performance parameters of the agricultural-solar hybrid system. The second data acquisition module 403 is used to acquire the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, the carbon emissions of the agricultural subsystem throughout its entire life cycle, the carbon sink of the agricultural subsystem throughout its entire life cycle, and the carbon emission reduction generated by the photovoltaic subsystem replacing the electricity used for agricultural production. The carbon footprint accounting module 404 is used to determine the total carbon footprint of the agricultural-photovoltaic complementary system throughout its entire life cycle based on the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, the carbon emissions of the agricultural subsystem throughout its entire life cycle, the carbon sink of the agricultural subsystem throughout its entire life cycle, and the carbon emission reduction generated by the photovoltaic subsystem replacing the electricity used for agricultural production.

[0100] Optionally, the second data acquisition module 403 includes: The photovoltaic subsystem-related data acquisition submodule is used to acquire activity level data of the photovoltaic subsystem throughout its entire life cycle and the corresponding carbon emission factors. The activity level data of the photovoltaic subsystem throughout its entire life cycle includes quantitative data of at least one stage of raw material acquisition and production, transportation, construction, operation and maintenance, and waste recycling. The photovoltaic subsystem carbon emission calculation submodule is used to determine the carbon emissions of the photovoltaic subsystem throughout its entire life cycle based on the activity level data of the photovoltaic subsystem and the corresponding carbon emission factors.

[0101] Optionally, the second data acquisition module 403 includes: The first agricultural subsystem related data acquisition submodule is used to acquire activity level data of the agricultural subsystem throughout its entire life cycle and the carbon emission factor corresponding to the activity level data; wherein, the activity level data of the agricultural subsystem throughout its entire life cycle includes quantitative data of at least one stage of agricultural input production and transportation, agricultural production activities, and agricultural product processing and transportation; The agricultural subsystem carbon emission calculation submodule is used to determine the total carbon emissions of the agricultural subsystem throughout its entire life cycle based on the activity level data and the corresponding carbon emission factors of the activity level data.

[0102] Optionally, the second data acquisition module 403 includes: The second agricultural subsystem related data acquisition submodule is used to acquire carbon sink accounting data and carbon sink factors corresponding to the entire life cycle of the agricultural subsystem; wherein, the carbon sink accounting data includes at least one of the aboveground biomass data of crops, underground biomass data of crops, and sampling and detection data of soil organic carbon content. The agricultural subsystem carbon sink calculation submodule is used to determine the carbon sink amount of the agricultural subsystem throughout its entire life cycle based on the carbon sink accounting data and the corresponding carbon sink factor of the carbon sink accounting data.

[0103] Optionally, the second data acquisition module 403 includes: The agricultural production electricity consumption acquisition submodule is used to acquire the agricultural production electricity consumption of the agricultural subsystem and the average carbon emission factor of the power grid; The carbon emission reduction calculation submodule is used to determine the carbon emission reduction generated by the photovoltaic subsystem replacing the electricity used for agricultural production based on the electricity consumption of the agricultural subsystem and the average carbon emission factor of the power grid.

[0104] Optionally, the device further includes: The decision variable parameter acquisition module is used to acquire several sets of decision variable parameters for the photovoltaic subsystem and the agricultural subsystem. The multi-objective optimization module is used to determine, from several sets of decision variable parameters of photovoltaic subsystem and agricultural subsystem, a set of decision variable parameters of photovoltaic subsystem and agricultural subsystem that satisfy the optimization objective and constraints, and to use the set of decision variable parameters of photovoltaic subsystem and agricultural subsystem that satisfy the optimization objective and constraints as the optimization parameters of agricultural-photovoltaic complementary system. The agricultural-solar complementary system optimization module is used to optimize the agricultural-solar complementary system according to the optimization parameters of the agricultural-solar complementary system, so as to obtain the optimized agricultural-solar complementary system.

[0105] Optionally, the photovoltaic subsystem includes photovoltaic panels, and the agricultural subsystem includes crops; The decision variables of the photovoltaic subsystem include at least one of the following: the spacing of the photovoltaic panels, the tilt angle of the photovoltaic panels, the installation height of the photovoltaic panels, and the type of photovoltaic panels. The decision variables of the agricultural subsystem include at least one of the following: crop planting structure, crop irrigation method, crop fertilization program, and crop farming system.

[0106] Optionally, the optimization objectives include at least one of the following: minimizing the total carbon footprint of the agricultural-photovoltaic complementary system throughout its entire life cycle, maximizing photovoltaic power generation, and maximizing total economic benefits; The constraints include at least one of the following: the spacing between photovoltaic panels is greater than or equal to the preset spacing; the yield of crops is greater than or equal to the preset yield; the soil erosion modulus is less than or equal to the preset soil erosion modulus; the initial investment is less than or equal to the preset budget; the groundwater extraction volume is less than or equal to the preset groundwater extraction volume; and the biodiversity index is greater than or equal to the preset biodiversity index.

[0107] Figure 5 This is a structural diagram of an electronic device M00 provided in an embodiment of this application. In the diagram, the electronic device M00 includes a processor M01 and a memory M02. The memory M02 stores a program or instructions that can run on the processor M01. When the program or instructions are executed by the processor M01, they implement the various steps of the above-mentioned method embodiment for calculating the carbon footprint of the entire life cycle of the agricultural-photovoltaic complementary system and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0108] In embodiments of this application, the memory M02 can be used to store software programs and various data. The memory M02 may primarily include a first storage area for storing programs or instructions and a second storage area for storing data. The first storage area may store the operating system, applications or instructions required for at least one function (such as sound playback, image playback, etc.). Furthermore, the memory M02 may include volatile memory or non-volatile memory, or both. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), static random access memory (SRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct memory bus RAM (DRRAM). The memory M02 in the embodiments of this application includes, but is not limited to, these and any other suitable types of memory.

[0109] The processor M01 may include one or more processing units; optionally, the processor M01 integrates an application processor and a modem processor, wherein the application processor mainly handles operations involving the operating system, user interface, and applications, and the modem processor mainly handles wireless communication signals, such as a baseband processor. It is understood that the aforementioned modem processor may also not be integrated into the processor M01.

[0110] This application also provides a readable storage medium storing a program or instructions. When the program or instructions are executed by a processor, they implement the various processes of the above-described embodiment of the carbon footprint accounting method for the entire life cycle of the agricultural-solar complementary system and achieve the same technical effect. To avoid repetition, they will not be described again here.

[0111] The processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0112] This application embodiment also provides a chip, which includes a processor and a communication interface. The communication interface and the processor are coupled. The processor is used to run programs or instructions to implement the various processes of the above-described embodiment of the carbon footprint accounting method for the entire life cycle of the agricultural-photovoltaic complementary system, and can achieve the same technical effect. To avoid repetition, it will not be described again here.

[0113] It should be understood that the chip mentioned in the embodiments of this application may also be referred to as a system-on-a-chip, system chip, chip system, or system-on-a-chip, etc.

[0114] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element. Furthermore, it should be noted that the scope of the methods and apparatuses in the embodiments of this application is not limited to performing functions in the order shown or discussed, but may also include performing functions substantially simultaneously or in the reverse order, depending on the functions involved. For example, the described methods may be performed in a different order than described, and various steps may be added, omitted, or combined. Additionally, features described with reference to certain examples may be combined in other examples.

[0115] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0116] The embodiments of this application have been described above with reference to the accompanying drawings. However, this application is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of this application without departing from the spirit and scope of the claims, and all of these forms are within the protection scope of this application.

Claims

1. A method for calculating the carbon footprint of an agro-solar hybrid system throughout its entire life cycle, characterized in that, The agro-photovoltaic complementary system includes a photovoltaic subsystem and an agricultural subsystem, and the method includes: Obtain the system performance parameters of the agricultural-solar hybrid system; The full life cycle of the agricultural-solar hybrid system is determined based on the system performance parameters of the agricultural-solar hybrid system. Obtain the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, the carbon emissions of the agricultural subsystem throughout its entire life cycle, the carbon sink of the agricultural subsystem throughout its entire life cycle, and the carbon emission reduction generated by the photovoltaic subsystem replacing the electricity used for production in the agricultural subsystem. The total carbon footprint of the agricultural-photovoltaic complementary system is determined based on the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, the carbon emissions of the agricultural subsystem throughout its entire life cycle, the carbon sink of the agricultural subsystem throughout its entire life cycle, and the carbon emission reduction generated by the photovoltaic subsystem replacing the electricity used for production in the agricultural subsystem.

2. The method according to claim 1, characterized in that, The process of obtaining the carbon emissions of the photovoltaic subsystem throughout its entire lifecycle includes: Acquire activity level data for the entire lifecycle of the photovoltaic subsystem and the corresponding carbon emission factor; wherein, the activity level data for the entire lifecycle of the photovoltaic subsystem includes quantitative data for at least one stage of raw material acquisition and production, transportation, construction, operation and maintenance, and waste recycling; The carbon emissions of the photovoltaic subsystem throughout its entire life cycle are determined based on the activity level data of the photovoltaic subsystem and the carbon emission factor corresponding to the activity level data.

3. The method according to claim 1, characterized in that, The process of obtaining the carbon emissions of the agricultural subsystem throughout its entire life cycle includes: Acquire activity level data of the entire life cycle of the agricultural subsystem and the carbon emission factor corresponding to the activity level data; wherein, the activity level data of the entire life cycle of the agricultural subsystem includes quantitative data of at least one stage of agricultural input production and transportation, agricultural production activities, and agricultural product processing and transportation; The carbon emissions of the agricultural subsystem throughout its entire life cycle are determined based on the activity level data of the agricultural subsystem and the carbon emission factor corresponding to the activity level data.

4. The method according to claim 1, characterized in that, The process of obtaining the carbon sequestration of the agricultural subsystem throughout its entire life cycle includes: Obtain carbon sink accounting data for the entire life cycle of the agricultural subsystem and the carbon sink factor corresponding to the carbon sink accounting data; wherein, the carbon sink accounting data includes at least one of the aboveground biomass data of crops, the underground biomass data of crops, and the sampling and detection data of soil organic carbon content; The carbon sink amount of the agricultural subsystem throughout its entire life cycle is determined based on the carbon sink accounting data of the agricultural subsystem and the carbon sink factor corresponding to the carbon sink accounting data.

5. The method according to claim 1, characterized in that, The carbon emission reduction generated by replacing the electricity used in agricultural production by the photovoltaic subsystem includes: Obtain the average carbon emission factor of the agricultural power grid and the agricultural production electricity consumption of the agricultural subsystem. The carbon emission reduction generated by replacing the electricity used for agricultural production in the agricultural subsystem is determined based on the electricity consumption of the agricultural subsystem and the average carbon emission factor of the power grid.

6. The method according to claim 1, characterized in that, The method further includes: Obtain several sets of decision variable parameters for the photovoltaic subsystem and the agricultural subsystem; Under the preset optimization objective and preset constraints, a set of decision variable parameters of photovoltaic subsystem and agricultural subsystem that satisfy the optimization objective and the constraints is determined from the several sets of decision variable parameters of photovoltaic subsystem and agricultural subsystem, and the set of decision variable parameters of photovoltaic subsystem and agricultural subsystem that satisfy the optimization objective and the constraints is used as the optimization parameters of the agricultural-photovoltaic complementary system. The agricultural-solar complementary system is optimized based on the optimization parameters of the agricultural-solar complementary system to obtain the optimized agricultural-solar complementary system.

7. The method according to claim 6, characterized in that, The photovoltaic subsystem includes photovoltaic panels, and the agricultural subsystem includes crops; The decision variable parameters of the photovoltaic subsystem include at least one of the following: the spacing of the photovoltaic panels, the tilt angle of the photovoltaic panels, the installation height of the photovoltaic panels, and the type of photovoltaic panels. The decision variable parameters of the agricultural subsystem include at least one of the following: the planting structure of the crop, the irrigation method of the crop, the fertilization plan of the crop, and the farming system of the crop.

8. The method according to claim 6, characterized in that, The optimization objectives include at least one of the following: minimizing the total carbon footprint of the agricultural-photovoltaic complementary system throughout its entire life cycle, maximizing photovoltaic power generation, and maximizing total economic benefits. The constraints include at least one of the following: the spacing between the photovoltaic panels is greater than or equal to a preset spacing; the yield of the crops is greater than or equal to a preset yield; the soil erosion modulus is less than or equal to a preset soil erosion modulus; the initial investment is less than or equal to a preset budget; the groundwater extraction volume is less than or equal to a preset groundwater extraction volume; and the biodiversity index is greater than or equal to a preset biodiversity index.

9. A carbon footprint accounting device for the entire life cycle of an agricultural-solar hybrid system, characterized in that, The agro-photovoltaic complementary system includes a photovoltaic subsystem and an agricultural subsystem. The carbon footprint accounting device for the entire life cycle of the agro-photovoltaic complementary system includes: The first data acquisition module is used to acquire the system performance parameters of the agricultural-photovoltaic complementary system; The full life cycle determination module is used to determine the full life cycle of the agricultural-solar hybrid system based on the system performance parameters of the agricultural-solar hybrid system. The second data acquisition module is used to acquire the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, the carbon emissions of the agricultural subsystem throughout its entire life cycle, the carbon sink of the agricultural subsystem throughout its entire life cycle, and the carbon emission reduction generated by the photovoltaic subsystem generating electricity to replace the electricity used for production in the agricultural subsystem. The carbon footprint accounting module is used to determine the total carbon footprint of the agricultural-photovoltaic complementary system throughout its entire life cycle based on the carbon emissions of the photovoltaic subsystem throughout its entire life cycle, the carbon emissions of the agricultural subsystem throughout its entire life cycle, the carbon sink of the agricultural subsystem throughout its entire life cycle, and the carbon emission reduction generated by the photovoltaic subsystem replacing the electricity used for production in the agricultural subsystem.

10. An electronic device, characterized in that, It includes a processor, a memory, and a program or instructions stored in the memory and executable on the processor, wherein when the program or instructions are executed by the processor, they implement the steps of the carbon footprint accounting method for the entire life cycle of an agro-photovoltaic complementary system as described in any one of claims 1-8.

11. A readable storage medium, characterized in that, The program or instructions are stored on the readable storage medium, and when the program or instructions are executed by the processor, they implement the steps of the carbon footprint accounting method for the entire life cycle of the agricultural-photovoltaic complementary system as described in any one of claims 1-8.

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