Tobacco carbon accounting parameter analysis and prediction system

Abstract

The invention discloses a tobacco carbon accounting parameter analysis and prediction system, and relates to the technical field of carbon accounting analysis and prediction management. The tobacco carbon accounting parameter analysis and prediction system comprises an initial accounting analysis module which is used for continuously acquiring a carbon accounting parameter set of each period and analyzing an initial carbon emission index; the soil environment correction analysis module extracts an environment correction factor based on the soil environment data; the growth correction analysis module extracts a growth correction factor based on the tobacco growth image sequence and a pre-trained growth situation recognition model; the prediction and analysis module is used for calculating the comprehensive tobacco carbon emission index based on the analysis result of the prediction and analysis module and predicting the comprehensive tobacco carbon emission index of the next period, and management measures are taken for the tobacco planting area according to the comprehensive tobacco carbon emission index of the next period in the prediction and feedback management module; therefore, the accuracy of carbon accounting prediction is improved, abnormal carbon emission fluctuation is found in time, and corresponding management measures are taken.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon accounting analysis, prediction and management, and in particular to a tobacco carbon accounting parameter analysis and prediction system. Background Art

[0002] As an important part of the national economy, the tobacco industry involves multiple links in its production process and contains complex carbon emission factors. Tobacco carbon accounting refers to the process of quantitatively evaluating the carbon emissions and carbon sink behaviors generated at each stage of the tobacco life cycle. It aims to comprehensively measure the contribution of tobacco production activities to regional carbon balance and climate impact. The accounting process usually covers planting, harvesting, baking, primary processing and circulation processes, involving the identification and quantification of multiple carbon source and carbon sink elements such as energy consumption, mechanical operations, and biological carbon sinks.

[0003] In the entire process, the planting stage is the link with the most concentrated and variable carbon emissions and carbon absorption activities, and its carbon accounting work is particularly critical. The carbon accounting of this stage mainly focuses on the carbon emissions and carbon absorption caused by soil disturbance, fertilizer application, plant growth and other behaviors during field operations. It aims to dynamically capture the specific impact of agricultural operations on carbon flux, and provide basic support for optimizing planting management strategies and implementing low-carbon agriculture, so as to reduce carbon emissions and achieve green and sustainable development.

[0004] However, in traditional technologies, rough estimates are generally made based on annual average or periodic manual measurements of carbon emissions, which makes it difficult to achieve detailed modeling and dynamic control of carbon emission behaviors in specific planting areas and specific time periods. In addition, especially in the tobacco planting stage, traditional carbon emission accounting methods often lack systematic consideration of the comprehensive influence of multiple factors such as soil microenvironment changes, growth stage differences, and agronomic operations, which easily leads to insufficient accuracy of accounting results.

[0005] Among them, the limitations of existing technologies include at least the following problems: it is difficult for existing technologies to reflect the dynamic changes in carbon emissions in tobacco-growing areas in real time. Carbon emissions in tobacco-growing areas are affected by complex and interrelated factors such as soil and plant growth. These factors change dynamically within the cycle. For example, soil moisture fluctuates rapidly due to rainfall and evaporation, and the respiration rate of tobacco plants changes accordingly with changes in sunlight intensity, thereby affecting the overall level of carbon absorption and emission, so that the carbon emissions in tobacco-growing areas show complex fluctuation characteristics throughout the growth cycle. Existing technologies lack multi-dimensional data fusion capabilities, and it is difficult to synchronously collect and accurately integrate data from multiple influencing factors, and thus it is difficult to comprehensively integrate multiple factors for accurate carbon accounting, which easily leads to the difficulty in effectively capturing and analyzing the fluctuation characteristics of carbon emissions, thereby affecting the accuracy of management decisions. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a tobacco carbon accounting parameter analysis and prediction system, which solves the problem that the existing technology lacks multi-dimensional data fusion, making it difficult to accurately capture the carbon emission characteristics of tobacco-growing areas and affecting the accuracy of carbon accounting.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a tobacco carbon accounting parameter analysis and prediction system, comprising the following steps: an initial accounting analysis module, used to continuously obtain the carbon accounting parameter sets of several cycles of a set tobacco planting area, and analyze the initial carbon emission index of the corresponding cycle; a soil environment correction analysis module, used to obtain the soil environment data of each cycle of the set tobacco planting area, and analyze the soil environment correction factor of the corresponding cycle; a growth correction analysis module, used to obtain the tobacco growth image sequence data of each cycle of the set tobacco planting area, and analyze the growth correction factor of the corresponding cycle based on a pre-trained growth trend recognition model; a prediction analysis module, used to analyze the comprehensive tobacco carbon emission index of the corresponding cycle based on the initial carbon emission index, soil environment correction factor and growth correction factor of each cycle of the set tobacco planting area, and predict the comprehensive tobacco carbon emission index of the next cycle; a prediction feedback management module, used to take preset management measures for the set tobacco planting area based on the comprehensive tobacco carbon emission index of the next cycle.

[0008] Furthermore, the carbon accounting parameter set includes the planting area value, tobacco plant biomass value, tobacco plant respiration rate value, tobacco plant carbon sequestration value, soil carbon sequestration value, the usage value of each agricultural activity and the corresponding carbon emission factor, application area value. The specific steps for analyzing the initial carbon emission index of each cycle of the set tobacco planting area are as follows: based on the carbon accounting parameter set of each cycle of the set tobacco planting area, analyze the accounting evaluation set of the corresponding cycle, including the comprehensive carbon emission index and the comprehensive carbon sink index; obtain the carbon emission influencing factor and carbon sink influencing factor of each cycle of the set tobacco planting area, and conduct a comprehensive analysis with the emission evaluation set of the corresponding cycle to obtain the initial carbon emission index of the corresponding cycle.

[0009] Furthermore, the specific steps of analyzing the accounting evaluation set for each cycle of the set tobacco planting area are as follows: based on the planting area value, tobacco plant biomass, tobacco plant respiration rate value, dosage value of each agricultural activity and corresponding carbon emission factor, application area value of each cycle of the set tobacco planting area, analyze the comprehensive carbon emission index of the corresponding cycle; based on the tobacco plant carbon sink value and soil carbon sink value of each cycle of the set tobacco planting area, analyze the comprehensive carbon sink index of the corresponding cycle.

[0010] Furthermore, the specific steps for obtaining the carbon emission influencing factors and carbon sequestration influencing factors of each cycle of the set tobacco planting area are as follows: obtain the tillage intensity value, tillage frequency value, and tillage layer disturbance depth value of each cycle of the set tobacco planting area, and analyze the carbon emission influencing factors of the corresponding cycle; obtain the canopy absorption rate, chlorophyll relative content index, and soil structure stability index of each cycle of the set tobacco planting area, and analyze the carbon sequestration influencing factors of the corresponding cycle.

[0011] Furthermore, the soil environmental data includes soil thermal diffusivity value, soil environmental regulation factor, root decomposition rate value, soil electrical conductivity gradient value, and soil dissolved oxygen concentration value. The specific steps for analyzing the soil environmental correction factor of each period of the set tobacco planting area are as follows: obtaining the climate factor of each period of the set tobacco planting area, and combining it with the soil environmental data of the corresponding period, analyzing the soil environmental assessment set of the corresponding period, including the soil energy regulation factor and the soil carbon conversion factor; based on the soil environmental assessment set of each period of the set tobacco planting area, analyzing the soil environmental correction factor of the corresponding period.

[0012] Furthermore, the specific steps for analyzing the soil environmental assessment set for each period of the set tobacco planting area are as follows: based on the soil thermal diffusivity value, soil environmental regulation factor, and soil electrical conductivity gradient value for each period of the set tobacco planting area, the soil energy regulation factor for the corresponding period is analyzed; based on the root decomposition rate value and soil dissolved oxygen concentration value for each period of the set tobacco planting area, the soil carbon conversion factor for the corresponding period is analyzed.

[0013] Furthermore, the tobacco growth image sequence data includes tobacco growth image data of each time window, which includes multi-band pixel values ​​and two-dimensional coordinates of each pixel point. The specific steps of analyzing the growth correction factor of each cycle in the set tobacco planting area are as follows: inputting the tobacco growth image data of each time window of each cycle in the set tobacco planting area into a pre-trained growth status recognition model for comprehensive analysis to obtain a growth correction evaluation set of the corresponding cycle, including a carbon absorption index, a tobacco vitality index, and a tobacco light adaptation index; based on the growth evaluation set of each cycle in the set tobacco planting area, analyzing the growth correction factor of the corresponding cycle.

[0014] Furthermore, the specific formula for calculating the growth correction factor for a certain period of a set tobacco planting area is as follows: ; in, To set the growth correction factor for a certain period in a tobacco growing area, To set the carbon absorption index for a tobacco growing area for a certain period, is the absorption coefficient stored in the database, To set the tobacco vitality index for a certain period in a tobacco growing area, is the vitality coefficient stored in the database, To set the tobacco light adaptation index for a certain period in the tobacco growing area, is the adaptation coefficient stored in the database, is the adjustment coefficient stored in the database.

[0015] Furthermore, the growth situation recognition model includes a feature extraction subnetwork, a temporal state modeling subnetwork, and a decoding output subnetwork. The specific steps of analyzing the growth evaluation set of each period of the set tobacco planting area are as follows: in the feature extraction subnetwork of the growth situation recognition model, tobacco growth image data of each time window of each period of the set tobacco planting area is received, and feature encoding processing is performed to obtain the feature vector of its corresponding time window; in the temporal state modeling subnetwork of the growth situation recognition model, temporal analysis is performed on the feature vector of each time window of each period of the set tobacco planting area to obtain the temporal feature vector of its corresponding period; in the decoding output subnetwork of the growth situation recognition model, prediction processing is performed on the temporal feature vector of each period of the set tobacco planting area to obtain the growth evaluation set of its corresponding period.

[0016] Furthermore, the specific formula for calculating the comprehensive tobacco carbon emission index for a certain period of a given tobacco growing area is as follows: ; in, To set the comprehensive tobacco carbon emission index for a certain period in tobacco growing areas, To set the initial carbon emission index for a certain period in a tobacco growing area, To set the soil environment correction factor for a certain period in tobacco growing areas, is the soil environmental coefficient stored in the database, To set the growth correction factor for a certain period in a tobacco growing area, is the growth coefficient stored in the database, is the synergy coefficient stored in the database.

[0017] The present invention has the following beneficial effects: (1) The tobacco carbon accounting parameter analysis and prediction system analyzes data from multiple dimensions and obtains the initial carbon emission index, soil environment correction factor, and growth correction factor, thereby accurately capturing the carbon emission characteristics of tobacco planting areas. The system cross-integrates the analyzed features to accurately reflect the complex impact of tobacco plant growth and soil environment changes on carbon emissions, and can then comprehensively evaluate the carbon emission status of tobacco planting areas. It can not only accurately calculate the carbon emission index of the current cycle, but also predict the carbon emission trend of the next cycle in real time, thereby effectively improving the accuracy of carbon emission accounting predictions and timely discovering abnormal carbon emission fluctuations so that corresponding management measures can be taken to improve the sustainability of tobacco planting.

[0018] (2) The tobacco carbon accounting parameter analysis and prediction system extracts the growth status features of tobacco plants from tobacco growth image sequence data based on the pre-trained growth status recognition model. It can not only accurately capture the details of the tobacco plant growth process, but also deeply analyze how these details affect the formation and changes of carbon emissions. If the tobacco growth is in the high photosynthesis stage, the plant's carbon absorption capacity is strong, which easily leads to lower carbon emissions; on the contrary, during the growth stagnation period, carbon emissions are likely to increase, thereby accurately identifying the impact of the tobacco plant growth stage on carbon emissions, and forming a growth correction factor, thereby optimizing carbon emission accounting and prediction, and then enhancing the accuracy of carbon emission accounting and prediction.

[0019] (3) The tobacco carbon accounting parameter analysis and prediction system can dynamically predict the carbon emission trend of the next cycle by using the set prediction window and combining the comprehensive tobacco carbon emission index of each cycle within the prediction window. When the system detects that the fluctuation of carbon emissions exceeds the preset threshold, it can adjust the prediction value in time and give an early warning, thereby providing an accurate carbon emission risk assessment, so as to take corresponding management measures in advance and effectively control the fluctuation of carbon emissions, thereby improving the predictability of carbon emission accounting and helping to accurately achieve carbon emission control in the agricultural production process.

[0020] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a block diagram of a tobacco carbon accounting parameter analysis and prediction system of the present invention; Figure 2 This is a flowchart of the specific steps for analyzing and setting the initial carbon emission index of each cycle in a tobacco planting area in a tobacco carbon accounting parameter analysis and prediction system of the present invention; Figure 3 A schematic diagram of a time series of setting the carbon absorption index of a tobacco planting area in a tobacco carbon accounting parameter analysis and prediction system of the present invention; Figure 4 This is a schematic diagram of the time series of setting tobacco vitality index of tobacco planting areas in a tobacco carbon accounting parameter analysis and prediction system of the present invention; Figure 5 This is a schematic diagram of the tobacco light adaptation index time series for setting tobacco planting areas in a tobacco carbon accounting parameter analysis and prediction system of the present invention; Figure 6 This is a flowchart of the specific steps for analyzing and setting the growth correction factor of each cycle in a tobacco planting area in a tobacco carbon accounting parameter analysis and prediction system of the present invention. DETAILED DESCRIPTION

[0022] See also Figure 1 The embodiment of the present invention provides a technical solution: a tobacco carbon accounting parameter analysis and prediction system, comprising: an initial accounting analysis module, used to continuously obtain a set of carbon accounting parameter sets for a set of tobacco planting areas for several cycles (each cycle is 10 days), and analyze the initial carbon emission index of the corresponding cycle; a soil environment correction analysis module, used to obtain soil environment data for each cycle of the set tobacco planting area, and analyze the soil environment correction factor of the corresponding cycle; a growth correction analysis module, used to obtain tobacco (plant) growth image sequence data for each cycle of the set tobacco planting area, and analyze the growth correction factor of the corresponding cycle based on a pre-trained growth trend recognition model; The prediction and analysis module is used to analyze the comprehensive tobacco carbon emission index of the corresponding period based on the initial carbon emission index, soil environment correction factor, and growth correction factor of each period of the set tobacco planting area, and predict the comprehensive tobacco carbon emission index of the next period. Specifically, the module sets a prediction window, such as three consecutive periods, and extracts the mean value of the comprehensive tobacco carbon emission index, the standard deviation value of the comprehensive tobacco carbon emission index, and the range value of the comprehensive tobacco carbon emission index (i.e., the difference between the maximum and minimum values ​​of the comprehensive tobacco carbon emission index in the prediction window) within the risk window, performs weighted processing on the standard deviation value of the comprehensive tobacco carbon emission index and the range value of the comprehensive tobacco carbon emission index to obtain the fluctuation index of the prediction window, and determines whether it is higher than a preset fluctuation index threshold. If it is higher, the mean value of the comprehensive tobacco carbon emission index and the fluctuation index are added to obtain the predicted comprehensive tobacco carbon emission index (and marked as the comprehensive tobacco carbon emission index of the next period). If it is not higher, the comprehensive tobacco carbon emission index and the fluctuation index are subtracted to obtain the comprehensive tobacco carbon emission index of the next period. The prediction feedback management module is used to take preset management measures for the set tobacco planting area based on the comprehensive tobacco carbon emission index of the next cycle. Specifically, it is used to determine whether it is higher than the preset comprehensive tobacco carbon emission index threshold; if it is higher, then the preset management measures are taken for the set tobacco planting area, namely, increase the input of organic fertilizer, improve soil microbial activity, promote carbon fixation capacity, reduce carbon release rate, plant low-carbon sink plants at the edge of the tobacco planting area or intercrop, improve the overall carbon absorption capacity of the region, reduce the frequency of agricultural machinery operations, and adopt low-carbon agricultural machinery and equipment to reduce mechanical fuel consumption and related carbon emissions; if it is not higher, then no preset management measures are taken for the set tobacco planting area.

[0023] The specific formula for calculating the comprehensive tobacco carbon emission index for a given tobacco growing area over a certain period is as follows: ; in, To set the comprehensive tobacco carbon emission index for a certain period in tobacco growing areas, To set the initial carbon emission index for a certain period in a tobacco growing area, To set the soil environment correction factor for a certain period in tobacco growing areas, is the soil environmental coefficient stored in the database, To set the growth correction factor for a certain period in a tobacco growing area, is the growth coefficient stored in the database, is the synergy coefficient stored in the database.

[0024] What needs to be explained is that the specific form of the tanh function is: , in, is a natural constant and can be taken as 2.71 in this embodiment, with a domain of (−∞, +∞) and a range of (−1, +1).

[0025] Soil environmental coefficients stored in the database The acquisition steps are as follows: obtain the historical soil temperature values, historical soil moisture values, and historical soil pH values ​​of several historical periods, perform standard deviation processing on each of them, perform weighted processing based on the standard deviation processing results, and use the weighted processing results as the soil environmental coefficient ; Growth coefficients stored in the database The acquisition steps are as follows: obtain the historical growth correction factors of several historical periods, analyze the mean and standard deviation of the historical growth correction factors respectively, and perform ratio processing, that is, the standard deviation of the historical growth correction factor / the mean of the historical growth correction factor, and use the ratio processing result as the growth coefficient ; Synergy coefficients stored in the database The acquisition steps are as follows: obtain the historical growth correction factor and historical soil environment correction factor of several historical periods, and perform interaction analysis (historical growth correction factor × historical soil environment correction factor) to obtain the interaction value of each historical period, and perform weighted average processing, and use the weighted average processing result as the synergy coefficient .

[0026] Specifically, if Figure 2 As shown, the carbon accounting parameter set includes the planting area value (measured by on-site RTK and the measurement results are uploaded to the database), tobacco plant biomass value, tobacco plant respiration rate value, tobacco plant carbon sink value, soil carbon sink value, the usage value of each agricultural activity and the corresponding carbon emission factor (that is, the conversion ratio of the usage value of each agricultural activity to the corresponding carbon dioxide emission, obtained through the carbon accounting manual stored in the database. For example, during a certain fertilization operation, the type of fertilizer used for the tobacco crop is recorded, and the carbon emission factor corresponding to this type of fertilizer is found in the carbon accounting manual), the application area value, and the specific steps for analyzing the initial carbon emission index of each cycle of the set tobacco planting area are as follows: based on the carbon accounting parameter set of each cycle of the set tobacco planting area, analyze the accounting evaluation set of its corresponding cycle, including the comprehensive carbon emission index and the comprehensive carbon sink index; obtain the carbon emission impact factor and the carbon sink impact factor of each cycle of the set tobacco planting area, and conduct a comprehensive analysis with the emission evaluation set of its corresponding cycle to obtain the initial carbon emission index of its corresponding cycle.

[0027] The specific formula for calculating the initial carbon emission index for a given tobacco planting area is as follows: ; in, To set the initial carbon emission index for a certain period in a tobacco growing area, To set the comprehensive carbon emission index for a tobacco growing area in a certain period, To set the carbon emission impact factor of a tobacco growing area in a certain period, To set the comprehensive carbon sink index for a tobacco growing area in a certain period, To set the carbon sequestration impact factor of a tobacco growing area in a certain period.

[0028] The tobacco plant biomass value is the total fresh weight of tobacco plants per unit area during the period, reflecting the growth status of the plants and the accumulation of organic matter. It is obtained through field sampling. Representative sample plots are selected, and several tobacco plants are randomly collected. After weighing their fresh weight, the dry matter mass value of the tobacco plants is obtained, which is divided by the sampling area of ​​the sample plot to obtain the tobacco plant biomass value.

[0029] The tobacco plant respiration rate is the rate at which tobacco plants release carbon dioxide through respiration per unit area, reflecting the intensity of the plant's metabolic activity. It is obtained through real-time monitoring in the field using a closed photosynthesis-respiration measurement instrument (such as the LI-COR LI-6800) and uploaded to a database. The instrument measures the rate of change of carbon dioxide concentration in a closed space of the sample plant, the chamber volume, the measurement time, and the plant's leaf area, and calculates the amount of carbon dioxide released per unit area per unit time (based on the instrument's built-in inference algorithm).

[0030] The carbon sink value of tobacco plants is the net amount of carbon fixed by tobacco plants through photosynthesis, reflecting the plant's ability to absorb atmospheric carbon. It is calculated by reading the dry matter mass value of the tobacco plants and the carbon content coefficient of the tobacco plants stored in the data, and then multiplying the dry matter mass value of the tobacco plants by the carbon content coefficient of the tobacco plants. The result is the carbon sink value of the tobacco plants.

[0031] Soil carbon sequestration value refers to the net accumulation of organic carbon in the soil, which reflects the soil's ability to absorb and store atmospheric carbon dioxide. It is obtained by jointly analyzing soil organic carbon content determination and soil respiration rate monitoring data. Representative soil samples from the designated tobacco-growing area are collected, and the organic carbon content in the samples is quantitatively analyzed using laboratory dry combustion method or chemical oxidation method to obtain the organic carbon content value per unit mass of soil. At the same time, an in-situ automatic soil respiration meter (such as LI-8100) is used to measure the rate of change of carbon dioxide concentration in the soil surface in a closed chamber in real time, and record the change value of carbon dioxide concentration per unit time. Combined with the measurement chamber, The volume and measurement area of ​​the chamber are used to calculate the carbon dioxide flux per unit area, which is specifically the carbon dioxide concentration change rate multiplied by the chamber volume and divided by the measurement area. Then, the carbon dioxide flux is converted into carbon flux, converted according to the molar mass ratio of carbon to carbon dioxide, and finally converted into soil respiration rate value. By comparing the change values ​​of soil organic carbon content at different times and adding full average processing, the change value of soil organic carbon content is generated. Combined with the total amount of soil respiration release (the result of multiplying the soil respiration rate value by the duration of the cycle), the net accumulation of soil organic carbon (change value of soil organic carbon content - total amount of soil respiration release), i.e., the soil carbon sink value, is calculated.

[0032] The usage value of each agricultural activity is the unit input of various operations such as fertilization, irrigation, mulching, mechanical tillage, etc. (for example, the amount of fertilizer used in a certain fertilization operation, the amount of irrigation used in a certain irrigation operation), which can be obtained through the operation record table stored in the database.

[0033] The applied area value is the area involved in each agricultural activity operation. For example, in a fertilization operation, after the fertilization is completed, the land area fertilized in this fertilization is recorded, which can be obtained through the operation record table stored in the database.

[0034] The specific steps for analyzing the accounting evaluation set for each cycle of the set tobacco planting area are as follows: based on the planting area value, tobacco plant biomass, tobacco plant respiration rate value, the usage value of each agricultural activity and the corresponding carbon emission factor and application area value of each cycle of the set tobacco planting area, the comprehensive carbon emission index of the corresponding cycle is analyzed, which is specifically: reading the duration value and soil respiration rate value of each cycle, multiplying the duration value, soil respiration rate value and planting area value of the corresponding cycle to obtain the soil carbon emission value of the corresponding cycle, and multiplying the duration value, tobacco plant biomass and tobacco plant respiration rate value of the corresponding cycle to obtain the soil carbon emission value of the corresponding cycle. The carbon emission values ​​of the plants in the corresponding period are multiplied by the carbon absorption rate values, and the carbon emission factors and application area values ​​of each agricultural activity in the corresponding period are cumulatively multiplied and summed to obtain the activity carbon emission value. The soil carbon emission values, plant carbon emission values ​​and activity carbon emission values ​​of the corresponding period are weighted and summed to obtain the comprehensive carbon emission index of the corresponding period. Based on the tobacco plant carbon sink values ​​and soil carbon sink values ​​of each period in the set tobacco planting area, the comprehensive carbon sink index of the corresponding period is analyzed (i.e., the tobacco plant carbon sink values ​​and soil carbon sink values ​​are weighted and summed).

[0035] The specific steps for obtaining the carbon emission influencing factors and carbon sequestration influencing factors of each cycle in the set tobacco planting area are as follows: obtain the tillage intensity value, tillage frequency value, and tillage layer disturbance depth value of each cycle in the set tobacco planting area, and analyze the carbon emission influencing factors of the corresponding cycle (standardize the tillage intensity value, tillage frequency value, and tillage layer disturbance depth value of the corresponding cycle, and perform weighted processing based on the standardized processing results); obtain the canopy absorption rate, chlorophyll relative content index, and soil structure stability index of each cycle in the set tobacco planting area, and analyze the carbon sequestration influencing factors of the corresponding cycle (standardize the canopy absorption rate, chlorophyll relative content index, and soil structure stability index of the corresponding cycle, and perform weighted processing based on the standardized processing results).

[0036] The tillage intensity value is the sum of the energy consumption during each operation in the cycle, reflecting the disturbance intensity of tillage on the soil carbon cycle. The energy consumption during each operation is obtained by obtaining the total fuel consumption during each operation through a thermal sensor, and the calorific value constant of the fuel type used stored in the database is obtained. The total fuel consumption is multiplied by the corresponding calorific value constant to obtain the energy consumption of each operation, and the sum is processed. The result is the tillage intensity value.

[0037] The tillage frequency value is the cumulative number of tillage behaviors that occurred during the time period, which is obtained from the agricultural production operation log stored in the database.

[0038] The tillage layer disturbance depth is the vertical depth of the soil disturbed by mechanical or manual operations. It is measured directly in the plot after tillage using a soil profile ruler (and uploaded to the database). Specifically, five representative points are selected, and after each mechanical or manual operation, the depth of the disturbance traces is observed by digging the profile and the average value is taken as the tillage layer disturbance depth.

[0039] The canopy absorptivity is the ratio of light energy intensity absorbed by the tobacco plant canopy per unit area within the photosynthetic band (400–700 nm), reflecting the plant's light energy utilization efficiency and carbon absorption potential. It is obtained by comparing photosynthetically active radiation sensors set up in the field. That is, photosynthetically active radiation (PAR) sensors are placed above the tobacco canopy (incident light) and below the canopy (transmitted light), respectively, to collect the PAR values ​​of the upper and lower layers per unit time in real time. The real-time canopy absorptivity at each time point within the period = (incident PAR - transmitted PAR) / incident PAR, and the average is processed to obtain the canopy absorptivity.

[0040] The relative chlorophyll content index is an indicator of the chlorophyll concentration in the functional leaves of tobacco plants. It is measured by using a handheld SPAD meter (such as SPAD-502Plus) to measure the SPAD value of the third or fourth main functional leaf on the upper part of each tobacco plant. Each plant is sampled three times and the average is taken. No less than 10 plants are sampled in each area, and the final average is taken as the relative chlorophyll content index.

[0041] The soil structure stability index is the ability of particle-size aggregates (water-stable aggregates) in the soil to maintain structural integrity under external disturbance conditions (such as rainfall and tillage), reflecting the soil's ability to protect organic matter and store carbon. It is obtained through an indoor aggregate wet screening test, which specifically includes: collecting representative tillage layer (0–20 cm) soil samples, drying them in the shade and passing them through a 2 mm sieve, and using the wet screening method to classify the soil samples for water stability. Large, medium, and small aggregates (>2 mm, 0.25–2 mm, <0.25 mm) are screened out, and the proportion of water-stable aggregates (>0.25 mm) is calculated, that is, soil structure stability index = water-stable aggregate mass / total soil sample mass.

[0042] In this implementation scheme, through the integration of field measurements and real-time sensor data, the growth status, respiration rate, carbon sequestration capacity and carbon absorption status of tobacco plants can be accurately reflected. This enables the system to capture changes in carbon emissions in different aspects, thereby improving the accuracy of carbon accounting. Secondly, by combining the amount of agricultural activities with the calculation of carbon emission factors, it can not only analyze the various sources of carbon emissions in the planting process in more detail, but also evaluate the impact of different operations on carbon emissions, helping farmers optimize agricultural management measures. Finally, through the analysis of carbon emission influencing factors and carbon sequestration influencing factors, it can deeply analyze the factors related to carbon emissions and carbon sequestration, thereby achieving a comprehensive assessment of the carbon emission process in tobacco growing areas, thereby improving the accuracy of carbon accounting.

[0043] Specifically, the soil environmental data include soil thermal diffusivity value, soil environmental regulation factor, root decomposition rate value, soil electrical conductivity gradient value, and soil dissolved oxygen concentration value. The specific steps for analyzing the soil environmental correction factor of each period in the set tobacco planting area are as follows: Based on the soil environmental data of each period in the set tobacco planting area, analyze the soil environmental assessment set of the corresponding period, including soil energy regulation factor (the indirect regulation ability of soil temperature, moisture and oxygen dynamic state in the tobacco planting area on microbial carbon metabolism), soil carbon conversion factor (dynamic distribution trend of organic carbon in the soil to gaseous carbon emission or to stable carbon pool, which is an internal driving factor of the carbon process); obtain the climate factor of each period in the set tobacco planting area, and combine it with the soil environmental assessment set of the corresponding period to analyze the soil environmental correction factor of the corresponding period (that is, weight the soil energy regulation factor, soil carbon conversion factor, and climate factor of each period, and the result is the soil environmental correction factor of the corresponding period, and in the process of weighted processing, the soil energy regulation factor, soil carbon conversion factor, and climate factor corresponding to the soil The weight coefficient can be obtained through the particle swarm optimization algorithm, which is as follows: in the particle swarm optimization algorithm, each particle represents a possible solution, and each solution consists of the weight coefficients corresponding to the soil energy regulation factor, soil carbon conversion factor, and climate factor. Initially, the position and velocity of the particle are randomly generated, and these positions correspond to different weight combinations. The fitness of each particle is evaluated by the objective function, which is related to the optimization of tobacco emissions or carbon conversion efficiency. The particle swarm optimization algorithm measures the quality of each solution by calculating the objective function value. A smaller objective function value indicates a better solution. According to the value of the fitness function, the particle swarm optimization algorithm updates the speed and position of the particle. The particle will adjust its own speed according to its historical optimal position and the global optimal position, thereby searching in the solution space, gradually approaching the optimal solution, and gradually converge after multiple iterations until the termination condition is met, such as stopping the iteration when the maximum number of iterations is reached or the fitness change is less than the preset threshold. After multiple iterations and updates, the particles will eventually find a set of optimal solutions, namely the weight coefficients corresponding to the soil energy regulation factor, soil carbon conversion factor, and climate factor).

[0044] Among them, the soil thermal diffusivity value is the ability of the soil to transfer heat within the cycle, reflecting the impact of soil temperature changes on microbial metabolism and carbon cycle. It is obtained through the sampling method, that is, sampling is carried out in an area selected within the set tobacco planting area, and multiple depth layers are set along the vertical profile of the soil (such as 10 cm, 20 cm, and 30 cm below the surface). Based on high-precision soil temperature sensors (such as Pt100 or thermistor sensors) and soil heat flux sensors (such as HFP01 type heat flux plates), the soil temperature, vertical heat flux, and soil temperature of each set time window in the cycle are respectively obtained. The soil density value is obtained from the vertical heat flux density value, and the soil density value (measured by a portable soil density meter and uploaded to the database) and specific heat capacity value (obtained through the soil texture specific heat capacity table stored in the database) of the corresponding set sampling time window (such as one day) are obtained at the same time. The temperature gradient value (i.e., standard deviation) is calculated based on the soil temperature of each depth layer within the time window, and a comprehensive analysis is performed to obtain the thermal diffusivity of each set time window, i.e., vertical heat flux density value / (soil density value × specific heat capacity value × temperature gradient value), and the average processing is performed. The result is the soil thermal diffusivity value.

[0045] The soil environmental regulation factor is the comprehensive influence of the soil environmental status on the carbon conversion process during this period. It obtains the soil temperature value, soil pH value, and soil moisture value at each time point during this period through soil temperature sensors, soil pH sensors, and soil moisture sensors, respectively, and performs normalization processing. Based on the normalization processing results, weighted processing is performed, and the result obtained is the soil environmental regulation factor.

[0046] The root decomposition rate value is the decomposition rate of organic matter in the plant roots during this period. It characterizes the rate at which root residues (such as dead roots, rhizosphere secretions, etc.) are decomposed by microorganisms in the soil, affecting the storage and release of soil organic carbon. It can be obtained through the sampling method, that is, the plant roots together with the attached soil are selected in the set tobacco planting area and dug out as a whole, and then divided into two parts of equal mass. One part is placed in a soil microbial active culture medium in a sealed container and placed in a temperature-controlled incubator to simulate natural soil environmental conditions. The amount of carbon dioxide released in the culture medium is collected based on a preset collection window (such as 30 minutes), and the carbon dioxide concentration is quantitatively detected using an infrared gas analyzer. The other part is measured by the drying method to determine the initial dry weight of the root residue. The root decomposition rate within the preset collection window = the amount of carbon dioxide released divided by the initial dry weight of the root residue. Multiple samplings are performed and the average value is taken to obtain the root decomposition rate value.

[0047] The soil conductivity gradient value is the rate of change of conductivity between different depths of the soil. It is obtained by placing multiple conductivity sensors in the soil at different depths to obtain the conductivity value at each time point within the cycle, and performing standard deviation processing. The mean value is then processed based on the standard deviation processing results. The result is the soil conductivity gradient value.

[0048] The soil dissolved oxygen concentration value is the content of dissolved oxygen in the soil pore water during the period, which affects the aerobic respiration activity of soil microorganisms and the decomposition rate of organic matter. By burying a dissolved oxygen sensor in the soil to measure the dissolved oxygen concentration value at each time point during the period and performing average processing, the result is the soil dissolved oxygen concentration value.

[0049] The specific steps for analyzing the soil environmental assessment set for each period of the set tobacco-growing area are as follows: based on the soil thermal diffusivity value, soil environmental regulation factor, and soil electrical conductivity gradient value of each period of the set tobacco-growing area, the soil energy regulation factor of the corresponding period is analyzed (the soil thermal diffusivity value, soil environmental regulation factor, and soil electrical conductivity gradient value of the corresponding period are standardized, and weighted processing is performed based on the standardized processing results); based on the root decomposition rate value and soil dissolved oxygen concentration value of each period of the set tobacco-growing area, the soil carbon conversion factor of the corresponding period is analyzed (the root decomposition rate value and soil dissolved oxygen concentration value of the corresponding period are standardized, and weighted processing is performed based on the standardized processing results).

[0050] The specific steps for obtaining the climate factors for each cycle of a given tobacco growing area are as follows: The air temperature value of each period in the set tobacco planting area is obtained (the temperature value of each set time window is obtained through a temperature sensor, and average processing is performed, and the result is the air temperature value), the air humidity value (the humidity value of each set time window is obtained through a humidity sensor, and average processing is performed, and the result is the air humidity value), the rainfall value (through a tipping bucket rain gauge set in the planting area, the number of tipping buckets is counted within the period and converted into the corresponding rainfall, the result is the rainfall value, and the result is uploaded to the database), and the light intensity value (the light intensity value of each set time window is obtained through a light sensor, and average processing is performed, and the result is the air light intensity value) are obtained, and standardized. Based on the standardized processing result, weighted processing is performed to obtain the climate factor of the corresponding period.

[0051] In this embodiment, by analyzing multiple soil environmental data, we can fully understand the regulatory effects of soil on microbial activity, carbon metabolism and carbon conversion processes. For example, soil thermal diffusivity can help reveal the impact of soil temperature changes on carbon metabolism, while the root decomposition rate value can accurately reflect the decomposition rate of plant residues in the soil, correct soil carbon storage and release, and integrate various parameters into soil energy regulation factors and soil carbon conversion factors through standardization and weighting processing, so that we can more intuitively and clearly see the specific impact of soil on carbon emissions and carbon sinks in each cycle, which in turn helps to optimize carbon management in tobacco-growing areas.

[0052] Specifically, the tobacco growth image sequence data includes tobacco growth image data of each time window (such as 2 days) (the image is a multispectral image), which includes multi-band pixel values ​​of each pixel point (such as red light, green light, blue light, near-infrared reflectivity, etc.) and two-dimensional coordinates. The specific steps for analyzing the growth correction factor of each cycle of the set tobacco planting area are as follows: the tobacco growth image data of each time window of each cycle of the set tobacco planting area is input into the pre-trained growth status recognition model for comprehensive analysis to obtain the growth correction evaluation set of the corresponding cycle, including the carbon absorption index, the tobacco vitality index, and the tobacco light adaptation index; based on the growth evaluation set of each cycle of the set tobacco planting area, the growth correction factor of the corresponding cycle is analyzed.

[0053] The specific formula for calculating the growth correction factor for a given tobacco growing area for a certain period is as follows: ; in, To set the growth correction factor for a certain period in a tobacco growing area, To set the carbon absorption index for a tobacco growing area for a certain period, is the absorption coefficient stored in the database, To set the tobacco vitality index for a certain period in a tobacco growing area, is the vitality coefficient stored in the database, To set the tobacco light adaptation index for a certain period in the tobacco growing area, is the adaptation coefficient stored in the database, is the adjustment coefficient stored in the database, and in this embodiment, the value is 3.000.

[0054] It should be explained that the absorption coefficients stored in the database The acquisition steps are as follows: Based on the feature extraction sub-network, the chlorophyll index features of each time window of each cycle are extracted, and the chlorophyll index feature mean and chlorophyll index feature standard deviation are analyzed respectively, and the ratio processing is performed, that is, the chlorophyll index feature standard deviation / chlorophyll index feature mean, and the ratio processing result is used as the absorption coefficient ; Vitality coefficient stored in the database The acquisition steps are as follows: Based on the feature extraction sub-network, the stem diameter characteristics and plant height characteristics of each time window of each cycle are extracted, and the stem diameter characteristic change rate and plant height characteristic change rate of adjacent time windows are respectively calculated, and the mean processing is performed, and the weighted processing is performed based on the mean processing result, and the weighted processing is used as the vitality coefficient ; Adaptation coefficients stored in the database The acquisition steps are as follows: extract the spectral response intensity features of each time window of each cycle based on the feature extraction sub-network, perform standard deviation processing, perform normalization processing based on the standard deviation processing result, and use the normalized processing result as the adaptation coefficient .

[0055] The specific implementation example of calculating the growth correction factor of a certain period in a set tobacco planting area is as follows. The existing data includes the carbon absorption index, tobacco vitality index, and tobacco light adaptation index of three periods in the set tobacco planting area. Figure 3-5 As shown: Table 1 Example of growth correction evaluation time series data for a given tobacco planting area Carbon absorption index Tobacco Vitality Index Tobacco light adaptation index Cycle 1 0.712 0.734 0.684 Cycle 2 0.764 0.823 0.791 Cycle 3 0.812 0.857 0.826 Absorption coefficients stored in the database Approximately: 0.137; Vitality coefficient stored in the database Approximately: 0.189; Adaptation coefficients stored in the database Approximately: 0.174; The adjustment factor stored in the database is: 3.000; Substituting the data in Table 1 and the above coefficients into the specific formula for calculating the growth correction factor for a certain period of a given tobacco planting area, we obtain: Set the growth correction factor for the first cycle of the tobacco growing area = ; Set the growth correction factor for the second cycle of tobacco growing area = ; Set the growth correction factor for the third cycle of tobacco growing area = .

[0056] The growth state recognition model includes a feature extraction subnetwork, a temporal state modeling subnetwork, and a decoding output subnetwork. The specific steps of analyzing the growth evaluation set of each period of the set tobacco planting area are as follows: in the feature extraction subnetwork of the growth state recognition model, tobacco growth image data of each time window of each period of the set tobacco planting area is received, and feature encoding processing is performed to obtain the feature vector of the corresponding time window, which is specifically: based on the chlorophyll index feature extraction layer, the plant density feature extraction layer, the leaf area index feature extraction layer, the plant height feature extraction layer, the stem diameter feature extraction layer, the spectral response intensity feature extraction layer, and the texture uniformity feature extraction layer, the tobacco growth image data are respectively characterized, and the extracted features are combined into a feature vector; In the temporal state modeling subnetwork of the growth status recognition model, a temporal analysis is performed on the feature vectors of each time window of each cycle of the set tobacco planting area to obtain the temporal feature vectors of the corresponding cycle. Specifically, a temporal correlation analysis is performed on the feature vectors of each time window based on the temporal convolutional network (TCN). For example, for the plant height feature of each time window, the change rate of its adjacent time windows is calculated, such as (plant height feature of the first time window - plant height feature of the second time window) / plant height feature of the second time window, and the calculation is performed to extract the growth rate feature. For the spectral response intensity feature of each time window, the standard deviation processing is performed to extract the spectral stability. For the chlorophyll index feature, plant density feature, leaf area index feature, stem diameter feature, and texture uniformity of each time window, the mean processing is performed respectively to obtain the corresponding feature stability, and the features are combined into a temporal feature vector. In the decoding output subnetwork of the growth status recognition model, the time series feature vector of each period of the set tobacco planting area is predicted and processed to obtain the growth evaluation set of the corresponding period. Based on the carbon absorption index output layer, tobacco vitality index output layer, and tobacco light adaptation index output layer, the corresponding features of the time series feature vector are extracted and weighted to output the carbon absorption index, tobacco vitality index, and tobacco light adaptation index.

[0057] The feature extraction subnetwork includes chlorophyll index feature extraction layer, plant density feature extraction layer, leaf area index feature extraction layer, plant height feature extraction layer, stem diameter feature extraction layer, spectral response intensity feature extraction layer, and texture uniformity feature extraction layer.

[0058] The chlorophyll index feature extraction layer extracts the reflectance values ​​of the red and near-infrared bands for each pixel in the tobacco growth image within each time window and performs analysis, namely (reflectance value of the near-infrared band - reflectance value of the red band) / (reflectance value of the near-infrared band + reflectance value of the red band). After obtaining the results, average processing is performed to extract the chlorophyll index features.

[0059] The plant density feature extraction layer extracts the pixel values ​​of the green band (G channel) and the near-infrared band (NIR) from the multispectral image, binarizes the plant area using a set threshold to obtain a binary image, and performs a morphological closing operation on the binary image. This operation includes dilation and then erosion, using a structural element (such as a 5x5 square kernel) to mark the connected components of the closing operation result, that is, identifying all interconnected plant clusters in the image. Each plant cluster corresponds to an independent plant area, and the Euclidean distance between each plant cluster (the distance between cluster center points) is calculated and the average is taken to extract the plant density feature.

[0060] The leaf area index feature extraction layer uses edge detection algorithms (such as Canny edge detection) to extract leaf contours in the image, calculates the projected area of ​​the leaf contours, converts it into actual area values ​​based on the physical proportions of the image, and analyzes the ratio of the total leaf area to the actual plot area to extract the leaf area index features.

[0061] In the plant height feature extraction layer, the edge detection algorithm (such as the Canny operator) detects several plant contours. For each plant contour, the skeleton extraction algorithm is combined to locate the center line of the trunk and determine the pixel positions corresponding to the base (the lowest point of the bottom contour) and the top (the highest point) of the plant. Through the calibration of the camera's intrinsic parameters (focal length, principal point position) and extrinsic parameters (camera position, posture), a mapping relationship between the image pixel coordinates and the actual three-dimensional space coordinates is established (the two-dimensional coordinates of the pixel points in the image are converted into spatial coordinates in the real world). The actual vertical height between the base and top pixel points of the plant is calculated, and the mean processing is performed to extract the plant height features.

[0062] The stem diameter feature extraction layer extracts several main trunk contour lines based on edge detection algorithms (such as Canny edge detection), determines the longitudinal axis of the trunk, and extracts the corresponding cross-sectional contours at multiple equally spaced sampling points on the longitudinal axis to form a series of cross-sectional slices. For each cross-sectional slice, the maximum width of the contour is calculated as the stem diameter pixel value at that position. Statistical methods are used to filter out measurement errors (such as eliminating outliers that exceed the mean ± 2 times the standard deviation) to obtain a valid cross-sectional diameter set, establish a mapping relationship between image pixel coordinates and actual three-dimensional space coordinates, convert the valid stem diameter pixel values ​​in the valid cross-sectional diameter set into physical dimensions of millimeters or centimeters, and perform mean processing to extract the stem diameter features.

[0063] The spectral response intensity feature extraction layer extracts the reflectance values ​​of the red, green, blue, and near-infrared bands for each pixel point, analyzes the average value and standard deviation of each band respectively, and performs weighted processing based on the analysis results to extract the spectral response intensity characteristics.

[0064] The texture uniformity feature extraction layer calculates the texture features of the leaf area in the image based on the gray-level co-occurrence matrix (GLCM), including contrast, energy, entropy, etc., and performs mean processing on the values ​​in the texture features to extract the texture uniformity features.

[0065] The decoding output subnetwork includes the carbon absorption index output layer, the tobacco vitality index output layer, and the tobacco light adaptation index output layer.

[0066] The carbon absorption index output layer extracts the stability of the chlorophyll index characteristics, the stability of the plant density characteristics, and the growth rate characteristics, and performs weighted processing to output the carbon absorption index, which characterizes the efficiency of photosynthesis and carbon fixation capacity of tobacco plants during their growth period, reflects the persistence of the plant's chlorophyll content, the rationality of the plant's spatial distribution, and the overall growth rate, and thus evaluates the tobacco's utilization of environmental carbon resources and growth vitality.

[0067] The tobacco vitality index output layer extracts the characteristic stability of leaf area index, plant height, and stem diameter, and performs weighted processing to output the tobacco vitality index, which characterizes the growth health and physical strength of tobacco plants, reflects the completeness of leaf expansion, the vertical growth potential of the plant, and the thickness of the stem, thereby comprehensively evaluating the growth vitality of tobacco plants.

[0068] The tobacco light adaptation index output layer extracts the spectral stability and texture uniformity feature stability, and performs weighted processing to output the tobacco light adaptation index, which characterizes the adaptability of tobacco plants to the lighting environment and the uniformity of spectral reflectance characteristics, reflects the stable reflectance state of tobacco leaves in different spectral bands and the uniform distribution of surface texture, and then evaluates the physiological regulation and light energy utilization efficiency of tobacco under changing light conditions.

[0069] The pre-training process of the growth trend recognition model is as follows: In the pre-training stage of the growth status recognition model, it is first necessary to construct a training data set covering multi-dimensional growth characteristics, and collect historical multi-source monitoring image data of tobacco-growing areas under different growth stages and environmental conditions. The data categories include but are not limited to: environmental status data (such as temperature, humidity, light intensity, soil moisture, etc.), plant growth status data (such as chlorophyll index, plant density, leaf area index, plant height, stem diameter, etc.), time series data (such as daily image data series, dynamic growth rate, etc.). For each type of data, time series segmentation, image enhancement (such as rotation, scaling, cropping, etc.) and feature labeling operations are performed.

[0070] After completing the data preparation, each sub-network is pre-trained independently. The pre-training process adopts a supervised learning method, inputs the corresponding type of standardized time series data (such as image data of each time window and its corresponding growth features), and outputs the known growth change trend and feature labels.

[0071] The pre-training process of each sub-network uses the mean square error (MSE) loss function to evaluate the error of various feature outputs, and uses the Adam optimizer to adjust the network weights to ensure that the network can effectively capture subtle feature changes during the growth process.

[0072] After the independent pre-training of each sub-network is completed, the weight parameters obtained from the training are loaded as initial values ​​into the complete growth status recognition model, and the multi-dimensional feature fusion optimization stage is entered. In this stage, by constructing a joint training sample containing multiple cycles and multiple growth status features, the growth characteristics of different dimensions (such as chlorophyll index, plant density, leaf area index, etc.) are simultaneously input into the temporal state modeling sub-network, the obtained temporal feature vector is extracted, and it is input into the decoding output sub-network. In the decoding output sub-network, a fully connected fusion mechanism is used to jointly model the multi-source temporal features, focusing on optimizing the connection weight parameters between the fusion layer and each output layer (such as the carbon absorption index output layer, the tobacco vitality index output layer, and the tobacco light adaptation index output layer). The training goal is to minimize the overall growth status assessment error.

[0073] Through the optimization training in this stage, the model has the ability to accurately extract, fuse and classify growth features under different growth states and environmental changes, thereby improving its recognition accuracy of complex growth trends. Ultimately, the trained model can quickly and accurately identify and evaluate growth trends in practical applications.

[0074] In this implementation, multispectral images of multiple time windows within each cycle are analyzed to ensure that the data has temporal continuity, which helps to fully reflect the actual growth status of tobacco plants. Secondly, a pre-trained growth status recognition model is used to extract the carbon absorption index, tobacco vitality index and light adaptation index, so that the photosynthesis efficiency, growth ability and adaptation to the environment of the plants can be evaluated in multiple dimensions. At the same time, the introduced absorption coefficient, vitality coefficient and adaptation coefficient are all derived from the statistical characteristics of historical cycle data, which makes the current cycle evaluation results highly comparative, thereby improving the accuracy of tobacco growth evaluation.

[0075] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0076] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A tobacco carbon accounting parameter analysis and prediction system, characterized in that: include: The initial accounting analysis module is used to continuously obtain the carbon accounting parameter sets of several cycles in the set tobacco planting area and analyze the initial carbon emission index of the corresponding cycles; The soil environment correction analysis module is used to obtain the soil environment data of each cycle of the set tobacco planting area and analyze the soil environment correction factor of the corresponding cycle; A growth correction analysis module is used to obtain tobacco growth image sequence data for each cycle of a set tobacco planting area and analyze the growth correction factor of the corresponding cycle based on a pre-trained growth trend recognition model; The prediction and analysis module is used to analyze the comprehensive tobacco carbon emission index of each cycle of the set tobacco planting area based on the initial carbon emission index, soil environment correction factor, and growth correction factor, and predict the comprehensive tobacco carbon emission index of the next cycle; The prediction feedback management module is used to take preset management measures for the set tobacco growing area based on the comprehensive tobacco carbon emission index of the next cycle.

2. The tobacco carbon accounting parameter analysis and prediction system according to claim 1, characterized in that: The carbon accounting parameter set includes the planting area value, tobacco plant biomass value, tobacco plant respiration rate value, tobacco plant carbon sequestration value, soil carbon sequestration value, the usage value of each agricultural activity, the corresponding carbon emission factor, and the application area value. The specific steps for analyzing and setting the initial carbon emission index for each cycle of the tobacco planting area are as follows: Based on the carbon accounting parameter set for each period of the tobacco growing area, the accounting assessment set for the corresponding period is analyzed, including the comprehensive carbon emission index and the comprehensive carbon sink index; The carbon emission impact factor and carbon sink impact factor of each cycle of the set tobacco planting area are obtained, and a comprehensive analysis is performed with the emission assessment set of the corresponding cycle to obtain the initial carbon emission index of the corresponding cycle.

3. The tobacco carbon accounting parameter analysis and prediction system according to claim 2, characterized in that: The specific steps for analyzing and setting the accounting evaluation set for each period of the tobacco growing area are as follows: Based on the planting area value, tobacco plant biomass, tobacco plant respiration rate value, the amount value of each agricultural activity, the corresponding carbon emission factor, and the application area value of each cycle in the set tobacco planting area, the comprehensive carbon emission index of the corresponding cycle is analyzed; Based on the tobacco plant carbon sequestration value and soil carbon sequestration value of each cycle in the set tobacco planting area, the comprehensive carbon sequestration index of the corresponding cycle is analyzed.

4. The tobacco carbon accounting parameter analysis and prediction system according to claim 2, characterized in that: The specific steps for obtaining the carbon emission impact factor and carbon sink impact factor for each cycle of a set tobacco planting area are as follows: Obtain the tillage intensity, tillage frequency, and tillage layer disturbance depth values ​​for each cycle of the set tobacco planting area, and analyze the carbon emission influencing factors of the corresponding cycle; The canopy absorption rate, chlorophyll relative content index, and soil structure stability index of each cycle in the set tobacco planting area were obtained, and the carbon sequestration influencing factors of the corresponding cycle were analyzed.

5. The tobacco carbon accounting parameter analysis and prediction system according to claim 1, characterized in that: The soil environmental data includes soil thermal diffusivity value, soil environmental control factor, root decomposition rate value, soil electrical conductivity gradient value, and soil dissolved oxygen concentration value. The specific steps for analyzing and setting the soil environmental correction factor for each cycle of the tobacco planting area are as follows: Based on the soil environmental data of each period in the set tobacco planting area, the soil environmental assessment set of the corresponding period was analyzed, including soil energy regulation factors and soil carbon conversion factors; The climatic factors of each period of the set tobacco planting area are obtained, and the soil environmental correction factors of the corresponding period are analyzed in combination with the soil environmental assessment set of the corresponding period.

6. The tobacco carbon accounting parameter analysis and prediction system according to claim 5, characterized in that: The specific steps for analyzing the soil environmental assessment set for each cycle of tobacco growing areas are as follows: Based on the soil thermal diffusivity value, soil environmental regulation factor, and soil electrical conductivity gradient value of each period in the set tobacco planting area, the soil energy regulation factor of the corresponding period is analyzed; Based on the root decomposition rate and soil dissolved oxygen concentration values ​​of each cycle in the set tobacco planting area, the soil carbon conversion factor of the corresponding cycle was analyzed.

7. The tobacco carbon accounting parameter analysis and prediction system according to claim 1, characterized in that: The tobacco growth image sequence data includes tobacco growth image data of each time window, which includes multi-band pixel values ​​and two-dimensional coordinates of each pixel point. The specific steps of analyzing and setting the growth correction factor of each cycle of the tobacco planting area are as follows: The tobacco growth image data of each time window of each cycle in the set tobacco planting area is input into the pre-trained growth status recognition model for comprehensive analysis to obtain the growth correction evaluation set of the corresponding cycle, including carbon absorption index, tobacco vitality index, and tobacco light adaptation index; Based on the growth evaluation set of each cycle in the set tobacco planting area, the growth correction factor of the corresponding cycle is analyzed.

8. The tobacco carbon accounting parameter analysis and prediction system according to claim 7, characterized in that: The specific formula for calculating the growth correction factor for a given tobacco growing area for a certain period is as follows: ； in, 、 、 、 The following are the growth correction factor, carbon absorption index, tobacco vitality index, and tobacco light adaptation index of a certain period of the tobacco planting area. 、 、 、 They are the absorption coefficient, vitality coefficient, adaptation coefficient and adjustment coefficient stored in the database respectively.

9. The tobacco carbon accounting parameter analysis and prediction system according to claim 7, characterized in that: The growth situation recognition model includes a feature extraction subnetwork, a temporal state modeling subnetwork, and a decoding output subnetwork. The specific steps of analyzing the growth evaluation set of each cycle in the tobacco planting area are as follows: In the feature extraction subnetwork of the growth state recognition model, tobacco growth image data of each time window of each period of the set tobacco planting area is received and feature encoding processing is performed to obtain the feature vector of the corresponding time window; In the time series state modeling subnetwork of the growth status recognition model, a time series analysis is performed on the feature vectors of each time window of each period of the set tobacco planting area to obtain the time series feature vectors of the corresponding period; In the decoding output subnetwork of the growth status recognition model, the time series feature vector of each period of the set tobacco planting area is predicted and processed to obtain the growth evaluation set of its corresponding period.

10. The tobacco carbon accounting parameter analysis and prediction system according to claim 1, characterized in that: The specific formula for calculating the comprehensive tobacco carbon emission index for a given tobacco growing area over a certain period is as follows: ； in, 、 、 、 The following are the comprehensive tobacco carbon emission index, initial carbon emission index, soil environment correction factor, and growth correction factor for a certain period of the tobacco planting area. 、 、 They are the soil environment coefficient, growth coefficient and synergy coefficient stored in the database respectively.

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