Agricultural rural environment emission reduction analysis method and system based on carbon-nitrogen cooperation, electronic equipment and storage medium

Abstract

The invention belongs to the field of agricultural environmental protection, and discloses an agricultural rural environment emission reduction analysis method and system based on carbon-nitrogen collaboration, electronic equipment and a storage medium, and the method comprises the following steps: S1, obtaining carbon-nitrogen coupling dynamic data in an agricultural rural production and living ecosystem, and carrying out the preprocessing of the carbon-nitrogen coupling dynamic data, obtaining processed data; s2, performing time sequence matching and spatial scale alignment on the processed data through a data fusion algorithm to obtain a fused data set; s3, constructing a neural network model, and training the neural network model by using the fusion data set to obtain a carbon-nitrogen coupling dynamic model; s4, utilizing the carbon-nitrogen coupling dynamic model to predict the carbon-nitrogen coupling dynamic change trend in the production and living ecosystem of the agricultural rural area, and calculating a carbon-nitrogen collaborative emission reduction index; and S5, based on the carbon-nitrogen collaborative emission reduction index, generating an emission reduction strategy according to different production, life, ecological application and management scenes of agricultural villages.

Description

Technical Field

[0001] The present invention relates to the field of agricultural environmental protection technology, and in particular to an agricultural and rural environmental emission reduction analysis method, system, electronic equipment and storage medium based on carbon-nitrogen synergy. Background Art

[0002] A key technical challenge in analyzing agricultural and rural environmental emissions reductions based on carbon-nitrogen synergy is how to accurately capture the dynamic changes in carbon-nitrogen coupling within complex agricultural and rural production and living ecosystems and convert them into quantifiable emission reduction indicators. During agricultural production, the interaction between the carbon and nitrogen cycles is not only influenced by soil physical and chemical properties and climatic conditions, but is also closely related to factors such as crop type, fertilization management, and tillage practices. However, existing monitoring methods often struggle to simultaneously track the full-process carbon-nitrogen coupling effect. For example, changes in soil carbon storage typically require long-term sampling and laboratory analysis, while nitrogen fertilizer use efficiency relies on real-time field gas emission monitoring, resulting in a mismatch between the temporal and spatial scales. Furthermore, assessments of crop carbon sequestration capacity are often based on a single growth stage or specific environmental conditions, failing to reflect dynamic changes throughout the entire growth cycle. This fragmented and incomplete monitoring data results in a lack of systematic and precise quantitative analysis of carbon-nitrogen synergy. Furthermore, due to the complexity of agricultural and rural production and living ecosystems, mathematical models of carbon-nitrogen coupling often require a large number of input parameters, and the uncertainty in obtaining these parameters further complicates quantitative analysis. Therefore, how to construct a quantitative system based on multi-source heterogeneous data that can not only reflect the dynamic coupling relationship between carbon and nitrogen, but also adapt to different agricultural and rural production, life, ecological application and management scenarios has become a key technical bottleneck in the practical application of this method. Summary of the Invention

[0003] In order to solve the above technical problems, the present invention provides an agricultural and rural environmental emission reduction analysis method based on carbon-nitrogen synergy, comprising the following steps:

[0004] S1. Obtaining carbon-nitrogen coupling dynamic data in agricultural and rural production and living ecosystems, and preprocessing the carbon-nitrogen coupling dynamic data to obtain processed data;

[0005] S2. performing time series matching and spatial scale alignment on the processed data using a data fusion algorithm to obtain a fused data set;

[0006] S3. Constructing a neural network model and training the neural network model using the fusion data set to obtain a carbon-nitrogen coupling dynamic model;

[0007] S4. Using the carbon-nitrogen coupling dynamic model to predict the dynamic trend of carbon-nitrogen coupling in agricultural and rural production and living ecosystems, and calculate carbon-nitrogen synergistic emission reduction indicators;

[0008] S5. Based on the carbon-nitrogen synergistic emission reduction indicators, generate emission reduction strategies according to different agricultural and rural production, life, ecological application and management scenarios.

[0009] Preferably, the S1 includes:

[0010] Collect the carbon-nitrogen coupling dynamic data in the production and living ecosystems of agricultural and rural areas through Internet of Things sensors, remote sensing monitoring and field fixed-point sampling. The carbon-nitrogen coupling dynamic data includes: carbon cycle data, nitrogen cycle data and environmental driving data;

[0011] The Tukey elimination method is used to identify and eliminate abnormal data in the carbon-nitrogen coupling dynamic data, and then the missing data is supplemented using the interpolation method to obtain the processed data.

[0012] Preferably, the S2 includes:

[0013] Using linear interpolation to interpolate the quarterly data in the processed data to obtain a monthly data series, thereby obtaining time-matched data;

[0014] The processed data are unified into a grid scale of 1 km × 1 km, and the missing grids are interpolated using the Kriging interpolation method to obtain spatially aligned data;

[0015] The time-matched data and the space-aligned data are fused using a data fusion algorithm to obtain the fused data set.

[0016] Preferably, the S3 includes:

[0017] Constructing a hybrid neural network model including a spatial and temporal dual channel, wherein the hybrid neural network model includes: a spatial feature extraction layer, a temporal feature extraction layer, and an output layer;

[0018] The hybrid neural network model is dynamically weighted trained using the fused data set to obtain the carbon-nitrogen coupling dynamic model.

[0019] Preferably, the S4 includes:

[0020] Performing multi-dimensional feature splicing on the environmental driving data of the target prediction area and the multi-scenario agricultural management plan, and inputting the data into the carbon-nitrogen coupling dynamic model to obtain the dynamic change trend of the carbon-nitrogen coupling in the future period;

[0021] The carbon-nitrogen synergistic emission reduction index is constructed based on the dynamic change trend of the carbon-nitrogen coupling:

[0022]

[0023] Among them, S synergy represents the carbon and nitrogen synergistic emission reduction index, C eq represents the carbon equivalent conversion function, NUE represents the nitrogen use efficiency index, R reduction Represents emission reduction, R baseline Indicates baseline emissions.

[0024] The present invention also provides an agricultural and rural environmental emission reduction analysis system based on carbon-nitrogen synergy, wherein the analysis system applies any of the above methods and comprises: a data preprocessing module, a data fusion module, a model building module, an indicator calculation module, and a strategy generation module;

[0025] The data preprocessing module is used to obtain carbon-nitrogen coupling dynamic data in the production and living ecosystem of agricultural and rural areas, and preprocess the carbon-nitrogen coupling dynamic data to obtain processed data;

[0026] The data fusion module performs time series matching and spatial scale alignment on the processed data through a data fusion algorithm to obtain a fused data set;

[0027] The model building module is used to build a neural network model and use the fusion data set to train the neural network model to obtain a carbon-nitrogen coupling dynamic model;

[0028] The index calculation module uses the carbon-nitrogen coupling dynamic model to predict the dynamic change trend of carbon-nitrogen coupling in the production and living ecosystem of agricultural and rural areas, and calculates the carbon-nitrogen synergistic emission reduction index;

[0029] The strategy generation module generates emission reduction strategies based on the carbon-nitrogen coordinated emission reduction indicators and according to different agricultural and rural production, life, ecological application and management scenarios.

[0030] The present invention also provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, the above-mentioned agricultural and rural environmental emission reduction analysis method based on carbon-nitrogen synergy is implemented.

[0031] The present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed, it implements the above-mentioned agricultural and rural environmental emission reduction analysis method based on carbon-nitrogen synergy.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The present invention discloses a method for analyzing agricultural and rural environmental emission reduction based on carbon-nitrogen synergy. The method obtains multi-source heterogeneous data such as soil carbon storage, nitrogen fertilizer utilization efficiency, and crop carbon sequestration capacity, pre-processes and fuses the data, and constructs a carbon-nitrogen coupling dynamic model. The model is trained and optimized using a machine learning algorithm to achieve real-time prediction of the dynamic trend of carbon-nitrogen coupling in the production and living ecosystems of agricultural and rural areas. Based on the prediction results, the carbon-nitrogen synergistic emission reduction index is calculated, and cluster analysis is performed on different agricultural and rural production, life, ecological application and management scenarios to derive emission reduction strategies suitable for different scenarios. By integrating multidimensional data and advanced algorithms, the present invention provides accurate dynamic prediction and decision-making support for carbon and nitrogen management in the production and living ecosystems of agricultural and rural areas, which helps to improve the sustainability and environmental friendliness of agricultural production. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solution of the present invention, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0035] Figure 1 Schematic diagram of a method flow in an embodiment of the present invention;

[0036] Figure 2 Schematic diagram of the structure of an electronic device according to an embodiment of the present invention.

[0037] Description of reference numerals:

[0038] 1010 , processor; 1020 , memory; 1030 , input / output interface; 1040 , communication interface; 1050 , bus. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0040] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present disclosure should have the usual meanings understood by people with ordinary skills in the field to which the present disclosure belongs. The "first", "second" and similar words used in the embodiments of the present disclosure do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word include the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative position relationships. When the absolute position of the described object changes, the relative position relationship may also change accordingly.

[0041] Example 1

[0042] In this embodiment, if Figure 1 As shown, a carbon-nitrogen synergy-based agricultural and rural environmental emission reduction analysis method includes the following steps:

[0043] S1. Obtain carbon-nitrogen coupling dynamic data in agricultural and rural production and living ecosystems, and preprocess the carbon-nitrogen coupling dynamic data to obtain processed data.

[0044] S1 includes: collecting carbon-nitrogen coupling dynamic data in agricultural and rural production and living ecosystems through Internet of Things sensors, remote sensing monitoring and field fixed-point sampling. The carbon-nitrogen coupling dynamic data includes: carbon cycle data, nitrogen cycle data and environmental driving data; using the Tukey elimination method to identify abnormal data in the carbon-nitrogen coupling dynamic data and eliminate them, and then using the interpolation method to fill in the missing data to obtain processed data.

[0045] In this embodiment, the carbon-nitrogen coupling dynamic data of agricultural and rural production and living ecosystems are collected by combining the Internet of Things sensor network, remote sensing monitoring platform and field fixed-point sampling: (1) Carbon cycle data: including soil respiration flux (using the LI-8100A soil carbon flux system), crop biomass carbon storage (determined by the biomass harvesting method), organic carbon input (straw return amount, organic fertilizer application amount) (2) Nitrogen cycle data: covering nitrogen fertilizer application records (base fertilizer / topdressing type and amount), ammonia volatilization (dynamic chamber method determination), nitrate nitrogen leaching loss (leakage liquid collection and analysis), N2O emission flux (gas chromatography) (3) Environmental driving data: temperature, humidity, precipitation, and radiation data recorded by the weather station, pH value, bulk density, and texture parameters monitored by the soil profile. Afterwards, Tukey's fences method (IQR=1.5) was used to identify outliers, and manual verification was performed in combination with field records. The sensor breakpoint data were supplemented using cubic spline interpolation, and the agricultural operation data were estimated using the adjacent similar field analogy method to obtain the processed data.

[0046] S2. Use the data fusion algorithm to perform time series matching and spatial scale alignment on the processed data to obtain a fused dataset.

[0047] S2 includes: using linear interpolation to interpolate the quarterly data in the processed data to obtain monthly data series and time-matched data; unifying the processed data to a grid scale of 1 km × 1 km, using Kriging interpolation to interpolate the missing grids to obtain spatially aligned data; and using a data fusion algorithm to fuse the time-matched data and the spatially aligned data to obtain a fused data set.

[0048] In this embodiment, the time series matching algorithm can be used to process data with different sampling frequencies. For example, soil carbon storage data may be collected once a month, while nitrogen fertilizer use efficiency data may be collected once a quarter. The data can be unified to a monthly scale through time series matching. In specific implementation, the quarterly data can be interpolated using linear interpolation to obtain a monthly data sequence. During the spatial scale alignment process, since the sampling points of soil carbon storage, nitrogen fertilizer use efficiency, and crop carbon sequestration capacity may be different, the data needs to be unified to the same spatial grid; for example, the sampling data is unified to a grid scale of 1km×1km, and the missing grids are interpolated using the Kriging interpolation method. There may be multiple monitoring stations in the production and living ecosystem research area of a certain agricultural and rural area. The data collected at each station are not representative. Complete spatial distribution data can be obtained through spatial scale alignment. The Bayesian fusion method can be used in the data fusion process to comprehensively consider the reliability of different data sources. For example, the soil carbon storage in a certain study area has both field sampling data and remote sensing inversion data. The two types of data have their own advantages and disadvantages. Bayesian fusion can obtain a more reliable estimate. The fused data set retains the accuracy of field sampling and has the spatial continuity of remote sensing data.

[0049] S3. Construct a neural network model and use the fused data set to train the neural network model to obtain a carbon-nitrogen coupling dynamic model.

[0050] S3 includes: constructing a hybrid neural network model with dual time and space channels, the hybrid neural network model includes: a spatial feature extraction layer, a temporal feature extraction layer and an output layer; using the fusion data set to perform dynamic weight training on the hybrid neural network model to obtain a carbon-nitrogen coupling dynamic model.

[0051] In this embodiment, a hybrid neural network structure with dual spatial and temporal channels is established, wherein the spatial feature extraction layer uses a three-dimensional convolutional neural network (3D-CNN) to process the spatial distribution data of carbon and nitrogen in the soil profile, and the temporal feature extraction layer uses a bidirectional long short-term memory network (Bi-LSTM) to process the time series data of the carbon and nitrogen cycle; the output layer fuses the spatial and temporal features through an attention mechanism to construct a carbon-nitrogen interaction matrix. The fused dataset is converted into a model input tensor, wherein: (1) spatial dimension: the 1km×1km grid data is converted into a three-channel three-dimensional matrix (longitude×latitude×depth) containing soil organic carbon, nitrate nitrogen, and ammonium nitrogen; (2) temporal dimension: a time series feature vector containing environmental driving factors such as temperature, precipitation, and fertilizer application is constructed; (3) collaborative dimension: the time series markers of crop growth stages and farming activities are added as auxiliary features. The model then undergoes dynamic weight training using the input tensor. Adaptive loss functions are used for multi-objective optimization. The primary loss function combines mean squared error (MSE) with dynamic time warping (DTW) loss to capture the temporal correlations between carbon and nitrogen variations. An auxiliary loss function maximizes mutual information to enhance the model's ability to characterize carbon-nitrogen synergistic effects. Regularization employs spatial smoothness constraints and temporal periodic consistency constraints to prevent overfitting. The model is then iteratively optimized using a phased training strategy. Phase 1 freezes the spatial feature extraction layer and focuses on optimizing the time series modeling module. Phase 2 unfreezes the entire network and dynamically adjusts the learning rate using a cosine annealing algorithm. Phase 3 introduces adversarial training samples to enhance the model's robustness to extreme climate events. Finally, a spatiotemporal cross-validation strategy is used to partition the dataset into training and validation sets based on crop rotation cycles. The iterative model is validated, and if it meets the criteria, a dynamic carbon-nitrogen coupling model is obtained.

[0052] S4. Use the carbon-nitrogen coupling dynamic model to predict the dynamic changing trends of carbon-nitrogen coupling in agricultural and rural production and living ecosystems, and calculate the carbon-nitrogen synergistic emission reduction indicators.

[0053] S4 includes: multi-dimensional feature splicing of the environmental driving data of the target prediction area and the multi-scenario agricultural management plan, and inputting them into the carbon-nitrogen coupling dynamic model to obtain the dynamic change trend of carbon-nitrogen coupling in the future period; constructing carbon-nitrogen synergistic emission reduction indicators based on the dynamic change trend of carbon-nitrogen coupling.

[0054] In this embodiment, the environmental driving data of the target prediction area (including temperature gradient data, precipitation time series pattern, soil heterogeneity distribution) and the multi-scenario agricultural management plan (including organic fertilizer replacement ratio matrix, rotation system combination, irrigation mode parameters) are multi-dimensionally spliced to form a spatiotemporal prediction input tensor; through the spatiotemporal dual-channel structure of the carbon-nitrogen coupling dynamic model, the spatial variation characteristics of soil organic matter are analyzed in the spatial dimension, and the lag effect of fertilization events and climate fluctuations is captured in the temporal dimension. The carbon flux dynamic surface (including interannual fluctuation rate of soil carbon pool and crop residue decomposition flux) and nitrogen loss three-dimensional matrix (including nitrate nitrogen leaching flux, nitrous oxide emission flux, and ammonia volatilization spatiotemporal distribution) for the next five years are output; and a carbon equivalent conversion function is established:

[0055] C eq =α·ΔSOC+β·(N2O base -N2O pred )·GWP N2O ,

[0056] Among them, α represents the soil carbon sequestration efficiency coefficient (0.25-0.35); β represents the nitrogen fertilizer management correction factor; ΔSOC represents the change in soil organic carbon; N2O base represents the nitrous oxide emissions under the standard scenario; N2O pred represents the nitrous oxide emissions under the predicted scenario; GWP N2O Indicates the global warming potential of nitrous oxide, which indicates the greenhouse effect intensity of nitrous oxide per unit mass relative to carbon dioxide, and is usually taken as 298. Constructing the nitrogen use efficiency index:

[0057]

[0058] Where T represents the time period, N uptake (t) represents the nitrogen absorption of crops, N input (t) represents nitrogen input, σ(N leach ) represents the standard deviation of the nitrogen leaching vector. A carbon-nitrogen synergistic emission reduction index was constructed based on the carbon equivalent conversion function and the nitrogen use efficiency index.

[0059]

[0060] Among them, S synergy represents the carbon and nitrogen synergistic emission reduction index, R reduction Represents emission reduction, R baseline Indicates baseline emissions.

[0061] S5. Based on the carbon-nitrogen synergistic emission reduction indicators, generate emission reduction strategies according to different agricultural and rural production, life, ecological application and management scenarios.

[0062] It should be noted that the method of the embodiments of the present disclosure can be performed by a single device, such as a computer or server. The method of the embodiments of the present disclosure can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the multiple devices will interact with each other to complete the method.

[0063] It should be noted that the above describes some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, it should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution, and the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous.

[0064] Example 2

[0065] In this embodiment, a carbon-nitrogen synergy-based agricultural and rural environmental emission reduction analysis system includes: a data preprocessing module, a data fusion module, a model building module, an indicator calculation module and a strategy generation module.

[0066] The data preprocessing module is used to obtain the carbon-nitrogen coupling dynamic data in the production and living ecosystems of agricultural and rural areas, and preprocess the carbon-nitrogen coupling dynamic data to obtain processed data.

[0067] In this embodiment, the data preprocessing module collects dynamic data on carbon-nitrogen coupling of agricultural and rural production and living ecosystems by combining the Internet of Things sensor network, remote sensing monitoring platform and field fixed-point sampling: (1) Carbon cycle data: including soil respiration flux (using the LI-8100A soil carbon flux system), crop biomass carbon storage (determined by the biomass harvesting method), organic carbon input (straw return amount, organic fertilizer application amount) (2) Nitrogen cycle data: covering nitrogen fertilizer application records (base fertilizer / topdressing type and amount), ammonia volatilization (dynamic chamber method determination), nitrate nitrogen leaching loss (leakage liquid collection and analysis), N2O emission flux (gas chromatography) (3) Environmental driving data: temperature, humidity, precipitation, and radiation data recorded by the weather station, and pH value, bulk density, and texture parameters monitored by the soil profile. Afterwards, Tukey's fences method (IQR=1.5) was used to identify outliers, and manual verification was performed in combination with field records. The sensor breakpoint data were supplemented using cubic spline interpolation, and the agricultural operation data were estimated using the adjacent similar field analogy method to obtain the processed data.

[0068] The data fusion module uses the data fusion algorithm to perform time series matching and spatial scale alignment on the processed data to obtain a fused data set.

[0069] In the data fusion module of this embodiment, the time series matching algorithm can be used to process data with different sampling frequencies. For example, soil carbon storage data may be collected once a month, while nitrogen fertilizer use efficiency data may be collected once a quarter. The data can be unified to a monthly scale through time series matching. In specific implementation, the quarterly data can be interpolated using linear interpolation to obtain a monthly data sequence. During the spatial scale alignment process, since the sampling points of soil carbon storage, nitrogen fertilizer use efficiency, and crop carbon sequestration capacity may be different, the data needs to be unified to the same spatial grid; for example, the sampling data is unified to a grid scale of 1km×1km, and the missing grids are interpolated using the Kriging interpolation method. There may be multiple monitoring stations in the production and living ecosystem research area of a certain agricultural and rural area. The data collected at each station are not representative. Complete spatial distribution data can be obtained through spatial scale alignment. The Bayesian fusion method can be used in the data fusion process to comprehensively consider the reliability of different data sources. For example, the soil carbon storage in a certain study area has both field sampling data and remote sensing inversion data. The two types of data have their own advantages and disadvantages. Bayesian fusion can obtain a more reliable estimate. The fused data set retains the accuracy of field sampling and has the spatial continuity of remote sensing data.

[0070] The model building module is used to build a neural network model and use the fusion data set to train the neural network model to obtain a carbon-nitrogen coupling dynamic model.

[0071] In this embodiment, the model construction module establishes a hybrid neural network structure with dual time and space channels, in which the spatial feature extraction layer uses a three-dimensional convolutional neural network (3D-CNN) to process the spatial distribution data of carbon and nitrogen in the soil profile, and the temporal feature extraction layer uses a bidirectional long short-term memory network (Bi-LSTM) to process the carbon and nitrogen cycle time series data; the output layer fuses the spatiotemporal features through the attention mechanism to construct a carbon-nitrogen interaction matrix. The fused dataset is converted into a model input tensor, in which: (1) spatial dimension: the 1km×1km grid data is converted into a three-channel three-dimensional matrix (longitude×latitude×depth) containing soil organic carbon, nitrate nitrogen, and ammonium nitrogen; (2) temporal dimension: a time series feature vector containing environmental driving factors such as temperature, precipitation, and fertilizer application is constructed; (3) collaborative dimension: the time series markers of crop growth stages and farming activities are added as auxiliary features. The model then undergoes dynamic weight training using the input tensor. Adaptive loss functions are used for multi-objective optimization. The primary loss function combines mean squared error (MSE) with dynamic time warping (DTW) loss to capture the temporal correlations between carbon and nitrogen variations. An auxiliary loss function maximizes mutual information to enhance the model's ability to characterize carbon-nitrogen synergistic effects. Regularization employs spatial smoothness constraints and temporal periodic consistency constraints to prevent overfitting. The model is then iteratively optimized using a phased training strategy. Phase 1 freezes the spatial feature extraction layer and focuses on optimizing the time series modeling module. Phase 2 unfreezes the entire network and dynamically adjusts the learning rate using a cosine annealing algorithm. Phase 3 introduces adversarial training samples to enhance the model's robustness to extreme climate events. Finally, a spatiotemporal cross-validation strategy is used to partition the dataset into training and validation sets based on crop rotation cycles. The iterative model is validated, and if it meets the criteria, a dynamic carbon-nitrogen coupling model is obtained.

[0072] The indicator calculation module uses the carbon-nitrogen coupling dynamic model to predict the dynamic change trend of carbon-nitrogen coupling in the production and living ecosystems of agricultural and rural areas, and calculates the carbon-nitrogen synergistic emission reduction indicators.

[0073] In this embodiment, the indicator calculation module performs multi-dimensional feature splicing on the environmental driving data of the target prediction area (including temperature gradient data, precipitation time series pattern, soil heterogeneity distribution) and the multi-scenario agricultural management plan (including organic fertilizer replacement ratio matrix, rotation system combination, irrigation mode parameters) to form a spatiotemporal prediction input tensor; through the spatiotemporal dual-channel structure of the carbon-nitrogen coupling dynamic model, the spatial variation characteristics of soil organic matter are analyzed in the spatial dimension, and the lag effect of fertilization events and climate fluctuations is captured in the temporal dimension. The carbon flux dynamic surface (including interannual fluctuation rate of soil carbon pool and crop residue decomposition flux) and nitrogen loss three-dimensional matrix (including nitrate nitrogen leaching flux, nitrous oxide emission flux, and ammonia volatilization spatiotemporal distribution) for the next five years are output; and a carbon equivalent conversion function is established:

[0074] C eq =α·ΔSOC+β·(N2O base -N2O pred )·GWP N2O ,

[0075] Among them, α represents the soil carbon sequestration efficiency coefficient (0.25-0.35); β represents the nitrogen fertilizer management correction factor; ΔSOC represents the change in soil organic carbon; N2O base represents the nitrous oxide emissions under the standard scenario; N2O pred represents the nitrous oxide emissions under the predicted scenario; GWP N2O Indicates the global warming potential of nitrous oxide, which indicates the greenhouse effect intensity of nitrous oxide per unit mass relative to carbon dioxide, and is usually taken as 298. Constructing the nitrogen use efficiency index:

[0076]

[0077] Where T represents the time period, N uptake (t) represents the nitrogen absorption of crops, N input (t) represents nitrogen input, σ(N leach ) represents the standard deviation of the nitrogen leaching vector. A carbon-nitrogen synergistic emission reduction index was constructed based on the carbon equivalent conversion function and the nitrogen use efficiency index.

[0078]

[0079] Among them, S synergy represents the carbon and nitrogen synergistic emission reduction index, R reduction Represents emission reduction, R baseline Indicates baseline emissions.

[0080] The strategy generation module generates emission reduction strategies based on carbon-nitrogen coordinated emission reduction indicators and according to different agricultural and rural production, life, ecological application and management scenarios.

[0081] The system of the above embodiment is used to implement the corresponding agricultural and rural environmental emission reduction analysis method based on carbon-nitrogen synergy in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0082] It should be noted that the above-mentioned carbon-nitrogen synergy-based agricultural and rural environmental emission reduction analysis system is embodied in the form of functional units. The term "module" here can be implemented in the form of software and / or hardware, and is not specifically limited to this.

[0083] For example, a "module" may be a software program, a hardware circuit, or a combination of the two that implements the aforementioned functionality. The hardware circuit may include an application specific integrated circuit (ASIC), an electronic circuit, a processor (e.g., a shared processor, a dedicated processor, or a group of processors) and memory for executing one or more software or firmware programs, combined logic circuits, and / or other suitable components that support the described functionality.

[0084] Example 3

[0085] Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, the present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and runnable on the processor. When the processor executes the program, it implements the agricultural and rural environmental emission reduction analysis method based on carbon-nitrogen synergy as described in any of the above embodiments.

[0086] Figure 2 10 is a schematic diagram showing a more specific hardware structure of an electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other within the device via the bus 1050.

[0087] The processor 1010 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0088] The memory 1020 can be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage devices, dynamic storage devices, etc. The memory 1020 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1020 and is called and executed by the processor 1010.

[0089] The input / output interface 1030 is used to connect input / output modules to implement information input and output. The input / output modules can be configured as components within the device (not shown in the figure) or can be externally connected to the device to provide corresponding functions. Input devices may include a keyboard, mouse, touch screen, microphone, various sensors, etc., and output devices may include a display, speaker, vibrator, indicator light, etc.

[0090] The communication interface 1040 is used to connect to a communication module (not shown) to enable communication between the device and other devices. The communication module can communicate via a wired method (e.g., USB (Universal Serial Bus), network cable, etc.) or a wireless method (e.g., mobile network, WIFI (Wireless Fidelity), Bluetooth, etc.).

[0091] The bus 1050 comprises a path for transmitting information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0092] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in a specific implementation, the device may also include other components necessary for normal operation. In addition, it will be understood by those skilled in the art that the above device may only include the components necessary to implement the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0093] The system of the above embodiment is used to implement the corresponding agricultural and rural environmental emission reduction analysis method based on carbon-nitrogen synergy in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0094] Example 4

[0095] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present invention also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute the agricultural and rural environmental emission reduction analysis method based on carbon-nitrogen synergy as described in any of the above embodiments.

[0096] The computer-readable media of this embodiment include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0097] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the agricultural and rural environmental emission reduction analysis method based on carbon-nitrogen synergy as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0098] Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present disclosure (including the claims) is limited to these examples. Within the scope of the present disclosure, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present disclosure as described above, which are not provided in detail for the sake of simplicity.

[0099] In addition, to simplify the description and discussion, and so as not to obscure the embodiments of the present disclosure, known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided figures. In addition, devices may be shown in the form of block diagrams to avoid obscuring the embodiments of the present disclosure, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present disclosure are to be implemented (i.e., these details should be fully within the purview of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present disclosure, it will be apparent to those skilled in the art that the embodiments of the present disclosure may be implemented without these specific details or with variations in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0100] Although the present disclosure has been described in conjunction with specific embodiments thereof, many alternatives, modifications, and variations of these embodiments will be apparent to those skilled in the art based on the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

[0101] Therefore, the units of each example described in the embodiments of this application can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0102] The embodiments of the present disclosure are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present disclosure should be included in the scope of protection of the present disclosure.

Claims

1. A method for analyzing agricultural and rural environmental emission reduction based on carbon-nitrogen synergy, characterized in that: The following steps are involved: S1. Obtaining carbon-nitrogen coupling dynamic data in agricultural and rural production and living ecosystems, and preprocessing the carbon-nitrogen coupling dynamic data to obtain processed data; S2. performing time series matching and spatial scale alignment on the processed data using a data fusion algorithm to obtain a fused data set; S3. Constructing a neural network model and training the neural network model using the fusion data set to obtain a carbon-nitrogen coupling dynamic model; S4. Using the carbon-nitrogen coupling dynamic model to predict the dynamic trend of carbon-nitrogen coupling in agricultural and rural production and living ecosystems, and calculate carbon-nitrogen synergistic emission reduction indicators; S5. Based on the carbon-nitrogen synergistic emission reduction indicators, generate emission reduction strategies according to different agricultural and rural production, life, ecological application and management scenarios.

2. The agricultural and rural environmental emission reduction analysis method based on carbon-nitrogen synergy according to claim 1 is characterized in that: Said S1 comprises: Collect the carbon-nitrogen coupling dynamic data in the production and living ecosystems of agricultural and rural areas through Internet of Things sensors, remote sensing monitoring and field fixed-point sampling. The carbon-nitrogen coupling dynamic data includes: carbon cycle data, nitrogen cycle data and environmental driving data; The Tukey elimination method is used to identify and eliminate abnormal data in the carbon-nitrogen coupling dynamic data, and then the missing data is supplemented using the interpolation method to obtain the processed data.

3. The agricultural and rural environmental emission reduction analysis method based on carbon-nitrogen synergy according to claim 1 is characterized in that: The S2 includes: Using linear interpolation to interpolate the quarterly data in the processed data to obtain a monthly data series, thereby obtaining time-matched data; The processed data are unified into a grid scale of 1 km × 1 km, and the missing grids are interpolated using the Kriging interpolation method to obtain spatially aligned data; The time-matched data and the space-aligned data are fused using a data fusion algorithm to obtain the fused data set.

4. The agricultural and rural environmental emission reduction analysis method based on carbon-nitrogen synergy according to claim 1 is characterized in that: The S3 includes: Constructing a hybrid neural network model including a spatial and temporal dual channel, wherein the hybrid neural network model includes: a spatial feature extraction layer, a temporal feature extraction layer, and an output layer; The hybrid neural network model is dynamically weighted trained using the fused data set to obtain the carbon-nitrogen coupling dynamic model.

5. The agricultural and rural environmental emission reduction analysis method based on carbon-nitrogen synergy according to claim 2 is characterized in that: The S4 includes: Performing multi-dimensional feature splicing on the environmental driving data of the target prediction area and the multi-scenario agricultural management plan, and inputting the data into the carbon-nitrogen coupling dynamic model to obtain the dynamic change trend of the carbon-nitrogen coupling in the future period; The carbon-nitrogen synergistic emission reduction index is constructed based on the dynamic change trend of the carbon-nitrogen coupling: Among them, S synergy represents the carbon and nitrogen synergistic emission reduction index, C eq represents the carbon equivalent conversion function, NUE represents the nitrogen use efficiency index, R reduction Represents emission reduction, R baseline Indicates baseline emissions.

6. An agricultural and rural environmental emission reduction analysis system based on carbon-nitrogen synergy, wherein the analysis system applies the method according to any one of claims 1 to 5, characterized in that: include: Data preprocessing module, data fusion module, model building module, indicator calculation module and strategy generation module; The data preprocessing module is used to obtain carbon-nitrogen coupling dynamic data in the production and living ecosystem of agricultural and rural areas, and preprocess the carbon-nitrogen coupling dynamic data to obtain processed data; The data fusion module performs time series matching and spatial scale alignment on the processed data through a data fusion algorithm to obtain a fused data set; The model building module is used to build a neural network model and use the fusion data set to train the neural network model to obtain a carbon-nitrogen coupling dynamic model; The index calculation module uses the carbon-nitrogen coupling dynamic model to predict the dynamic change trend of carbon-nitrogen coupling in the production and living ecosystem of agricultural and rural areas, and calculates the carbon-nitrogen synergistic emission reduction index; The strategy generation module generates emission reduction strategies based on the carbon-nitrogen coordinated emission reduction indicators and according to different agricultural and rural production, life, ecological application and management scenarios.

7. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the agricultural and rural environmental emission reduction analysis method based on carbon-nitrogen synergy as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed, implements the agricultural and rural environmental emission reduction analysis method based on carbon-nitrogen synergy as described in any one of claims 1 to 5.

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