Soil carbon sequestration and carbon enhancement optimization method and system based on artificial intelligence

Abstract

The present invention provides a soil carbon fixation and carbon increase optimization method and system based on artificial intelligence, which belongs to the technical field of agricultural carbon fixation and carbon increase optimization, and includes the following steps: S101: obtaining the area and boundary of the agricultural planting soil area, and obtaining weather data; S102: obtaining different plant species planted, performing regional division operations, and performing soil area numbering; S103: obtaining a data change fluctuation curve group and a weather factor group, and obtaining a carbon fixation-time change fluctuation curve group; S104: establishing an agricultural soil carbon fixation model, and obtaining carbon fixation prediction data matching the soil area number; S105: designing and establishing a carbon fixation verification model, obtaining carbon fixation verification data matching the soil area number, and performing data verification and checking operations; S106: performing carbon increase optimization operations on sub-regions with different soil area numbers based on the verified carbon fixation prediction data, thereby improving the accuracy of the prediction data.

Description

Technical Field

[0001] The present invention belongs to the technical field of agricultural carbon fixation and carbon enhancement optimization, and more specifically, particularly relates to a soil carbon fixation and carbon enhancement optimization method and system based on artificial intelligence. Background Art

[0002] In the agricultural field, soil quality is the cornerstone of crop growth and yield, and soil carbon content is one of the core indicators for measuring soil quality. Carbon in the soil exists in the form of organic carbon and inorganic carbon. Soil organic carbon not only provides a long-term source of nutrients for crops, but also improves soil structure and enhances soil's ability to retain water and fertilizer. It plays a key role in improving the stress resistance and quality of crops. However, traditional carbon fixation and carbon increase optimization methods have low data accuracy when predicting and obtaining future soil carbon fixation data, and there is often a lag in time when increasing soil carbon. Summary of the invention

[0003] In order to solve the above technical problems, the present invention provides an artificial intelligence-based soil carbon fixation and carbon enhancement optimization method and system to solve the technical problems in the prior art that the traditional carbon fixation and carbon enhancement optimization method has low data accuracy when predicting and acquiring future soil carbon fixation data, and often lags in time when increasing soil carbon.

[0004] The purpose and efficacy of the soil carbon fixation and carbon enhancement optimization method and system based on artificial intelligence of the present invention are achieved by the following specific technical means: The soil carbon sequestration and carbon enhancement optimization method based on artificial intelligence includes the following steps: S101: Conduct a survey on the agricultural planting soil area, obtain the area and boundary of the agricultural planting soil area, establish an agricultural weather monitoring station and an agricultural weather monitoring database, detect the weather in the agricultural planting soil area based on the agricultural weather monitoring station, and obtain weather data, the weather data includes past dates in the agricultural planting soil area and weather types matching the past dates, and import the weather data into the agricultural weather monitoring database for storage; S102: different plant species planted in the agricultural planting soil area are obtained, different plant species are numbered by characters, and a region division operation is performed based on the plant species in the agricultural planting soil area. Based on the region division operation, the agricultural planting soil area is divided into a plurality of different agricultural planting soil sub-regions according to the planted plant species, different sub-regions are numbered, and the number numbers are matched and combined with the character numbers to form soil region numbers; S103: Obtain historical carbon fixation data and historical dates of different sub-regions, perform a timestamp alignment operation to match the historical carbon fixation data with the historical dates, obtain a data change fluctuation curve group based on the historical carbon fixation data and the historical dates, and import the soil region number into the data change fluctuation curve group. At the same time, call the agricultural weather detection database, obtain a weather factor group based on the weather data in the agricultural weather detection database, and obtain a carbon fixation-time change fluctuation curve group based on the data change fluctuation curve group, the weather factor group and the soil region number; S104: Establishing an agricultural soil carbon sequestration model, making a copy of the carbon sequestration-time variation fluctuation curve group, importing the copy as a training sample into the agricultural soil carbon sequestration model, and deriving an agricultural soil carbon sequestration prediction model based on iterative training. The agricultural soil carbon sequestration prediction model predicts the carbon sequestration of different sub-regions in the next year based on the monthly change rate of the carbon sequestration-time variation fluctuation curve group, thereby obtaining carbon sequestration prediction data matching the soil region number; S105: designing and establishing a carbon sequestration verification model, obtaining carbon sequestration verification data matching the soil region number based on the carbon sequestration verification model, performing data verification and calibration operations based on the carbon sequestration verification data and the carbon sequestration prediction data, and verifying the carbon sequestration prediction data based on the data verification and calibration operations; S106: Based on the verified carbon sequestration prediction data, carbon enhancement optimization operations are performed on sub-regions with different soil area numbers.

[0005] As a further solution of the present invention, a carbon sequestration verification model is designed and established, and carbon sequestration verification data matching the soil region number is obtained based on the carbon sequestration verification model, including: A1: Construct a small-scale regional digital map, which is constructed based on the actual geographical data of the actual soil area; construct a small-scale agricultural planting soil area digital sand table based on the small-scale regional digital map, which contains planted plants. The small-scale agricultural planting soil area digital sand table is used to simulate the environment of the agricultural planting soil area, divide the small-scale agricultural planting soil area digital sand table into regions, and number the soil areas at the same time; A2: A virtual weather observation station and a virtual weather generation station are established in the digital sandbox of the small agricultural planting soil area. The virtual weather generation station is used to dynamically simulate the actual weather, and the virtual weather observation station is used to detect the weather in the sandbox of the small agricultural planting soil area and obtain virtual weather data; A3: Construct a carbon sequestration verification model. The carbon sequestration verification model is constructed using the DAYCENT model; A4: Import virtual weather data and agricultural soil area related parameters into the carbon sequestration verification model, perform verification simulation through the carbon sequestration verification model, and generate carbon sequestration verification data matching the soil area number.

[0006] As a further solution of the present invention, the carbon fixation verification model is simulated based on the carbon cycle function, which can be expressed as: ; in, Indicates time of soil carbon, Indicates the last moment of soil carbon, Indicates time The total primary productivity, Indicates time The amount of carbon released by plant and soil respiration, It is expressed as the amount of carbon released from soil organic matter; During the simulation, virtual weather data is imported into the carbon fixation verification model as a virtual weather factor. After the virtual weather factor is imported, the carbon fixation verification model uses dynamic calculation simulation to perform simulation.

[0007] As a further solution of the present invention, the region division operation specifically includes: B1: Obtaining rectangular NDVI images of agricultural planting soil areas based on remote sensing technology; B2: De-noising the rectangular NDVI image based on wavelet transform technology, and then sharpening and contrast enhancing the rectangular NDVI image after denoising; B3: The Canny edge detection algorithm is used to detect the boundary of the soil area based on the brightness gradient of the rectangular NDVI image. At the same time, the Canny edge detection algorithm is used to remove useless images from the rectangular NDVI image to obtain a curved NDVI image. The useless images are the image parts of the rectangular image that do not contain the agricultural soil area. B4: Image segmentation of curved NDVI images is performed based on the region growing algorithm, and the areas with similar spectral characteristics in the NDVI images are divided into multiple sub-areas; B5: Create a color gradient table that matches the character numbers of different plant species. The color of the bare soil roads in the area is defined as white. The NDVI values ​​in the sub-areas of the curved NDVI image are mapped to different colors based on the color mapping technology. The color mapping is based on the color gradient table.

[0008] As a further solution of the present invention, the useless images in the rectangular NDVI image are removed based on the Canny edge detection algorithm, so as to obtain the curved NDVI image, including: After obtaining the curved NDVI image, the curved NDVI image is corrected based on the area of ​​the agricultural planting soil region and the boundary of the agricultural planting soil region.

[0009] As a further solution of the present invention, a rectangular image of an agricultural planting soil area is obtained based on remote sensing technology, including: The rectangular image of the agricultural planting soil area obtained is a high-resolution image.

[0010] As a further solution of the present invention, the digital number and the character number are matched and combined to form a soil area number, including: When numbering, use numbers 1.2.3.4 to n, where n represents the number of agricultural soil sub-regions; When performing character numbering, the names of different plant species are obtained, and the first letters are extracted based on the pinyin of the different plant species, and character encoding is performed based on the pinyin first letter combination of the different plant species. If the character codes are the same, a number is added to the end of the pinyin first letter combination, and at the same time, the character codes are assigned to different plant species for character numbering; Based on the matching combination of digital numbers and character numbers, the combination is a soil area number, which can be expressed as ,in, Represented as a digital number, Represented as a character number.

[0011] As a further solution of the present invention, the carbon enrichment optimization operation specifically includes: Carbon is added to the soil, and when adding carbon, a combined carbon addition method is used; A large amount of first carbon-increasing materials are used to increase carbon in the soil, wherein the first carbon-increasing materials include decomposed straw and harmlessly treated feces; Using a second carbon-enhancing material in an appropriate amount to increase carbon in the soil, wherein the second carbon-enhancing material is a strain of bacteria that meets the standards of food, feed and fertilizer; A small amount of accurate third carbon-enhancing materials are added to increase the carbon content of the soil. The third carbon-enhancing materials are mineral elements that meet the standards of organic cultivation. The user can adjust the ratio of the first carbon increasing material, the second carbon increasing material and the third carbon increasing material based on the carbon fixation prediction data, and can also adjust the total usage of the first carbon increasing material, the second carbon increasing material and the third carbon increasing material based on the carbon fixation prediction data.

[0012] As a further solution of the present invention, the data verification operation specifically includes: Obtaining a verification threshold, the verification threshold is used to verify the carbon fixation prediction data and the carbon fixation verification data, subtracting the carbon fixation prediction data from the carbon fixation verification data and taking the absolute value to obtain a carbon fixation comparison value, and comparing the carbon fixation comparison value with the verification threshold; If the carbon fixation comparison value is less than or equal to the verification threshold, the carbon fixation prediction data is derived as the verified carbon fixation prediction data; If the carbon sequestration comparison value is greater than the verification threshold, a re-prediction is performed based on the agricultural soil carbon sequestration prediction model.

[0013] The soil carbon sequestration and carbon enhancement optimization system based on artificial intelligence includes: An acquisition module is used to acquire the area and boundary of the agricultural planting soil area, acquire different plant species planted in the agricultural planting soil area, and acquire historical carbon sequestration data and historical dates of different sub-regions; Weather module, the weather module includes agricultural weather detection stations and agricultural weather detection database; Agricultural weather monitoring stations are used to detect weather in agricultural soil areas and obtain weather data; Agricultural weather detection database, used to store weather data detected by agricultural weather detection stations; A region module, used to perform region division operations; A numbering module, used for performing character numbering and digital numbering, and generating soil area numbering based on the character numbering and digital numbering; A processing module is used to obtain a data change fluctuation curve group, import the soil area number into the data change fluctuation curve group, and call the agricultural weather detection database to obtain a weather factor group, and obtain a carbon fixation-time change fluctuation curve group based on the weather factor group, the data change fluctuation curve group and the soil area number; A model module is used to establish an agricultural soil carbon sequestration model and generate an agricultural soil carbon sequestration prediction model based on a carbon sequestration-time variation fluctuation curve group, thereby obtaining carbon sequestration prediction data matching the soil area number based on the agricultural soil carbon sequestration prediction model; A verification simulation module is used to design and establish a carbon sequestration verification model, and obtain carbon sequestration verification data matching the soil area number based on the carbon sequestration verification model; The verification module is used to perform data verification and calibration operations on the carbon fixation verification data and the carbon fixation prediction data.

[0014] Compared with the prior art, the present invention has the following beneficial effects: First, the agricultural planting soil area is investigated to obtain the area and boundary of the agricultural planting soil area, and an agricultural weather monitoring station and an agricultural weather monitoring database are established. The weather data is obtained based on the agricultural weather monitoring station and imported into the agricultural weather monitoring database for storage. Then, different plant species planted in the agricultural planting soil area are obtained, and regional division operations are performed based on the plant species in the agricultural planting soil area, so that the agricultural planting soil area is divided into multiple groups of different agricultural planting soil sub-regions according to the planted plant species, and the sub-regions are numbered. Then, historical carbon fixation data and historical dates of different sub-regions are obtained, and a data change fluctuation curve group is obtained based on the historical carbon fixation data and historical dates, and a weather factor group is obtained. Based on the data change fluctuation curve group, weather factor group and soil area The carbon fixation-time fluctuation curve group is obtained by numbering, an agricultural soil carbon fixation model is established, and an agricultural soil carbon fixation prediction model is derived based on iterative training, so as to obtain carbon fixation prediction data matching the soil area number. Subsequently, a carbon fixation verification model is designed and established to obtain carbon fixation verification data matching the soil area number, and data verification and calibration operations are performed. Based on the verified carbon fixation prediction data, carbon increase optimization operations are performed on sub-regions with different soil area numbers. This method can predict the carbon fixation of soil sub-regions. If the predicted carbon fixation amount is too small, the user can perform carbon increase operations in time through manual intervention, and manual intervention can be performed in different regions, so as to perform carbon increase operations more accurately. When obtaining the carbon fixation prediction data for each sub-region, the data can be verified and checked based on the carbon fixation verification model to ensure the accuracy of the predicted data. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of the steps of the soil carbon fixation and carbon enhancement optimization method based on artificial intelligence of the present invention; Figure 2 It is a flowchart of the steps of designing and establishing a carbon fixation verification model in the soil carbon fixation and carbon enhancement optimization method based on artificial intelligence of the present invention; Figure 3 It is a flow chart of the steps of the regional division operation in the soil carbon fixation and carbon enhancement optimization method based on artificial intelligence of the present invention; Figure 4 It is an example schematic diagram of agricultural planting soil areas after the regional division operation in the soil carbon fixation and carbon enhancement optimization method based on artificial intelligence of the present invention. DETAILED DESCRIPTION

[0016] The following is a further detailed description of the embodiments of the present invention in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the technical solutions of the present invention, but cannot be used to limit the scope of protection of the present invention.

[0017] Embodiment 1: As shown in the attached Figure 1 , Figure 2 , Figure 3 , Figure 4 As shown: The present invention provides an artificial intelligence-based soil carbon fixation and carbon enhancement optimization method, which is applicable to soil carbon fixation and carbon enhancement, and includes the following steps: S101: Conduct a survey on the agricultural planting soil area, obtain the area and boundary of the agricultural planting soil area, establish an agricultural weather monitoring station and an agricultural weather monitoring database, monitor the weather in the agricultural planting soil area based on the agricultural weather monitoring station, and obtain weather data, the weather data includes past dates in the agricultural planting soil area and weather types matching the past dates, and import the weather data into the agricultural weather monitoring database for storage.

[0018] Specifically, when investigating agricultural planting soil areas, users can use drones and other means to investigate agricultural planting soil areas. The weather conditions in agricultural planting soil areas are important data in assessing carbon sequestration. The establishment of agricultural weather monitoring stations can detect and record the weather in agricultural planting soil areas.

[0019] S102: Acquire different plant species planted in the agricultural planting soil area, character number the different plant species, perform area division operation based on the plant species in the agricultural planting soil area, divide the agricultural planting soil area into multiple groups of different agricultural planting soil sub-regions according to the planted plant species based on the area division operation, digitally number the different sub-regions, match and combine the digital numbers with the character numbers to form soil area numbers.

[0020] Specifically, the region division operation includes: B1: Obtain rectangular NDVI images of agricultural planting soil areas based on remote sensing technology.

[0021] When images of agricultural soil areas are obtained through remote sensing technology, the rectangular images of agricultural soil areas obtained are high-resolution images. It is understandable that finer-grained soil area divisions require higher-resolution image data to ensure the accuracy of the division.

[0022] B2: De-noise the rectangular NDVI image based on wavelet transform technology, and then perform sharpening and contrast enhancement operations on the rectangular NDVI image after denoising.

[0023] It is understandable that after remote sensing image data is acquired, preprocessing and image enhancement are usually required to improve the effect of edge detection. There may be noise in the remote sensing image, and denoising can reduce the impact of noise on subsequent processing. After denoising, the edges in the enhanced image can be sharpened to make the boundaries between different areas clearer. By sharpening the image, details such as soil areas and vegetation boundaries can be highlighted, which is helpful for regional division and analysis. By contrast enhancement operations, the contrast in local areas of the image can be enhanced, especially for areas with less plant coverage, which can better show the difference between soil and other areas.

[0024] B3: The Canny edge detection algorithm is used to detect the boundary of the soil area based on the brightness gradient of the rectangular NDVI image. At the same time, the Canny edge detection algorithm is used to remove useless images from the rectangular NDVI image to obtain a curved NDVI image. The useless images are the image parts in the rectangular image that do not contain the agricultural soil area.

[0025] Furthermore, after obtaining the curved NDVI image, the curved NDVI image is corrected based on the area of ​​the agricultural planting soil area and the boundary of the agricultural planting soil area. Through the correction operation, the curved NDVI image can be matched with the agricultural planting soil area under investigation to prevent errors in image division during subsequent steps.

[0026] B4: Image segmentation of curved NDVI images is performed based on the region growing algorithm, and regions with similar spectral characteristics in the NDVI images are divided into multiple sub-regions.

[0027] Specifically, the region growing algorithm can select multiple "seed points" in the curved NDVI image, and then expand the region based on the "seed points" and according to relevant thresholds. It can accurately identify boundaries and merge similar regions.

[0028] B5: Create a color gradient table that matches the character numbers of different plant species. The color of the bare soil roads in the area is defined as white. The NDVI values ​​in the sub-areas of the curved NDVI image are mapped to different colors based on the color mapping technology. The color mapping is based on the color gradient table.

[0029] It can be understood that using color mapping technology to map NDVI values ​​into different colors can help enhance the visual effects of different plant species in the image, making the differences between different plant species more obvious, and can enhance the contrast between different plant species or soil areas, making the visual effects of the curved NDVI image clearer, making it easier for users to conduct subsequent analysis of the soil area.

[0030] Specifically, the digital number and the character number are matched and combined to form a soil area number, including: when performing digital numbering, numbers 1.2.3.4 to n are used for numbering, and n represents the number of agricultural planting soil sub-regions; when performing character numbering, the names of different plant species are obtained, and the first letters are extracted based on the pinyin of the different plant species, and character encoding is performed based on the pinyin first letter combination of the different plant species. If the character codes are the same, numbers are added to the end of the pinyin first letter combination. At the same time, the character codes are assigned to different plant species for character numbering.

[0031] Furthermore, when numbering characters, if the first pinyin letter is the same, the character is added to the second pinyin letter. If the second pinyin letter is the same, the character is added to the third pinyin letter, and so on. If the pinyin letter is used up and the characters are the same, a number is added to the end of the character.

[0032] After the digital number and character number are completed, the digital number and character number are matched and combined to form a soil area number. The soil area number can be expressed as ,in, Represented as a digital number, Represented as a character number, for example, , the digital number is 1, indicating that it is in sub-area 1, and the character number is , indicating that the plant species is pumpkin, the soil area number can be represented as sub-area 1 where the plant is pumpkin.

[0033] For example, Figure 4 As shown, Indicates that the plant is spinach in sub-region 1, Indicates that the sub-region where the plant is green vegetables 2, Indicates sub-region 3 where the plant is leek, It shows sub-region 4 where apples are grown.

[0034] S103: Obtain historical carbon fixation data and historical dates for different regions, align timestamps to match historical carbon fixation data with historical dates, obtain a data change fluctuation curve group based on the historical carbon fixation data and historical dates, and import the soil region number into the data change fluctuation curve group; at the same time, call the agricultural weather detection database, obtain a weather factor group based on the weather data in the agricultural weather detection database, and obtain a carbon fixation-time change fluctuation curve group based on the data change fluctuation curve group, the weather factor group and the soil region number.

[0035] For example, the carbon fixation-time fluctuation curve group can be expressed as: ; Among them, F represents the carbon fixation-time fluctuation curve group, Represented as a weather factor group, , , and Expressed as weight, Expressed as a constant, when calculations are performed, they are based on the appropriate weather factor in the weather factor group.

[0036] S104: Establish an agricultural soil carbon sequestration model, copy the carbon sequestration-time variation fluctuation curve group, import the copy as a training sample into the agricultural soil carbon sequestration model, and derive an agricultural soil carbon sequestration prediction model based on iterative training. The agricultural soil carbon sequestration prediction model predicts the carbon sequestration in different sub-regions in the next year based on the monthly change rate of the carbon sequestration-time variation fluctuation curve group, thereby obtaining carbon sequestration prediction data matching the soil region number.

[0037] Furthermore, when establishing an agricultural soil carbon sequestration model, a hybrid model of the CENTURY model and the random forest model can be used. By combining the process model with the data-driven model, the physical interpretability of the process model is retained, and the strong nonlinear fitting ability of the data-driven model is utilized to achieve higher prediction accuracy. The hybrid model can also reduce the overfitting risk of a single model and improve the robustness of the overall model.

[0038] In this method, soil carbon sequestration is affected by multiple factors, and there are complex nonlinear relationships between these factors. The hybrid model can capture these complex relationships through data-driven parts (such as neural networks, random forests, etc.), and use process-based models to explain the basic mechanism of the soil carbon sequestration process. The impact of weather and climate factors on carbon sequestration is dynamic and nonlinear. The hybrid model can combine weather factor groups and adjust the prediction model through machine learning algorithms to provide more accurate carbon sequestration predictions.

[0039] S105: Design and establish a carbon sequestration verification model, obtain carbon sequestration verification data matching the soil area number based on the carbon sequestration verification model, perform data verification and check operations based on the carbon sequestration verification data and the carbon sequestration prediction data, and verify the carbon sequestration prediction data based on the data verification and check operations.

[0040] Specifically, the data verification and validation operations include: Obtaining a verification threshold, the verification threshold is used to verify the carbon fixation prediction data and the carbon fixation verification data, subtracting the carbon fixation prediction data from the carbon fixation verification data and taking the absolute value to obtain a carbon fixation comparison value, and comparing the carbon fixation comparison value with the verification threshold; If the carbon fixation comparison value is less than or equal to the verification threshold, the carbon fixation prediction data is derived as the verified carbon fixation prediction data; If the carbon sequestration comparison value is greater than the verification threshold, a re-prediction is performed based on the agricultural soil carbon sequestration prediction model.

[0041] Specifically, a carbon sequestration verification model is designed and established, and carbon sequestration verification data matching the soil area number is obtained based on the carbon sequestration verification model, including: A1: Construct a small-scale regional digital map. The small-scale regional digital map is constructed based on the actual geographical data of the actual soil area. Construct a small-scale agricultural planting soil area digital sand table based on the small-scale regional digital map. The small-scale agricultural planting soil area digital sand table contains planted plants. The small-scale agricultural planting soil area digital sand table is used to simulate the environment of the agricultural planting soil area. The small-scale agricultural planting soil area digital sand table is divided into regions, and the soil areas are numbered at the same time.

[0042] A2: A virtual weather observation station and a virtual weather generation station are established in the digital sandbox of the small agricultural planting soil area. The virtual weather generation station is used to dynamically simulate the actual weather, and the virtual weather observation station is used to detect the weather in the sandbox of the small agricultural planting soil area and obtain virtual weather data.

[0043] The simulated soil area information is combined with the data collected by the weather observation station to form a complete virtual environment, based on which the soil carbon fixation process is simulated.

[0044] A3: Construct a carbon sequestration verification model. The carbon sequestration verification model is constructed using the DAYCENT model.

[0045] A4: Import virtual weather data and agricultural soil area related parameters into the carbon sequestration verification model, perform verification simulation through the carbon sequestration verification model, and generate carbon sequestration verification data matching the soil area number.

[0046] Furthermore, the carbon sequestration verification model is simulated based on the carbon cycle function, which can be expressed as: ; in, Indicates time of soil carbon, Indicates the last moment of soil carbon, Indicates time The total primary productivity, Indicates time The amount of carbon released by plant and soil respiration, Expressed as the amount of carbon released from soil organic matter.

[0047] During the simulation, virtual weather data is imported into the carbon fixation verification model as a virtual weather factor. After the virtual weather factor is imported, the carbon fixation verification model uses dynamic calculation simulation to perform simulation.

[0048] The carbon sequestration verification model can be used to test the carbon sequestration prediction data and the carbon sequestration verification data to ensure the accuracy of the data. In practical applications, the carbon sequestration prediction model can also be calibrated through the carbon sequestration verification model. If the verification results show that the carbon sequestration prediction deviation in certain areas or situations is large, the model can be optimized by adjusting the parameters of the prediction model or improving the input variables to make it more suitable for actual conditions.

[0049] S106: Based on the verified carbon sequestration prediction data, carbon enhancement optimization operations are performed on sub-regions with different soil area numbers.

[0050] It can be understood that this method can be used to predict the carbon fixation amount in different regions. If the predicted carbon fixation amount is too little, carbon addition operations can be carried out in time through manual intervention, and manual intervention can be carried out in different regions to make carbon addition operations more accurate.

[0051] Specifically, the carbon addition optimization operation includes: Carbon is added to the soil, and when adding carbon, a combined carbon addition method is used; A large amount of first carbon-increasing materials are used to increase carbon in the soil, wherein the first carbon-increasing materials include decomposed straw and harmlessly treated feces; Using a second carbon-enhancing material in an appropriate amount to increase carbon in the soil, wherein the second carbon-enhancing material is a strain of bacteria that meets the standards of food, feed and fertilizer; A small amount of accurate third carbon-enhancing materials are added to increase the carbon content of the soil. The third carbon-enhancing materials are mineral elements that meet the standards of organic cultivation. The user can adjust the ratio of the first carbon increasing material, the second carbon increasing material and the third carbon increasing material based on the carbon fixation prediction data, and can also adjust the total usage of the first carbon increasing material, the second carbon increasing material and the third carbon increasing material based on the carbon fixation prediction data.

[0052] The soil carbon sequestration and carbon enhancement optimization system based on artificial intelligence includes: An acquisition module is used to acquire the area and boundary of the agricultural planting soil area, acquire different plant species planted in the agricultural planting soil area, and acquire historical carbon sequestration data and historical dates of different sub-regions; Weather module, the weather module includes agricultural weather detection stations and agricultural weather detection database; Agricultural weather monitoring stations are used to detect weather in agricultural soil areas and obtain weather data; Agricultural weather detection database, used to store weather data detected by agricultural weather detection stations; A region module, used to perform region division operations; A numbering module, used for performing character numbering and digital numbering, and generating soil area numbering based on the character numbering and digital numbering; A processing module is used to obtain a data change fluctuation curve group, import the soil area number into the data change fluctuation curve group, and call the agricultural weather detection database to obtain a weather factor group, and obtain a carbon fixation-time change fluctuation curve group based on the weather factor group, the data change fluctuation curve group and the soil area number; A model module is used to establish an agricultural soil carbon sequestration model and generate an agricultural soil carbon sequestration prediction model based on a carbon sequestration-time variation fluctuation curve group, thereby obtaining carbon sequestration prediction data matching the soil area number based on the agricultural soil carbon sequestration prediction model; A verification simulation module is used to design and establish a carbon sequestration verification model, and obtain carbon sequestration verification data matching the soil area number based on the carbon sequestration verification model; The verification module is used to perform data verification and calibration operations on the carbon fixation verification data and the carbon fixation prediction data.

[0053] The specific usage and function of the first embodiment are as follows: First, the agricultural planting soil area is investigated to obtain the area and boundary of the agricultural planting soil area, and an agricultural weather monitoring station and an agricultural weather monitoring database are established. The weather data is obtained based on the agricultural weather monitoring station and imported into the agricultural weather monitoring database for storage. Then, different plant species planted in the agricultural planting soil area are obtained, and regional division operations are performed based on the plant species in the agricultural planting soil area, so that the agricultural planting soil area is divided into multiple groups of different agricultural planting soil sub-regions according to the planted plant species, and the sub-regions are numbered. Then, historical carbon fixation data and historical dates of different sub-regions are obtained, and a data change fluctuation curve group is obtained based on the historical carbon fixation data and historical dates, and a weather factor group is obtained. Based on the data change fluctuation curve group, weather factor group and soil area The carbon fixation-time fluctuation curve group is obtained by numbering, an agricultural soil carbon fixation model is established, and an agricultural soil carbon fixation prediction model is derived based on iterative training, so as to obtain carbon fixation prediction data matching the soil area number. Subsequently, a carbon fixation verification model is designed and established to obtain carbon fixation verification data matching the soil area number, and data verification and calibration operations are performed. Based on the verified carbon fixation prediction data, carbon increase optimization operations are performed on sub-regions with different soil area numbers. This method can predict the carbon fixation of soil sub-regions. If the predicted carbon fixation amount is too small, the user can perform carbon increase operations in time through manual intervention, and manual intervention can be performed in different regions, so as to perform carbon increase operations more accurately. When obtaining the carbon fixation prediction data for each sub-region, the data can be verified and checked based on the carbon fixation verification model to ensure the accuracy of the predicted data.

[0054] An electronic device, comprising: At least one processor; and, a memory connected to the at least one processor for communication; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method proposed in the first embodiment of the present invention.

[0055] The following is a detailed introduction to the various components of electronic equipment: The processor is the control center of the electronic device, which can be a processor or a general term for multiple processing elements. For example, the processor is one or more central processing units (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the first embodiment of the present invention, such as one or more microprocessors (digital signal processors, DSP), or one or more field programmable gate arrays (FPGA).

[0056] The processor can perform various functions of the electronic device by running or executing software programs stored in the memory and calling data stored in the memory.

[0057] The memory is used to store the software program for executing the solution of the present invention, and the execution is controlled by the processor. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0058] The memory may be a real-only memory (ROM) or other types of static storage devices that can store static information and instructions, a random access memory (RAM) or other types of dynamic storage devices that can store information and instructions, or an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only (CD-ROM), or other optical disc storage, optical disc storage (including compressed optical disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory may be integrated with the processor, or may exist independently and be coupled to the processor through an interface circuit of the electronic device, and the embodiments of the present invention do not specifically limit this.

[0059] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by a wireless (such as infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0060] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can represent the existence of A alone, the existence of A and B at the same time, and the existence of B alone, where A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0061] It should be understood that in the embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. 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 embodiments of the present invention.

[0062] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. An artificial intelligence-based soil carbon sequestration and carbon enhancement optimization method, characterized in that: The following steps are included: S101: Conduct a survey on the agricultural planting soil area, obtain the area and boundary of the agricultural planting soil area, establish an agricultural weather monitoring station and an agricultural weather monitoring database, detect the weather in the agricultural planting soil area based on the agricultural weather monitoring station, and obtain weather data, the weather data includes past dates in the agricultural planting soil area and weather types matching the past dates, and import the weather data into the agricultural weather monitoring database for storage; S102: different plant species planted in the agricultural planting soil area are obtained, different plant species are numbered by characters, and a region division operation is performed based on the plant species in the agricultural planting soil area. Based on the region division operation, the agricultural planting soil area is divided into a plurality of different agricultural planting soil sub-regions according to the planted plant species, different sub-regions are numbered, and the number numbers are matched and combined with the character numbers to form soil region numbers; S103: Obtain historical carbon fixation data and historical dates of different sub-regions, perform a timestamp alignment operation to match the historical carbon fixation data with the historical dates, obtain a data change fluctuation curve group based on the historical carbon fixation data and the historical dates, and import the soil region number into the data change fluctuation curve group. At the same time, call the agricultural weather detection database, obtain a weather factor group based on the weather data in the agricultural weather detection database, and obtain a carbon fixation-time change fluctuation curve group based on the data change fluctuation curve group, the weather factor group and the soil region number; S104: Establishing an agricultural soil carbon sequestration model, making a copy of the carbon sequestration-time variation fluctuation curve group, importing the copy as a training sample into the agricultural soil carbon sequestration model, and deriving an agricultural soil carbon sequestration prediction model based on iterative training. The agricultural soil carbon sequestration prediction model predicts the carbon sequestration of different sub-regions in the next year based on the monthly change rate of the carbon sequestration-time variation fluctuation curve group, thereby obtaining carbon sequestration prediction data matching the soil region number; S105: designing and establishing a carbon sequestration verification model, obtaining carbon sequestration verification data matching the soil region number based on the carbon sequestration verification model, performing data verification and calibration operations based on the carbon sequestration verification data and the carbon sequestration prediction data, and verifying the carbon sequestration prediction data based on the data verification and calibration operations; S106: Based on the verified carbon sequestration prediction data, carbon enhancement optimization operations are performed on sub-regions with different soil area numbers.

2. The artificial intelligence-based soil carbon sequestration and carbon enhancement optimization method according to claim 1 is characterized in that: Design and establish a carbon sequestration verification model, and obtain carbon sequestration verification data matching the soil area number based on the carbon sequestration verification model, including: A1: Construct a small-scale regional digital map, which is constructed based on the actual geographical data of the actual soil area; construct a small-scale agricultural planting soil area digital sand table based on the small-scale regional digital map, which contains planted plants. The small-scale agricultural planting soil area digital sand table is used to simulate the environment of the agricultural planting soil area, divide the small-scale agricultural planting soil area digital sand table into regions, and number the soil areas at the same time; A2: A virtual weather observation station and a virtual weather generation station are established in the digital sandbox of the small agricultural planting soil area. The virtual weather generation station is used to dynamically simulate the actual weather, and the virtual weather observation station is used to detect the weather in the sandbox of the small agricultural planting soil area and obtain virtual weather data; A3: Construct a carbon sequestration verification model. The carbon sequestration verification model is constructed using the DAYCENT model; A4: Import virtual weather data and agricultural soil area related parameters into the carbon sequestration verification model, perform verification simulation through the carbon sequestration verification model, and generate carbon sequestration verification data matching the soil area number.

3. The artificial intelligence-based soil carbon sequestration and carbon enhancement optimization method according to claim 2 is characterized in that: The carbon fixation verification model is simulated based on the carbon cycle function, which can be expressed as: ; in, Indicates time of soil carbon, Indicates the last moment of soil carbon, Indicates time The total primary productivity, Indicates time The amount of carbon released by plant and soil respiration, It is expressed as the amount of carbon released from soil organic matter; During the simulation, virtual weather data is imported into the carbon fixation verification model as a virtual weather factor. After the virtual weather factor is imported, the carbon fixation verification model uses dynamic calculation simulation to perform simulation.

4. The artificial intelligence-based soil carbon fixation and carbon enhancement optimization method according to claim 1 or 2, characterized in that: The regional division operation specifically includes: B1: Obtaining rectangular NDVI images of agricultural planting soil areas based on remote sensing technology; B2: De-noising the rectangular NDVI image based on wavelet transform technology, and then sharpening and contrast enhancing the rectangular NDVI image after denoising; B3: The Canny edge detection algorithm is used to detect the boundary of the soil area based on the brightness gradient of the rectangular NDVI image. At the same time, the Canny edge detection algorithm is used to remove useless images from the rectangular NDVI image to obtain a curved NDVI image. The useless images are the image parts of the rectangular image that do not contain the agricultural soil area. B4: Image segmentation of curved NDVI images is performed based on the region growing algorithm, and the areas with similar spectral characteristics in the NDVI images are divided into multiple sub-areas; B5: Create a color gradient table that matches the character numbers of different plant species. The color of the bare soil roads in the area is defined as white. The NDVI values ​​in the sub-areas of the curved NDVI image are mapped to different colors based on the color mapping technology. The color mapping is based on the color gradient table.

5. The artificial intelligence-based soil carbon sequestration and carbon enhancement optimization method according to claim 4 is characterized in that: Based on the Canny edge detection algorithm, the useless images in the rectangular NDVI image are removed to obtain the curved NDVI image, including: After obtaining the curved NDVI image, the curved NDVI image is corrected based on the area of ​​the agricultural planting soil region and the boundary of the agricultural planting soil region.

6. The artificial intelligence-based soil carbon sequestration and carbon enhancement optimization method according to claim 5 is characterized in that: Obtain rectangular images of agricultural planting soil areas based on remote sensing technology, including: The rectangular image of the agricultural planting soil area obtained is a high-resolution image.

7. The artificial intelligence-based soil carbon sequestration and carbon enhancement optimization method according to claim 1 is characterized in that: Based on the matching combination of digital numbers and character numbers, the combination is a soil area number, including: When numbering, use numbers 1.2.3.4 to n, where n represents the number of agricultural soil sub-regions; When performing character numbering, the names of different plant species are obtained, and the first letters are extracted based on the pinyin of the different plant species, and character encoding is performed based on the pinyin first letter combination of the different plant species. If the character codes are the same, a number is added to the end of the pinyin first letter combination, and at the same time, the character codes are assigned to different plant species for character numbering; Based on the matching combination of digital numbers and character numbers, the combination is a soil area number, which can be expressed as ,in, Represented as a digital number, Represented as a character number.

8. The artificial intelligence-based soil carbon sequestration and carbon enhancement optimization method according to claim 1 is characterized in that: Carbon addition optimization operations specifically include: Carbon is added to the soil, and when adding carbon, a combined carbon addition method is used; A large amount of first carbon-increasing materials are used to increase carbon in the soil, wherein the first carbon-increasing materials include decomposed straw and harmlessly treated feces; Using a second carbon-enhancing material in an appropriate amount to increase carbon in the soil, wherein the second carbon-enhancing material is a strain of bacteria that meets the standards of food, feed and fertilizer; A small amount of accurate third carbon-enhancing materials are added to increase the carbon content of the soil. The third carbon-enhancing materials are mineral elements that meet the standards of organic cultivation. The user can adjust the ratio of the first carbon increasing material, the second carbon increasing material and the third carbon increasing material based on the carbon fixation prediction data, and can also adjust the total usage of the first carbon increasing material, the second carbon increasing material and the third carbon increasing material based on the carbon fixation prediction data.

9. The artificial intelligence-based soil carbon sequestration and carbon enhancement optimization method according to claim 1, characterized in that: The data verification and validation operations specifically include: Obtaining a verification threshold, the verification threshold is used to verify the carbon fixation prediction data and the carbon fixation verification data, subtracting the carbon fixation prediction data from the carbon fixation verification data and taking the absolute value to obtain a carbon fixation comparison value, and comparing the carbon fixation comparison value with the verification threshold; If the carbon fixation comparison value is less than or equal to the verification threshold, the carbon fixation prediction data is derived as the verified carbon fixation prediction data; If the carbon sequestration comparison value is greater than the verification threshold, a re-prediction is performed based on the agricultural soil carbon sequestration prediction model.

10. The soil carbon sequestration and carbon enhancement optimization system based on artificial intelligence is characterized by: include: An acquisition module is used to acquire the area and boundary of the agricultural planting soil area, acquire different plant species planted in the agricultural planting soil area, and acquire historical carbon sequestration data and historical dates of different sub-regions; Weather module, the weather module includes agricultural weather detection stations and agricultural weather detection database; Agricultural weather monitoring stations are used to detect weather in agricultural soil areas and obtain weather data; Agricultural weather detection database, used to store weather data detected by agricultural weather detection stations; A region module, used to perform region division operations; A numbering module, used for performing character numbering and digital numbering, and generating soil area numbering based on the character numbering and digital numbering; A processing module is used to obtain a data change fluctuation curve group, import the soil area number into the data change fluctuation curve group, and call the agricultural weather detection database to obtain a weather factor group, and obtain a carbon fixation-time change fluctuation curve group based on the weather factor group, the data change fluctuation curve group and the soil area number; A model module is used to establish an agricultural soil carbon sequestration model and generate an agricultural soil carbon sequestration prediction model based on a carbon sequestration-time variation fluctuation curve group, thereby obtaining carbon sequestration prediction data matching the soil area number based on the agricultural soil carbon sequestration prediction model; A verification simulation module is used to design and establish a carbon sequestration verification model, and obtain carbon sequestration verification data matching the soil area number based on the carbon sequestration verification model; The verification module is used to perform data verification and calibration operations on the carbon fixation verification data and the carbon fixation prediction data.

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