A fertilization method and device based on returning corn straw to the field

Abstract

The present invention relates to the field of agricultural technology, and is a fertilization method and device based on returning corn straw to the field, comprising: receiving planting data sent by corn straw returning growers, obtaining multiple historical soil samples based on the straw returning area, detecting the multiple historical soil samples using a pre-constructed soil data detection method to obtain historical soil data, obtaining target soil data based on target crops, calculating the amount of straw per unit area according to the planting data, extracting a straw decomposition curve set from a pre-constructed database based on the amount of straw per unit area and the type of corn, obtaining unit mixed fertilizer amount data using the straw decomposition curve set, performing a fertilization operation on the straw returning area based on the unit mixed fertilizer amount data, and completing fertilization based on returning corn straw to the field. The present invention can achieve that after the initial addition of fertilizer when returning corn straw to the field, the soil corresponding to the target planting time can meet the growth requirements of the target crop.

Description

Technical Field

[0001] The present invention relates to the field of agricultural technology, and in particular to a fertilization method, device, electronic equipment and computer-readable storage medium based on returning corn stalks to fields. Background Art

[0002] Returning corn straw to the fields can alleviate the decline in organic matter quality and improve the soil's carbon sequestration capacity. Therefore, returning corn straw to the fields is often used in agricultural production to maintain and improve the farmland soil ecosystem.

[0003] When returning corn straw to the fields, it is necessary to estimate the amount of straw based on the total corn yield. In a more practical agricultural production process, after all the corn straw in the straw returning area is buried in the soil, a compound fertilizer with a nitrogen, phosphorus and potassium ratio of 1:1:1 is usually applied. When the target crops are planted at the target planting time, corresponding fertilizers are added according to the characteristics of the target crops to achieve secondary fertilization.

[0004] Although this method can provide fertilizer for the target crops, it requires applying fertilizer once when all the corn stalks in the straw return area are buried in the soil, and applying fertilizer a second time when planting the target crops, for a total of two fertilizer applications. If the physical and chemical properties of the soil are not known during the second fertilization, it is very easy to cause too much fertilizer in the soil, resulting in seedling burn. Therefore, a method is needed to convert agricultural experience into quantitative fertilization, so that after the initial addition of fertilizer when returning corn stalks to the field, the soil corresponding to the target planting time can meet the growth needs of the target crop, thereby better promoting the growth of the target crop. Summary of the invention

[0005] The present invention provides a fertilization method based on returning corn stalks to the field. A computer can realize that after the initial addition of fertilizer when returning corn stalks to the field, the soil corresponding to the target planting time can meet the growth requirements of the target crop.

[0006] To achieve the above-mentioned purpose, the present invention provides a fertilization method based on returning corn straw to the field, comprising: receiving planting data sent by a corn straw returning grower, wherein the planting data includes: straw returning area, corn type, planting interval estimation, returning area area, target crop and target planting time; obtaining multiple historical soil samples based on the straw returning area, detecting the multiple historical soil samples using a pre-constructed soil data detection method to obtain historical soil data, and obtaining target soil data based on the target crop; calculating the amount of straw per unit area according to the planting data, and extracting a straw decomposition curve set from a pre-constructed database based on the amount of straw per unit area and the corn type, wherein the straw decomposition curve set includes multiple element decomposition curves, and the multiple element decomposition curves are used as the decomposition curves. The element decomposition curves are: organic matter decomposition curve, total nitrogen decomposition curve, total phosphorus decomposition curve, total potassium decomposition curve, alkaline nitrogen decomposition curve, effective phosphorus decomposition curve and effective potassium decomposition curve; the initial missing quantity set is calculated using the database, straw decomposition curve set, target soil data, and historical soil data; the initial missing quantity set is parsed based on the database to obtain unit mixed fertilizer quantity data; the total amount of mixed fertilizer data is calculated based on the unit mixed fertilizer quantity data and the area of ​​the returned field area; the initial fertilizer data is generated based on the total amount of mixed fertilizer data and the unit mixed fertilizer quantity data; the initial fertilizer data is imported into the pre-constructed fertilization unit to obtain the initialized fertilization unit; after performing the fertilization operation on the straw returned field area based on the initialized fertilization unit, the fertilization based on the corn straw returned field is completed.

[0007] Optionally, the method of obtaining multiple historical soil samples based on the straw returning area includes: obtaining a top-view image of the straw returning area, performing a preprocessing operation on the top-view image to obtain a field contour feature image, extracting a closed edge contour based on the field contour feature image, and generating a minimum circumscribed rectangle based on the closed edge contour, wherein the pixel values ​​of all pixels corresponding to the edge contour of the straw returning area in the field contour feature image are 0, and the pixel values ​​of all pixels outside the edge contour of the straw returning area are 255; mapping the minimum circumscribed rectangle to the field contour feature image to obtain an image to be analyzed, obtaining the size of the top-view image, and generating a coarse grid based on the size of the top-view image, a preset field sampling spacing, and the area of ​​the returning area. Grid size, perform a gridding operation on the image to be analyzed based on the coarse gridding size to obtain a coarse gridding map, wherein the coarse gridding map includes multiple coarse grid rectangles, and the length and width of each of the multiple coarse grid rectangles are equal; obtain a fine grid map based on a pre-constructed fine gridding method and the coarse gridding map; use a pre-constructed sampling point setting method to extract a shallow sampling point set and a deep sampling point set from the fine grid map, perform a shallow sampling operation on all shallow sampling points in the shallow sampling point set to obtain a shallow sampling soil set, perform a deep sampling operation on all deep sampling points in the deep sampling point set to obtain a deep sampling soil set, summarize the shallow sampling soil set and the deep sampling soil set to obtain multiple historical soil samples.

[0008] Optionally, the method for obtaining a fine grid map based on the pre-constructed fine gridding method and the coarse gridding map includes: extracting coarse grid rectangles from the coarse gridding map in sequence to obtain a target large rectangle, and performing the following operations on the target large rectangle: obtaining the centroid of the target large rectangle, and judging whether the centroid of the target large rectangle is inside the closed edge contour based on the coarse gridding map; if the centroid of the target large rectangle is inside the closed edge contour, rasterizing the target large rectangle in the coarse gridding map based on a preset fine grid size and the centroid of the target large rectangle to obtain multiple fine grid rectangles; otherwise, skipping the extracted coarse grid rectangle; and aggregating multiple fine grid rectangles to obtain a fine grid map corresponding to the coarse gridding map.

[0009] Optionally, the method of extracting a shallow sampling point set and a deep sampling point set from a fine grid map using a pre-constructed sampling point setting method includes: identifying the centroids of all coarse grid rectangles in the fine grid map to obtain an unidentified point set, performing an identification operation on the unidentified point set in a pre-constructed identification order to obtain an identified point set; extracting multiple identified points from the identified point set using a pre-constructed random number extraction method to obtain a repeated sampling point set, removing the repeated sampling point set from the identified point set, and summarizing the remaining identified points after removing the repeated sampling point set to obtain a single sampling point set; identifying and summarizing the coarse grid rectangles corresponding to each repeated sampling point in the repeated sampling point set to obtain a repeated sampling rectangle set, and performing the following operations on each repeated sampling rectangle in the repeated sampling rectangle set The method comprises the following steps: identifying multiple fine grid rectangles corresponding to repeated sampling rectangles to obtain multiple rectangles to be sampled; randomly extracting a rectangle to be sampled from the multiple rectangles to be sampled, identifying and marking the centroid of the extracted rectangle to be sampled, and obtaining a shallow sampling point; eliminating the rectangle to be sampled corresponding to the shallow sampling point from the multiple rectangles to be sampled, and summarizing all the rectangles to be sampled after eliminating the rectangle to be sampled corresponding to the shallow sampling point, to obtain multiple deep sampling rectangles; randomly extracting a deep sampling rectangle from the multiple deep sampling rectangles, identifying and marking the centroid corresponding to the extracted deep sampling rectangle, and obtaining a deep sampling point; after confirming each single sampling point in the single sampling point set as a shallow sampling point, summarizing the shallow sampling points to obtain a shallow sampling point set, and summarizing the deep sampling points to obtain a deep sampling point set.

[0010] Optionally, the calculating the amount of straw per unit area according to the planting data includes: obtaining a corn planting interval sequence and a plurality of corn planting types, extracting corn planting types from the plurality of corn planting types in sequence, and associating the extracted corn planting types with the corn planting interval sequence to obtain a corn-interval sequence, summarizing the corn-interval sequences to obtain a plurality of blank planting data groups, wherein the blank planting data group includes a corn planting type, a corn planting interval and a corresponding blank yield estimate; obtaining a historical planting data set, wherein the historical planting data set includes a plurality of historical planting data, and the historical planting data include: historical planting intervals, historical corn types and historical corn yields; using the historical planting data set to fill in the plurality of The method comprises the following steps: first, determining the blank planting data set, obtaining an initial filled data set, wherein the initial filled data set includes a filled data set and an unfilled data set; performing data simplification operation on the initial filled data set, obtaining a data set to be interpolated, identifying the unfilled data set in the data set to be interpolated, and using a pre-built interpolation method to perform an interpolation operation on the blank corn yield of each unfilled data set in the unfilled data set, and confirming the interpolated unfilled data set as a filled data set, summarizing the filled data sets, and obtaining a corn yield estimation model; and importing the field return area, corn planting type and planting interval estimation into the corn yield estimation model to obtain the amount of straw per unit area, wherein the calculation formula for the amount of straw per unit area is:

[0011] ,in, represents the estimated amount of corn stover per unit, It represents the corn yield per unit area in the straw returning area, and the corn yield per unit area is the ratio of the total corn yield in the straw returning area to the area of ​​the returning area. The grass-to-grain ratio of corn is expressed as represents the collectible coefficient of corn stalks.

[0012] Optionally, the method of using the historical planting data set to fill the multiple blank planting data groups to obtain an initial filled data set includes: extracting historical planting data from the historical planting data set in sequence, and performing the following operations on the extracted historical planting data: identifying the corresponding historical planting intervals in the historical planting data to obtain the interval to be analyzed, and extracting the first corn planting interval and the second corn planting interval adjacent to the interval to be analyzed from the corn planting interval sequence, and calculating the absolute value of the difference between the interval to be analyzed and the first corn planting interval and the second corn planting interval, respectively, to obtain a first difference absolute value and a second difference absolute value, wherein the value of the first corn planting interval is greater than the value of the second corn planting interval, and the first corn planting interval corresponds to the first difference absolute value, and the second corn planting interval corresponds to the second difference absolute value; identifying the historical planting intervals; and The historical corn type corresponding to the data is obtained to obtain the corn type to be analyzed, wherein the corn type to be analyzed belongs to multiple corn planting types; if the absolute value of the first difference is greater than the absolute value of the second difference, the blank planting data group corresponding to the second corn planting interval and the corn type to be analyzed is extracted, and the extracted blank planting data group is filled with the historical corn yield to obtain a filled data group; if the absolute value of the first difference is less than or equal to the absolute value of the second difference, the filled data group is obtained based on the absolute value of the first difference; after confirming that all historical planting data sets have been extracted, the filled data groups are summarized to obtain a filled data group set, all blank planting data groups that have not been filled with historical planting data in multiple blank planting data groups are summarized to obtain an unfilled data group set, and the filled data group set and the unfilled data group set are summarized to obtain an initial filled data group set.

[0013] Optionally, the data simplification operation is performed on the initial filled data set to obtain the data set to be interpolated, including: extracting the planting groups to be analyzed from the filled data sets in the initial filled data set in sequence, and performing the following operations on the extracted planting groups to be analyzed: if the extracted planting group to be analyzed includes multiple historical corn yields, then calculating the mean of the multiple historical corn yields, and updating the historical corn yields in the planting group to be analyzed with the mean of the multiple historical corn yields to obtain a simplified analysis planting group; otherwise, confirming the extracted planting group to be analyzed as a simplified analysis planting group; and summarizing the simplified analysis planting group and the unfilled data set separately to obtain the data set to be interpolated.

[0014] Optionally, the use of a database, a straw decomposition curve set, target soil data, and historical soil data to calculate an initial missing amount set includes: extracting element decomposition curves from the straw decomposition curve set in sequence, performing the following operations on the element decomposition curves: confirming a target element corresponding to the element decomposition curve, extracting a unit initial element content corresponding to the target element from the historical soil data, and extracting a unit target element content corresponding to the target element from the target soil data, extracting an element loss curve of the target element in the straw return area from a pre-constructed database, and calculating a missing amount curve of the target element based on the unit initial element content, the unit target element content, the element decomposition curve, and the element loss curve, wherein the horizontal axis of the missing amount curve is time, the vertical axis is the element missing amount, and the calculation formula for the element missing amount is:

[0015] ,in, Indicates the time The number of missing elements corresponding to the target element, Indicates the unit target element content, Indicates the unit initial element content, Indicates the time The release amount of the target element in the element decomposition curve, Indicates the time The target element loss amount in the element loss curve at the time of planting is obtained; based on the target planting time, the initial-target curve is intercepted from the missing amount curve, and the element missing amount corresponding to the target planting time in the initial-target curve is identified to obtain the target time required filling amount; the initial missing amount is calculated according to the target time required filling amount, the initial-target curve and the preset translation correction coefficient; the initial missing amounts are summarized to obtain the initial missing amount set.

[0016] Optionally, after the initialization fertilization unit performs the fertilization operation on the straw return area, the fertilization based on returning corn straw to the field is completed, including: wherein the fertilization unit includes a data processing unit and a fertilizer spreading unit; extracting the total amount data of mixed fertilizer based on the initial fertilizer data, and parsing the extracted total amount data of mixed fertilizer based on the data processing unit in the fertilization unit to obtain a variety of fertilizer raw materials, and calculating the ratio of the multiple fertilizer raw materials to obtain mixed fertilizer proportion data; extracting a plurality of target raw materials from the multiple fertilizer raw materials based on the mixed fertilizer proportion data and the mixed fertilizer total amount data, mixing the multiple target raw materials to obtain the mixed fertilizer to be used. The fertilizer to be used is introduced into the initialization fertilization unit, and the fertilizer spreading unit in the initialization fertilization unit performs a fertilization operation on the straw return area, thereby completing the fertilization based on corn straw return. To achieve the above purpose, the present invention also provides a fertilization device based on corn straw return, including: a planting data acquisition module, which is used to receive planting data sent by a corn straw return grower, wherein the planting data includes: straw return area, corn type, planting interval estimation, return area area, target crop and target planting time; a soil data acquisition module, which is used to acquire soil data based on the straw return area A plurality of historical soil samples are detected by a pre-constructed soil data detection method to obtain historical soil data, and target soil data is obtained based on target crops; a fertilizer initial data generation module is used to calculate the amount of straw per unit area according to the planting data, and extract a straw decomposition curve set from a pre-constructed database based on the amount of straw per unit area and the type of corn, wherein the straw decomposition curve set includes a plurality of element decomposition curves, and the plurality of element decomposition curves are respectively: an organic matter decomposition curve, a total nitrogen decomposition curve, a total phosphorus decomposition curve, a total potassium decomposition curve, an alkaline nitrogen decomposition curve, and an effective phosphorus decomposition curve. line and effective potassium decomposition curve, calculate the initial missing quantity set using the database, straw decomposition curve set, target soil data, and historical soil data, parse the initial missing quantity set based on the database, obtain unit mixed fertilizer quantity data, calculate the total amount of mixed fertilizer data based on the unit mixed fertilizer quantity data and the area of ​​the returned field area, and generate initial fertilizer data based on the total amount of mixed fertilizer data and the unit mixed fertilizer quantity data; a fertilization module is used to import the initial fertilizer data into a pre-built fertilization unit to obtain an initialized fertilization unit, and after performing a fertilization operation on the straw returned field area based on the initialized fertilization unit, fertilization based on the corn straw returned field is completed.

[0017] In order to solve the above problems, the present invention also provides an electronic device, which includes: a memory storing at least one instruction; and a processor executing the instructions stored in the memory to implement the above-mentioned fertilization method based on returning corn straw to the field.

[0018] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned fertilization method based on returning corn straw to the field.

[0019] The present invention is to solve the problem described in the background technology. The present invention first receives planting data sent by corn straw return growers, wherein the planting data includes: straw return area, corn type, planting interval estimation, field return area area, target crop and target planting time. After receiving the planting data sent by corn straw return growers, the present invention can parse the amount of fertilizer that needs to be applied after all the corn straws in the straw return area are buried in the soil based on the planting data, and use the fertilization unit to apply the corresponding amount of fertilizer while all the corn straws are buried in the soil. The present invention obtains multiple historical soil samples based on the straw return area, detects the multiple historical soil samples using a pre-constructed soil data detection method, obtains historical soil data, obtains target soil data based on the target crop, and obtains multiple historical soil samples by reasonably setting sampling points, so that the obtained historical soil data can more accurately represent the soil physical and chemical properties of the straw return area. The amount of straw per unit area is calculated according to the planting data, and a straw decomposition curve set is extracted from a pre-constructed database based on the amount of straw per unit area and the type of corn, wherein the straw decomposition curve set includes a plurality of element decomposition curves, and the plurality of element decomposition curves are respectively: an organic matter decomposition curve, a total nitrogen decomposition curve, a total phosphorus decomposition curve, a total potassium decomposition curve, an alkaline nitrogen decomposition curve, an effective phosphorus decomposition curve and an effective potassium decomposition curve; an initial missing quantity set is calculated using the database, the straw decomposition curve set, the target soil data and the historical soil data; the initial missing quantity set is parsed based on the database to obtain unit mixed fertilizer quantity data; the total amount of mixed fertilizer data is calculated based on the unit mixed fertilizer quantity data and the area of ​​the returned field area; initial fertilizer data is generated based on the total amount of mixed fertilizer data and the unit mixed fertilizer quantity data; the corn yield per unit area and the amount of straw per unit area are estimated by fuzzifying the corn planting interval; while reducing the amount of calculation, the initial missing quantity is converted from being added arbitrarily based on agricultural experience into quantitative data, so that when the target planting time is reached, the physical and chemical properties of the soil in the straw returned field area can basically meet the growth requirements of the target crop, and better promote the growth of the target crop. At the same time, while ensuring soil fertility, the number of fertilization times is reduced and the efficiency of agricultural production is improved. The initial fertilizer data is imported into the pre-built fertilization unit to obtain the initialization fertilization unit. After the fertilization operation is performed on the straw return area based on the initialization fertilization unit, the fertilization based on the corn straw return is completed. Therefore, the present invention can achieve that after the initial addition of fertilizer when the corn straw is returned to the field, the soil corresponding to the target planting time can meet the growth requirements of the target crop. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 A schematic diagram of a fertilization method based on returning corn stalks to the field provided in one embodiment of the present invention; Figure 2A functional module diagram of a fertilizing device based on returning corn stalks to the field provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of an electronic device for implementing the fertilization method based on returning corn stalks to the field provided in one embodiment of the present invention.

[0021] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0023] The embodiment of the present application provides a fertilization method based on returning corn stalks to the field. The execution subject of the fertilization method based on returning corn stalks to the field includes but is not limited to at least one of the electronic devices such as a server and a terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the fertilization method based on returning corn stalks to the field can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc.

[0024] Reference Figure 1 FIG. 1 is a flow chart of a fertilization method based on returning corn stalks to fields according to an embodiment of the present invention. In this embodiment, the fertilization method based on returning corn stalks to fields includes: S1, receiving planting data from a corn stalk returning planter.

[0025] Among them, the planting data includes: straw return area, corn type, planting interval estimation, return area area, target crops and target planting time.

[0026] Exemplarily, Xiao Zhang is the owner of a farmland, and Xiao Zhang planted corn in the farmland. After the corn is harvested, Xiao Zhang decides to use corn straw to return to the field to maintain and improve the farmland soil ecosystem, because corn stalks can alleviate the decline in organic matter quality and improve the soil's carbon sequestration capacity, and sends out planting data, wherein the straw return area is the geographical location of Xiao Zhang's farmland, and the geographical location includes the province, city, district and township where Xiao Zhang's farmland is located. After receiving the straw return area, the embodiment of the present invention can parse the soil type according to the straw return area. This is because different soils have different decomposition effects on straw and different elements retention capabilities. However, in the same type of soil, the same straw return method is used to decompose the straw and plant crops, and the effects are similar. The embodiment of the present invention determines that the same straw return method is used to decompose the straw in the same soil, and the effects are the same. The corn type is the corn type planted by Xiao Zhang, and the estimated planting interval is the estimated value filled in by Xiao Zhang. It should be noted that the estimated planting interval is an estimated value. Xiao Zhang uses mechanical sowing to sow corn. The estimated planting interval filled in by Xiao Zhang is the preset interval when Xiao Zhang uses mechanical sowing. The area of ​​the field return area is the area of ​​the straw return area. The target crop is the crop that Xiao Zhang needs to plant in the straw return area after returning the corn straw to the field. The target planting time is the time from the corn straw return to the field when Xiao Zhang plants the target crop in the straw return area. For example, Xiao Zhang buried all the corn straws in the straw return area in the soil on September 1 of a certain year and applied compost and fertilizer. At the same time, Xiao Zhang planned to plant the target crop on December 1 of the same year. Then the target planting time is the number of days between September 1 of the certain year and December 1 of the same year.

[0027] It should be noted that, under normal circumstances, after all the corn stalks in the straw return area are buried in the soil, a compound fertilizer with a nitrogen, phosphorus and potassium ratio of 1:1:1 is usually applied, and when the target crops are planted at the target planting time, the corresponding fertilizer is added according to the characteristics of the target crops to achieve secondary fertilization. Although this method can provide fertilizer for the target crops, it requires burying all the corn stalks in the straw return area in the soil and applying fertilizer once, and applying fertilizer a second time when planting the target crops, a total of two fertilizer applications, and if the physical and chemical properties of the soil are not understood during the secondary fertilization, it is very easy to cause too much fertilizer in the soil, resulting in seedling burns, or cause insufficient fertilizer in the soil, resulting in the target crops being unable to grow normally.

[0028] It is understandable that after receiving the planting data sent by the corn stalks returning to the field growers, the embodiment of the present invention can parse the amount of fertilizer that needs to be applied per unit area in the straw returning area after all the corn stalks in the straw returning area are buried in the soil, based on the target crop and the target planting time, and use the fertilization unit to apply the corresponding amount of fertilizer while all the corn stalks are buried in the soil, so that when the target planting time is reached, the physical and chemical properties of the soil in the straw returning area can meet the growth requirements of the target crop and better promote the growth of the target crop. At the same time, while ensuring soil fertility, the number of fertilizations is reduced, and the efficiency of agricultural production is improved.

[0029] S2. Acquire multiple historical soil samples based on the straw return area, use a pre-constructed soil data detection method to detect the multiple historical soil samples to obtain historical soil data, and acquire target soil data based on the target crop.

[0030] It is understandable that the soil data detection method is a conventional analysis method in soil agrochemical analysis, which is a prior art and will not be described in detail here. The historical soil sample is a sample of soil in the straw return area before the corn straw is returned to the field and the straw has not yet begun to decompose. The historical soil data is the soil data of the straw return area obtained by detecting multiple historical soil samples. The target soil data is the soil data of the normal growth of the target crop obtained by querying the database, and the target soil data corresponds to the historical soil data.

[0031] The historical soil data include organic matter content, total nitrogen content, total phosphorus content, total potassium content, alkaline nitrogen content, available phosphorus content and available potassium content.

[0032] Furthermore, the method of obtaining multiple historical soil samples based on the straw returning area includes: obtaining a top-view image of the straw returning area, performing a preprocessing operation on the top-view image to obtain a field contour feature image, extracting a closed edge contour based on the field contour feature image, and generating a minimum circumscribed rectangle based on the closed edge contour, wherein the pixel values ​​of all pixels corresponding to the edge contour of the straw returning area in the field contour feature image are 0, and the pixel values ​​of all pixels outside the edge contour of the straw returning area are 255; mapping the minimum circumscribed rectangle to the field contour feature image to obtain an image to be analyzed, obtaining the size of the top-view image, and generating a coarse grid based on the size of the top-view image, a preset field sampling spacing, and the area of ​​the returning area. Grid size, perform a gridding operation on the image to be analyzed based on the coarse gridding size to obtain a coarse gridding map, wherein the coarse gridding map includes multiple coarse grid rectangles, and the length and width of each of the multiple coarse grid rectangles are equal; obtain a fine grid map based on a pre-constructed fine gridding method and the coarse gridding map; use a pre-constructed sampling point setting method to extract a shallow sampling point set and a deep sampling point set from the fine grid map, perform a shallow sampling operation on all shallow sampling points in the shallow sampling point set to obtain a shallow sampling soil set, perform a deep sampling operation on all deep sampling points in the deep sampling point set to obtain a deep sampling soil set, summarize the shallow sampling soil set and the deep sampling soil set to obtain multiple historical soil samples.

[0033] It is understandable that, optionally, the overhead image of the straw return area can be obtained from the image data of the third national land survey, or can be taken by an unmanned aerial vehicle. This technology is a prior art and the embodiment of the present invention does not limit this. The preprocessing operation includes: image noise reduction processing operation, image graying operation, edge feature extraction operation and binarization operation. Optionally, the image noise reduction processing operation uses a Gaussian filtering algorithm to perform an image noise reduction processing operation on the overhead image to obtain a noise reduction image, uses a graying algorithm to perform an image graying operation on the noise reduction image to obtain a grayscale image, uses a Canny operator to perform an edge feature extraction operation on the grayscale image to obtain an edge feature image, and uses a binarization method to perform a binarization operation on the edge feature image, so that the pixel values ​​of all pixels corresponding to the edge contour of the straw return area in the edge feature image are 0, and the pixel values ​​of all pixels outside the edge contour of the straw return area are 255, so as to obtain a field contour feature image.

[0034] It should be noted that the image denoising operation, image graying operation, edge feature extraction operation and binarization operation are all existing technologies and will not be described in detail here.

[0035] Furthermore, the interior of the closed edge contour is the straw return area, and the interior of the non-closed edge contour corresponds to the area not the straw return area. The minimum circumscribed rectangle is the rectangle corresponding to the closed edge contour, and the method for generating the minimum circumscribed rectangle is prior art and will not be described in detail here.

[0036] It is understandable that after the minimum bounding rectangle is mapped to the field contour feature image, the image outside the closed rectangle formed by the minimum bounding rectangle is clipped, and the area inside the minimum bounding rectangle is retained, and the area inside the retained minimum bounding rectangle is the image to be analyzed. The embodiment of the present invention uses the minimum bounding rectangle to eliminate irrelevant image areas in the overhead image, and only rasterizes the image to be analyzed, thereby reducing the amount of calculation.

[0037] It should be noted that, according to the parameters of the equipment used when taking the overhead image and the size of the overhead image, the preset field sampling spacing can be mapped to the overhead image. Therefore, the scale corresponding to the preset field sampling spacing can be confirmed in the overhead image. For example, the preset field sampling spacing is 2m, which is shown as The length of the horizontal arrangement of pixels is formed. Since the embodiment of the present invention does not change the size of the pixels in the image during the process of acquiring the image to be analyzed based on the overhead image, the coarse rasterization size is ,by The rectangle formed by pixels is a coarse grid rectangle, and the image to be analyzed is divided to obtain a coarse grid map. The grid operation is a prior art and will not be described in detail here.

[0038] It is understandable that the shallow sampling operation for the shallow sampling point is to perform soil sampling on the soil at a depth of 10 cm in the straw return area of ​​the shallow sampling point. Similarly, the deep sampling operation is to perform soil sampling on the soil at a depth of 30 cm. It is understandable that when all the corn straws in the straw return area are buried in the soil, the depth of soil turning is 20 cm. The operation of soil sampling is a prior art and will not be repeated here.

[0039] Furthermore, the method for obtaining a fine grid map based on the pre-constructed fine gridding method and the coarse gridding map comprises: extracting coarse grid rectangles from the coarse gridding map in sequence to obtain a target large rectangle, and performing the following operations on the target large rectangle: obtaining the centroid of the target large rectangle, and judging whether the centroid of the target large rectangle is inside the closed edge contour based on the coarse gridding map; if the centroid of the target large rectangle is inside the closed edge contour, rasterizing the target large rectangle in the coarse gridding map based on a preset fine grid size and the centroid of the target large rectangle to obtain multiple fine grid rectangles; otherwise, skipping the extracted coarse grid rectangle; and aggregating multiple fine grid rectangles to obtain a fine grid map corresponding to the coarse gridding map.

[0040] It is understandable that the target large rectangle is a coarse grid rectangle extracted from the coarse grid map. It is understandable that since the coarse grid rectangle is a square, the centroid of the target large rectangle is also the centroid of the target large rectangle. According to the above embodiment, the interior of the closed edge contour is the straw return area, so the soil collection site can only be set inside the closed edge contour. Therefore, the embodiment of the present invention determines whether the centroid of the target large rectangle is inside the closed edge contour, and only considers further rasterization for the interior of the closed edge contour, which reduces the amount of calculation for data processing.

[0041] It should be noted that the preset fine grid size is the grid size set to further refine the target large rectangle corresponding to the coarse grid rectangle. For example, if the coarse grid rectangle is 2×2, the fine grid size is 1×1, and the centroid of the target large rectangle is used as the base point to divide the coarse grid rectangle into four fine grid rectangles of size 1×1.

[0042] For example, for ease of explanation, assume that in a plane rectangular coordinate system, the coordinates of the four vertices of the coarse grid rectangle are: (0,0), (0,2), (2,2) and (2,0), then the centroid of the target large rectangle is (1,1), and among the four fine grid rectangles of size 1×1, the four vertex coordinates of the first fine grid rectangle are: (0,0), (0,1), (1,1) and (1,0), the four vertex coordinates of the second fine grid rectangle are: (1,0), (2,0), (2,1) and (1,1), the four vertex coordinates of the third fine grid rectangle and the four vertex coordinates of the fourth fine grid rectangle are deduced by analogy, and will not be repeated here.

[0043] It can be understood that in the coarse gridded map, after all the coarse grid rectangles whose centroids are inside the closed edge contour are rasterized at the fine grid size, the resulting image is a fine grid map.

[0044] Furthermore, the method of using a pre-constructed sampling point setting method to extract a shallow sampling point set and a deep sampling point set from a fine grid map includes: identifying the centroids of all coarse grid rectangles in the fine grid map to obtain an unidentified point set, performing an identification operation on the unidentified point set in a pre-constructed identification order to obtain an identified point set; extracting multiple identified points from the identified point set using a pre-constructed random number extraction method to obtain a repeated sampling point set, removing the repeated sampling point set from the identified point set, and summarizing the remaining identified points after removing the repeated sampling point set to obtain a single sampling point set; identifying and summarizing the coarse grid rectangles corresponding to each repeated sampling point in the repeated sampling point set to obtain a repeated sampling rectangle set, and performing the following operations on each repeated sampling rectangle in the repeated sampling rectangle set The method comprises the following steps: identifying multiple fine grid rectangles corresponding to repeated sampling rectangles to obtain multiple rectangles to be sampled; randomly extracting a rectangle to be sampled from the multiple rectangles to be sampled, identifying and marking the centroid of the extracted rectangle to be sampled, and obtaining a shallow sampling point; eliminating the rectangle to be sampled corresponding to the shallow sampling point from the multiple rectangles to be sampled, and summarizing all the rectangles to be sampled after eliminating the rectangle to be sampled corresponding to the shallow sampling point, to obtain multiple deep sampling rectangles; randomly extracting a deep sampling rectangle from the multiple deep sampling rectangles, identifying and marking the centroid corresponding to the extracted deep sampling rectangle, and obtaining a deep sampling point; after confirming each single sampling point in the single sampling point set as a shallow sampling point, summarizing the shallow sampling points to obtain a shallow sampling point set, and summarizing the deep sampling points to obtain a deep sampling point set.

[0045] It is understandable that the pre-constructed identification order is: preferably from left to right and from top to bottom. Taking the image coordinate system in the fine grid map as an example, it is preferably marked in the direction of increasing horizontal axis coordinate values, and then in the direction of increasing vertical axis coordinate values. For ease of explanation, take a matrix as an example, and assume that the matrix is ​​as follows:

[0046] , then according to the pre-built identification order, is 1-soil sampling point, is 2-soil sampling point, For 3-soil sampling points, There are 4 soil sampling points.

[0047] It should be noted that, optionally, the pre-constructed random number extraction method is to use multiple random numbers generated by existing technologies such as a Mersenne twiddle, and use the multiple random numbers to extract multiple identified points corresponding to the multiple random numbers from the identified point set, extract the multiple identified points, and obtain a repeated sampling point set.

[0048] Exemplarily, the identified point set includes: 1-soil sampling point, 2-soil sampling point, 3-soil sampling point and 4-soil sampling point, and the generated multiple random numbers are 1 and 4, respectively, then 1-soil sampling point and 4-soil sampling point are confirmed as repeated sampling point sets. 1-soil sampling point and 4-soil sampling point are removed from the identified point set, and the retained 2-soil sampling point and 3-soil sampling point are confirmed as single sampling point sets.

[0049] It should be noted that the random method of randomly extracting a rectangle to be sampled from multiple rectangles to be sampled is consistent with the pre-built random number extraction method, and the effect achieved is the same, which will not be repeated here. In the embodiment of the present invention, since a coarse grid rectangle includes four fine grid rectangles, the number of the multiple rectangles to be sampled is four.

[0050] Exemplarily, for the sake of convenience, taking the plane rectangular coordinate system as an example, the 1-soil sampling point corresponding to the repeated sampling rectangle, and the corresponding four vertex coordinates are (0,0), (0,2), (2,2) and (2,0), respectively. The four vertex coordinates of a randomly extracted rectangle to be sampled are (0,0), (0,1), (1,1) and (1,0), respectively. The centroid of the extracted rectangle to be sampled is marked as the shallow sampling point, that is, the coordinates corresponding to the 1-shallow sampling point are (0.5,0.5). By analogy, except for the randomly extracted rectangle to be sampled, The other three rectangles to be sampled are all confirmed as deep sampling rectangles, then the coordinates of the four vertices of the second deep sampling rectangle are (1,0), (2,0), (2,1) and (1,1), and the coordinates of the four vertices of the third deep sampling rectangle and the four vertices of the fourth deep sampling rectangle are deduced in the same way. If the four vertex coordinates of a randomly extracted deep sampling rectangle are (1,0), (2,0), (2,1) and (1,1), then the centroid of the extracted deep sampling rectangle is identified as the deep sampling point, that is, the coordinates corresponding to 1-deep sampling point are (1.5,0.5).

[0051] S3. Calculate the amount of straw per unit area according to the planting data, and extract a straw decomposition curve set from a pre-constructed database based on the amount of straw per unit area and the type of corn.

[0052] The straw decomposition curve set includes multiple element decomposition curves, and the multiple element decomposition curves are: organic matter decomposition curve, total nitrogen decomposition curve, total phosphorus decomposition curve, total potassium decomposition curve, alkaline nitrogen decomposition curve, effective phosphorus decomposition curve and effective potassium decomposition curve.

[0053] It is understandable that the straw decomposition curve set is a set of curves of the elements released when corn straw is returned to the field over time, and the straw decomposition curve set corresponds to the historical soil data, that is, the organic matter decomposition curve corresponds to the organic matter content in the historical soil data, and the organic matter decomposition curve is: the curve of the organic matter released when corn straw is returned to the field over time, the total nitrogen decomposition curve and the total nitrogen content in the historical soil data, the total phosphorus decomposition curve, the total potassium decomposition curve, the alkaline nitrogen decomposition curve, the effective phosphorus decomposition curve and the effective potassium decomposition curve, and so on, which will not be repeated here. The database includes a straw decomposition curve set of corn straw returned to the field, and the straw decomposition curve sets corresponding to corns of different corn planting types and different planting densities are all included. The method for obtaining the straw decomposition curve set can be obtained by experiments or a variety of existing technologies. The embodiment of the present invention is not limited to this, and it will not be repeated here.

[0054] Furthermore, the calculation of the amount of straw per unit area based on the planting data includes: obtaining a corn planting interval sequence and a plurality of corn planting types, extracting corn planting types from the plurality of corn planting types in sequence, and associating the extracted corn planting types with the corn planting interval sequence to obtain a corn-interval sequence, summarizing the corn-interval sequences to obtain a plurality of blank planting data groups, wherein the blank planting data group includes a corn planting type, a corn planting interval and a corresponding blank yield estimate; obtaining a historical planting data set, wherein the historical planting data set includes a plurality of historical planting data, and the historical planting data include: historical planting intervals, historical corn types and historical corn yields; using the historical planting data set to fill in the plurality of blank planting data groups to obtain an initial filled data set, wherein the initial filled data set includes a filled data set and an unfilled data set; performing data simplification operations on the initial filled data set to obtain a data set to be interpolated, identifying the unfilled data set in the data set to be interpolated, and using a pre-built interpolation method to perform interpolation operations on the blank corn yield of each unfilled data group in the unfilled data set, and confirming the interpolated unfilled data set as a filled data set, summarizing the filled data sets, and obtaining a corn yield estimation model; the area of ​​the field return area, the corn planting type and the planting interval estimation are imported into the corn yield estimation model to obtain the amount of straw per unit area, wherein the calculation formula for the amount of straw per unit area is:

[0055] ,in, represents the estimated amount of corn stover per unit, It represents the corn yield per unit area in the straw returning area, and the corn yield per unit area is the ratio of the total corn yield in the straw returning area to the area of ​​the returning area. The grass-to-grain ratio of corn is expressed as represents the collectible coefficient of corn stalks.

[0056] It is understandable that the corn planting types are the types of corn planted in agriculture, such as glutinous corn, sweet corn, shoot corn, silage corn, etc. Different corn planting types correspond to different corn production capacities, grass-grain ratio coefficients, and collectible coefficients, and the embodiment of the present invention uses the corn yield per unit area to estimate the estimated amount of corn per unit straw, so it is necessary to distinguish different corn planting types. The corn planting interval sequence is a sequence composed of different corn planting intervals. For example, the multiple corn planting types are sweet corn and glutinous corn, respectively, and the corn planting interval sequences are: 60cm and 80cm, then the corn-interval sequence composed of sweet corn is: [sweet corn-60cm, sweet corn-80cm], and the corn-interval sequence composed of glutinous corn is: [glutinous corn-60cm, glutinous corn-80cm], so that the multiple blank planting data groups generated are: sweet corn-60cm-blank yield estimation, sweet corn-80cm-blank yield estimation, glutinous corn-60cm-blank yield estimation, and glutinous corn-80cm-blank yield estimation. It is understandable that the blank yield estimate is the corn yield per unit area, but when calculating and counting the historical corn yield, what is collected is the total corn planting area and the total corn yield corresponding to the historical planting data, and the ratio of the total corn planting area to the total corn yield is stored as the historical corn yield in the historical planting data to obtain the historical planting data.

[0057] It is understandable that the data set to be interpolated is an initial filled data set including blank yield estimation. Optionally, the pre-constructed interpolation method can be a polynomial interpolation method, a spline interpolation method, or other existing technologies, which can perform an interpolation operation on the blank corn yield of each unfilled data set in the unfilled data set, and the embodiment of the present invention does not limit this.

[0058] Further, the corn yield estimation model is a model for estimating the amount of straw per unit area based on planting data. For example, if the area of ​​the field return region is a, the corn planting type is sweet corn, and the planting interval is estimated to be 60 cm, then after importing this data into the corn yield estimation model, since the total corn planting area and the total corn planting yield corresponding to the historical planting data are collected, the total corn planting area, the total corn planting yield, the grass-to-grain ratio coefficient of sweet corn, and the corn straw collectible coefficient corresponding to the planting data can be called out from the corn yield estimation model, and the field return region area corresponds to the total corn planting area, the corn planting type is sweet corn, which is used to call out the grass-to-grain ratio coefficient of sweet corn and the corn straw collectible coefficient, and the planting interval is estimated to be 60 cm, which is used to call out the historical planting intervals in the historical planting data. Therefore, according to the planting data, the filled data group can be called out from the corn yield estimation model: sweet corn-60 cm-M, where M is the corn yield per unit area in the straw return region, and the straw per unit area can be calculated using the calculation formula of the straw per unit area.

[0059] It should be noted that historical planting data is data composed of historical planting intervals, historical corn varieties, and historical corn yields obtained through big data retrieval or experiments. For example, there is data from a certain city: Xiao Zhao planted sweet corn in a farmland of area a, and the interval between the planted sweet corns was 60cm, and the total yield was b, so the historical planting data is: sweet corn-60cm-b / a.

[0060] Furthermore, the method of using the historical planting data set to fill the multiple blank planting data groups to obtain an initial filled data set includes: extracting the historical planting data from the historical planting data set in sequence, and performing the following operations on the extracted historical planting data: identifying the corresponding historical planting intervals in the historical planting data to obtain the interval to be analyzed, and extracting the first corn planting interval and the second corn planting interval adjacent to the interval to be analyzed from the corn planting interval sequence, and respectively calculating the absolute value of the difference between the interval to be analyzed and the first corn planting interval and the second corn planting interval to obtain the first difference absolute value and the second difference absolute value, wherein the value of the first corn planting interval is greater than the value of the second corn planting interval, and the first corn planting interval corresponds to the first difference absolute value, and the second corn planting interval corresponds to the second difference absolute value; identifying the historical planting intervals; and obtaining the first difference absolute value. The historical corn type corresponding to the data is obtained to obtain the corn type to be analyzed, wherein the corn type to be analyzed belongs to multiple corn planting types; if the absolute value of the first difference is greater than the absolute value of the second difference, the blank planting data group corresponding to the second corn planting interval and the corn type to be analyzed is extracted, and the extracted blank planting data group is filled with the historical corn yield to obtain a filled data group; if the absolute value of the first difference is less than or equal to the absolute value of the second difference, the filled data group is obtained based on the absolute value of the first difference; after confirming that all historical planting data sets have been extracted, the filled data groups are summarized to obtain a filled data group set, all blank planting data groups that have not been filled with historical planting data in multiple blank planting data groups are summarized to obtain an unfilled data group set, and the filled data group set and the unfilled data group set are summarized to obtain an initial filled data group set.

[0061] It can be understood that the extraction of the first corn planting interval and the second corn planting interval adjacent to the interval to be analyzed from the corn planting interval sequence is as follows: finding two corn planting intervals adjacent to the interval to be analyzed from the corn planting interval sequence, searching for the interval to be analyzed in the corn planting interval sequence, and if there is a corn planting interval identical to the interval to be analyzed in the corn planting interval sequence, then confirming the interval to be analyzed as the second corn planting interval, and extracting the next corn planting interval adjacent to the interval to be analyzed from the corn planting interval sequence in the order of the corn planting intervals in the corn planting interval sequence, and confirming the extracted next corn planting interval adjacent to the interval to be analyzed as the first corn planting interval, which ensures that the first corn planting interval is greater than the second corn planting interval; if the corn planting interval If there is no corn planting interval that is the same as the interval to be analyzed in the interval sequence, then two adjacent corn planting intervals are found in the corn planting interval sequence, and the characteristics of the two adjacent corn planting intervals are: one of the corn planting intervals is larger than the interval to be analyzed, and the other corn planting interval is smaller than the interval to be analyzed. Then the two adjacent corn planting intervals are the first corn planting interval and the second corn planting interval adjacent to the interval to be analyzed. At the same time, since the value of the first corn planting interval is larger than the value of the second corn planting interval, the first corn planting interval is: the corn planting interval of the two adjacent corn planting intervals that is larger than the interval to be analyzed, and the second corn planting interval is: the corn planting interval of the two adjacent corn planting intervals that is smaller than the interval to be analyzed. For example, the corn planting interval sequence is: [40, 45, 50, 55]. When the interval to be analyzed is 55, since the value of the first corn planting interval is larger than the value of the second corn planting interval, the first corn planting interval is 60 and the second corn planting interval is 55.

[0062] Exemplarily, when the interval to be analyzed is 54, 54 is between 50 and 55, the first corn planting interval is 55, the second corn planting interval is 50, and the first difference absolute value is obtained by subtracting the interval to be analyzed 54 from the first corn planting interval 55, that is, the first difference absolute value is 1, and similarly, the second difference absolute value is 4, and the first difference absolute value 1 is less than or equal to the second difference absolute value 4. If the corn type to be analyzed is sweet corn and the historical corn yield is M, then the blank planting data group corresponding to the first corn planting interval and the corn type to be analyzed is extracted: sweet corn-55cm-blank yield estimation, and the blank planting data group is filled with the historical corn yield to obtain the filled data group, wherein the filled data group is: sweet corn-55cm-M.

[0063] It should be noted that the filled data set includes: one corn planting type, one corn planting interval and historical corn yield, and the unfilled data set includes: one corn planting type, one corn planting interval and blank corn yield.

[0064] Exemplarily, the plurality of blank planting data sets are: sweet corn-60 cm-blank yield estimation, sweet corn-80 cm-blank yield estimation, waxy corn-60 cm-blank yield estimation, and waxy corn-80 cm-blank yield estimation, and the historical planting data are: sweet corn-60 cm-b / a, sweet corn-60 cm-b / d, waxy corn-60 cm-c, and waxy corn-80 cm- , the use of historical planting data sets to fill the multiple blank planting data sets, the resulting initial filling data set is: sweet corn-60cm-b / ab / d, sweet corn-80cm-blank yield estimation, waxy corn-60cm-c and waxy corn-80cm- At this time, the filled data set is: [sweet corn-60cm-b / ab / d, waxy corn-60cm-c, waxy corn-80cm- ], the unfilled data sets include: [sweet corn-80cm-blank yield estimation]. Among them, when the historical planting data is sweet corn-60cm-b / d, b represents the total yield when the historical planting data is sweet corn-60cm-b / d, and d represents the area of ​​the field when the historical planting data is sweet corn-60cm-b / d. Similarly, when the historical planting data is glutinous corn-60cm-c, c represents the historical corn yield when the historical planting data is glutinous corn-60cm-c, and the historical planting data is glutinous corn-80cm- hour, Indicates that the historical planting data is glutinous corn -80cm- Historical corn yield. It should be noted that, in actual agricultural planting, corn is often planted at a fixed corn planting interval, for example, 60 cm or 80 cm. The embodiment of the present invention fuzzifies the corn planting interval. For example, the historical planting data with a corn planting interval of 54 is filled into the blank planting data group with a corn planting interval of 55. The embodiment of the present invention ignores the impact of a planting interval of 1 cm on the corn yield per unit area and the amount of straw per unit area, and only estimates the corn yield per unit area and the amount of straw per unit area to reduce the amount of calculation.

[0065] Furthermore, the data simplification operation is performed on the initial filled data set to obtain the data set to be interpolated, including: extracting the planting groups to be analyzed from the filled data sets in the initial filled data set in sequence, and performing the following operations on the extracted planting groups to be analyzed: if the extracted planting group to be analyzed includes multiple historical corn yields, then calculating the average of the multiple historical corn yields, and updating the historical corn yields in the planting group to be analyzed with the average of the multiple historical corn yields to obtain a simplified analysis planting group; otherwise, confirming the extracted planting group to be analyzed as a simplified analysis planting group; and summarizing the simplified analysis planting group and the unfilled data set separately to obtain the data set to be interpolated.

[0066] It is understandable that the planting group to be analyzed is a filled data group extracted from the filled data group set. For example, the filled data group set is: [sweet corn-60cm-b / ab / d, waxy corn-60cm-c, waxy corn-80cm- ], the unfilled data set includes: [sweet corn-80cm-blank yield estimation], the extracted planting group to be analyzed is sweet corn-60cm-b / ab / d, then calculate the mean of b / a and b / d to get n, then use n to update the planting group to be analyzed, and get the simplified analysis planting group: sweet corn-60cm-n. If the extracted planting group to be analyzed is glutinous corn-60cm-c, then glutinous corn-60cm-c is confirmed as the simplified analysis planting group, and so on, so as to summarize the simplified analysis planting group and the unfilled data set respectively, and the obtained data set to be interpolated is: [sweet corn-n, glutinous corn-60cm-c, glutinous corn-80cm- , sweet corn-80cm-blank yield estimation].

[0067] S4. Calculate the initial missing quantity set using the database, straw decomposition curve set, target soil data, and historical soil data. Analyze the initial missing quantity set based on the database to obtain unit mixed fertilizer quantity data.

[0068] Furthermore, the use of the database, the straw decomposition curve set, the target soil data, and the historical soil data to calculate the initial missing amount set includes: extracting element decomposition curves from the straw decomposition curve set in sequence, performing the following operations on the element decomposition curve: confirming the target element corresponding to the element decomposition curve, extracting the unit initial element content corresponding to the target element from the historical soil data, and extracting the unit target element content corresponding to the target element from the target soil data, extracting the element loss curve of the target element in the straw return area from the pre-constructed database, and calculating the missing amount curve of the target element based on the unit initial element content, the unit target element content, the element decomposition curve, and the element loss curve, wherein the horizontal axis of the missing amount curve is time, the vertical axis is the element missing amount, and the calculation formula of the element missing amount is:

[0069] ,in, Indicates the time The number of missing elements corresponding to the target element, Indicates the unit target element content, Indicates the unit initial element content, Indicates the time The release amount of the target element in the element decomposition curve, Indicates the time The target element loss amount in the element loss curve at the time of planting is obtained; based on the target planting time, the initial-target curve is intercepted from the missing amount curve, and the element missing amount corresponding to the target planting time in the initial-target curve is identified to obtain the target time required filling amount; the initial missing amount is calculated according to the target time required filling amount, the initial-target curve and the preset translation correction coefficient; the initial missing amounts are summarized to obtain the initial missing amount set.

[0070] It is understandable that the element decomposition curve is a curve extracted from the straw decomposition curve set. For example, if the curve extracted from the straw decomposition curve set is a total nitrogen decomposition curve, then the element decomposition curve is a total nitrogen decomposition curve, and the target element corresponding to the element decomposition curve is total nitrogen. Then the unit initial element content is the total nitrogen content of the soil that needs to return corn straw to the field after harvesting corn, the element loss curve is the curve corresponding to the loss of total nitrogen in the straw return area, and the unit target element content is the total nitrogen content that can meet the growth needs of the target crop queried from the database. It should be noted that the element loss amount can be obtained by experiment or database, and the technology for obtaining the element loss amount is a prior art, which will not be repeated here.

[0071] For example, if the target planting time is 100 and the missing amount curve has a time range of 0 to 130, then the curve corresponding to the time range of 0 to 100 is extracted from the missing amount curve, and the resulting curve is the initial-target curve. When the target planting time is 100, the missing amount of the element is , then the target time needs to be filled , if the preset translation correction coefficient is , then the initial-target curve is translated in the direction of decreasing the amount of missing elements , get the intercept of the translated curve, which is the initial missing amount.

[0072] Furthermore, the initial missing quantity set is a set consisting of initial missing quantities corresponding to each element decomposition curve in the straw decomposition curve set.

[0073] It is understandable that during the fertilization process, the soil properties required for the target crops to thrive are different. For example, when the target crop is a seed, the content of each target element included in the required soil is basically the same. When the target crop is a leafy vegetable, such as Chinese cabbage, Chinese mustard greens, etc., the content of each target element included in the required soil is basically the same. The target crop is a tree, such as apple trees, pear trees, etc., and the content of each target element included in the required soil is basically the same. Therefore, a lot of estimation processing is done in the embodiment of the present invention. While reducing the amount of calculation, it realizes the conversion of the initial missing amount from arbitrary addition based on agricultural experience into quantitative data, so that when the target planting time is reached, the physical and chemical properties of the soil in the straw return area can basically meet the growth needs of the target crop, and better promote the growth of the target crop.

[0074] It is understandable that, based on the initial missing amount set parsed by the database, the unit mixed fertilizer amount data obtained is the data formed by integrating all the missing elements in the straw return area per unit area. For example, in the initial missing amount set, the initial missing amount of organic matter content is q, the initial missing amount of total nitrogen content is w, the initial missing amount of total phosphorus content is e, the initial missing amount of total potassium content is r, the initial missing amount of alkaline nitrogen content is t, the initial missing amount of effective phosphorus content is y, and the initial missing amount of effective potassium content is u, then the unit mixed fertilizer amount data is qwertyu. That is, fertilizer with a unit mixed fertilizer amount data of qwertyu needs to be applied per unit area of ​​the straw return area.

[0075] S5. Calculate the total amount data of mixed fertilizer based on the unit mixed fertilizer amount data and the area of ​​the returned field region, and generate initial fertilizer data according to the total amount data of mixed fertilizer and the unit mixed fertilizer amount data.

[0076] It is understandable that the total amount of mixed fertilizer data is the total amount of fertilizer required in the straw return area. The initial fertilizer data includes unit mixed fertilizer amount data and total amount of mixed fertilizer data.

[0077] S6. Importing the initial fertilizer data into a pre-built fertilization unit to obtain an initialized fertilization unit, and performing a fertilization operation on the straw return area based on the initialized fertilization unit to complete the fertilization based on the corn straw return area.

[0078] Furthermore, after the initialization fertilization unit performs a fertilization operation on the straw return area, the fertilization based on returning corn straw to the field is completed, including: wherein the fertilization unit includes a data processing unit and a fertilizer spreading unit; based on the fertilizer initial data, the total amount data of the mixed fertilizer is extracted, and based on the data processing unit in the fertilization unit, the extracted total amount data of the mixed fertilizer is analyzed to obtain a variety of fertilizer raw materials, and the ratio of the multiple fertilizer raw materials is calculated to obtain mixed fertilizer proportion data; based on the mixed fertilizer proportion data and the total amount data of the mixed fertilizer, multiple target raw materials are extracted from the multiple fertilizer raw materials, the multiple target raw materials are mixed to obtain fertilizer to be used, the fertilizer to be used is introduced into the initialization fertilization unit, and after the straw return area is fertilized by the fertilizer spreading unit in the initialization fertilization unit, the fertilization based on returning corn straw to the field is completed.

[0079] It is understandable that the data processing unit is a unit for parsing the total amount data of the mixed fertilizer and obtaining a plurality of fertilizer raw materials. After parsing the total amount data of the mixed fertilizer, the data processing unit calls a plurality of fertilizer raw materials stored in the database from the database, and calculates the ratio of each of the plurality of fertilizer raw materials to obtain the mixed fertilizer ratio data. After mixing a plurality of target raw materials corresponding to the plurality of fertilizer raw materials according to the mixed fertilizer ratio data, the fertilizer to be used can be obtained.

[0080] Exemplarily, the unit mixed fertilizer amount data is qwertyu, and the total mixed fertilizer amount data is aq-bw-ce-dr-et-fy-gu. After the data processing unit parses the extracted total mixed fertilizer amount data, it is obtained that when the ratio of the first fertilizer, the second fertilizer, the third fertilizer and the fourth fertilizer in the database is v:m:k:l, the fertilization demand can be met, and thus the mixed fertilizer ratio data is: [first fertilizer v: second fertilizer m: third fertilizer k: fourth fertilizer l].

[0081] It is understandable that the target raw material is the corresponding fertilization raw material extracted according to the mixed fertilizer ratio data and the mixed fertilizer total amount data. Optionally, the mixing of multiple target raw materials is performed by using a blender, which is a prior art, and a variety of technologies can achieve the same effect, which will not be described in detail here.

[0082] Furthermore, the fertilizer spreading unit is a unit that spreads the fertilizer to be used on the straw returning area according to the unit mixed fertilizer amount data.

[0083] The present invention is to solve the problem described in the background technology. The present invention first receives planting data sent by corn straw return growers, wherein the planting data includes: straw return area, corn type, planting interval estimation, field return area area, target crop and target planting time. After receiving the planting data sent by corn straw return growers, the present invention can parse the amount of fertilizer that needs to be applied after all the corn straws in the straw return area are buried in the soil based on the planting data, and use the fertilization unit to apply the corresponding amount of fertilizer while all the corn straws are buried in the soil. The present invention obtains multiple historical soil samples based on the straw return area, detects the multiple historical soil samples using a pre-constructed soil data detection method, obtains historical soil data, obtains target soil data based on the target crop, and obtains multiple historical soil samples by reasonably setting sampling points, so that the obtained historical soil data can more accurately represent the soil physical and chemical properties of the straw return area. The amount of straw per unit area is calculated according to the planting data, and a straw decomposition curve set is extracted from a pre-constructed database based on the amount of straw per unit area and the type of corn, wherein the straw decomposition curve set includes a plurality of element decomposition curves, and the plurality of element decomposition curves are respectively: an organic matter decomposition curve, a total nitrogen decomposition curve, a total phosphorus decomposition curve, a total potassium decomposition curve, an alkaline nitrogen decomposition curve, an effective phosphorus decomposition curve and an effective potassium decomposition curve; an initial missing quantity set is calculated using the database, the straw decomposition curve set, the target soil data and the historical soil data; the initial missing quantity set is parsed based on the database to obtain unit mixed fertilizer quantity data; the total amount of mixed fertilizer data is calculated based on the unit mixed fertilizer quantity data and the area of ​​the returned field area; initial fertilizer data is generated based on the total amount of mixed fertilizer data and the unit mixed fertilizer quantity data; the corn yield per unit area and the amount of straw per unit area are estimated by fuzzifying the corn planting interval; while reducing the amount of calculation, the initial missing quantity is converted from being added arbitrarily based on agricultural experience into quantitative data, so that when the target planting time is reached, the physical and chemical properties of the soil in the straw returned field area can basically meet the growth requirements of the target crop, and better promote the growth of the target crop. At the same time, while ensuring soil fertility, the number of fertilization times is reduced and the efficiency of agricultural production is improved. The initial fertilizer data is imported into the pre-built fertilization unit to obtain the initialization fertilization unit. After the fertilization operation is performed on the straw return area based on the initialization fertilization unit, the fertilization based on the corn straw return is completed. Therefore, the present invention can achieve that after the initial addition of fertilizer when the corn straw is returned to the field, the soil corresponding to the target planting time can meet the growth requirements of the target crop.

[0084] like Figure 2 1 is a functional module diagram of a fertilization device based on returning corn straw to the field provided by an embodiment of the present invention.

[0085] The fertilization device 100 based on returning corn stalks to the field of the present invention can be installed in an electronic device. According to the functions to be implemented, the fertilization device 100 based on returning corn stalks to the field can include a planting data acquisition module 101, a soil data acquisition module 102, a fertilizer initial data generation module 103 and a fertilization module 104. The module of the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0086] The planting data acquisition module 101 is used to receive planting data sent by corn straw return growers, wherein the planting data includes: straw return area, corn type, planting interval estimation, field return area area, target crop and target planting time; the soil data acquisition module 102 is used to obtain multiple historical soil samples based on the straw return area, use a pre-constructed soil data detection method to detect the multiple historical soil samples to obtain historical soil data, and obtain target soil data based on the target crop; the fertilizer initial data generation module 103 is used to calculate the amount of straw per unit area according to the planting data, and extract a straw decomposition curve set from a pre-constructed database based on the amount of straw per unit area and the corn type, wherein the straw decomposition curve set includes multiple element decomposition curves, and The multiple element decomposition curves are: organic matter decomposition curve, total nitrogen decomposition curve, total phosphorus decomposition curve, total potassium decomposition curve, alkaline nitrogen decomposition curve, effective phosphorus decomposition curve and effective potassium decomposition curve. The database, straw decomposition curve set, target soil data and historical soil data are used to calculate the initial missing quantity set. The initial missing quantity set is parsed based on the database to obtain unit mixed fertilizer quantity data. The total amount of mixed fertilizer data is calculated based on the unit mixed fertilizer quantity data and the area of ​​the field return area. The initial fertilizer data is generated according to the total amount of mixed fertilizer data and the unit mixed fertilizer quantity data. The fertilization module 104 is used to import the initial fertilizer data into a pre-constructed fertilization unit to obtain an initialized fertilization unit. After performing a fertilization operation on the straw return area based on the initialized fertilization unit, the fertilization based on the corn straw return to the field is completed.

[0087] In detail, each module in the fertilization device 100 based on returning corn stalks to the field according to the embodiment of the present invention is used in the same manner as described above. Figure 1 The fertilization method based on returning corn straw to the field has the same technical means as described in the method and can produce the same technical effects, so I will not go into details here.

[0088] like Figure 3 2 is a schematic diagram of the structure of an electronic device for implementing a fertilization method based on returning corn stalks to fields according to an embodiment of the present invention.

[0089] The electronic device 1 may include a processor 10, a memory 11 and a bus 12, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a fertilization method program based on returning corn stalks to the field.

[0090] Wherein, the memory 11 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a mobile hard disk of the electronic device 1. In other embodiments, the memory 11 can also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device 1. Further, the memory 11 also includes an internal storage unit of the electronic device 1 and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device 1, such as the code of the fertilization method program based on returning corn stalks to the field, but also can be used to temporarily store data that has been output or is to be output.

[0091] The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor 10 is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (for example, a fertilization method program based on returning corn stalks to the field, etc.), and calls data stored in the memory 11, so as to execute various functions of the electronic device 1 and process data.

[0092] The bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus 12 may be divided into an address bus, a data bus, a control bus, etc. The bus 12 is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0093] Figure 3 Only an electronic device with components is shown, and those skilled in the art will understand that Figure 3 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0094] For example, although not shown, the electronic device 1 may also include a power source (such as a battery) for supplying power to various components. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0095] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device 1 and other electronic devices.

[0096] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode) touch device. The display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device 1 and to display a visual user interface.

[0097] The fertilization method program based on returning corn straw to the field stored in the memory 11 in the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve: receiving planting data sent by corn straw returning growers, wherein the planting data includes: straw returning area, corn type, planting interval estimation, returning area, target crop and target planting time; obtaining multiple historical soil samples based on the straw returning area, using a pre-constructed soil data detection method to detect the multiple historical soil samples to obtain historical soil data, and obtaining target soil data based on the target crop; calculating the amount of straw per unit area according to the planting data, and extracting a straw decomposition curve set from a pre-constructed database based on the amount of straw per unit area and the corn type, wherein the straw decomposition curve set includes The method comprises a plurality of element decomposition curves, and the plurality of element decomposition curves are respectively: an organic matter decomposition curve, a total nitrogen decomposition curve, a total phosphorus decomposition curve, a total potassium decomposition curve, an alkaline nitrogen decomposition curve, an effective phosphorus decomposition curve and an effective potassium decomposition curve; an initial missing quantity set is calculated using a database, a straw decomposition curve set, a target soil data and a historical soil data; the initial missing quantity set is parsed based on the database to obtain unit mixed fertilizer quantity data; the total amount of mixed fertilizer data is calculated based on the unit mixed fertilizer quantity data and the area of ​​the returned field region; and initial fertilizer data is generated based on the total amount of mixed fertilizer data and the unit mixed fertilizer quantity data; the initial fertilizer data is imported into a pre-constructed fertilization unit to obtain an initialized fertilization unit; after performing a fertilization operation on the straw returned field region based on the initialized fertilization unit, fertilization based on the corn straw returned field is completed.

[0098] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to Figures 1 to 3 The description of the relevant steps in the corresponding embodiments will not be repeated here.

[0099] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0100] The present invention also provides a computer-readable storage medium, wherein the computer program is stored in the computer program. When the computer program is executed by the processor of the electronic device, the computer program can achieve: receiving planting data sent by a corn straw return planter, wherein the planting data includes: straw return area, corn type, planting interval estimation, field return area area, target crop and target planting time; obtaining multiple historical soil samples based on the straw return area, using a pre-constructed soil data detection method to detect the multiple historical soil samples to obtain historical soil data, and obtaining target soil data based on the target crop; calculating the amount of straw per unit area according to the planting data, and extracting a straw decomposition curve set from a pre-constructed database based on the amount of straw per unit area and the corn type, wherein the straw decomposition curve set The method comprises a plurality of element decomposition curves, and the plurality of element decomposition curves are respectively: an organic matter decomposition curve, a total nitrogen decomposition curve, a total phosphorus decomposition curve, a total potassium decomposition curve, an alkaline nitrogen decomposition curve, an effective phosphorus decomposition curve and an effective potassium decomposition curve; an initial missing quantity set is calculated by using a database, a straw decomposition curve set, a target soil data and a historical soil data; the initial missing quantity set is parsed based on the database to obtain unit mixed fertilizer quantity data; the total amount of mixed fertilizer data is calculated based on the unit mixed fertilizer quantity data and the area of ​​the returning field region; and initial fertilizer data is generated based on the total amount of mixed fertilizer data and the unit mixed fertilizer quantity data; the initial fertilizer data is imported into a pre-constructed fertilization unit to obtain an initialized fertilization unit; after performing a fertilization operation on the straw returning field region based on the initialized fertilization unit, fertilization based on returning corn straw to the field is completed.

[0101] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, and actual implementation may have other division methods.

[0102] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0103] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0104] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A fertilization method based on returning corn stalks to the field, characterized in that: The method comprises: receiving planting data sent by a corn stalk returning planter, wherein the planting data comprises: a stalk returning area, a corn variety, an estimated planting interval, an area of ​​the returning area, a target crop and a target planting time; acquiring a plurality of historical soil samples based on the stalk returning area, detecting the plurality of historical soil samples using a pre-built soil data detection method to obtain historical soil data, and acquiring target soil data based on the target crop; wherein the acquiring of the plurality of historical soil samples based on the stalk returning area comprises: acquiring a top view image of the stalk returning area, performing a pre-processing operation on the top view image to obtain a field contour feature image, extracting a closed edge contour based on the field contour feature image, and extracting a closed edge contour based on the closed edge contour feature image. The minimum circumscribed rectangle is generated by the contour, wherein the pixel values ​​of all pixels corresponding to the edge contour of the straw returning area in the field contour feature image are 0, and the pixel values ​​of all pixels outside the edge contour of the straw returning area are 255; the minimum circumscribed rectangle is mapped to the field contour feature image to obtain the image to be analyzed, the size of the overhead image is obtained, the coarse rasterization size is generated based on the size of the overhead image, the preset field sampling spacing and the area of ​​the returning area, and the image to be analyzed is rasterized based on the coarse rasterization size to obtain a coarse rasterization map, wherein the coarse rasterization map includes multiple coarse raster rectangles, and the length and width of each of the multiple coarse raster rectangles are equal; based on the pre-built fine rasterization method and obtaining a fine grid map from the coarse grid map; wherein, obtaining a fine grid map based on the pre-constructed fine grid method and the coarse grid map comprises: extracting coarse grid rectangles from the coarse grid map in sequence to obtain a target large rectangle, and performing the following operations on the target large rectangle: obtaining the centroid of the target large rectangle, and judging whether the centroid of the target large rectangle is inside the closed edge contour based on the coarse grid map; if the centroid of the target large rectangle is inside the closed edge contour, rasterizing the target large rectangle in the coarse grid map based on a preset fine grid size and the centroid of the target large rectangle to obtain multiple fine grid rectangles; otherwise, skipping the extracted coarse grid rectangle; and summarizing multiple fine grid rectangles to obtain a coarse grid map. A corresponding fine grid map; extracting a shallow sampling point set and a deep sampling point set from the fine grid map using a pre-constructed sampling point setting method, performing shallow sampling operations on shallow sampling points in the shallow sampling point set to obtain a shallow sampling soil set, performing deep sampling operations on deep sampling points in the deep sampling point set to obtain a deep sampling soil set, summarizing the shallow sampling soil set and the deep sampling soil set to obtain a plurality of historical soil samples; wherein, extracting a shallow sampling point set and a deep sampling point set from the fine grid map using a pre-constructed sampling point setting method comprises: identifying the centroids of all coarse grid rectangles in the fine grid map to obtain an unmarked point set, performing a marking operation on the unmarked point set in a pre-constructed marking order to obtain a marked point set;A pre-constructed random number extraction method is used to extract multiple identified points from the identified point set to obtain a repeated sampling point set, the repeated sampling point set is eliminated from the identified point set, and the remaining identified points after eliminating the repeated sampling point set are summarized to obtain a single sampling point set; the coarse grid rectangle corresponding to each repeated sampling point in the repeated sampling point set is identified and summarized to obtain a repeated sampling rectangle set, and the following operations are performed on each repeated sampling rectangle in the repeated sampling rectangle set: multiple fine grid rectangles corresponding to the repeated sampling rectangle are identified to obtain multiple rectangles to be sampled; a rectangle to be sampled is randomly extracted from the multiple rectangles to be sampled, and the centroid of the extracted rectangle to be sampled is identified and marked to obtain a shallow sampling point; the rectangle to be sampled corresponding to the shallow sampling point is eliminated from the multiple rectangles to be sampled, and all the rectangles to be sampled after eliminating the rectangle to be sampled corresponding to the shallow sampling point are summarized to obtain multiple deep sampling rectangles, a deep sampling rectangle is randomly extracted from the multiple deep sampling rectangles, and the centroid corresponding to the extracted deep sampling rectangle is identified and marked to obtain a deep sampling point; each single sampling point in the single sampling point set is confirmed After shallow sampling points are considered, shallow sampling points are summarized to obtain a shallow sampling point set, and deep sampling points are summarized to obtain a deep sampling point set; the amount of straw per unit area is calculated according to the planting data, and a straw decomposition curve set is extracted from a pre-constructed database based on the amount of straw per unit area and the type of corn, wherein the straw decomposition curve set includes multiple element decomposition curves, and the multiple element decomposition curves are respectively: an organic matter decomposition curve, a total nitrogen decomposition curve, a total phosphorus decomposition curve, a total potassium decomposition curve, an alkaline nitrogen decomposition curve, an effective phosphorus decomposition curve, and an effective potassium decomposition curve; using The initial missing quantity set is calculated based on the database, the straw decomposition curve set, the target soil data, and the historical soil data. The initial missing quantity set is parsed based on the database to obtain the unit mixed fertilizer quantity data. The total amount of mixed fertilizer data is calculated based on the unit mixed fertilizer quantity data and the area of ​​the returned field area. The initial fertilizer data is generated based on the total amount of mixed fertilizer data and the unit mixed fertilizer quantity data; the initial fertilizer data is imported into the pre-built fertilization unit to obtain the initial fertilization unit. After performing the fertilization operation on the straw returned field area based on the initial fertilization unit, the fertilization based on the corn straw returned field is completed. ; 2. The fertilization method based on returning corn stalks to the field according to claim 1, characterized in that: The method of calculating the amount of straw per unit area according to the planting data includes: obtaining a corn planting interval sequence and a plurality of corn planting types, extracting corn planting types from the plurality of corn planting types in sequence, and associating the extracted corn planting types with the corn planting interval sequence to obtain a corn-interval sequence, summarizing the corn-interval sequences to obtain a plurality of blank planting data groups, wherein the blank planting data group includes a corn planting type, a corn planting interval and a corresponding blank yield estimate; obtaining a historical planting data set, wherein the historical planting data set includes a plurality of historical planting data, and the historical planting data includes: a historical planting interval, a historical corn type and a historical corn yield; and filling the plurality of blanks with the historical planting data set. The method comprises the following steps: obtaining an initial filled data set, wherein the initial filled data set includes a filled data set and an unfilled data set; performing data simplification operation on the initial filled data set to obtain a data set to be interpolated, identifying an unfilled data set in the data set to be interpolated, and performing interpolation operation on the blank corn yield of each unfilled data set in the unfilled data set using a pre-built interpolation method, and confirming the interpolated unfilled data set as a filled data set, summarizing the filled data sets, and obtaining a corn yield estimation model; importing the field return area, corn planting type and planting interval estimation into the corn yield estimation model to obtain the amount of straw per unit area, wherein the calculation formula of the amount of straw per unit area is: ,in, represents the estimated amount of corn stover per unit, It represents the corn yield per unit area in the straw returning area, and the corn yield per unit area is the ratio of the total corn yield in the straw returning area to the area of ​​the returning area. The grass-to-grain ratio of corn is expressed as represents the collectible coefficient of corn stalks.

3. The fertilization method based on returning corn stalks to the field as claimed in claim 2, characterized in that: The method of using the historical planting data set to fill the multiple blank planting data groups to obtain the initial filling data group set includes: extracting the historical planting data from the historical planting data set in sequence, and performing the following operations on the extracted historical planting data: identifying the corresponding historical planting intervals in the historical planting data to obtain the interval to be analyzed, and extracting the first corn planting interval and the second corn planting interval adjacent to the interval to be analyzed from the corn planting interval sequence, and respectively calculating the absolute value of the difference between the interval to be analyzed and the first corn planting interval and the second corn planting interval to obtain the first difference absolute value and the second difference absolute value, wherein the value of the first corn planting interval is greater than the value of the second corn planting interval, and the first corn planting interval corresponds to the first difference absolute value, and the second corn planting interval corresponds to the second difference absolute value; identifying the historical planting data The historical corn type corresponding to the first difference is obtained to obtain the corn type to be analyzed, wherein the corn type to be analyzed belongs to multiple corn planting types; if the absolute value of the first difference is greater than the absolute value of the second difference, the blank planting data group corresponding to the second corn planting interval and the corn type to be analyzed is extracted, and the extracted blank planting data group is filled with the historical corn yield to obtain a filled data group; if the absolute value of the first difference is less than or equal to the absolute value of the second difference, the filled data group is obtained based on the absolute value of the first difference; after confirming that all historical planting data sets have been extracted, the filled data groups are summarized to obtain a filled data group set, all blank planting data groups that have not been filled with historical planting data in multiple blank planting data groups are summarized to obtain an unfilled data group set, and the filled data group set and the unfilled data group set are summarized to obtain an initial filled data group set.

4. The fertilization method based on returning corn stalks to the field as claimed in claim 3, characterized in that: The data simplification operation is performed on the initial filled data set to obtain the data set to be interpolated, including: extracting the planting groups to be analyzed from the filled data sets in the initial filled data set in sequence, and performing the following operations on the extracted planting groups to be analyzed: if the extracted planting group to be analyzed includes multiple historical corn yields, then calculating the average of the multiple historical corn yields, and updating the historical corn yields in the planting group to be analyzed with the average of the multiple historical corn yields to obtain a simplified analysis planting group; otherwise, confirming the extracted planting group to be analyzed as a simplified analysis planting group; and summarizing the simplified analysis planting group and the unfilled data set respectively to obtain the data set to be interpolated.

5. The fertilization method based on returning corn stalks to the field as claimed in claim 4, characterized in that: The method uses a database, a straw decomposition curve set, target soil data, and historical soil data to calculate an initial missing amount set, including: extracting element decomposition curves from the straw decomposition curve set in sequence, performing the following operations on the element decomposition curve: confirming a target element corresponding to the element decomposition curve, extracting a unit initial element content corresponding to the target element from the historical soil data, and extracting a unit target element content corresponding to the target element from the target soil data, extracting an element loss curve of the target element in a straw return area from a pre-constructed database, and calculating a missing amount curve of the target element based on the unit initial element content, the unit target element content, the element decomposition curve, and the element loss curve, wherein the horizontal axis of the missing amount curve is time, the vertical axis is the element missing amount, and the calculation formula of the element missing amount is: ,in, Indicates the time The number of missing elements corresponding to the target element, Indicates the unit target element content, Indicates the unit initial element content, Indicates the time The release amount of the target element in the element decomposition curve, Indicates the time The target element loss amount in the element loss curve at the time of planting is obtained; based on the target planting time, the initial-target curve is intercepted from the missing amount curve, and the element missing amount corresponding to the target planting time in the initial-target curve is identified to obtain the target time required filling amount; the initial missing amount is calculated according to the target time required filling amount, the initial-target curve and the preset translation correction coefficient; the initial missing amounts are summarized to obtain the initial missing amount set.

6. The fertilization method based on returning corn stalks to the field as claimed in claim 5, characterized in that: After the initialization fertilization unit performs a fertilization operation on the straw return area, the fertilization based on returning corn straw to the field is completed, including: wherein the fertilization unit includes a data processing unit and a fertilizer spreading unit; based on the fertilizer initial data, the total amount data of the mixed fertilizer is extracted, and based on the data processing unit in the fertilization unit, the extracted total amount data of the mixed fertilizer is parsed to obtain a variety of fertilizer raw materials, and the ratio of the multiple fertilizer raw materials is calculated to obtain mixed fertilizer proportion data; based on the mixed fertilizer proportion data and the total amount data of the mixed fertilizer, multiple target raw materials are extracted from the multiple fertilizer raw materials, the multiple target raw materials are mixed to obtain fertilizer to be used, the fertilizer to be used is introduced into the initialization fertilization unit, and after the straw return area is fertilized by the fertilizer spreading unit in the initialization fertilization unit, the fertilization based on returning corn straw to the field is completed.

7. A fertilization device based on returning corn stalks to the field, characterized in that: Used to implement the fertilization method based on returning corn straw to the field as claimed in claim 1, the device includes: a planting data acquisition module, used to receive planting data sent by corn straw return growers, wherein the planting data includes: straw return area, corn type, planting interval estimation, field return area area, target crop and target planting time; a soil data acquisition module, used to obtain multiple historical soil samples based on the straw return area, use a pre-constructed soil data detection method to detect the multiple historical soil samples to obtain historical soil data, and obtain target soil data based on the target crop; a fertilizer initial data generation module, used to calculate the amount of straw per unit area according to the planting data, and extract a straw decomposition curve set from a pre-constructed database based on the amount of straw per unit area and the corn type, wherein the straw decomposition curve set The invention comprises a plurality of element decomposition curves, and the plurality of element decomposition curves are respectively: an organic matter decomposition curve, a total nitrogen decomposition curve, a total phosphorus decomposition curve, a total potassium decomposition curve, an alkaline nitrogen decomposition curve, an effective phosphorus decomposition curve and an effective potassium decomposition curve. The initial missing quantity set is calculated by using a database, a straw decomposition curve set, target soil data and historical soil data. The initial missing quantity set is parsed based on the database to obtain unit mixed fertilizer quantity data. The total amount of mixed fertilizer data is calculated based on the unit mixed fertilizer quantity data and the area of ​​the returning field region. The initial fertilizer data is generated based on the total amount of mixed fertilizer data and the unit mixed fertilizer quantity data. The fertilization module is used to import the initial fertilizer data into a pre-constructed fertilization unit to obtain an initialized fertilization unit. After performing a fertilization operation on the straw returning field region based on the initialized fertilization unit, the fertilization based on the returning of corn straw to the field is completed.

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