Intelligent decision-making of rice basal fertilizer based on big data and variable fertilization method of intelligent agricultural machinery

Abstract

This invention provides a big data-based intelligent decision-making method for rice basal fertilizer and a variable-rate fertilization method using intelligent agricultural machinery, belonging to the field of intelligent agriculture technology. The method includes utilizing intelligent nutrient decision-making technology for cold-region rice to generate a nutrient decision prescription adapted to the nutritional needs of the target plot, obtaining a plot basal fertilizer prescription map, and connecting the plot prescription map to an SQL Server database on a cloud computing platform; using a mobile cloud platform as a big data fusion subsystem, and constructing a program interface API through a backend entity framework; based on the mobile cloud platform, using intelligent agricultural machinery at the field operation end, and obtaining fertilization information by calling the program interface API. This invention solves the problem of how to couple fertilization prescription maps with intelligent agricultural machinery under large-scale planting conditions to achieve precise application, improving overall operational efficiency and nutrient resource utilization efficiency.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent agriculture, and in particular relates to an intelligent decision-making method for rice basal fertilizer based on big data and an intelligent agricultural machinery variable fertilization method. Background Art

[0002] Promoting and developing smart agriculture requires the deep integration and application of information technology with agricultural machinery and agronomy. Smart planting is the deep integration of computer technology, communication technology, management technology, and engineering technology to achieve intelligent perception, precise regulation, quantitative decision-making, and intelligent services in crop production. Building a smart planting technology system for field crops based on unmanned farms is the main direction of my country's modern agricultural development.

[0003] Rice growth is often affected by a variety of variable factors such as meteorological environment, cultivation management conditions, and soil nutrients. With the development of precision agriculture, crop growth models and photoelectric sensors are used to obtain crop growth information for remote sensing diagnosis, which can provide accurate fertilization models and precise nutrient decision-making plans for crop growth. Modern intelligent agricultural machinery and equipment can perform precise variable operations based on precise nutrient decision-making plans, which will be more conducive to overcoming the spatiotemporal environmental heterogeneity of farmland at the regional scale and improving resource utilization efficiency. Precision variable fertilization technology is one of the core technologies in precision agriculture. By dividing farmland soil into spatial grid units, variable input fertilizers are applied as needed according to the growth and nutrient profit and loss of crops in each grid, so as to reduce regional differences in field biochemical parameters and improve fertilizer utilization and crop production capacity. Variable fertilization technology based on prescription maps is to pre-produce a spatial geographic layer with spatial location and fertilization attribute information. After that, intelligent agricultural machinery combines satellite positioning technology to obtain real-time positioning information data, and then performs spatial grid recognition based on this positioning information to determine the spatial geographical location of the intelligent agricultural machinery, so as to obtain the amount of fertilizer from the pre-made prescription map to achieve the purpose of variable adjustment of the amount of fertilizer. The application of precise variable fertilization based on prescription maps can help change the large-scale and extensive fertilization management methods of traditional agriculture and achieve scientific and effective fertilization that is adapted to local conditions. With the continuous improvement of domestic navigation technology and agricultural machinery unmanned driving technology, unmanned driving systems based on satellite navigation technology are widely used in rice transplanting. The intelligent unmanned rice transplanter is equipped with a navigation satellite positioning system and a side-deep fertilization control system. It can be positioned, navigated and timed during field transplanting, realizing unmanned driving and side-deep fertilization. The human-computer interaction interface and corresponding actuators of the vehicle-mounted control terminal provide the hardware conditions for the intelligent unmanned rice transplanter to obtain the prescription information of the plot and realize real-time precise variables, which improves the level of intelligence and makes the intelligent unmanned rice transplanter more controllable when performing variable fertilization of the plot.

[0004] Cloud computing is a new network access mode that can quickly and conveniently realize the sharing service of network information resources between different computer network devices. Cloud computing technology is widely used in the development of modern agriculture. In the agricultural Internet of Things system, cloud computing technology can be used to connect with agricultural sensors such as temperature sensors and photoelectric sensors. The collected data can be stored and decided through the cloud computing center using the Internet as a carrier. The decision analysis results can also be published to cloud service detection terminals such as computers and mobile phones to achieve real-time detection and management of crop growth and environment. At present, relying on information technologies such as the Internet of Things, the Internet, and big data, an agricultural data sharing service platform based on cloud computing can be built. Therefore, farms with large-scale production and operation methods can build a cloud service platform based on farm agricultural big data. The cloud service platform application based on cloud computing technology needs to use Mysql, Sqlsever, Oracale and other databases to store structured and standardized data, and can realize data sharing and exchange between subsystems integrated with big data through shared services. Data exchange is often realized by processing XML / or JSON format. Users can obtain and use data through queries in data sharing services. Smartphones are becoming more and more important modern agricultural equipment due to their portability, convenience and flexible operation. Many researchers have established crop nutrition relationships through images collected by smartphone cameras to provide a new and convenient channel for precision fertilization. However, smartphones and intelligent unmanned agricultural machinery control systems can be used as subsystems for big data integration and cloud service terminals to achieve remote query and acquisition of data, thereby serving the agricultural production process and improving the level of intelligent agricultural production. Obtaining crop nutrient management decision information and cloud-coupled intelligent unmanned agricultural machinery control systems through cloud service platforms or smartphone cloud service terminals, and coordinating variable actuators for efficient operation, thereby realizing the integration of agricultural machinery and agronomy and improving the level of intelligent management of rice nutrients are current problems that need to be solved in the intelligent cultivation of rice fields. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides a smart decision-making method for rice basal fertilizer and intelligent agricultural machinery variable fertilization based on big data, which solves the problem of how to couple nutrient decisions with intelligent agricultural machinery in large-scale planting to achieve precise implementation, thereby improving the overall operation efficiency and nutrient resource utilization efficiency.

[0006] In order to achieve the above purpose, the technical solution adopted by the present invention is: a method for intelligent decision-making of rice basal fertilizer and intelligent agricultural machinery variable fertilization based on big data, comprising the following steps:

[0007] S1. Using the intelligent nutrient decision-making technology for cold-region rice, a nutrient decision prescription adapted to the nutritional needs of crops in the target plot is generated to obtain a base fertilizer prescription map for the plot, and the plot prescription map is connected to the SQL Serve database of the cloud computing platform;

[0008] S2. Using the mobile phone cloud platform as a big data fusion subsystem, and building a program interface API through the application backend entity framework; wherein the application backend entity framework and the program interface API are connected to the SQL Server database, and the visual interface on the mobile phone cloud platform and the function buttons for controlling remote access are displayed through the front-end framework;

[0009] S3. Based on the mobile phone cloud platform, in cooperation with intelligent agricultural machinery equipment at the field operation end, the fertilization information is obtained by calling the program interface API, and the intelligent decision-making of rice base fertilizer and intelligent agricultural machinery variable fertilization based on big data are completed.

[0010] The beneficial effect of the present invention is as follows: the present invention obtains precise basal fertilizer prescription information for rice based on multi-source data and multi-model simulation technology, and utilizes the Internet of Things, cloud computing service platform, the Internet, etc. to integrate and process the prescription information of plots, and then transmits the basal fertilizer prescription information back to the matching intelligent agricultural machinery control system through remote communication, thereby realizing data interaction and linkage control of intelligent agricultural machinery equipment terminals in the cloud, so that intelligent agricultural machinery can be accurately controlled and operated efficiently and with high quality according to agronomic requirements, and an integrated technical integration solution for intelligent nutrient decision-making and precise operation of intelligent agricultural machinery is established, in order to realize large-scale intelligent fertilization decision-making coupled with precise variable application of intelligent agricultural machinery, and promote the development of rice fertilization towards intelligence, intelligence, and precision.

[0011] Furthermore, the step S1 includes the following steps:

[0012] S101. Using the intelligent nutrient decision-making technology for cold-region rice, a nutrient decision prescription adapted to the nutritional needs of the target plot of crops is generated to obtain a plot basal fertilizer prescription map, wherein the plot basal fertilizer prescription map is a plot Shapefile file composed of a plot map and attribute table information. Shapefile is a spatial vector data format file that expresses vector data and stores map elements, and is used to store and express vector data and describe spatial data geometry and attribute characteristics;

[0013] S102. Use the SQL Server database of the cloud computing platform to store the plot map, convert the attribute table information into a .xls format table, and connect to the SQL Server database through a database management tool, wherein the plot map attribute fields correspond to the .xls table one by one through the same fields, thereby completing the database connection between the plot prescription map and the cloud computing platform.

[0014] The beneficial effect of the above further scheme is that the Shapefile file produced by the ArcMap application tool contains the map and the corresponding attribute information, which can realize the storage and visualization of the spatial location plot information and the plot nutrient decision data. The SQLServer database, as a software for storing library tables, can connect the attribute table or table (.xls) file to the database through Navicat Premium (database management tool), and then call the database data to the front-end interface of the PC cloud platform and the mobile phone cloud platform through the API, so as to realize the visualization of the plot map and database attribute information on the front-end cloud platform interface, and real-time linkage with the database data.

[0015] Furthermore, the attribute table information includes the plot number, farmer's name, planting variety, per-acre yield potential, per-acre fertilizer application amount, and the longitude and latitude of the plot boundary;

[0016] The per mu fertilizer application amount includes the per mu compound fertilizer application amount, the per mu pure nitrogen, pure phosphorus and pure potassium application amount and the plot fertilizer application amount. The plot fertilizer application amount corresponds to the longitude and latitude information of the plot boundary through the plot number.

[0017] The spatial information of the plot map is obtained through unmanned aerial vehicles or high-resolution remote sensing satellite image data and drawn using ArcGIS software.

[0018] The beneficial effects of the above further scheme are: obtaining the necessary plot attribute information and saving it in an .xls table, opening the RGB image obtained by the drone using ArcGIS drawing software and drawing vector plots, the vector plots have attribute tables and contain plot location coordinate data, and the attributes are connected based on the specific plot number. The .xls table realizes the visualization of the plot map and the accurate storage of all necessary plot attribute information.

[0019] Furthermore, step S2 includes the following steps:

[0020] S201, the attribute table information and the corresponding plot map in the SQL Server database are sent to the PC-side cloud platform by calling the URL, and a mobile phone cloud platform is constructed as a big data fusion subsystem, wherein the back-end entity framework is applied to connect to the SQL Server database with the constructed program interface API, and the visual interface on the mobile phone cloud platform and the function buttons for controlling remote access are displayed through the front-end framework;

[0021] S202: Build a program interface API by applying the backend entity framework and deploy it to a public network server.

[0022] The beneficial effect of the above further scheme is: the prescription map based on cloud computing is coupled with the integrated technology of precise operation of intelligent unmanned rice transplanter. The base fertilizer prescription map and attribute information of the plot scale are uploaded to the cloud database of the cloud service platform for storage and management. Then, based on the constructed API interface and combined with the Beidou navigation and positioning technology of the fuselage, the fertilization control system of the intelligent unmanned rice transplanter is interconnected with the cloud service platform database, so that the intelligent unmanned rice transplanter can automatically obtain the fertilization data of the cloud plot and cooperate with the fertilizer discharge mechanism to realize precise variable fertilization.

[0023] Furthermore, the mobile phone cloud platform constructed as a big data fusion subsystem is specifically:

[0024] By applying the back-end entity framework and the constructed program interface API, the plot maps and plot fertilizer application attributes of the SQL Server database are connected to the mobile cloud platform, and the visual interface on the mobile cloud platform and the function keys for controlling remote access are displayed on the front-end framework to complete the construction of the mobile cloud platform.

[0025] The beneficial effect of the above further scheme is that the above function key refers to the interface button for coupling the plot fertilization information with the intelligent unmanned rice transplanter to obtain the prescription map data format through the mobile phone. The button controls the database specific attribute column information through the API interface, and is responsible for controlling the release of the intelligent nutrient prescription map data format, i.e., the Json data format, to the intelligent unmanned agricultural machinery host computer, thereby determining whether the intelligent unmanned agricultural machinery host computer system can query and accurately obtain the fertilization information of the target plot in the database through the communication protocol. It can realize the sending of the precise fertilization plan for the plot to the mobile phone cloud platform, and can also couple the precise nutrient decision plan for the database plot to the intelligent unmanned agricultural machinery control system by operating the function keys of the mobile phone cloud platform.

[0026] Furthermore, the construction program interface API is specifically:

[0027] Use the longitude and latitude coordinates as API interface parameters, and query the longitude and latitude of the land boundary and the fertilization information fields in the SQL Server database list by using the ray method;

[0028] After the query is successful, the status code and JSON format information including the longitude and latitude of the plot boundary and the fertilization information field are returned to the on-board control system of the intelligent unmanned rice transplanter to complete the construction of the program interface API.

[0029] The beneficial effect of the above further scheme is that the JSON format data contains the attribute information of the plot, including the plot number, the longitude and latitude boundaries of the plot, the amount of fertilizer applied per mu of the plot, the status code and the corresponding response information. By building an API interface, the intelligent agricultural machinery host computer system can remotely access the database, and accurately query the plot boundary data in the database based on the longitude and latitude coordinates of the intelligent agricultural machinery. At the same time, the database fertilization decision data column of the plot where the intelligent agricultural machinery is located is accurately obtained based on the plot number. After the query, the data is returned to the intelligent agricultural machinery in JSON format to execute the fertilization decision plan.

[0030] Furthermore, the intelligent agricultural machinery equipment in step S3 includes:

[0031] Beidou satellite navigation system, used to obtain the geographic coordinates of the smart rice transplanter in real time;

[0032] The vehicle host system is used to carry application software that can carry a program interface API, and display the geographic coordinate position of the intelligent rice transplanter in real time through the vehicle control terminal, and call the program interface API and parse the data format of the plot prescription map to obtain the plot fertilization information;

[0033] The vehicle body lower computer system is used to control the fertilization motor by receiving the field fertilization information from the vehicle-mounted control terminal, and to provide feedback on the rotation speed of the fertilizer discharge shaft and the displacement of the lead screw guide rail. It also uses a variable fertilizer controller to perform closed-loop control of the fertilization motor and the fertilizer discharger to achieve optimal fertilizer discharge.

[0034] The beneficial effects of the above further scheme are: the Beidou satellite navigation system is used for unmanned navigation and positioning, the upper computer system includes a flat-panel display terminal carrying application software, the application software can be equipped with an API interface server, and the database plot information is queried by parsing the prescription map data format, and the lower computer system includes a fertilization controller, which is used to receive the command control of the display terminal to control the fertilization motor. The upper computer can obtain and display the current longitude and latitude according to the Beidou navigation and positioning system and the positioning antenna, so as to obtain the JSON data of the plot fertilization prescription map based on the longitude and latitude data and API, and the intelligent agricultural machinery fertilization controller receives the fertilization prescription execution command, and realizes the coordinated control of the lower computer fertilization execution motor and the fertilizer discharge mechanism through the CAN bus, so as to accurately discharge fertilizer according to the prescribed fertilization amount. The upper computer and the lower computer further provide technical support for the integration of the fertilization prescription map coupling intelligent agricultural machinery precise variable fertilization.

[0035] Furthermore, step S3 includes the following steps:

[0036] S301, uploading the program interface API address to the vehicle control terminal;

[0037] S302, using the Beidou satellite navigation system on the intelligent unmanned rice transplanter to read the real-time geographic location coordinates of the intelligent rice transplanter, and send them to the vehicle control terminal for real-time display;

[0038] S303, according to the real-time display result, return the real-time latitude and longitude parameters to the program interface API address through the Get request method in the Http protocol, and request data from the Http interface;

[0039] S304, according to the request instruction, the Http interface queries the SQL Server database for the land fertilization information by the ray method, and returns the JSON format information and the response status code;

[0040] S305, parse the Http interface and confirm the response status code;

[0041] S306, using the mobile phone cloud platform, revising the returned response status code by controlling the function key of remote access to determine whether to parse the string in the JSON format information;

[0042] S307, parsing the string in the JSON format information, and converting the parsed JSON string into an object to retrieve the plot fertilization information;

[0043] S308, after the vehicle-mounted control terminal obtains the plot fertilization information, the plot fertilization information is converted into hexadecimal, and the converted fertilization instruction is transmitted to the variable fertilization controller;

[0044] S309, the variable fertilization controller analyzes the fertilization instruction, and sends the control analysis result to the fertilization motor via the CAN bus, and uses the sensor to feed back the rotation speed of the fertilizer discharge shaft and the displacement of the screw guide rail to the variable fertilization controller;

[0045] S3010. Based on the feedback results, the variable fertilization controller is used to perform closed-loop control of the fertilization motor and the fertilizer dispenser to achieve optimal fertilizer discharge, complete intelligent decision-making of rice base fertilizer based on big data and variable fertilization of intelligent agricultural machinery.

[0046] The beneficial effect of the above further scheme is that the intelligent unmanned rice transplanter on-board control terminal application software realizes remote and accurate query and acquisition of the database plot boundaries and parsing of JSON data containing plot identification codes and fertilizer prescription maps through the Http interface, so as to accurately obtain the nutrient decision-making plan, that is, the amount of fertilizer for the target plot where the intelligent agricultural machinery is located, and the function buttons of the mobile phone cloud platform are responsible for controlling and modifying the corresponding column fields of the database, thereby determining the successful query and return data of the Http interface, so that at the planting end, according to the intelligent decision-making plan of the mobile phone cloud platform, the intelligent agricultural machinery can accurately perform plot-level precise variable fertilization operations based on the controller and the actuator, and finally realize the integration of intelligent fertilization decision-making and coupled intelligent agricultural machinery precise variable fertilization operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 The present invention is a flow chart of the method.

[0048] Figure 2 It is a base fertilizer prescription diagram of an embodiment of the present invention;

[0049] Figure 3 It is the technical roadmap of the present invention.

[0050] Figure 4 This is a schematic diagram of the integrated technology solution for intelligent precision fertilization. DETAILED DESCRIPTION

[0051] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0052] Example

[0053] Traditional large-scale rice production often relies on regional, fixed-amount basal fertilizer application mode. Although the use of agricultural machinery has improved the fertilization level to a certain extent, the intelligent level of field nutrient management is still low, and the problem of fertilization decision-making coupled with intelligent agricultural machinery for precise variable application is generally not realized. Therefore, in order to improve the level of integration of agricultural machinery and agronomy in crop fertilization, and realize intelligent decision-making and intelligent precise variable application of rice nutrient management, the technical idea of ​​the present invention is proposed, that is, using the Internet of Things, cloud computing service platform, Internet and other technologies to realize intelligent agricultural machinery to obtain precise crop nutrient prescription information through remote communication, and efficiently coordinate variable fertilization control system and execution device to perform precise variable fertilization, so that the intelligent unmanned rice transplanter can more intelligently complete the execution command of the prescription map and efficient and precise fertilization operation in the field, thereby forming an integrated solution for intelligent rice fertilization integrating intelligent decision-making and intelligent agricultural machinery precise operation, solving the problem of how to couple nutrient decision-making with intelligent agricultural machinery in large-scale planting to achieve precise implementation, and improving the comprehensive operation efficiency and nutrient resource utilization efficiency.

[0054] like Figure 1 As shown, the present invention provides a method for intelligent decision-making of rice basal fertilizer and intelligent agricultural machinery variable fertilization based on big data, and the implementation method thereof is as follows:

[0055] S1. Using the intelligent nutrient decision-making technology for cold-region rice, a nutrient decision prescription adapted to the nutritional needs of crops in the target plot is generated to obtain a base fertilizer prescription map for the plot, and the plot prescription map is connected to the SQL Serve database of the cloud computing platform. The implementation method is as follows:

[0056] S101. Using the intelligent nutrient decision-making technology for cold-region rice, a nutrient decision prescription adapted to the nutritional needs of the target plot of crops is generated to obtain a plot basal fertilizer prescription map, wherein the plot basal fertilizer prescription map is a plot Shapefile file composed of a plot map and attribute table information. Shapefile is a spatial vector data format file that expresses vector data and stores map elements, and is used to store and express vector data and describe spatial data geometry and attribute characteristics;

[0057] S102, using the SQL Server database of the cloud computing platform to store the plot map, and converting the attribute table information into a .xls format table, and connecting to the SQL Server database through a database management tool, wherein the plot map attribute fields and the .xls table correspond one to one through the same fields, completing the connection between the plot prescription map and the database of the cloud computing platform;

[0058] The attribute table information includes the plot number, farmer's name, planting variety, per-acre yield potential, per-acre fertilizer application amount, and the longitude and latitude of the plot boundary;

[0059] The per mu fertilizer application amount includes the per mu compound fertilizer application amount, the per mu pure nitrogen, pure phosphorus and pure potassium application amount and the plot fertilizer application amount. The plot fertilizer application amount corresponds to the longitude and latitude information of the plot boundary through the plot number.

[0060] The spatial information of the plot map is obtained through unmanned aerial vehicles or high-resolution remote sensing satellite image data and drawn using ArcGIS software;

[0061] S2. Use the mobile phone cloud platform as a big data fusion subsystem, and build a program interface API through the application backend entity framework; the application backend entity framework and the program interface API are connected to the SQL Server database, and the visual interface on the mobile phone cloud platform and the function buttons for controlling remote access are displayed through the front-end framework. The implementation method is as follows:

[0062] S201, the attribute table information and the corresponding plot map in the SQL Server database are sent to the PC-side cloud platform by calling the URL, and a mobile phone cloud platform is constructed as a big data fusion subsystem, wherein the back-end entity framework is applied to connect to the SQL Server database with the constructed program interface API, and the visual interface on the mobile phone cloud platform and the function buttons for controlling remote access are displayed through the front-end framework;

[0063] S202, constructing a program interface API by applying a backend entity framework and deploying it to a public network server;

[0064] The mobile phone cloud platform constructed as a big data fusion subsystem is specifically:

[0065] By applying the backend entity framework and the constructed program interface API, the plot map and plot fertilizer application attributes of the SQL Server database are connected to the mobile cloud platform, and the visual interface on the mobile cloud platform and the function keys for controlling remote access are displayed on the front-end framework to complete the construction of the mobile cloud platform;

[0066] The construction program interface API is specifically:

[0067] Use the longitude and latitude coordinates as API interface parameters, and query the longitude and latitude of the land boundary and the fertilization information fields in the SQL Server database list by using the ray method;

[0068] After the query is successful, the status code and JSON format information including the longitude and latitude of the plot boundary and the fertilization information field are returned to the on-board control system of the intelligent unmanned rice transplanter to complete the construction of the program interface API;

[0069] S3. Based on the mobile phone cloud platform, in the field operation end, with the cooperation of intelligent agricultural machinery equipment, the fertilization information is obtained by calling the program interface API, and the intelligent decision-making of rice base fertilizer and intelligent agricultural machinery variable fertilization based on big data are completed. The implementation method is as follows:

[0070] S301, uploading the program interface API address to the vehicle control terminal;

[0071] S302, using the Beidou satellite navigation system on the intelligent unmanned rice transplanter to read the real-time geographic location coordinates of the intelligent rice transplanter, and send them to the vehicle control terminal for real-time display;

[0072] S303, according to the real-time display result, return the real-time latitude and longitude parameters to the program interface API address through the Get request method in the Http protocol, and request data from the Http interface;

[0073] S304, according to the request instruction, the Http interface queries the SQL Server database for the land fertilization information by the ray method, and returns the JSON format information and the response status code;

[0074] S305, parse the Http interface and confirm the response status code;

[0075] S306, using the mobile phone cloud platform, revising the returned response status code by controlling the function key of remote access to determine whether to parse the string in the JSON format information;

[0076] S307, parsing the string in the JSON format information, and converting the parsed JSON string into an object to retrieve the fertilization information of the plot; in object-oriented programming, the object is an instance of a class, which has the attributes and behaviors defined by the class, namely, member variables and behaviors, also called methods or functions. A new object can be created using "new+constructor", and the object members (including attributes and methods) can be accessed using the "object.object member" method, thereby achieving the calling of attributes or methods;

[0077] S308, after the vehicle-mounted control terminal obtains the plot fertilization information, the plot fertilization information is converted into hexadecimal, and the converted fertilization instruction is transmitted to the variable fertilization controller;

[0078] S309, the variable fertilization controller analyzes the fertilization instruction, and sends the control analysis result to the fertilization motor via the CAN bus, and uses the sensor to feed back the rotation speed of the fertilizer discharge shaft and the displacement of the screw guide rail to the variable fertilization controller;

[0079] S3010. Based on the feedback results, the variable fertilization controller is used to perform closed-loop control on the fertilization motor and the fertilizer dispenser to achieve optimal fertilizer discharge, and complete intelligent decision-making of rice basal fertilizer based on big data and intelligent agricultural machinery variable fertilization;

[0080] The intelligent agricultural machinery equipment in step S3 includes:

[0081] Beidou satellite navigation system, used to obtain the geographic coordinates of the smart rice transplanter in real time;

[0082] The vehicle host system is used to carry application software that can carry a program interface API, and display the geographic coordinate position of the intelligent rice transplanter in real time through the vehicle control terminal, and call the program interface API and parse the data format of the plot prescription map to obtain the plot fertilization information;

[0083] The vehicle body lower computer system is used to control the fertilization motor by receiving the field fertilization information from the vehicle-mounted control terminal, and to provide feedback on the rotation speed of the fertilizer discharge shaft and the displacement of the lead screw guide rail. It also uses a variable fertilizer controller to perform closed-loop control of the fertilization motor and the fertilizer discharger to achieve optimal fertilizer discharge.

[0084] In this embodiment, the present invention first uses the intelligent nutrient decision-making technology of cold-region rice to generate an accurate nutrient decision-making prescription adapted to the nutritional needs of crops in the target plot. Afterwards, the database and database management tools of the cloud computing platform storage library table are used to import and store the attribute information or table files, and at the same time, a program interface API is constructed to call the data and attribute fields of the query database, and the program interface API is deployed to the public network server for remote access to the external network; the mobile phone cloud platform is used as a subsystem for big data integration, and the back-end entity framework and program interface API are applied to connect to the SQLServer database, and the visual interface on the mobile phone cloud and the function buttons for controlling remote access are displayed through the front-end framework: clicking the "on" and "off" of the function button can modify the corresponding plot list field of the database, and the software system on the intelligent transplanter recognizes the call status API to control the software system to successfully parse the returned fertilization data. The field operation end cooperates with the Beidou satellite navigation system, the vehicle body upper computer and the lower computer system of the intelligent agricultural machinery equipment technology. The Beidou satellite navigation system is used for unmanned navigation, positioning and speed measurement of the vehicle body. The vehicle body upper computer system includes a vehicle-mounted control terminal system, which carries application software that can be equipped with a program interface API. The vehicle-mounted control terminal is equipped with a human-computer interaction interface, calls the program interface API, and parses the source prescription map data format to obtain the fertilization information of the plot. The vehicle-mounted controller is the core control component of the vehicle body lower computer system. It controls the fertilization motor by receiving instructions from the vehicle-mounted control terminal, and detects the displacement of the fertilizer discharge shaft and the lead screw slide rail, forming a closed-loop control of the controller on the actuator to achieve the purpose of accurately controlling fertilizer discharge, thereby realizing the intelligent unmanned rice transplanter to accurately apply variable fertilizers according to the information of the prescription map (for details of the technical roadmap, see Figure 3 ). As mentioned above, a replicable integrated solution for intelligent precision fertilization of cold-region rice is finally formed, which integrates four technical modules: intelligent nutrient management technology module, nutrient decision dynamic release technology module, intelligent agricultural machinery precision operation technology module, and mobile cloud platform nutrient decision information coupling intelligent agricultural machinery equipment module. It is used to achieve intelligent precision variable fertilization in large areas by large-scale use of this technology, such as Figure 4 shown.

[0085] In this embodiment, the cold-region rice plot-level precise prescription information is connected to the cloud platform database management system:

[0086] First, if Figure 2 and Figure 3 As shown, the plot prescription map, i.e., the Shapefile consisting of the plot map and attribute table information, is obtained. The spatial location information of the plot map is obtained through DJI drone images, and then imported into ArcGIS for drawing. Therefore, the plot boundaries and vertices have longitude and latitude coordinates. The attribute table information mainly includes the plot number ID, farmer name, planting variety, per mu yield potential, per mu fertilizer application, and longitude and latitude of the plot boundary. Among them, the fertilizer application includes the amount of compound fertilizer per mu and the amount of pure nitrogen, pure phosphorus, and pure potassium per mu. The fertilizer application amount of the plot corresponds to the longitude and latitude information of the plot boundary through the plot number ID. The plot Shapefile map is published to ArcGIS Server. The plot map is saved in PNG format and the URL address of the corresponding attribute field is retained and saved in SQL Server (database management system). After that, the attribute table information is converted into a .xls format table and connected to the SQL Server database through Navicat Premium (database management tool), where the map attribute field and the .xls table correspond to each other through the same field. The above-mentioned farmer names and planting variety data were obtained through surveys, and the per-mu yield potential and per-mu fertilizer application data were obtained through recommendations from the rice crop model based on data such as the soil properties of the actual plots, the climate type of the planting year, the rice varieties and cultivation information.

[0087] In this embodiment, the mobile phone cloud publishing and API interface creation for the plot attribute information are as follows:

[0088] like Figure 3 and Figure 4As shown, the SQL Server database plot attribute information and corresponding maps are transferred to the PC-side cloud platform (cloud server PC platform) by calling the URL address, and a mobile cloud platform system integrating big data is established at the same time, that is, the SQL Server database maps and plot fertilizer amount attributes are connected to the mobile cloud through the back-end .NET Core and Entity Framework framework, and the Vue.js framework is used to display the visual interface on the mobile cloud and the function buttons for controlling remote access through uni-app development, and the API interface is written through the back-end .Net Core WebApi framework to achieve the effect of calling the interface of the function button status to control the intelligent unmanned rice transplanter vehicle control system to obtain the fertilizer amount, and the longitude and latitude coordinates are used as interface parameters. Then, the SQL Server database list of plot longitude and latitude and fertilizer information fields are queried by the ray method. If the query is successful, the status code and JSON format information containing the plot longitude and latitude and fertilizer information strings will be returned to the intelligent unmanned rice transplanter vehicle control system. The created API complies with the OpenAPI specification, and the program interface API is deployed to the Window Server cloud server to publish so that the external network can remotely call the fertilization plan in the SQL Server database in real time.

[0089] In this embodiment, the plot-level precise nutrient decision-making scheme is coupled with the intelligent unmanned rice transplanter vehicle control system and collaborative operation, as follows:

[0090] like Figure 3 and Figure 4As shown, the program interface API address is uploaded to the unmanned driving software system of the vehicle control terminal. The Beidou navigation and positioning system carried by the intelligent unmanned rice transplanter continuously reads the real-time geographic location coordinates of the intelligent rice transplanter and transmits them to the vehicle control terminal for real-time display. At the same time, the vehicle control terminal returns the real-time latitude and longitude parameters to the program interface API address through the Get request method in the Http protocol to request data from the Http interface. After that, the Http interface queries the fertilizer prescription information of the plot in the SQL Server database according to the ray method, and returns JSON format information and response status code. After that, the vehicle control terminal parses the Http interface, confirms the response status code, parses the JSON string after confirmation, and converts the JSON string into an object after parsing to retrieve the fertilization data. This process is to read the response prescription map information and return it to the software system of the vehicle control terminal of the intelligent unmanned rice transplanter for storage. In the above process, the mobile phone cloud can revise the returned status code through the function button, so as to determine whether the software system can parse the string in JSON format, so as to achieve the effect of verifying the fertilizer amount of the plot in production. After the on-board control terminal obtains the fertilizer amount from the fertilizer prescription map of the plot, it converts the fertilizer amount into hexadecimal. Then, according to the standard of the international ISO11783 communication protocol and the fertilizer variable controller local area network bus (CAN bus), the fertilization instruction is transmitted to the controller. The fertilizer variable controller parses the instruction and sends the control instruction to the lower machine position electronic control motor again through the CAN bus. The sensor is used to feed back the fertilizer shaft speed or the screw slide displacement to the fertilizer variable controller. The fertilizer variable controller uses the proportional, integral and differential (PID) algorithm to achieve closed-loop control of the fertilizer motor and the fertilizer discharger to achieve the purpose of accurately controlling the fertilizer discharge amount. Finally, the intelligent unmanned rice transplanter can perform unmanned driving and precise variable fertilization operations according to the information of the prescription map.

[0091] In summary, the present invention integrates nutrient decision-making, prescription map cloud storage management, cloud database prescription information coupling intelligent unmanned rice transplanter and coordinated fertilizer execution mechanism to achieve precise fertilization operation, integrating cloud computing, Internet, API, Beidou navigation satellite positioning, information communication, and multiple technologies of rice transplanter fertilizer control. Combined with actual production needs, the precise fertilization plan of the plot is sent to the mobile phone cloud platform, and the mobile phone cloud function module key is operated to control the prescription map data information response to the intelligent unmanned agricultural machinery control system, so that the intelligent unmanned agricultural machinery obtains the precise fertilization plan of the target plot and accurately executes the operation. Finally, an integrated solution for intelligent fertilization of rice integrating intelligent decision-making for cold-region rice and precise operation of intelligent agricultural machinery is constructed. Compared with the traditional agricultural machinery management according to fixed production experience, it can reduce labor intensity and labor costs and improve the utilization efficiency of agricultural production resources, and provide ideas and technical support for intelligent precision fertilization and large-scale implementation in field crop planting.

Claims

1. A method for intelligent decision-making of rice basal fertilizer and intelligent agricultural machinery variable fertilization based on big data, characterized in that: The following steps are involved: S1. Using the intelligent nutrient decision-making technology for cold-region rice, a nutrient decision prescription adapted to the nutritional needs of crops in the target plot is generated to obtain a base fertilizer prescription map for the plot, and the base fertilizer prescription map for the plot is connected to the SQL Serve database of the cloud computing platform; S2. Using the mobile phone cloud platform as a big data fusion subsystem, and building a program interface API through the application backend entity framework; wherein the application backend entity framework and the program interface API are connected to the SQL Server database, and the visual interface on the mobile phone cloud platform and the function buttons for controlling remote access are displayed through the front-end framework; S3. Based on the mobile phone cloud platform, in the field operation end, cooperate with intelligent agricultural machinery equipment to obtain fertilization information by calling the program interface API, and complete the intelligent decision-making of rice base fertilizer based on big data and intelligent agricultural machinery variable fertilization; The step S3 comprises the following steps: S301, uploading the program interface API address to the vehicle control terminal; S302, using the Beidou satellite navigation system on the intelligent unmanned rice transplanter to read the real-time geographic location coordinates of the intelligent rice transplanter, and send them to the vehicle control terminal for real-time display; S303, according to the real-time display result, return the real-time latitude and longitude parameters to the program interface API address through the Get request method in the Http protocol, and request data from the Http interface; S304, according to the request instruction, the Http interface queries the SQL Server database for the plot fertilization information by using the ray method, and returns the JSON format information and the response status code; S305, parse the Http interface and confirm the response status code; S306, using the mobile phone cloud platform, revising the returned response status code by controlling the function key of remote access to determine whether to parse the string in the JSON format information; S307, parsing the string in the JSON format information, and converting the parsed JSON string into an object to retrieve the plot fertilization information; S308, after the vehicle-mounted control terminal obtains the plot fertilization information, the plot fertilization information is converted into hexadecimal, and the converted fertilization instruction is transmitted to the variable fertilization controller; S309, the variable fertilization controller analyzes the fertilization instruction, and sends the control analysis result to the fertilization motor via the CAN bus, and uses the sensor to feed back the rotation speed of the fertilizer discharge shaft and the displacement of the screw guide rail to the variable fertilization controller; S3010. Based on the feedback results, the variable fertilization controller is used to perform closed-loop control of the fertilization motor and the fertilizer dispenser to achieve optimal fertilizer discharge, complete intelligent decision-making on rice base fertilizer based on big data and variable fertilization of intelligent agricultural machinery.

2. The method for intelligent decision-making of rice basal fertilizer and intelligent agricultural machinery variable fertilization based on big data according to claim 1 is characterized in that: The step S1 comprises the following steps: S101. Using the intelligent nutrient decision-making technology for cold-region rice, a nutrient decision prescription adapted to the nutritional needs of the target plot of crops is generated to obtain a plot basal fertilizer prescription map, wherein the plot basal fertilizer prescription map is a plot Shapefile file composed of a plot map and attribute table information. Shapefile is a spatial vector data format file that expresses vector data and stores map elements, and is used to store and express vector data and describe spatial data geometry and attribute characteristics; S102. Use the SQL Server database of the cloud computing platform to store the plot map, convert the attribute table information into a .xls format table, and connect to the SQL Server database through a database management tool, wherein the plot map attribute fields correspond to the .xls table one by one through the same fields, thereby completing the database connection between the plot prescription map and the cloud computing platform.

3. The method for intelligent decision-making of rice basal fertilizer and intelligent agricultural machinery variable fertilization based on big data according to claim 2 is characterized in that: The attribute table information includes the plot number, farmer's name, planting variety, per-acre yield potential, per-acre fertilizer application amount, and the longitude and latitude of the plot boundary; The per mu fertilizer application amount includes the per mu compound fertilizer application amount, the per mu pure nitrogen, pure phosphorus and pure potassium application amount and the plot fertilizer application amount. The plot fertilizer application amount corresponds to the longitude and latitude information of the plot boundary through the plot number. The spatial information of the plot map is obtained through unmanned aerial vehicles or high-resolution remote sensing satellite image data and drawn using ArcGIS software.

4. The method for intelligent decision-making of rice basal fertilizer and intelligent agricultural machinery variable fertilization based on big data according to claim 3 is characterized in that: The step S2 comprises the following steps: S201, the attribute table information and the corresponding plot map in the SQL Server database are sent to the PC-side cloud platform by calling the URL, and a mobile phone cloud platform is constructed as a big data fusion subsystem, wherein the back-end entity framework is applied to connect to the SQL Server database with the constructed program interface API, and the visual interface on the mobile phone cloud platform and the function buttons for controlling remote access are displayed through the front-end framework; S202: Build a program interface API by applying the backend entity framework and deploy it to a public network server.

5. The method for intelligent decision-making of rice basal fertilizer and intelligent agricultural machinery variable fertilization based on big data according to claim 4 is characterized in that: The mobile phone cloud platform constructed as a big data fusion subsystem is specifically: By applying the back-end entity framework and the constructed program interface API, the plot maps and plot fertilizer application attributes of the SQL Server database are connected to the mobile cloud platform, and the visual interface on the mobile cloud platform and the function keys for controlling remote access are displayed on the front-end framework to complete the construction of the mobile cloud platform.

6. The method for intelligent decision-making of rice basal fertilizer and intelligent agricultural machinery variable fertilization based on big data according to claim 4 is characterized in that: The construction program interface API is specifically: Use the longitude and latitude coordinates as API interface parameters, and query the longitude and latitude of the land boundary and the fertilization information fields in the SQL Server database list by using the ray method; After the query is successful, the status code and JSON format information including the longitude and latitude of the plot boundary and the fertilization information field are returned to the on-board control system of the intelligent unmanned rice transplanter to complete the construction of the program interface API.

7. The method for intelligent decision-making of rice basal fertilizer and intelligent agricultural machinery variable fertilization based on big data according to claim 1, characterized in that: The intelligent agricultural machinery equipment in step S3 includes: Beidou satellite navigation system, used to obtain the geographic coordinates of the smart rice transplanter in real time; The vehicle host system is used to carry application software that can carry a program interface API, and display the geographic coordinate position of the intelligent rice transplanter in real time through the vehicle control terminal, and call the program interface API and parse the data format of the plot prescription map to obtain the plot fertilization information; The vehicle body lower computer system is used to control the fertilization motor by receiving the field fertilization information from the vehicle-mounted control terminal, and to provide feedback on the rotation speed of the fertilizer discharge shaft and the displacement of the lead screw guide rail. It also uses a variable fertilizer controller to perform closed-loop control of the fertilization motor and the fertilizer discharger to achieve optimal fertilizer discharge.

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