An adaptive dynamic adjustment water and fertilizer ratio decision system and method

Abstract

The application discloses a kind of self-adapting dynamic adjustment's water and fertilizer proportioning decision system and method, system includes: water supply unit, for providing water source to water and fertilizer machine;Filter module, for filtering the impurities contained in water source;Fertilizer supply unit, for providing fertilizer to water and fertilizer machine;Water and fertilizer machine, for according to the fertilizer demand law of crop, accurately proportion water and fertilizer ratio;Irrigation module, for accurately proportioned water and fertilizer is efficiently delivered to crop;Data acquisition module, for collecting the data of relevant factors capable of influencing water and fertilizer proportioning ratio;Database, for storing collected data and manually input data, and provide data support for simulation analysis unit;Through the implementation of the application, after data collection and reanalysis, according to the irrigation mode of user, analysis and adjustment are carried out, the current more optimal water and fertilizer application scheme is pushed to user, realizes self-adapting dynamic adjustment processing.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of agricultural irrigation technology, and particularly relates to a water and fertilizer proportioning decision system and method with adaptive dynamic adjustment. BACKGROUND

[0002] The water and fertilizer integrated automatic control system can help producers to conveniently realize automatic water and fertilizer integrated management. The water and fertilizer integrated system usually includes a water source project, a head hub, a water and fertilizer machine, a filtration system, a field water delivery pipe network system, a valve controller, an electromagnetic valve and a control software platform and the like, and is also provided with different sensing devices such as field climate monitoring, soil moisture monitoring, PH detection and soil salinity detection.

[0003] The existing technology has the following disadvantages: it cannot analyze and adjust according to the irrigation mode of the user, the user who has started irrigation cannot be used for the evaluation standard of the original model, and the effect brought by the water and fertilizer proportioning decision needs to be further improved.

[0004] In view of the above, a water and fertilizer proportioning decision system and method with adaptive dynamic adjustment are needed to solve the problems in the prior art. SUMMARY

[0005] In view of the problems in the prior art, the present application provides a water and fertilizer proportioning decision system and method with adaptive dynamic adjustment, which aims to solve the above problems.

[0006] To achieve the above purpose, the present application provides the following technical scheme: a water and fertilizer proportioning decision system with adaptive dynamic adjustment, comprising:

[0007] a water supply unit for providing a water source to the water and fertilizer machine;

[0008] a filtration module for filtering impurities contained in the water source;

[0009] a fertilizer supply unit for providing fertilizer to the water and fertilizer machine;

[0010] a water and fertilizer machine for accurately adjusting the water and fertilizer ratio according to the fertilizer demand law of crops;

[0011] an irrigation module for efficiently delivering the accurately adjusted water and fertilizer to crops;

[0012] a data acquisition module for acquiring data of related factors that can affect the water and fertilizer adjustment ratio;

[0013] a database for storing the acquired data and manually input data and providing data support for the simulation analysis unit;

[0014] a simulation analysis unit for analyzing and simulating according to the acquired data, and real-time adaptive dynamic reconstruction of a new model;

[0015] A control terminal is configured to issue an instruction to control the water and fertilizer machine and the irrigation module.

[0016] The water supply unit is connected to the water and fertilizer machine through a filter module, the fertilizer supply unit is connected to the water and fertilizer machine, and the water and fertilizer machine is connected to the irrigation module. Through the cooperation of the data acquisition module and the analog analysis unit, the irrigation mode of the user is analyzed and adjusted, and after data collection and reanalysis, the current optimal water and fertilizer application scheme is pushed to the user, realizing self-adaptive dynamic adjustment.

[0017] Further, the irrigation module includes a main pipe and a plurality of branch pipes, and an electromagnetic valve is arranged at the connection between each branch pipe and the main pipe. The electromagnetic valve is communicatively connected to the control terminal, and each branch pipe is adapted with any one or more of the irrigation units, such as ground-inserted micro-spraying, inverted micro-spraying, drip irrigation, hole irrigation, and trench irrigation.

[0018] Further, the artificially input data is the conventional data of crop fertilizer requirement regularity.

[0019] Further, the data acquisition module includes soil moisture monitoring, soil nitrogen, phosphorus and potassium, soil temperature and humidity, ultrasonic six-parameter, atmospheric temperature and pressure, wind speed and direction, and rainfall sensors.

[0020] Further, the filter module includes a sand filter and a laminated filter, and the water source of the water supply unit passes through the sand filter and the laminated filter in sequence and is input into the water and fertilizer machine.

[0021] A self-adaptive dynamic adjustment water and fertilizer ratio decision method is used for a self-adaptive dynamic adjustment water and fertilizer ratio decision system as described above, comprising the following steps:

[0022] Step 1: First, input the conventional data of crop fertilizer requirement regularity into the database, and use the current data as the basis for the first water and fertilizer irrigation.

[0023] Step 2: Filter the water source through the water pump and the filter, and then input it into the water and fertilizer machine. The fertilizer is input into the water and fertilizer machine through the cooperation of the electromagnetic valve and the fertilizer pump. The water and fertilizer machine mixes the water source and the fertilizer according to the ratio in step 1, and the mixed water and fertilizer is applied to the crops by different irrigation methods.

[0024] Step 3: After water and fertilizer irrigation is performed in step 2, the relevant data of the surrounding environment of the crops is collected and real-time feedback is given to the database. The collected data is sorted according to the required data format.

[0025] Step 4: Data preliminary screening, compare the collected and sorted data according to the standard range. If the collected data is completely within the standard range, all the data is accepted. If there is abnormal data, it is excluded.

[0026] Step 5: plot the data collected in step 4 in three-dimensional coordinates, observe the trend of the data and the general curve trend, preliminarily determine the data modeling method, dynamically reconstruct a new irrigation model, and push the current new water and fertilizer ratio decision scheme to the user.

[0027] Further, the irrigation model uses the least squares method model, and each user uses a separate database. After receiving the new data collected by the user, the data is scattered together with the previous data, and is redistributed according to a fixed ratio. Some data is selected to reconstruct the model. According to the data collected from the user, a graph is first plotted to determine the data trend.

[0028] Further, the model establishing method comprises the following steps:

[0029] Step 1: plot a three-dimensional graph according to all the data. As the water and fertilizer consumption increases, the curve gradually rises, and when it reaches a certain point, it will decrease with the increase of water and fertilizer consumption;

[0030] Step 2: construct a polynomial equation Since x and y have a clear amount, the equation is considered as an equation about a, b, c, e, f, and g. If you want to predict the trend of this data, you can simulate the trend of this data, find the curve that fits all (x, y, w), and then use the partial derivative method to make the derivative equal to zero, and get 6 linear equations about (a, b, c, e, f, g) ; ; ; ; ; Solve the equation set, which can give different solutions of the six unknowns. In the actual solving process, there will be multiple values of a, b, and c, and multiple equations in the form of F(z) = ;

[0031] Step 3: In order to make the coefficients more consistent with the original data, further calculate these equations and data, so that the final function can have the smallest distance from all data. Use the Euler formula method, that is, the sum of squares, to calculate the distance between each point and each surface: Calculate the final sum value, and record the maximum and minimum standard deviation in each surface as the fluctuation range (x min , y min ) of the surface. The surface with the smallest sum is the final required surface.

[0032] Step 4: F(z) = After the equation, the equation is further verified, the new data generated in the actual production process is brought into the equation, if it is in the fluctuation range, it is recorded as a correct one, if it is out of the fluctuation range, it is recorded as a failure, when the correct value exceeds 80% of all values, it is proved that the equation is effective, then the model can be put into the actual water and fertilizer ratio;

[0033] Wherein, x is the amount of water, y is the amount of fertilizer, w is the dependent variable, and F(z) is the target function.

[0034] Further, the standard range is a natural irrigation use range of each crop, a general data fluctuation range is determined according to the trend of historical data, if the collected data is not in the range, it will be initially excluded from the database, and then backed up.

[0035] Further, the data trend is a parabolic shape with water and fertilizer as independent variables (x, y) and yield as dependent variable (w). In this model, a regression surface that fits all data is found, and the shortest distance from all points to the surface is found. The parabolic surface is the target surface, recorded as the target function F(z).

[0036] The substantial effect of the application is:

[0037] 1. In the application, the data acquisition module cooperates with the simulation analysis unit to analyze and adjust according to the user's irrigation mode. After data collection and reanalysis, the current more optimal water and fertilizer application scheme is pushed to the user, realizing self-adaptive dynamic adjustment processing.

[0038] 2. In the application, the database stores the collected data and manually input data. The manually input data is the conventional data of crop fertilizer requirement law, which can be used as the initial water and fertilizer ratio basis and the data support for secondary model adjustment, and provides data support for the simulation analysis unit. DETAILED DESCRIPTION

[0039] Figure 1 The system block diagram of the application.

[0040] Figure 2 The database principle diagram of the application.

[0041] Figure 3 The ratio decision method flow of the application. CONCRETE EMBODIMENT

[0042] As shown in Figure 1 , 2 An adaptive dynamic adjustment water and fertilizer ratio decision system, comprising:

[0043] A water supply unit for providing water source to the water and fertilizer machine;

[0044] a filtering module for filtering impurities contained in the water source;

[0045] a fertilizer supply unit for supplying fertilizer to the water-fertilizer machine;

[0046] a water-fertilizer machine for accurately adjusting the water-fertilizer ratio according to the fertilizer requirement law of crops;

[0047] an irrigation module for efficiently delivering the accurately adjusted water-fertilizer to crops;

[0048] a data acquisition module for acquiring data of related factors that can affect the water-fertilizer adjustment ratio;

[0049] a database for storing the acquired data and manually input data and providing data support for the simulation analysis unit;

[0050] a simulation analysis unit for analyzing and simulating according to the acquired data, real-time self-adaptive dynamic reconstruction of a new model, solving the optimal water and fertilizer use problem under the current irrigation mode of the user, and making the resource utilization more rational;

[0051] a control terminal for issuing instructions to control the water-fertilizer machine and the irrigation module;

[0052] The water supply unit is connected to the water-fertilizer machine through the filtering module, the fertilizer supply unit is connected to the water-fertilizer machine, and the water-fertilizer machine is connected to the irrigation module.

[0053] The irrigation module includes a main pipeline and a plurality of branch pipelines, an electromagnetic valve is arranged at the connection between the branch pipeline and the main pipeline, the electromagnetic valve is communicatively connected to the control terminal, and the branch pipeline is adapted with multiple irrigation units such as ground-insertion micro-spraying, inverted micro-spraying, drip irrigation, hole irrigation, and trench irrigation.

[0054] The manually input data are conventional data of the fertilizer requirement law of crops.

[0055] The data acquisition module includes soil moisture monitoring, soil nitrogen, phosphorus and potassium, soil temperature and humidity, ultrasonic six-parameter, atmospheric temperature and pressure, wind speed and direction, and rainfall sensors, and provides secondary data support for model modification after initial water-fertilizer ratio adjustment through the soil moisture monitoring, soil nitrogen, phosphorus and potassium, soil temperature and humidity, ultrasonic six-parameter, atmospheric temperature and pressure, wind speed and direction, and rainfall sensors.

[0056] The filtering module includes a sandstone filter and a laminated filter, the water source of the water supply unit is input to the water-fertilizer machine through the sandstone filter and the laminated filter in sequence, the sandstone particles in the water source are filtered through the sandstone filter, then a filter disc with different precision is selected according to the water requirement, there are 20 microns, 55 microns, 100 microns, 130 microns, 200 microns, 400 microns and other specifications, and the filtration ratio is greater than 85%.

[0057] In another aspect, as shown in Figure 3 The embodiment provides a self-adaptive dynamic adjustment water and fertilizer ratio decision method, which is used for a self-adaptive dynamic adjustment water and fertilizer ratio decision system as described above, and includes the following steps.

[0058] Step 1: The conventional data of crop fertilizer requirement rules are input and stored into a database, and the current data is used as the basis for the first water and fertilizer irrigation.

[0059] Step 2: The water source is filtered by a water pump and a filter and then is fed into a water and fertilizer machine, the fertilizer is fed into the water and fertilizer machine by cooperating an electromagnetic valve and a fertilizer suction pump, the water source and the fertilizer are mixed by the water and fertilizer machine according to the ratio in step 1, and the mixed water and fertilizer is used on crops by different irrigation methods.

[0060] Step 3: After the water and fertilizer irrigation in step 2, the related data of the environment around the crops are collected and are fed back to the database in real time, and the collected data are arranged according to the required data format.

[0061] Step 4: The collected and arranged data are compared according to the irrigation dosage range of each crop naturally existing, if the collected data are completely within the standard range, all the data are received, if there is an exceeding part, the 20% part is collected, and the 80% exceeding part is excluded, a general data fluctuation range is determined according to the trend of the historical data, if the collected data are not within the range, the data are preliminarily excluded from the database, and then are backed up.

[0062] Step 5: The data collected in step 4 are drawn into a three-dimensional coordinate graph, the trend and the general curve trend of the data are observed, the data model establishment method is preliminarily determined, a new irrigation model is dynamically reconstructed, and a new water and fertilizer ratio decision scheme is pushed to the user.

[0063] The irrigation model adopts a least square method model, each user uses a separate database, after receiving the new collected data of the user, the data are scattered together with the previous data, are redistributed according to a 3:3:4 ratio, 30% of the data are selected to reconstruct the model, a graph is drawn according to the data initially collected from the user, the trend of the data is determined, the parabolic surface shape of the water amount and the fertilizer amount as the independent variables (x, y) and the yield as the dependent variable (w) is determined, in the model, a regression surface that is more fitted to all the data is found, the shortest distance of all the points from the surface is found, and then the parabolic surface is the target surface, which is recorded as a target function F(z).

[0064] The model establishment method includes the following steps.

[0065] Step 1: According to all the data, a three-dimensional graph is drawn, which shows a roughly parabolic form. As the water and fertilizer consumption increases, the curve gradually rises, and then decreases as the water and fertilizer consumption increases to a certain point;

[0066] Step 2: Construct a polynomial equation of Since x and y have specific quantities, this equation can be considered as an equation for a, b, c, e, f, and g. If we want to predict the trend of this data, we can simulate the trend of this data and find a curve that fits all (x, y, w). Then we can find the derivative by taking the partial derivative, and set it to zero to get six linear equations for (a, b, c, e, f, g), ; ; ; ; ; Solve the equation set, and we can get different solutions for the six unknowns. In the actual solving process, we may get multiple values for a, b, and c, and we may also get multiple equations in the form of F(z) = ;

[0067] Step 3: To make the coefficients more consistent with the original data, we further calculate these equations and data to make the final function have the smallest distance from all data. We use the method of Euler formula, i.e. square sum, to calculate the distance between each point and each surface: Calculate the final sum value, and record the maximum and minimum standard deviation in each surface, denoted as the fluctuation range of the surface (x min , y min ). The surface with the smallest sum is the final required surface;

[0068] Step 4: After obtaining the equation F(z) = , we further verify the equation by bringing new data generated in the actual production process into the equation. If it is within the fluctuation range, it is correct. If it is outside the fluctuation range, it is a mistake. When the correct value exceeds 80% of all values, the equation is proved to be effective, and the model can be put into actual water and fertilizer ratio;

[0069] By bringing in a known water and fertilizer consumption, we can deduce more suitable coefficients, which can be a, b, c, e, f, or more. After deducing the coefficients, we can bring them into the original standard equation. When the corresponding water amount X changes, the corresponding fertilizer amount Y will also change. When the corresponding fertilizer amount Y changes, the corresponding water amount X will also change.

[0070] The above merely provides the preferred embodiment of the present application, and is not used to limit the present application, and any modification, equivalent replacement or improvement made in the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. An adaptive dynamic adjustment of water and fertilizer ratio decision method for an adaptive dynamic adjustment of water and fertilizer ratio decision system, characterized in that the system include: The water supply unit is used to provide water to the fertigation machine; The filter module is used to filter impurities contained in the water source; Fertilizer supply unit, used to supply fertilizer to the fertigation unit; A water and fertilizer machine is used to precisely adjust the water and fertilizer ratio according to the fertilizer requirements of crops. Irrigation modules are used to efficiently deliver precisely allocated water and fertilizer to crops; The data acquisition module is used to collect data on factors that can affect the water-fertilizer ratio. The database is used to store collected data and manually input data, and to provide data support for the simulation analysis unit. The simulation analysis unit is used to analyze and simulate the collected data, and dynamically reconstruct new models in real time. The control terminal is used to issue commands to control the water and fertilizer machine and the irrigation module; The water supply unit is connected to the fertigation unit via a filtration module, the fertilizer supply unit is connected to the fertigation unit, and the fertigation unit is connected to the irrigation module. It also includes the following steps: Step 1: First, input and store the regular data on crop fertilizer requirements into the database, and use the current data as the basis for the first irrigation. Step 2: The water source is filtered by a water pump and filter and then sent to the water and fertilizer machine. The fertilizer is sent to the water and fertilizer machine through a solenoid valve and fertilizer suction pump. The water and fertilizer machine mixes the water source and fertilizer according to the ratio in Step 1. The mixed water and fertilizer is applied to the crops using different irrigation methods. Step 3: After water and fertilizer irrigation in Step 2, collect relevant data on the environment around the crops and transmit it back to the database in real time. Organize the collected data according to the required data format. Step 4: Initial data screening. The collected and organized data is compared according to the standard range. If the collected data is completely within the standard range, all data is accepted. Any abnormal data is removed. Step 5: Plot a three-dimensional coordinate graph of the data collected in Step 4, observe the trend of the data and the general curve trend, preliminarily determine the data model building method, adaptively and dynamically reconstruct a new irrigation model, and push the current new water and fertilizer ratio decision scheme to the user. The standard range refers to the natural range of irrigation usage for each crop. A general data fluctuation range is determined based on the trend of historical data. If the collected data is not within this range, it will be initially excluded from the database and then backed up. The data trend is the shape of a parabola with water and fertilizer as independent variables (x, y) and yield as dependent variable (w). In this model, a regression surface that fits all the data is found. The shortest distance from all points to the surface is found. This parabola is the target surface, denoted as the objective function F(z).

2. The self-adaptive dynamic adjustment water and fertilizer ratio decision method according to claim 1, characterized in that, The irrigation module includes a main pipeline and several branch pipelines. The connection between the branch pipelines and the main pipeline is provided with a solenoid valve. The solenoid valve is communicatively connected to a control terminal. The branch pipelines are adapted to any one or more irrigation units among ground-mounted micro-sprinklers, inverted micro-sprinklers, drip irrigation, hole irrigation, and furrow irrigation.

3. The adaptive dynamic adjustment water-fertilizer ratio decision method according to claim 1, characterized in that, The manually input data is routine data on the fertilizer requirements of crops.

4. The adaptive dynamic adjustment water-fertilizer ratio decision method according to claim 1, characterized in that, The data acquisition module includes soil moisture monitoring, soil nitrogen, phosphorus and potassium, soil temperature and humidity, ultrasonic six parameters, atmospheric temperature and pressure, wind speed and direction, and rainfall sensors.

5. The adaptive dynamic adjustment water-fertilizer ratio decision method according to claim 1, characterized in that, The filtration module includes a sand filter and a disc filter. The water source of the water supply unit is fed into the water fertilizer after passing through the sand filter and the disc filter.

6. The adaptive dynamic adjustment water-fertilizer ratio decision method according to claim 1, characterized in that, The irrigation model uses the least squares method. Each user uses a separate database. After receiving new data collected from the user, the data is broken down together with the previous data and redistributed according to a fixed ratio. A portion of the data is selected to reconstruct the model. Based on the data initially collected from the user, a graph is drawn to determine the data trend.

7. The adaptive dynamic adjustment water-fertilizer ratio decision method according to claim 1, characterized in that, The method for establishing the model includes the following steps: Step 1: Draw a 3D graph based on all the data. As the amount of water and fertilizer increases, the curve gradually rises. When it reaches a certain point, it will decrease as the amount of water and fertilizer increases. Step 2: Construction Since x and y are definite quantities, this polynomial equation can be viewed as an equation about finding a, b, c, e, f, and g. If we want to predict the trend of this data, by simulating the trend and finding a curve that better fits all (x, y, w), we can obtain six sets of linear equations about (a, b, c, e, f, g) by taking partial derivatives until the derivative is zero. ; ; ; ; ; Solving the system of equations can yield different solutions for the six unknowns. In the actual solution process, multiple values ​​for a, b, and c will appear, resulting in multiple sets of forms such as F(z) = The equation; Step 3: To make the coefficients fit the original data better, these equations and data are calculated again to ensure that the final function has the minimum distance from all data. Euler's formula, i.e., the sum of squares, is used to calculate the distance between each point and each surface: Calculate the final summation value and record the maximum and minimum standard deviations for each surface, denoted as the fluctuation range (x) of that surface. min y min The surface with the smallest summation is the final required surface. Step 4: F(z) = appears After formulating the equation, further verification is performed by incorporating new data generated during actual production into the equation. If the data is within the fluctuation range, it is recorded as correct; if it is outside the fluctuation range, it is recorded as incorrect. When the correct value exceeds 80% of all values, the equation is proven to be effective, and the model can be applied to the actual water and fertilizer ratio. Where x is the amount of water, y is the amount of fertilizer, w is the dependent variable, and F(z) is the objective function.

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