Intelligent water and fertilizer management method and operation system

Abstract

The application relates to the technical field of intelligent water and fertilizer management, and particularly discloses an intelligent water and fertilizer management method and an operation system, which comprises the following steps: acquiring regional parameters of a water and fertilizer management area, determining sampling points and their motion trajectories according to the regional parameters; acquiring environmental data in real time according to sensors installed at the sampling points, determining the inhibition rate distribution of the water and fertilizer management area at each moment according to the environmental data; acquiring actual growth parameters of crops at regular time intervals, correcting the inhibition rate distribution at each moment according to the actual growth parameters; superimposing the corrected inhibition rate distribution to determine a water and fertilizer management work order; and the application obtains environmental data according to the sampling points, uniformly converts the environmental data, obtains the inhibition rate as a parameter, and statistically obtains the inhibition rate at each position within a period of time as a reference for the water and fertilizer management process, so that the obtained water and fertilizer management scheme has extremely high refinement and intelligence.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent water and fertilizer management, and particularly relates to an intelligent water and fertilizer management method and an operation system. BACKGROUND

[0002] Traditional agricultural irrigation and fertilization methods mostly rely on manual experience or timed and quantitative irrigation systems, and have problems such as water and fertilizer waste, unbalanced crop nutrition, and inaccurate growth cycle control. In particular, in the face of large-area planting or complex terrain, manual fertilization and irrigation are difficult to achieve fine management, resulting in low resource utilization efficiency, and it is difficult to improve crop yield and quality. With the development of sensors, the Internet of Things and artificial intelligence technology, agricultural production is gradually transforming towards intelligence and precision. Therefore, there is an urgent need for a fine intelligent water and fertilizer management method to improve agricultural resource allocation efficiency. SUMMARY

[0003] The present application aims to provide an intelligent water and fertilizer management method and an operation system to solve the problems raised in the background.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical solutions:

[0005] An intelligent water and fertilizer management method, the method comprising:

[0006] Obtaining the area parameters of the water and fertilizer management area, and determining the sampling points and their motion trajectories according to the area parameters;

[0007] Real-time acquisition of environmental data from sensors installed at the sampling points, and determination of the inhibition rate distribution of the water and fertilizer management area at each time according to the environmental data; the inhibition rate is used to represent the inhibition degree of environmental data on the growth process of crops, and the inhibition rate distribution is represented by a layer;

[0008] Timely acquisition of actual growth parameters of crops, and correction of the inhibition rate distribution at each time according to the actual growth parameters;

[0009] Superimposition of the corrected inhibition rate distribution to determine the management work order of water and fertilizer; the management work order includes the amount of water and fertilizer for each area.

[0010] As a further solution of the present application, the step of obtaining the area parameters of the water and fertilizer management area and determining the sampling points and their motion trajectories according to the area parameters comprises:

[0011] Timely acquisition of remote sensing images of the water and fertilizer management area, and positioning of the motion trajectory according to the remote sensing images;

[0012] Selecting the to-be-analyzed points in the water and fertilizer management area based on a preset density;

[0013] For any to-be-analyzed point, the distance between the to-be-analyzed point and each passing trajectory is calculated, and the exposure of each to-be-analyzed point is determined according to the distance;

[0014] The exposure of adjacent to-be-analyzed points is compared in sequence, and when the difference in exposure is less than a preset tolerance, the two to-be-analyzed points are merged;

[0015] After all to-be-analyzed points are compared with adjacent to-be-analyzed points, a sampling area composed of to-be-analyzed points is obtained;

[0016] At least one sampling point is set in the sampling area, the sampling area is divided according to the sampling range of the sampling point, and a motion trajectory is generated.

[0017] As a further scheme of the present application, the step of acquiring environmental data in real time according to the sensor installed at the sampling point, and determining the inhibition rate distribution of the water and fertilizer management area at each time according to the environmental data comprises:

[0018] The environmental data containing time labels and position labels are acquired in real time according to the sensor installed at the sampling point; the environmental data at least include illumination parameters, air parameters and soil components;

[0019] The crop planting duration is acquired, and demand data is acquired according to the crop planting duration;

[0020] The demand data and the environmental data are compared, and the inhibition rate containing the time label and the position label is determined according to the comparison result; wherein the initial time node of the crop planting duration is updated regularly;

[0021] All inhibition rates containing position labels of the same time label are counted to obtain the inhibition rate distribution containing the time label.

[0022] As a further scheme of the present application, the step of counting all inhibition rates containing position labels of the same time label to obtain the inhibition rate distribution containing the time label comprises:

[0023] The counted inhibition rate containing the time label and the position label is obtained;

[0024] The time labels of the inhibition rate are compared two by two, the time difference is calculated, and when the time difference is less than a preset difference threshold, the time label of the inhibition rate is taken as the same time label;

[0025] All inhibition rates containing position labels of the same time label are counted, and a radiation layer is created with the position label as the center;

[0026] The radiation layers of all position labels are superimposed, and the superimposed result is taken as the inhibition rate distribution.

[0027] As a further scheme of the present application: the step of acquiring the actual growth parameter of the crop in real time, and correcting the inhibition rate distribution of each time point according to the actual growth parameter comprises:

[0028] The remote sensing image of the water and fertilizer management area is acquired in real time, the remote sensing image is input into the trained crop recognition model, and the actual growth parameter of the recognizable crop is determined; the actual growth parameter is used to represent the change of the crop in the remote sensing image of the adjacent time point;

[0029] The inhibition rate of the recognizable crop in the time interval of the current remote sensing image and the previous remote sensing image is acquired, and the theoretical growth parameter is simulated according to the inhibition rate; the mapping relationship between the inhibition rate and the theoretical growth parameter is set in advance;

[0030] The difference rate is calculated by comparing the actual growth parameter and the theoretical growth parameter;

[0031] The theoretical growth parameter of all crops is corrected according to the difference rate, and the corrected inhibition rate is reversely acquired based on the corrected theoretical growth parameter;

[0032] The position of the recognizable crop is acquired, and the inhibition rate distribution is corrected according to the corrected inhibition rate.

[0033] As a further scheme of the present application: the step of superimposing the corrected inhibition rate distribution to determine the management work order of the water and fertilizer comprises:

[0034] The time period input by the management personnel is received;

[0035] All the corrected inhibition rate distribution in the time period is acquired, and the corrected inhibition rate distribution is superimposed;

[0036] The superimposed corrected inhibition rate distribution is profile-recognized to obtain a sub-area;

[0037] For each sub-area, the water and fertilizer demand is determined according to the value of the pixel point in the sub-area;

[0038] The management work order of the water and fertilizer is determined based on each sub-area and the water and fertilizer demand thereof.

[0039] The present application also provides an intelligent water and fertilizer management operation system, which comprises:

[0040] A point setting module is used to acquire the area parameter of the water and fertilizer management area, and determine the sampling point and its motion trail according to the area parameter;

[0041] An inhibition rate analysis module is configured to acquire environmental data in real time according to sensors installed at sampling points, and determine an inhibition rate distribution of the water and fertilizer management area at each time according to the environmental data; the inhibition rate is used to represent the inhibition degree of the environmental data on the growth process of crops, and the inhibition rate distribution is represented by a layer;

[0042] An inhibition rate correction module is configured to acquire actual growth parameters of the crops at regular time intervals, and correct the inhibition rate distribution at each time according to the actual growth parameters;

[0043] A management work order generation module is configured to superimpose the corrected inhibition rate distribution, and determine a management work order of water and fertilizer; the management work order includes the amount of water and fertilizer for each area.

[0044] As a further scheme of the present application, the point setting module comprises:

[0045] A passing trajectory positioning unit is configured to acquire remote sensing images of the water and fertilizer management area at regular time intervals, and position passing trajectories according to the remote sensing images;

[0046] An analysis point selection unit is configured to select to-be-analyzed points in the water and fertilizer management area based on a preset density;

[0047] An exposure degree calculation unit is configured to calculate the distance between any to-be-analyzed point and each passing trajectory, and determine the exposure degree of each to-be-analyzed point according to the distance;

[0048] A point merging unit is configured to compare the exposure degrees of adjacent to-be-analyzed points in sequence, and merge two to-be-analyzed points when the difference between the exposure degrees is less than a preset tolerance;

[0049] A sampling area construction unit is configured to obtain a sampling area composed of to-be-analyzed points after all to-be-analyzed points are compared with adjacent to-be-analyzed points;

[0050] A trajectory generation unit is configured to set at least one sampling point in the sampling area, divide the sampling area according to the sampling range of the sampling point, and generate a motion trajectory.

[0051] As a further scheme of the present application, the inhibition rate analysis module comprises:

[0052] An environmental data acquisition unit is configured to acquire environmental data containing time labels and position labels in real time according to sensors installed at sampling points; the environmental data at least includes illumination parameters, air parameters and soil components;

[0053] A demand data acquisition unit is configured to acquire the planting duration of crops, and acquire demand data according to the planting duration of the crops;

[0054] The data comparison unit is used for comparing the demand data and the environment data, and determining the inhibition rate containing a time label and a position label according to a comparison result; wherein, an initial time node of the crop planting time length is updated in time;

[0055] The statistical output unit is used for statistically outputting all inhibition rates containing a position label of a same time label, and obtaining an inhibition rate distribution containing a time label.

[0056] As a further scheme of the present application, the inhibition rate correction module comprises:

[0057] The actual parameter acquisition unit is used for acquiring a remote sensing image of the water and fertilizer management area in time, inputting the remote sensing image into the trained crop recognition model, and determining an actual growth parameter of the recognizable crop; the actual growth parameter is used for representing a change of the crop in a remote sensing image of a neighboring time;

[0058] The theoretical parameter determination unit is used for acquiring the inhibition rate of the recognizable crop in a time interval of the current remote sensing image and the previous remote sensing image, and simulating a theoretical growth parameter according to the inhibition rate; a mapping relationship between the inhibition rate and the theoretical growth parameter is pre-set;

[0059] The difference rate calculation unit is used for comparing the actual growth parameter and the theoretical growth parameter, and calculating a difference rate;

[0060] The correction execution unit is used for correcting the theoretical growth parameter of all crops according to the difference rate, and inversely acquiring a corrected inhibition rate based on the corrected theoretical growth parameter;

[0061] The distribution correction unit is used for acquiring a position of the recognizable crop, and correcting an inhibition rate distribution according to the corrected inhibition rate.

[0062] Compared with the prior art, the present application has the beneficial effects that: the present application acquires environment data according to a sampling point, uniformly converts the environment data, obtains the inhibition rate as a parameter, and statistically obtains the inhibition rate at each position in a period of time as a reference for a water and fertilizer management process; the determination process of the inhibition rate is accurate to a point, the water and fertilizer management scheme obtained is extremely fine, and the intelligent level is also very high. BRIEF DESCRIPTION OF DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only some embodiments of the present application.

[0064] Figure 1 The flowchart of the intelligent water and fertilizer management method.

[0065] Figure 2The application discloses an intelligent water and fertilizer management operation system. DETAILED DESCRIPTION

[0066] In order to make the technical problems, technical solutions and beneficial effects of the present application clearer, the present application will be further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0067] Figure 1 The application discloses an intelligent water and fertilizer management operation system.

[0068] Step S100: acquiring region parameters of a water and fertilizer management region, and determining sampling points and motion trajectories according to the region parameters;

[0069] The water and fertilizer management region is a region that needs water and fertilizer management, and the region parameters are generally region contours and geological types of positions in the region. In the technical solution of the present application, the range of the region is determined according to the region contours. The region parameters are analyzed, and some points are selected. The unique feature of the technical solution of the present application is that the selected points are dynamic points that can move. Therefore, after the points are selected, the motion trajectories need to be determined synchronously. In an actual scene, the sampling points correspond to collecting devices installed on mobile robots, the collecting devices are used to collect data, and the mobile robots are used to drive the collecting devices to move along the motion trajectories.

[0070] Step S200: acquiring environmental data in real time according to sensors installed at the sampling points, and determining an inhibition rate distribution of the water and fertilizer management region at each moment according to the environmental data; the inhibition rate is used to represent the inhibition degree of the environmental data on the growth process of crops, and the inhibition rate distribution is represented by a layer.

[0071] The collecting devices are limited to sensors, the environmental data is acquired in real time according to the sensors installed at the sampling points, and the types of the environmental data are various and need to be unified. In the technical solution of the present application, the environmental data is unified into a parameter representing the inhibition degree on the growth process of crops, that is, the inhibition rate. The inhibition rate of each position in the water and fertilizer management region at each moment is determined according to the environmental data, which is called the inhibition rate distribution of the region at a moment, and the inhibition rate distribution is represented by a layer.

[0072] Step S300: acquiring actual growth parameters of crops at regular time intervals, and correcting the inhibition rate distribution at each moment according to the actual growth parameters.

[0073] Real-time acquisition of actual growth parameters of crops, such as height and chlorophyll content of crops, which are set by staff, can represent the growth situation, and the acquisition method can be assisted by remote sensing identification technology; after the actual growth parameters are obtained, the inhibition rate distribution at each time is corrected according to the actual growth parameters, since the inhibition rate distribution is determined by the environmental data obtained by the sensor, and its influence on the crops is theoretical, combined with the actual growth parameters, the inhibition rate distribution can be corrected.

[0074] It should be noted that the specific cause of the above correction process is that the frequency of obtaining the actual growth parameter is very low, which may be once a day, while the inhibition rate distribution is obtained by the sensor, and the frequency is very high, which may be once a minute, and a correction relationship (generally a coefficient) is determined by the actual growth parameter once a day, which is applied to the newly obtained inhibition rate distribution, which can greatly improve the accuracy of the inhibition rate distribution with high frequency.

[0075] Step S400: superimposing the corrected inhibition rate distribution to determine the management work order of water and fertilizer; the management work order includes the amount of water and fertilizer pointing to which area;

[0076] The corrected inhibition rate distribution contains a time label, indicating the inhibition degree of all crops at each time, and the superimposed range of the corrected inhibition rate distribution is a preset time span, such as half-day inhibition rate distribution, so that the inhibition degree of crops at each position in the water and fertilizer management area after half-day time can be obtained, and the management work order of water and fertilizer is determined, the management work order includes the amount of water and fertilizer pointing to which area, and the staff can distribute fertilizer or directly apply fertilizer according to the management work order, since the robot has been introduced in the technical scheme of the application, these work can be completely completed by the robot.

[0077] Regarding step S100, the step of acquiring the area parameters of the water and fertilizer management area and determining the sampling points and their motion trajectories according to the area parameters includes:

[0078] Timely acquire remote sensing images of the water and fertilizer management area, and locate the motion trajectories according to the remote sensing images;

[0079] Select the to-be-analyzed points in the water and fertilizer management area based on a preset density;

[0080] For any to-be-analyzed point, calculate the distance between it and each motion trajectory, and determine the exposure of each to-be-analyzed point according to the distance;

[0081] Compare the exposure of adjacent to-be-analyzed points in sequence, and when the difference in exposure is less than a preset tolerance, combine the two to-be-analyzed points;

[0082] After all the to-be-analyzed point positions are compared with adjacent to-be-analyzed point positions, a sampling area composed of to-be-analyzed point positions is obtained.

[0083] At least one sampling point position is arranged in the sampling area, and the sampling area is divided according to the sampling range of the sampling point position, to generate a motion track.

[0084] In an example of the technical scheme of the present application, the determination process of the sampling point position and the motion track thereof is described. A remote sensing image of the water and fertilizer management area is acquired in real time, and the motion track is located according to the remote sensing image. The locating process is very simple, and the motion track is located in the water and fertilizer management area by means of preset image features, such as some small roads and the like, which are the areas passed by the staff when working. Then, to-be-analyzed point positions are selected in the water and fertilizer management area based on a preset density. The density is generally represented by the interval of the to-be-analyzed point positions. The smaller the interval, the greater the density, and the more the to-be-analyzed point positions. The distance between each to-be-analyzed point position and each motion track is calculated, the exposure of each to-be-analyzed point position is determined according to the distance, adjacent to-be-analyzed point positions are compared in turn, when the difference in exposure is less than a preset tolerance, the two to-be-analyzed point positions are merged, and after each to-be-analyzed point position is compared and merged with the surrounding to-be-analyzed point positions, a sampling area composed of to-be-analyzed point positions is obtained. Finally, at least one sampling point position is arranged in the sampling area, and the sampling area is divided according to the sampling range of the sampling point position, to generate a motion track.

[0085] In the above, the calculation process of the exposure is that an exposure is determined for each distance. The smaller the distance, the greater the exposure, and the smaller the distance, the smaller the absolute value of the change rate of the exposure. The exposure corresponding to all distances is accumulated to obtain the final exposure. The to-be-analyzed point positions are aggregated according to the exposure, to obtain a sampling area. The navigation technology is applied in the sampling area to generate a motion track, which is the motion track of the selected sampling point position.

[0086] Regarding step S200, the step of determining the inhibition rate distribution of the water and fertilizer management area at each time according to the environment data acquired in real time by the sensor installed at the sampling point position includes:

[0087] The environment data containing time labels and position labels is acquired in real time by the sensor installed at the sampling point position. The environment data at least includes illumination parameters, air parameters and soil composition.

[0088] The crop planting time length is acquired, and the demand data is acquired according to the crop planting time length;

[0089] The demand data and the environment data are compared, and the inhibition rate containing time labels and position labels is determined according to the comparison result. The initial time node of the crop planting time length is updated in real time.

[0090] The inhibition rates containing the time label are counted to obtain the inhibition rate distribution containing the time label.

[0091] The water and fertilizer management area is a crop planting site, and environment data can be obtained by a preset sensor in the crop planting site, wherein the environment data at least includes illumination parameters, air parameters and soil components; the process of obtaining the environment data has been described above, wherein the influence degree of the air parameters on the crop growth process is relatively low, and accordingly, the priority is relatively low compared with the illumination parameters and the soil components.

[0092] According to the existing growth data, the demand data of the crops in different stages can be determined, that is, what conditions are needed, such as: the temperature of seedlings during the day is 20-25℃, and the temperature at night is 10-15℃; such conditions are known data in the existing technical background, and can be directly obtained.

[0093] By comparing the demand data and the environment data at a certain moment, it can be judged whether the environment data can meet the demand, and a value is determined according to whether it meets the demand or not, which is used to reflect the environment, and the value is called the inhibition rate; the closer the environment data is to the demand data, the lower the inhibition rate is, and the greater the difference between the environment data and the demand data, the higher the inhibition rate is.

[0094] It is worth mentioning that the crop planting time needs to be explained, the current state of the crops will be obtained by the staff at regular intervals, and the current state is taken as the initial state, the crop planting time is obtained, and then the demand data is obtained, so that the real-time performance of the demand data can be ensured; generally, the initial state of the crops is updated once every time a new growth period is entered, such as entering the germination period, etc., in a popular way, the initial time node of the crop planting time is updated regularly.

[0095] Specifically, the step of counting all the inhibition rates containing the position label of the same time label to obtain the inhibition rate distribution containing the time label includes:

[0096] The inhibition rates containing the time label and the position label obtained by counting;

[0097] The time labels of the inhibition rates are compared two by two, the time difference is calculated, and when the time difference is less than a preset difference threshold, the time label of the inhibition rate is taken as the same time label;

[0098] All the inhibition rates containing the position label of the same time label are counted, and a radiation layer is created with the position label as the center;

[0099] The radiation layers of all the position labels are superimposed, and the superimposed result is taken as the inhibition rate distribution.

[0100] The above describes the statistical process of the inhibition rate. The inhibition rate containing the time label and the position label is obtained by statistics. If the time difference of the time labels of two inhibition rates is small enough, they are considered to be the same time label. All inhibition rates containing the position label of the same time label are statistically obtained. The radiation layer is created based on the position label as the center. The radiation layer generally uses the Gaussian kernel function to determine the weight. For example, a central color value is determined according to the size of the inhibition rate, and then the color value of each position away from the center is determined based on the Gaussian kernel function. The color value is related to the distance, and a gradual process is achieved. Finally, the radiation layers of all inhibition rates containing the position label of the same time label are superimposed, and the superimposed result is used as the inhibition rate distribution. The obtained inhibition rate distribution is the inhibition rate distribution of each time. Intuitively, there is a layer for each time.

[0101] Regarding step S300, the step of acquiring the actual growth parameter of the crop at a time and correcting the inhibition rate distribution of each time according to the actual growth parameter includes:

[0102] The remote sensing image of the water and fertilizer management area is acquired at a time, and the remote sensing image is input into the trained crop recognition model to determine the actual growth parameter of the recognizable crop. The actual growth parameter is used to represent the change of the crop in the remote sensing image at the adjacent time.

[0103] The inhibition rate of the recognizable crop in the time interval between the current remote sensing image and the previous remote sensing image is obtained, and the theoretical growth parameter is simulated according to the inhibition rate. The mapping relationship between the inhibition rate and the theoretical growth parameter is set in advance.

[0104] The difference rate is calculated by comparing the actual growth parameter and the theoretical growth parameter.

[0105] The theoretical growth parameter of all crops is corrected according to the difference rate, and the corrected inhibition rate is obtained reversely based on the corrected theoretical growth parameter.

[0106] The position of the recognizable crop is obtained, and the inhibition rate distribution is corrected according to the corrected inhibition rate.

[0107] In an example of the technical solution of the present application, the inhibition rate distribution is corrected, the remote sensing image of the water and fertilizer management area is obtained at regular intervals, the remote sensing image is input into the trained crop recognition model to determine the actual growth parameters of the recognizable crops. This process is a conventional scheme for identifying crop growth using remote sensing. Existing schemes can be used, and the actual growth parameters can only represent the changes in the remote sensing image at adjacent times. On this basis, the inhibition rate of the recognizable crops in the time interval between the current remote sensing image and the previous remote sensing image is obtained. The theoretical growth parameters are simulated according to the inhibition rate. The mapping relationship between the inhibition rate and the theoretical growth parameters is set in advance, which is generally a functional relationship. The difference rate is calculated by comparing the actual growth parameters and the theoretical growth parameters.

[0108] Regarding the application process of the difference rate, the technical solution of the present application is not directly applied to the inhibition rate. For the inhibition rate that needs to be corrected, it is first converted into a theoretical growth parameter according to the preset mapping relationship. The theoretical growth parameter is corrected by the difference rate to make it closer to the actual growth parameter. Then, the inhibition rate is converted into the inhibition rate by the mapping relationship in reverse. This correction process is more accurate because the difference rate calculated is actually a numerical value. Using it to correct the data is simply multiplying the data. The relationship between the inhibition rate and the growth parameter may be nonlinear. Directly multiplying the difference rate (or linear adjustment) by the inhibition rate is prone to errors.

[0109] Regarding step S400, the step of superimposing the corrected inhibition rate distribution to determine the water and fertilizer management work order includes:

[0110] Receiving the time period input by the management personnel;

[0111] Obtaining all the corrected inhibition rate distribution in the time period, superimposing the corrected inhibition rate distribution;

[0112] Contour recognition is performed on the superimposed corrected inhibition rate distribution to obtain sub-regions;

[0113] For each sub-region, the water and fertilizer demand is determined according to the value of the pixel points in the sub-region;

[0114] Based on each sub-region and its water and fertilizer demand, the water and fertilizer management work order is determined.

[0115] In an example of the technical solution of the present application, the generation process of the management work order is described, a time period input by a management personnel is received, the actual meaning of the time period is how long to manage once, such as three days, all the modified inhibition rate distribution situations in the time period are obtained, the modified inhibition rate distribution situations are superimposed, this superposition is to superimpose the inhibition rate distribution situations at different times together, since a large number of inhibition rate distribution situations are involved, the color values at each position will be very large, which is out of physical meaning, therefore, in actual application, the total number of inhibition rate distribution situations (total number of time points) in the time period is obtained, for any inhibition rate distribution situation, each color value in the inhibition rate distribution situation is divided by the total number, which is equivalent to adjusting, so that the superimposed inhibition rate distribution situation is still within the preset range.

[0116] Figure 2 The composition structure block diagram of the intelligent water and fertilizer management operation system, in the embodiment of the present application, an intelligent water and fertilizer management operation system, the system 10 comprises:

[0117] The point setting module 11 is used for obtaining the area parameters of the water and fertilizer management area, and determining the sampling point and its motion trajectory according to the area parameters.

[0118] The inhibition rate analysis module 12 is used for obtaining the environmental data in real time according to the sensor installed at the sampling point, and determining the inhibition rate distribution situation of the water and fertilizer management area at each time according to the environmental data; the inhibition rate is used for representing the inhibition degree of the environmental data on the crop growth process, and the inhibition rate distribution situation is represented by a layer.

[0119] The inhibition rate correction module 13 is used for obtaining the actual growth parameters of the crop at regular time intervals, and correcting the inhibition rate distribution situation at each time according to the actual growth parameters.

[0120] The management work order generation module 14 is used for superimposing the corrected inhibition rate distribution situation, and determining the management work order of the water and fertilizer; the management work order comprises the water and fertilizer amount of the water and fertilizer management area.

[0121] Further, the point setting module 11 comprises:

[0122] The motion trajectory positioning unit is used for obtaining the remote sensing image of the water and fertilizer management area at regular time intervals, and positioning the motion trajectory according to the remote sensing image.

[0123] The analysis point selection unit is used for selecting the to-be-analyzed point in the water and fertilizer management area based on the preset density.

[0124] The exposure calculation unit is used for calculating the distance between any to-be-analyzed point and each motion trajectory, and determining the exposure of each to-be-analyzed point according to the distance.

[0125] The point position merging unit is configured to compare exposure degrees of adjacent to-be-analyzed point positions in sequence, and merge two to-be-analyzed point positions when a difference between the exposure degrees is less than a preset tolerance.

[0126] The sampling area construction unit is configured to obtain a sampling area composed of to-be-analyzed point positions after all to-be-analyzed point positions are compared with adjacent to-be-analyzed point positions.

[0127] The trajectory generation unit is configured to set at least one sampling point position in the sampling area, divide the sampling area according to a sampling range of the sampling point position, and generate a motion trajectory.

[0128] Specifically, the inhibition rate analysis module 12 includes:

[0129] The environmental data acquisition unit is configured to acquire environmental data containing time labels and position labels in real time according to sensors installed at sampling point positions; the environmental data at least includes illumination parameters, air parameters, and soil components.

[0130] The demand data acquisition unit is configured to acquire a crop planting duration, and acquire demand data according to the crop planting duration.

[0131] The data comparison unit is configured to compare the demand data and the environmental data, and determine an inhibition rate containing time labels and position labels according to a comparison result; wherein an initial time node of the crop planting duration is updated in a timely manner.

[0132] The statistical output unit is configured to statistically analyze all inhibition rates containing position labels of a same time label, and obtain an inhibition rate distribution containing time labels.

[0133] Further, the inhibition rate correction module 13 includes:

[0134] The actual parameter acquisition unit is configured to acquire remote sensing images of a water and fertilizer management area in a timely manner, input the remote sensing images into a trained crop recognition model, and determine actual growth parameters of recognizable crops; the actual growth parameters are used to represent change of the crops in adjacent time instant remote sensing images.

[0135] The theoretical parameter determination unit is configured to acquire an inhibition rate of recognizable crops in a time interval between a current remote sensing image and a previous remote sensing image, and simulate a theoretical growth parameter according to the inhibition rate; a mapping relationship between the inhibition rate and the theoretical growth parameter is preset.

[0136] The difference rate calculation unit is configured to compare the actual growth parameter and the theoretical growth parameter, and calculate a difference rate.

[0137] The correction execution unit is configured to correct the theoretical growth parameter of all crops according to the difference rate, and inversely acquire a corrected inhibition rate based on the corrected theoretical growth parameter.

[0138] A distribution correction unit acquires the position of the identifiable crop and corrects the suppression rate distribution based on the corrected suppression rate.

[0139] The above descriptions are only the preferred embodiments of the present application and are not intended to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. An intelligent water and fertilizer management method, characterized in that, The method comprises: Obtaining the area parameters of the water and fertilizer management area, and determining the sampling points and their motion trajectories according to the area parameters; Real-time acquisition of environmental data by sensors installed at the sampling points, and determination of the inhibition rate distribution of the water and fertilizer management area at each time according to the environmental data; the inhibition rate is used to represent the inhibition degree of the environmental data on the crop growth process, and the inhibition rate distribution is represented by a layer; Timely acquisition of the actual growth parameters of the crops, and correction of the inhibition rate distribution at each time according to the actual growth parameters; Superimposition of the corrected inhibition rate distribution to determine the management work order of the water and fertilizer; the management work order includes the water and fertilizer amount for each area; The step of real-time acquisition of environmental data by sensors installed at the sampling points, and determination of the inhibition rate distribution of the water and fertilizer management area at each time according to the environmental data comprises: Real-time acquisition of environmental data containing time labels and location labels by sensors installed at the sampling points; the environmental data at least includes illumination parameters, air parameters and soil composition; Acquisition of the crop planting duration, and acquisition of the demand data according to the crop planting duration; Comparison of the demand data and the environmental data, and determination of the inhibition rate containing time labels and location labels according to the comparison result; wherein the initial time node of the crop planting duration is updated in time; Statistical analysis of all inhibition rates containing location labels of the same time label to obtain the inhibition rate distribution containing time labels; The step of statistical analysis of all inhibition rates containing location labels of the same time label to obtain the inhibition rate distribution containing time labels comprises: Statistical analysis of the obtained inhibition rates containing time labels and location labels; Two-by-two comparison of the time labels of the inhibition rates, calculation of the time difference, and setting the time labels of the inhibition rates as the same time label when the time difference is less than a preset difference threshold; Statistical analysis of all inhibition rates containing location labels of the same time label, and creation of a radiation layer centered on the location label; Superimposition of all radiation layers of the location labels, and setting the superimposition result as the inhibition rate distribution; The step of timely acquisition of the actual growth parameters of the crops, and correction of the inhibition rate distribution at each time according to the actual growth parameters comprises: Timely acquisition of remote sensing images of the water and fertilizer management area, input of the remote sensing images into a trained crop recognition model, and determination of the actual growth parameters of the identifiable crops; the actual growth parameters are used to represent the change of the crops in the adjacent time remote sensing images; Acquisition of the inhibition rates of the identifiable crops in the time interval between the current remote sensing image and the previous remote sensing image, and simulation of the theoretical growth parameters according to the inhibition rates; the mapping relationship between the inhibition rates and the theoretical growth parameters is preset; Comparison of the actual growth parameters and the theoretical growth parameters, and calculation of the difference rate; Correction of the theoretical growth parameters of all crops according to the difference rate, and reverse acquisition of the corrected inhibition rates based on the corrected theoretical growth parameters; Acquisition of the location of the identifiable crops, and correction of the inhibition rate distribution according to the corrected inhibition rates. 2.The intelligent water and fertilizer management method according to claim 1, characterized in that, The step of obtaining the area parameters of the water and fertilizer management area, and determining the sampling points and their motion trajectories according to the area parameters comprises: Timing acquisition of remote sensing images of the water and fertilizer management area, and positioning of the passing track according to the remote sensing images; Selecting a to-be-analyzed point in the water and fertilizer management area based on a preset density; For any to-be-analyzed point, calculating the distance between the to-be-analyzed point and each passing track, and determining the exposure of each to-be-analyzed point according to the distance; Sequentially comparing the exposure of adjacent to-be-analyzed points, and merging two to-be-analyzed points when the difference in exposure is less than a preset tolerance; After all to-be-analyzed points are compared with adjacent to-be-analyzed points, a sampling area composed of to-be-analyzed points is obtained; Setting at least one sampling point in the sampling area, and cutting the sampling area according to the sampling range of the sampling point to generate a motion track. 3.The intelligent water and fertilizer management method according to claim 1, characterized in that, The step of superimposing the corrected inhibition rate distribution to determine the water and fertilizer management work order includes: Receiving a time period input by a management personnel; Acquiring all corrected inhibition rate distributions in the time period, and superimposing the corrected inhibition rate distributions; Performing contour recognition on the superimposed corrected inhibition rate distribution to obtain a sub-area; For each sub-area, determining the water and fertilizer demand according to the numerical value of the pixel points in the sub-area; Determining the water and fertilizer management work order based on each sub-area and the water and fertilizer demand thereof.

4. An intelligent water and fertilizer management operation system, characterized in that, The operating system is used to implement the intelligent water and fertilizer management method according to any one of claims 1 to 3, and the operating system comprises: A point setting module configured to acquire area parameters of a water and fertilizer management area, and determine a sampling point and a motion track thereof according to the area parameters; An inhibition rate analysis module configured to acquire environmental data in real time according to a sensor installed at the sampling point, and determine an inhibition rate distribution of the water and fertilizer management area at each time point according to the environmental data; the inhibition rate is used to represent the inhibition degree of the environmental data on the growth process of crops, and the inhibition rate distribution is represented by a layer; An inhibition rate correction module configured to acquire actual growth parameters of crops in real time, and correct the inhibition rate distribution at each time point according to the actual growth parameters; A management work order generation module configured to superimpose the corrected inhibition rate distribution to determine a water and fertilizer management work order; the management work order comprises a water and fertilizer amount for a specific area; The inhibition rate analysis module comprises: An environmental data acquisition unit configured to acquire environmental data containing time labels and location labels in real time according to a sensor installed at the sampling point; the environmental data at least includes illumination parameters, air parameters and soil composition; A demand data acquisition unit configured to acquire a crop planting duration, and acquire demand data according to the crop planting duration; A data comparison unit configured to compare the demand data and the environmental data, and determine an inhibition rate containing time labels and location labels according to the comparison result; wherein, an initial time node of the crop planting duration is updated in real time; A statistical output unit configured to statistically obtain all inhibition rates containing location labels of the same time label to obtain an inhibition rate distribution containing time labels; The content of the statistical output unit includes: The statistically obtained inhibition rate containing time labels and location labels; The time labels of the inhibition rates are compared pairwise, a time difference is calculated, and when the time difference is less than a preset difference threshold, the time label of the inhibition rate is taken as a same time label; All inhibition rates containing position labels of the same time label are counted, and a radiation layer is created with the position label as a center; Radiation layers of all position labels are superimposed, and a superimposition result is taken as an inhibition rate distribution situation; The inhibition rate correction module comprises: An actual parameter acquisition unit is configured to acquire a remote sensing image of the water and fertilizer management area at a regular time, input the remote sensing image into a trained crop recognition model, and determine an actual growth parameter of the recognizable crop; the actual growth parameter is used to represent a change of the crop in a remote sensing image at a neighboring time; A theoretical parameter determination unit is configured to acquire an inhibition rate of the recognizable crop in a time interval between a current remote sensing image and a previous remote sensing image, and simulate a theoretical growth parameter according to the inhibition rate; a mapping relationship between the inhibition rate and the theoretical growth parameter is preset; A difference rate calculation unit is configured to compare the actual growth parameter and the theoretical growth parameter, and calculate a difference rate; A correction execution unit is configured to correct the theoretical growth parameter of all crops according to the difference rate, and inversely acquire a corrected inhibition rate based on the corrected theoretical growth parameter; A distribution situation correction unit is configured to acquire a position of the recognizable crop, and correct an inhibition rate distribution situation according to the corrected inhibition rate. 5.The intelligent water and fertilizer management operation system according to claim 4, characterized in that, The point setting module comprises: A passing trajectory positioning unit is configured to acquire a remote sensing image of the water and fertilizer management area at a regular time, and position a passing trajectory according to the remote sensing image; An analysis point selection unit is configured to select a to-be-analyzed point in the water and fertilizer management area based on a preset density; An exposure degree calculation unit is configured to calculate a distance between any to-be-analyzed point and each passing trajectory, and determine an exposure degree of each to-be-analyzed point according to the distance; A point merging unit is configured to compare exposure degrees of adjacent to-be-analyzed points in sequence, and merge two to-be-analyzed points when a difference between the exposure degrees is less than a preset tolerance; A sampling area construction unit is configured to obtain a sampling area composed of to-be-analyzed points after all to-be-analyzed points are compared with adjacent to-be-analyzed points; A trajectory generation unit is configured to set at least one sampling point in the sampling area, divide the sampling area according to a sampling range of the sampling point, and generate a motion trajectory.

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