CN117837366B - Agricultural supervision platform based on agricultural Internet of things - Google Patents
Agricultural supervision platform based on agricultural Internet of things Download PDFInfo
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- 238000005507 spraying Methods 0.000 claims abstract description 126
- 239000003814 drug Substances 0.000 claims abstract description 45
- 230000004720 fertilization Effects 0.000 claims abstract description 26
- 239000007921 spray Substances 0.000 claims abstract description 13
- 238000000889 atomisation Methods 0.000 claims abstract description 8
- 238000004891 communication Methods 0.000 claims abstract description 8
- 238000000034 method Methods 0.000 claims description 36
- 239000000575 pesticide Substances 0.000 claims description 29
- 238000004364 calculation method Methods 0.000 claims description 9
- CURLTUGMZLYLDI-UHFFFAOYSA-N Carbon dioxide Chemical compound O=C=O CURLTUGMZLYLDI-UHFFFAOYSA-N 0.000 claims description 8
- 229910002092 carbon dioxide Inorganic materials 0.000 claims description 4
- 239000001569 carbon dioxide Substances 0.000 claims description 4
- 239000002689 soil Substances 0.000 claims description 4
- 239000011159 matrix material Substances 0.000 claims description 3
- 241000196324 Embryophyta Species 0.000 abstract description 13
- 239000003337 fertilizer Substances 0.000 abstract description 8
- 230000000694 effects Effects 0.000 abstract description 6
- 239000002699 waste material Substances 0.000 abstract description 5
- 241000592344 Spermatophyta Species 0.000 abstract description 4
- 238000009826 distribution Methods 0.000 abstract description 3
- 238000010191 image analysis Methods 0.000 abstract description 3
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 240000006394 Sorghum bicolor Species 0.000 description 1
- 235000011684 Sorghum saccharatum Nutrition 0.000 description 1
- 241000209140 Triticum Species 0.000 description 1
- 235000021307 Triticum Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 235000013339 cereals Nutrition 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 210000003608 fece Anatomy 0.000 description 1
- 239000010871 livestock manure Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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Abstract
The invention discloses an agricultural supervision platform based on an agricultural Internet of things, which comprises a server, a marking rod and an unmanned aerial vehicle in communication connection with the server; the unmanned aerial vehicle is provided with a camera and an atomization medicine spraying device; the number of the identification rods is a plurality; the agricultural supervision platform based on the agricultural Internet of things can shoot the acquired images of all subareas of a planting field through the unmanned aerial vehicle, and carry out image analysis on the acquired images to determine the growth conditions of plants corresponding to all subareas, so as to determine the fertilization spraying quantity corresponding to all subareas, then control the unmanned aerial vehicle to fly to the upper air of all the acquisition points in sequence, and spray fertilization according to the corresponding fertilization spraying quantity through the atomization spraying device at the upper air of all the acquisition points; compared with the existing mode of uniformly spraying and fertilizing, the scheme provided by the invention can carry out targeted fertilization aiming at the growth distribution condition of the seed plants in the planting field, avoid fertilizer waste and promote fertilization effect.
Description
Technical Field
The invention relates to the technical field of agricultural Internet of things, in particular to an agricultural supervision platform based on the agricultural Internet of things.
Background
Agriculture refers to an important industry in national economy, agriculture includes five industrial forms of planting industry, forestry industry, animal husbandry, fishery industry and auxiliary industry, and narrow-sense agriculture refers to planting industry, including production activities of crops such as grain crops, cash crops, feed crops, green manure and the like. Agriculture is a basic industry for supporting national economy construction and development, and modern agriculture based on digitization and informatization is a development direction of future agriculture.
The current supervision platform of the agricultural Internet of things only can collect and check various data (such as temperature, humidity and the like) in a planting field, and the collected data is not fully utilized; the existing agricultural supervision platform can realize that the remote control unmanned aerial vehicle flies to automatically spray and fertilize (namely water-soluble fertilizer), but the existing unmanned aerial vehicle fertilizer spraying operation adopts a uniform spraying mode, and can not pertinently spray and fertilize aiming at the growth condition of the seed plants in the planting field, so that fertilizer waste is easily caused, and the fertilizer effect is poor.
Disclosure of Invention
The invention mainly aims to provide an agricultural supervision platform based on the agricultural Internet of things, and aims to solve the problems that the existing agricultural supervision platform cannot conduct targeted spraying fertilization aiming at the growth condition of plants in a planting field, fertilizer waste is easily caused, and the fertilization effect is poor.
The technical scheme provided by the invention is as follows:
An agricultural supervision platform based on the agricultural Internet of things comprises a server, a marking rod and an unmanned aerial vehicle which is in communication connection with the server; the unmanned aerial vehicle is provided with a camera and an atomization medicine spraying device; the number of the marking rods is multiple, the marking rods are distributed in a matrix and arranged in the planting field, marking rods are arranged at the edges and four corners of the planting field to divide the planting field into a plurality of subareas, the subareas are square, and the marking rods are arranged at the four corners of the subareas; the identification rod is provided with a position sensor in communication connection with the server; the server is used for: acquiring position data of each subarea through a position sensor, and determining an acquisition point corresponding to each subarea based on the position data of each subarea, wherein the acquisition point is a center point of each subarea; controlling the unmanned aerial vehicle to fly to the upper air of each acquisition point in sequence, and shooting corresponding acquisition images at the upper air of each acquisition point; determining the fertilizing and spraying amount corresponding to each subarea based on the acquired image; the unmanned aerial vehicle is controlled to fly to the upper air of each collecting point in sequence, and spraying fertilization is carried out by the atomizing pesticide spraying device according to the corresponding fertilization spraying amount above each collecting point.
Preferably, the spraying coverage area of the atomizing and spraying device is rectangular, and the spraying range of the atomizing and spraying device can just cover the sub-area; the server is further configured to:
The server acquires RGB values corresponding to the plants in the manually input acquisition image and marks the RGB values as target RGB values; the server sequentially performs image recognition on each acquired image: the method comprises the steps of obtaining actual RGB values of all pixel points in an acquisition image, marking the pixel points meeting preset requirements between the actual RGB values and target RGB values in the acquisition image as target pixel points, obtaining the number of target pixel points in the acquisition image, and determining fertilizing and spraying amounts corresponding to subareas based on the number of the target pixel points in the acquisition image, wherein the pixel points in each acquisition image are consistent, and the target pixel points are pixel points corresponding to plants.
Preferably, the server is further configured to:
the server calculates a first difference value, a second difference value and a third difference value corresponding to each pixel point based on the target RGB value and the actual RGB value of each pixel point in the acquired image:
，
，
，
In the method, in the process of the invention, The method comprises the steps of obtaining a first difference value corresponding to a jth pixel point in an ith acquired image; /(I)The second difference value corresponding to the jth pixel point in the ith acquired image is obtained; /(I)A third difference value corresponding to a j-th pixel point in the i-th acquired image; i is a positive integer, i is less than or equal to N, and N is the total number of acquired images; j is a positive integer, i is less than or equal to M, and M is the total number of pixel points in the acquired image; /(I)R component of RGB value of jth pixel point in ith collected image; /(I)R component being the target RGB value; G component of RGB value of jth pixel point in ith collected image; /(I) G component which is the target RGB value; b component of RGB value of jth pixel point in ith collected image; /(I) B component being the target RGB value;
The server calculates a color difference value corresponding to each pixel point based on the first difference value, the second difference value and the third difference value corresponding to each pixel point:
，
In the method, in the process of the invention, The color difference value corresponding to the jth pixel point in the ith acquired image is obtained;
And the server determines the pixel point with the color difference value smaller than the preset value as a target pixel point.
Preferably, the server is further configured to:
the server obtains the fertilizing and spraying amount corresponding to the subarea corresponding to the most acquired image of the target pixel point, and marks the fertilizing and spraying amount as the preset spraying amount;
the server marks the number of the target pixel points of the acquired image with the largest number of the target pixel points as a reference number;
the server calculates fertilization spraying amount corresponding to each subarea based on the preset spraying amount and the reference amount and the amount of target pixel points in each acquired image:
，
In the method, in the process of the invention, The fertilizing and spraying amount corresponding to the subarea corresponding to the ith acquired image is determined; /(I)The spraying quantity is preset; Is the reference number; /(I) The number of target pixel points in the ith acquired image.
Preferably, the server is further configured to: the method comprises the steps of obtaining pesticide spraying flight data of an unmanned aerial vehicle in the past within a first preset time period, wherein the pesticide spraying flight data comprise pesticide carrying capacity and power consumption rate corresponding to the pesticide carrying capacity, the pesticide carrying capacity is the weight of a pesticide carried by the unmanned aerial vehicle, and the power consumption rate is the power consumption of the unmanned aerial vehicle in unit time when the unmanned aerial vehicle carries the pesticide to fly at a preset speed; the load and power consumption prediction model is built, the medicine carrying capacity is used as an input variable of the load and power consumption prediction model, and the power consumption rate corresponding to the medicine carrying capacity is used as an output variable of the load and power consumption prediction model so as to train the load and power consumption prediction model; the method comprises the steps of obtaining initial medicament carrying capacity of an unmanned aerial vehicle before spraying, calculating estimated power consumption corresponding to the unmanned aerial vehicle when the unmanned aerial vehicle executes flying spraying operation according to a first track and a second track respectively based on an initial medicament carrying capacity and a load power consumption prediction model, marking the track with smaller estimated power consumption in the first track and the second track as a target track, and controlling the unmanned aerial vehicle to execute flying spraying operation according to the target track, wherein the first track is the unmanned aerial vehicle to spray the sub-area with larger spraying quantity preferentially, and the second track is the unmanned aerial vehicle to spray the pesticide sequentially according to the position arrangement sequence of each sub-area.
Preferably, the server is further configured to:
the server sorts all the subregions according to the sequence from the big to the small of the corresponding spraying amount, and the sequence numbers are [ P1, P2, ], pk, P (k+1), P (k+2),. The main, PK ], the Pk subregion represents the subregion with the big spraying amount, and K is the total number of subregions in the planting field;
The method comprises the steps that a server obtains time required by the unmanned aerial vehicle to fly from an initial position to the central position of a P1 sub-area according to a preset speed, and the time is marked as a 1 st first time;
The server sequentially acquires the time length required by the unmanned aerial vehicle to fly from the central position of the Pk sub-area to the central position of the P (k+1) sub-area according to the preset speed, and marks the time length as the k+1 first time length;
the server sequentially acquires the corresponding rotation stopping time length when the unmanned aerial vehicle performs the spraying operation at the center position of the Pk sub-area, and marks the rotation stopping time length as the kth second time length;
the server inputs the actual medicine carrying capacity of the unmanned aerial vehicle to a load and power consumption prediction model when the unmanned aerial vehicle flies to the central position of the Pk sub-area and the medicine spraying operation is not started, so as to obtain the Pk first estimated power consumption rate;
the server inputs the average carrying capacity of the unmanned aerial vehicle before spraying and after spraying in the central position of the Pk sub-area into a load and power consumption prediction model to obtain a Pk second estimated power consumption rate;
The server generates estimated power consumption of the first track based on the first duration, the first estimated power consumption rate, the second duration, and the second estimated power consumption rate.
Preferably, the calculation formula for generating the estimated power consumption of the first track by the server based on the first duration, the first estimated power consumption rate, the second duration and the second estimated power consumption rate is as follows:
，
In the method, in the process of the invention, The estimated power consumption of the first track; /(I)Is the first duration of the Pk; /(I)The power consumption rate is estimated for the first Pk; /(I)Is the Pk second duration; /(I)The power consumption rate is estimated for the second Pk.
Preferably, the server is further configured to:
the server sorts the subregions from left to right and from top to bottom in the top view, and the arrangement sequence numbers are [ F1, F2 ], fk, F (k+1), F (k+2), -FK, fk ] the subregion represents the subregion with the sequence of K, and K is the total number of subregions in the planting field;
The server obtains the time length required by the unmanned aerial vehicle to fly from the initial position to the central position of the F1 sub-area according to the preset speed, and marks the time length as the 1 st third time length;
The server sequentially acquires the time length required by the unmanned aerial vehicle to fly from the central position of the Fk sub-area to the central position of the F (k+1) sub-area according to the preset speed, and marks the time length as the k+1 third time length;
The server sequentially acquires the corresponding rotation stopping time length when the unmanned aerial vehicle performs the spraying operation at the central position of the Fk sub-area, and marks the rotation stopping time length as the k fourth time length;
The server inputs the actual medicine carrying capacity of the unmanned aerial vehicle to a load and power consumption prediction model when the unmanned aerial vehicle flies to the central position of the Fk sub-area and the medicine spraying operation is not started, so as to obtain Fk third estimated power consumption rate;
the server inputs the average carrying capacity of the unmanned aerial vehicle before spraying and after spraying to the central position of the Fk sub-area into a load power consumption prediction model to obtain Fk fourth estimated power consumption rate;
the server generates an estimated power consumption rate of the second track based on the third duration, the third estimated power consumption rate, the fourth duration, and the fourth estimated power consumption rate.
Preferably, the calculation formula for generating the estimated power consumption rate of the second track by the server based on the third duration, the third estimated power consumption rate, the fourth duration and the fourth estimated power consumption rate is as follows:
，
In the method, in the process of the invention, The estimated power consumption of the second track; /(I)Is the Fk third duration; /(I)The power consumption rate is estimated for the Fk third time; /(I)A fourth time period of Fk; /(I)The power consumption rate is estimated for the Fk fourth.
Preferably, the system further comprises an environment acquisition module which is communicated with the server and is arranged at the planting field; the environment acquisition module comprises a carbon dioxide sensor, a soil moisture sensor, a temperature sensor, a humidity sensor and an illuminance sensor.
Through the technical scheme, the following beneficial effects can be realized:
The agricultural supervision platform based on the agricultural Internet of things provided by the invention can shoot the acquired images of all the subareas of the planting field through the unmanned aerial vehicle, and carry out image analysis on the acquired images to determine the growth conditions of the plants corresponding to all the subareas, so as to determine the fertilization spraying quantity corresponding to all the subareas, then control the unmanned aerial vehicle to fly to the upper part of all the acquisition points in sequence, and spray fertilization is carried out by an atomization spraying device according to the corresponding fertilization spraying quantity above all the acquisition points; compared with the existing mode of uniformly spraying and fertilizing, the scheme provided by the invention can carry out targeted fertilization aiming at the growth distribution condition of the seed plants in the planting field, avoid fertilizer waste and promote fertilization effect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to the structures shown in these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of an embodiment of an agricultural supervision platform based on the agricultural internet of things.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The invention provides an agricultural supervision platform based on an agricultural Internet of things.
As shown in fig. 1, in an embodiment of an agricultural supervision platform based on the agricultural internet of things provided by the invention, the agricultural supervision platform based on the agricultural internet of things comprises a server, a marking rod and an unmanned aerial vehicle in communication connection with the server; the unmanned aerial vehicle is provided with a camera and an atomization medicine spraying device; the number of the marking rods is multiple, the marking rods are distributed in a matrix and arranged in a planting field (such as a sorghum, wheat or rice planting field), marking rods are arranged at the edges and four corners of the planting field so as to divide the planting field into a plurality of subareas, the subareas are square, and the marking rods are arranged at the four corners of the subareas; the identification rod is provided with a position sensor in communication connection with the server; the server is used for: acquiring position data of each subarea through a position sensor, and determining an acquisition point corresponding to each subarea based on the position data of each subarea, wherein the acquisition point is a center point of each subarea; controlling the unmanned aerial vehicle to fly to the upper air of each acquisition point in sequence, and shooting corresponding acquisition images at the upper air of each acquisition point; determining the corresponding fertilizing and spraying amount of each subarea based on the acquired images (specifically, determining the number of plants in different subareas by acquiring the images, further determining different fertilizing and spraying amounts based on different plant numbers, wherein the more the plants are, the more the crops in the subarea grow, the corresponding amount of needed fertilization is smaller, and otherwise, the more the crops in the subarea grow, the larger the corresponding amount of needed fertilization is; the unmanned aerial vehicle is controlled to fly to the upper air of each collecting point in sequence, and spraying fertilization is carried out by the atomizing pesticide spraying device according to the corresponding fertilization spraying amount above each collecting point.
The agricultural supervision platform based on the agricultural Internet of things provided by the invention can shoot the acquired images of all the subareas of the planting field through the unmanned aerial vehicle, and carry out image analysis on the acquired images to determine the growth conditions of the plants corresponding to all the subareas, so as to determine the fertilization spraying quantity corresponding to all the subareas, then control the unmanned aerial vehicle to fly to the upper part of all the acquisition points in sequence, and spray fertilization is carried out by an atomization spraying device according to the corresponding fertilization spraying quantity above all the acquisition points; compared with the existing mode of uniformly spraying and fertilizing, the scheme provided by the invention can carry out targeted fertilization aiming at the growth distribution condition of the seed plants in the planting field, avoid fertilizer waste and promote fertilization effect.
In addition, the server is further configured to:
The server acquires RGB values corresponding to the plants in the manually input acquisition image and marks the RGB values as target RGB values; the server sequentially performs image recognition on each acquired image: the method comprises the steps of obtaining actual RGB values of all pixel points in an acquisition image, marking the pixel points meeting preset requirements between the actual RGB values and target RGB values in the acquisition image as target pixel points (the preset requirements are that the actual RGB values are close to the target RGB values, and a specific calculation mode refers to a follow-up embodiment) so as to obtain the number of the target pixel points in the acquisition image, and determining fertilizing and spraying amounts corresponding to subareas based on the number of the target pixel points in the acquisition image, wherein the pixel points in each acquisition image are consistent, and the target pixel points are the pixel points corresponding to plants.
The present embodiment gives a specific scheme of how to determine the amount of spray corresponding to each sub-area based on the acquired image.
In addition, the server is further configured to:
the server calculates a first difference value, a second difference value and a third difference value corresponding to each pixel point based on the target RGB value and the actual RGB value of each pixel point in the acquired image:
，
，
，
In the method, in the process of the invention, The method comprises the steps of obtaining a first difference value corresponding to a jth pixel point in an ith acquired image; /(I)The second difference value corresponding to the jth pixel point in the ith acquired image is obtained; /(I)A third difference value corresponding to a j-th pixel point in the i-th acquired image; i is a positive integer, i is less than or equal to N, and N is the total number of acquired images; j is a positive integer, i is less than or equal to M, and M is the total number of pixel points in the acquired image; /(I)R component of RGB value of jth pixel point in ith collected image; /(I)R component being the target RGB value; G component of RGB value of jth pixel point in ith collected image; /(I) G component which is the target RGB value; b component of RGB value of jth pixel point in ith collected image; /(I) Is the B component of the target RGB value.
The server calculates a color difference value corresponding to each pixel point based on the first difference value, the second difference value and the third difference value corresponding to each pixel point:
，
In the method, in the process of the invention, And the color difference value corresponding to the jth pixel point in the ith acquired image is obtained.
The server determines a pixel point with a color difference value smaller than a preset value (for example, the preset value is 9, when the color difference value is smaller than the preset value, that is, the color of the corresponding pixel point is close to that of the plant, so that the pixel point is judged as the plant) as a target pixel point.
The present embodiment gives a scheme of how to determine the target pixel in detail.
In addition, the server is further configured to:
the server obtains the fertilizing and spraying amount corresponding to the subarea corresponding to the most acquired image of the target pixel point, and marks the fertilizing and spraying amount as the preset spraying amount;
the server marks the number of the target pixel points of the acquired image with the largest number of the target pixel points as a reference number;
the server calculates fertilization spraying amount corresponding to each subarea based on the preset spraying amount and the reference amount and the amount of target pixel points in each acquired image:
，
In the method, in the process of the invention, The fertilizing and spraying amount corresponding to the subarea corresponding to the ith acquired image is determined; /(I)The spraying quantity is preset; Is the reference number; /(I) The number of target pixel points in the ith acquired image.
The embodiment provides a specific scheme of how to determine the spraying quantity corresponding to the subareas based on the number of target pixel points in the acquired image.
In addition, the server is further configured to: acquiring pesticide spraying flight data of the unmanned aerial vehicle in the past within a first preset time period (for example, half a year), wherein the pesticide spraying flight data comprise a pesticide carrying capacity and a power consumption rate corresponding to the pesticide carrying capacity, the pesticide carrying capacity is the weight of a pesticide carried by the unmanned aerial vehicle, and the power consumption rate is the power consumption of the unmanned aerial vehicle in a unit time (for example, 1 second) when the unmanned aerial vehicle carries the pesticide to fly at a preset speed (for example, 1 meter per second); the load and power consumption prediction model is built, the medicine carrying capacity is used as an input variable of the load and power consumption prediction model, and the power consumption rate corresponding to the medicine carrying capacity is used as an output variable of the load and power consumption prediction model so as to train the load and power consumption prediction model; the method comprises the steps of obtaining initial medicament carrying capacity of an unmanned aerial vehicle before spraying, calculating estimated power consumption corresponding to the unmanned aerial vehicle when the unmanned aerial vehicle executes flying spraying operation according to a first track and a second track respectively based on an initial medicament carrying capacity and a load power consumption prediction model, marking the track with smaller estimated power consumption in the first track and the second track as a target track, and controlling the unmanned aerial vehicle to execute flying spraying operation according to the target track, wherein the first track is the unmanned aerial vehicle to spray the sub-area with larger spraying quantity preferentially, and the second track is the unmanned aerial vehicle to spray the pesticide sequentially according to the position arrangement sequence of each sub-area.
Specifically, the server is further configured to:
The server sorts all the subregions according to the sequence from big to small of corresponding spraying amount, and the sequence numbers are [ P1, P2, ], pk, P (k+1), P (k+2),. The sub-regions of the K-th subregion represent the subregion with big spraying amount, and K is the total number of the sub-regions in the planting field.
Specifically, in this embodiment, K is 9, that is, the planting field includes 9 sub-areas, and after the sub-areas are sequenced according to the sequence from the large to the small of the corresponding spraying amount, the sequence numbers of the sub-areas are P1, P2, P3, P4, P5, P6, P7, P8 and P9.
The server obtains the time length required by the unmanned aerial vehicle to fly from the initial position (namely the starting position of the unmanned aerial vehicle) to the central position of the P1 st sub-area (namely the sub-area with the largest spraying amount) according to the preset speed, and marks the time length as the 1 st first time length.
The server sequentially acquires the time length required by the unmanned aerial vehicle to fly from the central position of the Pk sub-area to the central position of the P (k+1) sub-area according to the preset speed, and marks the time length as the k+1 first time length.
For example, the 2 nd first time length is a time length required for the unmanned aerial vehicle to fly from the center position of the P1 st sub-area to the center position of the P2 nd sub-area. The first duration may be calculated based on the preset speed and the distance between the center position of the P1 st sub-area and the center position of the P2 nd sub-area.
The server sequentially acquires the corresponding rotation stopping time length when the unmanned aerial vehicle performs the spraying operation at the center position of the Pk sub-area, and marks the rotation stopping time length as the k second time length.
Specifically, the hovering time is the time for spraying the pesticide by the unmanned aerial vehicle; the kth second duration is the duration consumed by the unmanned aerial vehicle to perform the spraying operation at the center position of the Pk sub-region.
And the server inputs the actual medicine carrying capacity of the unmanned aerial vehicle to a load and power consumption prediction model when the unmanned aerial vehicle flies to the central position of the Pk sub-area and the medicine spraying operation is not started, so as to obtain the Pk first estimated power consumption rate.
Specifically, the first estimated power consumption rate is a power consumption rate corresponding to the first time period (i.e., the time period during which the unmanned aerial vehicle is flying). For example: and inputting the actual medicine carrying capacity (here, the initial medicine carrying capacity) of the unmanned aerial vehicle when the unmanned aerial vehicle flies to the central position of the P1 sub-area and the medicine spraying operation is not started into a load power consumption prediction model to obtain the P1 first estimated power consumption rate.
And the server inputs the average carrying capacity of the unmanned aerial vehicle before spraying and after spraying to the central position of the Pk sub-area into a load-carrying power consumption prediction model to obtain the Pk second estimated power consumption rate.
Specifically, the second estimated power consumption rate is the power consumption rate corresponding to the second duration (i.e. during the hovering and spraying process of the unmanned aerial vehicle). And in the process of spraying the medicine, the medicine carrying capacity of the unmanned aerial vehicle continuously drops, and the average carrying capacity before spraying the medicine and after finishing spraying the medicine is taken as the input variable of the load power consumption prediction model, so that the corresponding power consumption rate is obtained.
The server generates estimated power consumption of the first track based on the first duration, the first estimated power consumption rate, the second duration, and the second estimated power consumption rate.
The embodiment provides how to calculate the estimated power consumption corresponding to the unmanned aerial vehicle when the unmanned aerial vehicle executes the flight spraying operation according to the first track respectively based on the initial medicament carrying capacity and the load power consumption prediction model.
In addition, the calculation formula for generating the estimated power consumption of the first track based on the first duration, the first estimated power consumption rate, the second duration and the second estimated power consumption rate by the server is as follows:
，
In the method, in the process of the invention, The estimated power consumption of the first track; /(I)Is the first duration of the Pk; /(I)The power consumption rate is estimated for the first Pk; /(I)Is the Pk second duration; /(I)The power consumption rate is estimated for the second Pk.
Specifically, the embodiment provides a calculation formula of how to generate the estimated power consumption of the first track based on the first duration, the first estimated power consumption rate, the second duration and the second estimated power consumption rate.
In addition, the server is further configured to:
the server orders the subregions from left to right and from top to bottom in a top view, and the arrangement sequence numbers are [ F1, F2 ], fk, F (k+1), F (k+2), FK, fk subregions represent subregions with the order of K, and K is the total number of subregions in the planting field.
Specifically, in this embodiment, K is 9, that is, the planting field includes 9 sub-areas, and after the sub-areas are sequenced from left to right and from top to bottom in the top view, the sequence numbers of the sub-areas are F1, F2, F3, F4, F5, F6, F7, F8 and F9.
The server obtains the time length required by the unmanned aerial vehicle to fly from the initial position (namely the starting position of the unmanned aerial vehicle) to the central position of the F1 st sub-area (namely the leftmost uppermost sub-area) according to the preset speed, and marks the time length as the 1 st third time length.
The server sequentially acquires the time required by the unmanned aerial vehicle to fly from the central position of the Fk sub-area to the central position of the F (k+1) sub-area according to the preset speed, and marks the time as the k+1 third time.
For example, the 2 nd third duration is a duration required for the unmanned aerial vehicle to fly from the center position of the F1 st sub-area to the center position of the F2 nd sub-area. The third time period may be calculated based on the preset speed and the distance between the center position of the F1 th sub-area and the center position of the F2 nd sub-area.
The server sequentially acquires the corresponding rotation stopping time length when the unmanned aerial vehicle performs the spraying operation at the center position of the Fk sub-area, and marks the rotation stopping time length as the k fourth time length.
Specifically, the hovering time is the time for spraying the pesticide by the unmanned aerial vehicle; the kth fourth time period is the time period consumed by the unmanned aerial vehicle to perform the spraying operation at the center position of the Fk sub-area.
And the server inputs the actual medicine carrying capacity of the unmanned aerial vehicle to a load and power consumption prediction model when the unmanned aerial vehicle flies to the central position of the Fk sub-area and the medicine spraying operation is not started, so as to obtain the Fk third estimated power consumption rate.
Specifically, the third estimated power consumption rate is a power consumption rate corresponding to a third duration (i.e., the duration of the unmanned aerial vehicle in flight). For example: and inputting the actual medicine carrying capacity (here, the initial medicine carrying capacity) of the unmanned aerial vehicle when the unmanned aerial vehicle flies to the center position of the F1 sub-area and the medicine spraying operation is not started into a load power consumption prediction model to obtain the F1 third estimated power consumption rate.
And the server inputs the average carrying capacity of the unmanned aerial vehicle before spraying and after spraying to the central position of the Fk sub-area into a load-carrying power consumption prediction model to obtain the Fk fourth estimated power consumption rate.
Specifically, the fourth estimated power consumption rate is the power consumption rate corresponding to the fourth time period (i.e., the time period during which the unmanned aerial vehicle hovers and sprays the medicine). And in the process of spraying the medicine, the medicine carrying capacity of the unmanned aerial vehicle continuously drops, and the average carrying capacity before spraying the medicine and after finishing spraying the medicine is taken as the input variable of the load power consumption prediction model, so that the corresponding power consumption rate is obtained.
The server generates an estimated power consumption rate of the second track based on the third duration, the third estimated power consumption rate, the fourth duration, and the fourth estimated power consumption rate.
The embodiment provides how to calculate the estimated power consumption corresponding to the unmanned aerial vehicle when the unmanned aerial vehicle executes the flight spraying operation according to the second track based on the initial medicament carrying capacity and the load power consumption prediction model.
In addition, the calculation formula for generating the estimated power consumption rate of the second track based on the third duration, the third estimated power consumption rate, the fourth duration and the fourth estimated power consumption rate by the server is as follows:
，
In the method, in the process of the invention, The estimated power consumption of the second track; /(I)Is the Fk third duration; /(I)The power consumption rate is estimated for the Fk third time; /(I)A fourth time period of Fk; /(I)The power consumption rate is estimated for the Fk fourth.
Specifically, the present embodiment provides a calculation formula of how to generate the estimated power consumption of the second track based on the third duration, the third estimated power consumption rate, the fourth duration, and the fourth estimated power consumption rate.
In addition, the agricultural supervision platform based on the agricultural Internet of things further comprises an environment acquisition module which is communicated with the server and is arranged in a planting field; the environment acquisition module comprises a carbon dioxide sensor, a soil moisture sensor, a temperature sensor, a humidity sensor and an illuminance sensor.
Specifically, the agricultural supervision platform based on the agricultural Internet of things further comprises a display module in communication connection with the server; the display module is used for displaying the carbon dioxide concentration value, the soil water content, the temperature value, the humidity value and the illuminance of the planting field in real time.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and including several instructions for causing a terminal (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are to be protected by the present invention.
Claims (5)
1. The agricultural supervision platform based on the agricultural Internet of things is characterized by comprising a server, an identification rod and an unmanned aerial vehicle which is in communication connection with the server; the unmanned aerial vehicle is provided with a camera and an atomization medicine spraying device; the number of the marking rods is multiple, the marking rods are distributed in a matrix and arranged in the planting field, marking rods are arranged at the edges and four corners of the planting field to divide the planting field into a plurality of subareas, the subareas are square, and the marking rods are arranged at the four corners of the subareas; the identification rod is provided with a position sensor in communication connection with the server; the server is used for: acquiring position data of each subarea through a position sensor, and determining an acquisition point corresponding to each subarea based on the position data of each subarea, wherein the acquisition point is a center point of each subarea; controlling the unmanned aerial vehicle to fly to the upper air of each acquisition point in sequence, and shooting corresponding acquisition images at the upper air of each acquisition point; determining the fertilizing and spraying amount corresponding to each subarea based on the acquired image; controlling the unmanned aerial vehicle to fly to the upper space of each collecting point in sequence, and spraying and fertilizing by an atomization and pesticide spraying device according to the corresponding fertilizing and spraying amount above each collecting point;
The server is further configured to: the method comprises the steps of obtaining pesticide spraying flight data of an unmanned aerial vehicle in the past within a first preset time period, wherein the pesticide spraying flight data comprise pesticide carrying capacity and power consumption rate corresponding to the pesticide carrying capacity, the pesticide carrying capacity is the weight of a pesticide carried by the unmanned aerial vehicle, and the power consumption rate is the power consumption of the unmanned aerial vehicle in unit time when the unmanned aerial vehicle carries the pesticide to fly at a preset speed; the load and power consumption prediction model is built, the medicine carrying capacity is used as an input variable of the load and power consumption prediction model, and the power consumption rate corresponding to the medicine carrying capacity is used as an output variable of the load and power consumption prediction model so as to train the load and power consumption prediction model; acquiring initial medicament carrying capacity of the unmanned aerial vehicle before spraying, calculating estimated power consumption corresponding to the unmanned aerial vehicle when the unmanned aerial vehicle executes flying spraying operation according to a first track and a second track respectively based on an initial medicament carrying capacity and a load power consumption prediction model, marking the track with smaller estimated power consumption in the first track and the second track as a target track, and then controlling the unmanned aerial vehicle to execute flying spraying operation according to the target track, wherein the first track is the unmanned aerial vehicle to spray the sub-area with larger spraying amount preferentially, and the second track is the unmanned aerial vehicle to spray the pesticide sequentially according to the position arrangement sequence of each sub-area;
The server is further configured to:
the server sorts all the subregions according to the sequence from the big to the small of the corresponding spraying amount, and the sequence numbers are [ P1, P2, ], pk, P (k+1), P (k+2),. The main, PK ], the Pk subregion represents the subregion with the big spraying amount, and K is the total number of subregions in the planting field;
The method comprises the steps that a server obtains time required by the unmanned aerial vehicle to fly from an initial position to the central position of a P1 sub-area according to a preset speed, and the time is marked as a 1 st first time;
The server sequentially acquires the time length required by the unmanned aerial vehicle to fly from the central position of the Pk sub-area to the central position of the P (k+1) sub-area according to the preset speed, and marks the time length as the k+1 first time length;
the server sequentially acquires the corresponding rotation stopping time length when the unmanned aerial vehicle performs the spraying operation at the center position of the Pk sub-area, and marks the rotation stopping time length as the kth second time length;
the server inputs the actual medicine carrying capacity of the unmanned aerial vehicle to a load and power consumption prediction model when the unmanned aerial vehicle flies to the central position of the Pk sub-area and the medicine spraying operation is not started, so as to obtain the Pk first estimated power consumption rate;
the server inputs the average carrying capacity of the unmanned aerial vehicle before spraying and after spraying in the central position of the Pk sub-area into a load and power consumption prediction model to obtain a Pk second estimated power consumption rate;
The server generates estimated power consumption of the first track based on the first duration, the first estimated power consumption rate, the second duration and the second estimated power consumption rate;
The calculation formula for generating the estimated power consumption of the first track based on the first duration, the first estimated power consumption rate, the second duration and the second estimated power consumption rate by the server is as follows:
Wherein H 1 is the estimated power consumption of the first track; t 1,Pk is the first duration of the Pk; p 1,Pk is the first estimated power consumption rate of the Pk; t 2,Pk is the Pk second time period; p 2,Pk is the Pk second estimated power consumption rate;
The server is further configured to:
the server sorts the subregions from left to right and from top to bottom in the top view, and the arrangement sequence numbers are [ F1, F2 ], fk, F (k+1), F (k+2), -FK, fk ] the subregion represents the subregion with the sequence of K, and K is the total number of subregions in the planting field;
The server obtains the time length required by the unmanned aerial vehicle to fly from the initial position to the central position of the F1 sub-area according to the preset speed, and marks the time length as the 1 st third time length;
The server sequentially acquires the time length required by the unmanned aerial vehicle to fly from the central position of the Fk sub-area to the central position of the F (k+1) sub-area according to the preset speed, and marks the time length as the k+1 third time length;
The server sequentially acquires the corresponding rotation stopping time length when the unmanned aerial vehicle performs the spraying operation at the central position of the Fk sub-area, and marks the rotation stopping time length as the k fourth time length;
The server inputs the actual medicine carrying capacity of the unmanned aerial vehicle to a load and power consumption prediction model when the unmanned aerial vehicle flies to the central position of the Fk sub-area and the medicine spraying operation is not started, so as to obtain Fk third estimated power consumption rate;
the server inputs the average carrying capacity of the unmanned aerial vehicle before spraying and after spraying to the central position of the Fk sub-area into a load power consumption prediction model to obtain Fk fourth estimated power consumption rate;
the server generates estimated power consumption rate of the second track based on the third duration, the third estimated power consumption rate, the fourth duration and the fourth estimated power consumption rate;
the calculation formula for generating the estimated power consumption rate of the second track based on the third time length, the third estimated power consumption rate, the fourth time length and the fourth estimated power consumption rate by the server is as follows:
Wherein H 2 is the estimated power consumption of the second track; t 3,Pk is Fk third time period; p 3,Pk is the Fk third estimated power consumption; t 4,Pk is the Fk fourth time period; p 4,Pk is the Fk fourth estimated power consumption.
2. The agricultural supervisory platform based on the agricultural internet of things according to claim 1, wherein the server is further configured to:
The server acquires RGB values corresponding to the plants in the manually input acquisition image and marks the RGB values as target RGB values; the server sequentially performs image recognition on each acquired image: the method comprises the steps of obtaining actual RGB values of all pixel points in an acquisition image, marking the pixel points meeting preset requirements between the actual RGB values and target RGB values in the acquisition image as target pixel points, obtaining the number of target pixel points in the acquisition image, and determining fertilizing and spraying amounts corresponding to subareas based on the number of the target pixel points in the acquisition image, wherein the pixel points in each acquisition image are consistent, and the target pixel points are pixel points corresponding to plants.
3. The agricultural supervisory platform based on the agricultural internet of things according to claim 2, wherein the server is further configured to:
the server calculates a first difference value, a second difference value and a third difference value corresponding to each pixel point based on the target RGB value and the actual RGB value of each pixel point in the acquired image:
C1,i,j＝|Ri,j,S-RM|，
C2,i,j＝|Gi,j,S-GM|，
C3,i,j＝|Bi,j,S-BM|，
Wherein, C 1,i,j is the first difference value corresponding to the j-th pixel point in the i-th acquired image; c 2,i,j is a second difference value corresponding to the j-th pixel point in the i-th acquired image; c 3,i,j is a third difference value corresponding to the j-th pixel point in the i-th acquired image; i is a positive integer, i is less than or equal to N, and N is the total number of acquired images; j is a positive integer, i is less than or equal to M, and M is the total number of pixel points in the acquired image; r i,j,S is the R component of the RGB value of the jth pixel point in the ith acquired image; r M is the R component of the target RGB value; g i,j,S is the G component of the RGB value of the jth pixel point in the ith acquired image; g M is the G component of the target RGB value; b i,j,S is the B component of the RGB value of the jth pixel point in the ith acquired image; b M is the B component of the target RGB value;
The server calculates a color difference value corresponding to each pixel point based on the first difference value, the second difference value and the third difference value corresponding to each pixel point:
CY,i,j＝C1,i,j+C2,i,j+C3,i,j，
Wherein, C Y,i,j is the color difference value corresponding to the j-th pixel point in the i-th acquired image;
And the server determines the pixel point with the color difference value smaller than the preset value as a target pixel point.
4. An agricultural supervision platform based on the agricultural internet of things according to claim 3, wherein the server is further configured to:
the server obtains the fertilizing and spraying amount corresponding to the subarea corresponding to the most acquired image of the target pixel point, and marks the fertilizing and spraying amount as the preset spraying amount;
the server marks the number of the target pixel points of the acquired image with the largest number of the target pixel points as a reference number;
the server calculates fertilization spraying amount corresponding to each subarea based on the preset spraying amount and the reference amount and the amount of target pixel points in each acquired image:
Wherein L i is the fertilizing and spraying amount corresponding to the sub-region corresponding to the ith acquired image; l Y is a preset spraying amount; s J is the reference number; s i,M is the number of target pixel points in the ith acquired image.
5. The agricultural supervision platform based on the agricultural internet of things according to claim 1, further comprising an environment collection module which is communicated with the server and is arranged at the planting field; the environment acquisition module comprises a carbon dioxide sensor, a soil moisture sensor, a temperature sensor, a humidity sensor and an illuminance sensor.
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