Farmland intelligent precise irrigation direction control system based on Internet of Things

Abstract

The invention discloses an intelligent precise farmland irrigation direction control system based on the Internet of Things, and relates to the technical field related to intelligent irrigation. Comprising a data acquisition module, a data processing and analyzing module, a data labeling and classifying unit, a three-dimensional coordinate model building module, a nozzle angle adjusting device, a nozzle water quantity adjusting unit and a control instruction execution unit. According to the invention, the distance between the nearest nozzle and the water-requiring position is calculated, so that irrigation water can reach the water-requiring area more efficiently, the distance is accurately calculated to ensure that water can be quickly and accurately conveyed to the required area, water loss and energy waste caused by long-distance water conveying are avoided, the water spraying direction is adjusted in combination with the wind direction and the wind speed, and the water spraying efficiency is improved. The device can effectively cope with the influence of natural wind on irrigation, and by monitoring wind direction and wind speed information in real time and combining the wind direction and wind speed information with water spraying parameters of the spray head, the system accurately calculates horizontal and vertical angle adjustment values of the spray head, so that sprayed water flow can accurately reach a water needing position.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent irrigation, and particularly relates to a farmland intelligent precise irrigation direction control system based on the Internet of Things. Background Art

[0002] With the continuous advancement of agricultural modernization, farmland irrigation, as a key link in agricultural production, has become increasingly prominent. Traditional farmland irrigation methods mainly rely on manual experience and have many deficiencies. In terms of water demand monitoring, in the past, it mostly relied on farmers' subjective judgments or simple soil moisture detection tools. For example, the method of taking soil samples with a soil drill to observe moisture is relatively rough and cannot obtain real-time and accurate moisture information at different depths of the soil, nor can it visually understand the overall water demand status and related requirements of farmland crops.

[0003] The monitoring of the foliage state is often overlooked in traditional agriculture. The cleanliness, dryness, and pest situation of crop foliage have an important impact on the photosynthesis and healthy growth of crops. For example, in some dusty areas, dust is easily attached to crop foliage, affecting the photosynthesis efficiency, but it is difficult for farmers to detect and clean it in time, and the early symptoms of pests are also not easily noticed. Once discovered, it often causes greater damage to the crops. The traditional manual visual inspection method is inefficient and prone to omissions for large areas of farmland.

[0004] In terms of sprinkler control, the position of the sprinkler in the traditional irrigation system is usually fixed, and the spraying range and angle of the sprinkler are also relatively fixed and cannot be flexibly adjusted according to the actual situation. This may result in over-irrigation or under-irrigation in some areas of farmland with irregular terrain. In addition, most traditional irrigation systems do not consider the influence of wind direction and wind speed on the spraying effect. In the actual farmland environment, the presence of wind will cause the sprayed water droplets to deviate from the target area, reducing the accuracy and uniformity of irrigation. For example, in windy conditions, if irrigation is still carried out in the same way as in windless conditions, the sprayed water droplets may be blown to other areas that do not require irrigation, resulting in a waste of water resources.

[0005] In summary, these limitations of traditional farmland irrigation methods make it difficult to ensure the irrigation effect, result in low water resource utilization rate, and high labor intensity, and are increasingly unable to meet the development needs of modern agriculture for high efficiency, precision, and energy conservation. Therefore, it is necessary to propose a farmland intelligent precise irrigation direction control system based on the Internet of Things to solve the above problems. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides an intelligent and precise irrigation direction control system for farmland based on the Internet of Things, which solves the problems that the branch and leaf state of the existing irrigation system is ignored, the cleanliness, dryness and pest situation of the crop branches and leaves affect the photosynthesis of the crops, the plants cannot grow healthily, the positions of the nozzles in the irrigation system are usually fixed, there is too much or too little irrigation in some areas, and the existence of wind will cause the sprayed water droplets to deviate from the target area, reducing the accuracy and uniformity of irrigation.

[0007] To achieve the above objectives, the present invention is realized through the following technical solutions:

[0008] An intelligent and precise irrigation direction control system for farmland based on the Internet of Things, comprising:

[0009] Data acquisition module: including a soil humidity sensor, a spectrometer and a weather station. The spectrometer is used to analyze the branch and leaf state of plants (branch and leaf cleanliness, moisture content and pest situation). The weather station is equipped with a wind speed sensor and a wind direction sensor, and transmits the data to the central control device through wireless communication;

[0010] Data processing and analysis module: including a central control device and the internal water demand calculation algorithm, the optimal nozzle calculation algorithm and the nozzle angle adjustment algorithm. The central control device receives the data from the data acquisition module and processes it; the water demand calculation algorithm calculates the water demand at each water demand position according to the preset algorithm, the optimal nozzle calculation algorithm calculates the distance between the nearest nozzle and the water demand position, and the nozzle angle adjustment algorithm calculates the horizontal angle and vertical angle of the nozzle during water spraying according to the water demand position;

[0011] Data annotation and classification unit: obtains the water demand position and water demand according to the soil humidity measurement result of the soil humidity sensor and the branch and leaf state measured by the spectrometer, and classifies and marks them in the three-dimensional model, using different colors and symbols to represent the water shortage position, the cleaning demand position and the pest position;

[0012] Three-dimensional coordinate model construction module: takes a corner of the farmland as the origin, establishes a three-dimensional rectangular coordinate system, and marks the coordinates of the soil humidity sensor, the spectrometer and the nozzle module in the three-dimensional coordinate model;

[0013] Nozzle angle adjustment device: The nozzle is equipped with a device for adjusting the angle in the horizontal and vertical directions;

[0014] Nozzle water volume adjustment unit: A flow sensor and a flow adjustment valve are installed in the nozzle;

[0015] Control instruction execution unit: sends control instructions to the nozzle angle adjustment device according to the horizontal angle and vertical angle obtained by the nozzle angle adjustment algorithm, and controls and adjusts the water output of the nozzle according to the water demand.

[0016] Optionally, the acquisition method of the data acquisition module is as follows: Soil humidity measurement: Soil humidity sensors are distributed in a grid pattern in the farmland to measure soil humidity. In the xOy plane, sensors are arranged at intervals of Δx along the x-axis direction and at intervals of Δy along the y-axis direction, forming a number of rectangular grids with side lengths of Δx and Δy. Each grid point is (i, j), and the data measured by the sensors is sent to the data processing center through wireless transmission;

[0017] Collection of foliage status: The spectrometer is installed on a movable bracket and scans and measures the plant foliage along the direction perpendicular to the xOy plane. At each measurement position, the spectrometer obtains the spectral reflectance curve of the foliage at that position. By comparing and analyzing with the pre-established standard spectral database of foliage in different states, the cleanliness, moisture content, and presence of pests of the foliage at that position are determined.

[0018] Optionally, the construction method of the three-dimensional rectangular coordinate system in the three-dimensional coordinate model construction module is as follows: Taking a corner of the farmland as the origin, a three-dimensional rectangular coordinate system is established, with the x-axis along the length direction of the farmland, the y-axis along the width direction of the farmland, and the z-axis perpendicular to the ground and upward;

[0019] The x-axis is along the length direction of the farmland. The distance of a certain point in the length direction of the farmland from the origin is Δx, and its coordinate on the x-axis is (Δx, 0, 0); the y-axis is along the width direction of the farmland, and its coordinate on the y-axis is (0, Δy, 0); the z-axis is perpendicular to the ground and upward. When the height of a certain position from the ground is Δz, its coordinate on the z-axis is (0, 0, Δz).

[0020] Optionally, the coordinate marking method of the soil humidity sensor, spectrometer, and sprinkler module in the three-dimensional coordinate model construction module is as follows:

[0021] Coordinate marking of soil humidity sensor: The position of the sensor in the farmland plane is known. Its distance from the origin in the length direction is Δx s , and its distance in the width direction is Δy s ; the coordinate in the z-axis direction is 0, and the coordinate of the soil humidity sensor in the three-dimensional coordinate model is (Δx s , Δy s , 0);

[0022] Coordinate marking of spectrometer: The spectrometer is installed on a movable bracket. Its position in the farmland plane is at a distance of Δx from the origin in the length direction p , and at a distance of Δy from the origin in the width direction p ; since the spectrometer is above the ground, its coordinate in the z-axis direction is H, and the coordinate of the spectrometer in the three-dimensional coordinate model is (Δx p , Δy p, H);

[0023] Sprinkler module coordinate marking: There are n sprinklers in the farmland. For the a-th sprinkler (a = 1, 2, 3... n), its coordinates are (x a , y a , z a ).

[0024] Optionally, the classification marking method for the water demand position and water demand in the data annotation and classification unit is: Obtain the water demand position and water demand according to the soil humidity measurement results of the soil humidity sensor and the foliage state measured by the spectrometer;

[0025] Annotation of soil humidity measurement results in the three-dimensional coordinate model: For the grid point (i, j), its coordinates in the three-dimensional coordinate system are (x i , y j , z0), where z0 is the height of the soil surface;

[0026] Annotation of foliage state in the three-dimensional coordinate model: For the position (x, y) measured by the spectrometer, the position of the corresponding foliage in the three-dimensional space is (x, y, z t ), and the value of z t is determined by the height of the measuring instrument and the actual height of the plant foliage;

[0027] Cleanliness annotation: Represent the cleanliness of the foliage with different icons or colors;

[0028] Moisture content annotation: Represent the moisture content of the foliage with the height or color of a cube;

[0029] Pest damage part annotation: When pest damage is detected, use a red skull icon or different marking symbols at the corresponding foliage position.

[0030] Optionally, the water demand calculation algorithm in the data processing and analysis module is as follows:

[0031] Calculation of water demand for soil humidity: Determine the target value of soil humidity. For each grid point (i, j), calculate the soil humidity deficit, convert the soil humidity deficit into a soil water potential deficit according to the soil water characteristic curve, and then calculate the irrigation water volume required for each grid point in combination with the root distribution depth factor of the soil;

[0032] Calculation of water demand for foliage moisture content: Set the foliage water moisture threshold, and increase the water demand when the foliage moisture content is lower than the threshold.

[0033] Optionally, the classification marking method for the water demand position and water demand in the data annotation and classification unit is:

[0034] Water demand position marking: According to the calculation results of the water demand, distinguish the water demand positions in the three-dimensional coordinate model. If the water demand at the grid point (i, j) is greater than 0, mark this position as a water shortage position and display it at the coordinate (x i , y j , z0) with a red flashing icon. For the water demand situation of branches and leaves, mark it with a red water droplet icon at the corresponding branch and leaf position (x, y, z t );

[0035] Water demand marking: Display the specific water demand value next to the marked icon at the water demand position.

[0036] Optionally, the optimal nozzle calculation algorithm in the data processing and analysis module is as follows:

[0037] Determine the coordinate representation of the nozzle and the water demand position: Suppose in three-dimensional space, the nozzle position is represented by the coordinate (x a , y a , z a ), where a = 1, 2,..., n, and n is the number of nozzles; the water demand position is represented by the coordinate (x w , y w , z w ), where w = 1, 2,..., q, and q is the number of water demand positions;

[0038] Calculate the distance to all nozzles: The distance formula between two points is calculated based on the Euclidean distance formula;

[0039] Find the shortest distance: By comparing all the calculated distances of the nozzles, find the minimum distance.

[0040] Optionally, the nozzle angle adjustment algorithm in the data processing and analysis module is as follows:

[0041] Horizontal angle: In the case of no wind, the nozzle sprays directly at the water demand position, and the horizontal angle is 0 degrees; in the case of wind, considering the influence of the wind on the water flow deviation, the wind speed is v, the angle between the wind direction and the horizontal vector from the nozzle to the water demand position is α, and the horizontal offset acceleration a of the water flow is related to the wind speed and the wind direction. According to Newton's second law: a = k·v·cosα, where k is a coefficient related to the water flow characteristics and air resistance;

[0042] Vertical angle: Determine the coordinate system and related parameters, establish a space rectangular coordinate system with the nozzle position as the origin, the x-axis as the horizontal direction, the y-axis as the vertical direction upward, and the z-axis perpendicular to the xOy plane outward. Suppose the installation height of the nozzle is h1, the height of the water demand position is h2, and the lifting angle of the nozzle is β1; the vertical component of the water outlet speed of the nozzle is v 0y = v0·sinβ1, and the horizontal component is v 0x= v0·cosβ1;

[0043] The influence of water pressure on the range: Water pressure affects the range of the sprinkler. Let the water pressure be P (unit: Pa), the cross-sectional area of the sprinkler nozzle be A (unit: m 2 ), and the density of the water flow be ρ (unit: kg / m 3 ). According to Bernoulli's equation and the continuity equation, it is deduced that the range L of the sprinkler is related to the water pressure. For a certain sprinkler and pipeline system, the range L is expressed as where c is a coefficient related to the structure of the sprinkler and the pipeline;

[0044] Calculating the vertical angle: The water flow performs a vertical upward throwing motion in the vertical direction. The height difference from the sprinkler to the water demand position is: Δh = h2 - h1. According to the maximum height formula of the vertical upward throwing motion where g is the acceleration due to gravity. If Δh ≤ h, the water demand position is within the trajectory range of the vertical upward throwing motion of the sprinkler. At this time, the vertical angle is solved according to the kinematic formula. Let the time from the sprinkler to the water demand position be t1, then there is At the same time, in the horizontal direction, there is d = v 0x t1. By combining the two formulas to find t1, and then obtaining v 0y ; Based on obtain

[0045] Optionally, the central control device obtains the nearest sprinkler according to the optimal sprinkler calculation algorithm, then uses the angle sensor on the sprinkler to obtain the horizontal angle and vertical angle of the sprinkler at this time, and the horizontal angle and vertical angle of the sprinkler during water spraying obtained by the sprinkler angle adjustment algorithm, calculates the angle difference between the two, and uses the PID control algorithm to calculate the adjustment amount according to the angle difference, so as to control the sprinkler angle adjustment device, and control the water output of the sprinkler according to the water demand.

[0046] The present invention provides an intelligent precise irrigation direction control system for farmland based on the Internet of Things, having the following beneficial effects:

[0047] 1. The present invention constructs a three-dimensional rectangular coordinate system with the corner or center of the farmland as the origin, which can accurately determine the coordinate information of each position in the farmland. This enables the positions of the soil humidity sensors, spectrometers, and sprinklers to be accurately marked in three-dimensional space, realizing the visual management of farmland irrigation facilities and monitoring equipment. For cleaning, moistening, and pest control requirements, they are also intuitively displayed in the coordinate system with different regions or markings. This intuitive visualization method helps to quickly understand the overall water demand status and related requirements of farmland crops.

[0048] 2. The present invention uses a spectrometer to monitor the cleanliness of branches and leaves, can promptly detect dirt, dust or other attachments on the branches and leaves that may affect photosynthesis, issue a warning of low cleanliness of the branches and leaves in a timely manner, and the device takes corresponding cleaning measures to ensure normal photosynthesis of crops, promote crop growth, and monitor the dryness of the branches and leaves in real time, which helps prevent drought stress suffered by crops due to water shortage, ensures the water supply during the critical growth period of crops, and the monitoring of pest-infested parts can achieve early pest warning.

[0049] 3. The present invention calculates the distance between the nearest sprinkler and the water-required position, enabling irrigation water to reach the water-required area more efficiently. By accurately calculating the distance, the system can select the sprinkler closest to the water-required position for spraying water, reducing the transmission loss of water, ensuring that water can be quickly and accurately delivered to the required place, and avoiding water loss and energy waste caused by long-distance water conveyance.

[0050] 4. The present invention adjusts the spraying direction in combination with wind direction and wind speed, which can effectively cope with the influence of natural wind on irrigation. In the case of wind, if the wind direction and wind speed are not considered, the sprayed water may be blown off, resulting in uneven irrigation or deviation from the target area. By real-time monitoring of wind direction and wind speed information and combining it with the spraying parameters of the sprinkler, the system can accurately calculate the horizontal and vertical angle adjustment values of the sprinkler, so that the sprayed water flow can reach the water-required position accurately against or along the wind. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the modules of the intelligent precise irrigation direction control system for farmland of the present invention;

[0052] Figure 2 It is a schematic diagram of the working process of the intelligent precise irrigation direction control system for farmland of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0053] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0054] Embodiment 1

[0055] Please refer to Figure 1 , an intelligent precise irrigation direction control system for farmland based on the Internet of Things, including:

[0056] Data acquisition module: It includes multiple soil moisture sensors deployed at different positions in the farmland, a spectrometer installed in the farmland area for analyzing the state of plant branches and leaves (branch and leaf cleanliness, moisture content, and pest situation), and a weather station set at the surrounding position of the farmland. The weather station is equipped with a wind speed sensor and a wind direction sensor, and transmits data to the central control device through wireless communication;

[0057] Data processing and analysis module: It includes a central control device and the water demand calculation algorithm, the optimal nozzle calculation algorithm, and the nozzle angle adjustment algorithm inside. The central control device receives data from the soil moisture sensors and the spectrometer, and performs parsing, denoising, and synchronization processing on the data to ensure the accuracy and timeliness of the data; The water demand calculation algorithm calculates the water demand at each water demand position according to the preset algorithm, the optimal nozzle calculation algorithm calculates the nearest nozzle and the distance between the nozzle and the water demand position, and the nozzle angle adjustment algorithm calculates the horizontal angle and vertical angle of the nozzle during water spraying according to the water demand position;

[0058] Data annotation and classification unit: In the three-dimensional coordinate model, according to the calculated water demand and the detection results of the soil moisture sensors and the spectrometer, classify and mark the water demand positions and water demands, and use different colors and symbols to represent the water shortage positions, cleaning demand positions, and pest positions, which is convenient for formulating subsequent irrigation strategies and controlling the nozzles;

[0059] Three-dimensional coordinate model construction module: Taking a corner of the farmland as the origin, establish a three-dimensional rectangular coordinate system, with the x-axis along the length direction of the farmland, the y-axis along the width direction of the farmland, and the z-axis perpendicular to the ground upward to construct a three-dimensional coordinate model, and mark the coordinates of the soil moisture sensors, spectrometers, and nozzle modules in the three-dimensional coordinate model;

[0060] Nozzle module: The installation positions and spacings of the nozzles are reasonably planned according to factors such as the shape, size, irrigation requirements, and nozzle range of the farmland to ensure the uniformity and effectiveness of irrigation coverage. The position and number of each nozzle are accurately marked in the three-dimensional coordinate model;

[0061] Nozzle angle adjustment device: The nozzle is equipped with devices for adjusting the angle in the horizontal and vertical directions, such as a rotation mechanism driven by a stepper motor. By controlling the rotation of the motor, the functions of lifting and turning the nozzle are realized, thereby changing the spraying angle of the nozzle, and a nozzle angle sensor, including angle sensors in the horizontal and vertical directions, is used to measure the real-time angle of the nozzle;

[0062] Sprinkler water volume adjustment unit: Each sprinkler is equipped with an electrically controlled flow regulating valve to adjust the water output of the sprinkler. The opening and closing degree of the valve is precisely controlled according to the water demand and irrigation strategy to meet the water requirements of different regions and different crops. A flow sensor is installed in the sprinkler pipeline to monitor the actual water output in real time and feed the data back to the central control device. When there is a deviation between the actual water output and the set value, the central control device automatically adjusts the opening and closing size of the valve to ensure the accuracy of the irrigation water volume;

[0063] Control instruction execution unit: According to the horizontal angle and vertical angle calculated by the sprinkler angle adjustment algorithm in the central control device, send control instructions to the sprinkler angle adjustment device. The instructions include the rotation direction, angle value, and rotation speed information to ensure that the sprinkler can accurately and quickly adjust to the target angle, and control the sprinkler water volume adjustment unit according to the water demand at the water demand position to adjust the water output of the sprinkler;

[0064] Server unit: The server is responsible for storing and managing various data of the entire farm intelligent irrigation system, including historical irrigation data, sensor data, and meteorological data. The database management system (such as MySQL) is used to classify, store, index, and query the data to facilitate users to retrieve and analyze the data at any time;

[0065] Remote monitoring and control platform: Build a remote monitoring interface. Users log in to the server through the mobile phone APP or web browser to view the irrigation status of the farmland, sensor data, and sprinkler working status information in real time. At the same time, users manually adjust the irrigation parameters and start or stop the irrigation task on the remote platform to achieve remote control of the farmland irrigation;

[0066] Communication network unit: Build a wireless sensor network within the farmland to ensure stable and efficient data transmission between sensors, sprinklers, and other devices. The topology of the network is optimized according to the farmland terrain and device distribution to improve the reliability and coverage of communication.

[0067] The acquisition method of the data acquisition module is as follows:

[0068] Soil humidity measurement: Soil humidity sensors are distributed in a grid pattern in the farmland to measure soil humidity. In the xOy plane, sensors are arranged at intervals of Δx along the x-axis and at intervals of Δy along the y-axis, forming a number of rectangular grids with side lengths of Δx and Δy. For each grid point (i, j), where i = 1, 2, …, n and j = 1, 2, …, m, and n and m are the numbers of grids in the x-axis and y-axis directions respectively. The data measured by the sensors is sent to the data processing center through wireless transmission. The original soil humidity data collected is calibrated to eliminate the influence of the errors of the sensors themselves and environmental factors (such as temperature, soil texture) on the measurement results. The calibration coefficient can be obtained through experiments or by referring to relevant literature. Let the calibrated soil humidity data be θ ij , and its value range is generally between 0 and 1. 0 indicates that the soil is completely dry, and 1 indicates that the soil is water-saturated;

[0069] Collection of foliage status: Foliage of plants has different reflectance characteristics for light of different wavelengths under different conditions (clean, dirty, dry, normal water content, pest-infested, etc.). For example, in the visible light band (wavelength range is approximately 380 - 780 nm), healthy green plant leaves absorb more blue light (wavelength is approximately 380 - 495 nm) and red light (wavelength is approximately 620 - 780 nm) due to the presence of chlorophyll, and reflect green light (wavelength is approximately 495 - 570 nm), so they appear green. When there is dirt on the foliage, it will change its spectral reflectance, usually causing the reflectance to decrease and become more stable in each band; Dry foliage may change its spectral reflectance characteristics due to water loss leading to changes in cell structure; Foliage infested with pests may have lesions or tissue damage, which will also cause specific changes in its spectral reflectance;

[0070] The spectrometer is installed on a movable bracket and scans and measures the plant foliage along the direction perpendicular to the xOy plane. At each measurement position (x, y) (corresponding to (x, y, z t ) in the three-dimensional coordinates, where z t is the height of the foliage), the spectrometer obtains the spectral reflectance curve of the foliage at this position. By comparing and analyzing it with the pre-established standard spectral database of foliage in different states, the cleanliness, water content, and pest-infested situation of the foliage at this position are determined.

[0071] The construction method of the three-dimensional rectangular coordinate system in the three-dimensional coordinate model construction module is as follows: Taking a corner of the farmland as the origin, a three-dimensional rectangular coordinate system is established. The x-axis is along the length direction of the farmland, the y-axis is along the width direction of the farmland, and the z-axis is perpendicular to the ground and points upward. The corresponding position of this corner is the starting position in the length direction and the starting position in the width direction. Then, the abscissa x0 of the origin is 0, the ordinate y0 is 0, and taking the plane where the ground is located as the z = 0 plane, so the vertical coordinate z0 of the origin is 0.

[0072] The x-axis is along the length direction of the farmland. Starting from the origin, the right direction is the positive direction. The distance of a certain point on the length direction of the farmland from the origin is Δx, and the coordinate of this point on the x-axis is (Δx, 0, 0). The y-axis is along the width direction of the farmland. Starting from the origin, the forward direction is the positive direction. For a point at a distance of Δy from the origin in the width direction, its coordinate on the y-axis is (0, Δy, 0). The z-axis is perpendicular to the ground and points upward. Starting from the ground, the upward direction is the positive direction. When the height of a certain position from the ground is Δz, the coordinate of this point on the z-axis is (0, 0, Δz).

[0073] The coordinate marking method of the soil moisture sensor, spectrometer, and sprinkler module in the three-dimensional coordinate model construction module is as follows:

[0074] Coordinate marking of the soil moisture sensor: The soil moisture sensor is buried underground. The position of the sensor in the farmland plane is known. Its distance from the origin in the length direction is Δx s , and its distance from the origin in the width direction is Δy s ; Although the sensor is underground, when using sprinkler irrigation, the water can only be sprayed to the soil surface. Therefore, the coordinate in the z-axis direction is 0, and the coordinate of the soil moisture sensor in the three-dimensional coordinate model is (Δx s , Δy s , 0);

[0075] Coordinate marking of the spectrometer: The spectrometer is installed on a movable bracket, and its height is H (unit: meter). Its position in the farmland plane is at a distance of Δx from the origin in the length direction p , and its distance from the origin in the width direction is Δy p ; Since the spectrometer is above the ground, its coordinate in the z-axis direction is H, and the coordinate of the spectrometer in the three-dimensional coordinate model is (Δx p , Δy p , H);

[0076] Coordinate marking of the sprinkler module: There are n sprinklers in the farmland. For the a-th sprinkler (a = 1, 2, 3... n), its coordinate is (x a , y a , z a ), where x ax is the coordinate of the nozzle in the x-axis direction, y is the coordinate of the nozzle in the y-axis direction (the position of the nozzle in the farmland plane is known), and z a is the coordinate of the nozzle in the z-axis direction (since the nozzle has a height and is not on the ground layer).

[0077] In this embodiment, a three-dimensional rectangular coordinate system is constructed with the corner or center of the farmland as the origin, which can accurately determine the coordinate information of each position in the farmland. This enables the positions of soil moisture sensors, spectrometers, and nozzles to be accurately marked in three-dimensional space, realizing the visual management of farmland irrigation facilities and monitoring equipment. Through the three-dimensional model, farmers or agricultural managers can intuitively see the distribution of sensors and nozzles throughout the farmland, facilitating equipment maintenance and layout planning. When specific operations need to be performed on a particular area, such as checking the data of a certain sensor or adjusting the angle of a certain nozzle, its position in three-dimensional space can be quickly located, saving the time for searching and positioning and improving management efficiency;

[0078] Through the three-dimensional rectangular coordinate system, the spatial information of the water demand positions can be clearly determined. For soil water demand, the water demand conditions of different depths of soil can be accurately marked, such as the position differences of the surface soil (0 - 10 cm), middle soil (10 - 30 cm), and deep soil (30 - 50 cm or deeper) in the coordinate system, enabling managers to clearly know the drought or over-wet states of different soil layers at a glance. For the water demand of branches and leaves, according to the different heights and levels of the tree crown, the water demand positions of the branches and leaves can be accurately located in the coordinate system. For example, the water demand conditions of the upper, middle, and lower parts of the tree crown can be clearly distinguished in the vertical direction, facilitating targeted irrigation or other water replenishment measures; for cleaning, moistening, and pest control requirements, they are also visually displayed in the coordinate system with different regions or markings. This intuitive visualization method helps to quickly understand the overall water demand status and related requirements of farmland crops.

[0079] Embodiment 2

[0080] This embodiment further optimizes on the basis of Embodiment 1. Specifically, the classification marking method for the water demand position and water demand volume in the data annotation and classification unit is: obtaining the water demand position and water demand volume according to the soil moisture measurement results of the soil moisture sensor and the foliage state measured by the spectrometer;

[0081] Annotation of soil moisture measurement results in the three-dimensional coordinate model: For the grid point (i, j), its coordinates in the three-dimensional coordinate system are (x i , y j , z0), where x i =(i - 1)Δx, y j =(j - 1)Δy, and z0 is the height of the soil surface; in the three-dimensional coordinate model, in the form of a cube at the coordinate (xi , y j , the soil moisture information is marked at (x, y, z0), and the height of the cube changes dynamically according to the magnitude of the soil moisture. For example, when θ ij = 0.8, the height is h1; when θ ij = 0.5, the height is h2 (h1 > h2). In this way, the spatial distribution of the soil moisture is intuitively displayed. Color is used as one dimension of the marking, with blue indicating wet (high soil moisture), green indicating moderate, and brown indicating dry (low soil moisture);

[0082] Marking of the foliage state in the three - dimensional coordinate model: For the position (x, y) measured by the spectrometer, the corresponding position of the foliage in the three - dimensional space is (x, y, z t ), and the value of z t is determined by the height of the measuring instrument and the actual height of the plant foliage; if the spectrometer is installed at a height of H p and the average height of the plant foliage is h f , then z t = H p - h f ;

[0083] Marking of cleanliness: Different icons or colors are used to represent the cleanliness of the foliage. A green circular icon represents high cleanliness (above the cleanliness threshold), a yellow triangular icon represents medium cleanliness, and a red square icon represents low cleanliness (with dirt). These icons can be displayed at the corresponding foliage position (x, y, z t ) in the three - dimensional coordinate model;

[0084] Marking of moisture content: The height or color of a cube is used to represent the moisture content of the foliage. The cylinder corresponding to the foliage with a high moisture content has a higher height and a bright green color, while the cylinder corresponding to the foliage with a low moisture content has a lower height and a dark green color. The marking position is also at (x, y, z t );

[0085] Marking of pest - infested parts: When pests are detected, a red skull icon or different marking symbols are used at the corresponding foliage position.

[0086] The water requirement calculation algorithm in the data processing and analysis module is as follows:

[0087] Calculation of water requirement for soil moisture: Determine the target value θ tar get of the soil moisture. This value is determined according to the growth requirements and growth stages of different crops. For example, for a certain crop during the growth period, θ tar get = 0.7. Then, for each grid point (i, j), calculate the soil moisture deficit Δθ ij = θ tar get-θ ij , according to the soil moisture characteristic curve (the curve describing the relationship between soil water potential and soil moisture), the soil moisture deficit is converted into soil water potential deficit Combined with the root distribution depth of the soil r Factors, calculate the irrigation water volume W required for each grid point ij , the calculation formula is: Among them, ρ w is the density of water (take 1000kg / m 3 );

[0088] Calculation of water requirement for leaf moisture content: Set the leaf moisture threshold. When the leaf moisture content is lower than the threshold, increase the calculated water requirement appropriately. Assume that the leaf moisture content is W l , when W l <w lmin ,(w lmin is the lower limit threshold of the moisture content of branches and leaves), the adjustment coefficient is k l >1; when w lmin ≤W l ≤W lmax (W lmax is the upper threshold of the water content of branches and leaves), k l >1; when W l >W lmax When k l <1. Then the adjusted water demand W' ij =k l ×W ij .

[0089] The classification marking method for water demand location and water demand in data labeling and classification unit is as follows:

[0090] Water demand location marking: According to the calculation results of water demand, the water demand location is distinguished in the three-dimensional coordinate model. If the water demand W' at the grid point (i, j) ij >0, the location is marked as a water shortage location, with a red flashing icon (such as a red water drop icon) at the coordinate (x i ,y j ,z0), indicating that the location needs irrigation. For the water demand of branches and leaves, the corresponding branch and leaf position (x,y,z t ) is marked with a red water drop icon;

[0091] Water demand mark: Display the specific water demand value next to the mark icon of the water demand location, for example, in (x i ,y j ,z0) is marked with a red water drop icon and the message “Water Requirement: Xm 3” (X is the water demand value calculated for the grid point). For the water demand of branches and leaves, the water demand value is displayed at the corresponding marked position so as to accurately understand the water demand level of each position and provide a basis for precise irrigation.

[0092] In this embodiment, the water demand location and water demand are marked in the three-dimensional coordinate model, which can more accurately analyze the water demand situation at different locations of the farmland. The traditional irrigation method can often only determine the irrigation amount based on experience or rough regional division, while the marking under the three-dimensional coordinate system allows accurate water demand calculation for each specific water demand point. This accurate water demand analysis helps to realize personalized irrigation strategies, improve the utilization efficiency of water resources, and reduce the waste of water resources. At the same time, through long-term monitoring and data analysis of water demand in different regions, it can also provide a scientific basis for farmland soil improvement, crop planting structure adjustment, etc.

[0093] Using a spectrometer to monitor the cleanliness of branches and leaves can timely detect dirt, dust or other attachments on branches and leaves that may affect photosynthesis. For example, in a dusty environment, the surface of crop branches and leaves is prone to adsorb dust, which affects light absorption and gas exchange, thereby reducing photosynthesis efficiency. By issuing a warning of low branch and leaf cleanliness in a timely manner, the device takes corresponding cleaning measures to ensure normal photosynthesis of crops and promote crop growth;

[0094] Real-time monitoring of the dryness of branches and leaves helps prevent crops from suffering from drought stress due to water shortage. In the early stages of drought, branches and leaves will show slight signs of wilting and drying. At this time, the system can monitor and replenish water in time to avoid the serious impact of drought on crop growth, such as slow growth and reduced yield. At the same time, for areas with high water demand but poor soil water retention capacity, irrigation can be arranged in priority based on the monitoring results of branch and leaf dryness to ensure water supply during the critical period of crop growth.

[0095] Monitoring of pest sites can provide early warning of pests. Many pests tend to breed only in local areas in the early stages. If they are not discovered and dealt with in time, they can easily spread over a large area and cause devastating damage to crops. The spectrometer can detect early symptoms of pests on branches and leaves, such as slight changes in leaf color, minor damage to the leaf surface, etc. It is more sensitive and accurate than observation with the naked eye. For example, when corn borer larvae are first hatched, they will first feed on the heart leaves of corn. At this time, the spectrometer can be used to monitor the abnormalities in the spectral characteristics of the heart leaves, issue a warning in time, and use water to spray away the pests. By accurately identifying the type and location of pests, more effective prevention and control measures can be adopted.

[0096] Example 3

[0097] This embodiment is a further optimization based on the embodiment 1. Specifically, the optimal nozzle calculation algorithm in the data processing and analysis module is as follows:

[0098] Determine the coordinate representation of the nozzle and the water demand position: Suppose in a three-dimensional space, the position of the nozzle is represented by coordinates (x a , y a , z a ), where a = 1, 2,..., n, and n is the number of nozzles; the water demand position is represented by coordinates (x w , y w , z w ), where w = 1, 2,..., q, and q is the number of water demand positions (usually the number of grid points or the number of water demand positions of branches and leaves);

[0099] Calculate its distances to all nozzles: The distance formula between two points is calculated based on the Euclidean distance formula: where d aw represents the distance from the a-th nozzle to the w-th water demand position;

[0100] Find the shortest distance: By comparing all the calculated d aw (for each water demand position w), find the smallest d mirw , which is the distance from this water demand position to the nearest nozzle, and at the same time record the index a' of the corresponding nearest nozzle.

[0101] In this embodiment, calculating the distance between the nearest nozzle and the water demand position can enable irrigation water to reach the water demand area more efficiently. In traditional irrigation, there may be a situation where the irrigation position does not match the actual water demand position, resulting in water surplus in some areas and insufficient water in other areas. By accurately calculating the distance, the system can select the nozzle closest to the water demand position for spraying, reducing water transmission losses and ensuring that water can be quickly and accurately delivered to the required place. For example, in an irregularly shaped farmland, when the crops in the edge area need irrigation, the system can find the nozzle closest to this area, avoiding water loss and energy waste caused by long-distance water conveyance;

[0102] This precise nozzle selection also helps to optimize the layout of nozzles. By analyzing the distance relationship between different water demand positions and nozzles, agricultural engineers can discover the irrationalities in the nozzle layout, such as too many nozzles in some areas and insufficient nozzle coverage in other areas. Based on this information, the nozzles can be repositioned or adjusted to improve the uniformity and effectiveness of the entire farmland irrigation system;

[0103] When the soil moisture sensor detects water shortage in a certain area, it quickly calculates the nearest sprinkler head and sprays water, enabling a rapid response to water demand. Under some extreme weather conditions, such as high-temperature and dry weather, the water demand of crops will increase sharply. If irrigation is not carried out in time, it may lead to crop withering or even death. By quickly finding the nearest sprinkler head and starting irrigation, water can be supplied to the crops within a short time, alleviating the drought situation and ensuring the normal growth of crops. For example, during the hot afternoon in summer, if it is monitored that a certain area of crops shows water deficit, the system can immediately command the nearest sprinkler head to start working, so that the crops can obtain water supply in the shortest time and reduce the damage of high temperature to the crops.

[0104] Example 4

[0105] This embodiment is a further optimization based on Embodiment 1. Specifically, the sprinkler head angle adjustment algorithm in the data processing and analysis module is as follows:

[0106] Horizontal angle: In the case of no wind, the sprinkler head sprays directly towards the water demand position, and the horizontal angle is 0 degrees; in the case of wind, considering the influence of wind on the water flow deflection, the wind speed is v (unit: m / s), the angle between the wind direction and the horizontal vector from the sprinkler head to the water demand position is α, and the horizontal offset acceleration a of the water flow is related to the wind speed and wind direction. According to Newton's second law: a = k·v·cosα, where k is a coefficient related to water flow characteristics and air resistance, etc. (this coefficient needs to be determined through experiments or empirical data);

[0107] Suppose the initial velocity of the sprinkler head spraying water is v0 (unit: m / s). In the horizontal direction, the movement of the water flow is a uniformly variable linear motion under the action of wind. The horizontal distance from the sprinkler head to the water demand position is d (unit: m). According to the kinematic formula: where t is the time for the water flow to reach the water demand position from the sprinkler head. Since the height difference between the installation height of the sprinkler head and the water demand position is relatively small compared to the horizontal distance (this can be approximated in general cases), the time t is approximately calculated by dividing the horizontal distance by the initial velocity of the sprinkler head spraying water, that is Horizontal displacement: The actual horizontal angle θ1 is calculated through the arctangent function. Let Δx be the actual horizontal offset of the water flow: Then

[0108] Vertical Angle: Determine the coordinate system and related parameters. Establish a three-dimensional Cartesian coordinate system with the position of the nozzle as the origin. The x-axis is in the horizontal direction (pointing to the water-required position), the y-axis is in the vertical upward direction, and the z-axis is perpendicular to the xOy plane and points outward. Let the installation height of the nozzle be h1 (unit: m), the height of the water-required position be h2 (unit: m), and the elevation angle of the nozzle be β1 (i.e., the angle between the nozzle and the horizontal plane); the vertical component of the water outlet velocity of the nozzle is v 0y = v0·sinβ1, and the horizontal component is v 0x = v0·cosβ1;

[0109] Influence of water pressure on the range: Water pressure affects the range of the nozzle. Let the water pressure be P (unit: Pa), the cross-sectional area of the nozzle orifice be A (unit: m 2 ), and the density of the water flow be ρ (unit: kg / m 3 ). According to Bernoulli's equation and the continuity equation, it is deduced that the range L of the nozzle is related to the water pressure. For a certain nozzle and pipeline system, the range L is expressed as where c is a coefficient related to the structure of the nozzle and the pipeline;

[0110] Calculation of the vertical angle: The water flow performs a vertical upward throw motion in the vertical direction. The height difference from the nozzle to the water-required position is: Δh = h2 - h1. According to the formula for the maximum height of vertical upward throw motion where g is the acceleration due to gravity. If Δh ≤ h, the water-required position is within the trajectory range of the vertical upward throw motion of the nozzle. At this time, the vertical angle is solved according to the kinematic formula. Let the time for the water flow to reach the water-required position from the nozzle be t1, then there is At the same time, in the horizontal direction, there is d = v 0x t1. By combining the two formulas to find t1, and then obtaining v 0y ; Based on obtain

[0111] In this embodiment, by accurately calculating the water spray direction in combination with water pressure, wind direction and wind speed, the irrigation accuracy can be improved and energy and water resources can be saved; considering the water pressure factor can ensure that the water flow ejected by the nozzle can reach the expected distance and range. Different nozzles have different ranges and spraying radii under different water pressures. By accurately calculating the influence of water pressure on the water spray distance, the system can adjust the water outlet pressure of the nozzle or select a suitable nozzle type according to the actual situation, so that the water flow can accurately cover the water-required area. For example, when irrigating a relatively far area, the system can automatically increase the water pressure or select a nozzle with a longer range; while in the area close to the nozzle, appropriately reducing the water pressure can avoid over-irrigation, which can avoid problems such as insufficient irrigation area caused by insufficient water pressure or water resource waste and ground erosion caused by excessive water pressure;

[0112] Meanwhile, adjusting the water spraying direction in combination with the wind direction and speed can effectively address the impact of natural wind on irrigation. In windy conditions, if the wind direction and speed are not considered, the sprayed water may be deflected by the wind, resulting in uneven irrigation or deviation from the target area. By real-time monitoring of the wind direction and speed information and combining it with the water spraying parameters of the sprinkler head, the system can accurately calculate the horizontal and vertical angle adjustment values of the sprinkler head, enabling the sprayed water flow to reach the water-required position accurately against or along the wind. For example, in light wind weather, the sprinkler head can spray slightly towards the upwind direction, allowing the wind to blow the water mist towards the target area; in strong wind weather, it may be necessary to increase the water spraying angle of the sprinkler head or adjust the rotation speed of the sprinkler head to resist the interference of the wind and ensure the accuracy of irrigation.

[0113] Accurately calculating the water spraying direction can reduce unnecessary energy consumption. When the water flow can be directly aimed at the water-required area, no additional energy is needed to change the direction of the water flow or make up for the energy loss caused by wind direction deviation. This means that the water pump can operate at a lower power, thereby reducing energy consumption. For example, in a large farm, if each sprinkler head can avoid the increased energy consumption due to wind direction problems through accurate calculation of the water spraying direction, the irrigation energy consumption of the entire farm will be significantly reduced. From the perspective of water resources, accurate water spraying direction helps to avoid the unnecessary loss of water. If the water spraying direction is improper, the water may be sprayed onto unnecessary areas, such as open spaces or roads outside the farmland, causing waste of water resources. By accurately calculating the water spraying direction by combining various factors, water can be maximally sprayed onto places that need water, such as the roots of crops, improving the utilization rate of water resources, reducing the waste of water resources and the impact on the surrounding environment.

[0114] Example 5

[0115] This example is a further optimization based on Example 1. Specifically, the central control device obtains the nearest sprinkler head according to the optimal sprinkler head calculation algorithm, and then uses the angle sensors on the sprinkler head to obtain the current horizontal and vertical angles of the sprinkler head, as well as the horizontal and vertical angles of the sprinkler head during water spraying obtained by the sprinkler head angle adjustment algorithm. Calculate the angle difference between the two, and use the PID control algorithm to calculate the adjustment amount according to the angle difference, and then control the sprinkler head angle adjustment device, and control the water output of the sprinkler head according to the water demand.

[0116] In this embodiment, the central control device controls the angle of the sprinkler heads, realizing the automation of the irrigation process. Farmers or agricultural managers do not need to manually adjust the angle of the sprinkler heads in the field, greatly saving labor and time costs. For example, in large-scale farmlands, it is impractical to manually adjust the angle of each sprinkler head. However, through the intelligent control system, relevant instructions or parameters can be input on the server side to remotely control the angle adjustment of all sprinkler heads. This greatly improves the efficiency and convenience of irrigation management for farmlands with vast areas and complex terrains. The automatic control can also be integrated with other agricultural management systems. For example, it can be connected to a weather station system. When the meteorological data indicates that it is about to rain, the server can automatically adjust the sprinkler watering plan, suspend or reduce the irrigation water volume to avoid wasting water resources caused by repeated irrigation. At the same time, it can also be integrated with the soil fertility monitoring system. According to the soil nutrient status and crop requirements, the fertilizer addition amount in the irrigation water can be automatically adjusted to achieve integrated management of precise fertilization and irrigation.

[0117] The intelligent control relies on the server to collect and analyze a large amount of data, including soil humidity, foliage status, meteorological conditions, and sprinkler operation parameters. These data can provide a scientific basis for irrigation decision-making, making irrigation management more precise and optimized. For example, through the analysis of historical data and the application of machine learning algorithms, the server can predict the water demand patterns of different crops at different growth stages and adjust the irrigation strategy in advance. If it is found that a certain crop shows regular changes in water demand during a specific growth cycle, the system can automatically increase or decrease the irrigation volume during the corresponding time period to achieve the best irrigation effect.

[0118] As Figure 2 shown, the working process of the present invention is as follows:

[0119] S1. Taking a corner of the farmland as the origin, establish a three-dimensional rectangular coordinate system, with the x-axis along the length direction of the farmland, the y-axis along the width direction of the farmland, and the z-axis perpendicular to the ground upward, construct a three-dimensional coordinate model, and mark the soil humidity sensors, spectrometers, and sprinkler heads in the three-dimensional coordinate model;

[0120] S2. Use the soil humidity sensors to measure the soil humidity, use the spectrometers to measure including the cleanliness, water content, and pest-infested parts of the monitored foliage; and calculate the water demand, mark the water demand positions and water demand amounts in the three-dimensional coordinate model. The water demand position markings are water shortage, cleanliness, and pest infestation;

[0121] S3. Calculate the distance between the nearest sprinkler head and the water demand position;

[0122] S4. Analyze the horizontal and vertical angles of the sprinkler heads in combination with water pressure, wind speed, and wind direction;

[0123] S5. Use the central control device to control the angle and water spray volume of the sprinkler heads.

[0124] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An intelligent and precise irrigation direction control system for farmland based on the Internet of Things, characterized by: include: Data acquisition module: including soil moisture sensor, spectrometer and weather station. The spectrometer is used to analyze the state of branches and leaves, and the weather station is used to measure wind speed and direction, and transmit data to the central control device through wireless communication; Data processing and analysis module: including central control equipment, water demand calculation algorithm, optimal sprinkler calculation algorithm and sprinkler angle adjustment algorithm. The central control equipment receives and processes data; Data labeling and classification unit: According to the soil moisture measurement results and the state of branches and leaves, the water demand location and water demand are obtained, and classified and marked in the three-dimensional model; Three-dimensional coordinate model building module: Take a corner of the farmland as the origin to establish a three-dimensional rectangular coordinate system, and mark the coordinates of the soil moisture sensor, spectrometer and sprinkler module in the three-dimensional model; Nozzle angle adjustment device: The nozzle is equipped with a device for adjusting the angle in the horizontal and vertical directions; Nozzle water volume regulating unit: The nozzle is equipped with a flow sensor and a flow regulating valve; Control instruction execution unit: sends control instructions to the nozzle angle adjustment device according to the horizontal angle and vertical angle obtained by the nozzle angle adjustment algorithm, and controls and adjusts the water output of the nozzle according to the water demand.

2. According to the IoT-based farmland intelligent precision irrigation direction control system of claim 1, it is characterized by: The data acquisition module's acquisition method is: Soil moisture measurement: Soil moisture sensors are distributed in the farmland in a grid pattern to measure soil moisture. In the xOy plane, a sensor is arranged at a distance Δx along the x-axis direction, and a sensor is arranged at a distance Δy along the y-axis direction, forming a number of rectangular grids with side lengths of Δx and Δy. Each grid point is (i, j). The data measured by the sensor is sent to the data processing center via wireless transmission; Branch and leaf status collection: The spectrometer is installed on a movable bracket and scans and measures the plant branches and leaves in a direction perpendicular to the xOy plane. At each measurement position, the spectrometer obtains the spectral reflectance curve of the branches and leaves at that position, and compares and analyzes it with the pre-established standard spectral database of branches and leaves in different states to determine the cleanliness, moisture content and whether there are insect pests in the branches and leaves at that position.

3. According to claim 2, the intelligent precision irrigation direction control system for farmland based on the Internet of Things is characterized by: The three-dimensional rectangular coordinate system in the three-dimensional coordinate model construction module is constructed in the following manner: taking a corner of the farmland as the origin, a three-dimensional rectangular coordinate system is established, with the x-axis along the length direction of the farmland, the y-axis along the width direction of the farmland, and the z-axis perpendicular to the ground and upward; The x-axis is along the length of the farmland. The distance from a certain point in the length of the farmland to the origin is Δx, and the coordinate of this point on the x-axis is (Δx, 0, 0); the y-axis is along the width of the farmland, and its coordinate on the y-axis is (0, Δy, 0); the z-axis is perpendicular to the ground and upward. When the height of a certain position from the ground is Δz, the coordinate of this point on the z-axis is (0, 0, Δz).

4. According to claim 2, the intelligent precision irrigation direction control system for farmland based on the Internet of Things is characterized by: The coordinate marking method of the soil moisture sensor, spectrometer and sprinkler module in the three-dimensional coordinate model building module is as follows; Soil moisture sensor coordinate marking: The position of the sensor in the farmland plane is known, and its distance from the origin in the length direction is Δx s , the distance in the width direction is Δy s ; The coordinate of the z-axis direction is 0, and the coordinate of the soil moisture sensor in the three-dimensional coordinate model is (Δx s ,Δy s ,0); Spectrometer coordinate marking: The spectrometer is installed on a movable bracket, and the distance from the origin in the plane of the farmland is Δx in the length direction. p , the distance in the width direction is Δy p ; Since the spectrometer is above the ground, its coordinate in the z-axis direction is H, and the coordinate of the spectrometer in the three-dimensional coordinate model is (Δx p ,Δy p ,H); Nozzle module coordinate marking: There are n sprinklers in the farmland. For the ath sprinkler (a=1,2,3…n), its coordinate is (x a ,y a ,z a ).

5. According to claim 2, the intelligent precision irrigation direction control system for farmland based on the Internet of Things is characterized by: The method for classifying and marking the water demand position and water demand in the data labeling and classification unit is as follows: the water demand position and water demand are obtained according to the soil moisture measurement result of the soil moisture sensor and the state of branches and leaves measured by the spectrometer; The annotation of soil moisture measurement results in the three-dimensional coordinate model: For the grid point (i, j), its coordinates in the three-dimensional coordinate system are (x i ,y j ,z0), z0 is the height of the soil surface; The annotation of the branch and leaf status in the three-dimensional coordinate model: For the position (x, y) measured by the spectrometer, the corresponding branch and leaf position in the three-dimensional space is (x, y, z t ), z t The value is determined by the height of the measuring instrument and the actual height of the plant branches and leaves; Cleanliness labeling: Use different icons or colors to indicate the cleanliness of branches and leaves; Moisture content labeling: Use the height or color of the cube to indicate the moisture content of the branches and leaves; Pest location marking: When pests are detected, a red skull icon or different marking symbols will be used to indicate the corresponding branch and leaf locations.

6. The intelligent precision irrigation direction control system for farmland based on the Internet of Things according to claim 2 is characterized by: The water demand calculation algorithm in the data processing and analysis module is as follows: Calculation of water requirement for soil moisture: Determine the target value of soil moisture, calculate the soil moisture deficit for each grid point (i, j), convert the soil moisture deficit into soil water potential deficit according to the soil moisture characteristic curve, and then calculate the irrigation water required for each grid point in combination with the root distribution depth factor of the soil; Calculation of water requirement based on leaf and branch moisture content: Set a leaf moisture threshold. When the leaf and branch moisture content is lower than the threshold, increase the water requirement.

7. The intelligent precision irrigation direction control system for farmland based on the Internet of Things according to claim 2 is characterized by: The classification marking method of the water demand location and water demand in the data labeling and classification unit is as follows: Water demand location marking: Based on the calculation results of water demand, the water demand location is distinguished in the three-dimensional coordinate model. If the water demand at the grid point (i, j) is greater than 0, the location is marked as a water shortage location, and a red flashing icon is used at the coordinate (x i ,y j ,z0) shows that the water demand of branches and leaves is shown at the corresponding branch and leaf position (x,y,z t ) is marked with a red water drop icon; Water demand mark: The specific water demand value is displayed next to the mark icon of the water demand location.

8. The intelligent precision irrigation direction control system for farmland based on the Internet of Things according to claim 1 is characterized by: The optimal nozzle calculation algorithm in the data processing and analysis module is as follows: Determine the coordinates of the sprinkler and the water demand position: Assume that in three-dimensional space, the sprinkler position is represented by the coordinates (x a ,y a ,z a ), where a=1,2,…,n, n is the number of sprinklers; the location where water is needed is represented by the coordinates (x w ,y w ,z w ), where w = 1, 2, ..., q, q is the number of water demand locations; Calculate its distance to all sprinklers: The distance formula between two points is calculated based on the Euclidean distance formula; Find the shortest distance: By comparing all calculated nozzle distances, find the shortest distance.

9. The intelligent precision irrigation direction control system for farmland based on the Internet of Things according to claim 1, characterized in that: The nozzle angle adjustment algorithm in the data processing and analysis module is as follows: Horizontal angle: In the absence of wind, the nozzle sprays directly at the location where water is needed, and the horizontal angle is 0 degrees; in the presence of wind, the wind speed is v, and the angle between the wind direction and the horizontal vector from the nozzle to the location where water is needed is α. According to Newton's second law: a = k·v·cosα, where k is a coefficient related to water flow characteristics and air resistance; Assume that the initial velocity of the sprinkler is v0, and the horizontal distance from the sprinkler to the location where water is needed is d (unit: m). According to the kinematic formula: Among them, t is the time for water to flow from the nozzle to the water demand location, and the formula is: Horizontal displacement: The actual horizontal angle θ1 is calculated by the inverse tangent function, assuming Δx is the actual horizontal offset of the water flow: but Vertical angle: Determine the coordinate system and related parameters, establish a spatial rectangular coordinate system with the location of the nozzle as the origin, the x-axis is horizontal, the y-axis is vertical and upward, and the z-axis is perpendicular to the xOy plane and outward. Assume that the installation height of the nozzle is h1, the height of the water-requiring position is h2, and the lifting angle of the nozzle is β1; the component of the nozzle's water outlet velocity in the vertical direction is v 0y =v0·sinβ1, the horizontal component is v 0x =v0·cosβ1; The influence of water pressure on the range: Assume that the water pressure is P (unit: Pa), the nozzle cross-sectional area is A (unit: m 2 ), the density of water flow is ρ (unit: kg / m 3 ), the range L is expressed as Among them, c is a coefficient related to the sprinkler and pipeline structure; Calculate the vertical angle: The water flow performs vertical upward motion in the vertical direction. The height difference from the nozzle to the water-required position is: Δh = h2-h1. According to the formula for vertical upward motion Where g is the acceleration of gravity. If Δh≤h, the water-demanding position is within the trajectory of the vertical upward motion of the sprinkler. At this time, the vertical angle is solved according to the kinematic formula. Assuming that the time for the water flow from the sprinkler to the water-demanding position is t1, we have At the same time, in the horizontal direction, d = v 0x t1, combine the two formulas to find t1, and then get v 0y ;based on get 10. The intelligent precision irrigation direction control system for farmland based on the Internet of Things according to claim 1, characterized in that: The central control device obtains the nearest nozzle based on the optimal nozzle calculation algorithm, and then uses the angle sensor on the nozzle to obtain the horizontal angle and vertical angle of the nozzle at this time, as well as the horizontal angle and vertical angle of the nozzle when spraying water obtained by the nozzle angle adjustment algorithm, calculates the angle difference between the two, and uses the PID control algorithm to calculate the adjustment amount based on the angle difference, thereby controlling the nozzle angle adjustment device and controlling the water output of the nozzle according to the water demand.

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