A smart agricultural monitoring method and system based on the Internet of Things

Abstract

The application provides a smart agricultural monitoring method and system based on the Internet of Things, and relates to the field of agricultural informatization. The application marks water and nitrogen nutrients, obtains their morphological transformation path and rate in the crop and soil system, and constructs a biochemical substance flow network; uses a three-dimensional radio frequency sensing network to collect the spatial displacement trajectory of water and nitrogen nutrients, and constructs a physical migration network; fuses the two networks to construct a migration analysis mechanism, analyzes the substance form and stock at a specific position, quantitatively analyzes the competition and distribution of water and nitrogen among crops, soil and microorganisms, identifies the circulating high-loss area and the retention amount, generates path reconstruction instructions and morphological conversion instructions, realizes efficient resource recycling of water and nitrogen nutrients, can realize multi-dimensional accurate analysis of the migration and transformation of farmland water and nitrogen, identify nutrient circulation bottlenecks, actively optimize and control, effectively reduce nutrient loss, improve resource utilization efficiency, and provide a scientific basis for the accurate management of smart agriculture.

Description

TECHNICAL FIELD

[0001] The application relates to the field of agricultural information technology, and in particular to a smart agricultural monitoring method and system based on the Internet of Things. BACKGROUND

[0002] In smart agriculture, the efficient use of water and nitrogen nutrients such as water, ammonium nitrogen, and nitrate nitrogen directly affects crop growth and resource sustainability. Traditional extensive management cannot accurately monitor the dynamic migration and morphological transformation of water and nitrogen in the soil-crop system, leading to resource waste and environmental pollution. Therefore, an intelligent monitoring method that can analyze water and nitrogen migration paths, quantify nutrient distribution relationships, and dynamically optimize control is urgently needed to achieve precise fertilization, water-saving irrigation, and nutrient recycling.

[0003] Currently, some studies use a soil nutrient monitoring system based on a wireless sensor network to achieve data collection and analysis by deploying soil temperature and humidity, conductivity, and nitrogen content sensors combined with Internet of Things technology. This system can monitor key soil parameters and use machine learning algorithms to predict nutrient distribution, providing decision support for irrigation and fertilization.

[0004] However, this solution relies on discrete sensors and cannot construct a three-dimensional migration trajectory of water and nitrogen, leading to inaccurate physical migration path analysis. It can only detect the total amount of nitrogen and cannot distinguish between different chemical transformation processes, affecting nutrient utilization efficiency evaluation. Moreover, it relies on historical data modeling and cannot identify high-loss areas or actively adjust water and nitrogen flow paths, making optimization measures lack timeliness. SUMMARY

[0005] The application aims to provide a smart agricultural monitoring method and system based on the Internet of Things to solve the problems of inaccurate water and nitrogen migration trajectory monitoring, missing nutrient morphological transformation process, and lagging control decision-making in the prior art.

[0006] To solve the above technical problems, in a first aspect, the application provides a smart agricultural monitoring method based on the Internet of Things, comprising:

[0007] By marking the pre-set water and nitrogen nutrients, the morphological transformation path and reaction rate of the water and nitrogen nutrients in the crop and soil system caused by biological and chemical effects are obtained to construct a biochemical substance flow network, which is used to represent the material morphological changes of the water and nitrogen nutrients;

[0008] By deploying a three-dimensional radio frequency sensor network in the crop and soil system, the three-dimensional spatial displacement trajectory of the water and nitrogen nutrients is collected to construct a physical migration network, which is used to represent the physical location changes of the water and nitrogen nutrients;

[0009] Fusing the biochemical substance flow network and the physical migration network, constructing a migration analysis mechanism of the water and nitrogen nutrients, and analyzing the substance form and stock data of the water and nitrogen nutrients at a specific spatial position through the migration analysis mechanism;

[0010] Based on the analysis result, quantitatively analyzing the competitive allocation relationship between the water and nitrogen nutrients among crops, soil and microorganisms, and identifying a high loss area in the material circulation process and a loss amount of the water and nitrogen nutrients retained in the high loss area according to the competitive allocation relationship;

[0011] According to the high loss area and the loss amount, generating path reconstruction instructions and form conversion instructions, the path reconstruction instructions are used to reconstruct the flow path of the water and nitrogen nutrients, and the form conversion instructions convert the nitrogen retained in the water and nitrogen nutrients into a slow-release form, so as to realize the resource circulation of the water and nitrogen nutrients.

[0012] Optionally, the fusion of the biochemical substance flow network and the physical migration network, the construction of the migration analysis mechanism of the water and nitrogen nutrients, and the analysis of the substance form and stock data of the water and nitrogen nutrients at a specific spatial position through the migration analysis mechanism, comprising:

[0013] According to the position data and form data of the water and nitrogen nutrients, a position and form correspondence table is generated to associate the substance form nodes in the biochemical substance flow network with the spatial position points in the physical migration network;

[0014] According to the position data and form data of the water and nitrogen nutrients, the substance form nodes in the biochemical substance flow network are associated with the spatial position points in the physical migration network to generate a position and form correspondence table; through the position and form correspondence table, the nodes and edges of the physical migration network and the nodes and edges of the biochemical substance flow network are superimposed to form a joint network structure, the nodes in the joint network structure contain spatial position information and substance form information, and the edges contain displacement distance and reaction rate information;

[0015] Based on the joint network structure, a resolution function is defined to construct a migration analysis mechanism of the water and nitrogen nutrients, and the input of the migration analysis mechanism is a specific spatial position coordinate, and the output is the substance form and stock data of the water and nitrogen nutrients at the specific spatial position;

[0016] For the resolution function, the node corresponding to the input coordinate is queried in the joint network structure, the substance form information of the node is directly extracted, and the stock data of the specific spatial position is calculated through the reaction rate information and displacement distance of the adjacent edges.

[0017] Optionally, in the joint network structure, the node corresponding to the input coordinate is queried, the material form information of the node is directly extracted, and the inventory data of the specific spatial position is calculated through the reaction rate information and displacement distance of the adjacent edge, including:

[0018] The node with the minimum spatial distance from the input coordinate is located as a target node, and the material form information of the target node is directly read;

[0019] All adjacent edges directly connected with the target node are obtained, and are distinguished into inflow edges and outflow edges according to directions;

[0020] The sum of the product of the displacement distance and the reaction rate of the inflow edge is calculated as the input total amount, and the sum of the product of the displacement distance and the reaction rate of the outflow edge is calculated as the output total amount;

[0021] The difference between the input total amount and the output total amount is divided by the unit time to obtain the inventory data of the specific spatial position.

[0022] Optionally, based on the analysis result, the competitive allocation relationship of the water and nitrogen nutrient elements among crops, soil and microorganisms is quantitatively analyzed, and the high loss area in the material circulation process and the loss amount of the water and nitrogen nutrient elements retained in the high loss area are identified according to the competitive allocation relationship, including:

[0023] Based on the analysis result, the inventory data of crops, soil and microorganisms at a specific spatial position is extracted, including crop inventory data, soil inventory data and microorganism inventory data;

[0024] Based on the crop inventory data, soil inventory data and microorganism inventory data, dynamic competitive allocation calculation is performed combined with the proportional relationship between the inventory data to generate a set of dynamic allocation coefficients;

[0025] The actual allocation amount of the crop inventory data, soil inventory data and microorganism inventory data within a unit time is calculated according to the set of dynamic allocation coefficients, and a three-dimensional allocation relationship matrix is constructed based on the actual allocation amount as the competitive allocation relationship;

[0026] Based on the competitive allocation relationship, the dimensional position with the allocation amount continuously lower than a preset threshold in the specific spatial position is identified, the specific spatial position corresponding to the dimensional position is marked as a high loss area, and the loss amount of the water and nitrogen nutrient elements retained is calculated by subtracting the sum of the actual allocation amounts of all specific spatial positions from the total input amount of the water and nitrogen nutrient elements.

[0027] Optionally, the path reconstruction instruction is used to reconstruct the flow path of the water-nitrogen nutrient elements, and the morphological conversion instruction is used to convert the nitrogen element retained in the water-nitrogen nutrient elements into a slow-release form, so as to realize resource recycling of the water-nitrogen nutrient elements, comprising:

[0028] Based on the spatial attributes of the high-loss area, the path reconstruction instruction is generated in combination with the topological relationship of the physical migration network, and based on the loss amount, the slow-release conversion calculation is performed to generate the morphological conversion instruction;

[0029] Based on the path reconstruction instruction, the edge weight value of the physical migration network of the high-loss area to the target area is increased, and the edge weight value to the non-target area is reduced, so that the water-nitrogen nutrient elements are directed to migrate to the target area, and the loss of the water-nitrogen nutrient elements to the non-target area is inhibited, so as to reconstruct the flow path of the water-nitrogen nutrient elements, wherein the target area includes a crop root enrichment zone and a microbial activity zone, and the non-target area includes a groundwater infiltration zone and an atmospheric volatilization zone;

[0030] The morphological conversion instruction is executed to convert the nitrogen element retained in the high-loss area into a slow-release form;

[0031] The slow-release form of nitrogen element obtained by conversion is migrated to the target area, so that the slow-release form of nitrogen element participates in the material allocation cycle in the target area, so as to realize resource recycling of the water-nitrogen nutrient elements.

[0032] Optionally, the water-nitrogen nutrient elements are marked, the morphological conversion path and reaction rate caused by biological and chemical effects of the water-nitrogen nutrient elements in the crop and soil system are obtained, and a biochemical substance flow network is constructed, the biochemical substance flow network is used to characterize the material morphological change of the water-nitrogen nutrient elements, comprising:

[0033] The preset water-nitrogen nutrient elements are isotopically labeled, and the labeled water-nitrogen nutrient elements are introduced into the crop and soil system;

[0034] In the crop and soil system, the near-infrared spectrum sensor and the ion selective electrode array arranged in the crop stem and leaf, the root system and the soil layer are used to periodically capture the material morphological spectrum characteristics and ion concentration of the labeled water-nitrogen nutrient elements at continuous time points;

[0035] According to the changes of the material morphological spectrum characteristics and ion concentration in the time sequence, the continuous conversion sequence from the initial form to the derived form is extracted as the morphological conversion path;

[0036] A ratio of a concentration change value of the same substance form at adjacent time points to a time interval is calculated to obtain a reaction rate, and a directed weighted graph structure is constructed with the substance form as a node, a form conversion path as an edge, and the reaction rate as an edge weight, to form a biochemical substance flow network.

[0037] Optionally, the three-dimensional spatial displacement trajectory of the water and nitrogen nutrient elements is collected by the three-dimensional radio frequency sensing network deployed in the crop and soil system to construct a physical migration network, and the physical migration network is used to represent the physical position change of the water and nitrogen nutrient elements, including:

[0038] A three-dimensional radio frequency sensing network is deployed in the crop canopy, root zone, and soil profile.

[0039] Three-dimensional coordinate position data of the marked water and nitrogen nutrient elements at each time point is obtained by the three-dimensional radio frequency sensing network, and for the three-dimensional coordinate position data of adjacent time points, a displacement vector from a starting point position to an ending point position is calculated, and a corresponding modulus and displacement direction angle are recorded.

[0040] The starting point position points of the displacement vectors are connected in time sequence to form a three-dimensional spatial displacement trajectory composed of continuous spatial position points.

[0041] A directed weighted graph structure is constructed with the spatial position points as nodes, the three-dimensional spatial displacement trajectory between adjacent position points as directed edges, and the modulus and displacement direction angle of the displacement vector as complex edge weights, to form a physical migration network.

[0042] In a second aspect, the present application provides a smart agriculture monitoring system based on the Internet of Things, comprising:

[0043] A construction module is configured to mark a predetermined water and nitrogen nutrient element, obtain a form conversion path and a reaction rate caused by biological and chemical actions of the water and nitrogen nutrient element in a crop and soil system, and construct a biochemical substance flow network, wherein the biochemical substance flow network is used to represent a substance form change of the water and nitrogen nutrient element.

[0044] A second construction module is configured to collect a three-dimensional spatial displacement trajectory of the water and nitrogen nutrient element by a three-dimensional radio frequency sensing network deployed in the crop and soil system, and construct a physical migration network, wherein the physical migration network is used to represent a physical position change of the water and nitrogen nutrient element.

[0045] An analysis module is configured to fuse the biochemical substance flow network and the physical migration network, construct a migration analysis mechanism of the water and nitrogen nutrient element, and analyze substance form and stock data of the water and nitrogen nutrient element at a specific spatial position by the migration analysis mechanism.

[0046] The identification module is configured to quantitatively analyze a competitive distribution relationship among the crops, the soil and the microorganism based on the analysis result, and identify a high-loss area in a material circulation process and a loss amount of the water and nitrogen nutrient elements retained in the high-loss area according to the competitive distribution relationship.

[0047] The transformation module is configured to generate a path reconstruction instruction and a form conversion instruction according to the high-loss area and the loss amount, the path reconstruction instruction being used to reconstruct a flow path of the water and nitrogen nutrient elements, and the form conversion instruction being used to convert nitrogen retained in the water and nitrogen nutrient elements into a slow-release form, so as to realize resource circulation of the water and nitrogen nutrient elements.

[0048] In a third aspect, the present application provides an electronic device, comprising:

[0049] a memory configured to store a computer program;

[0050] a processor configured to execute the computer program to implement the steps of the method for monitoring smart agriculture based on Internet of Things according to the first aspect.

[0051] In a fourth aspect, the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method for monitoring smart agriculture based on Internet of Things according to the first aspect.

[0052] The method for monitoring smart agriculture based on Internet of Things provided by the present application can accurately track the form conversion process of water and nitrogen in a crop-soil system by marking water and nitrogen nutrient elements and constructing a biochemical material flow network, can monitor the three-dimensional space migration track of water and nitrogen by deploying a three-dimensional radio frequency sensing network to construct a physical migration network, can accurately analyze the form and stock of water and nitrogen at a specific spatial position by fusing the biochemical material flow network and the physical migration network to form a joint network structure, can identify a high-loss area in a material circulation process and a loss amount of the retained water and nitrogen nutrient elements by quantitatively analyzing the competitive distribution relationship among the crops, the soil and the microorganism, and can optimize the flow path of water and nitrogen and convert the retained nitrogen into a slow-release form to realize efficient resource recycling by generating a path reconstruction instruction and a form conversion instruction.

[0053] Further, in the process of dual-network fusion, by establishing a position and form correspondence table and constructing a joint network structure, the node contains spatial position and material form information, and the edge contains displacement distance and reaction rate information. Based on the analytical function, the water and nitrogen form and stock at a specific position are accurately analyzed, solving the problems of discontinuous spatial trajectory and invisible form transformation in traditional monitoring. In the competition and allocation analysis, by constructing a three-dimensional allocation relationship matrix and calculating the dynamic allocation coefficient, the accurate identification of high loss areas and the accurate calculation of loss amount are realized. In the regulation and execution stage, by adjusting the edge weight of the migration network to realize directional migration, and converting the retained nitrogen into slow-release form, the nutrient utilization rate is greatly improved, effectively solving the problems of water and nitrogen resource waste and environmental pollution in agricultural systems. BRIEF DESCRIPTION OF DRAWINGS

[0054] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0055] Figure 1 A flowchart of a smart agricultural monitoring method based on the Internet of Things provided by an embodiment of the present application;

[0056] Figure 2 A scene diagram of a smart agricultural monitoring method based on the Internet of Things provided by an embodiment of the present application;

[0057] Figure 3 An architecture diagram of a smart agricultural monitoring method based on the Internet of Things provided by an embodiment of the present application;

[0058] Figure 4 A structural schematic diagram of a smart agricultural monitoring system based on the Internet of Things provided by an embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the person skilled in the art better understand the present application, the present application will be further described in detail below in combination with the drawings and specific embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0060] The core of the present application is to provide a smart agricultural monitoring method based on the Internet of Things, and a flowchart of a specific embodiment thereof is shown in Figure 1 The method comprises:

[0061] S101, obtaining morphological transformation paths and reaction rates of the water and nitrogen nutrients in the crop and soil system caused by biological and chemical actions by marking the preset water and nitrogen nutrients, to construct a biochemical substance flow network for characterizing the material morphological changes of the water and nitrogen nutrients;

[0062] Optionally, S101 can specifically include the following steps:

[0063] 1011, isotopically marking the preset water and nitrogen nutrients, and introducing the marked water and nitrogen nutrients into the crop and soil system;

[0064] 1012, periodically capturing material morphological spectral features and ion concentrations of the marked water and nitrogen nutrients at consecutive time points in the crop and soil system through near-infrared spectrum sensors and ion selective electrode arrays deployed on crop stems and leaves, root systems, and soil layers;

[0065] 1013, extracting a continuous transformation sequence from an initial form to a derived form as a morphological transformation path according to changes in the material morphological spectral features and ion concentrations over time;

[0066] 1014, calculating a ratio of a concentration change value of the same material form at adjacent time points to a time interval to obtain a reaction rate, and constructing a directed weighted graph structure with the material form as a node, the morphological transformation path as an edge, and the reaction rate as an edge weight, to form a biochemical substance flow network.

[0067] In the above scheme, the water and nitrogen nutrients refer to the total of water and nitrogen nutrients necessary for crop growth, including forms such as ammonium nitrogen and nitrate nitrogen; isotopic labeling refers to labeling nitrogen with stable isotopes so that it can be distinguished from ordinary nitrogen in the natural environment in detection; the near-infrared spectrum sensor refers to a device that identifies chemical components by analyzing the light wave characteristics of the reflected material; the ion selective electrode array refers to a group of probes that measure the concentration of specific ions in a solution; the morphological transformation path refers to the continuous process of the transformation of a substance from one chemical state to another state; the reaction rate refers to the amount of change in the concentration of a substance per unit time, with units of mg / kg·h; the directed weighted graph structure refers to a visualization model that uses nodes to represent material forms, arrows to represent transformation directions, and link values to represent reaction rates.

[0068] In the embodiments of the present application, first, in step 1011, stable isotopes 15 N are used to chemically replace nitrogen atoms in substances such as ammonium nitrogen and nitrate nitrogen to prepare isotopically labeled water and nitrogen nutrients in a laboratory environment. 15N-labeled nitrogen-containing substance chemical solution; then, through a hydraulic injector or an automatic irrigation system, the labeled solution is uniformly injected into the soil of the crop root layer at a concentration of 5 mg / kg, so that the target nutrient is traceable.

[0069] Secondly, through step 1012, the near-infrared spectrum sensor array is deployed at the crop stem and leaf part, the near-infrared light is actively emitted, the leaf reflection spectrum is received, the spectrum data is collected, the intensity change of the spectrum characteristic wave peak at 1350 nm is identified, and the nitrate nitrogen and other substance forms are determined; at the same time, the ion selective electrode array is arranged at the root layer and the soil profile, the potential difference generated by the contact between the electrode and the soil solution is measured, the calibration formula C=k x V is applied, the potential value is converted into the ion concentration, C is the ion concentration, k is the calibration coefficient, and V is the electrode potential measurement value; the two types of equipment synchronously collect data through the Internet of Things node at a period of 10 minutes to form a time sequence form and concentration data set; for example, in a corn field, the stem and leaf sensor detects that the 1350 nm wave peak intensity value is 120 at t0, and after 0.5 hours, the wave peak intensity at the same position decreases to 90; at the same time, the root layer ammonium electrode potential increases from 0.25 V to 0.35 V, and according to the electrode calibration formula, the nitrate nitrogen concentration is converted from 5 mg / kg to 7 mg / kg.

[0070] Then, through step 1013, the system receives the time sequence form and concentration data set, iterates adjacent time point pairs in time sequence, and calculates the concentration change amount of all forms When it is detected that the concentration of a certain form ΔC decreases significantly and the concentration of another form ΔC increases significantly, it is determined according to the principle of conservation of mass that there is a conversion relationship between the two, the form with the reduced concentration is marked as the initial node, the form with the increased concentration is marked as the derived node, and a single-step path of "initial form→derived form" is generated; if a single form decreases and multiple forms increase, a branch path is generated; finally, the adjacent time period paths are concatenated in time sequence to form a continuous conversion sequence as the form conversion path; for example, in a certain corn field, the stem and leaf nitrate nitrogen concentration is 5 mg / kg and the root system ammonium nitrogen concentration is 5 mg / kg at t0, the nitrate nitrogen decreases to 3.75 mg / kg and the ammonium nitrogen increases to 7 mg / kg at t1; the system detects that the nitrate nitrogen concentration change ΔC=-1.25 mg / kg and the ammonium nitrogen concentration change ΔC=+2 mg / kg, both of which exceed the 5% change threshold and have opposite trends, and it is determined according to the principle of conservation of mass that there is a "nitrate nitrogen→ammonium nitrogen" conversion path; if the next time period detects that the ammonium nitrogen decreases from 7 mg / kg to 5 mg / kg and the organic nitrogen increases from 10 mg / kg to 12 mg / kg, a continuous conversion sequence "nitrate nitrogen→ammonium nitrogen→organic nitrogen" is formed by concatenation.

[0071] In actual application, as shown in Figure 2 , first, a certain laboratory prepares 15N-labeled ammonium nitrogen and nitrate nitrogen solution is injected into the corn root layer soil at a concentration of 5 mg / kg using a hydraulic injector to trace nitrogen; secondly, a near-infrared sensor array is installed on the corn stem and leaf to collect the 1350 nm wave peak intensity, and an ammonium ion electrode is buried in the root layer, and the two types of equipment automatically synchronize data every 10 minutes to form a dynamic database; for example, at 6:00 in the morning, the sensor records the stem and leaf wave peak intensity of 120 units and the root potential of 0.25 volts, and at 6:30, the wave peak intensity decreases to 90 units and the potential rises to 0.35 volts; then the adjacent time period data is automatically analyzed to identify a significant reverse fluctuation of a decrease of 1.25 mg / kg in nitrate nitrogen concentration and an increase of 2 mg / kg in ammonium nitrogen within half an hour, and a "nitrate nitrogen-ammonium nitrogen" conversion path is generated according to the conservation of nitrogen; if further detection at 12:00 noon shows that the ammonium nitrogen decreases from 7 mg / kg to 5 mg / kg and the organic nitrogen increases from 10 mg / kg to 12 mg / kg, the system will form a complete conversion chain of "nitrate nitrogen-ammonium nitrogen-organic nitrogen", and intuitively present the nitrification and synthesis process of nitrogen in the soil.

[0072] The overall scheme of S101 is that the flow realizes accurate tracking of water and nitrogen elements by isotope labeling, captures the time sequence changes of the form and concentration of substances by means of a sensor network, extracts a continuous conversion path to reveal a metabolic chain, finally quantifies the reaction rate and constructs a biochemical substance flow network, and forms a "form-path-rate" three-in-one material circulation analysis system to provide a data basis for identifying nutrient retention bottlenecks.

[0073] S102, a three-dimensional radio frequency sensing network deployed in the crop and soil system is used to collect three-dimensional spatial displacement trajectories of the water and nitrogen nutrients to construct a physical migration network, and the physical migration network is used to represent the physical position changes of the water and nitrogen nutrients.

[0074] Optionally, S102 can specifically include the following steps:

[0075] 1021, a three-dimensional radio frequency sensing network is deployed in the crop canopy, root zone and soil profile;

[0076] 1022, three-dimensional coordinate position data of the labeled water and nitrogen nutrients at each time point is obtained by the three-dimensional radio frequency sensing network, and for the three-dimensional coordinate position data of adjacent time points, a displacement vector from the starting point position to the ending point position is calculated, and the corresponding modulus and displacement direction angle are recorded;

[0077] 1023, the starting point position of the displacement vector is connected in time sequence to form a three-dimensional spatial displacement trajectory composed of continuous spatial position points;

[0078] 1024、with the spatial position points as nodes, the three-dimensional space displacement trajectory between adjacent position points as directed edges, and the modulus of the displacement vector and the displacement direction angle as the complex edge weight, a directed weighted graph structure is constructed to form a physical migration network.

[0079] In the above scheme, the three-dimensional radio frequency sensing network refers to the network of wireless signal tags and receiving devices installed in the crop branch and leaf area, root soil layer, and different depth soil layers. The displacement vector refers to the position change of the marker at adjacent time points, including the moving distance and direction angle. The physical migration network refers to a network diagram formed by taking all recorded position points as nodes, the moving trajectory between adjacent position points as connecting lines, and jointly labeling the moving distance and direction angle on the lines.

[0080] In the embodiments of the present application, first, wireless signal tags and receivers are arranged in the range of 0.5 to 2 meters in height in the crop branch and leaf area, 0 to 30 centimeters in depth in the root soil layer, and every 10 centimeters in the deeper soil layer to form a three-dimensional monitoring network. For example, in a corn field, tag A is fixed on the leaf, tag B is buried in the shallow root soil, and tag C is buried in the 40 centimeter deep soil layer.

[0081] Secondly, the tag emission and multi-receiving point positioning calculation of the wireless radio frequency signal of the three-dimensional radio frequency sensing network are performed through step 1022 to obtain the three-dimensional coordinate position data of the marked water and nitrogen nutrient elements at each time point, such as tag A located at (1.0, 2.0, 0.5) at the initial time and moved to (1.3, 2.2, 0.4) after 30 minutes. The displacement vector from the starting point position to the ending point position is calculated for the three-dimensional coordinate positions of adjacent time points. The modulus of the displacement vector is: ; and the displacement direction angle is calculated according to the direction angle formula, the horizontal direction angle is , and the vertical direction angle is .

[0082] Then, the starting point positions of the displacement vectors are connected in time sequence through step 1023 to form a polyline trajectory by sequentially connecting adjacent time nodes in three-dimensional space, thereby constructing a three-dimensional space displacement trajectory composed of continuous spatial position points. For example, the initial point (1.0, 2.0, 0.5) of tag A is connected to the moved position (1.3, 2.2, 0.4), and then this point is connected to the next time period position (1.5, 2.3, 0.3), and the continuous moving path of the marker is formed by sequentially connecting in time sequence.

[0083] Finally, each recorded spatial position point is defined as a node, the three-dimensional spatial displacement trajectory between adjacent time points is identified as a directed edge from the starting point to the ending point, and the complex attribute composed of the modulus of the displacement vector and the displacement direction angle is taken as the weight of the directed edge, thereby constructing a directed weighted graph structure containing spatial nodes, directed trajectory edges, and quantitative distance and direction complex weights to form a physical migration network. For example, the complex edge weight on the directed edge from node (1.0, 2.0, 0.5) to node (1.3, 2.2, 0.4) is annotated as: {modulus: 0.37 m, horizontal angle: 33.7°, pitch angle: -15.5°}.

[0084] In practical applications, a three-dimensional monitoring network is laid out in a test field, a crop canopy is fixed with a leaf tag A, a root layer is buried with a soil tag B, and a deep soil is buried with a tag C, thereby constructing a three-dimensional monitoring network. The coordinates of tag A at t0 are (1.0, 2.0, 0.5) and the coordinates of tag B at t0 are (0.5, 1.0, -0.2). After 30 minutes, It is detected that tag A moves to (1.3, 2.2, 0.4) and tag B moves to (0.7, 1.3, -0.1). The system calculates the displacement parameters: tag A moves 0.3 meters in the X direction, 0.2 meters in the Y direction, and 0.1 meters downward in the Z direction, the actual moving distance is 0.37 meters, the horizontal angle is about 33.7°, and the vertical angle is about -15.5°. Tag B moves 0.2 meters in the X direction, 0.3 meters in the Y direction, and 0.1 meters upward in the Z direction, the moving distance is also 0.37 meters, the horizontal angle is 56.3°, and the vertical angle is 14.0°. Then tag A forms a path from (1.0, 2.0, 0.5) to (1.3, 2.2, 0.4), and tag B forms a path from (0.5, 1.0, -0.2) to (0.7, 1.3, -0.1). Finally, the four coordinate points are converted into network nodes, a directed edge is established between nodes (1.0, 2.0, 0.5) and (1.3, 2.2, 0.4) and is annotated with the weight {distance 0.37 meters, horizontal angle 33.7°, pitch angle -15.5°}, a directed edge is established between nodes (0.5, 1.0, -0.2) and (0.7, 1.3, -0.1) and is annotated with the weight {distance 0.37 meters, horizontal angle 56.3°, pitch angle 14.0°}, thereby forming a complete visual network.

[0085] The overall scheme of S102 above accurately captures the spatial movement trajectory of nutrients in the crop system through three-dimensional wireless sensing technology, converts physical motion into a visual network graph, and intuitively presents the migration direction, distance, and dynamic path of water, nitrogen, and nutrients, thereby providing a spatial motion basis for precise management of irrigation and fertilization.

[0086] S103, fuse the biochemical substance flow network and the physical migration network, construct the migration analysis mechanism of the water and nitrogen nutrient elements, and analyze the substance form and stock data of the water and nitrogen nutrient elements at a specific spatial position through the migration analysis mechanism;

[0087] Optionally, S103 can specifically include the following steps:

[0088] 1031, generate a position and form correspondence table according to the position data and form data of the water and nitrogen nutrient elements, so as to associate the substance form nodes in the biochemical substance flow network and the spatial position points in the physical migration network;

[0089] 1032, superimpose the nodes and edges of the physical migration network and the nodes and edges of the biochemical substance flow network through the position and form correspondence table to form a joint network structure, wherein the nodes in the joint network structure contain spatial position information and substance form information, and the edges contain displacement distance and reaction rate information;

[0090] 1033, define an analysis function based on the joint network structure to construct the migration analysis mechanism of the water and nitrogen nutrient elements, wherein the input of the migration analysis mechanism is a specific spatial position coordinate, and the output is the substance form and stock data of the water and nitrogen nutrient elements at the specific spatial position;

[0091] 1034, for the analysis function, query the node corresponding to the input coordinate in the joint network structure, directly extract the substance form information of the node, and calculate the stock data of the specific spatial position through the reaction rate information and displacement distance of the adjacent edges.

[0092] In the above scheme, the position and form correspondence table refers to a matching relationship table recording each three-dimensional spatial coordinate point and its corresponding substance form and concentration value; the joint network structure refers to a fusion network formed by merging the spatial position nodes of the physical migration network and the substance form nodes of the biochemical substance flow network, and simultaneously integrating the displacement parameters and reaction rate parameters; the migration analysis mechanism refers to a query function constructed based on the joint network, which can output the substance form type and estimated stock data of the position when the spatial coordinate is input.

[0093] In the above scheme, the position and form correspondence table refers to a matching relationship table recording each three-dimensional spatial coordinate point and its corresponding substance form and concentration value; the joint network structure refers to a fusion network formed by merging the spatial position nodes of the physical migration network and the substance form nodes of the biochemical substance flow network, and simultaneously integrating the displacement parameters and reaction rate parameters; the migration analysis mechanism refers to a query function constructed based on the joint network, which can output the substance form type and estimated stock data of the position when the spatial coordinate is input.

[0094] In the embodiments of the present application, firstly, all spatial position point coordinate data of the physical migration network and substance form data of the biochemical substance flow network are acquired through step 1031, the spatial position points are matched with the substance forms at the corresponding time to form a position and form correspondence table; for example, in a corn field, when the position of tag A is at coordinate (1.0, 2.0, 0.5) at time t0, the biochemical sensor detects that the water and nitrogen nutrient elements at this position are in the form of nitrate nitrogen, so the coordinate position (1.0, 2.0, 0.5) is bound with the "nitrate nitrogen" state to generate a mapping relationship table record.

[0095] Secondly, the spatial position points in the physical migration network and the substance form nodes in the biochemical substance flow network are superimposed and merged through step 1032 using the position and form correspondence table, and the displacement trajectory edges in the physical migration network and the reaction edges in the biochemical substance flow network are superimposed and merged to form a joint network structure; all nodes of the joint network structure retain the original spatial position coordinates and the newly added substance form information, and all edges contain displacement movement distance and reaction rate information. For example, in a corn field, the node (1.0, 2.0, 0.5) of the physical migration network is merged with the "nitrate nitrogen" state node of the biochemical substance flow network into a new node with attributes of {coordinate: 1.0, 2.0, 0.5, form: nitrate nitrogen}; at the same time, the displacement edge (containing a movement distance of 0.37 meters) from (1.0, 2.0, 0.5) to (1.3, 2.2, 0.4) in the physical network and the reaction edge (containing a reaction rate of 0.2 mg / h) of the corresponding period in the biochemical network are merged into a new edge with attributes of {movement distance: 0.37 m, reaction rate: 0.2 mg / h}, realizing complete fusion of physical displacement trajectory and biochemical reaction process.

[0096] Then, a resolution function is defined through step 1033 to construct a migration resolution mechanism, the input quantity of the resolution function is defined as the specific spatial position coordinate , and the resolution function is constructed according to the preset attributes of the joint network nodes: ,

[0097] Among them, the is a form query function, is a substance form, is a soil stock, is a crop stock, is a microbial stock, is the sum of the displacement distance and the reaction rate of the water and nitrogen nutrient elements flowing into the joint network node, is the sum of the displacement distance and the reaction rate of the water and nitrogen nutrient elements flowing out of the joint network node, is the unit time, to obtain the migration resolution mechanism.

[0098] Finally, the migration analysis mechanism is executed by step 1034, first locating the node with the minimum spatial distance from the input coordinates as the target node in the joint network structure, and then directly returning the substance morphology information of the target node through the morphology query function ; then obtaining all adjacent edges directly connected to the target node, distinguishing the edges into inflow edges and outflow edges according to the direction, calculating the sum of the product of the displacement distance and the reaction rate of the inflow edges as the input total amount (I), and calculating the sum of the product of the displacement distance and the reaction rate of the outflow edges as the output total amount (O); then performing multi-medium stock calculation through the stock calculation function: soil stock , crop stock , and microbial stock , and outputting the stock data of the specific spatial position; the is the unit time, corresponds to the input total amount of the target node, is the output total amount flowing out of the target node.

[0099] In actual application, the migration analysis mechanism is constructed in a corn field: first, based on the spatial nodes of the physical migration network, such as root layer point A (1.0, 2.0, 0.5) and point B (1.3, 2.2, 0.4), and the morphology data of the biochemical substance flow network, a position and morphology correspondence table is generated to record the mapping relationship between coordinates and substance morphology; then the spatial nodes of the physical migration network and the morphology nodes of the biochemical substance flow network are fused into a joint network structure, so that point A is expanded to {coordinate (1.0, 2.0, 0.5), morphology: nitrate nitrogen}, point B is expanded to {coordinate (1.3, 2.2, 0.4), morphology: nitrate nitrogen}, and the physical edge displacement distance 0.37 meters from point A to point B and the biochemical edge reaction rate 2.5 milligrams per kilogram per hour are combined into a composite weight; finally, when the input coordinates (1.3, 2.2, 0.4) are input, the system locates point B with the minimum spatial distance from the coordinates as the target node, directly extracts the substance morphology "nitrate nitrogen" through the morphology query function , then obtains the adjacent edges directly connected to point B (only the inflow edge from point A to point B, no outflow edge), calculates the sum of the product of the displacement distance and the reaction rate of the inflow edge as the input total amount , the output total amount , sets the unit time , and generates the coefficient through dynamic allocation calculation; the formula is calculated through the analysis function:

[0100] ,

[0101] The final complete result is: the substance morphology is nitrate nitrogen, the soil stock 0.375 mg per kg, crop stock 0.4625 mg per kg, microorganism stock 0.088 mg per kg. The overall scheme of S103 realizes the analytical function of "location input and full-element nutrient state output" by establishing the accurate mapping of spatial coordinates to material form and multi-medium stock data through the fusion of physical migration and biochemical reaction network, and provides a scientific basis for zonal precision fertilization.

[0102] S104, based on the analysis result, quantitatively analyzes the competitive allocation relationship between the water and nitrogen nutrients among crops, soil and microorganisms, and identifies the high loss area in the material circulation process and the loss amount of the water and nitrogen nutrients retained according to the competitive allocation relationship;

[0103] Optionally, S104 can specifically include the following steps:

[0104] 1041, based on the analysis result, extracting stock data of crops, soil and microorganisms at a specific spatial location, the stock data including crop stock data, soil stock data and microorganism stock data;

[0105] 1042, based on the crop stock data, soil stock data and microorganism stock data, performing dynamic competitive allocation calculation combined with the proportional relationship among the stock data to generate a set of dynamic allocation coefficients;

[0106] 1043, calculating the actual allocation amount of the crop stock data, soil stock data and microorganism stock data in a unit time according to the set of dynamic allocation coefficients, and constructing a three-dimensional allocation relationship matrix based on the actual allocation amount as the competitive allocation relationship;

[0107] 1044, based on the competitive allocation relationship, identifying the dimensional position in the specific spatial location point whose allocation amount is continuously lower than a preset threshold, marking the specific spatial location point corresponding to the dimensional position as a high loss area, and simultaneously calculating the loss amount of the water and nitrogen nutrients retained by subtracting the sum of the actual allocation amounts of all specific spatial location points from the total amount of water and nitrogen nutrient input.

[0108] In the above scheme, the stock data refers to the water and nitrogen distribution amount obtained at a specific location by the migration analysis function, including crop stock, soil stock and microorganism stock; the dynamic allocation coefficient refers to the weight value calculated based on the stock proportion; the competitive allocation relationship refers to the actual nutrient allocation result quantified by the three-dimensional matrix; the high loss area refers to the position dimension whose allocation amount is continuously lower than the preset threshold; and the loss amount refers to the nutrient amount retained without participating in the allocation.

[0109] In the embodiments of the present application, firstly, the crop stock data, soil stock data and microbial stock data on the three-dimensional trajectory points of the water and nitrogen nutrients are obtained by calling the migration analysis function through step 1041.

[0110] Secondly, the crop stock data, soil stock data and microbial stock data are obtained through step 1042, and the daily crop, soil and microbial water and nitrogen stock proportions of the three-dimensional trajectory points are calculated, which are specifically defined as: , , the soil and microbial water and nitrogen proportions are calculated in the same way; then, the dynamic competition allocation calculation is performed through the dynamic smoothing formula: , the is a dynamic allocation coefficient of day t, is a dynamic allocation coefficient of day t-1, the dynamic allocation coefficient fuses historical data to represent the dynamic evolution of the competition ability, wherein t represents the current day, t-1 represents the previous day, is a time attenuation factor, is the water and nitrogen stock proportion of the crop, soil and microorganism; finally, all the three-dimensional trajectory points are iteratively calculated day by day to generate a dynamic allocation coefficient set containing time dimension, space dimension and object dimension.

[0111] Then, based on the dynamic allocation coefficient set, the total amount of water and nitrogen input per unit time is obtained through step 1043, and the input amount is distributed according to the actual spatial weight represented by the three-dimensional trajectory points, and the formula is ; then, the dynamic allocation coefficient is called, and the actual allocation amount is calculated for soil, crop and microorganism, for example, the actual water gain of crop = point water input amount x , the actual nitrogen gain of microorganism = point nitrogen input amount x ; in this way, a three-dimensional allocation relationship matrix is constructed, the row dimension is time sequence, the column dimension is spatial point, and the layer dimension is allocation amount category, and the matrix element is filled according to the actual allocation amount of the first day, the first point and the first category, for example, represents the microbial nitrogen allocation amount of the first 5 point in the first day; the matrix directly quantifies the resource competition result, for example, the microbial nitrogen allocation amount in the corn field is continuously higher than the crop nitrogen, which reveals the competition relationship of nutrient inclination to microorganism.

[0112] Finally, based on the three-dimensional allocation relationship matrix, the high loss area is identified through step 1044; taking the microbial nitrogen allocation amount layer as an example, a scientific threshold is set, such as the microbial nitrogen threshold , and the continuous N-day data sequence of all three-dimensional trajectory points is scanned, when the microbial nitrogen allocation amount meets the continuous N-day less than When the three-dimensional trajectory point is marked as a high loss area, the process of identifying the high loss area of other allocation layers is the same; Calculate the total loss amount: extract the total amount of water and nitrogen input in unit time , sum the actual allocation amount by element category from the matrix (J is the total number of three-dimensional trajectory points), and finally the loss amount is obtained , and the amount of retained elements is quantified.

[0113] In practical application, a 10-day water and nitrogen observation was carried out in a 10-hectare corn test field, with 500 cubic meters of irrigation water and 10 kilograms of nitrogen applied daily, and the space weight was evenly distributed (0.2 weight per point). Through isotope labeling analysis, the first day P1 point inventory data was obtained, among which the microbial water inventory = 0.6 x 1000 = 600 kg / ha, the microbial nitrogen inventory = 16 kg / ha, and the crop water inventory was 1300 kg / ha, and the soil water inventory was 160000 kg / ha.

[0114] Assume the time decay factor is 0.9 (determined by data fitting during water and nitrogen observation), is 0 (determined by the preset of no historical dynamic allocation coefficient at the initial moment), then by substituting the above data into the formula: , the proportion of microbial water inventory is calculated , and the dynamic smoothing formula is substituted , to further calculate the actual water gain of P1 point microorganisms = irrigation amount x weight x = (500 x 0.2) x 0.0033 = 0.33 cubic meters, since the matrix element filling rule is the actual allocation amount of the first point of the first category on the first day, so the actual water gain of P1 point microorganisms 0.33 cubic meters on the first day is stored in the three-dimensional matrix M[1,1,5].

[0115] Set the microbial water threshold to 0.3 cubic meters / day, and assume that the microbial water gain sequence of P1 point for 5 consecutive days is [M[1,1,5]=0.33, M[2,1,5]=0.29, M[3,1,5]=0.28, M[4,1,5]=0.31, M[5,1,5]=0.27], all of which are lower than the threshold, then mark P1 as a high loss area, and calculate the total loss of 10 days: total input water = 500 cubic meters / day x 10 days = 5000 cubic meters, , loss water = 5000-4720 = 280 cubic meters, indicating that 28% of the irrigation water is not utilized.

[0116] The overall scheme of S104 above establishes a basic database through spatial inventory monitoring, quantifies resource competition processes using a dynamic scaling algorithm, constructs a three-dimensional model to visualize distribution patterns, and finally accurately identifies resource utilization weak areas and calculates system loss. This is the first time that resource competition has been evaluated in the microbial dimension, revealing the phenomenon of microbial resource deficiency that has been overlooked in traditional management, and providing new evidence for optimizing agricultural resource allocation.

[0117] S105, according to the high loss area and the loss amount, generating path reconstruction instruction and form conversion instruction, the path reconstruction instruction is used to reconstruct the flow path of the water and nitrogen nutrient elements, the form conversion instruction converts the nitrogen element retained in the water and nitrogen nutrient elements into slow-release form, to realize the resource circulation of the water and nitrogen nutrient elements.

[0118] Optionally, S105 can specifically include the following steps:

[0119] 1051, based on the spatial attributes of the high loss area, combined with the topological relationship of the physical migration network to generate path reconstruction instructions, and based on the loss amount, perform slow-release conversion calculation to generate form conversion instructions;

[0120] 1052, based on the path reconstruction instruction, increase the edge weight value of the physical migration network of the high loss area pointing to the target area, and reduce the edge weight value pointing to the non-target area, so that the water and nitrogen nutrient elements are directed to migrate to the target area, and the loss of the water and nitrogen nutrient elements to the non-target area is inhibited, to reconstruct the flow path of the water and nitrogen nutrient elements, wherein the target area includes crop root enrichment area and microbial activity area, and the non-target area includes groundwater infiltration area and atmospheric volatilization area;

[0121] 1053, execute the form conversion instruction to convert the nitrogen element retained in the high loss area into slow-release form;

[0122] 1054, migrate the slow-release form nitrogen obtained by conversion to the target area, so that the slow-release form nitrogen participates in the material distribution cycle in the target area, to realize the resource circulation of the water and nitrogen nutrient elements.

[0123] In the above scheme, the path reconstruction instruction refers to an instruction for adjusting the water and nitrogen movement direction generated according to the spatial position of the high loss area and the connection relationship of the physical migration network; the slow-release conversion calculation refers to a calculation process of converting the easily lost available nitrogen into a slow-release form; the form conversion instruction is an operation command for guiding the nitrogen form conversion; the physical migration network refers to a directed graph structure describing the water and nitrogen movement path in space; the edge weight value represents the material migration tendency intensity of different paths, and increasing the weight can make the material preferentially select the path; the target area refers to an area that needs to be preferentially supplied with resources, such as a crop root enrichment area and a microbial activity area; the non-target area refers to an area that is prone to cause resource loss, such as a groundwater infiltration area and an atmospheric volatilization area; the slow-release form nitrogen refers to a nitrogen compound that can slowly release nutrients after chemical treatment, such as coated urea; and the resource circulation refers to making the retained nutrients re-enter the utilization process.

[0124] In the embodiment of the application, first, all edges starting from the high loss area are identified through step 1051, and the spatial distance ratio of the target area to the non-target area is calculated According to the r value, the weight adjustment range is set, the target area edge weight promotion ratio is , and the non-target area weight reduction ratio is , to form a weight adjustment instruction set as the path reconstruction instruction; at the same time, based on the loss amount, the slow-release conversion calculation is performed , the is the allocation amount loss day, and the nitrogen amount that needs to be converted and the target slow-release form are output to generate the form conversion instruction;

[0125] Then, based on the target area weight promotion coefficient and the non-target area weight reduction coefficient in the path reconstruction instruction, the physical migration network edge weight directional adjustment is performed: first, the target path weight of the high loss area is promoted: , the refers to the original weight value of the directed edge of the target area, refers to the new weight value of the directed edge of the target area adjusted according to the path reconstruction instruction; and the non-target path weight is compressed at the same time: , the refers to the original weight value of the directed edge of the non-target area, refers to the new weight value of the directed edge of the non-target area adjusted according to the path reconstruction instruction; after the weight adjustment, the selection probability of the water and nitrogen migration path is proportional to the edge weight value, so the probability of flowing to the target area is promoted to , and the directional migration is realized by improving the statistical selection probability of the target path; for example, the root area weight = 0.3 x 2.412 = 0.7236, and the groundwater area weight = 0.6 x 0.542 = 0.3252, so the probability of water and nitrogen nutrients flowing to the root area is promoted to , so that more water and nitrogen elements migrate to the root zone;

[0126] Then, when the morphological conversion instruction is executed through step 1053, first, the nitrogen element retained in the high-loss area is located, and the conversion amount determined according to the slow-release conversion calculation , wherein the is the number of consecutive loss days, is the retention amount; under the conditions of catalyst and temperature control, the available nitrogen is converted into slow-release form through chemical reaction, and the conversion efficiency , and the t is the number of reaction hours; for example, for 1.8 kg of nitrate, , kg, after 2 hours of reaction , that is, 1.61 (1.66 x 97.3) kg of coated urea is generated.

[0127] Finally, through step 1054, the slow-release form of nitrogen is directed to migrate to the target area according to the path weight ratio by using the above adjusted physical migration network; in the target area, the slow-release kinetic model , wherein the is the release amount, is the initial amount, is the release rate, t is the time, the available nitrogen is released day by day, the release amount participates in the stock monitoring, and through dynamic competition allocation, resource recycling is realized.

[0128] In actual application, after detecting that P3 point (coordinates x=2.1 m, y=1.5 m, z=-0.2 m) in corn field A area becomes a high-loss area, first, the spatial distance ratio =0.294 is calculated, and then two key instructions are generated according to this ratio: the target area weight promotion coefficient and the non-target area inhibition coefficient are obtained by the path reconstruction instruction; at the same time, the nitrogen amount to be converted is calculated by the morphological conversion instruction; then, the weight is adjusted, the weight of P3 pointing to the root zone is updated to =0.7236, and the weight of P3 pointing to the groundwater area is adjusted to =0.3252, which significantly improves the selection probability of the target path; then, 1.66 kg of nitrate is converted into coated urea by adding an iron-based catalyst, and finally 1.61 kg of slow-release nitrogen is obtained through a 97% efficient reaction. Finally, it is monitored that the slow-release nitrogen is directed to transport along the adjusted migration network, wherein the amount obtained by the root zone is , and the slow-release nitrogen continuously releases nutrients according to the release model , releasing 0.063 kg of available nitrogen on the first day; when these nutrients are integrated into the soil stock, through the dynamic allocation coefficient =0.6, the crop successfully obtains 0.038 kg of nitrogen, and finally achieves a resource circulation optimization effect of reducing groundwater loss by 58% and improving microbial nitrogen cycle utilization rate to 78%.

[0129] The overall scheme of S105 reconstructs the water and nitrogen migration path to realize directional delivery, converts the retained nitrogen into a slow-release form to reduce loss, and finally makes the nutrients in the target area participate in material circulation, forming a full-chain resource optimization system, which significantly improves the nutrient utilization efficiency and sustainability of the agricultural system.

[0130] The intelligent agricultural monitoring method based on the Internet of Things provided in the present application first acquires the spatial characteristics of the high-loss area through intelligent identification 15 The ammonium nitrate solution is labeled with N isotope and injected into the root layer, the near-infrared spectrum sensor is used to monitor the 1530nm wave peak, and the ion selective electrode array is used to collect data every 30 minutes; at t0, the nitrate nitrogen concentration is detected to be 8mg / kg, (1 hour later) the concentration decreases to 5mg / kg, and the ammonium nitrogen increases from 3mg / kg to 6mg / kg; then calculate the concentration change ΔC (nitrate nitrogen: -3mg / kg, ammonium nitrogen: +3mg / kg), extract the conversion path "nitrate nitrogen→ammonium nitrogen"; calculate the reaction rate to get r=ΔC / Δt=3mg / kg·h, and then construct a biochemical network with "nitrate nitrogen, ammonium nitrogen" as nodes, conversion path as edges, and 3mg / kg·h as weight.

[0131] Then, deploy a three-dimensional radio frequency sensor network in the canopy, root zone, and deep soil layer to obtain the nitrogen coordinates labeled at t0 , coordinates at t1 , then calculate the displacement vector module: =0.14m, the horizontal direction angle =225°; connect to form a trajectory, and construct a physical network with , as nodes, displacement trajectory as edges, and {0.14m, 225°} as composite weight.

[0132] Then, execute S103 to fuse the network and build a migration analysis mechanism: generate a position and form correspondence table, associate the point with the "nitrate nitrogen" form; superimpose the networks to form a joint node { (1.2, 0.8, -0.3), nitrate nitrogen}, and define its adjacent edge attributes (inflow edge: displacement distance =0.2812m, reaction rate =0.1mg / kg·h; outflow edge: displacement distance =0.001m, reaction rate = 20.12 mg / kg h); define the resolution function, input close to monitoring the associated coordinates (1.25, 0.75, -0.35), extract the substance form "nitrate nitrogen" by the morphological query function; then distinguish the inflow edge from the outflow edge, calculate the total input I = inflow edge displacement distance x reaction rate = 0.2812 x 0.1 = 0.02812 (m mg / kg h), the total output O = outflow edge displacement distance x reaction rate = 0.001 x 20.12 = 0.02012 (m mg / kg h); set the unit time Δt = 1 h; calculate the total storage S = 0.008 mg / kg by the analytical function formula.

[0133] Next, extract the B point stock data, calculate the microbial water proportion = 0.0037; through the calculation of the dynamic distribution coefficient = 0.0035; according to the irrigation amount 50 m³ / hm², the actual water gain of microorganisms = 50 x 0.0035 = 0.175 m³ / day, which is lower than the threshold value 0.2 m³ / day for 5 consecutive days, and the B point is marked as a high loss area; the total input water is 500 m³, the actual distribution is 482 m³, and the loss amount = 500-482 = 18 m³.

[0134] Finally, calculate the distance ratio r = 0.3 between the crop root enrichment area and the groundwater infiltration area, generate path reconstruction instructions, and calculate the target area weight increase ratio = 2.4, and the non-target area weight reduction ratio = 0.55 to adjust the edge weight, wherein the root path weight is from 0.4 to 0.4 x 2.4 = 0.96, and the groundwater path is from 0.6 to 0.6 x 0.55 = 0.33; using the morphological conversion instruction, convert 1.5 kg of residual nitrogen to 1.2 kg by the formula , and convert the residual nitrogen to coated urea; migrate to the root area through the adjusted network, release according to the slow-release model , release 0.06 kg of available nitrogen on the first day to participate in the cycle, thereby realizing closed-loop utilization.

[0135] Figure 3 A specific implementation architecture diagram of a smart agricultural monitoring method based on the Internet of Things provided by an embodiment of the present application, used to show the basic framework of water and nitrogen nutrient element migration analysis;

[0136] Figure 4 A specific implementation structure schematic diagram of a smart agricultural monitoring system based on the Internet of Things provided by an embodiment of the present application, with reference to Figure 4 , the system can include:

[0137] The construction module 41 is configured to obtain morphological transformation paths and reaction rates of the water and nitrogen nutrients caused by biological and chemical actions in a crop and soil system by marking preset water and nitrogen nutrients, to construct a biochemical substance flow network for representing morphological changes of the water and nitrogen nutrients.

[0138] The second construction module 42 is configured to collect three-dimensional space displacement trajectories of the water and nitrogen nutrients by deploying a three-dimensional radio frequency sensing network in the crop and soil system, to construct a physical migration network for representing physical position changes of the water and nitrogen nutrients.

[0139] The analysis module 43 is configured to fuse the biochemical substance flow network and the physical migration network, to construct a migration analysis mechanism of the water and nitrogen nutrients, and to analyze morphological and stock data of the water and nitrogen nutrients at a specific space position by using the migration analysis mechanism.

[0140] The identification module 44 is configured to quantitatively analyze competitive allocation relationships of the water and nitrogen nutrients among crops, soil and microorganisms based on the analysis result, and to identify a high-loss area in a material circulation process and a loss amount of the water and nitrogen nutrients retained in the high-loss area according to the competitive allocation relationships.

[0141] The transformation module 45 is configured to generate path reconstruction instructions and morphological conversion instructions according to the high-loss area and the loss amount, the path reconstruction instructions being used to reconstruct a flow path of the water and nitrogen nutrients, and the morphological conversion instructions being used to convert nitrogen in the water and nitrogen nutrients into a slow-release form, so as to realize resource circulation of the water and nitrogen nutrients.

[0142] The Internet of Things-based smart agricultural monitoring system according to the embodiments of the present application is used to implement the Internet of Things-based smart agricultural monitoring method described above, and therefore the specific embodiments of the Internet of Things-based smart agricultural monitoring system can refer to the embodiments of the Internet of Things-based smart agricultural monitoring method described above, and the specific embodiments can refer to the descriptions of the respective embodiments, which will not be repeated here.

[0143] The present application also provides an electronic device, comprising a memory for storing a computer program, and a processor for executing the computer program to implement the steps of the Internet of Things-based smart agricultural monitoring method described above.

[0144] The present application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the steps of the Internet of Things-based smart agricultural monitoring method described above.

[0145] In one example embodiment, the computer readable storage medium described above can include, but is not limited to, a U disk, a read-only memory, a random access memory, a mobile hard disk, a magnetic disk or an optical disk, and various media that can store computer programs.

[0146] Embodiments of the present application also provide a computer program product, the computer program product comprising a computer program, the computer program being executed by a processor to implement the steps in any of the above-mentioned embodiments of the method for monitoring smart agriculture based on Internet of Things.

[0147] Those skilled in the art will further appreciate that the functions of the examples described herein, including any related steps of a method, can be implemented using electronic hardware, computer software, or any combination thereof. To clearly illustrate this interchangeability of hardware and software, various examples have been described herein generally in terms of their functionality, without referring to the corresponding acts of a computer program. Whether such functionality is implemented as hardware or software depends on the particular application and design constraints imposed on the overall system. Those skilled in the art can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.

[0148] The above provides a method and system for monitoring smart agriculture based on Internet of Things. The principles and implementation modes of the present application are described by applying specific examples. The above description of the examples is only used to help understand the method and its core idea of the present application. It should be pointed out that, for those skilled in the art, without departing from the principles of the present application, some improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the present application.

Claims

1. A smart agriculture monitoring method based on Internet of Things, characterized in that, The method comprises the following steps: By marking the preset water and nitrogen nutrients, the morphological transformation path and reaction rate caused by biological and chemical effects of the water and nitrogen nutrients in the crop and soil system are obtained to construct a biochemical substance flow network for representing the morphological changes of the water and nitrogen nutrients; By deploying a three-dimensional radio frequency sensing network in the crop and soil system, the three-dimensional spatial displacement trajectory of the water and nitrogen nutrients is collected to construct a physical migration network for representing the physical position changes of the water and nitrogen nutrients; The biochemical substance flow network and the physical migration network are fused to construct a migration analysis mechanism of the water and nitrogen nutrients, and the morphological form and stock data of the water and nitrogen nutrients in a specific spatial position are analyzed through the migration analysis mechanism; Based on the analysis result, the competitive allocation relationship among the crop, soil and microorganism of the water and nitrogen nutrients is quantitatively analyzed, and the high loss area in the material circulation process and the loss amount of the water and nitrogen nutrients retained in the high loss area are identified according to the competitive allocation relationship; According to the high loss area and the loss amount, path reconstruction instructions and morphological conversion instructions are generated, the path reconstruction instructions are used to reconstruct the flow path of the water and nitrogen nutrients, and the morphological conversion instructions are used to convert the nitrogen in the water and nitrogen nutrients into a slow-release form to realize the resource circulation of the water and nitrogen nutrients.

2. The method of claim 1, wherein, The biochemical substance flow network and the physical migration network are fused to construct a migration analysis mechanism of the water and nitrogen nutrients, and the morphological form and stock data of the water and nitrogen nutrients in a specific spatial position are analyzed through the migration analysis mechanism, which comprises the following steps: According to the position data and morphological data of the water and nitrogen nutrients, a position and morphological corresponding table is generated to associate the material morphological nodes in the biochemical substance flow network with the spatial position points in the physical migration network; According to the position data and morphological data of the water and nitrogen nutrients, the material morphological nodes in the biochemical substance flow network are associated with the spatial position points in the physical migration network to generate a position and morphological corresponding table; through the position and morphological corresponding table, the nodes and edges of the physical migration network and the nodes and edges of the biochemical substance flow network are superimposed to form a joint network structure, the nodes in the joint network structure contain spatial position information and material morphological information, and the edges contain displacement distance and reaction rate information; Based on the joint network structure, an analysis function is defined to construct a migration analysis mechanism of the water and nitrogen nutrients, the input of the migration analysis mechanism is a specific spatial position coordinate, and the output is the material morphological form and stock data of the water and nitrogen nutrients in the specific spatial position; For the analysis function, the node corresponding to the input coordinate in the joint network structure is queried, the material morphological information of the node is directly extracted, and the stock data of the specific spatial position is calculated through the reaction rate information and displacement distance of the adjacent edges.

3. The method of claim 2, wherein, In the joint network structure, the node corresponding to the input coordinate is queried, the substance form information of the node is directly extracted, and the stock data of the specific spatial position is calculated through the reaction rate information and displacement distance of the adjacent edges, including: Positioning the node with the minimum spatial distance from the input coordinate as the target node, and directly reading the substance form information of the target node; Obtaining all adjacent edges directly connected to the target node, and distinguishing the edges into inflow edges and outflow edges according to the direction; Calculating the sum of the product of the displacement distance and the reaction rate of the inflow edges as the input total amount, and calculating the sum of the product of the displacement distance and the reaction rate of the outflow edges as the output total amount; Dividing the difference between the input total amount and the output total amount by the unit time to obtain the stock data of the specific spatial position.

4. The method of claim 1, wherein, Based on the analysis result, the competitive allocation relationship of the water and nitrogen nutrient elements among crops, soil and microorganisms is quantitatively analyzed, and according to the competitive allocation relationship, the high-loss area in the material circulation process and the loss amount of the water and nitrogen nutrient elements retained in the high-loss area are identified, including: Based on the analysis result, the stock data of crops, soil and microorganisms at a specific spatial position is extracted, including crop stock data, soil stock data and microorganism stock data; Based on the crop stock data, soil stock data and microorganism stock data, dynamic competitive allocation calculation is performed based on the proportional relationship between the stock data to generate a set of dynamic allocation coefficients; According to the set of dynamic allocation coefficients, the actual allocation amount of the crop stock data, soil stock data and microorganism stock data per unit time is calculated, and a three-dimensional allocation relationship matrix is constructed based on the actual allocation amount as the competitive allocation relationship; Based on the competitive allocation relationship, the dimension position with the allocation amount continuously lower than the preset threshold in the specific spatial position is identified, the specific spatial position corresponding to the dimension position is marked as a high-loss area, and the loss amount of the water and nitrogen nutrient elements retained in the high-loss area is calculated by subtracting the total actual allocation amount of all specific spatial positions from the total input amount of the water and nitrogen nutrient elements.

5. The method of claim 1, wherein, According to the high-loss area and the loss amount, path reconstruction instructions and form conversion instructions are generated, the path reconstruction instructions are used to reconstruct the flow path of the water and nitrogen nutrient elements, and the form conversion instructions convert the nitrogen retained in the water and nitrogen nutrient elements into a slow-release form to realize resource circulation of the water and nitrogen nutrient elements, including: Based on the spatial attributes of the high-loss area, path reconstruction instructions are generated based on the topological relationship of the physical migration network, and slow-release conversion calculation is performed based on the loss amount to generate form conversion instructions; Based on the path reconstruction instructions, the edge weight value of the physical migration network from the high-loss area to the target area is increased, and the edge weight value to the non-target area is reduced, so that the water and nitrogen nutrient elements are directed to migrate to the target area, and the loss of the water and nitrogen nutrient elements to the non-target area is inhibited, so as to reconstruct the flow path of the water and nitrogen nutrient elements, wherein the target area includes crop root enrichment area and microbial activity area, and the non-target area includes groundwater infiltration area and atmospheric volatilization area. Performing the morphological conversion instruction, converting the nitrogen element retained in the high loss area into slow-release form; Migrate the converted slow-release form of nitrogen to the target area, so that the slow-release form of nitrogen participates in the material distribution cycle in the target area to realize the resource cycle of water and nitrogen nutrients.

6. The method of claim 1, wherein, By marking the preset water and nitrogen nutrients, the morphological conversion path and reaction rate caused by biological and chemical effects of the water and nitrogen nutrients in the crop and soil system are obtained to construct a biochemical material flow network, which is used to represent the material form change of the water and nitrogen nutrients, including: Isotopically label the preset water and nitrogen nutrients, and introduce the labeled water and nitrogen nutrients into the crop and soil system; In the crop and soil system, the near-infrared spectrum sensor and ion selective electrode array deployed in the crop stem and leaf, root system and soil layer periodically capture the material form spectrum characteristics and ion concentration of the labeled water and nitrogen nutrients at consecutive time points; According to the changes of the material form spectrum characteristics and ion concentration on the time sequence, the continuous conversion sequence from the initial form to the derived form is extracted as the morphological conversion path; The ratio of the concentration change value of the same material form at adjacent time points to the time interval is calculated to obtain the reaction rate, and a directed weighted graph structure is constructed with the material form as the node, the morphological conversion path as the edge, and the reaction rate as the edge weight, to form a biochemical material flow network.

7. The method of claim 1, wherein, A three-dimensional radio frequency sensing network deployed in the crop and soil system is used to collect the three-dimensional spatial displacement trajectory of the water and nitrogen nutrients to construct a physical migration network, which is used to represent the physical position change of the water and nitrogen nutrients, including: Deploy a three-dimensional radio frequency sensing network in the crop canopy, root zone and soil profile; The three-dimensional coordinate position data of the labeled water and nitrogen nutrients at each time point is obtained through the three-dimensional radio frequency sensing network, and for the three-dimensional coordinate position data of adjacent time points, the displacement vector from the starting position to the ending position is calculated, and the corresponding modulus and displacement direction angle are recorded; The starting position points of the displacement vectors are connected in time sequence to form a three-dimensional spatial displacement trajectory composed of consecutive spatial position points; A directed weighted graph structure is constructed with the spatial position points as the nodes, the three-dimensional spatial displacement trajectory between adjacent position points as the directed edges, and the modulus and displacement direction angle of the displacement vector as the composite edge weight to form a physical migration network.

8. An Internet of Things based smart agriculture monitoring system characterized in that, Including: A construction module is configured to mark the preset water and nitrogen nutrients, and obtain the morphological conversion path and reaction rate caused by biological and chemical effects of the water and nitrogen nutrients in the crop and soil system to construct a biochemical material flow network, which is used to represent the material form change of the water and nitrogen nutrients; A second construction module is configured to deploy a three-dimensional radio frequency sensing network in the crop and soil system to collect the three-dimensional spatial displacement trajectory of the water and nitrogen nutrients to construct a physical migration network, which is used to represent the physical position change of the water and nitrogen nutrients; An analysis module is configured to fuse the biochemical substance flow network and the physical migration network, construct a migration analysis mechanism of the water and nitrogen nutrients, and analyze substance form and stock data of the water and nitrogen nutrients at a specific spatial position through the migration analysis mechanism; An identification module is configured to quantitatively analyze competitive allocation relationships among the water and nitrogen nutrients, crops, soil and microorganisms based on the analysis result, and identify a high loss area in a material circulation process and a loss amount of the water and nitrogen nutrients retained in the high loss area according to the competitive allocation relationships; A transformation module is configured to generate path reconstruction instructions and form conversion instructions according to the high loss area and the loss amount, the path reconstruction instructions being used to reconstruct a flow path of the water and nitrogen nutrients, and the form conversion instructions being used to convert nitrogen retained in the water and nitrogen nutrients into a slow-release form, so as to realize resource circulation of the water and nitrogen nutrients.

9. An electronic device, comprising: Comprise: a memory for storing a computer program; a processor for executing the computer program to realize the steps of the method for monitoring smart agriculture based on Internet of Things according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by the processor to realize the method for monitoring smart agriculture based on Internet of Things according to any one of claims 1 to 7.

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