Intelligent agriculture monitoring method and system based on Internet of Things

Abstract

The invention provides a smart agriculture monitoring method and system based on the Internet of Things, and relates to the field of agricultural informatization. The method comprises the following steps: marking water-nitrogen nutritional elements, obtaining a morphological transformation path and rate of the water-nitrogen nutritional elements in a crop and soil system, and constructing a biochemical substance flow network; a three-dimensional radio frequency sensor network is used for collecting a spatial displacement track of water nitrogen nutritional elements, and a physical migration network is constructed; fusing double networks to construct a migration analysis mechanism, and analyzing the substance form and stock at a specific position; quantitatively analyzing the competitive distribution of water and nitrogen among crops, soil and microorganisms, and identifying a circulating high-loss region and retention volume; according to the method, a path reconstruction instruction and a form conversion instruction are generated, efficient resource recycling of water and nitrogen nutritional elements is achieved, multi-dimensional accurate analysis of farmland water and nitrogen migration and conversion, nutrient circulation bottleneck identification and active optimization regulation can be achieved, nutrient loss is effectively reduced, and the resource utilization efficiency is improved. And a scientific basis is provided for precise management of intelligent 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, 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 three-dimensional migration trajectories of water and nitrogen, leading to inaccurate physical migration path analysis. It can only detect total 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 processes, and lagging control decisions 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: 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; 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; 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; 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; 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.

[0007] Optionally, the 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, comprises: According to the position data and form data of the water and nitrogen nutrients, generating a position and form correspondence table to associate the substance form nodes in the biochemical substance flow network with the spatial position points in the physical migration network; According to the position data and form data of the water and nitrogen nutrients, associating the substance form nodes in the biochemical substance flow network 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, superimposing the nodes and edges of the physical migration network and the nodes and edges of the biochemical substance flow network 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; Based on the joint network structure, defining an analysis function to construct a migration analysis mechanism of the water and nitrogen nutrients, the input of the migration analysis mechanism being a specific spatial position coordinate, and the output being the substance form and stock data of the water and nitrogen nutrients at the specific spatial position; For the analysis function, querying the node corresponding to the input coordinate in the joint network structure, directly extracting the substance form information of the node, and calculating the stock data of the specific spatial position through the reaction rate information and displacement distance of the adjacent edges.

[0008] Optionally, 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. Positioning and inputting the node with the minimum distance in the coordinate space as a target node, and directly reading the material form information of the target node; Obtaining all adjacent edges directly connected to the target node, and distinguishing the adjacent edges into inflow edges and outflow edges according to directions; Calculating the sum of the product of the displacement distance and the reaction rate of the inflow edges as an input total amount, and calculating the sum of the product of the displacement distance and the reaction rate of the outflow edges as an output total amount; Dividing the difference between the input total amount and the output total amount by a unit time to obtain stock data of the specific spatial position.

[0009] Optionally, based on the analysis result, a competitive allocation relationship of the water and nitrogen nutrient elements among crops, soil and microorganisms is quantitatively analyzed, and 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 are identified according to the competitive allocation relationship, including: Based on the analysis result, stock data of crops, soil and microorganisms at a specific spatial position is extracted, and the stock data includes crop stock data, soil stock data and microorganism stock data; Based on the crop stock data, the soil stock data and the microorganism stock data, a dynamic competitive allocation calculation is performed in combination with a proportional relationship among the stock data to generate a dynamic allocation coefficient set; Actual allocation amounts of the crop stock data, the soil stock data and the microorganism stock data in a unit time are calculated according to the dynamic allocation coefficient set, a three-dimensional allocation relationship matrix is constructed based on the actual allocation amounts as a competitive allocation relationship; Based on the competitive allocation relationship, a dimensional position with an 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 a loss amount of the water and nitrogen nutrient elements retained is calculated by subtracting a sum of actual allocation amounts of all specific spatial positions from a total input amount of the water and nitrogen nutrient elements.

[0010] Optionally, path reconstruction instructions and form conversion instructions are generated according to the high-loss area and the loss amount, the path reconstruction instructions are used to reconstruct a flow path of the water and nitrogen nutrient elements, and the form conversion instructions are used to convert 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: Path reconstruction instructions are generated based on spatial attributes of the high-loss area in combination with a topological relationship of the physical migration network, and form conversion instructions are generated by performing slow-release conversion calculation based on the loss amount; Based on the path reconstruction instruction, increase the edge weight value of the physical migration network of the high loss area to the target area, and reduce the edge weight value to the non-target area, so that the water and nitrogen nutrients are directed to migrate to the target area, and the loss of water and nitrogen nutrients to the non-target area is inhibited, so as to reconstruct the flow path of water and nitrogen nutrients, 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. Perform the morphological conversion instruction to convert the nitrogen retained in the high loss area into a slow-release form; Migrate the slow-release form of nitrogen obtained by conversion 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.

[0011] Optionally, the morphological conversion path and reaction rate of the water and nitrogen nutrients caused by biological and chemical effects in the crop and soil system are obtained by marking the preset water and nitrogen nutrients, to construct a biochemical material flow network, which is used to characterize the material form change of the water and nitrogen nutrients, including: Isotope labeling is performed on the preset water and nitrogen nutrients, and the labeled water and nitrogen nutrients are introduced into the crop and soil system; In the crop and soil system, the material form spectral characteristics and ion concentration of the labeled water and nitrogen nutrients at consecutive time points are periodically captured by deploying near-infrared spectrum sensors and ion selective electrode arrays in crop stems and leaves, roots and soil layers; According to the changes of the material form spectral characteristics and ion concentration in the time series, 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.

[0012] Optionally, the three-dimensional spatial displacement trajectory of the water and nitrogen nutrients is collected by deploying a three-dimensional radio frequency sensing network in the crop and soil system to construct a physical migration network, which is used to characterize 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 by 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; Connect the starting position points of the displacement vectors in chronological order to form a three-dimensional space displacement trajectory composed of continuous space position points; A directed weighted graph structure is constructed with space position points as nodes, three-dimensional space displacement trajectories 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.

[0013] In a second aspect, the present application provides a smart agricultural monitoring system based on Internet of Things, comprising: The construction module is configured to mark the preset water and nitrogen nutrients, obtain 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, and construct a biochemical substance flow network, which is used to represent the material form change of the water and nitrogen nutrients. The second construction module is configured to collect the three-dimensional space displacement trajectory of the water and nitrogen nutrients by deploying a three-dimensional radio frequency sensing network in the crop and soil system, and construct a physical migration network, which is used to represent the physical position change of the water and nitrogen nutrients. The 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 the material form and stock data of the water and nitrogen nutrients at a specific space position by using the migration analysis mechanism. The identification module is configured to quantitatively analyze the competitive allocation relationship between the water and nitrogen nutrients, crops, soil and microorganisms based on the analysis result, and identify 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. The 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 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.

[0014] In a third aspect, the present application provides an electronic device, comprising: A memory is configured to store a computer program. A processor is configured to execute the computer program to realize the steps of the smart agricultural monitoring method based on Internet of Things as described in the first aspect.

[0015] 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 realize the steps of the smart agricultural monitoring method based on Internet of Things as described in the first aspect.

[0016] The smart agriculture monitoring method based on the Internet of Things provided in the application can accurately track the morphological transformation process of water and nitrogen in the crop-soil system by marking water and nitrogen nutrients and constructing a biochemical substance flow network; can monitor the three-dimensional space migration trajectory of water and nitrogen by deploying a three-dimensional radio frequency sensing network to construct a physical migration network; can realize accurate analysis of the form and stock of water and nitrogen at a specific spatial position by fusing the biochemical substance flow network and the physical migration network to form a joint network structure; can identify high-loss areas and retention loss amounts in the material circulation process by quantitatively analyzing the competitive allocation relationship among crops, soil and microorganisms; and can optimize the water and nitrogen flow path and convert the retained nitrogen into a slow-release form to realize efficient recycling of resources by generating path reconstruction instructions and morphological conversion instructions.

[0017] Further, in the process of fusing the two networks, by establishing a position and form correspondence table and constructing a joint network structure, the nodes contain spatial position and material form information, and the edges contain displacement distance and reaction rate information, the water and nitrogen form and stock at a specific position are accurately analyzed based on the analytical function, the problems of discontinuous spatial trajectory and invisible morphological transformation in traditional monitoring are solved; in the competitive allocation analysis, by constructing a three-dimensional allocation relationship matrix and calculating a dynamic allocation coefficient, the accurate identification of high-loss areas and the accurate calculation of loss amounts are realized; in the regulation and execution stage, directional migration is realized by adjusting the edge weight of the migration network, and the retained nitrogen is converted into a slow-release form, so that the nutrient utilization rate is greatly improved, and the problems of waste of water and nitrogen resources and environmental pollution in the agricultural system are effectively solved. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the application or the prior art, a brief introduction will be given below to the drawings needed to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the application, and other drawings can also be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0019] Figure 1 A flowchart of a smart agriculture monitoring method based on the Internet of Things provided by an embodiment of the application; Figure 2 A scene diagram of a smart agriculture monitoring method based on the Internet of Things provided by an embodiment of the application; Figure 3 An architecture diagram of a smart agriculture monitoring method based on the Internet of Things provided by an embodiment of the application; Figure 4 A structural diagram of a smart agriculture monitoring system based on the Internet of Things provided by an embodiment of the application. DETAILED DESCRIPTION

[0020] In order to make the person skilled in the art better understand the scheme of 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 part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0021] The core of the present application is to provide a kind of smart agriculture monitoring method based on Internet of Things, and the flowchart of a specific embodiment of the present application is as shown in Figure 1 The method comprises: S101, 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 crop and soil system are obtained, to construct biochemical substance flow network, the biochemical substance flow network is used to characterize the material form change of the water and nitrogen nutrients; Optionally, S101 can specifically include the following steps: 1011, the preset water and nitrogen nutrients are isotopically labeled, and the labeled water and nitrogen nutrients are introduced into crop and soil system; 1012, in the crop and soil system, the material form spectrum characteristics and ion concentration of the labeled water and nitrogen nutrients at consecutive time points are periodically captured by deploying near-infrared spectrum sensor and ion selective electrode array in crop stem and leaf, root system and soil layer; 1013, according to the change of the material form spectrum characteristics and ion concentration on time sequence, the continuous transformation sequence from initial form to derived form is extracted as morphological transformation path; 1014, the ratio of the concentration change value of the same material form at adjacent time points and the time interval is calculated to obtain the reaction rate, and the material form is taken as node, the morphological transformation path is taken as edge, and the reaction rate is taken as edge weight, to construct directed weighted graph structure, to form biochemical substance flow network.

[0022] In the above scheme, water and nitrogen nutrient elements refer to the general term for water and nitrogen nutrients necessary for crop growth, including forms such as ammonium nitrogen and nitrate nitrogen; isotope labeling refers to labeling nitrogen with stable isotopes so that it can be distinguished from ordinary nitrogen in the natural environment during detection; near-infrared spectral sensor refers to a device that identifies chemical components by analyzing the characteristics of light waves reflected by substances; ion-selective electrode array refers to a group of probes that measure the concentration of specific ions in a solution; morphological transformation path refers to the continuous process of a substance changing from one chemical state to another; reaction rate refers to the change in the concentration of a substance per unit time, measured in mg / kg·h; directed weighted graph structure refers to a visual model that uses nodes to represent the form of a substance, arrow lines to represent the direction of transformation, and line values ​​to represent the reaction rate.

[0023] In the embodiment of the present application, first, in step 1011, in a laboratory environment, a stable isotope is used. 15 N chemically replaces nitrogen atoms in ammonium nitrogen, nitrate nitrogen and other substances to prepare 15 N-labeled chemical solution of nitrogen-containing substances; the labeled solution is then evenly injected into the crop root zone soil at a concentration of 5 mg / kg through a hydraulic syringe or automatic irrigation system, making the target nutrient traceable.

[0024] Secondly, in step 1012, a near-infrared spectral sensor array is deployed on the stems and leaves of the crop. The sensor actively emits near-infrared light, receives leaf reflectance spectra, collects spectral data, and identifies changes in the intensity of characteristic spectral peaks at 1350 nm to determine the form of substances such as nitrate nitrogen. At the same time, an ion-selective electrode array is arranged in the root layer and soil profile. The potential difference generated by the contact between the electrode and the soil solution is measured and the electrode calibration formula C=k×V is applied to convert the potential value into ion concentration, where C is the ion concentration, k is the calibration factor, and V is the electrode potential measurement value. The two types of equipment synchronously collect data in a 10-minute cycle through the Internet of Things node to form a time series morphology and concentration data set. For example, in a corn field, the stem and leaf sensor detects a 1350 nm peak intensity of 120 at time t0, and the peak intensity at the same location drops to 90 0.5 hours later. Simultaneously, the ammonium root electrode potential in the root layer increases from 0.25 V to 0.35 V, which is converted to an increase in nitrate nitrogen concentration from 5 mg / kg to 7 mg / kg according to the electrode calibration formula.

[0025] Next, in step 1013, the system receives the time series morphology and concentration data set, traverses the adjacent time point pairs in chronological order, and calculates the concentration changes of all morphologies. When a significant decrease in ΔC of a certain form and a significant increase in ΔC of another form are detected, a transformation relationship between the two is determined based on the principle of conservation of mass. The form with a decreasing concentration is marked as the initial node, and the form with an increasing concentration is marked as the derived node, generating a single-step path of "initial form → derived form". If a decrease in a single form corresponds to an increase in multiple forms, a branching path is generated. Finally, the paths of adjacent time periods are connected in chronological order to form a continuous transformation sequence as the form transformation path. For example, at time t0 in a corn field, the nitrate nitrogen concentration in the stems and leaves is 5 mg / kg, and the ammonium nitrogen concentration in the roots is 5 mg / kg. At this moment, nitrate nitrogen dropped to 3.75 mg / kg and ammonium nitrogen rose to 7 mg / kg; the system detected that the concentration change of nitrate nitrogen was ΔC = -1.25 mg / kg and ammonium nitrogen was ΔC = +2 mg / kg, both of which exceeded the 5% change threshold and had opposite trends. According to the principle of conservation of mass, it was determined that there was a "nitrate nitrogen → ammonium nitrogen" conversion path; if in the next period It was detected that ammonium nitrogen decreased from 7 mg / kg to 5 mg / kg while organic nitrogen increased from 10 mg / kg to 12 mg / kg, thus forming a continuous conversion sequence "nitrate nitrogen → ammonium nitrogen → organic nitrogen" in series.

[0026] In practical applications, such as Figure 2 As shown, first prepared in a laboratory 15 N-labeled ammonium nitrogen and nitrate nitrogen solutions were injected into the maize root layer soil at a concentration of 5 mg / kg using a hydraulic syringe to achieve nitrogen tracing. A near-infrared sensor array was then installed on the maize stems and leaves to collect 1350 nm peak intensity, while ammonium ion electrodes were buried in the root layer. The two types of equipment automatically synchronized data every 10 minutes to form a dynamic database. For example, at 6:00 a.m. in a certain maize field, the sensor recorded a peak intensity of 120 units for the stems and leaves and a root potential of 0.25 volts. At 6:30 a.m., the peak intensity dropped to 90 units and the potential rose to 0.35 volts. It then automatically analyzes data from adjacent time periods, identifying significant reverse fluctuations in nitrate nitrogen concentration, which dropped by 1.25 mg / kg within half an hour, and ammonium nitrogen, which rose by 2 mg / kg. Based on the law of nitrogen conservation, it generates a "nitrate nitrogen → ammonium nitrogen" conversion pathway. If, at 12:00 noon, it further detects that ammonium nitrogen has dropped from 7 mg / kg to 5 mg / kg and organic nitrogen has increased from 10 mg / kg to 12 mg / kg, the system will connect these pathways to form a complete conversion chain of "nitrate nitrogen → ammonium nitrogen → organic nitrogen," visually presenting the dynamic process of nitrification and synthesis of nitrogen in the soil.

[0027] The overall solution of S101 mentioned above achieves precise tracking of water and nitrogen elements through isotope labeling, captures the temporal changes of material form and concentration with the help of sensor networks, extracts continuous transformation pathways to reveal metabolic chains, and finally quantifies reaction rates and constructs a biochemical material flow network, forming a "form-path-rate" trinity material cycle analysis system, providing a data basis for identifying nutrient retention bottlenecks.

[0028] S102, collecting three-dimensional space displacement trajectories of the water and nitrogen nutrients by a three-dimensional radio frequency sensing network deployed in the crop and soil system, to construct a physical migration network for representing physical position changes of the water and nitrogen nutrients; Optionally, S102 can specifically include the following steps: 1021, deploying a three-dimensional radio frequency sensing network in the crop canopy, root zone and soil profile; 1022, obtaining three-dimensional coordinate position data of the marked water and nitrogen nutrients at each time point by the three-dimensional radio frequency sensing network, and calculating displacement vectors from the starting point position to the ending point position for the three-dimensional coordinate position data of adjacent time points, and recording the corresponding modulus and displacement direction angle; 1023, connecting the starting point positions of the displacement vectors in time sequence to form three-dimensional space displacement trajectories composed of continuous space position points; 1024, constructing a directed weighted graph structure with space position points as nodes, three-dimensional space displacement trajectories between adjacent position points as directed edges, and modulus and displacement direction angle of the displacement vectors as complex edge weights, to form a physical migration network.

[0029] In the above scheme, the three-dimensional radio frequency sensing network refers to a network of wireless signal tags and receiving devices installed in the crop 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.

[0030] 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 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.

[0031] Secondly, the tag emission and multi-receiving point positioning calculation of the three-dimensional radio frequency sensing network wireless radio frequency signal are performed by step 1022 to obtain the three-dimensional coordinate position data of the marked water and nitrogen nutrients 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; for the three-dimensional coordinate position of adjacent time points, the displacement vector from the starting point position to the ending point position is calculated, and the modulus of the displacement vector is: ; and the displacement direction angle is calculated according to the direction angle formula, the horizontal direction angle , the vertical direction angle is .

[0032] Next, the starting positions of the displacement vectors are connected in time sequence by step 1023, and the adjacent time nodes are sequentially connected to form a broken line trajectory 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 label 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 so on, to form a continuous movement path of the marker in time sequence.

[0033] Finally, by step 1024, each recorded spatial position point is defined as a node, the three-dimensional space displacement trajectory between adjacent time points is identified as a directed edge from the starting point to the ending point, and the composite attribute composed of the modulus of the displacement vector and the displacement direction angle is used 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 composite weights, to form a physical migration network. For example, the composite edge weight is marked on the directed edge from node (1.0, 2.0, 0.5) to node (1.3, 2.2, 0.4): {modulus: 0.37m, horizontal angle: 33.7°, pitch angle: -15.5°}.

[0034] In practical applications, a three-dimensional monitoring network is laid out in a test field, a crop canopy is fixed with a leaf label A, a root layer is buried with a soil label B, and a deep soil is buried with a label C, thereby constructing a three-dimensional monitoring network; the coordinates of label A at t0 are (1.0, 2.0, 0.5) and the coordinates of label B are (0.5, 1.0, -0.2), and 30 minutes later The system calculates the displacement parameters: label 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°; label 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 label A forms a path from (1.0, 2.0, 0.5) to (1.3, 2.2, 0.4), and label 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 labeled with the weight {distance 0.37 meters, horizontal angle 33.7°, pitch angle -15.5°}, and a directed edge is established between nodes (0.5, 1.0, -0.2) and (0.7, 1.3, -0.1) and labeled with the weight {distance 0.37 meters, horizontal angle 56.3°, pitch angle 14.0°}, forming a complete visual network.

[0035] 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 diagram, and intuitively presents the migration direction, distance, and dynamic path of water and nitrogen nutrients, providing spatial motion basis for precise management of irrigation and fertilization.

[0036] 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; Optionally, S103 can specifically include the following steps: 1031, generating a position and form correspondence table according to the position data and form data of the water and nitrogen nutrient elements, to associate the substance form nodes in the biochemical substance flow network and the spatial position points in the physical migration network; 1032, superimposing 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, 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; 1033. Based on the joint network structure, a resolution function is defined to construct a migration resolution mechanism of the water and nitrogen nutrients, an input of the migration resolution mechanism being a specific spatial position coordinate, and an output being material form and stock data of the water and nitrogen nutrients at the specific spatial position; 1034. For the resolution function, a node corresponding to the input coordinate is queried in the joint network structure, material form information of the node is directly extracted, and stock data of the specific spatial position is calculated through reaction rate information and displacement distance of adjacent edges.

[0037] In the step 1034, the node with the minimum spatial distance from the input coordinate can be located as a target node, and the material form information of the target node can be directly read. All adjacent edges directly connected with the target node can be obtained, and the adjacent edges can be distinguished into inflow edges and outflow edges according to directions. The sum of products of displacement distances and reaction rates of the inflow edges can be calculated as an input total amount, and the sum of products of displacement distances and reaction rates of the outflow edges can be calculated as an output total amount. The difference between the input total amount and the output total amount can be divided by a unit time to obtain the stock data of the specific spatial position.

[0038] In the above scheme, the position-form correspondence table refers to a matching relationship table recording each three-dimensional spatial coordinate point and its corresponding material form and concentration value; the joint network structure refers to a fusion network formed by combining spatial position nodes of a physical migration network and material form nodes of a biochemical material flow network, and integrating displacement parameters and reaction rate parameters; and the migration resolution mechanism refers to a query function constructed based on the joint network, which can output the material form type and estimated stock data of the position when the spatial coordinate is input.

[0039] In the embodiments of the present application, first, all spatial position coordinate data of a physical migration network and material form data of a biochemical material flow network are obtained through the step 1031, and the spatial position points are matched with the corresponding material forms to form a position-form correspondence table. For example, in a corn field, when the position of a label A is at a coordinate (1.0, 2.0, 0.5) at a time t0, the biochemical sensor detects that the water and nitrogen nutrients at the position are in a nitrate nitrogen form, so the coordinate position (1.0, 2.0, 0.5) is bound with the "nitrate nitrogen" state to generate a mapping relationship table record.

[0040] Secondly, by step 1032, the spatial position points in the physical migration network are superimposed and merged with the substance form nodes in the biochemical substance flow network, and the displacement trajectory edges in the physical migration network are superimposed and merged with the reaction edges in the biochemical substance flow network, 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 the displacement moving distance and the 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 to form a new node with the attribute of {coordinate: 1.0, 2.0, 0.5, form: nitrate nitrogen}; at the same time, the displacement edge (containing a moving distance of 0.37 meters) from (1.0, 2.0, 0.5) to (1.3, 2.2, 0.4) in the physical network is merged with the reaction edge (containing a reaction rate of 0.2 mg / h) in the biochemical network in the corresponding period to form a new edge with the attribute of {moving distance: 0.37 m, reaction rate: 0.2 mg / h}, so as to realize the complete fusion of the physical displacement trajectory and the biochemical reaction process.

[0041] Then, by step 1033, an analysis function is defined to construct a migration analysis mechanism, the input quantity of the analysis function is defined as the specific spatial position coordinates , and the analysis function is constructed according to the preset attributes of the joint network nodes: , wherein 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 product of the displacement distance and the reaction rate per unit time, so as to obtain the migration analysis mechanism.

[0042] Finally, by step 1034, the migration analysis mechanism is executed, first, the node with the minimum spatial distance from the input coordinates is located as a target node in the joint network structure, and the substance form information of the target node is directly returned by the form query function ; then all adjacent edges directly connected with the target node are obtained, which are distinguished into inflow edges and outflow edges according to the direction, the sum of the displacement distance and the reaction rate of the inflow edges is calculated as the input total amount (I), and the sum of the displacement distance and the reaction rate of the outflow edges is calculated as the output total amount (O); then, the multi-medium stock calculation is performed by a stock calculation function: wherein the soil stock , crop stock , microbial stock , output the stock data of the specific spatial location; the is the total input from the target node, corresponds to the total input pointing to the target node, is the total output from the target node.

[0043] In practical applications, the migration analysis mechanism is implemented 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 form data generation location and form correspondence table of the biochemical substance flow network, the mapping relationship between coordinates and substance forms is recorded; then the spatial nodes of the physical migration network and the form nodes of the biochemical substance flow network are fused into a joint network structure, so that point A is extended to {coordinate (1.0, 2.0, 0.5), form: nitrate nitrogen}, point B is extended to {coordinate (1.3, 2.2, 0.4), form: 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 coordinate (1.3, 2.2, 0.4) is input, the system locates the point B with the smallest spatial distance from the coordinate as the target node, and directly extracts the substance form "nitrate nitrogen" through the form query function , the adjacent edge directly connected to point B (only the inflow edge from point A to point B, no outflow edge), the sum of the product of the displacement distance and the reaction rate of the inflow edge as the total input , the total output , set the unit time , the coefficient generated by dynamic allocation calculation ; through the formula calculation of the analysis function: , Finally, the complete result is obtained: the substance form is nitrate nitrogen, the soil stock is 0.375 milligrams per kilogram, the crop stock is 0.4625 milligrams per kilogram, and the microbial stock is 0.088 milligrams per kilogram. The overall scheme of the above S103, by fusing the physical migration and biochemical reaction network, establishes a precise mapping of spatial coordinates to substance forms and multi-medium stock data, realizes the analysis function of "location input and full-element nutrient state output", and provides a scientific basis for precision fertilization in different regions.

[0044] S104, based on the analysis result, quantitatively analyzing the competitive allocation relationship of the water and nitrogen nutrient elements among crops, soil and microorganisms, and identifying a high loss area in a material circulation process and a loss amount of the water and nitrogen nutrient elements retained according to the competitive allocation relationship; Optionally, S104 can specifically include the following steps: 1041, based on the analysis result, extracting stock data of crops, soil and microorganisms at a specific spatial position, the stock data including crop stock data, soil stock data and microorganism stock data; 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 dynamic allocation coefficient set; 1043, calculating actual allocation amounts of the crop stock data, soil stock data and microorganism stock data in a unit time according to the dynamic allocation coefficient set, and constructing a three-dimensional allocation relationship matrix based on the actual allocation amounts as a competitive allocation relationship; 1044, based on the competitive allocation relationship, identifying a dimensional position in the specific spatial position point where the allocation amount is continuously lower than a preset threshold, marking the specific spatial position point corresponding to the dimensional position as a high loss area, and simultaneously calculating a loss amount of the water and nitrogen nutrient elements retained by subtracting a total actual allocation amount of all specific spatial position points from a total water and nitrogen nutrient element input amount.

[0045] In the above scheme, the stock data refers to the water and nitrogen distribution amount obtained at a specific position by a migration analysis function, including crop stock, soil stock and microorganism stock; the dynamic allocation coefficient refers to a weight value calculated based on the stock proportion; the competitive allocation relationship refers to an actual nutrient amount allocation result quantified by a three-dimensional matrix; the high loss area refers to a position dimension where the allocation amount is continuously lower than a preset threshold; and the loss amount refers to a nutrient amount retained without participating in allocation.

[0046] In the embodiment of the application, first, the migration analysis function is called by step 1041 to obtain crop stock data, soil stock data and microorganism stock data at three-dimensional trajectory points of the water and nitrogen nutrient elements.

[0047] Secondly, the crop stock data, soil stock data and microorganism stock data are obtained by step 1042 to calculate a crop, soil and microorganism water and nitrogen stock proportion of the three-dimensional trajectory points per day, which is specifically defined as: The soil and microorganism water and nitrogen proportion is calculated in the same way. Then, dynamic competitive allocation calculation is performed by a dynamic smoothing formula: The dynamic allocation coefficient of day t is ​​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, where t represents the current day, and t-1 represents the previous day, is a time decay factor, is the proportion of water and nitrogen reserves of crops, soil, and microorganisms, and finally all three-dimensional trajectory points are calculated day by day to generate a dynamic allocation coefficient set containing time, space, and object dimensions.

[0048] Then, in step 1043, based on the dynamic allocation coefficient set, the total amount of water and nitrogen input per unit time is obtained, and the input amount is distributed according to the actual spatial weight represented by the three-dimensional trajectory points, with the formula Then, the dynamic allocation coefficient is called to calculate the actual allocation amount for soil, crops, and microorganisms, such as crop actual water gain = point water input amount x , and microbial actual nitrogen gain = point nitrogen input amount x ; a three-dimensional allocation relationship matrix is constructed, with time series as the row dimension, spatial points as the column dimension, and allocation amount categories as the layer dimension, and the matrix elements are filled according to the rule that the actual allocation amount of the first day, the first point, and the first category, such as representing the microbial nitrogen allocation amount of the first 5 points on the first day; this matrix directly quantifies the resource competition results, such as the microbial nitrogen allocation amount in the corn field being consistently higher than the crop nitrogen, revealing the competition relationship of nutrients tilting towards microorganisms.

[0049] Finally, in step 1044, based on the three-dimensional allocation relationship matrix, high loss areas are identified; 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, and when the microbial nitrogen allocation amount satisfies the condition of being less than for consecutive N days, the three-dimensional trajectory point is marked as a high loss area, and the process of identifying high loss areas for other allocation amount layers is the same; the total loss amount is calculated synchronously: the total amount of water and nitrogen input per unit time is extracted, and the actual allocation amount is summed up according to the element category from the matrix (J is the total number of three-dimensional trajectory points), and finally the loss amount is obtained, completing the quantification of the retained elements.

[0050] In practical application, 10-day water and nitrogen observation was carried out in a 10-hectare corn test field. The whole field was irrigated with 500 cubic meters of water and 10 kilograms of nitrogen per day, 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, in 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.

[0051] 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 microbial water inventory ratio is calculated, and substituted into the dynamic smoothing formula , the actual microbial water gain of P1 point = 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 of the first day the first point the first category, so the actual microbial water gain of P1 point on the first day 0.33 cubic meters is stored in the three-dimensional matrix M[1,1,5].

[0052] Set the microbial water threshold value to 0.3 cubic meters / day. Assuming 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 value, 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 used.

[0053] The overall scheme of the above S104 establishes a basic database through spatial inventory monitoring, quantifies the resource competition process using dynamic proportion algorithm, constructs a three-dimensional model to visualize the allocation pattern, and finally accurately identifies the weak resource utilization area and calculates the system loss. For the first time, the resource competition evaluation in the microbial dimension is realized, which reveals the neglected microbial resource shortage phenomenon in traditional management, and provides new basis for optimizing agricultural resource allocation.

[0054] S105, generating 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 for reconstructing a flow path of the water-nitrogen nutrient element, and the form conversion instruction being used for converting nitrogen element remaining in the water-nitrogen nutrient element into a slow-release form, so as to realize resource circulation of the water-nitrogen nutrient element.

[0055] Optionally, S105 can specifically include the following steps: 1051, generating a path reconstruction instruction based on spatial attributes of the high-loss area and a topological relationship of the physical migration network, and performing slow-release conversion calculation to generate a form conversion instruction based on the loss amount; 1052, increasing an edge weight value of the physical migration network of the high-loss area pointing to a target area and decreasing an edge weight value pointing to a non-target area based on the path reconstruction instruction, so as to make the water-nitrogen nutrient element migrate to the target area and inhibit the water-nitrogen nutrient element from flowing to the non-target area, thereby reconstructing a flow path of the water-nitrogen nutrient element, wherein the target area includes a crop root enrichment area and a microbial activity area, and the non-target area includes a groundwater infiltration area and an atmospheric volatilization area; 1053, performing the form conversion instruction to convert nitrogen element remaining in the high-loss area into a slow-release form; 1054, migrating the slow-release form nitrogen element obtained by conversion to the target area, so that the slow-release form nitrogen element participates in material allocation circulation in the target area, thereby realizing resource circulation of the water-nitrogen nutrient element.

[0056] In the above scheme, the path reconstruction instruction refers to an instruction for adjusting the moving direction of the water-nitrogen, which is 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 element into a slow-release form; the form conversion instruction refers to an operation command for guiding the form conversion of the nitrogen element; the physical migration network refers to a directed graph structure describing the moving path of the water-nitrogen in space; the edge weight value represents the intensity of the material migration tendency 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 easy to cause resource loss, such as a groundwater infiltration area and an atmospheric volatilization area; the slow-release form nitrogen element refers to a nitrogen compound that is chemically treated and can slowly release nutrients, such as coated urea; and the resource circulation refers to making the remaining nutrients re-enter the utilization process.

[0057] In the embodiment of the application, first, all edges starting from the high-loss area are identified by 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, and the target area edge weight is increased by a proportion , non-target zone weight reduction ratio , form weight adjustment instruction set as path reconstruction instruction; at the same time, based on the loss amount, calculate the transformation by slow-release , the is the loss days of allocation amount, output the amount of nitrogen to be transformed and the target slow-release form to generate form conversion instruction; Then, based on the target zone weight increase coefficient in the path reconstruction instruction and the non-target zone weight reduction coefficient , execute physical migration network edge weight direction adjustment: first increase the target path weight of the high loss area: , the is the original directed edge weight value of the target area, is the new weight value of the directed edge of the target area after adjustment according to the path reconstruction instruction; at the same time, compress the non-target path weight: , the is the original directed edge weight value of the non-target area, is the new weight value of the directed edge of the non-target area after adjustment according to the path reconstruction instruction; after weight adjustment, the selection probability of water and nitrogen migration path is proportional to the edge weight value, so the probability of flowing to the target area is increased to , realize directional migration by improving the statistical selection probability of the target path; for example, root zone weight = 0.3 × 2.412 = 0.7236, groundwater zone weight = 0.6 × 0.542 = 0.3252, then the probability of water and nitrogen nutrient elements flowing to the root zone is increased to , so that more water and nitrogen nutrients migrate to the root zone; Then, when executing the form conversion instruction by step 1053, first locate the nitrogen retained in the high loss area, and determine the transformation amount according to the slow-release transformation calculation , wherein the is the continuous loss days, is the retention amount; under the conditions of catalyst and temperature control, convert the available nitrogen into slow-release form by chemical reaction, and the conversion efficiency is , the t is the reaction hours; for example, for 1.8 kg of nitrate, , kg, after 2 hours of reaction , 1.61 (1.66 × 97.3) kg of coated urea is generated.

[0058] Finally, by step 1054, use the above adjusted physical migration network to directionally migrate the slow-release form nitrogen to the target area according to the path weight ratio; in the target area, by the slow-release kinetics model , the is the release amount, is the initial amount, is the release rate, t is the time, and the fast-acting nitrogen is released every day. The released amount participates in the stock monitoring and is allocated through dynamic competition to achieve resource recycling.

[0059] In actual application, after detecting point P3 (coordinates x=2.1m, y=1.5m, z=-0.2m) in area A of the cornfield as a high loss area, the spatial distance ratio is first calculated. =0.294, and then two key instructions are generated based on this ratio: the path reconstruction instruction obtains the target area weight improvement coefficient and non-target area suppression coefficient ; At the same time, the form conversion instruction calculates the amount of nitrogen that needs to be converted ; Then adjust the weight and update the weight of P3 pointing to the root zone to =0.7236, and adjust the weight of P3 pointing to the groundwater area to =0.3252, which significantly increased the probability of selecting the target path. Subsequently, 1.66 kg of nitrate was converted into coated urea by adding an iron-based catalyst, and 1.61 kg of slow-release nitrogen was finally obtained after a 97% reaction efficiency. Finally, it was monitored that the slow-release nitrogen was transported in a directional manner along the adjusted migration network, with the root zone obtaining , slow-release nitrogen according to the release model Continuously release nutrients, releasing 0.063 kg of fast-acting nitrogen on the first day; when these nutrients are integrated into the soil stock, they are distributed through the dynamic distribution coefficient. =0.6, the crops successfully obtained 0.038 kg of nitrogen, ultimately achieving resource cycle optimization effects of reducing groundwater loss by 58% and increasing the microbial nitrogen recycling rate to 78%.

[0060] The above-mentioned S105 overall solution intelligently identifies the spatial characteristics of high-loss areas, reconstructs the water and nitrogen migration paths to achieve directional transport, converts retained nitrogen into a slow-release form to reduce loss, and ultimately enables nutrients to participate in material circulation in the target area, forming a full-chain resource optimization system, significantly improving the nutrient utilization efficiency and sustainability of the agricultural system.

[0061] The smart agriculture monitoring method based on the Internet of Things provided in this application first 15 N isotope-labeled ammonium nitrate solution was injected into the root zone, and the 1530 nm peak was monitored using a near-infrared spectroscopy sensor. Data was collected every 30 minutes using an ion-selective electrode array. At time t0, the nitrate nitrogen concentration was 8 mg / kg. (After 1 hour) the concentration dropped to 5 mg / kg, and ammonium nitrogen increased from 3 mg / kg to 6 mg / kg; then the concentration change ΔC (nitrate nitrogen: -3 mg / kg, ammonium nitrogen: +3 mg / kg) was calculated, and the conversion path "nitrate nitrogen → ammonium nitrogen" was extracted; the reaction rate was calculated to obtain r=ΔC / Δt=3 mg / kg·h, and then a biochemical network was constructed with "nitrate nitrogen, ammonium nitrogen" as nodes, the conversion path as edges, and 3 mg / kg·h as the weight.

[0062] Next, a three-dimensional radio frequency sensor network is deployed in the canopy, root zone, and deep soil layer to obtain the nitrogen coordinates marked at time t0. , Time coordinates , and then calculate the displacement vector magnitude: =0.14m, horizontal angle =225°; connection Form a trajectory, build 、 A physical network with nodes as nodes, displacement trajectories as edges, and {0.14m,225°} as the composite weight.

[0063] Then, execute S103 fusion network construction migration analysis mechanism: generate position and morphology correspondence table, associate Point and "nitrate nitrogen" form; superimposed network forms joint node { (1.2,0.8,-0.3), nitrate nitrogen}, and define its adjacent edge properties (inflow edge: displacement distance =0.2812m, reaction rate =0.1mg / kg·h; outflow side: displacement distance =0.001m, reaction rate =20.12mg / kg·h); define the analytical function, input close to The monitoring associated coordinates are (1.25, 0.75, -0.35), and the material form "nitrate nitrogen" is extracted through the form query function; then the inflow and outflow edges are distinguished, and the total input amount I = inflow edge displacement distance × reaction rate = 0.2812 × 0.1 = 0.02812 (m·mg / kg·h), and the total output amount O = outflow edge displacement distance × reaction rate = 0.001 × 20.12 = 0.02012 (m·mg / kg·h); the unit time Δt = 1h is set; the total stock S = 0.008mg / kg is calculated through the analytical function formula.

[0064] Next, extract the inventory data at point B and calculate the proportion of microbial water =0.0037; obtained by calculation Dynamic allocation coefficient = 0.0035; the actual water gain of microorganisms = 50 x 0.0035 = 0.175 m3 / day, which is less 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 is = 500 - 482 = 18 m3.

[0065] Finally, the distance ratio r = 0.3 between the crop root enrichment area and the groundwater infiltration area is calculated, the path reconstruction instruction is generated, the weight increase ratio of the target area is calculated = 2.4, and the weight reduction ratio of the non-target area is = 0.55, the root path weight is adjusted from 0.4 to 0.4 x 2.4 = 0.96, and the groundwater path is adjusted from 0.6 to 0.6 x 0.55 = 0.33; using the morphological conversion instruction, the conversion amount of 1.5 kg of residual nitrogen is calculated to be 1.2 kg by the formula , and the residual nitrogen is converted into coated urea; the network is migrated to the root area after adjustment, and the release is calculated according to the slow-release model , 0.06 kg of available nitrogen is released on the first day to participate in the cycle, so as to realize closed-loop utilization.

[0066] Figure 3 A specific implementation architecture diagram of a smart agricultural monitoring method based on the Internet of Things is provided for the embodiment of the present application, which is used to show the basic framework of water and nitrogen nutrient element migration analysis; Figure 4 A specific implementation structure diagram of a smart agricultural monitoring system based on the Internet of Things is provided for the embodiment of the present application, which is referred to Figure 4 , the system can include: The construction module 41 is used to mark the preset water and nitrogen nutrient elements, acquire the morphological conversion path and reaction rate caused by biological and chemical actions of the water and nitrogen nutrient elements in the crop and soil system, and construct a biochemical substance flow network, which is used to represent the material form change of the water and nitrogen nutrient elements; The second construction module 42 is used to deploy a three-dimensional radio frequency sensing network in the crop and soil system, collect the three-dimensional space displacement trajectory of the water and nitrogen nutrient elements, and construct a physical migration network, which is used to represent the physical position change of the water and nitrogen nutrient elements; The analysis module 43 is used to fuse the biochemical substance flow network and the physical migration network, construct a migration analysis mechanism of the water and nitrogen nutrient elements, and analyze the material form and stock data of the water and nitrogen nutrient elements at a specific spatial position through the migration analysis mechanism; The identification module 44 is configured to quantitatively analyze a competitive distribution relationship between the water and nitrogen nutrients and 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 distribution relationship. The transformation module 45 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 nutrients, and the form conversion instruction 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] In an exemplary 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.

[0071] The embodiments of the present application also provide a computer program product, which comprises 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.

[0072] Those skilled in the art will further realize that the mere concepts, teachings, and embodiments described herein are merely meant to provide an enabling description of the applications and are not intended to limit the scope of the applications. Therefore, embodiments or examples described herein are not meant to be limiting, but merely to aid in the understanding of the overall more complete disclosure of the applications. Accordingly, those skilled in the art will recognize that modifications and variations of the more complete description herein can be resorted to without departing from the spirit and scope of the applications. Therefore, it is intended that the applications encompass all such modifications and variations as fall within the scope of the applications. All articles, patents, and other publications that have been cited herein are incorporated herein by reference for the teachings relevant to the sentence and / or paragraph in which the article, patent, and / or publication is mentioned.

[0073] The above provides a kind of based on the principle and system of the method for monitoring of wisdom agricultural of internet of things provided in the application in detail.The principle and implementation mode of the present application are described in this paper by applying specific examples, the above example is only used to help understand the method and its core idea of the present application.It should be pointed out that, for the ordinary skilled in the art, without departing from the principle of the present application, the present application can be improved and modified, these improvements and modifications also fall within the protection scope of the present application.

Claims

1. A smart agriculture monitoring method based on the Internet of Things, characterized in that: include: By marking preset water and nitrogen nutrient elements, the form transformation pathways and reaction rates of the water and nitrogen nutrient elements caused by biological and chemical reactions in the crop and soil system are obtained to construct a biochemical material flow network, which is used to characterize the material form changes of the water and nitrogen nutrient elements; A three-dimensional radio frequency sensor network deployed in the crop and soil system is used to collect three-dimensional spatial displacement trajectories of the water and nitrogen nutrient elements to construct a physical migration network, wherein the physical migration network is used to characterize the physical position changes of the water and nitrogen nutrient elements; The biochemical material flow network and the physical migration network are integrated to construct a migration analysis mechanism for the water and nitrogen nutrient elements, and the material form and stock data of the water and nitrogen nutrient elements at specific spatial locations are analyzed through the migration analysis mechanism; Based on the analytical results, the competitive distribution relationship between the water and nitrogen nutrients among crops, soil, and microorganisms is quantitatively analyzed, and the high loss areas in the material circulation process and the corresponding loss amount of the retained water and nitrogen nutrients are identified based on the competitive distribution relationship; Based on the high-loss area and the loss amount, a path reconstruction instruction and a form conversion instruction are generated. The path reconstruction instruction is used to reconstruct the flow path of the water-nitrogen nutrient element, and the form conversion instruction converts the nitrogen retained in the water-nitrogen nutrient element into a slow-release form to achieve resource recycling of the water-nitrogen nutrient element.

2. The method according to claim 1, characterized in that The biochemical material flow network and the physical migration network are integrated to construct a migration analysis mechanism for the water and nitrogen nutrient elements, and the material form and inventory data of the water and nitrogen nutrient elements at specific spatial locations are analyzed through the migration analysis mechanism, including: generating a position-to-form correspondence table based on the position data and form data of the water-nitrogen nutrient element to associate the material form nodes in the biochemical material flow network with the spatial position points in the physical migration network; According to the position data and form data of the water and nitrogen nutrient elements, the material form nodes in the biochemical material 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 are superimposed with the nodes and edges of the biochemical material flow network to form a joint network structure, wherein the nodes in the joint network structure simultaneously contain spatial position information and material form information, and the edges simultaneously contain displacement distance and reaction rate information; Based on the joint network structure, an analytical function is defined to construct a migration analysis mechanism for 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 material form and stock data of the water and nitrogen nutrient elements at the specific spatial position; For the analytical function, the node corresponding to the input coordinate is queried in the joint network structure, the material 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.

3. The method according to claim 2, characterized in that Querying the node corresponding to the input coordinates in the joint network structure, directly extracting the material form information of the node, and calculating the stock data of the specific spatial position through the reaction rate information and displacement distance of the adjacent edges, including: Locate the node with the smallest distance from the input coordinate space as the target node, and directly read the material form information of the target node; Obtain all adjacent edges directly connected to the target node, and divide them into inflow edges and outflow edges according to their directions; Calculating the sum of the product of the displacement distance of the inflow edge and the reaction rate as the input total amount, and calculating the sum of the product of the displacement distance of the outflow edge and the reaction rate as the output total amount; The difference between the total input amount and the total output amount is divided by the unit time to obtain the inventory data of the specific spatial location.

4. The method according to claim 1, wherein Based on the analytical results, the competitive distribution relationship between the water and nitrogen nutrients among crops, soil, and microorganisms is quantitatively analyzed, and the high-loss areas in the material circulation process and the corresponding loss amounts of the retained water and nitrogen nutrients are identified based on the competitive distribution relationship, including: Extracting stock data of crops, soil, and microorganisms at specific spatial locations based on the analysis results, wherein the stock data includes crop stock data, soil stock data, and microorganism stock data; Based on the crop stock data, soil stock data, and microbial stock data, and in combination with the proportional relationship between the stock data, a dynamic competitive allocation calculation is performed to generate a dynamic allocation coefficient set; Calculating actual distribution amounts of the crop stock data, soil stock data, and microbial stock data per unit time according to the dynamic distribution coefficient set, and constructing a three-dimensional distribution relationship matrix based on the actual distribution amounts as a competitive distribution relationship; Based on the competitive allocation relationship, identify the dimensional position in the specific spatial position where the allocation amount is continuously lower than the preset threshold, mark the specific spatial position corresponding to the dimensional position as a high loss area, and simultaneously calculate the loss amount corresponding to the retained water and nitrogen nutrient elements by subtracting the total input amount of water and nitrogen nutrient elements from the sum of the actual allocation amounts of all specific spatial position points.

5. The method according to claim 1, wherein Generate a path reconstruction instruction and a form conversion instruction based on the high loss area and the loss amount, wherein the path reconstruction instruction is used to reconstruct the flow path of the water-nitrogen nutrient element, and the form conversion instruction converts the nitrogen retained in the water-nitrogen nutrient element into a slow-release form to achieve resource recycling of the water-nitrogen nutrient element, including: Based on the spatial attributes of the high-loss area and in combination with the topological relationship of the physical migration network, a path reconstruction instruction is generated, and based on the loss amount, a slow-release conversion calculation is performed to generate a morphological conversion instruction; Based on the path reconstruction instruction, the edge weight value of the physical migration network pointing from the high-loss area to the target area is increased, and the edge weight value pointing 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 suppressed, so as to reconstruct the flow path of the water and nitrogen nutrient elements, wherein the target area includes the crop root enrichment area and the microbial activity area, and the non-target area includes the groundwater infiltration area and the atmospheric volatilization area; executing the form conversion instruction to convert the nitrogen retained in the high loss area into a slow-release form; The slow-release nitrogen obtained by the conversion is migrated to the target area, so that the slow-release nitrogen participates in the material distribution cycle in the target area, thereby realizing the resource cycle of water and nitrogen nutrient elements.

6. The method according to claim 1, characterized in that By marking preset water and nitrogen nutrient elements, the form transformation paths and reaction rates of the water and nitrogen nutrient elements caused by biological and chemical reactions in the crop and soil system are obtained to construct a biochemical material flow network. The biochemical material flow network is used to characterize the material form changes of the water and nitrogen nutrient elements, including: Isotope labeling of pre-set water and nitrogen nutrient elements, and introduction of the labeled water and nitrogen nutrient elements into the crop and soil system; In the crop and soil system, near-infrared spectral sensors and ion-selective electrode arrays deployed on the crop stems, leaves, roots, and soil layers periodically capture the material form spectral characteristics and ion concentrations of labeled water and nitrogen nutrients at consecutive time points. According to the changes in the spectral characteristics of the substance form and the ion concentration in the time series, a continuous transformation sequence from the initial form to the derived form is extracted as the form transformation 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 form transformation path as the edge, and the reaction rate as the edge weight to form a biochemical material flow network.

7. The method according to claim 1, characterized in that A three-dimensional radio frequency sensor network deployed in the crop and soil system is used to collect the three-dimensional spatial displacement trajectory of the water and nitrogen nutrient elements to construct a physical migration network. The physical migration network is used to characterize the physical position changes of the water and nitrogen nutrient elements, including: Deploy a three-dimensional radio frequency sensor network in the crop canopy, root zone, and soil profile; Obtaining the three-dimensional coordinate position data of the marked water and nitrogen nutrient elements at each time point through the three-dimensional radio frequency sensor network, calculating the displacement vector from the starting position to the end position for the three-dimensional coordinate position data of adjacent time points, and recording the corresponding modulus and displacement direction angle; Connecting the starting point positions of the displacement vector in time sequence to form a three-dimensional spatial displacement trajectory consisting of continuous spatial position points; A directed weighted graph structure is constructed with spatial position points as nodes, three-dimensional spatial displacement trajectories between adjacent position points as directed edges, and the modulus of the displacement vector and the displacement direction angle as composite edge weights to form a physical migration network.

8. A smart agricultural monitoring system based on the Internet of Things, characterized in that: include: A construction module is used to obtain the form transformation pathways and reaction rates of the water and nitrogen nutrient elements caused by biological and chemical reactions in the crop and soil system by marking preset water and nitrogen nutrient elements, so as to construct a biochemical material flow network, wherein the biochemical material flow network is used to characterize the material form changes of the water and nitrogen nutrient elements; A second construction module is configured to collect three-dimensional spatial displacement trajectories of the water and nitrogen nutrient elements through a three-dimensional radio frequency sensor network deployed in the crop and soil system to construct a physical migration network, wherein the physical migration network is used to characterize the physical position changes of the water and nitrogen nutrient elements; An analysis module is used to integrate the biochemical material flow network and the physical migration network to construct a migration analysis mechanism for the water and nitrogen nutrient elements, and analyze the material form and inventory data of the water and nitrogen nutrient elements at a specific spatial location through the migration analysis mechanism; an identification module for quantitatively analyzing the competitive distribution relationship of the water and nitrogen nutrients among crops, soil, and microorganisms based on the analysis results, and identifying high-loss areas in the material circulation process and the corresponding loss amount of the retained water and nitrogen nutrients according to the competitive distribution relationship; The conversion module is used to generate a path reconstruction instruction and a form conversion instruction based on the high-loss area and the loss amount. The path reconstruction instruction is used to reconstruct the flow path of the water-nitrogen nutrient element. The form conversion instruction converts the nitrogen retained in the water-nitrogen nutrient element into a slow-release form to realize the resource circulation of the water-nitrogen nutrient element.

9. An electronic device, characterized in that: include: memory for storing computer programs; A processor, configured to implement the steps of the smart agriculture monitoring method based on the Internet of Things as described in any one of claims 1 to 7 when executing the computer program.

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

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