An intelligent agriculture facility management method and system based on intelligent perception

Abstract

The present invention relates to the technical field of agricultural facility management, and particularly to a smart agricultural facility management method and system based on intelligent perception. The method includes the following steps: acquiring farmland area survey information data; deploying hierarchical sensor nodes for the farmland area survey information data to generate farmland sensor node deployment data; connecting communication networks according to the farmland sensor node deployment data to generate a farmland perception network, where the farmland perception network includes a number of farmland sensor nodes; dividing farmland state area plots for the farmland perception node layout planning data through the farmland perception network to generate normal farmland state area plots and abnormal farmland state area plots; calculating the water content of the abnormal farmland state area plots to obtain abnormal farmland area water content data. The present invention improves the efficiency of smart agricultural facility management through refined farmland perception, irrigation prediction, facility adjustment and strategy optimization.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural facility management, and in particular, to a method and system for intelligent agricultural facility management based on intelligent perception. Background Art

[0002] Initially, traditional agriculture relied on manual management and empirical judgment, suffering from problems such as lagging information acquisition and unreasonable resource allocation. With the development of automation control technology, facilities such as greenhouse greenhouses and irrigation systems have gradually achieved automated management, but still lack intelligent perception and real-time feedback mechanisms. After entering the 21st century, the breakthrough of intelligent perception technology has enabled the precise and intelligent management of agricultural facilities. The development of sensor technology has made it possible to monitor environmental factors such as temperature and humidity, light, and soil moisture in real time, and the Internet of Things technology enables these data to be remotely transmitted and processed. At the same time, the application of artificial intelligence algorithms in data analysis, prediction, and decision support has improved the intelligent level of agricultural facility management. In recent years, agricultural big data analysis based on cloud computing platforms has enabled agricultural facility management not only to optimize single facilities but also to perform global scheduling and collaborative control, further improving agricultural production efficiency and resource utilization rate. However, currently, traditional farmland status monitoring has problems such as unclear regional division and inaccurate detection of farmland problems. At the same time, there is a lack of accurate prediction and regulation of water body spread, often resulting in low irrigation efficiency and resource waste, and thus low efficiency of intelligent agricultural facility management. Summary of the Invention

[0003] Based on this, it is necessary to provide a method and system for intelligent agricultural facility management based on intelligent perception to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for intelligent agricultural facility management based on intelligent perception includes the following steps:

[0005] Step S1: Obtain farmland area survey information data; deploy hierarchical sensor nodes for the farmland area survey information data to generate farmland sensor node deployment data; connect communication networks according to the farmland sensor node deployment data to generate a farmland perception network, where the farmland perception network includes several farmland sensor nodes;

[0006] Step S2: Divide the farmland state area plots for the farmland perception node layout planning data through the farmland perception network to generate normal farmland state area plots and abnormal farmland state area plots; calculate the water content of the abnormal farmland state area plots to obtain abnormal farmland area water content data; use the abnormal farmland area water content data to manage the performance of irrigation facilities for the abnormal farmland state area plots to generate irrigation facility performance management data;

[0007] Step S3: Predict the spread of irrigation water bodies in adjacent plots of normal farmland areas to generate irrigation water body spread prediction data; adjust the direction of irrigation facilities according to the irrigation water body spread prediction data to generate irrigation facility direction management data; integrate the irrigation facility performance management data and the irrigation facility direction management data for irrigation strategy integration to generate an irrigation facility irrigation strategy;

[0008] Step S4: Conduct simulated irrigation on the irrigation facility irrigation strategy to generate irrigation facility management simulation data; visualize the irrigation facility management simulation data to generate an irrigation facility management simulation report; optimize the management strategy of the irrigation facility irrigation strategy using the irrigation facility management simulation report to perform intelligent agricultural facility management operations.

[0009] The present invention can lay a foundation for the farmland perception network by acquiring the survey information data of the farmland area and deploying hierarchical sensor nodes. The rationality of sensor node deployment can ensure the extensiveness and accuracy of information collection, contribute to real-time monitoring of farmland environmental changes, and provide accurate data for subsequent management. By using the farmland perception network to accurately divide the farmland state area, it is possible to monitor and distinguish the normal farmland area from the abnormal farmland area in real time. Calculating the water content of the plots in the abnormal farmland state area and generating relevant data can timely detect irrigation problems and help accurately adjust irrigation strategies. By using the water content data of the abnormal farmland area for irrigation facility performance management, the working status and efficiency of irrigation equipment can be comprehensively evaluated to ensure that the irrigation facilities can maintain the best performance at different times, thereby effectively improving the water resource utilization rate. Predicting the spread of irrigation water bodies in adjacent plots in the normal farmland state area can accurately predict the distribution trend of irrigation water bodies and provide a scientific basis for adjusting the irrigation direction. By adjusting the direction of irrigation facilities, it is possible to effectively avoid excessive spread or waste of water flow and ensure the rational use of irrigation water. Integrating the irrigation facility performance management data and the irrigation facility direction management data into an irrigation strategy to ensure that the irrigation operation meets the specific needs of the farmland. Through intelligent strategy integration and management, the irrigation process can be optimized, irrigation efficiency can be improved, and resource waste can be reduced. By simulating irrigation operations, simulation data for irrigation facility management can be generated to further optimize the actual management process. Data visualization and simulation reports can help managers clearly understand the working conditions of irrigation facilities and provide intuitive and clear bases for subsequent decision-making. By optimizing and analyzing the irrigation facility management simulation reports, important support can be provided for the management strategies of smart agriculture. The optimized irrigation strategy can achieve precise water application and adapt to the dynamic changes of the farmland environment. The goal of the entire system is to optimize the allocation and use of water resources through precise irrigation facility management and dynamic adjustment of irrigation strategies, thereby reducing water resource waste. This method helps to achieve the goals of smart agriculture, combines Internet of Things technology, sensor networks, and data analysis, improves the automation and intelligence levels of farmland management, and promotes the modernization of agricultural management. Therefore, the present invention improves the efficiency of smart agriculture facility management through refined farmland perception, irrigation prediction, facility adjustment, and strategy optimization.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Acquire the survey information data of the farmland area; design the layout of perception nodes for the survey information data of the farmland area to generate the layout planning data of farmland perception nodes;

[0012] Step S12: Based on the layout planning data of the farmland perception nodes, hierarchical sensor nodes are deployed to generate the farmland sensor node deployment data, where the farmland sensor node deployment data includes the deployment data of the soil perception layer sensors, the deployment data of the air and water body perception layer sensors, and the deployment data of the microbial ecological monitoring layer sensors;

[0013] Step S13: Through the deployment data of the soil perception layer sensors, the deployment data of the air and water body perception layer sensors, and the deployment data of the microbial ecological monitoring layer sensors, the intersection points of the deployment areas are confirmed to obtain the deployment area intersection point data; according to the deployment area intersection point data, data fusion nodes are deployed to generate the data fusion node deployment data;

[0014] Step S14: Based on the data fusion node deployment data, a communication network connection is established for the farmland sensor node deployment data, thereby generating a farmland perception network, where the farmland perception network includes several farmland sensor nodes.

[0015] Through the step-by-step layout and node deployment processes of steps S11 to S13 of the present invention, through the layout design of multi-level sensors such as soil, air, water body, and microbial ecology, multi-dimensional perception of the farmland environment is achieved, which helps to improve the comprehensiveness of data collection and cover farmland ecological information at different levels. In step S12, through scientific perception node layout and hierarchical deployment, the rationality of node distribution is ensured, effectively reducing blind spots, thereby improving data collection efficiency. And the deployment of each layer of sensors is subdivided into soil, air and water body, microbial ecology, etc., improving the monitoring fineness. In step S13, the confirmation of the intersection points of the deployment areas and the deployment of data fusion nodes can aggregate data at the intersections of different sensing levels, which helps to improve the accuracy and reliability of data fusion and reduce the deviation of data collected by sensors alone. Step S14 constructs a stable farmland perception network through the communication network connection of nodes, which helps to improve the stability and efficiency of data transmission, ensure that data between nodes can be transmitted in real time, and thus form the continuity and real-time of farmland monitoring. Combining the execution results of all steps, the formed farmland perception network can provide support for intelligent decision-making such as farmland management, pest warning, and environmental monitoring, improving the intelligent level of farmland management. This layered and fusion sensor layout scheme makes the deployment of perception nodes more adaptable, and can be flexibly adjusted and optimized according to different farmland environments and requirements to adapt to diverse agricultural monitoring needs.

[0016] Preferably, step S14 includes the following steps:

[0017] Step S141: Analyze the signal coverage range of the farmland sensor node deployment data based on the data fusion node deployment data to generate farmland node coverage data; select a communication protocol based on the farmland node coverage data to obtain communication protocol selection data;

[0018] Step S142: Use the communication protocol selection data to connect the farmland sensor node deployment data and the data fusion node deployment data to generate an initial node connection network;

[0019] Step S143: Assign unique identifiers to the nodes in the initial node connection network to generate network node unique identifiers; detect the network stability of the initial node connection network based on the network node unique identifiers to generate network connection stability data;

[0020] Step S144: Use the network connection stability data to optimize the network topology of the initial node connection network to generate a farmland sensing network, where the farmland sensing network includes a number of farmland sensor nodes.

[0021] Through signal coverage analysis and communication protocol selection, the present invention ensures that the nodes reasonably distributed in the farmland can effectively cover the monitoring area, reduce blind spots and overlapping coverage, and improve the stability and accuracy of signal transmission. The selection of the communication protocol is combined with the node coverage data, enabling the nodes in different regions to use appropriate communication protocols according to the actual situation, thereby maximizing the signal transmission efficiency, reducing energy consumption and signal interference, and ensuring the high efficiency of data transmission. By assigning unique identifiers to each node, a network structure convenient for management is formed, which helps to easily identify and track the positions and states of each node in the complex farmland sensing network, facilitating subsequent network maintenance and management. The network connection stability detection ensures the communication quality between nodes, timely discovers potential unstable factors in the initial connection network, and optimizes them in subsequent steps, which helps to reduce the risk of communication interruption and ensure the continuity and reliability of data transmission. Through topology optimization, the structure of the farmland sensing network becomes more flexible and scalable. The optimized network topology reduces unnecessary connections, improves the overall efficiency of the network, reduces the transmission delay, and makes the sensing network more suitable for the distributed monitoring requirements of the farmland. The process design of Step S14 considers the steps of communication protocol, node unique identifier, and topology optimization, facilitating the easy addition of new nodes or adjustment of existing nodes in the future when the farmland environment changes or the expansion requirements increase, realizing the flexible expansion of the sensing network. Through coverage analysis, stability detection, and topology optimization, the farmland sensing network has high reliability and durability, can adapt to long-term operation in different environments, reduces the frequency and cost of subsequent maintenance, and is beneficial to the management of the farmland facility monitoring system.

[0022] Preferably, Step S2 includes the following steps:

[0023] Step S21: Divide the farmland monitoring status of the farmland perception node layout planning data through the farmland perception network to generate a normal farmland monitoring status and an abnormal farmland monitoring status; based on the normal farmland monitoring status and the abnormal farmland monitoring status, divide the farmland area survey information data to generate a normal farmland status area plot and an abnormal farmland status area plot;

[0024] Step S22: Extract the soil properties of the plots in the abnormal farmland status area to obtain farmland soil property data; calculate the water content of the plots in the abnormal farmland status area according to the farmland soil property data to obtain the water content data of the abnormal farmland area;

[0025] Step S23: Compare the water content data of the abnormal farmland area with the preset standard soil water content threshold. When the water content data of the abnormal farmland area is less than the preset standard soil water content threshold, mark the plots in the abnormal farmland status area as water-deficient plots;

[0026] Step S24: When the water content data of the abnormal farmland area is greater than the preset standard soil water content threshold, mark the plots in the abnormal farmland status area as waterlogged plots; perform irrigation facility performance management based on the water-deficient plots and the waterlogged plots to generate irrigation facility performance management data.

[0027] The present invention divides the farmland monitoring status into normal and abnormal areas through the farmland perception network, realizes the early detection and area division of abnormal statuses, helps managers quickly identify and locate the farmland areas that need attention, and improves the monitoring efficiency. Extracting soil properties and calculating water content in the abnormal areas enables the monitoring system to obtain detailed soil characteristic data of abnormal plots, accurately master soil humidity and other important properties, and provides a scientific basis for subsequent processing and decision-making. By comparing with the standard soil water content threshold, the water status of the abnormal areas is classified in real time, and the abnormal areas are divided into water-deficient plots and waterlogged plots, which helps improve the accuracy of water resource management and quickly respond to drought or waterlogging problems. Based on the division of water-deficient plots and waterlogged plots, Step S24 can generate irrigation facility performance management data, which helps managers optimize the configuration and operation of the irrigation system, achieve targeted water resource allocation, and avoid resource waste. The entire step process effectively combines the sensing network and data analysis to form an intelligent farmland status monitoring and management system, which can manage the water and soil properties of farmland in a refined manner and promote the development of farmland management towards refinement.

[0028] Preferably, the dynamic management of irrigation facilities based on water-deficient plots and waterlogged plots includes:

[0029] Perform the first liquid filling of the irrigation facilities for water - deficient plots to generate the first liquid filling data of the irrigation facilities; based on the first liquid filling data of the irrigation facilities, perform periodic irrigation on the water - deficient plots, and simultaneously collect the soil data of the water - deficient plots after irrigation to obtain the soil data of the water - deficient plots after irrigation;

[0030] Conduct irrigation penetration analysis on the soil data of the water - deficient plots after irrigation to generate soil irrigation penetration data; adjust the irrigation intensity of the irrigation facilities through the soil irrigation penetration data to generate irrigation management data for the water - deficient plots;

[0031] Perform the second liquid filling of the irrigation facilities according to the data of the microbial activity degree of the plots to generate the second liquid filling data of the irrigation facilities;

[0032] Extract the regional meteorological data of the water - logged plots to obtain the regional meteorological data of the water - logged plots; conduct temperature change analysis on the regional meteorological data of the water - logged plots to generate the regional temperature change data of the water - logged plots; calculate the microbial activity degree of the plots through the regional temperature change data of the water - logged plots to obtain the data of the microbial activity degree of the plots;

[0033] Adjust the irrigation concentration of the second liquid filling data of the irrigation facilities by using the data of the microbial activity degree of the plots, thereby generating irrigation management data for the water - logged plots; integrate the irrigation management data of the water - deficient plots and the irrigation management data of the water - logged plots to generate irrigation facility performance management data.

[0034] Through liquid filling, periodic irrigation, soil data collection, and irrigation infiltration analysis of water-deficient plots, the present invention can achieve precise management of farmland water shortage problems, ensure uniform infiltration of soil moisture, avoid over-irrigation or water shortage, improve irrigation efficiency, and reduce water resource waste. By adjusting irrigation intensity based on soil data after irrigation and guiding liquid filling of irrigation with data on the activity level of microorganisms, the liquid filling and irrigation concentration of irrigation facilities are dynamically adjusted, thereby forming a more intelligent irrigation plan to ensure the optimal allocation of water and nutrients. This management process can adjust the operating parameters of irrigation facilities in real time according to the status of different plots (water shortage or waterlogging) and the activity level of microorganisms, improve the flexibility and adaptability of the irrigation system, and ensure that each plot is precisely irrigated according to its actual needs. For waterlogged plots, by extracting meteorological data and analyzing temperature changes, and combining with data on the activity level of microorganisms to adjust irrigation concentration, it helps to optimize the irrigation management of waterlogged areas, prevent the further aggravation of waterlogging conditions, and promote moderate drainage and recovery of the soil. By adjusting irrigation intensity and liquid filling based on data on the activity level of microorganisms, not only is the reasonable distribution of water ensured, but it also has a positive impact on the soil ecosystem, promoting the health of soil microorganisms and the long-term sustainability of the soil. Through the integration of irrigation management data for water-deficient plots and waterlogged plots, a comprehensive irrigation plan and decision-making support can be provided for farmland, promoting the unified optimization and performance improvement of irrigation facilities, and enhancing the overall efficiency of the irrigation system. This system can respond to changes in the farmland environment in real time and dynamically adjust the irrigation plan according to data feedback. This flexibility and adaptability improve the management level of irrigation facilities and the ability to adapt to complex agricultural environments.

[0035] Preferably, the irrigation infiltration analysis of the soil data of the water-deficient plot after irrigation includes:

[0036] Calculating the temporal soil water content of the soil data of the water-deficient plot after irrigation to obtain the change data of the irrigation soil water content; analyzing the soil water distribution of the water-deficient plot through the change data of the irrigation soil water content to generate a soil water distribution map;

[0037] Extracting the soil structure of the soil data of the water-deficient plot after irrigation to generate the soil structure data of the water-deficient plot, where the soil structure data of the water-deficient plot includes soil physical structure data and soil chemical structure data; analyzing the soil infiltration characteristics according to the soil physical structure data and soil chemical structure data to generate soil infiltration characteristic data;

[0038] Using the soil infiltration characteristic data to detect the infiltration uniformity of the soil water distribution map to generate soil infiltration uniformity data; estimating the infiltration depth of the soil water distribution map through the soil infiltration uniformity data to generate soil irrigation infiltration data.

[0039] The present invention calculates the time-series soil water content of the soil data after irrigation to generate the data of the change in the soil water content of the irrigated soil, which helps to comprehensively understand the change trend of soil moisture, timely identify abnormal moisture changes, and thus achieve precise moisture management of water-deficient plots and optimize the irrigation plan. Through the analysis of the soil moisture distribution, a soil moisture distribution map is generated, which can clearly show the distribution of soil moisture after irrigation, help farm managers understand the penetration and distribution patterns of moisture in the soil, and contribute to the reasonable arrangement of the irrigation cycle to avoid uneven moisture distribution. By extracting the data of the physical and chemical structures of the soil and conducting soil structure analysis, in-depth soil background information can be provided for the irrigation penetration characteristics, ensuring targeted analysis of the penetration behaviors of different soil types, thereby improving the effectiveness of the irrigation system. Through the analysis of the soil penetration characteristics, the data of the soil penetration ability is obtained, which can accurately judge the soil penetration characteristics. This data provides a scientific basis for the optimization of the irrigation system, making the moisture penetration more uniform and avoiding over-irrigation or water resource waste caused by differences in permeability. The detection of soil penetration uniformity helps to evaluate the penetration uniformity of soil moisture in different regions and timely detect the problem of uneven penetration in the irrigation system. Based on this, targeted adjustments can be made to the irrigation system to ensure that moisture can penetrate evenly into the deep layer of the soil. By analyzing the data of soil penetration uniformity, the penetration depth is estimated, and the penetration depth and range of moisture after irrigation can be predicted more accurately. This helps to optimize the control of the irrigation amount, avoid shallow irrigation and over-deep irrigation, and ensure the reasonable utilization of irrigation water. The design of this process makes irrigation management more data-driven, provides a large amount of key data such as soil moisture, structure, and permeability, supports refined farm management decisions, enhances the intelligent level in the agricultural production process, and improves the scientific nature and efficiency of agricultural management.

[0040] Preferably, calculating the activity degree of plot microorganisms in the waterlogged plot based on the temperature change data of the waterlogged plot area includes:

[0041] Extracting the microbial monitoring data of the waterlogged plot to obtain the microbial monitoring data of the waterlogged plot; classifying the microbial monitoring data of the waterlogged plot to generate the data of beneficial microbial species and the data of harmful microbial species; calculating the proportion of the microbial density in the plot for the data of beneficial microbial species and the data of harmful microbial species to obtain the data of the proportion of the microbial density in the plot;

[0042] Screening the waterlogged plots with a high density of harmful microorganisms according to the data of the proportion of the microbial density in the plot to obtain the waterlogged plots with a high density of harmful microorganisms; analyzing the preferred temperature of the microorganisms in the waterlogged plots with a high density of harmful microorganisms to generate the data of the preferred temperature range of harmful microorganisms;

[0043] Calculating the microbial activity degree of a waterlogged plot by using the temperature change data of the waterlogged plot area to obtain the microbial activity degree data of the plot; the formula for calculating the microbial activity degree of the plot is as follows:

[0044] A = α·ΔT + β·H + γ·O + δ·M;

[0045] In the formula, A represents the microbial activity degree index, α represents the influence degree weight coefficient of the temperature change rate, ΔT represents the temperature change rate, β represents the influence degree weight coefficient of humidity, H represents the soil humidity, γ represents the influence weight coefficient of oxygen concentration, O represents the soil oxygen concentration, δ represents the influence degree weight coefficient of microbial density, and M represents the microbial density coefficient.

[0046] By comprehensively considering various factors such as temperature change, humidity, oxygen concentration, and microbial density, the calculation formula can accurately evaluate the activity degree of microorganisms in the soil. The weighted influence of different factors can comprehensively reflect the influence of actual environmental conditions on microbial activity, thereby providing more detailed data support for farmland management. Through the classification of microbial species and the calculation of density ratios, the types and densities of harmful microorganisms can be accurately identified, and plots with high densities can be screened. Combining the analysis of the preferred temperature of microorganisms can help detect potential disease risks in areas with high densities of harmful microorganisms in a timely manner, thereby providing a scientific basis for farmland disease prevention and control. The microbial activity degree index (A), through the comprehensive influence of temperature, humidity, oxygen concentration, and microbial density, can provide important information on the soil health status. This data provides a scientific basis for optimizing irrigation decisions and adjusting soil management strategies (such as improving soil permeability and adjusting fertilizer use), which helps to increase farmland yield and health. By quantitatively calculating the microbial activity of waterlogged plots, it is possible to help identify the changing trends of microbial communities in the soil and detect the risks of soil degradation or pollution in a timely manner. Increasing the microbial activity of waterlogged plots can promote soil restoration, reduce soil degradation problems, and ensure the sustainability of agricultural production. The microbial community in the soil directly affects the soil structure and fertility. By monitoring the microbial activity degree, it is possible to maintain and increase the activity of beneficial microorganisms in the soil, optimize the soil ecosystem, and enhance the soil's nutrient cycling ability. This method can provide multi-dimensional analysis of soil microbial activity by comprehensively considering various environmental factors (temperature, humidity, oxygen concentration, etc.). This comprehensive method can better reflect the true situation of microbial activity in a complex environment than single-factor analysis, providing stronger data support for precise farmland management and decision-making. According to the calculation results of the microbial activity degree, targeted management can be carried out on waterlogged plots, optimizing the use of irrigation, water, and nutrients, improving resource utilization efficiency, reducing unnecessary irrigation or fertilization, thereby reducing costs and minimizing the negative impact on the environment.

[0047] Preferably, step S3 includes the following steps:

[0048] Step S31: Determine the difference in the state attributes of adjacent plots in the area of normal farmland status. When it is recognized that the status of an adjacent plot is an area of abnormal farmland status, identify the regional boundary between the area of normal farmland status and the area of abnormal farmland status to obtain the adjacent plot regional boundary data;

[0049] Step S32: Predict the spread of irrigation water bodies for the area of normal farmland status and the area of abnormal farmland status based on the adjacent plot regional boundary data to generate irrigation water body spread prediction data;

[0050] Step S33: Adjust the direction of irrigation facilities according to the irrigation water body spread prediction data to generate irrigation facility direction management data;

[0051] Step S34: Integrate the irrigation facility performance management data and the irrigation facility direction management data to generate an irrigation strategy for irrigation facilities.

[0052] By discriminating the differences in the state attributes of adjacent plots between the normal farmland state area and the abnormal farmland state area, the present invention can accurately identify the changes in the farmland area and timely detect potential abnormal situations. This method helps to avoid the waste of farmland resources caused by improper irrigation or soil problems. After identifying the state differences of adjacent plots, the regional boundary is identified, which can clearly determine the boundary between the normal and abnormal areas. This boundary data is crucial for subsequent irrigation management and precision agriculture decision-making, ensuring that irrigation measures are highly targeted and reducing unnecessary resource waste. Based on the regional boundary data, the prediction of the spread of irrigation water bodies can effectively estimate the flow range of water bodies and their impact on farmland. This helps to plan and adjust irrigation strategies in advance to prevent the spread of irrigation water bodies in areas where irrigation is not required, improve the utilization efficiency of water resources, and reduce waste. By adjusting the direction of irrigation facilities according to the prediction results of the spread of irrigation water bodies, the water flow direction and irrigation volume can be precisely controlled. This management method helps to improve irrigation efficiency, reduce unnecessary water body spread, and ensure that the irrigation system can better serve the farmland needs. Integrating the irrigation facility performance management data and the irrigation facility direction management data to generate an irrigation strategy helps to provide a comprehensive irrigation management plan. Through this integration step, it is ensured that all irrigation facilities work together to optimize the operation of the overall irrigation system and maximize the utilization efficiency of water resources. By optimizing the adjustment of the direction of irrigation facilities and integrating irrigation strategies, water source waste can be effectively avoided, and the actual needs of farmland can be precisely matched. This precise management improves agricultural production efficiency and ensures that the farmland maintains the best moisture conditions. The adjustment of the direction of irrigation facilities and the integration of irrigation strategies effectively avoid over-irrigation of the abnormal farmland state area and reduce the waste of irrigation water bodies. This not only saves water resources but also reduces the cost of agricultural production. By real-time identifying the differences in farmland states and dynamically adjusting the prediction of the spread of irrigation water bodies, environmental changes and sudden farmland problems can be addressed. As factors such as weather and soil humidity change, the irrigation strategy can be timely optimized and adjusted to maintain the continuous and stable production state of the farmland. By automatically analyzing the farmland state and irrigation requirements, this method can improve the intelligent level of agricultural management. Combining modern Internet of Things and sensing technologies, farmland managers can make more precise and efficient decisions with the support of real-time data, promoting the development of agriculture towards the direction of intelligence and precision.

[0053] Preferably, step S32 includes the following steps:

[0054] Step S321: Classify the boundaries of the plots in the normal farmland state area and the plots in the abnormal farmland state area according to the adjacent plot regional boundary data to generate permeable boundary data and barrier boundary data;

[0055] Step S322: Calculate the terrain slope difference of adjacent plot area boundaries through seepage boundary data and barrier boundary data to generate terrain slope difference data of plots; analyze the natural water flow trend of adjacent plot area boundaries based on the terrain slope difference data of plots to generate natural water flow trend data;

[0056] Step S323: Use the experimental determination method to analyze the soil moisture diffusion of adjacent plot area boundaries to generate the soil moisture diffusion coefficient of plots; divide the data sets of the natural water flow trend data and the soil moisture diffusion coefficient to generate a model training set and a model test set;

[0057] Step S324: Use the diffusion equation to train the seepage model for the model training set to generate a preliminary water body spread prediction model; optimize and iterate the preliminary water body spread prediction model according to the model test set to generate a water body spread prediction model; import the adjacent plot area boundary data into the water body spread prediction model to predict the water body spread of adjacent plots, thereby generating irrigation water body spread prediction data.

[0058] By classifying the boundary data of adjacent plot areas into permeable boundaries and barrier boundaries, the present invention can clearly distinguish which areas can naturally accept water body spread and which areas will block the water flow. This provides clear spatial information support for subsequent prediction of irrigation water body spread and helps optimize irrigation efficiency. By calculating the terrain slope differences of adjacent plot areas, the flow characteristics of water in different terrain areas can be accurately understood. This analysis provides an important basis for understanding the flow trend of water between plots with different slopes and helps formulate irrigation strategies that conform to the terrain conditions. Based on the terrain slope difference data, the natural flow trend analysis of the water body can reveal the flow path and direction of the water between different plots. This analysis ensures that the water body can flow according to natural laws, avoids over-irrigation or water waste, and helps optimize the irrigation area. By analyzing the soil water diffusion through experimental determination methods, the absorption, diffusion, and infiltration characteristics of the soil to the water body can be better understood. The generation of the soil water diffusion coefficient provides accurate physical data for subsequent model training and can effectively simulate the infiltration process of irrigation water bodies. By dividing the data sets of the natural water flow trend data and the soil water diffusion coefficient into training sets and test sets, it is ensured that the model can accurately reflect the actual situation during the training process and at the same time ensure the generalization ability of the model. Through this process, the model can adapt to the water body spread laws under different soil and terrain conditions. Using the diffusion equation to train the infiltration model for the model training set can deeply understand the physical phenomena during the water body spread process, thereby generating a more accurate water body spread prediction model. The generation of this model provides a scientific basis for accurately controlling the spread of irrigation water bodies. By optimizing and iterating the model test set, continuously adjusting the model parameters and structure, the water body spread prediction model can better adapt to the actual needs of different farmlands. The iteration process ensures the accuracy and stability of the model and improves the accuracy of the irrigation water body spread prediction. After importing the boundary data of adjacent plot areas into the water body spread prediction model, the scope and trend of water body spread can be accurately predicted for different areas. This prediction data helps optimize the adjustment of irrigation facilities, achieve more efficient irrigation management, and avoid over-spread or insufficient coverage of water bodies. Through accurate prediction of irrigation water body spread, more scientific and accurate decision-making support can be provided for agricultural managers, ensuring resource optimization and efficiency maximization during the irrigation process.

[0059] In this specification, a smart agriculture facility management system based on intelligent perception is provided for implementing the above-mentioned smart agriculture facility management method based on intelligent perception. The smart agriculture facility management system based on intelligent perception includes:

[0060] A sensor network connection module, which is used to obtain the survey information data of the farmland area; deploy hierarchical sensor nodes for the survey information data of the farmland area to generate the farmland sensor node deployment data; conduct communication network connection according to the farmland sensor node deployment data, thereby generating a farmland perception network, where the farmland perception network includes a number of farmland sensor nodes;

[0061] An irrigation performance management module, which is used to divide the farmland state area plots for the farmland perception node layout planning data through the farmland perception network to generate normal farmland state area plots and abnormal farmland state area plots; calculate the water content of the abnormal farmland state area plots to obtain the water content data of the abnormal farmland area; use the water content data of the abnormal farmland area to manage the performance of the irrigation facilities for the abnormal farmland state area plots to generate the irrigation facility performance management data;

[0062] An irrigation direction management module, which is used to predict the spread of irrigation water bodies in adjacent plots for the normal farmland state area plots to generate the irrigation water body spread prediction data; adjust the direction of the irrigation facilities according to the irrigation water body spread prediction data, thereby generating the irrigation facility direction management data; integrate the irrigation facility performance management data and the irrigation facility direction management data to generate the irrigation strategy of the irrigation facilities;

[0063] An irrigation management simulation module, which is used to simulate the irrigation of the irrigation facility irrigation strategy to generate the irrigation facility management simulation data; visualize the irrigation facility management simulation data to generate the irrigation facility management simulation report; use the irrigation facility management simulation report to optimize the management strategy of the irrigation facility irrigation strategy to execute the intelligent agricultural facility management operation.

[0064] The beneficial effects of the present invention are as follows: By deploying hierarchical sensor nodes and connecting them into a farmland perception network, the environmental information of the farmland (such as soil humidity, temperature, light, etc.) can be obtained in real time, providing accurate perception data for agricultural management. The construction of the farmland perception network enables efficient communication between sensor nodes, thus building a sensing network covering the entire farmland, ensuring the timeliness and accuracy of information transmission, and avoiding the problems of lagging or incomplete information transmission in traditional agricultural management. By dividing the farmland state area plots through the perception network, it is possible to accurately distinguish the normal farmland state area and the abnormal farmland state area, which helps to timely discover problem areas and take measures. Calculating the water content in the abnormal area can accurately understand the soil moisture condition, so as to implement targeted irrigation in the abnormal area and improve the utilization efficiency of water resources. Using the water content data for performance management of irrigation facilities can monitor the working state of irrigation equipment, timely adjust or repair the equipment, and ensure the efficient operation of the irrigation system. Predicting the spread of irrigation water in adjacent plots of the normal farmland state area can effectively foresee the diffusion path of irrigation water, avoiding unnecessary water resource waste. Adjusting the direction of irrigation facilities according to the water body spread prediction data can ensure uniform water distribution, improve irrigation efficiency, and reduce the phenomena of over-irrigation or under-irrigation. Combining the irrigation facility performance management data and the facility direction management data can integrate a scientific irrigation strategy, further optimize resource utilization, and ensure that the water demand of crops is met throughout the growth cycle. By simulating the irrigation strategy of irrigation facilities, the effect of the strategy can be verified before actual implementation, avoiding wrong decisions. Data visualization can enable managers to more intuitively understand the irrigation effect and management status. According to the report generated from the simulation data, the irrigation strategy can be analyzed and optimized to ensure more efficient and scientific management of agricultural facilities. The optimized strategy can further improve the overall performance of the irrigation system, reduce energy consumption and water resource waste. Therefore, the present invention improves the efficiency of intelligent agricultural facility management through refined farmland perception, irrigation prediction, facility adjustment, and strategy optimization. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic diagram of the step flow of a method for managing intelligent agricultural facilities based on intelligent perception;

[0066] Figure 2 is Figure 1 a detailed implementation step flow schematic diagram of step S2 in

[0067] Figure 3 is Figure 1 a detailed implementation step flow schematic diagram of step S3 in

[0068] The realization, functional characteristics, and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. Detailed implementation manners

[0069] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those skilled in the art within the scope of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0070] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0071] It should be understood that although terms such as "first" and "second" may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly, the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed related items.

[0072] To achieve the above object, please refer to Figures 1 to 3 , a smart agriculture facility management method based on intelligent perception, the method comprising the following steps:

[0073] Step S1: Obtain the survey information data of the farmland area; deploy hierarchical sensor nodes for the survey information data of the farmland area to generate the farmland sensor node deployment data; perform communication network connection according to the farmland sensor node deployment data to generate a farmland perception network, where the farmland perception network includes a number of farmland sensor nodes;

[0074] Step S2: Divide the farmland state area plots for the farmland perception node layout planning data through the farmland perception network to generate normal farmland state area plots and abnormal farmland state area plots; calculate the water content of the abnormal farmland state area plots to obtain the water content data of the abnormal farmland area; use the water content data of the abnormal farmland area to perform irrigation facility performance management on the abnormal farmland state area plots to generate irrigation facility performance management data;

[0075] Step S3: Predict the spread of irrigation water bodies in adjacent plots of normal farmland areas to generate irrigation water body spread prediction data; adjust the direction of irrigation facilities according to the irrigation water body spread prediction data to generate irrigation facility direction management data; integrate the irrigation facility performance management data and the irrigation facility direction management data for irrigation strategy integration to generate an irrigation facility irrigation strategy;

[0076] Step S4: Conduct simulated irrigation on the irrigation facility irrigation strategy to generate irrigation facility management simulation data; visualize the irrigation facility management simulation data to generate an irrigation facility management simulation report; optimize the management strategy of the irrigation facility irrigation strategy using the irrigation facility management simulation report to execute the intelligent agricultural facility management operation.

[0077] The present invention can lay a foundation for the farmland perception network by acquiring the survey information data of the farmland area and deploying hierarchical sensor nodes. The rationality of the sensor node deployment can ensure the extensiveness and accuracy of information collection, contribute to the real-time monitoring of the changes in the farmland environment, and provide accurate data for subsequent management. By using the farmland perception network to accurately divide the farmland state area, it is possible to monitor and distinguish the normal farmland area and the abnormal farmland area in real time. Calculating the water content of the plots in the abnormal farmland state area and generating relevant data can timely detect irrigation problems and help accurately adjust the irrigation strategy. By using the water content data of the abnormal farmland area for the performance management of irrigation facilities, the working state and efficiency of irrigation equipment can be comprehensively evaluated to ensure that the irrigation facilities can maintain the best performance in different periods, thereby effectively improving the utilization rate of water resources. Predicting the spread of irrigation water bodies in adjacent plots in the normal farmland state area can accurately predict the distribution trend of irrigation water bodies and provide a scientific basis for adjusting the irrigation direction. By adjusting the direction of irrigation facilities, it is possible to effectively avoid the excessive spread or waste of water flow and ensure the reasonable use of irrigation water. Integrating the performance management data of irrigation facilities and the direction management data of irrigation facilities into an irrigation strategy to ensure that the irrigation operation meets the specific needs of the farmland. Through intelligent strategy integration and management, the irrigation process can be optimized, the irrigation efficiency can be improved, and resource waste can be reduced. By simulating irrigation operations, simulation data for irrigation facility management can be generated to further optimize the actual management process. Data visualization and simulation reports can help managers clearly understand the working conditions of irrigation facilities and provide an intuitive and clear basis for subsequent decision-making. By optimizing and analyzing the simulation reports of irrigation facility management, important support can be provided for the management strategy of smart agriculture. The optimized irrigation strategy can achieve precise water application and adapt to the dynamic changes in the farmland environment. The goal of the entire system is to optimize the allocation and use of water resources through precise irrigation facility management and dynamic adjustment of irrigation strategies, thereby reducing water resource waste. This method helps to achieve the goal of smart agriculture, combines Internet of Things technology, sensor networks, and data analysis to improve the automation and intelligence level of farmland management, and promotes the modernization of agricultural management. Therefore, the present invention improves the efficiency of smart agriculture facility management through refined farmland perception, irrigation prediction, facility adjustment, and strategy optimization.

[0078] In an embodiment of the present invention, with reference to Figure 1 as shown, it is a schematic diagram of the step flow of a smart agriculture facility management method based on intelligent perception according to the present invention. In this example, the smart agriculture facility management method based on intelligent perception includes the following steps:

[0079] Step S1: Obtain the survey information data of the farmland area; deploy hierarchical sensor nodes for the survey information data of the farmland area to generate the farmland sensor node deployment data; establish a communication network connection according to the farmland sensor node deployment data, thereby generating a farmland perception network, where the farmland perception network includes a number of farmland sensor nodes;

[0080] In the embodiment of the present invention, the target farmland area is preliminarily surveyed by using technologies such as unmanned aerial vehicles and satellite images to collect terrain, landform and vegetation information. The collected raw data is subjected to data cleaning, denoising and standardization processing to generate standardized farmland area survey information data, ensuring data consistency and accuracy. According to the farmland area survey information data, sensors suitable for monitoring specific environmental parameters are selected, such as temperature, humidity, light, soil humidity, etc. The sensor nodes are divided into multi-level deployments. The functions and layouts of each layer of nodes are different. For example: Basic node layer: Covers each small block of the farmland area and is used to collect real-time environmental data. Relay node layer: Arranged between each small block, responsible for aggregating and transmitting the data of the basic node layer. Master node layer: Centralizes the management of data transmission and is responsible for uploading the data of the relay node layer to the central database. The positions, types and connection relationships of each node are formed into structured data to generate farmland sensor node deployment data. According to the node deployment data, the communication network topology structure within the farmland area is designed, considering factors such as distance, signal strength, data transmission delay, etc. Wireless communication connections (such as LoRa, Zigbee, Wi-Fi, etc.) are configured between the nodes to ensure that the sensor nodes can communicate stably and efficiently. The network topology is optimized to ensure the stability of data transmission and the lowest latency, generating the final farmland perception network. The entire farmland perception network is tested to ensure that all nodes are in normal operation and can collect and transmit data in real time. Detect and record the fault points in the network, and set up an automatic repair mechanism to ensure the continuity of data. Finally, farmland perception network data is generated, which includes information such as the positions of farmland sensor nodes, hierarchical connection information, and real-time data transmission status.

[0081] Step S2: Divide the farmland state area plots for the farmland perception node layout planning data through the farmland perception network to generate normal farmland state area plots and abnormal farmland state area plots; calculate the water content of the abnormal farmland state area plots to obtain the water content data of the abnormal farmland area; use the water content data of the abnormal farmland area to manage the performance of the irrigation facilities for the abnormal farmland state area plots to generate irrigation facility performance management data;

[0082] In the embodiments of the present invention, real-time environmental parameter data, including temperature, humidity, soil water content, etc., are collected from each sensor node through a farmland perception network. The collected data is compared with the farmland state threshold to determine whether the states of each plot in the farmland are within the normal range. According to the data analysis results, the farmland is divided into "plots in the normal farmland state area" and "plots in the abnormal farmland state area". Among them: the plots in the normal farmland state area refer to the plots where all parameters are within the set range, and the plots in the abnormal farmland state area refer to the plots where the parameters are abnormal and need further management. The division results of the farmland state area plots are output to form a data file, marking the state category of each plot. The data of all plots in the abnormal farmland state area are screened out from the plot division data for further centralized analysis. Using the data of the soil humidity sensor nodes, the soil water content of each abnormal area plot is calculated, and a water content data file is generated to record the water content situation of each abnormal plot. The water content of each abnormal plot is compared with the standard water content range to determine whether irrigation intervention is required. According to the water content data of the abnormal area, the plots that need irrigation are identified, and the required water volume and time for irrigation are recorded. The performance states of the irrigation facilities (such as sprinkler irrigation systems, drip irrigation systems) associated with the abnormal plots are obtained, including water pressure, flow rate, etc. The current performance of the irrigation facilities is compared with the set standards to ensure the effectiveness of the facilities. If the facility performance is lower than the standard, the information of the facilities that need maintenance or optimization is recorded. The irrigation requirements and the performance states of the facilities corresponding to each abnormal plot are summarized to generate an irrigation facility performance management data file as a reference for irrigation management.

[0083] Step S3: Predict the spread of irrigation water bodies in adjacent plots of the plots in the normal farmland state area to generate irrigation water body spread prediction data; adjust the direction of the irrigation facilities according to the irrigation water body spread prediction data to generate irrigation facility direction management data; integrate the irrigation facility performance management data and the irrigation facility direction management data to generate an irrigation strategy for the irrigation facilities;

[0084] In the embodiments of the present invention, by using data such as the terrain, soil permeability, slope, etc. of the plots in the normal farmland state area, a water flow model for the spread of irrigation water bodies is constructed. Based on the water flow model, combined with parameters such as irrigation volume and irrigation rate, the spread of irrigation water bodies in each plot is predicted, and the diffusion of water bodies between plots is simulated. The adjacent plots affected by the predicted water body spread are determined to ensure that the irrigation water body does not flow into areas that do not require irrigation, avoiding water resource waste or farmland waterlogging. The predicted data of the spread of irrigation water bodies is output, marking the spread range of the water body in each plot and the impact on adjacent plots. The spread range and the impact on adjacent plots in the spread prediction data are obtained to determine whether the direction of the current irrigation facility needs to be adjusted. For the plots affected by the spread, the spraying angle, nozzle direction, and spraying radius of the irrigation facility are adjusted to ensure that the water flow direction accurately covers the required area. The adjusted irrigation facility is tested to ensure that the water body spread meets the expected range. If it does not meet the expectation, further optimization is carried out until the best effect is achieved. The direction adjustment situation of the irrigation facility is recorded, and a management data file for the direction of the irrigation facility is generated to ensure that the adjusted direction data is traceable and referenceable. The performance management data of the irrigation facility is combined with the direction management data to analyze their mutual influence, such as irrigation efficiency, coverage accuracy, etc. According to the integrated data, an irrigation strategy for each plot is formulated, specifically including content such as irrigation volume, time, frequency, facility direction, and spraying angle. An irrigation strategy file for the irrigation facility is output, containing the integrated operation guidance, providing a comprehensive control plan for farmland irrigation.

[0085] Step S4: Conduct simulated irrigation on the irrigation strategy of the irrigation facility to generate simulation data for the management of the irrigation facility; perform data visualization on the simulation data for the management of the irrigation facility to generate a simulation report for the management of the irrigation facility; use the simulation report for the management of the irrigation facility to optimize the management strategy of the irrigation strategy of the irrigation facility to execute the intelligent agricultural facility management operation.

[0086] In the embodiments of the present invention, by inputting the formulated irrigation facility irrigation strategy into the irrigation simulation system, including parameters such as irrigation volume, time, frequency, spraying direction, etc. Set the relevant conditions of the farmland environment (such as soil humidity, air temperature, wind speed, etc.) in the simulation system to be as close as possible to the actual farmland conditions to ensure the accuracy of the simulation results. Start the simulation, simulate the irrigation operations of each irrigation facility according to the input strategy, and record data such as the water absorption situation and water body spread range of each plot. Output the simulation data of the irrigation facility management, including the simulated water content, irrigation coverage range, and water body spread situation of each plot, etc., as the basic data for subsequent analysis. Organize the simulation data of the irrigation facility management, and screen out key indicators (such as soil humidity, irrigation coverage rate, water body penetration range, etc.). Use visualization tools to convert the simulation data into easy-to-understand charts, including but not limited to the following forms: Plot status chart: showing the water content, irrigation coverage range, etc. of each plot; Water body spread heat map: showing the spread distribution of the irrigation water body in the farmland; Irrigation coverage effect chart: intuitively showing the irrigation situation of each plot. Integrate the above charts into an irrigation facility management simulation report, including data description, analysis results, and visualization charts, to help users comprehensively understand the simulation results. According to the irrigation facility management simulation report, identify the effectiveness and deficiencies of the irrigation strategy. For example, if the water body coverage of some plots is insufficient or there is over-irrigation, corresponding adjustments need to be made.

[0087] Preferably, step S1 includes the following steps:

[0088] Step S11: Obtain the survey information data of the farmland area; design the layout of the sensing nodes for the survey information data of the farmland area to generate the layout planning data of the farmland sensing nodes;

[0089] Step S12: Based on the layout planning data of the farmland sensing nodes, deploy the hierarchical sensor nodes to generate the deployment data of the farmland sensor nodes, where the deployment data of the farmland sensor nodes includes the deployment data of the soil sensing layer sensors, the deployment data of the air and water body sensing layer sensors, and the deployment data of the microbial ecological monitoring layer sensors;

[0090] Step S13: Confirm the intersection points of the deployment areas through the deployment data of the soil sensing layer sensors, the deployment data of the air and water body sensing layer sensors, and the deployment data of the microbial ecological monitoring layer sensors to obtain the intersection point data of the deployment areas; according to the intersection point data of the deployment areas, deploy the data fusion nodes to generate the deployment data of the data fusion nodes;

[0091] Step S14: Based on the deployment data of the data fusion nodes, connect the communication network of the deployment data of the farmland sensor nodes to generate a farmland sensing network, where the farmland sensing network includes a number of farmland sensor nodes.

[0092] In the embodiments of the present invention, by comprehensively surveying the target farmland area, basic information such as terrain, soil type, humidity, temperature, and microbial ecology is obtained. According to the survey information of the farmland area, the farmland is functionally divided, such as areas with good soil quality, areas with sufficient water sources, areas with active microorganisms, etc. According to various survey information of the farmland, the monitoring requirements of different areas are determined to efficiently and reasonably arrange sensing nodes. Reasonably layout sensing nodes in different functional areas to ensure wide node coverage and good signal strength, and enable efficient information transmission. Organize the layout design results into farmland sensing node layout planning data to provide a basis for subsequent sensor node deployment. According to the farmland sensing node layout planning data, sensors are reasonably deployed in the soil layer, such as humidity sensors, temperature sensors, nutrient sensors, etc., to monitor soil moisture, temperature, fertility, etc., generate soil sensing layer sensor deployment data, and record the specific positions and functions of each soil sensor. In the air and water layers, air temperature and humidity sensors, CO 2 monitoring sensors, water quality sensors, etc. are deployed to monitor air quality, water quality parameters, etc., and generate air and water body sensing layer sensor deployment data, including the specific positions and monitoring ranges of air and water body sensors. For the farmland microbial ecological environment, microbial monitoring sensors are deployed on the soil surface or in the plant root area to generate microbial ecological monitoring layer sensor deployment data, and record the specific positions of the microbial ecological monitoring sensors and the types of microorganisms monitored. Based on the sensor deployment data of each sensing layer (i.e., soil, air and water body, microbial ecological monitoring layer data), the intersection points of the deployment areas are confirmed through coordinate and coverage analysis. Record the confirmed intersection points as deployment area intersection point data, which are used as key points for data fusion. At the confirmed intersection point positions, data fusion nodes are deployed to collect data from multiple sensors, converge and fuse them for unified transmission. Record the deployment positions of the fusion nodes and the types of sensors they are connected to for subsequent network communication design. Based on the data fusion node deployment data, network topology connections are established for each sensor node to ensure the smoothness of the data transmission channel. The network can adopt a multi-hop communication structure or a grid structure to improve network coverage and transmission efficiency. Establish communication links between various sensor nodes and fusion nodes to form a complete farmland sensing network. The finally formed farmland sensing network includes sensor nodes at multiple levels such as soil, air and water body, and microbial ecology, and has the ability to collect and transmit farmland data in real time.

[0093] Preferably, step S14 includes the following steps:

[0094] Step S141: Analyze the signal coverage range of the farmland sensor node deployment data based on the data fusion node deployment data to generate farmland node coverage range data; select a communication protocol based on the farmland node coverage range data to obtain communication protocol selection data;

[0095] Step S142: Use the communication protocol to select data to connect the farmland sensor node deployment data and the data fusion node deployment data to generate an initial node connection network;

[0096] Step S143: Assign unique identifiers to the nodes in the initial node connection network to generate network node unique identifiers; based on the network node unique identifiers, perform network stability detection on the initial node connection network to generate network connection stability data;

[0097] Step S144: Use the network connection stability data to optimize the network topology of the initial node connection network to generate a farmland perception network, where the farmland perception network includes a number of farmland sensor nodes.

[0098] In the embodiment of the present invention, by deploying data based on the data fusion node, analyze the signal coverage of each sensor node in the farmland, considering the effective coverage distance of the sensor node, signal interference factors, etc. Calculate the signal coverage range of each node to ensure full coverage of the farmland area and avoid overlapping coverage or blind spots. Generate farmland node coverage range data according to the analysis results, which provides basic data for subsequent communication protocol selection. According to the farmland node coverage range data, select a suitable communication protocol, such as LoRa, Zigbee or NB-IoT, etc. The specific selection depends on the coverage area of the farmland, node density, data transmission requirements, etc. Record the selected communication protocol as communication protocol selection data to provide a communication standard for subsequent node connection. Based on the communication protocol selection data, connect the farmland sensor nodes and the data fusion nodes according to the topological relationship. Consider the transmission distance of the nodes, signal interference and communication protocol limitations, and optimize the connection path to ensure data transmission efficiency. Record the connected node relationship as the initial node connection network for subsequent unique identifier assignment and network stability detection. Assign a unique identifier to each node in the initial node connection network to ensure that the data source of each node can be accurately identified during data transmission, generate network node unique identifier data, and record it in the network configuration file for management and monitoring. Based on the network node unique identifiers, perform stability tests on the initial node connection network, including signal strength detection, delay analysis, packet loss rate calculation, etc. Organize the test results into network connection stability data for subsequent network topology optimization to ensure the robustness of the network structure. Use the network connection stability data to identify signal weaknesses or bottlenecks in the connection and optimize the connection path. By fine-tuning the layout of the data fusion node and the sensor node, optimize the connection path and network topology structure to make the network connection more stable and efficient. Record the optimized network structure as the farmland perception network to ensure its efficient data transmission, low latency and good signal coverage. Conduct a final test on the farmland perception network to verify its stability and coverage.

[0099] As an example of the present invention, refer to Figure 2 As shown, in this example, step S2 includes:

[0100] Step S21: Divide the farmland monitoring status of the farmland sensing node layout planning data through the farmland sensing network to generate a normal farmland monitoring status and an abnormal farmland monitoring status; based on the normal farmland monitoring status and the abnormal farmland monitoring status, divide the farmland area survey information data to generate a normal farmland status area plot and an abnormal farmland status area plot;

[0101] Step S22: Extract the soil properties of the plots in the abnormal farmland status area to obtain farmland soil property data; calculate the water content of the plots in the abnormal farmland status area according to the farmland soil property data to obtain the water content data of the abnormal farmland area;

[0102] Step S23: Compare the water content data of the abnormal farmland area with the preset standard soil water content threshold. When the water content data of the abnormal farmland area is less than the preset standard soil water content threshold, mark the plots in the abnormal farmland status area as water-deficient plots;

[0103] Step S24: When the water content data of the abnormal farmland area is greater than the preset standard soil water content threshold, mark the plots in the abnormal farmland status area as waterlogged plots; perform irrigation facility performance management based on the water-deficient plots and the waterlogged plots to generate irrigation facility performance management data.

[0104] In the embodiments of the present invention, by using the farmland perception network to obtain the layout planning data of farmland perception nodes, the farmland environment data collected by each monitoring node is monitored and analyzed in real time. According to the preset standards and data analysis, it is identified and classified into "normal" or "abnormal" farmland monitoring states. The normal farmland monitoring state and the abnormal farmland monitoring state are recorded and generated to provide data support for subsequent regional division. Based on the normal and abnormal farmland monitoring states, the farmland area survey information data is divided into regions, and the plots with normal and abnormal states are marked. The division data of the normal farmland state area plots and the abnormal farmland state area plots is formed to facilitate further in-depth analysis of the soil properties in the abnormal area. In the plots of the abnormal farmland state area, the physical and chemical property data of the soil are collected by the sensors of the soil perception layer. The soil data is analyzed to extract the key property data such as soil pH value, organic matter content, and particle structure. The analysis results are recorded as the farmland soil property data for the subsequent accurate calculation of the water content. According to the farmland soil property data, the soil water content of the plots in the abnormal area is calculated by using the soil water content sensor. The water content data of the abnormal farmland area is recorded and generated to facilitate further judgment of whether there is water shortage or waterlogging in the area. The preset standard soil water content threshold is used to determine whether the farmland meets the appropriate water content conditions. The water content data of the abnormal farmland area is compared with the standard threshold. If the water content of the abnormal farmland area is lower than the standard soil water content threshold, the plot is marked as a water shortage plot. The plot information and status are marked as a water shortage plot to receive priority attention in irrigation management. If the water content data of the abnormal farmland area is higher than the standard soil water content threshold, the area is marked as a waterlogging plot. The waterlogging status of the plot is recorded and marked as a plot that needs drainage treatment to facilitate the subsequent management of irrigation facilities. Based on the status information of the water shortage plots and the waterlogging plots, the performance of the irrigation facilities is detected, such as the operation of the water pump and the water flow of the drip irrigation system. According to the detection results, the irrigation equipment is adjusted or maintained to ensure the irrigation efficiency and the reasonable utilization of water resources. The irrigation facility performance management data is recorded and generated to guide the precise irrigation and water resource allocation in the farmland area.

[0105] Preferably, the dynamic management of the irrigation facilities based on the water shortage plots and the waterlogging plots includes:

[0106] The first liquid filling of the irrigation facilities is carried out for the water shortage plots to generate the first liquid filling data of the irrigation facilities; based on the first liquid filling data of the irrigation facilities, the water shortage plots are irrigated periodically, and the soil data of the water shortage plots after irrigation are collected synchronously to obtain the soil data of the water shortage plots after irrigation;

[0107] The irrigation penetration analysis of the soil data of the water shortage plots after irrigation is carried out to generate the soil irrigation penetration data; the irrigation intensity of the irrigation facilities is adjusted through the soil irrigation penetration data to generate the irrigation management data of the water shortage plots;

[0108] Perform the second liquid filling of the irrigation facility based on the data of the microbial activity degree of the plot to generate the second liquid filling data of the irrigation facility;

[0109] Extract the regional meteorological data of the waterlogged plot to obtain the regional meteorological data of the waterlogged plot; perform the temperature change analysis on the regional meteorological data of the waterlogged plot to generate the regional temperature change data of the waterlogged plot; calculate the microbial activity degree of the plot through the regional temperature change data of the waterlogged plot to obtain the data of the microbial activity degree of the plot;

[0110] Adjust the irrigation concentration of the second liquid filling data of the irrigation facility by using the data of the microbial activity degree of the plot, so as to generate the irrigation management data of the waterlogged plot; integrate the irrigation management data of the water-deficient plot and the irrigation management data of the waterlogged plot to generate the performance management data of the irrigation facility.

[0111] In the embodiments of the present invention, by evaluating the soil moisture content and nutrient data of water-deficient plots, the required liquid volume and formula are determined. The formula may include water and necessary nutrients such as nitrogen, phosphorus, potassium, etc. The required liquid is loaded into the irrigation facility to ensure that the liquid filling volume is suitable for the irrigation needs of the plot. Record the time of filling completion, liquid type, and filling volume to generate "the first liquid filling data of the irrigation facility" for subsequent use. Irrigate the water-deficient plot according to the set irrigation cycle (e.g., daily, every two days). Immediately after each irrigation, collect relevant data such as soil moisture, nutrients, and infiltration rate. Use sensors to monitor the soil status of the irrigation area in real time, including humidity sensors, soil conductivity sensors, and temperature sensors, etc., to generate "soil data of the water-deficient plot after irrigation" for analyzing the irrigation effect and providing data support for subsequent adjustments. Read parameters such as moisture content and infiltration rate in the "soil data of the water-deficient plot after irrigation". Calculate the infiltration depth and diffusion range of water in the soil through an infiltration model (e.g., an infiltration model based on Darcy's law). Analyze the water holding capacity of different soil layers to generate "soil irrigation infiltration data", which is used to judge whether the irrigation has achieved the expected effect and whether the irrigation volume needs to be further adjusted. Determine whether the current irrigation intensity is appropriate by analyzing the "soil irrigation infiltration data", and whether there is water waste or insufficient irrigation. If the infiltration is too fast, reduce the irrigation intensity to avoid water loss; if the infiltration is slow, the irrigation volume can be appropriately increased. Record the parameters and reasons for each adjustment to generate "irrigation management data of the water-deficient plot" for long-term irrigation strategy optimization. Use a microbial activity sensor to monitor the microbial activity in the soil of the water-deficient plot. Record the microbial quantity, activity, and their response to soil moisture changes to form "microbial activity data". According to the influence of seasonality and irrigation cycle on microbial activity, the data is used to optimize soil biological health. Analyze the "microbial activity data" to determine the liquid composition, such as adding microbial nutrients such as organic matter or amino acids. Record the liquid type, filling volume, and filling time of the second filling to generate "the second liquid filling data of the irrigation facility". Collect real-time meteorological data including temperature, humidity, and precipitation through a weather station or sensors. Record the collected data as "meteorological data of the waterlogged plot area". Analyze the temperature change trend in the "meteorological data of the waterlogged plot area" and calculate the evaporation effect of the day-night temperature difference on soil moisture. Record the analysis result as "temperature change data of the waterlogged plot area" for subsequent judgment of microbial activity. Based on the "temperature change data of the waterlogged plot area", combined with soil humidity and nutrient status, calculate the microbial activity under the current environmental conditions to generate "plot microbial activity degree data" for guiding subsequent adjustment of irrigation concentration. Read the "plot microbial activity degree data" and "the second liquid filling data of the irrigation facility". Optimize the microbial activity in the soil by adjusting the microbial nutrient concentration in the irrigation liquid.If the microbial activity is low, the concentration of organic nutrients can be increased; if it is too high, the concentration can be decreased. After adjustment, "irrigation management data for waterlogged plots" is generated. Integrate "irrigation management data for water - deficient plots" and "irrigation management data for waterlogged plots" to generate "irrigation facility performance management data", including information such as irrigation frequency, liquid type, concentration, and microbial activity. Input the "irrigation facility performance management data" into the control system of the irrigation facility to optimize the use of the irrigation facility in future decisions.

[0112] Preferably, the irrigation penetration analysis of the soil data of the water - deficient plots after irrigation includes:

[0113] Calculate the temporal soil water content of the soil data of the water - deficient plots after irrigation to obtain the irrigation soil water content change data; conduct soil water distribution analysis on the water - deficient plots through the irrigation soil water content change data to generate a soil water distribution map;

[0114] Extract the soil structure of the soil data of the water - deficient plots after irrigation to generate soil structure data for water - deficient plots, where the soil structure data for water - deficient plots includes soil physical structure data and soil chemical structure data; conduct soil permeability characteristic analysis based on the soil physical structure data and soil chemical structure data to generate soil permeability characteristic data;

[0115] Use the soil permeability characteristic data to detect the permeability uniformity of the soil water distribution map to generate soil permeability uniformity data; estimate the penetration depth of the soil water distribution map through the soil permeability uniformity data to generate soil irrigation penetration data.

[0116] In the embodiments of the present invention, soil water content data of water-deficient plots before and after irrigation is obtained. Data collection is carried out through sensors (such as soil moisture sensors) or remote sensing technologies (such as terrestrial laser scanning, satellite remote sensing, etc.), covering multiple moments (such as different periods before and after irrigation). Based on the time-series data, the soil water content change at each moment is calculated using a soil water formula (such as a water content formula based on soil density), generating irrigation soil water content change data, which reflects the change trend of soil moisture after irrigation. According to the soil water content change data, a spatial interpolation algorithm (such as Kriging interpolation, inverse distance weighting method, etc.) is used to analyze the spatial distribution of soil moisture in the water-deficient plots. This process can utilize the data obtained by the sensor network and perform spatial mapping in combination with a geographic information system (GIS). Based on the spatial distribution data, a soil moisture distribution map is drawn to show the moisture levels in different regions. Through this map, regions with poor irrigation effects or uneven moisture distribution can be identified. Analyze the physical properties of the soil (such as soil particle size distribution, soil porosity, soil density, etc.). Data can be obtained through laboratory analysis, on-site tests (such as soil permeability experiments, density tests, etc.). Test and analyze the chemical composition of the soil (such as pH value, organic matter content, salt content, nutrient content, etc.). Data is obtained through soil chemical analysis instruments (such as mass spectrometers, ion chromatography instruments). Combine the physical structure data with the chemical structure data to form complete soil structure data of the water-deficient plots. Based on the physical and chemical structure data of the soil, a soil infiltration model (such as the Kostiakov formula, Horton formula, etc.) is used to analyze the infiltration characteristics of the soil. Considering the soil porosity, particle composition, and chemical properties (such as the influence of soil salinity on infiltration), calculate the soil infiltration coefficient. Through infiltration tests or numerical simulations, soil infiltration characteristic data such as infiltration rate and infiltration depth are obtained. According to the soil infiltration characteristic data, statistical methods or image processing techniques are used to analyze the infiltration uniformity of the soil moisture distribution map. For example, variance analysis, standard deviation, and geostatistical methods are used to detect the uniformity of irrigation water distribution. Obtain the soil infiltration uniformity index or indicator to evaluate the infiltration effects in different regions to determine whether there are regions with water concentration or uneven water distribution. Based on the soil infiltration uniformity data, a physical model or regression model (such as Darcy's law, Philip model, etc.) is used to estimate the infiltration depth. Combining with actual observation data, infer the water infiltration depth in different soil regions through simulation or theoretical calculation. Combining the above analysis, complete soil irrigation infiltration data is generated, which will show the infiltration depth and infiltration effects in different regions and provide a reference for subsequent irrigation management and soil improvement.

[0117] Preferably, calculating the microbial activity degree of the waterlogged plot through the temperature change data of the waterlogged plot area includes:

[0118] Extract microbial monitoring data for waterlogged plots to obtain microbial monitoring data for waterlogged plots; classify the microbial species in the microbial monitoring data of waterlogged plots to generate data on beneficial microbial species and data on harmful microbial species; calculate the proportion of microbial density in the plots based on the data on beneficial microbial species and harmful microbial species to obtain data on the proportion of microbial density in the plots;

[0119] Screen waterlogged plots for plots with a high density of harmful microorganisms based on the data on the proportion of microbial density in the plots to obtain plots with a high density of harmful microorganisms; analyze the preferred temperatures of microorganisms in plots with a high density of harmful microorganisms to generate data on the range of preferred temperatures of harmful microorganisms;

[0120] Calculate the activity level of microorganisms in the plots using the regional temperature change data of waterlogged plots to obtain data on the activity level of microorganisms in the plots; the formula for calculating the activity level of microorganisms in the plots is as follows:

[0121] A = α·ΔT + β·H + γ·O + δ·M;

[0122] In the formula, A represents the microbial activity index, α represents the influence degree weight coefficient of the temperature change rate, ΔT represents the temperature change rate, β represents the influence degree weight coefficient of humidity, H represents the soil humidity, γ represents the influence weight coefficient of oxygen concentration, O represents the soil oxygen concentration, δ represents the influence degree weight coefficient of microbial density, and M represents the microbial density coefficient.

[0123] In the embodiments of the present invention, in waterlogged plots, microbial monitoring devices (such as soil microbial sensors, sampling devices) are used to regularly collect microbial data in the soil. These devices can capture changes in microbial species, quantity, and different microbial communities in the soil. Microbial monitoring data of waterlogged plots are extracted from the monitoring system, mainly including information such as microbial species, density, and distribution. The monitoring data can include the classification results of microbial species and the biomass of each microbial community. Classifying the microbial species in waterlogged plots usually determines beneficial microorganisms (such as nitrogen-fixing bacteria, microorganisms that decompose organic matter) and harmful microorganisms (such as pathogenic bacteria, molds, etc.) through methods such as microbial genome analysis, culture medium detection, PCR technology, or high-throughput sequencing. Beneficial and harmful microbial species data are divided. Each microbial species is associated with its corresponding function, density, and ecological role, generating detailed classification data of beneficial and harmful microorganisms. According to the microbial monitoring data, the quantity of different microorganisms in each area (unit: individuals / unit soil volume) is calculated. Based on the species data, the densities of beneficial and harmful microorganisms are calculated separately. The proportion of each species is calculated using the density data, that is, the proportion of beneficial and harmful microorganisms in the total microbial density, generating plot microbial density proportion data. Based on the plot microbial density proportion data, screening criteria are set (for example, a harmful microbial density higher than a certain threshold is considered a high-density plot). High-density harmful microbial density plots are screened out according to these criteria. The areas where these high-density harmful microorganisms gather are marked, providing key areas for further monitoring or intervention. According to the growth environment data of harmful microorganisms, combined with existing research on the temperature adaptability of microbial populations, the growth activity of various harmful microorganisms under different temperature conditions is analyzed. The optimal temperature range of different harmful microorganisms is obtained through literature or experimental data. For each high-density harmful microorganism, its preferred temperature range is obtained, that is, harmful microorganism preferred temperature range data, providing temperature conditions for subsequent microbial activity calculation. Using the regional temperature change data of waterlogged plots, the temperature change rate ΔT at different time points is calculated. The temperature change rate can be determined by measuring the temperature changes of the soil and the environment. Based on the soil moisture and oxygen concentration data of waterlogged plots, the soil moisture (H) and oxygen concentration (O) are calculated. These data are usually provided by moisture sensors and oxygen sensors. The microbial density coefficient M is calculated through the aforementioned plot microbial density proportion data. This coefficient considers the combined density of beneficial and harmful microorganisms and its impact on activity. The microbial activity index (A) is calculated using the following formula: A = α·ΔT + β·H + γ·O + δ·M; The microbial activity index A of each waterlogged plot is calculated according to the formula, generating plot microbial activity data.

[0124] As an example of the present invention, refer to Figure 3As shown, in this example, step S3 includes:

[0125] Step S31: Determine the difference in the status attributes of adjacent plots in the area with normal farmland status. When it is recognized that the status of an adjacent plot is a plot in the area with abnormal farmland status, identify the regional boundary between the plot in the area with normal farmland status and the plot in the area with abnormal farmland status to obtain the adjacent plot regional boundary data;

[0126] Step S32: Predict the spread of irrigation water bodies for the plot in the area with normal farmland status and the plot in the area with abnormal farmland status based on the adjacent plot regional boundary data to generate irrigation water body spread prediction data;

[0127] Step S33: Adjust the direction of the irrigation facilities according to the irrigation water body spread prediction data to generate irrigation facility direction management data;

[0128] Step S34: Integrate the irrigation facility performance management data and the irrigation facility direction management data to generate an irrigation strategy for the irrigation facilities.

[0129] In an embodiment of the present invention, farmland status data of plots in the normal farmland state area and surrounding plots are collected. The farmland status data may include soil moisture, temperature, vegetation growth, crop health, irrigation conditions, etc. The farmland status attribute data of adjacent plots are obtained by remote sensing technology, geographic information system (GIS) or drone monitoring. Data analysis methods (such as machine learning classification algorithms, cluster analysis, etc.) are used to compare the state attributes of the normal farmland state area and the adjacent plots to identify whether there are abnormalities. Abnormal states include excessive soil moisture (water accumulation), crop yellowing, and the occurrence of pests and diseases. When the adjacent plot is determined to be in an abnormal farmland state, it is marked as an abnormal state area. According to the identified boundary line between normal farmland and abnormal farmland, boundary identification is performed by image processing, edge detection and other technologies to determine the boundary between the normal farmland area and the abnormal farmland area. GIS technology is used to mark the boundary data, including boundary coordinates, area area and other information. The regional boundary data of the adjacent plots is output, and the data will be used for irrigation water spread prediction in subsequent steps. Based on the boundary data of adjacent plots and factors such as soil moisture, precipitation, temperature, and irrigation water source flow, an irrigation water body spread prediction model is established. The spread of irrigation water bodies can be predicted using hydrodynamic models, machine learning algorithms, or physics-based water flow models. Combined with the state attributes of adjacent plots (such as the availability of water sources, the efficiency of irrigation systems, etc.), the spread process of irrigation water bodies in farmland is simulated. Considering the topography, soil permeability, and irrigation methods of farmland, the expansion trend of water flow in farmland is predicted. By simulating the spread of irrigation water bodies, irrigation water body spread prediction data is generated. This data includes information such as the range, speed, and direction of water body spread. Output irrigation water body spread prediction data to provide a basis for subsequent adjustments to irrigation facilities. Obtain the working status data of current irrigation facilities to understand the layout, coverage, and irrigation effect of irrigation facilities. Collect the operation data of irrigation facilities through sensors, remote sensing monitoring, or data recording devices built into irrigation equipment. Evaluate the efficiency and scope of existing irrigation facilities to determine whether there are problems such as uneven irrigation, inefficiency, or untimely irrigation. Based on the irrigation water spread prediction data, determine which areas need to be irrigated more and which areas are over-irrigated (such as irrigation water spreading excessively to abnormal farmland areas). Adjust the direction of irrigation facilities to more accurately cover the areas that need irrigation. Adjustments include nozzle direction adjustment, flow control, irrigation area division, etc. The adjusted facility direction data will be used to optimize irrigation strategies. Output management data after the irrigation facility direction is adjusted, including information such as the working direction of the irrigation equipment, coverage area, flow control, etc. These data will help managers adjust and optimize irrigation operations. Collect performance data of irrigation facilities, such as irrigation water volume, pressure, work efficiency, fault records, etc. Use these data to analyze the working status of irrigation facilities and their support for crop growth.If there are performance problems with the irrigation facilities (such as nozzle blockage, unstable flow rate, etc.), improve the facility performance through maintenance and optimization. Combine the irrigation facility direction management data (i.e., the adjusted irrigation facility direction and flow control data) with the facility performance data to analyze the comprehensive performance of the irrigation system. Considering the irrigation water body spread prediction data, adjust the irrigation strategy to make rational use of water sources and avoid resource waste or insufficient irrigation. Develop a new irrigation strategy by integrating the facility performance management data and the facility direction management data. These strategies will guide irrigation operations, equipment configuration, irrigation frequency, irrigation volume, etc., generate irrigation facility irrigation strategies, and provide specific irrigation operation plans.

[0130] Preferably, step S32 includes the following steps:

[0131] Step S321: Classify the boundaries of the plots in the normal farmland state area and the abnormal farmland state area according to the adjacent plot area boundary data to generate seepage boundary data and barrier boundary data;

[0132] Step S322: Calculate the terrain slope difference of the adjacent plot area boundary data through the seepage boundary data and the barrier boundary data to generate terrain slope difference data of the plot; Analyze the natural water flow trend of the adjacent plot area boundary data based on the terrain slope difference data of the plot to generate natural water flow trend data;

[0133] Step S323: Use the experimental determination method to analyze the soil moisture diffusion of the adjacent plot area boundary data to generate the soil moisture diffusion coefficient of the plot; Divide the data sets of the natural water flow trend data and the soil moisture diffusion coefficient to generate a model training set and a model test set;

[0134] Step S324: Use the diffusion equation to train the seepage model for the model training set to generate a pre-model for predicting the spread of the water body; Optimize and iterate the pre-model for predicting the spread of the water body according to the model test set to generate a model for predicting the spread of the water body; Import the adjacent plot area boundary data into the model for predicting the spread of the water body in the adjacent plot to generate irrigation water body spread prediction data.

[0135] In the embodiments of the present invention, farmland status data of adjacent plots are obtained by using remote sensing technology (such as satellite images or drone photography), and the boundaries of the normal farmland status area and the abnormal farmland status area are marked by combining GIS technology. Based on the characteristics of the farmland, such as water condition, soil type, terrain, etc., the farmland area is classified to clarify which areas belong to the areas with good permeability (permeability boundary) and which areas are water-blocking areas (blocking boundary). The permeability boundary data refers to the boundary areas where water bodies can spread freely due to factors such as soil type and irrigation system, and these areas usually have good water permeability. The blocking boundary data refers to the areas where the water flow is blocked due to factors such as soil density, obstacles (such as buildings, vegetation belts, etc.) or uneven water source distribution. According to the classification results, permeability boundary and blocking boundary data are generated, and different identifiers are assigned to different areas. Digital elevation model (DEM) data of adjacent plots are obtained for slope analysis. By analyzing the slope differences between different plots, the natural flow trend of water bodies in different areas is evaluated. The slope differences are calculated and the corresponding plot terrain slope difference data are generated, which reflect the acceleration or deceleration trend of the water flow. Based on the plot slope difference data, soil type and water source distribution, the natural flow trend of water bodies in different areas is analyzed. For example, the water flow is faster in areas with larger slopes and slower in areas with smaller slopes. Combining the boundary data of the plots, water body natural flow trend data are generated to describe how the water flow spreads and the flow direction in the farmland. The soil water diffusion coefficient is measured by experimental determination methods (such as field experiments or simulation experiments). The diffusion rate of soil moisture is measured using soil samples, considering factors such as soil type, humidity, temperature, etc. Based on the experimental results, soil water diffusion coefficient data for each plot are generated. This coefficient reflects the diffusion rate of moisture in the soil and directly affects the speed and range of water body spread. The generated water body natural flow trend data and soil water diffusion coefficient data are combined, and the training set and test set are divided through data preprocessing (such as standardization, normalization). Usually, a division method of 70% for the training set and 30% for the test set is adopted to ensure the effectiveness of model training and evaluation. A mathematical model of water body spread is established using the diffusion equation. Common diffusion equations include Fick's law or diffusion models based on soil water transport. The model is trained using the training set, with the soil water diffusion coefficient, natural flow trend data, etc. as input features to predict the speed and direction of water body spread. During the training process, the model parameters are adjusted to ensure that the model can accurately reflect the water body spread process. The trained water body spread prediction model is evaluated using the model test set. By comparing the error between the model prediction results and the actual observation data, the parameters of the model are optimized. Methods such as cross-validation and parameter tuning are applied to ensure the adaptability and accuracy of the water body spread prediction model in various situations. After multiple iterations and optimizations, the final water body spread prediction model is generated.This model can predict the spread behavior of water bodies in different plots, including the expansion speed, spread range, and affected areas of the water bodies. Import the boundary data of adjacent plot areas into the water body spread prediction model to conduct the water body spread prediction. The output of this step is the prediction of the irrigation water body spread process, including how the irrigation water body expands in the farmland, which areas it spreads to, and the layout of irrigation facilities that need to be adjusted. Output the water body spread prediction data to provide a scientific basis for the adjustment of irrigation strategies. The data will include information such as the time, spatial distribution, and spread speed of the water body spread, helping farmland managers make timely irrigation adjustments.

[0136] In this specification, a smart agriculture facility management system based on intelligent perception is provided for implementing the above-mentioned smart agriculture facility management method based on intelligent perception. This smart agriculture facility management system based on intelligent perception includes:

[0137] A sensor network connection module, which is used to obtain the survey information data of the farmland area; deploy hierarchical sensor nodes for the survey information data of the farmland area to generate the farmland sensor node deployment data; conduct communication network connection according to the farmland sensor node deployment data to generate a farmland perception network, where the farmland perception network includes several farmland sensor nodes;

[0138] An irrigation performance management module, which is used to divide the plot areas of the farmland state into normal farmland state plot areas and abnormal farmland state plot areas through the farmland perception network for the layout planning data of the farmland perception nodes; calculate the water content of the abnormal farmland state plot areas to obtain the water content data of the abnormal farmland areas; use the water content data of the abnormal farmland areas to conduct irrigation facility performance management for the abnormal farmland state plot areas to generate irrigation facility performance management data;

[0139] An irrigation direction management module, which is used to predict the spread of irrigation water bodies in adjacent plots for the normal farmland state plot areas to generate irrigation water body spread prediction data; adjust the irrigation facility directions according to the irrigation water body spread prediction data to generate irrigation facility direction management data; integrate the irrigation facility performance management data and the irrigation facility direction management data to generate an irrigation strategy for the irrigation facilities;

[0140] An irrigation management simulation module, which is used to simulate the irrigation of the irrigation facility irrigation strategy to generate irrigation facility management simulation data; visualize the irrigation facility management simulation data to generate an irrigation facility management simulation report; use the irrigation facility management simulation report to optimize the management strategy of the irrigation facility irrigation strategy to execute the smart agriculture facility management operation.

[0141] The beneficial effects of the present invention are as follows: By deploying hierarchical sensor nodes and connecting them into a farmland perception network, the environmental information of the farmland (such as soil humidity, temperature, light, etc.) can be obtained in real time, providing accurate perception data for agricultural management. The construction of the farmland perception network enables efficient communication between sensor nodes, thus building a sensing network covering the entire farmland to ensure the timeliness and accuracy of information transmission, avoiding the problems of lagging or incomplete information transmission in traditional agricultural management. By dividing the farmland state area plots through the perception network, the normal farmland state area and the abnormal farmland state area can be accurately distinguished, which helps to timely discover problem areas and take measures. Calculating the water content in the abnormal area can accurately understand the soil moisture condition, so as to implement targeted irrigation in the abnormal area and improve the utilization efficiency of water resources. Using the water content data for performance management of irrigation facilities can monitor the working state of irrigation equipment, adjust or repair the equipment in time, and ensure the efficient operation of the irrigation system. Predicting the spread of irrigation water in adjacent plots of the normal farmland state area can effectively foresee the diffusion path of irrigation water, avoiding unnecessary water resource waste. Adjusting the direction of irrigation facilities according to the water body spread prediction data can ensure uniform distribution of water, improve irrigation efficiency, and reduce the phenomena of over-irrigation or under-irrigation. Combining the irrigation facility performance management data and the facility direction management data can integrate a scientific irrigation strategy, further optimize resource utilization, and ensure that the water requirements of crops are met throughout the growth cycle. By simulating the irrigation strategy of irrigation facilities, the effect of the strategy can be verified before actual implementation, avoiding wrong decisions. Data visualization can enable managers to more intuitively understand the irrigation effect and management status. According to the report generated from the simulation data, the irrigation strategy can be analyzed and optimized to ensure more efficient and scientific management of agricultural facilities. The optimized strategy can further improve the overall performance of the irrigation system, reduce energy consumption and water resource waste. Therefore, the present invention improves the efficiency of intelligent agricultural facility management through refined farmland perception, irrigation prediction, facility adjustment and strategy optimization.

[0142] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be included in the present invention.

[0143] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A smart agricultural facility management method based on intelligent perception, characterized in that: The following steps are involved: Step S1: Acquire farmland area survey information data; Performing hierarchical sensor node deployment on farmland area survey information data to generate farmland sensor node deployment data; performing communication network connection according to the farmland sensor node deployment data to generate a farmland sensing network, wherein the farmland sensing network includes a number of farmland sensor nodes; Step S2: using the farmland sensing network to divide the farmland sensing node layout planning data into farmland status area plots, generating normal farmland status area plots and abnormal farmland status area plots; Calculate the water content of the plots in the abnormal farmland status area to obtain the water content data of the abnormal farmland area; Use the water content data of abnormal farmland areas to manage the performance of irrigation facilities in areas with abnormal farmland status and generate irrigation facility performance management data; Step S3: For the plots in the normal farmland state area, the irrigation water body spread prediction is performed to the adjacent plots, and the irrigation water body spread prediction data is generated; Adjust the direction of irrigation facilities according to the irrigation water spread prediction data, thereby generating irrigation facility direction management data; integrate the irrigation facility performance management data and the irrigation facility direction management data into irrigation strategies, thereby generating irrigation facility irrigation strategies; Step S4: simulate irrigation of the irrigation facility irrigation strategy to generate irrigation facility management simulation data; visualize the irrigation facility management simulation data to generate an irrigation facility management simulation report; use the irrigation facility management simulation report to optimize the irrigation facility irrigation strategy management strategy to perform smart agricultural facility management operations.

2. The intelligent agricultural facility management method based on intelligent perception according to claim 1 is characterized in that: Step S1 includes the following steps: Step S11: Acquire farmland area survey information data; perform sensing node layout design on the farmland area survey information data to generate farmland sensing node layout planning data; Step S12: hierarchical sensor node deployment is performed based on the farmland sensing node layout planning data to generate farmland sensor node deployment data, wherein the farmland sensor node deployment data includes soil sensing layer sensor deployment data, air and water sensing layer sensor deployment data, and microbial ecological monitoring layer sensor deployment data; Step S13: confirm the intersection of the deployment area through the soil perception layer sensor deployment data, the air and water perception layer sensor deployment data and the microbial ecological monitoring layer sensor deployment data to obtain the deployment area intersection data; perform data fusion node deployment according to the deployment area intersection data to generate data fusion node deployment data; Step S14: Based on the data fusion node deployment data, the farmland sensor node deployment data is connected to a communication network to generate a farmland perception network, wherein the farmland perception network includes a plurality of farmland sensor nodes.

3. The intelligent agricultural facility management method based on intelligent perception according to claim 2 is characterized in that: Step S14 includes the following steps: Step S141: performing signal coverage analysis on the farmland sensor node deployment data based on the data fusion node deployment data to generate farmland node coverage data; performing communication protocol selection based on the farmland node coverage data to obtain communication protocol selection data; Step S142: using the communication protocol selection data to perform node connection on the farmland sensor node deployment data and the data fusion node deployment data to generate an initial node connection network; Step S143: performing node unique identifier allocation on the initial node connection network to generate a network node unique identifier; performing network stability detection on the initial node connection network based on the network node unique identifier to generate network connection stability data; Step S144: Utilize the network connection stability data to perform network topology optimization on the initial node connection network to generate a farmland sensing network, wherein the farmland sensing network includes a number of farmland sensor nodes.

4. The intelligent agricultural facility management method based on intelligent perception according to claim 1 is characterized in that: Step S2 includes the following steps: Step S21: using the farmland sensing network to divide the farmland sensing node layout planning data into farmland monitoring states, generating normal farmland monitoring states and abnormal farmland monitoring states; based on the normal farmland monitoring states and abnormal farmland monitoring states, the farmland area survey information data is divided into regions, generating normal farmland state area plots and abnormal farmland state area plots; Step S22: extracting soil properties of the land in the abnormal farmland state area to obtain farmland soil property data; calculating the water content of the land in the abnormal farmland state area according to the farmland soil property data to obtain the water content data of the abnormal farmland area; Step S23: comparing the moisture content data of the abnormal farmland area with the preset standard soil moisture content threshold, when the moisture content data of the abnormal farmland area is less than the preset standard soil moisture content threshold, marking the abnormal farmland state area plot as a water-deficient plot; Step S24: When the moisture content data of the abnormal farmland area is greater than the preset standard soil moisture content threshold, the land in the abnormal farmland status area is marked as an over-watered land; irrigation facility performance management is performed based on the water-deficient land and the over-watered land, and irrigation facility performance management data is generated.

5. The intelligent agricultural facility management method based on intelligent perception according to claim 4 is characterized in that: Dynamic management of irrigation facilities based on water-deficient and overwatered plots includes: Performing a first liquid filling operation on the water-deficient plot with the irrigation facility to generate first liquid filling data for the irrigation facility; periodically irrigating the water-deficient plot based on the first liquid filling data for the irrigation facility, and synchronously collecting soil data for the water-deficient plot after irrigation to obtain soil data for the water-deficient plot after irrigation; Conduct irrigation infiltration analysis on the soil data of water-deficient plots after irrigation to generate soil irrigation infiltration data; adjust the irrigation intensity of irrigation facilities based on the soil irrigation infiltration data to generate irrigation management data for water-deficient plots; Filling the irrigation facility with a second liquid according to the data on the activity level of microorganisms in the plot, and generating irrigation facility second liquid filling data; Extract regional meteorological data from overwatered plots to obtain regional meteorological data for overwatered plots; perform temperature change analysis on regional meteorological data for overwatered plots to generate regional temperature change data for overwatered plots; calculate the activity level of microorganisms in overwatered plots using regional temperature change data for overwatered plots to obtain the activity level of microorganisms in the plots; The irrigation concentration of the second liquid filling data of the irrigation facility is adjusted using the data on the activity level of microorganisms in the plot, thereby generating irrigation management data for the overwatered plot; the irrigation management data for the water-deficient plot and the overwatered plot are integrated to generate irrigation facility performance management data.

6. The intelligent agricultural facility management method based on intelligent perception according to claim 5 is characterized in that: Irrigation infiltration analysis of soil data of water-deficient plots after irrigation includes: The soil moisture content of the water-deficient plots after irrigation is calculated in time series to obtain the irrigation soil moisture content change data; the soil moisture distribution of the water-deficient plots is analyzed based on the irrigation soil moisture content change data to generate a soil moisture distribution map; Extract the soil structure of the water-deficient plot soil data after irrigation to generate the water-deficient plot soil structure data, wherein the water-deficient plot soil structure data includes soil physical structure data and soil chemical structure data; perform soil permeability characteristic analysis based on the soil physical structure data and soil chemical structure data to generate soil permeability characteristic data; The soil permeability characteristic data is used to detect the permeability uniformity of the soil moisture distribution map to generate soil permeability uniformity data; the soil permeability uniformity data is used to estimate the permeability depth of the soil moisture distribution map to generate soil irrigation permeability data.

7. The intelligent agricultural facility management method based on intelligent perception according to claim 5 is characterized in that: The calculation of the microbial activity level of the flooded plots through the temperature change data of the flooded plots includes: Extract the microbial monitoring data of the over-waterlogged plots to obtain the microbial monitoring data of the over-waterlogged plots; classify the microbial species of the microbial monitoring data of the over-waterlogged plots to generate beneficial microbial species data and harmful microbial species data; calculate the microbial density ratio of the plots for the beneficial microbial species data and the harmful microbial species data to obtain the microbial density ratio data of the plots; Screen the overwatered plots for plots with high harmful microorganism density according to the plot microbial density ratio data to obtain plots with high harmful microorganism density; perform microbial preferred temperature analysis on plots with high harmful microorganism density to generate harmful microorganism preferred temperature range data; The temperature change data of the flooded plots were used to calculate the activity of the microorganisms in the flooded plots, and the data of the activity of the microorganisms in the plots were obtained. The formula for calculating the activity of the microorganisms in the plots is as follows: In the formula, It is expressed as the microbial activity index. It is expressed as the weight coefficient of the influence degree of soil temperature change rate, Expressed as the rate of temperature change, Expressed as the weight coefficient of soil moisture influence, is expressed as soil moisture, Expressed as the influence weight coefficient of soil oxygen concentration, Expressed as soil oxygen concentration, It is expressed as the influence weight coefficient of microbial density coefficient, Expressed as microbial density coefficient.

8. The intelligent agricultural facility management method based on intelligent perception according to claim 1 is characterized in that: Step S3 includes the following steps: Step S31: for the normal farmland state area plot, the adjacent plot state attribute difference is judged. When the adjacent plot state is identified as the abnormal farmland state area plot, the area boundary of the normal farmland state area plot and the abnormal farmland state area plot is identified to obtain the adjacent plot area boundary data; Step S32: predicting the spread of irrigation water for the plots in the normal farmland state area and the plots in the abnormal farmland state area according to the boundary data of the adjacent plots, and generating irrigation water spread prediction data; Step S33: adjusting the direction of the irrigation facilities according to the irrigation water body spread prediction data, thereby generating irrigation facility direction management data; Step S34: Integrate the irrigation facility performance management data and the irrigation facility direction management data into an irrigation strategy to generate an irrigation facility irrigation strategy.

9. The intelligent agricultural facility management method based on intelligent perception according to claim 8 is characterized in that: Step S32 includes the following steps: Step S321: classifying the boundaries of the plots in the normal farmland state area and the plots in the abnormal farmland state area according to the boundary data of the adjacent plots, and generating the permeable boundary data and the blocking boundary data; Step S322: Calculate the land terrain slope difference of the adjacent land area boundary data through the permeable boundary data and the barrier boundary data to generate land terrain slope difference data; perform water body natural flow trend analysis on the adjacent land area boundary data based on the land terrain slope difference data to generate water body natural flow trend data; Step S323: using the experimental measurement method to perform soil moisture diffusion analysis on the boundary data of adjacent plots to generate the soil moisture diffusion coefficient of the plots; dividing the data sets of the natural flow trend data of the water body and the soil moisture diffusion coefficient to generate a model training set and a model test set; Step S324: Use the diffusion equation to train the infiltration model of the model training set to generate a water spread prediction pre-model; perform model optimization iteration on the water spread prediction pre-model according to the model test set to generate a water spread prediction model; import the adjacent plot area boundary data into the water spread prediction model to perform adjacent plot water spread prediction, thereby generating irrigation water spread prediction data.

10. A smart agricultural facility management system based on intelligent perception, characterized in that: Used to execute the intelligent agricultural facility management method based on intelligent perception as claimed in claim 1, the intelligent agricultural facility management system based on intelligent perception includes: The sensor network connection module is used to obtain farmland area survey information data; hierarchical sensor node deployment is performed on the farmland area survey information data to generate farmland sensor node deployment data; communication network connection is performed according to the farmland sensor node deployment data to generate a farmland perception network, wherein the farmland perception network includes a number of farmland sensor nodes; The irrigation performance management module is used to divide the farmland status area plots according to the farmland sensing node layout planning data through the farmland sensing network, and generate normal farmland status area plots and abnormal farmland status area plots; calculate the moisture content of the abnormal farmland status area plots to obtain the abnormal farmland area moisture content data; use the abnormal farmland area moisture content data to perform irrigation facility performance management on the abnormal farmland status area plots, and generate irrigation facility performance management data; The irrigation direction management module is used to predict the spread of irrigation water bodies in adjacent plots of land in normal farmland areas and generate irrigation water body spread prediction data; adjust the irrigation facility direction of irrigation facilities according to the irrigation water body spread prediction data, thereby generating irrigation facility direction management data; integrate the irrigation facility performance management data and the irrigation facility direction management data into irrigation strategies, and generate irrigation facility irrigation strategies; The irrigation management simulation module is used to simulate the irrigation of irrigation facilities and generate irrigation facility management simulation data; visualize the irrigation facility management simulation data and generate an irrigation facility management simulation report; and use the irrigation facility management simulation report to optimize the irrigation facility irrigation strategy to perform smart agricultural facility management operations.

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