Intelligent rice irrigation system and method

Abstract

The invention discloses an intelligent rice irrigation system and method, and relates to the technical field of rice irrigation, and the method specifically comprises the following steps: analyzing the water level state information, obtained in real time, of each land parcel, and evaluating the water level balance condition of each land parcel in a rice field region; dividing each plot into a low-water-level plot, a normal-water-level plot and a high-water-level plot according to a comparison result; according to the division result of each land parcel in the rice field area, constructing a regulation and control mechanism for dynamically adjusting water levels of different land parcels, and applying the regulation and control mechanism to a current irrigation system after construction; in the regulation and control mechanism application process, water level feedback information of all the land parcels is obtained in real time, analysis is conducted after obtaining, whether the regulation and control effect of the regulation and control mechanism in all the land parcels can balance the water level of the rice field area or not is evaluated, and the regulation and control mechanism is optimized according to the evaluation result. The problem that the water level of the rice field is not adjusted and controlled evenly is solved, accurate and automatic water level management is achieved, and efficient growth of rice is guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of rice irrigation, and in particular to an intelligent rice irrigation system and method. Background Art

[0002] Rice irrigation refers to providing sufficient water to rice fields to meet the water needs of rice throughout its entire growth cycle from seedling to maturity, ensuring its healthy growth and yield. As a crop with high water demand, rice has different requirements for water levels at different growth stages. An appropriate water layer can promote root development, enhance nutrient absorption, and improve field microclimate, thereby reducing the occurrence of diseases and insect pests. Current rice irrigation technologies mostly use automated irrigation systems, which transport water through pipes and use electrically driven pumps and control devices to adjust the water level, thereby accurately controlling the water layer height in the rice fields and achieving a stable rice growth environment.

[0003] The existing rice irrigation technology uses an automated irrigation system to complete the specific irrigation process. Its core includes laying pipes over long distances, powered by an external power supply, using an electric-driven water pump system to transport water to the rice fields, and using a water level control device to adjust the water layer height to ensure the appropriate water level required for rice growth. At the same time, the system relies on an electric-driven control system to monitor and adjust the water level, ensuring accurate control of the water layer in the rice fields at different growth stages, and ultimately providing a stable irrigation environment for rice.

[0004] The prior art has the following deficiencies: In large rice fields with scattered plots, the water level requirements of each plot vary due to regional soil water absorption and terrain differences. When the system performs remote monitoring, the lack of rapid judgment of water level changes in different plots will cause some areas to have local water levels that are too high or too low. Since the existing irrigation system cannot make targeted adjustments in a timely and accurate manner when the water levels of different plots are uneven, this delay in water level regulation will cause some plots to have low water levels and drought, and some plots to have high water levels and waterlogging, thereby affecting the normal development of the rice root system and resulting in uneven yields. At the same time, farmers need to conduct additional inspections of the water level conditions of each plot, which increases the management burden and reduces the efficiency of automated management of the irrigation system.

[0005] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure and therefore it may contain information that does not constitute the prior art that is already known to one of ordinary skill in the art. Summary of the invention

[0006] The object of the present invention is to provide an intelligent rice irrigation system and method to solve the problems in the above-mentioned background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: an intelligent rice irrigation method, which specifically comprises the following steps: In the process of large-scale rice field irrigation, the rice field area is evenly divided into several plots, and a sensor network is deployed in each plot to obtain the water level status information of each plot in real time; Analyze the water level status information of each plot obtained in real time, evaluate the water level balance of each plot in the rice field area, and divide each plot into low water level plot, normal water level plot and high water level plot according to the comparison results; According to the division results of each plot in the rice field area, a control mechanism for dynamically adjusting the water level of different plots is constructed and applied to the current irrigation system after construction; In the process of applying the control mechanism, the water level feedback information of each plot is obtained in real time, and analyzed after acquisition to evaluate whether the control effect of the control mechanism in each plot can balance the water level in the rice field area, and optimize the control mechanism according to the evaluation results; Based on the long-term collected water level status information and water level feedback information, the regulation mechanism is dynamically optimized and updated, and the irrigation strategies of various plots are continuously adjusted to adapt to environmental and seasonal changes, ensuring the continuous balance of rice field water levels and the stability of the irrigation system.

[0008] Preferably, the water level status information of each plot obtained in real time is analyzed to evaluate the water level balance of each plot in the paddy field area, and each plot is divided into a low water level plot, a normal water level plot and a high water level plot according to the comparison result, which specifically includes the following steps: Preprocess the water level status information of each plot obtained in real time; Extracting the water dynamic information and irrigation response information from the pre-processed water level status information of each plot, and analyzing them after extraction to generate the water level demand coefficient and water level response index of each plot respectively; A water level balance assessment model is constructed for the water level demand coefficient and water level response index generated for each plot, and the water level balance coefficient of each plot is generated. The generated water level balance coefficient of each plot is compared with the pre-set water level balance coefficient threshold range, and the water level balance status of each plot in the rice field area is evaluated according to the comparison results, and each plot is divided into low water level plots, normal water level plots and high water level plots according to the comparison results.

[0009] Preferably, the logic for obtaining the water level demand coefficient and water level response index of each plot is as follows: The water dynamic information is extracted from the preprocessed water level status information of each plot, including the soil moisture content, water loss rate and average temperature of each plot in the paddy field at different times over a period of time, and calibrated as , and , Indicates the first plots within a period of time The soil moisture content at the moment, Indicates the first plots within a period of time The rate of soil water loss at any given moment, Indicates the first plots within a period of time The average soil temperature at the time, , , and All are positive integers; Calculate the water level demand coefficient for each plot. The specific calculation formula is as follows:

[0010] In the formula, For the Water level demand coefficient for each plot; The irrigation response information is extracted from the preprocessed water level status information of each plot, including the water level change rate of each plot in the rice field area at different times within a period of time, the rate of water penetration into the soil, the natural loss rate of water, and the irrigation delay time of each plot in the rice field area, and calibrated as , , and , Indicates the first plots within a period of time The rate of change of water level at a given moment, Indicates the first plots within a period of time The rate at which water penetrates into the soil at any given moment. Indicates the first plots within a period of time The natural rate of water loss at any time, Indicates the first Irrigation delay time for each plot; Calculate the water level response index of each plot. The specific calculation formula is as follows:

[0011] In the formula, For the Water level response index for each plot.

[0012] Preferably, the water level demand coefficient for each plot generated and water level response index Construct a water level balance assessment model and generate the water level balance coefficient of each plot by weighted summation , and the water level balance coefficient of each plot generated The water level balance coefficient threshold interval is set in advance Compare and evaluate the water level balance of each plot in the paddy field area according to the comparison results, and divide each plot into low water level plot, normal water level plot and high water level plot according to the comparison results. The specific comparison analysis and division are as follows: like , the water level balance condition of the plot in the rice field area is low, and the plot is classified as a low-water-level plot; like , the water level balance condition of the plot in the rice field area is water level balance, and the plot is divided into a normal water level plot; like The water level balance condition of the plot in the rice field area is high, and the plot is divided into a high water level plot.

[0013] Preferably, according to the division results of each plot in the rice field area, a control mechanism for dynamically adjusting the water level of different plots is constructed, specifically: according to the division results of low-water-level plots, normal-water-level plots and high-water-level plots, different water-level control adjustment parameters are set respectively to form a dynamic water-level control mechanism; the dynamic water-level control mechanism is based on the water-level balance coefficient and the current water-level state of each plot, and automatically determines the amount and method of irrigation water to be increased, maintained and decreased through pre-set rules; For low-water plots, the increase parameter in the dynamic water level control mechanism is used to increase the irrigation water volume for the low-water plots; For normal water level plots with balanced water levels, the maintenance parameters in the dynamic water level control mechanism are used to keep the current irrigation water volume unchanged; For high-water plots with high water levels, the reduction parameters in the dynamic water level control mechanism are used to reduce the amount of irrigation water for the high-water plots.

[0014] Preferably, in the process of applying the control mechanism, the water level feedback information of each plot is obtained in real time, and analyzed after acquisition to evaluate whether the control effect of the control mechanism on each plot can balance the water level in the rice field area, and optimize the control mechanism according to the evaluation results, which specifically includes the following steps: In the process of applying the control mechanism, the water level feedback information of each plot is obtained in real time and pre-processed after acquisition; Extracting the regulation response information and water balance information from the pre-processed water level feedback information of each plot, and analyzing them after acquisition, respectively generating the water level regulation response coefficient and water balance index of each plot; A regulation effect evaluation model is constructed for the water level regulation response coefficient and water balance index generated for each plot, and the regulation coefficient of each plot is generated. The generated regulation coefficient of each plot is compared with the pre-set regulation coefficient threshold of each plot. According to the comparison results, it is evaluated whether the regulation effect of the regulation mechanism in each plot can balance the water level in the rice field area, and the regulation mechanism is optimized according to the evaluation results.

[0015] Preferably, the logic for obtaining the water level control response coefficient and water balance index of each plot is as follows: The control response information in the pre-processed water level feedback information of each plot is extracted, including the actual water level change rate of each plot at different times during a period of time during the application of the control mechanism, the preset water level change rate of each plot during this time, the delay time of the water level change of each plot, and the number of times the control mechanism controls each plot, and they are calibrated as , , and , Indicates that during the application of the control mechanism, Moment The actual water level change rate of each plot, Indicates that during this time The preset water level change rate of each plot, Indicates that during this time The delay time for the water level of each plot to start changing, Indicates the effect of the control mechanism on the The number of times a plot is regulated, , , and are all positive integers; Calculate the water level control response coefficient of each plot. The specific calculation formula is as follows:

[0016] In the formula, For the Water level control response coefficient of each plot; The water balance information in the pre-processed water level feedback information of each plot is extracted, including the soil moisture content, soil moisture loss rate, water level recovery rate of each plot at different times during a period of time during the application of the regulation mechanism, and the preset soil moisture content of each plot during the period of time, and calibrated as , , and , Indicates that during the application of the control mechanism, Moment The soil moisture content of the plot, Indicates that during the application of the control mechanism, Moment The soil moisture loss rate of the plots, Indicates that during the application of the control mechanism, Moment The water level recovery rate of each plot, Indicates the time period Preset soil moisture content for each plot; Calculate the water balance index of each plot. The specific calculation formula is as follows:

[0017] In the formula, For the Water balance index of a plot of land.

[0018] Preferably, the water level control response coefficient for each plot is generated and water balance index Construct a control effect evaluation model and generate the control coefficient of each plot through weighted summation , and the control coefficients of each plot generated The control coefficient thresholds of each plot are set in advance Compare and evaluate whether the control effect of the control mechanism in each plot can balance the water level in the rice field area according to the comparison results, and optimize the control mechanism according to the evaluation results. The specific comparison and analysis are as follows: like , the control effect of the control mechanism in this plot can balance the water level in the rice field area, and there is no need to optimize the control mechanism; like The control effect of the control mechanism on this plot cannot balance the water level in the rice field area, and the control mechanism needs to be optimized, including: if the plot is a low-water-level plot, increase the irrigation amount of the plot to ensure that the water level returns to a balanced state; if the plot is a high-water-level plot, reduce the irrigation amount of the plot to prevent excessive accumulation of water; dynamically adjust the control parameters, and optimize the control mechanism according to feedback data to adapt to different environmental conditions; continuously monitor the plots with normal water levels to prevent the water level from deviating from the normal state.

[0019] Preferably, the intelligent rice irrigation system includes a plot division module, a water level assessment module, a dynamic control construction module, a control effect assessment module and a control optimization update module; The plot division module evenly divides the paddy field area into several plots during large-scale paddy field irrigation, and deploys a sensor network in each plot to obtain the water level status information of each plot in real time; The water level assessment module analyzes the water level status information of each plot obtained in real time, assesses the water level balance of each plot in the rice field area, and divides each plot into low water level plot, normal water level plot and high water level plot according to the comparison results; The dynamic control construction module constructs a control mechanism for dynamically adjusting the water level of different plots according to the division results of each plot in the rice field area, and applies it to the current irrigation system after construction; The control effect evaluation module obtains the water level feedback information of each plot in real time during the application of the control mechanism, and analyzes it after acquisition to evaluate whether the control effect of the control mechanism in each plot can balance the water level in the rice field area, and optimizes the control mechanism according to the evaluation results; The control optimization and update module dynamically optimizes and updates the control mechanism based on the water level status information and water level feedback information collected over a long period of time, and continuously adjusts the irrigation strategies of various plots to adapt to environmental and seasonal changes, ensuring the continuous balance of rice field water levels and the stability of the irrigation system.

[0020] In the above technical solution, the technical effects and advantages provided by the present invention are: 1. The present invention can collect and monitor the water level status information of each plot in real time by dividing the rice field area into multiple plots and deploying a sensor network. By establishing an evaluation model of the water level control response coefficient and the water balance index, the system can quickly evaluate the water level balance status of each plot and achieve accurate judgment of water level changes. This real-time monitoring and evaluation method effectively solves the problem of uneven water levels caused by the lack of rapid judgment ability in the prior art, and fundamentally improves the accuracy and timeliness of water level management of each plot in the rice field area.

[0021] 2. The present invention realizes the automated management of water levels in various plots through the construction and application of a dynamic control mechanism. Based on the water level assessment results of different plots, the system can increase the irrigation volume for low-water-level plots and reduce the irrigation volume for high-water-level plots. At the same time, it continuously monitors plots with normal water levels to ensure that all plots receive appropriate water supply at different growth stages. This type of precise zoning management significantly improves the automation of irrigation, reduces the need for farmers to conduct manual inspections, greatly reduces the management burden, and meets the efficient and intelligent management needs of modern agriculture.

[0022] 3. The present invention continuously updates the control parameters through a dynamic optimization algorithm, so that the system can adapt to seasonal and environmental changes. This dynamic update mechanism ensures that the system can always provide accurate water level control strategies for each plot under changing environmental conditions, and optimizes the resource allocation efficiency of the irrigation system. Overall, the present invention not only effectively solves the key problem of water level imbalance, but also significantly improves the automation and intelligence level of rice field water level management, so that the irrigation system can operate efficiently and stably, ensuring the sustainability and high yield of rice production. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0024] Figure 1 It is a schematic diagram of the process of the intelligent rice irrigation method of the present invention.

[0025] Figure 2 It is a module schematic diagram of the intelligent rice irrigation system of the present invention. DETAILED DESCRIPTION

[0026] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these example embodiments are provided so that the description of the present disclosure will be more comprehensive and complete, and the concept of the example embodiments will be fully conveyed to those skilled in the art.

[0027] The present invention provides Figure 1 The smart rice irrigation method shown specifically comprises the following steps: In the process of large-scale rice field irrigation, the rice field area is evenly divided into several plots, and a sensor network is deployed in each plot to obtain the water level status information of each plot in real time; In order to achieve precise irrigation control in large rice fields, the rice field area can be evenly divided into several plots through GIS (Geographic Information System) technology. The specific method is to first use drones or satellite remote sensing technology to obtain high-resolution images of rice fields, and combine digital elevation model (DEM) data to analyze the topography, slope and other geographical characteristics of rice fields. Based on these data, the system automatically divides the rice fields into several suitable plots through the plot division algorithm, so that each plot has a relatively uniform area and similar geographical conditions. In addition, the system can also apply the K-means clustering algorithm in the software to divide the area into several plots with more similar characteristics according to parameters such as rice field topography and soil water absorption, laying the foundation for subsequent water level monitoring and irrigation adjustment.

[0028] After the rice field area is divided, a water level monitoring sensor network can be deployed in each plot through the Internet of Things (IoT) technology. The precise geographic location of each sensor is recorded through the GIS platform, and soil moisture sensors, water level sensors, etc. are installed in the center of the plot or at the intersection of water flows to accurately monitor the water level status. Data transmission can use low-power wide area networks (LPWAN) such as LoRa or NB-IoT protocols to transmit the water level information of each plot to the cloud in real time. The central system will collect, store and update the water level data of each plot in the database, and visualize the real-time water level status information through data collection and edge computing, so that the control system can monitor the water level status of the rice field in real time.

[0029] Through the division of rice fields and the deployment of water level monitoring sensors, the system can grasp the water level requirements of different areas of the rice fields in real time according to the water level status of each plot. This is a key step to solve the problem of lagging and uneven water level regulation in the current irrigation system. Since the water level demand of each plot is greatly affected by soil water absorption and terrain, personalized regulation of the water level of each plot can be achieved through precise division and monitoring. Furthermore, the real-time data provided by the sensor network provides reliable data support for the calculation of the water level demand coefficient and the water level response index, which helps the system dynamically build a water level regulation mechanism, realize accurate control of the water level of each plot, avoid the phenomenon of high or low water level in some plots, and ensure the automation and efficiency of the irrigation system.

[0030] Analyze the water level status information of each plot obtained in real time, evaluate the water level balance of each plot in the rice field area, and divide each plot into low water level plot, normal water level plot and high water level plot according to the comparison results; In this embodiment, the water level status information of each plot obtained in real time is analyzed to evaluate the water level balance of each plot in the rice field area, and each plot is divided into a low water level plot, a normal water level plot and a high water level plot according to the comparison result, which specifically includes the following steps: Preprocess the water level status information of each plot obtained in real time; The purpose of preprocessing water level status information is to improve the accuracy and consistency of the data so that the subsequent analysis and calculation process will be more accurate and reliable. Preprocessing includes steps such as data cleaning, format standardization, and missing value filling: First, data cleaning is used to remove noise and outliers that may be generated during sensor acquisition. If unreasonable water level or soil moisture values ​​are detected, they are removed or corrected. Next, the format of the water level status information of each plot is standardized to ensure that all data items (such as water level, humidity, evaporation rate, etc.) are stored and processed in the same unit or dimension for unified analysis. For missing data, interpolation or inference based on surrounding plot data is used to fill in the data so that data integrity can be maintained. Through these preprocessing steps, data quality can be ensured, thereby supporting the accurate calculation of water level demand coefficients and water level response indexes.

[0031] Extracting the water dynamic information and irrigation response information from the pre-processed water level status information of each plot, and analyzing them after extraction to generate the water level demand coefficient and water level response index of each plot respectively; A water level balance assessment model is constructed for the water level demand coefficient and water level response index generated for each plot, and the water level balance coefficient of each plot is generated. The generated water level balance coefficient of each plot is compared with the pre-set water level balance coefficient threshold range, and the water level balance status of each plot in the rice field area is evaluated according to the comparison results, and each plot is divided into low water level plots, normal water level plots and high water level plots according to the comparison results.

[0032] The determination of the threshold interval of the water level balance coefficient can be carried out through historical data analysis and machine learning algorithms to ensure that the water level demand of each plot is accurately met. First, the water level status data of the paddy field area over a long period of time is collected, including historical data of the water level demand coefficient and water level response index under different seasons, climate conditions, and soil types. Then, the historical data is clustered and analyzed using data mining methods to identify the natural distribution range of the water level balance coefficient, thereby determining the initial threshold intervals of low water level, normal water level, and high water level. On this basis, machine learning algorithms (such as classification algorithms based on decision trees) can be further introduced to optimize the threshold intervals through model training to ensure the accuracy of the water level classification of the system under different conditions. Finally, these optimized water level balance coefficient threshold intervals are set as the reference standard of the system, so as to accurately evaluate the water level balance status of each plot in real-time monitoring.

[0033] In this embodiment, the logic for obtaining the water level demand coefficient and water level response index of each plot is as follows: The water dynamic information is extracted from the preprocessed water level status information of each plot, including the soil moisture content, water loss rate and average temperature of each plot in the paddy field at different times over a period of time, and calibrated as , and , Indicates the first plots within a period of time The soil moisture content at the moment, Indicates the first plots within a period of time The rate of soil water loss at any given moment, Indicates the first plots within a period of time The average soil temperature at the time, , , and All are positive integers; The water dynamics information of each plot can be extracted through the data acquisition and analysis system. First, the system uses the sensor network deployed in each plot of the rice field to collect soil moisture content, water loss rate and temperature data at different time points within a set time interval (for example, every hour). After the collected data is uploaded to the cloud platform, the data is cleaned and formatted through the data preprocessing module to ensure the accuracy and consistency of the data. Then, the data analysis module extracts the water dynamics information from the preprocessed data, and summarizes the soil moisture content, water loss rate and temperature information at different times in chronological order for calculation of the water level demand coefficient. The whole process realizes the automatic extraction and organization of data, which is convenient for subsequent analysis.

[0034] Various quantitative data are obtained in real time through different types of sensors installed in rice fields. Soil moisture content is measured by soil moisture sensors, which are implanted in the soil and can sense changes in soil conductivity and convert them into moisture content data. The water loss rate is calculated by combining evaporation sensors with drainage sensors. Evaporation sensors monitor the evaporation of surface water, and drainage sensors record the water loss rate. These data are combined in the cloud to calculate the total water loss rate. Temperature data is obtained by ambient temperature sensors, which directly record the average temperature of the plot. These sensor data are automatically aggregated in the cloud to form a continuous time series, providing a basis for the extraction and analysis of water dynamic information.

[0035] Calculate the water level demand coefficient for each plot. The specific calculation formula is as follows:

[0036] In the formula, For the Water level demand coefficient for each plot; The calculation formula of the water level demand coefficient is designed to comprehensively reflect the impact of soil moisture content, water loss rate and temperature changes on water level demand of each plot, so as to obtain a dynamic water demand coefficient to help accurately evaluate the irrigation demand of the plot. Specifically, the cumulative symbol in the formula represents the accumulation of water dynamic data at each moment, ensuring that the water level demand under different conditions is fully considered over a period of time. The numerator of the fraction Represents the The direct impact of the water loss rate and temperature on the water demand at each moment, where the temperature value is added by 1 to avoid the temperature value being zero and to improve the calculation accuracy during low temperature periods. The soil moisture content and its rate of change are combined to adjust the water demand: higher soil moisture will reduce the water demand coefficient, indicating that the plot has a lower water demand. Normalization is performed to make the water level demand coefficient comparable in different plots and time periods. This design ensures that dynamic environmental factors and soil characteristics are considered simultaneously in the water level demand calculation, making the results more reasonable and accurate.

[0037] No. Water level demand coefficient for each plot It reflects the intensity of water demand of the plot in a specific period of time, and its size directly affects the assessment of the water level balance in the paddy field area. Specifically, the higher the water level demand coefficient, the lower the soil moisture content of the plot or the higher the water loss rate and temperature, and the more urgent the demand for water; on the contrary, a lower water level demand coefficient indicates that the water content of the plot is relatively sufficient and the demand is small. By comparing the water level demand coefficients of each plot, it can be determined which plots need to increase irrigation and which plots have water levels in a relatively balanced state, so that the overall balanced regulation of the water level in the paddy field area can be achieved. Therefore, the size of the water level demand coefficient provides a key basis for the subsequent division of plots into low water level, normal water level and high water level, ensuring the precise allocation of irrigation resources and the dynamic balance of paddy field water levels.

[0038] The irrigation response information is extracted from the preprocessed water level status information of each plot, including the water level change rate of each plot in the rice field area at different times within a period of time, the rate of water penetration into the soil, the natural loss rate of water, and the irrigation delay time of each plot in the rice field area, and calibrated as , , and , Indicates the first plots within a period of time The rate of change of water level at a given moment, Indicates the first plots within a period of time The rate at which water penetrates into the soil at any given moment. Indicates the first plots within a period of time The natural rate of water loss at any time, Indicates the first Irrigation delay time for each plot; The irrigation response information of each plot can be extracted through the rice field sensor network and data processing system. First, the sensor network deployed in the rice field plot will collect data such as water level change rate, water infiltration rate, water loss rate and irrigation delay time at preset time intervals (for example, every hour), and transmit these data to the cloud system. After the data is uploaded, the system uses the preprocessing module to denoise and standardize the data format to ensure its accuracy. Then, the data analysis module extracts the water level response related data at different times from the preprocessed data in time series, and classifies and archives these data into water level change rate, water infiltration rate, natural loss rate and irrigation delay time, forming a complete set of irrigation response information, which provides a basis for calculating the water level response index.

[0039] The acquisition of these quantitative data relies on different types of sensors in the rice fields to monitor the status information of each plot in real time. Specifically, the water level change rate is calculated by the water level change recorded by the water level sensor at different times; the water infiltration rate is calculated by the soil moisture sensor measuring the change in soil internal moisture and combining the time interval to calculate the water infiltration rate. The natural loss rate is calculated based on the data of the surface evaporation sensor and the drainage monitoring sensor, reflecting the loss of water in the absence of irrigation; the irrigation delay time is automatically generated by the system recording the time difference between the start of irrigation and the start of water level change. These sensor data are transmitted to the cloud system in real time, and are automatically calculated and sorted to form a complete irrigation response information database.

[0040] Calculate the water level response index of each plot. The specific calculation formula is as follows:

[0041] In the formula, For the Water level response index for each plot.

[0042] The calculation formula of the water level response index is designed to comprehensively evaluate the responsiveness of each plot to irrigation, especially under the influence of multiple factors such as water level changes, water infiltration and natural loss in different time periods, to evaluate its actual response to water level regulation. The cumulative symbol in the formula represents the sum of the water level change rate, water infiltration rate and natural loss rate at each moment in a period of time. This method can accurately capture the dynamic process of water level response. In the denominator, the irrigation delay time is added and added by 1 to avoid the response index being too low due to the long delay time, and to make the formula more stable. Adding these rates and squaring them can amplify the index value of the plots that respond quickly to water changes, so that those plots with higher water level change rates and infiltration rates can obtain higher response indexes, so that the water level regulation effect of the plots can be judged in time. Finally, use The normalization factor of the water level response index of each plot is averaged to make it comparable between different plots, ensuring the standardization of the response index in different regions. This design method can not only fully reflect the dynamic characteristics of the water level of each plot, but also effectively improve the regulation accuracy of the irrigation system.

[0043] No. Water level response index for each plot The size of is directly related to the accuracy of assessing the water level balance in the paddy field. The larger the water level response index, the faster the plot responds to water level regulation, which is manifested as a higher water level change rate, water infiltration rate and natural loss rate, indicating that the plot can quickly adjust the water level to adapt to irrigation needs; on the contrary, a lower response index indicates that the plot responds slowly to water level changes, and there may be a lag in water absorption or difficulty in infiltration, which requires a higher water level demand to maintain water balance. Therefore, by comparing the water level response index of each plot, it can be determined which plots need to increase irrigation to maintain balance and which plots have been in a stable state of water level adjustment, thereby achieving balanced regulation of water levels in the paddy field area.

[0044] A high water level response index does not mean a high demand for water. A high water level response index mainly indicates that the plot responds quickly to irrigation, can quickly absorb water or discharge excess water, and adjust its own water level. However, this does not mean that the plot has a higher demand for water, but rather that it can quickly adapt to changes in water volume and therefore responds sensitively in irrigation regulation. In contrast, plots with a low water level response index have a slow absorption rate or low permeability for water, and are not easy to quickly reach the ideal water level state after receiving irrigation water. Such plots may have a lag in water absorption or difficulty in infiltration, so in order to maintain the water balance of these plots, a higher water level demand may be required to compensate for their slower response speed. In other words, in order to achieve an ideal water level balance, plots with a low response index usually require more water or more frequent irrigation to "make up" for their slow absorption and adjustment capabilities.

[0045] In this embodiment, the water level demand coefficient of each plot is generated and water level response index Construct a water level balance assessment model and generate the water level balance coefficient of each plot by weighted summation , and the water level balance coefficient of each plot generated The water level balance coefficient threshold interval is set in advance Compare and evaluate the water level balance of each plot in the paddy field area according to the comparison results, and divide each plot into low water level plot, normal water level plot and high water level plot according to the comparison results. The specific comparison analysis and division are as follows: like , the water level balance condition of the plot in the rice field area is low, and the plot is classified as a low-water-level plot; Low water level plots indicate that the water supply of the plot is insufficient and the water level demand is high, but the current water level fails to meet the soil's absorption demand, which may cause the rice roots to be unable to obtain enough water to support growth. In this case, the soil moisture is low, which is prone to cause drought stress, affecting the nutrient absorption and growth rate of rice, and thus leading to a decrease in yield. Therefore, for low water level plots, the system needs to increase irrigation to quickly restore soil moisture content to avoid adverse effects on rice growth.

[0046] like , the water level balance condition of the plot in the rice field area is water level balance, and the plot is divided into a normal water level plot; The normal water level plot indicates that the water level balance of the plot is good, and the current water supply is moderate, which can meet the normal growth needs of rice. This state indicates that the water level demand and water level response are within the appropriate range, the soil moisture is at an ideal level, and the rice roots can smoothly absorb water and nutrients without additional adjustment of irrigation volume. This balanced state can effectively save water resources, ensure the healthy growth of rice fields, and improve the efficiency and stability of the irrigation system.

[0047] like The water level balance condition of the plot in the rice field area is high, and the plot is divided into a high water level plot.

[0048] A high water level plot indicates that the plot has too much water supply, which exceeds the soil's absorption capacity, resulting in excess water. Excessive water levels may cause a decrease in soil oxygen content, suffocate rice roots, increase the risk of root diseases, and may even cause soil compaction, affecting the normal growth of rice. In addition, excess water also wastes water resources and increases drainage costs. Therefore, for high water level plots, the system needs to reduce irrigation or stop irrigation to avoid the negative impact of water accumulation and ensure that the water supply to the rice field is moderate.

[0049] In order to construct a water level balance assessment model, the water level demand coefficients of each plot were generated. and water level response index Perform weighted summation to generate the water level balance coefficient for each plot Specifically, considering that the inverse of the water level demand coefficient reflects the intensity of the plot's demand for water (i.e., the larger the water level demand coefficient, the smaller the demand), and the larger the water level response index, the faster the plot responds to irrigation, the water demand and response speed can be balanced by the following weighted summation formula: ;in, and is a weight coefficient used to balance the impact of water level demand and response. Reflects the importance of water demand, usually with a higher weight when the water demand of the plot is high To give priority to meeting water needs. It reflects the emphasis on water level response speed and is suitable for plots that require rapid water level regulation. and It can be determined by machine learning algorithms or expert experience based on historical data and actual irrigation needs to ensure the water level balance coefficient The calculation is adapted to the specific paddy field environment. Through this weighted summation method, the water demand and response of each plot can be comprehensively evaluated, and a suitable water level balance coefficient can be generated to provide a basis for subsequent water level regulation.

[0050] According to the division results of each plot in the rice field area, a control mechanism for dynamically adjusting the water level of different plots is constructed and applied to the current irrigation system after construction; In this embodiment, according to the division results of each plot in the rice field area, a control mechanism for dynamically adjusting the water level of different plots is constructed, specifically: according to the division results of low-water-level plots, normal-water-level plots and high-water-level plots, different water level control adjustment parameters are set respectively to form a dynamic water level control mechanism; the dynamic water level control mechanism is based on the water level balance coefficient and the current water level state of each plot, and automatically determines the amount and method of irrigation water to be increased, maintained and reduced through pre-set rules; To realize the dynamic water level control mechanism according to the results of plot division, it can be realized through a rule-based automatic control system combined with a data analysis module. First, the system inputs the plot classification results (low water level, normal water level, high water level) and the water level balance coefficient and current water level status of each plot into the decision module. The system's built-in rule base will automatically determine the irrigation demand of the plot based on the water level balance evaluation model. Specifically, the system presets corresponding control parameters for each water level state in the rule base: low water level plots are set to "increase irrigation" parameters to ensure rapid water replenishment; normal water level plots are set to "maintain irrigation" parameters to maintain water level balance; high water level plots are set to "reduce irrigation" parameters to avoid excess water. The decision module dynamically adjusts the irrigation instructions of each plot (increase, maintain or reduce irrigation water) based on the water level balance coefficient and water level status, compared with the preset threshold. The purpose of this design is to achieve automated and precise irrigation control, so that the system can dynamically allocate water according to the actual needs of the plot, optimize the efficiency of water resource utilization, and avoid agricultural production problems caused by water resource waste or water level imbalance.

[0051] For low-water plots, the increase parameter in the dynamic water level control mechanism is used to increase the irrigation water volume for the low-water plots; For low-water plots with low water levels, the system will automatically call the increase parameters in the dynamic water level control mechanism to increase the irrigation water volume of the plot through the control module. The specific implementation method is that the system monitors the water level balance coefficient and current water level status of each plot in real time. When it is judged to be low water level, the irrigation control software will send instructions to the irrigation equipment to increase the irrigation volume to the preset increase value. The system will increase the water flow rate or extend the irrigation time for low-water plots based on the increase parameters to ensure that the soil moisture reaches an appropriate level. The purpose of this is to quickly increase the water level of the plot to prevent insufficient water from affecting the normal growth of rice, and at the same time realize the automatic control of low-water plots to ensure that they are irrigated in time.

[0052] For normal water level plots with balanced water level, the maintenance parameters in the dynamic water level control mechanism are used to keep the current irrigation water volume unchanged; For normal water level plots with balanced water levels, the system will automatically select the maintenance parameters in the dynamic water level control mechanism to keep the current irrigation water volume unchanged. The system determines the state of the water level balanced plot through the preset parameters in the rule base. After determining that it is in the ideal water level range, no irrigation volume adjustment is made. The control module monitors water level changes to ensure that the irrigation equipment maintains the current flow and irrigation cycle unchanged in this state. The purpose of this is to save water resources and avoid unnecessary irrigation. At the same time, it maintains the stability of moisture in normal water level plots, ensures that rice continues to obtain appropriate moisture, does not waste water resources, and avoids water level fluctuations affecting the moisture balance of the plot.

[0053] For high-water plots with high water levels, the reduction parameters in the dynamic water level control mechanism are used to reduce the amount of irrigation water for the high-water plots.

[0054] For high-water plots with high water levels, the system will call the reduction parameters in the dynamic water level control mechanism to reduce the irrigation water volume of the plot through the control module. The specific implementation method is that when the system detects that the water level of a certain plot is higher than the set threshold, the software will issue instructions according to the reduction parameters to suspend or reduce the water flow rate of the irrigation equipment, reduce the amount of irrigation water, and even stop irrigation in some cases until the water level returns to normal. The purpose of this is to prevent problems such as root hypoxia and increased disease risk caused by excessive soil moisture, and to ensure the rational allocation of water resources. By automatically reducing the amount of irrigation water, the system can efficiently maintain the water level balance of each plot in the rice field, reduce water resource waste, and improve the regulation accuracy of the irrigation system.

[0055] In the process of applying the control mechanism, the water level feedback information of each plot is obtained in real time, and analyzed after acquisition to evaluate whether the control effect of the control mechanism in each plot can balance the water level in the rice field area, and optimize the control mechanism according to the evaluation results; In this embodiment, during the application of the control mechanism, water level feedback information of each plot is obtained in real time, and analyzed after acquisition to evaluate whether the control effect of the control mechanism on each plot can balance the water level in the rice field area, and optimize the control mechanism according to the evaluation results, which specifically includes the following steps: In the process of applying the control mechanism, the water level feedback information of each plot is obtained in real time and pre-processed after acquisition; In the application process of the regulation mechanism, real-time water level feedback information of each plot can be obtained through the Internet of Things (IoT) sensor network. The specific method is to deploy sensors such as water level, soil moisture, temperature and loss rate in each plot, which automatically collect water level and moisture-related data at fixed time intervals (for example, every hour or every 30 minutes). The collected data is transmitted to the cloud server through wireless communication technology (such as LoRa, NB-IoT or Wi-Fi), and the system's software platform receives and records the real-time data stream. Once the data is uploaded, the system can call the data analysis module for preliminary analysis and store it in the database to support subsequent regulation effect evaluation. In this way, through the integration of the Internet of Things and the cloud platform, real-time acquisition and processing of water level feedback information of each plot is realized.

[0056] Preprocessing is to ensure the accuracy and consistency of water level feedback data so as to reduce errors in subsequent analysis. The preprocessing steps include data cleaning, format standardization, and missing value filling. First, data cleaning is used to remove noise and outliers to prevent data deviation caused by sensor failure or environmental interference. For example, obviously abnormal water level or humidity values ​​are filtered out. Next, the feedback information is formatted and stored in a unified unit and format for subsequent processing and analysis. For missing data, the system will use interpolation or fill in the values ​​of neighboring plots to ensure data integrity and consistency. Through these preprocessing steps, the high quality of water level feedback information can be guaranteed and the accuracy of the regulation effect evaluation model can be improved.

[0057] Extracting the regulation response information and water balance information from the pre-processed water level feedback information of each plot, and analyzing them after acquisition, respectively generating the water level regulation response coefficient and water balance index of each plot; A regulation effect evaluation model is constructed for the water level regulation response coefficient and water balance index generated for each plot, and the regulation coefficient of each plot is generated. The generated regulation coefficient of each plot is compared with the pre-set regulation coefficient threshold of each plot. According to the comparison results, it is evaluated whether the regulation effect of the regulation mechanism in each plot can balance the water level in the rice field area, and the regulation mechanism is optimized according to the evaluation results.

[0058] The thresholds of the control coefficients for each plot can be pre-set by historical data analysis and machine learning algorithms to ensure that the thresholds meet the water level control requirements and regional characteristics of each plot. First, the system collects historical data on the control coefficients of each plot of rice fields under different seasons, climate conditions and soil types to build a comprehensive water level response database. Then, the historical data is classified using a cluster analysis method to classify plots with similar water level requirements and response characteristics into one category, and a set of control coefficient thresholds is preliminarily set for each type of plot. Then, the model is trained using a machine learning algorithm (such as random forest or support vector machine) to further optimize the threshold interval to ensure that the threshold has good adaptability to different environmental conditions and plot differences. The system will set the final optimized threshold interval as the control standard for each plot, so that it can accurately determine whether each plot has reached a water level balance state in real-time monitoring and make corresponding adjustments.

[0059] In this embodiment, the logic for obtaining the water level control response coefficient and water balance index of each plot is as follows: The control response information in the pre-processed water level feedback information of each plot is extracted, including the actual water level change rate of each plot at different times during a period of time during the application of the control mechanism, the preset water level change rate of each plot during this time, the delay time of the water level change of each plot, and the number of times the control mechanism controls each plot, and they are calibrated as , , and , Indicates that during the application of the control mechanism, Moment The actual water level change rate of each plot, Indicates that during this time The preset water level change rate of each plot, Indicates that during this time The delay time for the water level of each plot to start changing, Indicates the effect of the regulatory mechanism on the The number of times a plot is regulated, , , and All are positive integers; In the process of applying the control mechanism, the control response information of each plot can be extracted through the Internet of Things (IoT) sensor network and data management system. Specifically, water level sensors, humidity sensors and data acquisition devices are deployed in the rice field area. These devices can automatically record the water level changes, the start time of control, and the response status after control at fixed time intervals (for example, every 10 minutes or every hour). After data collection, the data is transmitted to the cloud system through wireless communication technology (such as LoRa or NB-IoT). The data analysis module in the system can automatically extract the actual water level change rate, preset water level change rate, delay time and control times of each plot, and store these data in the database for subsequent analysis and calculation of water level control response coefficients. Through the integration of the Internet of Things and the data management system, the control response information can be collected in real time and comprehensively.

[0060] The acquisition of these quantitative data relies on the information automatically generated by the sensors and control systems in the rice fields. Specifically, the actual water level change rate is calculated by the water level sensor continuously monitoring the water level change during the control period; the preset water level change rate is a reference value automatically generated by the system according to the set water level target, which is used to compare the actual effect; the delay time is the time difference recorded by the system from the issuance of the control instruction to the beginning of the water level change; the number of controls is automatically accumulated by the control system, and the system updates the count every time the irrigation of the plot is adjusted. These data are sorted in the cloud system through the data analysis module to further analyze the response effect of water level control.

[0061] Calculate the water level control response coefficient of each plot. The specific calculation formula is as follows:

[0062] In the formula, For the Water level control response coefficient of each plot; The calculation formula of the water level control response coefficient is designed to comprehensively evaluate the response speed and effect of the plot to the control command, so as to reflect the actual control effect of the control system. Specifically, the cumulative symbol in the formula indicates the accumulation of data at different times over a period of time to ensure that the calculation covers the control effects under different conditions. Actual water level change rate The water level change rate is The product of indicates the degree to which the actual change in the water level of the plot matches the expected change. By taking the square root, the effect of the fast-responding plot is amplified while smoothing the data changes. The delay time in the denominator Add 2 in logarithmic form to avoid abnormally low index due to long delay time and ensure calculation stability. In addition, the cumulative number of controls in the denominator Take the square root and add 1 to reduce the impact of frequent adjustments on the response coefficient and emphasize stability. Finally, the normalization factor It is used to standardize the results of each plot to ensure that the response coefficients of different plots are comparable. This design method can effectively reflect the response effect and frequency of the plot to the regulation, and provide a scientific basis for the optimization of the irrigation system.

[0063] No. Water level control response coefficient of each plot The size of directly reflects the response effect of the plot to the regulation mechanism, and is therefore used to evaluate whether the regulation mechanism can achieve water level balance. A higher water level regulation response coefficient indicates that the actual water level change of the plot is relatively consistent with the preset target, with a fast response speed, short delay time, and moderate regulation frequency, indicating that the application effect of the regulation mechanism in the plot is good and helps to achieve water level balance; conversely, a lower response coefficient indicates that the actual water level change of the plot has not met expectations, and there may be a long delay or too high regulation frequency, resulting in unsatisfactory water level regulation effect. Therefore, by comparing the water level regulation response coefficients of various plots, the applicability and effectiveness of the regulation mechanism in different plots can be evaluated, and plots that need further adjustment can be identified to ensure that the water level in the entire rice field area is dynamically balanced.

[0064] The water balance information in the pre-processed water level feedback information of each plot is extracted, including the soil moisture content, soil moisture loss rate, water level recovery rate of each plot at different times during a period of time during the application of the regulation mechanism, and the preset soil moisture content of each plot during the period of time, and calibrated as , , and , Indicates that during the application of the control mechanism, Moment The soil moisture content of the plot, Indicates that during the application of the control mechanism, Moment The soil moisture loss rate of the plots, Indicates that during the application of the control mechanism, Moment The water level recovery rate of each plot, Indicates the time period Preset soil moisture content for each plot; In the process of applying the control mechanism, the water balance information of each plot can be extracted through the sensor network and data analysis system. The specific method is to deploy soil moisture sensors, water loss rate sensors and water level recovery monitoring equipment in different plots of rice fields. These sensors will automatically collect data on soil moisture content, water loss rate and water level recovery rate at set time intervals (for example, every hour). At the same time, the system is equipped with a preset soil moisture database. According to the target water demand values ​​set for different plots, crop growth stages and environmental conditions, the preset values ​​in the database will be automatically extracted at a specific time point for comparison with the actual collected data. All collected data are transmitted to the cloud system via wireless transmission (such as LoRa, NB-IoT, etc.), and the data analysis module automatically classifies and stores each data for subsequent calculation of the water balance index.

[0065] Various quantitative data are obtained in real time through different sensors. Soil moisture content is monitored by soil moisture sensors, which are buried in the soil and can sense changes in soil moisture in real time. The soil moisture loss rate is calculated by combining surface evaporation sensors and drainage monitoring sensors to monitor the natural loss rate of moisture in the plot. The water level recovery rate is recorded by the water level recovery monitoring equipment, that is, the speed at which the water level recovers to the preset value after irrigation. The preset soil moisture content is directly read through the preset database in the system, reflecting the target moisture content of the plot under different conditions. All data are uploaded and automatically sorted in real time to ensure the accuracy and completeness of the data when calculating the water balance index.

[0066] Calculate the water balance index of each plot. The specific calculation formula is as follows:

[0067] In the formula, For the Water balance index of a plot of land.

[0068] The water balance index The calculation formula is designed to comprehensively evaluate the degree of deviation of the moisture status of the plot from the preset target, thereby reflecting the moisture balance. In the formula, It represents the difference between the actual soil moisture content of the plot at each moment and the preset target moisture content. The deviation is magnified by taking the absolute value and then taking the cube. This can more significantly reflect the situation of large moisture deviation and give it a greater weight in the index. The denominator combines the soil moisture loss rate and water level recovery rate , and add 1 to avoid the denominator being zero. The product of the loss rate and the recovery rate is used in the denominator to balance the effect of water changes: a higher loss rate and a lower recovery rate will result in Increased, indicating water imbalance, while low loss rate and high recovery rate will Finally, the cumulative sign and normalization factor It is used to accumulate the balance index at each moment and average it, so as to make a comprehensive assessment of the water balance status of the plot over the entire time period. This design can comprehensively consider the relationship between the water change, loss and recovery capacity of the plot at different moments, and provide a scientific measurement indicator for water balance.

[0069] No. Water balance index of the plot The size of directly reflects the degree of deviation between the moisture status of the plot and the preset target, and is therefore used to evaluate whether the regulation mechanism is effective in achieving water level balance. A higher moisture balance index indicates that there is a large deviation between the actual soil moisture content of the plot and the target moisture content, and the moisture loss rate is high or the water level recovery rate is low, which means that the application effect of the regulation mechanism in the plot is not ideal and it is difficult to maintain moisture balance; on the contrary, a lower moisture balance index indicates that the actual moisture content is close to the preset target, and the moisture retention and recovery capabilities are good, indicating that the regulation mechanism can effectively maintain water level balance in the plot. Therefore, through the moisture balance index of each plot, plots with poor regulation effects can be identified, and irrigation strategies can be adjusted in a timely manner to achieve dynamic balance of water levels in the entire rice field area.

[0070] In this embodiment, the water level control response coefficients for each plot are generated. and water balance index Construct a control effect evaluation model and generate the control coefficient of each plot through weighted summation , and the control coefficients of each plot generated The control coefficient thresholds of each plot are set in advance Compare and evaluate whether the control effect of the control mechanism in each plot can balance the water level in the rice field area according to the comparison results, and optimize the control mechanism according to the evaluation results. The specific comparison and analysis are as follows: like , the control effect of the control mechanism in this plot can balance the water level in the rice field area, and there is no need to optimize the control mechanism; This means that the control mechanism has been well applied in this plot, and can effectively maintain the water level at the preset equilibrium state to meet the water demand of the rice field area. In this case, the water level control mechanism can operate stably without further adjustment, thus saving management and operating costs, reducing human intervention, ensuring the continuous stability of the water level in each plot, and facilitating the healthy growth of rice and the efficient use of resources.

[0071] like The control effect of the control mechanism on this plot cannot balance the water level in the rice field area, and the control mechanism needs to be optimized, including: if the plot is a low-water-level plot, increase the irrigation amount of the plot to ensure that the water level returns to a balanced state; if the plot is a high-water-level plot, reduce the irrigation amount of the plot to prevent excessive accumulation of water; dynamically adjust the control parameters, and optimize the control mechanism according to feedback data to adapt to different environmental conditions; continuously monitor the plots with normal water levels to prevent the water level from deviating from the normal state.

[0072] This situation shows that the application effect of the control mechanism in this plot is not ideal, and it fails to meet the requirements of water level balance, which may cause the water level in this plot to be low or high. If no adjustment is made, it may cause water shortage or excess water in the rice root system, affecting the healthy growth of crops and thus affecting yield. Therefore, the control mechanism needs to be optimized in time to restore the water level balance by increasing irrigation, reducing irrigation or adjusting the control parameters, so as to ensure that the overall water supply of the rice field is balanced.

[0073] To optimize the control mechanism, data collection based on sensor networks and real-time analysis of intelligent algorithms can be used. First, sensors arranged in each plot will continuously monitor soil moisture content, water level change rate and environmental conditions, and transmit data to the cloud system in real time. The data processing module in the cloud system will analyze the data, automatically identify low-water, high-water or normal-water plots, and determine the corresponding control measures according to preset rules. For low-water plots, the system will automatically generate instructions to increase irrigation, adjust the irrigation frequency or increase the amount of irrigation water through the intelligent irrigation control module to ensure that the water level returns to a balanced state; for high-water plots, the system will reduce the irrigation frequency or water volume to prevent excessive accumulation of water. In the part of dynamically adjusting the control parameters, the system uses machine learning algorithms to optimize irrigation parameters based on long-term historical data and real-time feedback to adapt to changes in different seasons and climate conditions, thereby improving the adaptability and control accuracy of the system. For normal water level plots, the system will continuously monitor data changes. Once the water level deviates from the normal range, it will immediately trigger fine-tuning instructions to ensure the stability of the water level. Through these methods, the system can intelligently adapt to the water level requirements of different plots of land, achieve overall water level balance in the rice field area, and ensure the best environmental conditions for rice growth.

[0074] The water level control response coefficient for each plot generated and water balance index Constructing a regulatory effect evaluation model, the regulatory coefficient of each plot can be generated by weighted summation , in order to comprehensively reflect the water level balance status and regulation response effect of each block. Specifically, the regulation coefficient calculation formula is: Among them, the weight coefficient and Used to balance the impact of water level regulation response and water balance. This reflects the importance the system places on the speed of response. If the water level control response capability of the plot is good, the system is more inclined to increase the water level. The value of can reduce additional intervention to improve the degree of automation. It reflects the system's attention to the water balance state; for plots with a low water balance index (i.e., water close to the target state), the water balance index can be appropriately reduced. , in order to reduce excessive regulatory operations. Therefore, by flexibly adjusting the weights and The value of can be used to optimize the control coefficient according to the specific plot characteristics and balance requirements, so that the water level balance and control response of each plot can achieve the best effect.

[0075] Based on the long-term collected water level status information and water level feedback information, the regulation mechanism is dynamically optimized and updated, and the irrigation strategies of various plots are continuously adjusted to adapt to environmental and seasonal changes, ensuring the continuous balance of rice field water levels and the stability of the irrigation system.

[0076] The water level demand of rice fields fluctuates with changes in the environment and seasons. Different climatic conditions (such as drought and increased rainfall) will significantly affect the rate of soil moisture loss and the water demand of crops. At the same time, the differences in soil structure and terrain of each plot in the rice field will also make the water demand personalized. Therefore, by collecting and analyzing water level status and feedback information over a long period of time, and dynamically optimizing and updating the regulation mechanism, the irrigation strategy can be made more flexible to meet the actual needs of rice fields. This method can ensure that rice obtains appropriate water at different growth stages and environmental conditions, prevent growth and yield from being affected by insufficient or excessive water, and thus improve the efficiency and reliability of the irrigation system.

[0077] Dynamic optimization and updating can be achieved by combining data acquisition systems, historical data analysis, and machine learning algorithms. First, the sensor network will collect water level status information of each plot in real time, including soil moisture, temperature, and water level change rate, and upload the data to the cloud. Secondly, the system will compare and analyze the current data with historical data, and identify the main factors affecting water level demand by establishing a correlation model between the environment and water level demand. Finally, with the help of machine learning algorithms (such as random forests or neural networks), the system can predict the impact of different climate conditions and seasonal changes on water level demand, thereby generating dynamically adjusted irrigation strategies. This method can effectively adapt to changing environmental needs and provide data support for the optimization of the regulation mechanism.

[0078] In the specific implementation, the software platform will analyze the water level status and feedback data collected over a long period of time in stages, build a control model, and adjust the irrigation strategy of each plot based on the prediction results. First, the data analysis module will classify and regress the historical data of each plot to find out the impact pattern of seasonal and environmental changes on water level demand. Then, the machine learning module uses these patterns to generate water level demand prediction models for each plot to provide support for irrigation strategies in different seasons and climatic conditions. When similar conditions in the current environment and historical data are detected, the system will automatically optimize irrigation parameters, including adjusting irrigation frequency, duration, and water volume to ensure continuous balance of water levels in each plot. This data-driven dynamic optimization mechanism enables the rice field irrigation system to maintain stability and efficiency under uncertain conditions.

[0079] like Figure 2 The intelligent rice irrigation system shown includes a plot division module, a water level assessment module, a dynamic control construction module, a control effect assessment module, and a control optimization update module; The plot division module evenly divides the paddy field area into several plots during large-scale paddy field irrigation, and deploys a sensor network in each plot to obtain the water level status information of each plot in real time; The water level assessment module analyzes the water level status information of each plot obtained in real time, assesses the water level balance of each plot in the rice field area, and divides each plot into low water level plot, normal water level plot and high water level plot according to the comparison results; The dynamic control construction module constructs a control mechanism for dynamically adjusting the water level of different plots according to the division results of each plot in the rice field area, and applies it to the current irrigation system after construction; The control effect evaluation module obtains the water level feedback information of each plot in real time during the application of the control mechanism, and analyzes it after acquisition to evaluate whether the control effect of the control mechanism in each plot can balance the water level in the rice field area, and optimizes the control mechanism according to the evaluation results; The control optimization and update module dynamically optimizes and updates the control mechanism based on the water level status information and water level feedback information collected over a long period of time, and continuously adjusts the irrigation strategies of various plots to adapt to environmental and seasonal changes, ensuring the continuous balance of rice field water levels and the stability of the irrigation system.

[0080] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0081] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0082] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0083] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0084] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0085] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0086] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0087] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. An intelligent rice irrigation method, characterized in that: The specific steps include: In the process of large-scale rice field irrigation, the rice field area is evenly divided into several plots, and a sensor network is deployed in each plot to obtain the water level status information of each plot in real time; Analyze the water level status information of each plot obtained in real time, evaluate the water level balance of each plot in the rice field area, and divide each plot into low water level plot, normal water level plot and high water level plot according to the comparison results; According to the division results of each plot in the rice field area, a control mechanism for dynamically adjusting the water level of different plots is constructed and applied to the current irrigation system after construction; In the process of applying the control mechanism, the water level feedback information of each plot is obtained in real time, and analyzed after acquisition to evaluate whether the control effect of the control mechanism in each plot can balance the water level in the rice field area, and optimize the control mechanism according to the evaluation results; Based on the long-term collected water level status information and water level feedback information, the regulation mechanism is dynamically optimized and updated, and the irrigation strategies of various plots are continuously adjusted to adapt to environmental and seasonal changes, ensuring the continuous balance of rice field water levels and the stability of the irrigation system.

2. The intelligent rice irrigation method according to claim 1, characterized in that: The water level status information of each plot obtained in real time is analyzed to evaluate the water level balance of each plot in the rice field area, and each plot is divided into low water level plot, normal water level plot and high water level plot according to the comparison results, which specifically includes the following steps: Preprocess the water level status information of each plot obtained in real time; Extracting the water dynamic information and irrigation response information from the pre-processed water level status information of each plot, and analyzing them after extraction to generate the water level demand coefficient and water level response index of each plot respectively; A water level balance assessment model is constructed for the water level demand coefficient and water level response index generated for each plot, and the water level balance coefficient of each plot is generated. The generated water level balance coefficient of each plot is compared with the pre-set water level balance coefficient threshold range, and the water level balance status of each plot in the rice field area is evaluated according to the comparison results, and each plot is divided into low water level plots, normal water level plots and high water level plots according to the comparison results.

3. The intelligent rice irrigation method according to claim 2, characterized in that: The logic for obtaining the water level demand coefficient and water level response index of each plot is as follows: The water dynamic information is extracted from the preprocessed water level status information of each plot, including the soil moisture content, water loss rate and average temperature of each plot in the paddy field at different times over a period of time, and calibrated as , and , Indicates the first plots within a period of time The soil moisture content at the moment, Indicates the first plots within a period of time The rate of soil water loss at any given moment, Indicates the first plots within a period of time The average soil temperature at the time, , , and All are positive integers; Calculate the water level demand coefficient for each plot. The specific calculation formula is as follows: ； In the formula, For the Water level demand coefficient for each plot; The irrigation response information is extracted from the preprocessed water level status information of each plot, including the water level change rate of each plot in the rice field area at different times within a period of time, the rate of water penetration into the soil, the natural loss rate of water, and the irrigation delay time of each plot in the rice field area, and calibrated as , , and , Indicates the first plots within a period of time The rate of change of water level at a given moment, Indicates the first plots within a period of time The rate at which water penetrates into the soil at any given moment. Indicates the first plots within a period of time The natural rate of water loss at any time, Indicates the first Irrigation delay time for each plot; Calculate the water level response index of each plot. The specific calculation formula is as follows: ； In the formula, For the Water level response index for each plot.

4. The intelligent rice irrigation method according to claim 3, characterized in that: Water level demand coefficient for each generated plot and water level response index Construct a water level balance assessment model and generate the water level balance coefficient of each plot by weighted summation , and the water level balance coefficient of each plot generated The water level balance coefficient threshold interval is set in advance Compare and evaluate the water level balance of each plot in the paddy field area according to the comparison results, and divide each plot into low water level plot, normal water level plot and high water level plot according to the comparison results. The specific comparison analysis and division are as follows: like , the water level balance condition of the plot in the rice field area is low, and the plot is classified as a low-water-level plot; like , the water level balance condition of the plot in the rice field area is water level balance, and the plot is divided into a normal water level plot; like The water level balance condition of the plot in the rice field area is high, and the plot is divided into a high water level plot.

5. The intelligent rice irrigation method according to claim 4, characterized in that: According to the division results of each plot in the rice field area, a control mechanism for dynamically adjusting the water level of different plots is constructed, specifically: according to the division results of low-water-level plots, normal-water-level plots and high-water-level plots, different water-level control adjustment parameters are set respectively to form a dynamic water-level control mechanism; the dynamic water-level control mechanism is based on the water-level balance coefficient and the current water-level status of each plot, and automatically determines the amount and method of irrigation water to be increased, maintained and reduced through pre-set rules; For low-water plots, the increase parameter in the dynamic water level control mechanism is used to increase the irrigation water volume for the low-water plots; For normal water level plots with balanced water levels, the maintenance parameters in the dynamic water level control mechanism are used to keep the current irrigation water volume unchanged; For high-water plots with high water levels, the reduction parameters in the dynamic water level control mechanism are used to reduce the amount of irrigation water for the high-water plots.

6. The intelligent rice irrigation method according to claim 5, characterized in that: In the process of applying the control mechanism, the water level feedback information of each plot is obtained in real time, and analyzed after acquisition to evaluate whether the control effect of the control mechanism in each plot can balance the water level in the rice field area, and optimize the control mechanism according to the evaluation results, which specifically includes the following steps: In the process of applying the control mechanism, the water level feedback information of each plot is obtained in real time and pre-processed after acquisition; Extracting the regulation response information and water balance information from the pre-processed water level feedback information of each plot, and analyzing them after acquisition, respectively generating the water level regulation response coefficient and water balance index of each plot; A regulation effect evaluation model is constructed for the water level regulation response coefficient and water balance index generated for each plot, and the regulation coefficient of each plot is generated. The generated regulation coefficient of each plot is compared with the pre-set regulation coefficient threshold of each plot. According to the comparison results, it is evaluated whether the regulation effect of the regulation mechanism in each plot can balance the water level in the rice field area, and the regulation mechanism is optimized according to the evaluation results.

7. The intelligent rice irrigation method according to claim 6, characterized in that: The logic for obtaining the water level control response coefficient and water balance index of each plot is as follows: The control response information in the pre-processed water level feedback information of each plot is extracted, including the actual water level change rate of each plot at different times during a period of time during the application of the control mechanism, the preset water level change rate of each plot during this time, the delay time of the water level change of each plot, and the number of times the control mechanism controls each plot, and they are calibrated as , , and , Indicates that during the application of the control mechanism, Moment The actual water level change rate of each plot, Indicates that during this time The preset water level change rate of each plot, Indicates that during this time The delay time for the water level of each plot to start changing, Indicates the effect of the control mechanism on the The number of times a plot is regulated, , , and All are positive integers; Calculate the water level control response coefficient of each plot. The specific calculation formula is as follows: ； In the formula, For the Water level control response coefficient of each plot; The water balance information in the pre-processed water level feedback information of each plot is extracted, including the soil moisture content, soil moisture loss rate, water level recovery rate of each plot at different times during a period of time during the application of the regulation mechanism, and the preset soil moisture content of each plot during the period of time, and calibrated as , , and , Indicates that during the application of the control mechanism, Moment The soil moisture content of the plot, Indicates that during the application of the control mechanism, Moment The soil moisture loss rate of the plots, Indicates that during the application of the control mechanism, Moment The water level recovery rate of each plot, Indicates the time period Preset soil moisture content for each plot; Calculate the water balance index of each plot. The specific calculation formula is as follows: ； In the formula, For the Water balance index of a plot of land.

8. The intelligent rice irrigation method according to claim 7, characterized in that: The water level control response coefficient for each plot generated and water balance index Construct a control effect evaluation model and generate the control coefficient of each plot through weighted summation , and the control coefficients of each plot generated The control coefficient thresholds of each plot are set in advance Compare and evaluate whether the control effect of the control mechanism in each plot can balance the water level in the rice field area according to the comparison results, and optimize the control mechanism according to the evaluation results. The specific comparison and analysis are as follows: like , the control effect of the control mechanism in this plot can balance the water level in the rice field area, and there is no need to optimize the control mechanism; like The control effect of the control mechanism on this plot cannot balance the water level in the rice field area, and the control mechanism needs to be optimized, including: if the plot is a low-water-level plot, increase the irrigation amount of the plot to ensure that the water level returns to a balanced state; if the plot is a high-water-level plot, reduce the irrigation amount of the plot to prevent excessive accumulation of water; dynamically adjust the control parameters, and optimize the control mechanism according to feedback data to adapt to different environmental conditions; continuously monitor the plots with normal water levels to prevent the water level from deviating from the normal state.

9. An intelligent rice irrigation system, used to implement the intelligent rice irrigation method according to any one of claims 1 to 8, characterized in that: It includes land parcel division module, water level assessment module, dynamic control construction module, control effect assessment module and control optimization and update module; The plot division module evenly divides the paddy field area into several plots during large-scale paddy field irrigation, and deploys a sensor network in each plot to obtain the water level status information of each plot in real time; The water level assessment module analyzes the water level status information of each plot obtained in real time, assesses the water level balance of each plot in the rice field area, and divides each plot into low water level plot, normal water level plot and high water level plot according to the comparison results; The dynamic control construction module constructs a control mechanism for dynamically adjusting the water level of different plots according to the division results of each plot in the rice field area, and applies it to the current irrigation system after construction; The control effect evaluation module obtains the water level feedback information of each plot in real time during the application of the control mechanism, and analyzes it after acquisition to evaluate whether the control effect of the control mechanism in each plot can balance the water level in the rice field area, and optimizes the control mechanism according to the evaluation results; The control optimization and update module dynamically optimizes and updates the control mechanism based on the water level status information and water level feedback information collected over a long period of time, and continuously adjusts the irrigation strategies of various plots to adapt to environmental and seasonal changes, ensuring the continuous balance of rice field water levels and the stability of the irrigation system.

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