Rice sowing time dynamic regulation and control and irrigation and drainage collaborative optimization method

Abstract

The invention discloses a rice sowing time dynamic regulation and control and irrigation and drainage collaborative optimization method, and the technical means of the method comprises the steps: obtaining the continuous observation data of a meteorological station of a target region, building an improved phenological model based on a three-critical temperature control algorithm, calculating the progress of a rice growth period, and building a dynamic water balance model. A leakage amount dual-mode calculation mechanism is integrated, a multi-target evaluation system is established, a TOPSIS-entropy weight method is adopted to carry out broadcast time optimization, a suitable broadcast time window is determined, and a dynamic adjustment mechanism based on standard deviation is established. According to the invention, through comprehensive meteorological data acquisition, a temperature response model, a water balance model and a multi-target evaluation system, dynamic regulation and control and irrigation and drainage collaborative optimization of the rice sowing time are realized; through the combination of various technical means, the problems of rice sowing time regulation and irrigation and drainage collaborative optimization are solved, and the overall benefits and environmental adaptability of rice planting are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural water conservancy engineering, and in particular to a method for dynamic regulation of rice sowing period and coordinated optimization of irrigation and drainage. Background Art

[0002] As one of the world's major food crops, optimizing rice cultivation techniques is crucial for increasing both yield and quality. However, current rice cultivation techniques still have numerous shortcomings in terms of sowing date selection and water management, particularly in the face of climate change and the increasing frequency of extreme weather events.

[0003] Traditional rice sowing date experiments have significant limitations. These trials typically last only one to two years and employ only a limited number of sowing date treatments, typically only three to five. This limited experimental design makes it difficult to fully reflect the complex relationship between rice growth and climatic conditions, making it difficult to develop reliable climate response models. Consequently, farmers face difficulties in accurately selecting the optimal sowing time based on local climate characteristics.

[0004] When it comes to water management, existing irrigation systems are overly simplistic, focusing primarily on controlling irrigation volume while neglecting the coordinated regulation of drainage volume. This single approach to water management is unsuitable for complex and changing climatic conditions and can easily lead to water waste or insufficient water supply, impacting rice growth and yield.

[0005] Furthermore, existing technologies fail to fully utilize long-term observational data from multiple meteorological stations. The lack of integration and in-depth analysis of this valuable data makes it difficult to develop a decision-making system based on a coupled phenological and water quantity model. Such a system could provide more precise guidance for rice planting, but its potential has been underutilized due to insufficient data utilization.

[0006] Existing technologies for optimizing sowing dates often rely on a single evaluation criterion, lacking a comprehensive, multi-criteria evaluation system. This simplistic approach fails to comprehensively consider irrigation and drainage volumes, making it difficult to provide farmers with optimal sowing date recommendations. Addressing these issues, existing technologies urgently need improvement. Summary of the Invention

[0007] The purpose of the present invention is to overcome the defects and shortcomings of the existing technology and provide a method for dynamic regulation of rice sowing period and coordinated optimization of irrigation and drainage, which solves the various problems existing in the existing technology and has the advantages of improving the accuracy of rice planting and optimizing the efficiency of water resource utilization.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A method for dynamic regulation of rice sowing period and coordinated optimization of irrigation and drainage, comprising the following steps:

[0010] S1: Obtain continuous observation data from meteorological stations in the target area;

[0011] S2: Establish an improved phenological model based on the three-critical temperature control algorithm to calculate the rice growth period process;

[0012] S3: Construct a dynamic water balance model and integrate a dual-mode leakage calculation mechanism;

[0013] S4: Establish a multi-objective evaluation system and use the TOPSIS-entropy weight method to optimize the sowing date;

[0014] S5: Determine the appropriate sowing window and establish a dynamic adjustment mechanism based on standard deviation.

[0015] Step S1 specifically includes: determining the geographical range parameters of the target area, including latitude and longitude coordinates and altitude, and deploying detection equipment of automatic weather stations in open areas.

[0016] The detection equipment includes temperature sensors, humidity sensors, and wind speed sensors. The temperature sensor is 1-2 meters from the ground, and the wind speed sensor is 8-12 meters from the ground. The data collection frequency is set to once an hour. The collected meteorological data include temperature, precipitation, sunshine hours, wind speed and relative humidity. The data collection frequency is daily or hourly, and the data is transmitted to the cloud storage system in real time via the 4G network.

[0017] In step S2, based on the mathematical model of rice growth response to temperature, a three-critical temperature phenological model is constructed by defining three critical temperatures for rice growth: the minimum temperature, the optimum temperature, and the maximum temperature. This model quantifies the effect of temperature on the rice growth period, thereby predicting each growth stage of rice from sowing to maturity.

[0018] The improved phenological model is constructed based on three critical temperature parameters, including:

[0019] Basic growth temperature range: 8-12℃;

[0020] Optimum growth temperature range: 25-35℃;

[0021] Growth inhibition temperature range: 38-42°C;

[0022] The model calculates the growth period progression by:

[0023] (2) Calculate the effective accumulated temperature HUH per hour:

[0024]

[0025] Where: Topt is the optimum growth temperature, T base is the basic growth temperature, T high is the growth inhibition temperature;

[0026]

[0027] Where h is the hour of the day, T d is the hourly temperature, based on the daily maximum temperature T max and the minimum temperature T min Calculation of the sine function;

[0028] (2) Daily effective temperature DTU

[0029]

[0030] DTU is calculated from the sowing date. When the accumulated DTU reaches a fixed temperature threshold, the rice growth process is completed. The accumulated daily effective temperature (DTU) is used. When the accumulated DTU reaches the preset temperature threshold, the rice is judged to have completed the corresponding growth stage.

[0031] The temperature response rules of the improved phenological model include:

[0032] When the temperature drops below the minimum growth temperature or exceeds the maximum growth temperature, rice stops developing;

[0033] When the temperature is within the optimum range, rice maintains its best growth rate;

[0034] When the temperature is higher than the optimum temperature but lower than the maximum temperature, the growth rate decreases as the temperature increases.

[0035] According to the rainfall collected during the rice growing period in step S1, the dynamic water balance equation is:

[0036] h i+1 =h i +I i +P i -ET Ci -S i -D i

[0037] Where: h i is the field water level on day i, I i is the irrigation amount on day i; P i is the precipitation on day i; ET Ci is the crop water requirement on day i; S i is the field leakage on day i; D i Discharge volume on day i;

[0038] The amount of irrigation and drainage is determined by the set irrigation level and rainwater storage depth;

[0039] Crop water requirement ET C Calculated according to the crop coefficient method, that is:

[0040] ET C =K c ×ET0

[0041] Where: K c is the crop coefficient; ET o It is the reference crop water requirement, calculated according to the Penman-Monteith formula recommended by FAO;

[0042] Rice field leakage when there is a water layer in the field:

[0043] S i =ah i +b

[0044] Where: S i is the field leakage on day i; h i is the field water level on day i, a and b are fitting parameters reflecting the linear relationship between soil permeability and water level;

[0045] When there is a layer of water in the field:

[0046]

[0047] Where: K0 is the saturated hydraulic conductivity, α is the empirical constant, T i is the time it takes for the soil moisture content to reach saturation on the i-th day; H i is the main root depth of rice on day i.

[0048] Step S4 specifically includes:

[0049] Determine evaluation objectives, including yield, quality, stress resistance, economic benefits and environmental impact;

[0050] Collect data from different sowing periods under different evaluation objectives and perform standardization;

[0051] The entropy weight method is used to calculate the weight of each evaluation target;

[0052] Based on the TOPSIS method, a comprehensive evaluation was conducted on each sowing period, and the distance between each sowing period and the positive ideal solution and the negative ideal solution was calculated, and the relative proximity was determined.

[0053] The method for determining the suitable sowing window in step S5 is:

[0054] Calculate the arithmetic mean μ of the optimal sowing dates over many years;

[0055] Take [μ-1 / 2σ,μ+1 / 2σ] as the recommended sowing window, where σ is the standard deviation;

[0056] The recommended sowing window [μ-1 / 2σ, μ+1 / 2σ] needs to be dynamically adjusted in combination with the following parameters:

[0057] If the climate forecast for that year is an El Niño year, the window is narrowed to [μ-1 / 3σ, μ+1 / 3σ];

[0058] If the forecast precipitation exceeds 20 mm in the first week of sowing, the window will be postponed to 3 days after the precipitation ends;

[0059] When the real-time soil moisture content is lower than 60% of the field water holding capacity, sowing should be started after irrigation meets the standard.

[0060] Compared with the prior art, the present invention has the following beneficial effects:

[0061] The present invention obtains meteorological data, establishes an improved phenological model and a dynamic water balance model, and combines a multi-objective evaluation system with a dynamic adjustment mechanism for the appropriate sowing window to achieve coordinated optimization of rice sowing date regulation and irrigation and drainage. Compared with the existing technology, the present application can more accurately predict the rice growth period, optimize the control of irrigation and drainage, improve rice yield and quality, enhance stress resistance, improve economic benefits, and reduce environmental impact. As a result, the present application solves the problems of traditional rice cultivation technology, such as short sowing date test cycle, few treatment solutions, uncoordinated irrigation and drainage regulation, and lack of integrated application of long-term observation data from multiple meteorological stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A distribution diagram of meteorological data of the actual growth period and the simulated growth period in an embodiment of the present invention;

[0063] Figure 2 It is the distribution diagram of the actual water level and the simulated water level in the present invention;

[0064] Figure 3 The distribution diagram of irrigation and drainage volume of rice after sowing, and the average data over many years;

[0065] Figure 4 This is the distribution map of the best sowing periods from 1958 to 2022. DETAILED DESCRIPTION

[0066] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0067] See attached Figure 1-4 ;

[0068] A method for dynamic regulation of rice sowing period and coordinated optimization of irrigation and drainage, comprising the following steps:

[0069] S1: Obtaining continuous observation data from meteorological stations in the target area provides basic data support for subsequent model building;

[0070] S2: Develop an improved phenological model based on a three-critical temperature control algorithm to calculate the rice growth period. This improved phenological model defines three critical temperatures for rice growth: the minimum temperature, the optimum temperature, and the maximum temperature. This model quantifies the impact of temperature on the rice growth period, thereby predicting each growth stage of rice from sowing to maturity.

[0071] S3: Construct a dynamic water balance model and integrate the dual-mode calculation mechanism of seepage. The dynamic water balance model optimizes the control of irrigation and drainage by integrating the dual-mode calculation mechanism of seepage.

[0072] S4: Establish a multi-objective evaluation system and use the TOPSIS-entropy weight method to optimize the sowing date. The multi-objective evaluation system uses the TOPSIS-entropy weight method to comprehensively evaluate multiple sowing dates and determine the optimal sowing date.

[0073] S5: Determine the suitable sowing window and establish a dynamic adjustment mechanism based on standard deviation. The suitable sowing window is adjusted according to the arithmetic mean and standard deviation of the optimal sowing period over many years through the dynamic adjustment mechanism of standard deviation, and further dynamic adjustment is made in combination with climate forecasts and soil moisture content.

[0074] This method relies on continuous observation data from weather stations. Automatic weather stations are deployed to collect data such as temperature, precipitation, sunshine hours, wind speed, and relative humidity. This data is transmitted in real time via a 4G network to a cloud storage system, providing the foundation for subsequent model development.

[0075] The core of this method is an improved phenological model based on a three-critical temperature control algorithm. By defining three critical temperatures for rice growth: minimum, optimum, and maximum, this model quantifies the impact of temperature on the rice growth period and thus predicts each growth stage from sowing to maturity. The model predicts rice growth by calculating hourly and daily effective temperatures.

[0076] The dynamic water balance model integrates a dual-mode seepage calculation mechanism to achieve dynamic management of irrigation and drainage. Based on collected meteorological data, it calculates field water levels, irrigation volume, precipitation, crop water requirements, field seepage, and drainage. Irrigation and drainage volumes are determined based on the configured irrigation levels and rainwater storage depth. Crop water requirements are calculated using the crop coefficient method, and paddy field seepage is calculated based on field water levels and soil permeability characteristics.

[0077] The multi-objective evaluation system determines the evaluation objectives, collects data for each sowing period under different evaluation objectives, and performs standardized processing. The entropy weight method is used to calculate the weight of each evaluation objective. Based on the TOPSIS method, a comprehensive evaluation of each sowing period is conducted. The distance between each sowing period and the positive ideal solution and the negative ideal solution is calculated, and the relative proximity is determined to optimize the sowing period decision.

[0078] The recommended sowing window is determined by calculating the arithmetic mean of multiple years of optimal sowing dates and combining the standard deviation. The sowing window is dynamically adjusted based on climate forecasts, precipitation, and other parameters. For example, if the climate forecast for the year is El Niño, the window is narrowed to a certain range; if the precipitation forecast exceeds 20 mm in the first week of sowing, the window is extended to three days after the precipitation ends; and if the real-time soil moisture content falls below 60% of field capacity, sowing can begin only after irrigation meets the standard.

[0079] Compared to existing technologies, this method achieves dynamic regulation of rice sowing dates and coordinated optimization of irrigation and drainage through comprehensive meteorological data collection, temperature response models, water balance models, and a multi-objective evaluation system. By combining multiple technical approaches, it solves the problem of rice sowing date regulation and coordinated optimization of irrigation and drainage, improving the overall efficiency and environmental adaptability of rice cultivation.

[0080] By acquiring continuous observation data from meteorological stations in the target area, an improved phenological model based on a three-critical temperature control algorithm was established, a dynamic water balance model was constructed, and a multi-objective evaluation system was established to determine the appropriate sowing window, thus achieving dynamic regulation of rice sowing dates and coordinated optimization of irrigation and drainage. Specifically, meteorological data provides basic data support for the model, the temperature response model predicts the growth process of rice, the water balance model achieves dynamic management of irrigation and drainage, the multi-objective evaluation system optimizes sowing date decisions, and the appropriate sowing window uses statistical methods to determine the optimal sowing date range and dynamically adjusts it. As a result, the problem of rice sowing date regulation and coordinated optimization of irrigation and drainage is solved, and the overall benefits and environmental adaptability of rice cultivation are improved.

[0081] Furthermore, step S1 specifically includes: determining the geographical range parameters of the target area, including latitude and longitude coordinates and altitude, deploying detection equipment of automatic weather stations in open areas,

[0082] The detection equipment includes temperature sensors, humidity sensors, and wind speed sensors. The temperature sensor is 1-2 meters from the ground, and the wind speed sensor is 8-12 meters from the ground. The data collection frequency is set to once an hour. The collected meteorological data include temperature, precipitation, sunshine hours, wind speed and relative humidity. The data collection frequency is daily or hourly, and the data is transmitted to the cloud storage system in real time via the 4G network.

[0083] The aforementioned technical features include determining the geographic scope parameters of the target area, deploying automatic weather station detection equipment, setting the data collection frequency, collecting meteorological data, and transmitting the data in real time to a cloud storage system via a 4G network. These features work together to ensure accurate and real-time acquisition of meteorological data for the target area. By determining the geographic scope parameters and deploying the detection equipment, coverage and data accuracy are guaranteed. The positioning of the temperature and wind speed sensors ensures representative and accurate data collection. The data collection frequency is set to ensure data continuity and real-time availability. Real-time data transmission to a cloud storage system via a 4G network ensures data immediacy and accessibility.

[0084] The detection equipment of an automatic weather station can be adjusted and optimized based on actual needs. For example, the height of the temperature and wind speed sensors can be adjusted appropriately based on the specific terrain and vegetation conditions to ensure representative and accurate data. The data collection frequency can also be adjusted based on specific application requirements. For example, under unusual weather conditions, the data collection frequency can be increased to obtain more detailed data. Furthermore, 4G network transmission can be replaced with other transmission methods, such as 5G networks or satellite communications, to ensure stable and reliable data transmission.

[0085] The above solution can effectively solve the problem of obtaining continuous observation data from meteorological stations in the target area, ensure the accuracy, real-time and continuity of the data, and provide reliable basic data support for the subsequent dynamic regulation of rice sowing period and coordinated optimization of irrigation and drainage. Compared with the existing technology, this application significantly improves the efficiency and quality of meteorological data acquisition through the deployment of automatic weather stations and real-time data transmission, ensures the continuity and real-time nature of the data, and can provide more accurate and timely meteorological data support for the dynamic regulation of rice planting.

[0086] Furthermore, in step S2, based on the mathematical model of rice growth response to temperature, the minimum temperature, the optimum temperature for rice growth and the optimum temperature for rice growth are defined.

[0087] The three critical temperatures of temperature and maximum temperature are used to construct a three-critical temperature phenological model to quantify the impact of temperature on the growth period of rice, thereby predicting the various growth stages of rice from sowing to maturity;

[0088] The improved phenological model is constructed based on three critical temperature parameters, including:

[0089] Basic growth temperature range: 8-12℃;

[0090] Optimum growth temperature range: 25-35℃;

[0091] Growth inhibition temperature range: 38-42°C;

[0092] The model calculates the growth period progression by:

[0093] (3) Calculate the effective accumulated temperature HUH per hour:

[0094]

[0095] Where: T opt is the optimum growth temperature, T base is the basic growth temperature, T high Growth inhibition temperature

[0096]

[0097] Where h is the hour of the day, T d is the hourly temperature, based on the daily maximum temperature T max and the minimum temperature T min Calculation of the sine function;

[0098] (2) Daily effective temperature DTU

[0099]

[0100] DTU is calculated from the sowing date. When the accumulated DTU reaches a fixed temperature threshold (TT), the rice growth process is completed. The accumulated daily effective temperature (DTU) is used. When the accumulated DTU reaches the preset temperature threshold, the rice is judged to have completed the corresponding growth stage.

[0101] By defining three critical temperatures for rice growth (basal, optimum, and inhibitory temperatures), a three-critical temperature phenology model was constructed to quantify the impact of temperature on the rice growth period. The basal temperature range is 8-12°C, the optimum temperature range is 25-35°C, and the inhibitory temperature range is 38-42°C. The model calculates the growth period progression by calculating the hourly accumulated effective temperature (HUH) and the daily effective temperature (DTU). Hourly accumulated effective temperature (HUH) is calculated using a sinusoidal function based on the daily maximum and minimum temperatures. Specifically, the calculation of HUH takes into account the hour of the day (h) and the hourly temperature (Td), data collected by automatic weather stations. Daily effective temperature (DTU) is calculated starting from the sowing date. When the accumulated DTU reaches a preset temperature threshold, the rice is considered to have completed the corresponding growth stage. This model can accurately predict each rice growth stage from sowing to maturity, solving the technical problem of how to quantify the impact of temperature on the rice growth period and predict each growth stage using a temperature response model. The model is calibrated and validated with local meteorological data to improve prediction accuracy.

[0102] Furthermore, the temperature response rules of the improved phenological model include:

[0103] When the temperature is lower than the basic growth temperature or higher than the growth inhibition temperature, rice stops developing;

[0104] When the temperature is within the optimum range, rice maintains its best growth rate;

[0105] When the temperature is higher than the optimum temperature but lower than the growth inhibition temperature, the growth rate decreases with increasing temperature.

[0106] The improved phenological model's temperature response rules address the issue of rice's growth response under extreme temperatures and within its optimum temperature range by specifying the rice's growth behavior under different temperature conditions. Specifically, when the temperature falls below the base growth temperature or rises above the growth-inhibiting temperature, rice stops growing, thus preventing the adverse effects of inappropriate temperatures on rice growth. When the temperature is within the optimum temperature range, rice maintains an optimal growth rate, thereby improving growth efficiency and yield. When the temperature rises above the optimum temperature but below the growth-inhibiting temperature, the rice's growth rate gradually slows as the temperature rises. This mechanism helps rice adapt to temperature changes and mitigates the negative effects of high temperatures on growth. By combining these technical features, the improved phenological model can dynamically regulate the rice growth process, ensuring a reasonable growth response under different temperature conditions. This allows for optimized rice sowing schedules and irrigation and drainage management, ultimately improving rice yield and quality.

[0107] The temperature response rules of the improved phenological model can be implemented in a variety of ways. For example, sensors can be deployed to monitor field temperatures in real time and, combined with the model's temperature thresholds, automatically adjust the rice growing environment. Furthermore, software can be used to simulate growth under different temperature conditions, allowing for advance prediction and planning of sowing and management measures. As a preferred implementation, this model can be integrated into intelligent agricultural management systems and combined with other environmental parameters to achieve comprehensive agricultural production optimization.

[0108] The improved phenological model's temperature response rules address the issue of rice's growth response under varying temperature conditions. Compared to existing technologies, this model more accurately reflects rice growth under varying temperatures, providing a scientific basis for rice sowing and management, and improving rice growth efficiency and yield. Consequently, this application offers significant advantages in optimizing rice cultivation.

[0109] Furthermore, according to the rainfall collected during the rice growing period in step S1, the dynamic water balance equation is:

[0110] h i+1 =h i +I i +P i -ET Ci -S i -D i

[0111] Where: h i is the field water level on day i, I i is the irrigation amount on day i; P i is the precipitation on day i; ET Ci is the crop water requirement on day i; S i is the field leakage on day i; D i Discharge volume on day i;

[0112] The amount of irrigation and drainage is determined by the set irrigation level and rainwater storage depth;

[0113] Crop water requirement ET C Calculated according to the crop coefficient method, that is:

[0114] ET C =K c ×ET0

[0115] Where: Kc is the crop coefficient; ET o It is the reference crop water requirement, calculated according to the Penman-Monteith formula recommended by FAO;

[0116] Rice field leakage when there is a water layer in the field:

[0117] Si =ah i +b

[0118] Where: S i is the field leakage on day i; h i is the field water level on day i, a and b are fitting parameters reflecting the linear relationship between soil permeability and water level;

[0119] When there is a layer of water in the field:

[0120]

[0121] Where: K0 is the saturated hydraulic conductivity, α is the empirical constant, T i is the time it takes for the soil moisture content to reach saturation on the i-th day; H i is the main root depth of rice on day i.

[0122] The application of the dynamic water balance equation can be achieved through the following steps: First, rainfall data during the rice growing season is collected. This data can be monitored and transmitted in real time through an automatic weather station. Then, parameters such as field water level, irrigation volume, precipitation, crop water requirement, field seepage, and drainage are calculated based on the dynamic water balance equation. The determination of irrigation and drainage volume is based on the set upper and lower irrigation limits and rainwater storage depth to ensure reasonable irrigation and drainage under different rainfall conditions. Crop water requirement is calculated using the crop coefficient method, referring to the Penman-Monteith formula recommended by the FAO to ensure the accuracy of the calculation. For rice field seepage, when there is a water layer in the field, the calculation is based on the field water level and soil permeability characteristic fitting parameters; when there is no water layer in the field, the calculation is based on the saturated hydraulic conductivity and the saturated state of the soil moisture content.

[0123] As a preferred embodiment, meteorological data and soil moisture data can be collected through a variety of sensors and monitoring equipment. Specifically, temperature sensors, humidity sensors, wind speed sensors and other equipment can be installed in the field to monitor in real time and transmit data to a cloud storage system. With this data, irrigation and drainage strategies can be dynamically adjusted to ensure water balance during the rice growing period. Furthermore, the irrigation and drainage plans can be optimized by combining meteorological forecast information and soil moisture data. For example, when heavy rainfall is predicted, the irrigation amount can be reduced in advance to avoid excessive water accumulation in the field; when the soil moisture content is low, the irrigation amount can be appropriately increased to ensure normal growth of rice.

[0124] Therefore, by combining a dynamic water balance equation with real-time monitoring technology, this application can effectively solve the technical problem of water balance during the rice growth period, ensuring that the water needs of rice are met at different growth stages, avoiding overirrigation or water shortages, and improving rice growth efficiency and yield. Compared with existing technologies, the advantage of this application is that it can more accurately control the water balance in rice fields, improving the management level of rice cultivation and the stability of yield.

[0125] Step S4 specifically includes:

[0126] Determine evaluation objectives, including yield, quality, stress resistance, economic benefits and environmental impact;

[0127] Collect data from different sowing periods under different evaluation objectives and perform standardization;

[0128] The entropy weight method is used to calculate the weight of each evaluation target;

[0129] Based on the TOPSIS method, a comprehensive evaluation was conducted on each sowing period, and the distance between each sowing period and the positive ideal solution and the negative ideal solution was calculated, and the relative proximity was determined.

[0130] Determining evaluation objectives clarifies the indicator system for comprehensive evaluation, specifically, irrigation and drainage volumes. Data collection and standardization ensure comparability and accuracy across different sowing dates. Data standardization can employ methods such as linear normalization and Z-score standardization. The entropy weighting method is used to objectively determine the importance of each evaluation objective. This method calculates the information entropy of each indicator to determine its weight. The greater the information entropy, the smaller the indicator's weight, and vice versa. Comprehensive evaluation based on the TOPSIS method determines the optimal sowing date by calculating the distance between each sowing date and the ideal solution. The TOPSIS method calculates the distance between each sowing date and the positive and negative ideal solutions and determines the relative proximity. The greater the proximity, the more optimal the sowing date.

[0131] This application proposes a comprehensive rice sowing date evaluation method based on the entropy weight method and the TOPSIS method, which can more objectively and comprehensively evaluate different sowing dates. Compared with the existing technology, the method of this application can comprehensively consider multiple evaluation objectives, ensure data comparability through standardization, objectively determine weights through the entropy weight method, and conduct a comprehensive evaluation through the TOPSIS method, ultimately determining the optimal sowing date. This method not only improves the scientific nature and accuracy of the evaluation, but also provides more reliable decision-making support for rice planting.

[0132] The method for determining the suitable sowing window in step S5 is:

[0133] Calculate the arithmetic mean μ of the optimal sowing dates over many years;

[0134] Take [μ-1 / 2σ,μ+1 / 2σ] as the recommended sowing window, where σ is the standard deviation;

[0135] The recommended sowing window [μ-1 / 2σ, μ+1 / 2σ] needs to be dynamically adjusted in combination with the following parameters:

[0136] If the climate forecast for that year is an El Niño year, the window is narrowed to [μ-1 / 3σ, μ+1 / 3σ];

[0137] If the forecast precipitation exceeds 20 mm in the first week of sowing, the window will be postponed to 3 days after the precipitation ends;

[0138] When the real-time soil moisture content is lower than 60% of the field water holding capacity, sowing should be started after irrigation meets the standard.

[0139] The basic sowing window is determined by calculating the arithmetic mean μ of the preferred sowing dates over many years; a preliminary sowing range is provided by taking [μ-1 / 2σ, μ+1 / 2σ] as the recommended sowing window, where σ is the standard deviation; dynamic adjustments are made in combination with climate forecast parameters, such as narrowing the window in El Niño years, postponing sowing when precipitation exceeds 20 mm, and starting sowing after irrigation when soil moisture content is low; these technical features work together to dynamically adapt to different climatic conditions, ensuring rice sowing under the most suitable conditions, thereby solving the technical problem of determining the appropriate rice sowing window under different climatic conditions.

[0140] The method for determining the suitable sowing window first determines the basic sowing window by calculating the arithmetic mean μ of multiple years of optimal sowing dates. This process can be achieved through statistical analysis of historical data. The recommended sowing window [μ-1 / 2σ, μ+1 / 2σ] is determined by calculating the standard deviation σ of these historical data. Dynamic adjustments are made based on climate forecast parameters. Specifically, if the climate forecast for the current year is El Niño, the window is narrowed to [μ-1 / 3σ, μ+1 / 3σ]. This adjustment is based on the impact of El Niño on the climate. If the forecast precipitation exceeds 20 mm in the first week of sowing, the window is postponed until three days after the precipitation ends. This adjustment is based on the impact of precipitation on sowing conditions. If the real-time soil moisture content falls below 60% of field capacity, sowing must be started only after irrigation meets the standard. This adjustment is based on the impact of soil moisture content on sowing success rate.

[0141] By introducing a dynamic adjustment mechanism for standard deviation, combined with climate forecasts and real-time soil moisture monitoring, this method proposes a method for dynamically adjusting the recommended sowing window. This method can dynamically adapt to different climate conditions and ensure that rice is sown under the most suitable conditions. Compared with existing technologies, this method provides a more reliable and scientific method for determining sowing dates by combining years of climate data analysis with real-time meteorological conditions, thereby improving the success rate and yield of rice planting.

[0142] Improve the prediction accuracy of growth period by 10-15% through the three-critical temperature model;

[0143] Achieve coordinated optimization of irrigation and drainage, reducing irrigation by 14% and drainage by 8%;

[0144] Establish a dynamic sowing window to adapt to climate change fluctuations of ±1.5°C;

[0145] Comprehensive evaluation indicators improve the accuracy of solution optimization by more than 25%.

[0146] Take the mid-season rice in the Jianghuai region as an example:

[0147] (1) Phenology modeling:

[0148] The basic temperature for the growth of single-season rice is T base =9.35℃, optimal growth temperature T opt =27.265℃ and growth inhibition temperature T high =38.61℃, cumulative effective temperature TT=2388℃

[0149] The phenological model derived from the meteorological data from 2000 to 2022 can well simulate the growth and development of rice (e.g. Figure 1 shown).

[0150] (2) Water volume simulation:

[0151] (a) Water management of rice

[0152]

[0153] Note: The upper limit for irrigation in the table refers to the water level in the field after a single irrigation session, measured in mm. The lower limit for irrigation refers to the value at which irrigation is initiated when the soil relative moisture content reaches this value. The rainwater storage depth refers to the maximum water level allowed in the field after rainfall; any water exceeding this level is promptly drained to this level, measured in mm.

[0154] (b) Parameter selection

[0155] Kc = 1.0 at the greening stage; Kc = 1.15 at the early tillering stage; Kc = 1.30 at the late tillering stage; Kc = 1.50 at the jointing and booting stage, heading and flowering stage; Kc = 1.25 at the milky stage; Kc = 1.0 at the greening stage;

[0156] a=0.125,b=0.262

[0157] K0 = 0.3, α = 140, soil saturation moisture content is 36%, soil bulk density is 1.4 kg / m3;

[0158] The model can well predict the field water level (such as Figure 2 shown):

[0159] (3) Evaluation index weight: Taking the meteorological data from 1958 to 2022 as an example, the irrigation and drainage of rice after sowing from April 21 to May 20 were simulated. The multi-year average data is as follows Figure 3 The data entropy weight method for 2022 was used to obtain the weights of irrigation (0.5639) and drainage (0.4361). The TOPSIS method was also used to obtain the optimal sowing date for that year: May 9.

[0160] (4) Calculation of sowing window: The best sowing window is obtained year by year using the topsis-entropy weight method. The best sowing window from 1958 to 2022 is as follows: Figure 4 As shown in the figure, when the average value of the optimal sowing period is μ = May 6 and σ = 9.7 days, the recommended sowing period is May 1-10.

[0161] Although this specification is described according to implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment can also be appropriately combined to form other implementation methods that can be understood by those skilled in the art.

[0162] Therefore, the above description is only a preferred embodiment of the present application and is not intended to limit the scope of implementation of the present application; that is, all equivalent modifications made according to the scope of the claims of the present application are within the scope of protection of the claims of the present application.

Claims

1. A method for dynamic control of rice sowing time and coordinated optimization of irrigation and drainage, characterized in that: The specific steps include: S1: Obtain continuous observation data from meteorological stations in the target area; S2: Establish an improved phenological model based on the three-critical temperature control algorithm to calculate the rice growth period process; S3: Construct a dynamic water balance model and integrate a dual-mode leakage calculation mechanism; S4: Establish a multi-objective evaluation system and use the TOPSIS-entropy weight method to optimize the sowing date; S5: Determine the appropriate sowing window and establish a dynamic adjustment mechanism based on standard deviation.

2. The method for dynamic control of rice sowing date and coordinated optimization of irrigation and drainage according to claim 1, characterized in that: Step S1 specifically includes: determining the geographical range parameters of the target area, including latitude and longitude coordinates and altitude, and deploying detection equipment of an automatic weather station in an open area; The detection equipment includes temperature sensors, humidity sensors, and wind speed sensors. The temperature sensor is 1-2 meters from the ground, and the wind speed sensor is 8-12 meters from the ground. The data collection frequency is set to once an hour. The collected meteorological data includes temperature, precipitation, sunshine hours, wind speed and relative humidity. The data collection frequency is daily or hourly. The data is transmitted in real time to the cloud storage system via the 4G network.

3. The method for dynamic control of rice sowing date and coordinated optimization of irrigation and drainage according to claim 1, characterized in that: In step S2, based on the mathematical model of rice growth response to temperature, a three-critical temperature phenological model is constructed by defining three critical temperatures for rice growth: the minimum temperature, the optimum temperature, and the maximum temperature. This model quantifies the effect of temperature on the rice growth period, thereby predicting each growth stage of rice from sowing to maturity. The improved phenological model is constructed based on three critical temperature parameters, including: Basic growth temperature range: 8-12℃; Optimum growth temperature range: 25-35℃; Growth inhibition temperature range: 38-42°C; The model calculates the growth period progression by: (1) Calculate the effective accumulated temperature HUH per hour: Where: T opt is the optimum growth temperature, T base is the basic growth temperature, T high is the growth inhibition temperature; Where h is the hour of the day, T d is the hourly temperature, based on the daily maximum temperature T max and the minimum temperature T min Calculation of the sine function; (2) Daily effective temperature DTU DTU is calculated from the sowing date. When the accumulated DTU reaches a fixed temperature threshold, the rice growth process is completed. The accumulated daily effective temperature DTU, when the accumulated DTU reaches the preset temperature threshold, is considered to have completed the corresponding growth stage of rice.

4. The method for dynamic control of rice sowing date and coordinated optimization of irrigation and drainage according to claim 3, characterized in that: The temperature response rules of the improved phenological model include: When the temperature is lower than the basic growth temperature or higher than the growth inhibition temperature, rice stops developing; When the temperature is within the optimum range, rice maintains its best growth rate; When the temperature is higher than the optimum temperature but lower than the growth inhibition temperature, the growth rate decreases with increasing temperature.

5. The method for dynamic control of rice sowing date and coordinated optimization of irrigation and drainage according to claim 1, characterized in that: According to the rainfall collected during the rice growing period in step S1, the dynamic water balance equation is: h i+1 =h i +1 i +P i -AND Ci -S i -D i Where: h i is the field water level on day i, I i is the irrigation amount on day i; P i is the precipitation on day i; ET Ci is the crop water requirement on day i; S i is the field leakage on day i; D i Discharge volume on day i; The amount of irrigation and drainage is determined by the set irrigation level and rainwater storage depth; Crop water requirement ET C Calculated according to the crop coefficient method, that is: AND C =K c ×ET0 Where: K c is the crop coefficient; ET o It is the reference crop water requirement, calculated according to the Penman-Monteith formula recommended by FAO; Rice field leakage when there is a water layer in the field: S i =ah i +b Where: S i is the field leakage on day i; h i is the field water level on day i, a and b are fitting parameters reflecting the linear relationship between soil permeability and water level; When there is a layer of water in the field: Where: K0 is the saturated hydraulic conductivity, α is the empirical constant, T i is the time it takes for the soil moisture content to reach saturation on the i-th day; H i is the main root depth of rice on day i.

6. The method for dynamic control of rice sowing date and coordinated optimization of irrigation and drainage according to claim 1, characterized in that: Step S4 specifically includes: Determine evaluation objectives, including irrigation and drainage volumes; Collect data from different sowing periods under different evaluation targets and perform standardization; The entropy weight method is used to calculate the weight of each evaluation target; Based on the TOPSIS method, a comprehensive evaluation of each sowing period was conducted, the distance between each sowing period and the positive ideal solution and the negative ideal solution was calculated, and the relative proximity was determined.

7. The method for dynamic control of rice sowing date and coordinated optimization of irrigation and drainage according to claim 1, characterized in that: The method for determining the suitable sowing window in step S5 is: Calculate the arithmetic mean μ of the optimal sowing dates over many years; Take [μ-1 / 2σ,μ+1 / 2σ] as the recommended sowing window, where σ is the standard deviation; The recommended sowing window [μ-1 / 2σ, μ+1 / 2σ] needs to be dynamically adjusted in combination with the following parameters: If the climate forecast for that year is an El Niño year, the window is narrowed to [μ-1 / 3σ, μ+1 / 3σ]; If the forecast precipitation exceeds 20 mm in the first week of sowing, the window will be postponed to 3 days after the precipitation ends; When the real-time soil moisture content is lower than 60% of the field water holding capacity, sowing should be started after irrigation meets the standard.

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