Agrometeorological disaster early warning and emergency response system

Abstract

The invention discloses an early warning and emergency response system for agrometeorological disasters, and relates to the technical field of intelligent agriculture, and the system comprises the following components: a meteorological data acquisition and early warning module, a soil humidity and rainfall monitoring module, an automatic irrigation and drainage control module, and a remote monitoring and management platform. By accurately monitoring meteorological data and soil humidity in real time and combining an early warning algorithm, agricultural meteorological disasters such as drought and flood can be predicted and responded in advance, before the disasters occur, the system can automatically adjust an irrigation strategy to ensure that crops obtain a proper amount of water, and when the disasters occur, the system can quickly start a drainage pump to prevent water accumulation in farmland, so that the system is safe and reliable. Therefore, crops are effectively protected from being affected by extreme weather, the risk resistance of agricultural production is remarkably improved through an automatic emergency response mechanism, and yield loss caused by disasters is reduced.

Description

Technical Field

[0001] The present invention relates to the field of smart agricultural technology, and specifically to an early warning and emergency response system for agricultural meteorological disasters. Background Art

[0002] With the rapid development of science and technology, smart agriculture, as an important development direction of modern agriculture, is gradually being integrated into all aspects of agricultural production. Against the backdrop of global climate change, extreme weather events such as droughts, floods and other agricultural meteorological disasters occur frequently, posing a serious threat to crop growth. This not only affects food production and quality, but also increases the uncertainty and risk of agricultural production. Therefore, how to effectively monitor meteorological changes, issue timely warnings and automatically respond to agricultural meteorological disasters has become a key issue that needs to be urgently addressed in smart agriculture.

[0003] Traditional early warning systems for agricultural meteorological disasters often rely on fixed-point observation data from meteorological stations and combine manual experience to predict disasters. There are problems with limited data acquisition and low prediction accuracy. At the same time, traditional emergency response systems also mostly rely on manual operations for irrigation and drainage control, which makes it difficult to make real-time and accurate adjustments. This manual-dependent approach is not only inefficient, but also prone to untimely or inappropriate response measures due to human misjudgment or delays. It is unable to effectively respond to the rapid changes brought about by extreme weather, especially when droughts and floods occur. Traditional systems are often unable to quickly and accurately adjust irrigation volumes or start drainage facilities, thereby exacerbating the damage to crops caused by disasters.

[0004] In view of the above problems, it is necessary to optimize the existing early warning and emergency response system for agricultural meteorological disasters, so as to achieve rapid response and effective response to drought and flood disasters by early warning of drought and flood disasters, and quickly and automatically adjust the irrigation volume or start the drainage pump when the disaster occurs. Therefore, it is of great significance to develop an early warning and emergency response system for agricultural meteorological disasters that can comprehensively realize the above characteristics. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the existing technology and provide an early warning and emergency response system for agricultural meteorological disasters. It can realize real-time monitoring, intelligent analysis and precise regulation of farmland environmental parameters by integrating advanced meteorological data acquisition and early warning modules, soil moisture and rainfall monitoring modules, automatic irrigation and drainage control modules and remote monitoring and management platforms. It can not only provide early warning of drought and flood disasters, but also quickly and automatically adjust the irrigation volume or start the drainage pump when a disaster occurs, effectively reducing human intervention and improving the speed and accuracy of emergency response.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: an agricultural meteorological disaster early warning and emergency response system, which includes the following components: a meteorological data acquisition and early warning module, a soil moisture and rainfall monitoring module, an automatic irrigation and drainage control module and a remote monitoring and management platform;

[0007] The meteorological data collection and early warning module collects meteorological data in real time through the meteorological stations installed in the farmland, and uses cloud computing technology to store, process and analyze the data. At the same time, combined with the agricultural meteorological disaster rule library, it conducts early warning analysis on the collected data. For the predicted risks of drought and flood disasters, early warning information is immediately generated and sent to the automatic irrigation and drainage control module;

[0008] The soil moisture and rainfall monitoring module monitors soil moisture and rainfall in real time through an intelligent sensor network;

[0009] The automatic irrigation and drainage control module automatically adjusts the flow value of the irrigation equipment and starts the drainage pump according to the meteorological warning information and soil moisture and rainfall data, and analyzes and correlates the humidity, temperature and light environment parameters through algorithms to achieve precise irrigation and drainage;

[0010] The remote monitoring and management platform provides a user interface, allowing users to remotely view farmland environmental parameters, irrigation and drainage status, and meteorological warning information, thereby realizing remote monitoring and management of farmland. The platform also supports historical data query and report generation functions, providing data support for agricultural production.

[0011] Furthermore, the meteorological data collection and early warning module installs a meteorological station at a selected location in the farmland area to fully cover and accurately collect meteorological data around the farmland, and collects atmospheric temperature, humidity, pressure, wind speed, wind direction, rainfall and light intensity data in real time through sensors, and transmits the data to a data center at the same time. The data center receives and stores meteorological data, establishes a historical database, and performs pre-processing operations on the collected meteorological data. A cloud computing platform is used in combination with an agricultural meteorological disaster rule library and algorithm to perform early warning analysis on the processed meteorological data. By comparing the current meteorological conditions with the meteorological characteristics when historical disasters occurred, the risk of drought or flood disasters is assessed. For predicted disaster risks, the system immediately generates early warning information and sends it to relevant personnel via SMS, email and APP push.

[0012] Furthermore, the meteorological data collection and warning module performs warning analysis on the processed meteorological data through an algorithm. Specifically, key features are extracted from the meteorological data, including rainfall R, soil moisture M, temperature T, humidity H and wind speed V, through the algorithm formula: The disaster risk index is derived to comprehensively assess the risks of drought and floods, where R ων and R ωd are the historical mean rainfall and standard deviation, M ων and M ωamp are the optimal value and range of soil moisture, respectively. TempRisk(T), HumidityRisk(H), and WindRisk(V) are the risk functions of temperature, humidity, and wind speed, and α, β, γ, δ, ∈ are weight coefficients.

[0013] Furthermore, the soil moisture and rainfall monitoring module reasonably deploys soil moisture sensors and rainfall sensors in the farmland, and collects soil moisture and rainfall data in real time, and transmits the data to the data center at the same time. The data center verifies the received data and stores the verified data in the database.

[0014] Furthermore, the automatic irrigation and drainage control module receives warning information from the meteorological data collection and warning module, as well as data from the soil moisture and rainfall monitoring module, and conducts a comprehensive analysis of the soil moisture, rainfall and meteorological warning information data through an algorithm to determine the current irrigation and drainage needs of the farmland. Based on the analysis results, a specific irrigation or drainage strategy is formulated, and the decision results are sent to the irrigation equipment and drainage pump control system through a control algorithm to automatically adjust the flow value of the irrigation equipment or start the drainage pump. At the same time, during the irrigation or drainage process, the soil moisture and rainfall are continuously monitored, and the irrigation and drainage strategies are adjusted based on real-time data feedback.

[0015] Furthermore, the automatic irrigation and drainage control module performs a comprehensive analysis of soil moisture, rainfall and meteorological warning information data through an algorithm, and the algorithm formula is: Where I is the demand index, S cur Indicates the current soil moisture, S ideal represents the ideal soil moisture, R cur Indicates the current rainfall, R reasonable represents reasonable rainfall, W represents the intensity of meteorological warning, α, β and γ are the weight coefficients of soil moisture deviation rate, rainfall deviation rate and meteorological warning intensity respectively, F is the adjustment factor, and the larger the value of the demand index is, the more urgent the need for irrigation or drainage is.

[0016] Furthermore, the specific steps of the automatic irrigation and drainage control module to formulate a specific irrigation or drainage strategy according to the analysis results are as follows:

[0017] (1) For irrigation strategy formulation, select appropriate irrigation methods according to crop needs and soil conditions, including drip irrigation, sprinkler irrigation and micro-sprinkler irrigation, and determine the amount of water for each irrigation based on irrigation needs and crop water requirements;

[0018] (2) Arrange irrigation time by taking into account the crop growth cycle, weather conditions, and the operating efficiency of irrigation equipment;

[0019] (3) For drainage strategy formulation, when soil moisture exceeds the safety threshold, the drainage plan is initiated to determine the drainage method and drainage volume so that the accumulated water can be discharged in time while avoiding excessive drainage that causes soil nutrient loss;

[0020] (4) Generate specific control instructions based on the formulated irrigation and drainage strategies, and send the control instructions to the irrigation equipment and drainage pump control system through the algorithm.

[0021] Furthermore, the automatic irrigation and drainage control module sends the decision result to the irrigation equipment and drainage pump control system through the control algorithm, automatically adjusts the flow value of the irrigation equipment or starts the drainage pump, and its algorithm formula is: Where u(t) is the output of the controller, i.e., the flow adjustment value of the irrigation equipment or the start / stop signal of the drainage pump, e(t) is the error signal, i.e., the difference between the target soil moisture and the actual soil moisture, and K p is the proportional gain, is the integral term of the error, K i is the integral gain K d is the differential gain.

[0022] Furthermore, the automatic irrigation and drainage control module adjusts the irrigation and drainage strategies according to the real-time data feedback, and adjusts the parameters and structure of the model through the algorithm, and the calculation formula is: Where: θ t+1 is the updated irrigation and drainage strategy, θ t is the current irrigation and drainage strategy, η is the learning rate, In the current dataset D t The gradient of the loss function L with respect to the model parameter θ, λ is the weight factor, Δθ hist It is the adjustment amount calculated based on historical feedback results.

[0023] Compared with the existing technology, the agricultural meteorological disaster early warning and emergency response system has the following beneficial effects:

[0024] 1. The present invention can predict and respond to agricultural meteorological disasters such as droughts and floods in advance by real-time and accurate monitoring of meteorological data and soil moisture, combined with early warning algorithms. Before a disaster occurs, the system can automatically adjust the irrigation strategy to ensure that crops get an appropriate amount of water. When a disaster occurs, the system can quickly start the drainage pump to prevent water accumulation in the farmland, thereby effectively protecting crops from the impact of extreme weather. This automated emergency response mechanism significantly improves the risk resistance of agricultural production and reduces production losses caused by disasters.

[0025] 2. Through intelligent analysis and precise control of irrigation and drainage strategies, the present invention can carry out refined irrigation according to the actual water demand of crops and soil moisture, avoiding ineffective waste of water resources. At the same time, when encountering flood disasters, the system can start the drainage pump in time to prevent waterlogging in farmland, protect the health of crop roots, and further improve the utilization efficiency of water resources.

[0026] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 It is the process operation diagram of the agricultural meteorological disaster early warning and emergency response system;

[0029] Figure 2 This is the flow chart of the automated irrigation and drainage control module of the agricultural meteorological disaster early warning and emergency response system. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] Embodiment 1

[0032] This embodiment describes in detail the specific implementation of the agricultural meteorological disaster early warning and emergency response system.

[0033] First, the meteorological data collection and early warning module selects a suitable location to install a meteorological station in the farmland area to fully cover and accurately collect meteorological data around the farmland. The sensor collects atmospheric temperature, humidity, pressure, wind speed, wind direction, rainfall and light intensity data in real time, and transmits the data to the data center. The data center receives and stores meteorological data, establishes a historical database, and pre-processes the collected meteorological data. The cloud computing platform is used to combine the agricultural meteorological disaster rule library and algorithm to perform early warning analysis on the processed meteorological data. Specifically, key features are extracted from the meteorological data, including rainfall R, soil moisture M, temperature T, humidity H and wind speed V. The algorithm formula is: The disaster risk index is derived to comprehensively assess the risks of drought and floods, where R ων and R ωd are the historical mean rainfall and standard deviation, M ων and M ωamp are the optimal value and range of soil moisture, respectively. TempRisk(T), HumidityRisk(H), and WindRisk(V) are risk functions of temperature, humidity, and wind speed. α, β, γ, δ, and ∈ are weight coefficients. The risk of drought or flood disasters is assessed by comparing the current meteorological conditions with the meteorological characteristics when historical disasters occurred. For predicted disaster risks, the system immediately generates warning information and sends it to relevant personnel through SMS, email, APP push, etc.

[0034] Secondly, the soil moisture and rainfall monitoring module reasonably deploys soil moisture sensors and rainfall sensors in the farmland. The sensors regularly collect soil moisture and rainfall data and transmit the data to the data center. The data center verifies the received data and stores the verified data in the database.

[0035] Next, the automatic irrigation and drainage control module receives the warning information from the meteorological data collection and warning module, as well as the data from the soil moisture and rainfall monitoring module. It uses an algorithm to conduct a comprehensive analysis of the soil moisture, rainfall and meteorological warning information data to determine the current irrigation and drainage needs of the farmland. Based on the analysis results, it formulates specific irrigation or drainage strategies, and sends the decision results to the irrigation equipment and drainage pump control system through the control algorithm. It automatically adjusts the flow value of the irrigation equipment or starts the drainage pump. During the irrigation or drainage process, it continuously monitors soil moisture and rainfall, and adjusts the irrigation and drainage strategies based on real-time data feedback.

[0036] Then, based on the analysis results, the automated irrigation and drainage control module formulates specific irrigation or drainage strategies. For irrigation strategies, appropriate irrigation methods (such as drip irrigation, sprinkler irrigation or micro-sprinkler irrigation) are selected according to crop needs and soil conditions, and the amount of water for each irrigation is determined based on irrigation needs and crop water requirements. At the same time, the irrigation time is arranged taking into account the crop growth cycle, weather conditions and the operating efficiency of the irrigation equipment. For drainage strategies, when soil moisture exceeds the safety threshold, the drainage plan is started, the drainage method and drainage amount are determined, so that the accumulated water can be discharged in time while avoiding excessive drainage that causes soil nutrient loss. The decision results are sent to the irrigation equipment and drainage pump control system through the control algorithm to automatically adjust the flow value of the irrigation equipment or start the drainage pump.

[0037] Finally, the remote monitoring and management platform provides a user interface that allows users to remotely view farmland environmental parameters, irrigation and drainage status, and meteorological warning information, thereby realizing remote monitoring and management of farmland. The platform also supports historical data query and report generation functions, providing data support for agricultural production, helping users better understand farmland conditions and optimize agricultural production management.

[0038] In summary, the above embodiments can predict and respond to agricultural meteorological disasters such as droughts and floods in advance by real-time and accurate monitoring of meteorological data and soil moisture, combined with early warning algorithms. Before a disaster occurs, the system can automatically adjust the irrigation strategy to ensure that crops get an appropriate amount of water. When a disaster occurs, the system can quickly start the drainage pump to prevent waterlogging in the farmland, thereby effectively protecting crops from the impact of extreme weather.

[0039] Embodiment 2

[0040] Based on the first embodiment, this embodiment describes in detail the specific implementation method of the automatic irrigation and drainage control module of the agricultural meteorological disaster early warning and emergency response system.

[0041] The automatic irrigation and drainage control module receives the warning information from the meteorological data collection and warning module, as well as the data from the soil moisture and rainfall monitoring module, and conducts a comprehensive analysis of the soil moisture, rainfall and meteorological warning information data through an algorithm. The algorithm formula is: Where I is the demand index, S cur Indicates the current soil moisture, S ideal represents the ideal soil moisture, R cur Indicates the current rainfall, R reasonable represents reasonable rainfall, W represents the intensity of meteorological warning, α, β and γ are the weight coefficients of soil moisture deviation rate, rainfall deviation rate and meteorological warning intensity respectively, F is the adjustment factor, and the larger the value of the demand index is, the more urgent the need for irrigation or drainage is.

[0042] Determine the current irrigation and drainage needs of farmland, and formulate specific irrigation or drainage strategies based on the analysis results. The specific steps are as follows:

[0043] (1) For irrigation strategy formulation, select appropriate irrigation methods according to crop needs and soil conditions, including drip irrigation, sprinkler irrigation and micro-sprinkler irrigation, and determine the amount of water for each irrigation based on irrigation needs and crop water requirements;

[0044] (2) Arrange irrigation time by taking into account the crop growth cycle, weather conditions, and the operating efficiency of irrigation equipment;

[0045] (3) For drainage strategy formulation, when soil moisture exceeds the safety threshold, the drainage plan is initiated to determine the drainage method and drainage volume so that the accumulated water can be discharged in time while avoiding excessive drainage that causes soil nutrient loss;

[0046] (4) Generate specific control instructions based on the formulated irrigation and drainage strategies, and send the control instructions to the irrigation equipment and drainage pump control system through the algorithm.

[0047] The decision results are sent to the irrigation equipment and drainage pump control system through the control algorithm to automatically adjust the flow value of the irrigation equipment or start the drainage pump. During the irrigation or drainage process, the soil moisture and rainfall are continuously monitored, and the irrigation and drainage strategies are adjusted according to the real-time data feedback. The algorithm formula is: Where u(t) is the output of the controller, i.e., the flow adjustment value of the irrigation equipment or the start / stop signal of the drainage pump, e(t) is the error signal, i.e., the difference between the target soil moisture and the actual soil moisture, and K p is the proportional gain, is the integral term of the error, K i is the integral gain K d is the differential gain.

[0048] In summary, this embodiment can automatically optimize irrigation and drainage strategies according to actual environmental parameters through intelligent analysis and precise control, achieve precise irrigation and drainage, and reduce the waste of water resources.

[0049] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. Agricultural meteorological disaster early warning and emergency response system, characterized by: The system includes the following components: meteorological data collection and early warning module, soil moisture and rainfall monitoring module, automatic irrigation and drainage control module and remote monitoring and management platform; The meteorological data collection and early warning module collects meteorological data in real time through the meteorological stations installed in the farmland, and uses cloud computing technology to store, process and analyze the data. At the same time, combined with the agricultural meteorological disaster rule library, it conducts early warning analysis on the collected data. For the predicted risks of drought and flood disasters, early warning information is immediately generated and sent to the automatic irrigation and drainage control module; The soil moisture and rainfall monitoring module monitors soil moisture and rainfall in real time through an intelligent sensor network; The automatic irrigation and drainage control module automatically adjusts the flow value of the irrigation equipment and starts the drainage pump according to the meteorological warning information and soil moisture and rainfall data, and analyzes and correlates the humidity, temperature and light environment parameters through algorithms to achieve precise irrigation and drainage; The remote monitoring and management platform provides a user interface, allowing users to remotely view farmland environmental parameters, irrigation and drainage status, and meteorological warning information, thereby realizing remote monitoring and management of farmland. The platform also supports historical data query and report generation functions, providing data support for agricultural production.

2. The agricultural meteorological disaster early warning and emergency response system according to claim 1 is characterized in that: The meteorological data collection and early warning module selects a location to install a meteorological station in the farmland area to fully cover and accurately collect meteorological data around the farmland, and collects atmospheric temperature, humidity, pressure, wind speed, wind direction, rainfall and light intensity data in real time through sensors, and transmits the data to the data center at the same time. The data center receives and stores meteorological data, establishes a historical database, and performs pre-processing operations on the collected meteorological data. The cloud computing platform is combined with the agricultural meteorological disaster rule library and algorithm to perform early warning analysis on the processed meteorological data. By comparing the current meteorological conditions with the meteorological characteristics when historical disasters occurred, the risk of drought or flood disasters is evaluated. For predicted disaster risks, the system immediately generates early warning information and sends it to relevant personnel via SMS, email and APP push.

3. The agricultural meteorological disaster early warning and emergency response system according to claim 2 is characterized in that: The meteorological data collection and early warning module performs early warning analysis on the processed meteorological data through an algorithm. Specifically, key features are extracted from the meteorological data, including rainfall R, soil moisture M, temperature T, humidity H and wind speed V, through the algorithm formula: The disaster risk index is derived to comprehensively assess the risks of drought and floods, where R ων and R ωd are the historical mean rainfall and standard deviation, M ων and M ωamp are the optimal value and range of soil moisture, respectively. TempRisk(T), HumidityRisk(H), and WindRisk(V) are the risk functions of temperature, humidity, and wind speed, and α, β, γ, δ, ∈ are weight coefficients.

4. The agricultural meteorological disaster early warning and emergency response system according to claim 1 is characterized in that: The soil moisture and rainfall monitoring module reasonably deploys soil moisture sensors and rainfall sensors in the farmland, collects soil moisture and rainfall data in real time, and transmits the data to the data center, which verifies the received data and stores the verified data in the database.

5. The agricultural meteorological disaster early warning and emergency response system according to claim 1 is characterized in that: The automatic irrigation and drainage control module receives the warning information from the meteorological data collection and warning module, as well as the data from the soil moisture and rainfall monitoring module, and conducts a comprehensive analysis of the soil moisture, rainfall and meteorological warning information data through an algorithm to determine the current irrigation and drainage needs of the farmland. Based on the analysis results, a specific irrigation or drainage strategy is formulated, and the decision results are sent to the irrigation equipment and drainage pump control system through a control algorithm to automatically adjust the flow value of the irrigation equipment or start the drainage pump. At the same time, during the irrigation or drainage process, the soil moisture and rainfall are continuously monitored, and the irrigation and drainage strategies are adjusted according to real-time data feedback.

6. The agricultural meteorological disaster early warning and emergency response system according to claim 5 is characterized in that: The automatic irrigation and drainage control module uses an algorithm to comprehensively analyze soil moisture, rainfall and meteorological warning information data. The algorithm formula is: Where I is the demand index, S cur Indicates the current soil moisture, S ideal represents the ideal soil moisture, R cur Indicates the current rainfall, R reasonable represents reasonable rainfall, W represents the intensity of meteorological warning, α, β and γ are the weight coefficients of soil moisture deviation rate, rainfall deviation rate and meteorological warning intensity respectively, F is the adjustment factor, and the larger the value of the demand index is, the more urgent the need for irrigation or drainage is.

7. The agricultural meteorological disaster early warning and emergency response system according to claim 5 is characterized in that: The specific steps for the automatic irrigation and drainage control module to formulate specific irrigation or drainage strategies according to the analysis results are as follows: (1) For irrigation strategy formulation, select appropriate irrigation methods according to crop needs and soil conditions, including drip irrigation, sprinkler irrigation and micro-sprinkler irrigation, and determine the amount of water for each irrigation based on irrigation needs and crop water requirements; (2) Arrange irrigation time by taking into account the crop growth cycle, weather conditions, and the operating efficiency of irrigation equipment; (3) For drainage strategy formulation, when soil moisture exceeds the safety threshold, the drainage plan is initiated to determine the drainage method and drainage volume so that the accumulated water can be discharged in time while avoiding excessive drainage that causes soil nutrient loss; (4) Generate specific control instructions based on the formulated irrigation and drainage strategies, and send the control instructions to the irrigation equipment and drainage pump control systems through algorithms.

8. The agricultural meteorological disaster early warning and emergency response system according to claim 7 is characterized in that: The automatic irrigation and drainage control module sends the decision results to the irrigation equipment and drainage pump control system through the control algorithm, automatically adjusts the flow value of the irrigation equipment or starts the drainage pump. The algorithm formula is: Where u(t) is the output of the controller, i.e., the flow adjustment value of the irrigation equipment or the start / stop signal of the drainage pump, e(t) is the error signal, i.e., the difference between the target soil moisture and the actual soil moisture, and K p is the proportional gain, is the integral term of the error, K i is the integral gain K d is the differential gain.

9. The agricultural meteorological disaster early warning and emergency response system according to claim 5, characterized in that: The automatic irrigation and drainage control module adjusts the irrigation and drainage strategies according to real-time data feedback and adjusts the parameters and structure of the model through the algorithm. The calculation formula is: Where: θ t+1 is the updated irrigation and drainage strategy, θ t is the current irrigation and drainage strategy, η is the learning rate, In the current dataset D t The gradient of the loss function L with respect to the model parameter θ, λ is the weight factor, Δθ hist It is the adjustment amount calculated based on historical feedback results.

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