Intelligent water and fertilizer management method and system for crops

Abstract

The invention relates to an intelligent water and fertilizer management system for crops. The intelligent water and fertilizer management system comprises a data acquisition and monitoring module for monitoring soil environment data and meteorological conditions; the data storage and analysis module is responsible for storing all data acquired from the data acquisition and monitoring module; the model customization module is responsible for formulating irrigation and fertilization rules according to different growth stages and characteristics of different crops, and introducing different random forest models customized for different crops to optimize irrigation and fertilization strategies on the basis of basic rule setting, so as to achieve the purpose of irrigation and fertilization. The random forest model finds an association rule between crop growth and water and fertilizer supply, adjusts irrigation and fertilization strategies according to the association rule, and optimizes the rule and the model according to abnormal information provided by the alarm and fault processing module; and the intelligent control module is used for automatically adjusting the water and fertilizer proportion and the irrigation scheme according to the real-time monitoring data and the crop growth requirements in combination with the model provided by the model customization module.

Description

Technical Field

[0001] The present invention relates to the technical field of crop management, and in particular to an intelligent crop water and fertilizer management method and system. Background Art

[0002] Intelligent water and fertilizer management for crops refers to the use of advanced sensors, controllers, actuators and other equipment to monitor soil moisture, nutrient status and crop growth needs in real time, automatically adjust irrigation and fertilization, and achieve precise water and fertilizer supply. Its principle is mainly based on precise sensor monitoring and intelligent control. Through various high-precision sensors installed in the field, field environmental data is collected in real time, and the data is transmitted to the intelligent control system for analysis and decision-making, and ultimately precise water and fertilizer management is achieved through actuators.

[0003] The intelligent control algorithm is the core of the intelligent water and fertilizer management system, which needs to make decisions based on real-time monitoring data and adjust the amount of irrigation and fertilization. However, the water and fertilizer requirements of different crops, different growth stages and different soil conditions vary greatly. Suppose that in an intelligent water and fertilizer management system, two crops, wheat and tomatoes, are planted at the same time. As a food crop, wheat has a relatively long growth cycle and a relatively stable demand for water and fertilizer; while tomatoes, as vegetable crops, have a short growth cycle and different water and fertilizer requirements at different growth stages. The intelligent control algorithm does not fully consider the differences in water and fertilizer requirements of these two crops and may adopt the same irrigation and fertilization strategy. This may cause wheat to affect yield and quality due to excess water and fertilizer in the late growth period, while tomatoes are hindered in growth due to insufficient water and fertilizer during the critical growth period. Therefore, an intelligent water and fertilizer management method and system for crops is proposed. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent water and fertilizer management method and system for crops.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] An intelligent water and fertilizer management system for crops, comprising:

[0007] Data acquisition and monitoring module: real-time monitoring of soil environmental data (soil moisture, soil conductivity, soil pH value, nutrient content) and meteorological conditions (temperature, humidity, light, etc.) through sensors (such as soil moisture sensors, nutrient sensors, meteorological sensors, etc.), and the soil environmental data and meteorological conditions are sent to the data storage and analysis module and the intelligent control module;

[0008] Data storage and analysis module: responsible for storing all data obtained from the data collection and monitoring module, and performing preliminary data cleaning, integration and preprocessing, providing data analysis functions such as data trend analysis, anomaly detection, etc., providing the analyzed data to the remote monitoring and management module, and at the same time, providing the stored historical data to the model customization module;

[0009] Model customization module: responsible for formulating irrigation and fertilization rules according to the different growth stages and characteristics of different crops. At the same time, based on the basic rule setting, different random forest models customized for different crops are introduced to optimize the irrigation and fertilization strategies. The random forest model discovers the correlation between crop growth and water and fertilizer supply, and adjusts the irrigation and fertilization strategies according to the correlation law. The rules and models are optimized according to the abnormal information provided by the alarm and fault handling module. For crops with a long growth cycle and relatively stable water and fertilizer requirements (such as wheat), a relatively stable irrigation and fertilization plan is adopted to ensure that the crops receive a continuous and appropriate supply of water and fertilizer during the growth process. For crops with a short growth cycle and large differences in water and fertilizer requirements (such as tomatoes), water and fertilizer supply is increased during their critical growth period (such as flowering and fruiting period) to meet their needs for rapid growth and fruiting;

[0010] Intelligent control module: According to real-time monitoring data and crop growth requirements, combined with the model provided by the model customization module, the water-fertilizer ratio and irrigation plan are automatically adjusted, including the control of intelligent valves, irrigation pipe networks and water-fertilizer machines. The intelligent control module sends the adjusted water-fertilizer ratio and irrigation plan as well as the real-time operating status to the remote monitoring and management module. When adjusting the water-fertilizer ratio and irrigation plan, if the intelligent control module detects an abnormal situation or equipment failure, it will immediately send an alarm message to the alarm and fault handling module;

[0011] Remote monitoring and management module: allows users to view various monitoring data and equipment operation status of the irrigation area in real time through mobile phone APP, computer software and other management platforms, and perform remote operation and management, such as adjusting irrigation and fertilization parameters, viewing historical data, etc. Users adjust irrigation and fertilization parameters through the remote monitoring and management module, and the parameters are sent to the model customization module for updating. At the same time, the model customization module sends the customized random forest model and rules to the remote monitoring and management module for display and application;

[0012] Alarm and fault handling module: When an abnormal situation or equipment failure is monitored, an alarm message is automatically issued and corresponding fault handling measures are taken. The fault handling measures include the identification of abnormal data, the sending of alarm information (such as SMS, email, APP push, etc.) and the provision of fault handling suggestions. The alarm and fault handling module sends the monitored abnormal information to the model customization module and sends the alarm information to the remote monitoring and management module.

[0013] The above technical solution further includes:

[0014] Furthermore, the data acquisition and monitoring module includes a sensor unit, an Internet of Things transmission device and a data acquisition terminal, which is responsible for directly measuring and collecting soil environmental data and meteorological conditions. The sensors include soil moisture sensors, nutrient sensors, meteorological sensors, etc., which can monitor in real time and convert them into electrical signals or digital signals for subsequent processing and transmission. The Internet of Things transmission device is responsible for transmitting the data collected by the sensor unit to the data acquisition terminal. The data acquisition terminal is responsible for receiving the data sent by the Internet of Things transmission device and performing preliminary processing and storage. The data acquisition terminal sends the processed data to the data storage and analysis module and the intelligent control module.

[0015] Furthermore, the data acquisition and transmission module includes a data acquisition interface unit, a data storage management unit, a data cleaning and integration unit, a data preprocessing unit and a data analysis engine unit. The data acquisition interface unit is responsible for communicating with the data acquisition and monitoring module, receiving the data transmitted by it, and providing data format conversion functions to ensure that the received data meets the internal processing requirements of the module. The data storage management unit is responsible for data storage and retrieval, managing databases or data warehouses, ensuring data security and integrity, and providing efficient data indexing and query mechanisms. The data cleaning and integration unit implements a data cleaning algorithm, removes invalid and erroneous data, integrates data from different data sources, and forms a unified data view. The data preprocessing unit preprocesses the data to improve the accuracy of data analysis and provides data smoothing, standardization, dimensionality reduction and other processing functions. The data analysis engine unit implements data analysis, such as trend analysis, anomaly detection, etc.

[0016] Furthermore, the model customization module includes a rule setting unit, a random forest customization unit and an irrigation and fertilization strategy adjustment unit. The rule setting unit receives growth data, environmental data and historical irrigation and fertilization data of different crops, formulates irrigation and fertilization rules according to different growth stages and characteristics of crops, and transmits the formulated rules to the irrigation and fertilization strategy adjustment unit. The random forest customization unit receives growth data, environmental data and historical irrigation and fertilization data of different crops, customizes different random forest models for different crops, uses customized models to analyze data, discovers the correlation between crop growth and water and fertilizer supply, and transmits the discovered correlation and adjustment suggestions to the irrigation and fertilization strategy adjustment unit. The irrigation and fertilization strategy adjustment unit receives information transmitted by the rule setting unit and the random forest model customization unit, comprehensively considers the results of rule setting and model prediction, and adjusts the irrigation and fertilization strategies.

[0017] Furthermore, the intelligent control module includes a data receiving unit, an intelligent control unit, an equipment control unit and an abnormal data identification unit. The data receiving unit is responsible for receiving data from the sensor network and the equipment control unit, and transmitting the received data to the intelligent control unit and the abnormal data identification unit. The intelligent control unit receives the data transmitted by the data receiving unit, and at the same time, accepts the random forest model provided by the model customization module, formulates the water-fertilizer ratio and irrigation plan, and transmits the control instruction to the equipment control unit. The equipment control unit receives the control instruction transmitted by the intelligent control unit, controls the switching and adjustment of the intelligent valve, irrigation pipeline network and water-fertilizer machine, and feeds back the operating status of the equipment and the information of the irrigation and fertilization process to the intelligent control unit and the abnormal processing and alarm unit in real time. The abnormal data identification unit receives the data transmitted by the data receiving unit, analyzes and judges the data according to preset rules and thresholds, identifies abnormal conditions or equipment failures, and transmits the identification results to the alarm and fault handling module.

[0018] Furthermore, the remote monitoring and management module includes a device management unit, a database unit and a management platform unit. The device management unit is responsible for the control and management of the intelligent valve, the irrigation pipe network and the water and fertilizer machine, receives the control instructions sent by the management platform unit, and executes the corresponding device operations, and transmits the operating status and feedback information of the equipment to the management platform of the remote monitoring and management module. The database unit stores the monitoring data of the irrigation area, the equipment operating status, the user operation record and other historical data, provides data query and export functions, and supports users to query the unit historical data through the management platform. The management platform unit provides a user interaction interface, receives the user's operation instructions, and transmits them to the device management unit for execution, and provides historical data query, alarm and notification functions;

[0019] Furthermore, the alarm and fault handling module includes an alarm information sending unit, a fault handling suggestion providing unit and a fault recording and tracking unit. The alarm information sending unit receives the identification result transmitted by the abnormal data identification unit, generates abnormal information according to the identification result, sends the abnormal information to relevant personnel according to the preset alarm method and recipient information, records the sending status of the abnormal information and the recipient feedback, and passes the abnormal information to the model customization module for model optimization. The fault handling suggestion providing unit receives the equipment fault information transmitted by the abnormal data identification unit, provides corresponding fault handling suggestions according to the fault type and the specific situation of the equipment, and passes the fault handling suggestions to the user or relevant maintenance personnel. The fault recording and tracking unit records each abnormal situation and related information of the fault handling, provides fault tracking and query functions, and facilitates users to view historical fault records.

[0020] Furthermore, the specific steps of the model customization module are:

[0021] Data collection and preprocessing: Collect growth data, environmental data, and historical irrigation and fertilization data of different crops, and preprocess them to ensure data accuracy and consistency;

[0022] Establishment of basic rule base: Establish a basic rule base based on the growth characteristics and needs of different crops, including irrigation rules, fertilization rules, etc.;

[0023] Random forest model training: Select features related to crop growth and water and fertilizer supply from the preprocessed data, such as soil moisture, soil nutrient content, meteorological conditions (such as temperature, humidity, light intensity), historical irrigation volume, fertilizer application amount, etc., to determine the target that the model needs to predict, such as crop growth rate, yield, quality, etc., or more directly, whether irrigation or fertilization is needed;

[0024] Constructing decision trees: Random forests consist of multiple decision trees, each of which is independently constructed based on a subset of the training data (obtained through bootstrap sampling). When constructing each tree, a portion of all features are randomly selected as candidate split features;

[0025] Splitting nodes: For each node of the decision tree, select the best feature for splitting to maximize the information gain. The calculation formula for information gain is:

[0026]

[0027] Where D is the data set, A is the feature, Values(A) is all possible values ​​of feature A, and D v It is a subset of the dataset where feature A takes the value v;

[0028] Integrated prediction: The final prediction result is determined through a voting mechanism;

[0029] Model evaluation and optimization: Use k-fold cross validation to evaluate the performance of the model and provide an assessment of feature importance, which helps understand which features have the greatest impact on the model's prediction results. Use grid search, random search and other methods to adjust the hyperparameters of the random forest (such as the number of trees, maximum depth, minimum number of sample splits, etc.) to optimize model performance.

[0030] Furthermore, the model customization module optimizes the rules and model specific steps according to the abnormal information:

[0031] Adjust rules: Analyze and adjust the irrigation rules and fertilization rules in the basic rule base based on abnormal information. For example, if it is detected that the soil moisture is continuously low, it may be necessary to increase the irrigation frequency or the irrigation amount.

[0032] Introducing new rules: In response to newly discovered abnormal situations, new rules are introduced to adapt to crop growth needs;

[0033] Update the data set: add abnormal data and its processing results (such as manually adjusted irrigation and fertilization strategies) to the training data set;

[0034] Retrain the model: Retrain the random forest model using the updated dataset so that it can learn new rules and patterns.

[0035] A control method for an intelligent water and fertilizer management system for crops, comprising the following steps:

[0036] Data collection: Soil moisture, soil conductivity, soil pH, nutrient content and meteorological conditions are collected in real time through the data collection and monitoring module, and the data is transmitted to the data storage and analysis module and the intelligent control module through the Internet of Things technology;

[0037] Data analysis and decision-making: The intelligent control module uses the model provided by the model customization module to analyze and process the collected data. Based on the analysis results, it automatically selects the optimal irrigation and fertilization strategy and generates corresponding control instructions.

[0038] Instruction issuance: The intelligent control module sends control instructions to the intelligent valve and water and fertilizer machine. The intelligent valve adjusts the flow and proportion of water and fertilizer according to the instructions to ensure accurate irrigation and fertilization;

[0039] Irrigation mode selection: According to the growth characteristics and needs of crops, the intelligent control module selects the appropriate irrigation mode (such as timed irrigation, remote irrigation, cycle irrigation, etc.). The intelligent control module automatically adjusts the irrigation strategy according to the preset irrigation plan and the actual growth of crops;

[0040] Remote monitoring: Users can view various monitoring data and equipment operation status of the irrigation area in real time through the remote monitoring and management module. Through the visual interface provided by the platform, users can intuitively understand the irrigation and fertilization conditions of the farmland;

[0041] Alarm processing: When an abnormal situation or equipment failure is detected, the alarm and fault processing module automatically sends out an alarm message. After receiving the alarm message, the user takes measures to ensure the normal growth of crops.

[0042] The present invention has the following beneficial effects:

[0043] 1. In the present invention, more refined irrigation and fertilization rules are formulated for different growth stages and characteristics of crops such as wheat and tomatoes. At the same time, different machine learning models are customized for different crops to automatically discover the correlation between crop growth and water and fertilizer supply. And dynamically adjust the irrigation and fertilization strategies according to the type and growth stage of the crop.

[0044] 2. In the present invention, the rules and models are optimized according to the abnormal information. Through the optimized rules and models, the system can more accurately control the amount and time of irrigation and fertilization, so as to meet the growth needs of crops and improve the yield and quality of crops. In addition, after introducing new rules and retraining the model, it can better adapt to different environments and crop types. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a system block diagram of an intelligent water and fertilizer management method and system for crops proposed by the present invention;

[0046] Figure 2 This is a method step diagram of an intelligent water and fertilizer management method and system for crops proposed by the present invention. DETAILED DESCRIPTION

[0047] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0048] See also Figure 1-Figure 2 As shown, the present invention is an intelligent water and fertilizer management system for crops, comprising:

[0049] Data acquisition and monitoring module: real-time monitoring of soil environmental data (soil moisture, soil conductivity, soil pH value, nutrient content) and meteorological conditions (temperature, humidity, light, etc.) through sensors (such as soil moisture sensors, nutrient sensors, meteorological sensors, etc.), and sending soil environmental data and meteorological conditions to the data storage and analysis module and the intelligent control module;

[0050] Data storage and analysis module: responsible for storing all data obtained from the data acquisition and monitoring module, and performing preliminary data cleaning, integration and preprocessing, providing data analysis functions such as data trend analysis, anomaly detection, etc., and providing the analyzed data to the remote monitoring and management module;

[0051] Model customization module: responsible for formulating irrigation and fertilization rules according to the different growth stages and characteristics of different crops. At the same time, based on the basic rule setting, different random forest models customized for different crops are introduced to optimize the irrigation and fertilization strategies. The random forest model discovers the correlation between crop growth and water and fertilizer supply, and adjusts the irrigation and fertilization strategies according to the correlation. The rules and models are optimized according to the abnormal information provided by the alarm and fault handling module.

[0052] Intelligent control module: Based on real-time monitoring data and crop growth requirements, combined with the model provided by the model customization module, the water-fertilizer ratio and irrigation plan are automatically adjusted, including the control of intelligent valves, irrigation pipe networks and water-fertilizer machines. The intelligent control module sends the adjusted water-fertilizer ratio and irrigation plan as well as the real-time operating status to the remote monitoring and management module. When adjusting the water-fertilizer ratio and irrigation plan, if the intelligent control module detects an abnormal situation or equipment failure, it will immediately send an alarm message to the alarm and fault handling module;

[0053] Remote monitoring and management module: allows users to view various monitoring data and equipment operation status of the irrigation area in real time through mobile phone APP, computer software and other management platforms, and perform remote operations and management, such as adjusting irrigation and fertilization parameters, viewing historical data, etc. Users adjust irrigation and fertilization parameters through the remote monitoring and management module, and the parameters are sent to the model customization module for updating. At the same time, the model customization module sends the customized random forest model and rules to the remote monitoring and management module for display and application;

[0054] Alarm and fault handling module: When an abnormal situation or equipment failure is detected, an alarm message is automatically issued and corresponding fault handling measures are taken. The fault handling measures include the identification of abnormal data, the sending of alarm information (such as SMS, email, APP push, etc.) and the provision of fault handling suggestions. The alarm and fault handling module sends the monitored abnormal information to the model customization module and sends the alarm information to the remote monitoring and management module.

[0055] In one embodiment, for the above-mentioned data acquisition and monitoring module, the data acquisition and monitoring module includes a sensor unit, an Internet of Things transmission device and a data acquisition terminal, which is responsible for directly measuring and collecting soil environmental data and meteorological conditions. The sensors include soil moisture sensors, nutrient sensors, meteorological sensors, etc., which can monitor in real time and convert them into electrical signals or digital signals for subsequent processing and transmission. The Internet of Things transmission device is responsible for transmitting the data collected by the sensor unit to the data acquisition terminal. The data acquisition terminal is responsible for receiving the data sent by the Internet of Things transmission device and performing preliminary processing and storage. The data acquisition terminal sends the processed data to the data storage and analysis module and the intelligent control module.

[0056] In one embodiment, for the above-mentioned data acquisition and transmission module, the data acquisition and transmission module includes a data acquisition interface unit, a data storage management unit, a data cleaning and integration unit, a data preprocessing unit and a data analysis engine unit. The data acquisition interface unit is responsible for communicating with the data acquisition and monitoring module, receiving the data transmitted by it, and providing data format conversion functions to ensure that the received data meets the internal processing requirements of the module. The data storage management unit is responsible for data storage and retrieval, managing the database or data warehouse, ensuring the security and integrity of the data, and providing an efficient data indexing and query mechanism. The data cleaning and integration unit implements a data cleaning algorithm, removes invalid and erroneous data, integrates data from different data sources, and forms a unified data view. The data preprocessing unit preprocesses the data to improve the accuracy of data analysis, and provides data smoothing, standardization, dimensionality reduction and other processing functions. The data analysis engine unit implements data analysis, such as trend analysis, anomaly detection, etc.

[0057] In one embodiment, for the above-mentioned model customization module, the model customization module includes a rule setting unit, a random forest customization unit and an irrigation and fertilization strategy adjustment unit. The rule setting unit receives growth data, environmental data and historical irrigation and fertilization data of different crops, formulates irrigation and fertilization rules according to different growth stages and characteristics of crops, and passes the formulated rules to the irrigation and fertilization strategy adjustment unit. The random forest customization unit receives growth data, environmental data and historical irrigation and fertilization data of different crops, customizes different random forest models for different crops, uses customized models to analyze data, discovers the correlation between crop growth and water and fertilizer supply, and passes the discovered correlation and adjustment suggestions to the irrigation and fertilization strategy adjustment unit. The irrigation and fertilization strategy adjustment unit receives the information transmitted by the rule setting unit and the random forest model customization unit, comprehensively considers the results of rule setting and model prediction, and adjusts the irrigation and fertilization strategies.

[0058] In one embodiment, for the above-mentioned intelligent control module, the intelligent control module includes a data receiving unit, an intelligent control unit, a device control unit and an abnormal data identification unit. The data receiving unit is responsible for receiving data from the sensor network and the device control unit, and transmitting the received data to the intelligent control unit and the abnormal data identification unit. The intelligent control unit receives the data transmitted by the data receiving unit, and at the same time, accepts the random forest model provided by the model customization module, formulates the water-fertilizer ratio and irrigation plan, and transmits the control instructions to the device control unit. The device control unit receives the control instructions transmitted by the intelligent control unit, controls the switching and adjustment of the intelligent valve, irrigation pipeline network and water-fertilizer machine, and feeds back the operating status of the equipment and the information of the irrigation and fertilization process to the intelligent control unit and the abnormal processing and alarm unit in real time. The abnormal data identification unit receives the data transmitted by the data receiving unit, analyzes and judges the data according to preset rules and thresholds, identifies abnormal conditions or equipment failures, and transmits the identification results to the alarm and fault handling module.

[0059] In one embodiment, for the above-mentioned remote monitoring and management module, the remote monitoring and management module includes a device management unit, a database unit and a management platform unit. The device management unit is responsible for the control and management of intelligent valves, irrigation pipe networks and water and fertilizer machines, receives control instructions sent by the management platform unit, and executes corresponding device operations, and transmits the operating status and feedback information of the equipment to the management platform of the remote monitoring and management module. The database unit stores historical data such as monitoring data of the irrigation area, equipment operating status, user operation records, etc., provides data query and export functions, and supports users to query unit historical data through the management platform. The management platform unit provides a user interaction interface, receives user operation instructions, and transmits them to the device management unit for execution, providing historical data query, alarm and notification functions.

[0060] In one embodiment, for the above-mentioned alarm and fault handling module, the alarm and fault handling module includes an alarm information sending unit, a fault handling suggestion providing unit, and a fault recording and tracking unit. The alarm information sending unit receives the recognition result transmitted by the abnormal data identification unit, generates abnormal information according to the recognition result, sends the abnormal information to relevant personnel according to the preset alarm method and recipient information, records the sending status of the abnormal information and the recipient feedback, and passes the abnormal information to the model customization module for model optimization. The fault handling suggestion providing unit receives the equipment fault information transmitted by the abnormal data identification unit, provides corresponding fault handling suggestions according to the fault type and the specific situation of the equipment, and passes the fault handling suggestions to the user or relevant maintenance personnel. The fault recording and tracking unit records each abnormal situation and related information of the fault handling, provides fault tracking and query functions, and facilitates users to view historical fault records.

[0061] In one embodiment, for the above-mentioned model customization module, the specific steps of the model customization module are:

[0062] Data collection and preprocessing: Collect growth data, environmental data, and historical irrigation and fertilization data of different crops, and preprocess them to ensure data accuracy and consistency;

[0063] Establishment of basic rule base: Establish a basic rule base based on the growth characteristics and needs of different crops, including irrigation rules, fertilization rules, etc.;

[0064] Random forest model training: Select features related to crop growth and water and fertilizer supply from the preprocessed data, such as soil moisture, soil nutrient content, meteorological conditions (such as temperature, humidity, light intensity), historical irrigation volume, fertilizer application amount, etc., to determine the target that the model needs to predict, such as crop growth rate, yield, quality, etc., or more directly, whether irrigation or fertilization is needed;

[0065] Constructing decision trees: Random forests consist of multiple decision trees, each of which is independently constructed based on a subset of the training data (obtained through bootstrap sampling). When constructing each tree, a portion of all features are randomly selected as candidate split features;

[0066] Splitting nodes: For each node of the decision tree, select the best feature for splitting to maximize the information gain. The calculation formula for information gain is:

[0067]

[0068] Where D is the data set, A is the feature, Values(A) is all possible values ​​of feature A, and D v It is a subset of the dataset where feature A takes the value v;

[0069] Integrated prediction: The final prediction result is determined through a voting mechanism;

[0070] Model evaluation and optimization: Use k-fold cross validation to evaluate the performance of the model and provide an assessment of feature importance, which helps understand which features have the greatest impact on the model's prediction results. Use grid search, random search and other methods to adjust the hyperparameters of the random forest (such as the number of trees, maximum depth, minimum number of sample splits, etc.) to optimize model performance.

[0071] In one embodiment, for the above-mentioned model customization module, the model customization module optimizes the rules and model specific steps according to the abnormal information:

[0072] Adjust rules: Analyze and adjust the irrigation rules and fertilization rules in the basic rule base based on abnormal information. For example, if it is detected that the soil moisture is continuously low, it may be necessary to increase the irrigation frequency or the irrigation amount.

[0073] Introducing new rules: In response to newly discovered abnormal situations, new rules are introduced to adapt to crop growth needs;

[0074] Update the data set: add abnormal data and its processing results (such as manually adjusted irrigation and fertilization strategies) to the training data set;

[0075] Retrain the model: Use the updated data set to retrain the random forest model so that it can learn new rules and patterns;

[0076] Assume that during wheat planting, the soil moisture detected by the sensor is continuously lower than the set threshold for a long time. Based on the abnormal information, we can make the following optimizations:

[0077] Rule optimization: Adjust irrigation rules, increase irrigation frequency or increase irrigation amount to ensure soil moisture remains within an appropriate range.

[0078] Model retraining: Add the data from this period (including abnormal data and manually adjusted irrigation strategies) to the training data set and retrain the random forest model. Through model retraining, the model can better learn the relationship between soil moisture and irrigation strategies, making it more accurate in future predictions.

[0079] A management method for an intelligent water and fertilizer management system for crops, comprising the following steps:

[0080] Data collection: Soil moisture, soil conductivity, soil pH, nutrient content and meteorological conditions are collected in real time through the data collection and monitoring module, and the data is transmitted to the data storage and analysis module and the intelligent control module through the Internet of Things technology;

[0081] Data analysis and decision-making: The intelligent control module uses the model provided by the model customization module to analyze and process the collected data. Based on the analysis results, it automatically selects the optimal irrigation and fertilization strategy and generates corresponding control instructions.

[0082] Instruction issuance: The intelligent control module sends control instructions to the intelligent valve and water and fertilizer machine. The intelligent valve adjusts the flow and proportion of water and fertilizer according to the instructions to ensure accurate irrigation and fertilization;

[0083] Irrigation mode selection: According to the growth characteristics and needs of crops, the intelligent control module selects the appropriate irrigation mode (such as timed irrigation, remote irrigation, cycle irrigation, etc.). The intelligent control module automatically adjusts the irrigation strategy according to the preset irrigation plan and the actual growth of crops;

[0084] Remote monitoring: Users can view various monitoring data and equipment operation status of the irrigation area in real time through the remote monitoring and management module. Through the visual interface provided by the platform, users can intuitively understand the irrigation and fertilization conditions of the farmland;

[0085] Alarm processing: When an abnormal situation or equipment failure is detected, the alarm and fault processing module automatically sends out an alarm message. After receiving the alarm message, the user takes measures to ensure the normal growth of crops.

[0086] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent water and fertilizer management system for crops, characterized in that: include: Data acquisition and monitoring module: real-time monitoring of soil environmental data and meteorological conditions through sensors, which are sent to the data storage and analysis module and the intelligent control module; Data storage and analysis module: responsible for storing all data obtained from the data acquisition and monitoring module, and performing preliminary data cleaning, integration and preprocessing, providing data analysis functions, providing the analyzed data to the remote monitoring and management module, and providing the stored historical data to the model customization module; Model customization module: responsible for formulating irrigation and fertilization rules according to the different growth stages and characteristics of different crops. At the same time, based on the basic rule setting, different random forest models customized for different crops are introduced to optimize the irrigation and fertilization strategies. The random forest model discovers the correlation between crop growth and water and fertilizer supply, and adjusts the irrigation and fertilization strategies according to the correlation. The rules and models are optimized according to the abnormal information provided by the alarm and fault handling module; Intelligent control module: According to real-time monitoring data and crop growth requirements, combined with the model provided by the model customization module, the water-fertilizer ratio and irrigation plan are automatically adjusted, including the control of intelligent valves, irrigation pipe networks and water-fertilizer machines. The intelligent control module sends the adjusted water-fertilizer ratio and irrigation plan as well as the real-time operating status to the remote monitoring and management module. When adjusting the water-fertilizer ratio and irrigation plan, if the intelligent control module detects an abnormal situation or equipment failure, it will immediately send an alarm message to the alarm and fault handling module; Remote monitoring and management module: allows users to view various monitoring data and equipment operation status of the irrigation area in real time through the management platform, and perform remote operation and management. Users adjust irrigation and fertilization parameters through the remote monitoring and management module, and the parameters are sent to the model customization module for update. At the same time, the model customization module sends the customized random forest model and rules to the remote monitoring and management module for display and application; Alarm and fault handling module: When an abnormal situation or equipment failure is monitored, an alarm message is automatically issued and corresponding fault handling measures are taken. The fault handling measures include the identification of abnormal data, the sending of alarm information and the provision of fault handling suggestions. The alarm and fault handling module sends the monitored abnormal information to the model customization module and sends the alarm information to the remote monitoring and management module.

2. The intelligent water and fertilizer management system for crops according to claim 1, characterized in that: The data acquisition and monitoring module includes a sensor unit, an Internet of Things transmission device and a data acquisition terminal. The sensor unit is responsible for directly measuring and collecting soil environmental data and meteorological conditions. The Internet of Things transmission device is responsible for transmitting the data collected by the sensor unit to the data acquisition terminal. The data acquisition terminal is responsible for receiving the data sent by the Internet of Things transmission device and performing preliminary processing and storage. The data acquisition terminal sends the processed data to the data storage and analysis module and the intelligent control module.

3. The intelligent water and fertilizer management system for crops according to claim 1 is characterized in that: The data acquisition and transmission module includes a data acquisition interface unit, a data storage management unit, a data cleaning and integration unit, a data preprocessing unit and a data analysis engine unit. The data acquisition interface unit is responsible for communicating with the data acquisition and monitoring module and receiving the data transmitted by it. The data storage management unit is responsible for data storage and retrieval and managing databases or data warehouses. The data cleaning and integration unit implements a data cleaning algorithm, removes invalid and erroneous data, integrates data from different data sources, and forms a unified data view. The data preprocessing unit preprocesses the data, and the data analysis engine unit implements data analysis.

4. The intelligent water and fertilizer management system for crops according to claim 1, characterized in that: The model customization module includes a rule setting unit, a random forest customization unit and an irrigation and fertilization strategy adjustment unit. The rule setting unit receives growth data, environmental data and historical irrigation and fertilization data of different crops, formulates irrigation and fertilization rules according to different growth stages and characteristics of crops, and transmits the formulated rules to the irrigation and fertilization strategy adjustment unit. The random forest customization unit receives growth data, environmental data and historical irrigation and fertilization data of different crops, customizes different random forest models for different crops, uses customized models to analyze data, discovers the correlation between crop growth and water and fertilizer supply, and transmits the discovered correlation and adjustment suggestions to the irrigation and fertilization strategy adjustment unit. The irrigation and fertilization strategy adjustment unit receives information transmitted by the rule setting unit and the random forest model customization unit, comprehensively considers the results of rule setting and model prediction, and adjusts the irrigation and fertilization strategies.

5. The intelligent water and fertilizer management system for crops according to claim 4 is characterized in that: The intelligent control module includes a data receiving unit, an intelligent control unit, an equipment control unit and an abnormal data identification unit. The data receiving unit is responsible for receiving data from the sensor network and the equipment control unit, and transmitting the received data to the intelligent control unit and the abnormal data identification unit. The intelligent control unit receives the data transmitted by the data receiving unit, and at the same time, accepts the random forest model provided by the model customization module, formulates the water-fertilizer ratio and irrigation plan, and transmits the control instruction to the equipment control unit. The equipment control unit receives the control instruction transmitted by the intelligent control unit, controls the switching and adjustment of the intelligent valve, irrigation pipeline network and water-fertilizer machine, and feeds back the operating status of the equipment and the information of the irrigation and fertilization process to the intelligent control unit and the abnormal processing and alarm unit in real time. The abnormal data identification unit receives the data transmitted by the data receiving unit, analyzes and judges the data according to preset rules and thresholds, identifies abnormal conditions or equipment failures, and transmits the identification results to the alarm and fault handling module.

6. The intelligent water and fertilizer management system for crops according to claim 1, characterized in that: The remote monitoring and management module includes a device management unit, a database unit and a management platform unit. The device management unit is responsible for the control and management of intelligent valves, irrigation pipe networks and water and fertilizer machines, receives control instructions sent by the management platform unit, and executes corresponding device operations, and transmits the operating status and feedback information of the equipment to the management platform of the remote monitoring and management module. The database unit stores historical data of the irrigation area, provides data query and export functions, and supports users to query unit historical data through the management platform. The management platform unit provides a user interaction interface, receives user operation instructions, and transmits them to the device management unit for execution.

7. The intelligent water and fertilizer management system for crops according to claim 5, characterized in that: The alarm and fault handling module includes an alarm information sending unit, a fault handling suggestion providing unit and a fault recording and tracking unit. The alarm information sending unit receives the recognition result transmitted by the abnormal data identification unit, generates abnormal information according to the recognition result, sends the abnormal information to relevant personnel, records the sending status of the abnormal information and the feedback of the recipient, and transmits the abnormal information to the model customization module for model optimization. The fault handling suggestion providing unit receives the equipment fault information transmitted by the abnormal data identification unit, provides corresponding fault handling suggestions according to the fault type and the specific situation of the equipment, and transmits the fault handling suggestions to the user or relevant maintenance personnel. The fault recording and tracking unit records each abnormal situation and related information of the fault handling, and provides fault tracking and query functions.

8. The intelligent water and fertilizer management system for crops according to claim 4, characterized in that: The specific steps of the model customization module are: Data collection and preprocessing: Collect growth data, environmental data, and historical irrigation and fertilization data of different crops and perform preprocessing; Establishment of basic rule base: Establish a basic rule base according to the growth characteristics and needs of different crops; Random forest model training: Select features related to crop growth and water and fertilizer supply from the preprocessed data to determine the target that the model needs to predict; Constructing decision trees: Random forests consist of multiple decision trees, each of which is independently constructed based on a subset of the training data. When constructing each tree, a portion of all features is randomly selected as candidate split features; Splitting nodes: For each node of the decision tree, select the best feature for splitting to maximize the information gain. The calculation formula for information gain is: Where D is the data set, A is the feature, Values(A) is all possible values ​​of feature A, and D υ It is a subset of the data set where feature A takes the value of υ; Integrated prediction: The final prediction result is determined through a voting mechanism; Model evaluation and optimization: Use k-fold cross validation to evaluate the performance of the model, provide an assessment of feature importance, and optimize model performance by adjusting the hyperparameters of the random forest.

9. The intelligent water and fertilizer management system for crops according to claim 4, characterized in that: The model customization module optimizes the rules and models according to the abnormal information: Adjust rules: Analyze and adjust the irrigation rules and fertilization rules in the basic rule base according to abnormal information; Introducing new rules: In response to newly discovered abnormal situations, new rules are introduced to adapt to crop growth needs; Update the data set: add the abnormal data and its processing results to the training data set; Retrain the model: Retrain the random forest model using the updated dataset so that it can learn new rules and patterns.

10. The management method of the intelligent water and fertilizer management system for crops according to claim 1, characterized in that: The following steps are involved: Data collection: Soil moisture, soil conductivity, soil pH, nutrient content and meteorological conditions are collected in real time through the data collection and monitoring module, and the data is transmitted to the data storage and analysis module and the intelligent control module through the Internet of Things technology; Data analysis and decision-making: The intelligent control module uses the model provided by the model customization module to analyze and process the collected data. Based on the analysis results, it automatically selects the optimal irrigation and fertilization strategy and generates corresponding control instructions. Instruction issuance: The intelligent control module sends control instructions to the intelligent valve and water and fertilizer machine, and the intelligent valve adjusts the flow and proportion of water and fertilizer according to the instructions; Irrigation mode selection: According to the growth characteristics and needs of crops, the intelligent control module selects the appropriate irrigation mode. The intelligent control module automatically adjusts the irrigation strategy according to the preset irrigation plan and the actual growth of crops; Remote monitoring: Users can view various monitoring data of the irrigation area and the operating status of the equipment in real time through the remote monitoring and management module; Alarm processing: When an abnormal situation or equipment failure is monitored, the alarm and fault processing module automatically sends an alarm message. After receiving the alarm message, the user takes measures to handle it. At the same time, the alarm and fault processing module sends the abnormal information to the model customization module to optimize the rules and models.

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