Paddy field aquaculture intelligent management system based on Internet of Things

Abstract

The invention discloses a rice field aquaculture intelligent management system based on the Internet of Things, and relates to the technical field of intelligent management. During operation of the system, various environmental parameters in a rice field are monitored and collected in real time by deploying an Internet of Things sensor network, and water quality changes and other factors of a farmland environment are monitored; water quality adjusting equipment is controlled to adjust a rice field water body, automatic adjustment is achieved through a feedback control mechanism, water quality changes are responded in real time, a crop growth monitoring sensor and a camera are installed, the growth state of rice is monitored in real time, visual feedback of the growth state is provided through the image recognition technology and the data analysis technology, and the rice field water quality monitoring system is established. The automatic irrigation system is used for controlling the water volume of the paddy field, automatically adjusting the irrigation frequency and the water volume, calculating the comprehensive benefit index TGI of the paddy field by using the data mining and statistical analysis technology, monitoring the environmental parameters of the paddy field and the growth state of crops in real time, identifying abnormal conditions and timely giving out early warning.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management technology, and in particular to an intelligent management system for rice field aquaculture based on the Internet of Things. Background Art

[0002] Paddy field aquaculture, as an integrated agricultural model that combines rice cultivation and aquaculture, has been widely used in agricultural production in recent years. This model not only improves the efficiency of land use, but also provides farmers with a diversified source of income. In the process of combining rice and aquaculture, farmland environment, crop growth and water quality regulation affect each other, requiring managers to carry out refined control under the influence of multiple factors. However, the traditional paddy field aquaculture management model still has great limitations, mainly reflected in the insufficient real-time monitoring of environmental parameters, the lag of control measures and the lack of intelligent data analysis.

[0003] The current management model of rice field aquaculture relies on manual experience and traditional monitoring methods, which leads to low management efficiency, waste of resources and failure to maximize production benefits. Due to the lag in manual monitoring, it is often difficult to detect environmental changes or crop growth problems in a timely manner. If these problems are not solved in a timely manner, they may lead to abnormal rice growth and even affect the final yield. For example, when the water quality is unstable, it may lead to insufficient oxygen in the rice roots, which in turn affects the growth of crops; at the same time, if the irrigation system fails to respond to changes in soil moisture in real time, it may cause too much or too little water, resulting in water shortage or waterlogging in the crop roots, affecting the yield. Due to the lack of intelligent and automated management tools, many farmers or managers can only rely on experience or visual inspection to judge the growth status of crops, resulting in slow discovery and resolution of problems, and even missing the best time for intervention. In addition, due to the lag in information transmission, agricultural management decisions are often lacking in accurate data support, which reduces the scientificity and effectiveness of the decisions. Due to these management blind spots and delayed responses, the benefits of rice farming are lost, and in severe cases, rice death or the collapse of the entire rice field ecosystem may occur. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an intelligent management system for rice field aquaculture based on the Internet of Things, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an intelligent management system for rice field aquaculture based on the Internet of Things, including an environmental monitoring module, a water quality control module, a crop growth monitoring module, an automatic irrigation module, a benefit analysis module and a remote management module;

[0006] The environmental monitoring module is used to monitor and collect various environmental parameters in the rice fields in real time by deploying an Internet of Things sensor network, monitoring water quality changes and other factors of the farmland environment;

[0007] The water quality control module is used to control the water quality regulating equipment to regulate the water body in the paddy field based on the real-time data collected by the environmental monitoring module, so as to ensure the stability of water quality and meet the needs of rice growth. It realizes automatic regulation through the feedback control mechanism, responds to water quality changes in real time, and avoids sharp fluctuations in water quality.

[0008] The crop growth monitoring module is used to monitor the growth status of rice in real time by installing crop growth monitoring sensors and cameras, including plant height, leaf color and photosynthesis efficiency indicators, to help identify abnormal growth, water or fertilizer shortage problems, and to provide visual feedback on the growth status through image recognition technology and data analysis technology;

[0009] The automatic irrigation module is used to control the amount of water in the rice field through an automatic irrigation system based on the data of the environmental monitoring module, and automatically adjust the irrigation frequency and water amount according to soil moisture, climate change and crop demand;

[0010] The benefit analysis module is used to integrate the data of the environment monitoring module, the crop growth monitoring module and the water quality control module, including the crop growth index, land productivity and irrigation efficiency, and calculate the rice field comprehensive benefit index TGI reflecting the rice growth and breeding benefits by using data mining and statistical analysis techniques;

[0011] The remote management module is used to monitor paddy field environmental parameters and crop growth status in real time, identify abnormal situations and issue early warnings in a timely manner, prompt farmers to take timely measures, perform fault diagnosis through data analysis, and optimize the rice planting and management process.

[0012] Preferably, the environmental monitoring module includes a soil environment monitoring unit, a crop growth health monitoring unit and a crop environment regulation capability monitoring unit;

[0013] The soil environment monitoring unit is used to monitor the moisture, pH, temperature and nutrient content of the soil in real time through soil moisture sensors, pH sensors, temperature sensors and nutrient sensors, measure soil quality, fertilization conditions and crop yields, and obtain: current rice field yield Y, maximum yield Y max , soil quality index T soil and reference soil quality index T soil,ref ;

[0014] The crop growth health monitoring unit is used to monitor the growth status, leaf color, photosynthesis and growth rate of crops using cameras and sensors, and obtain: the current height H of the crop, the reference height Href , crop leaf area L, reference leaf area L ref , current photosynthesis efficiency P and maximum photosynthesis efficiency P max ;

[0015] The crop environment regulation capability monitoring unit is used to monitor and adjust the environmental response time, accuracy, frequency and energy efficiency through the irrigation system and environmental control equipment, including the automatic ventilation and temperature control system in the greenhouse, to obtain: the ideal amount of water W required for crop growth needed , actual irrigation water volume W actual , the amount of irrigation water supplemented by actual precipitation W rain , precipitation E rain and ideal precipitation E ideal .

[0016] Preferably, the water quality control module includes a water quality parameter monitoring unit and a water quality regulating unit;

[0017] The water quality parameter monitoring unit is used to configure a group of water quality sensors in the paddy field water body to monitor the key water quality indicators of the water body in real time, including pH value, dissolved oxygen, ammonia nitrogen content and temperature;

[0018] The water quality regulation unit is used to manage and control water quality regulation equipment, including aerators and dosing equipment. According to the water quality data fed back by the environmental monitoring module, the equipment is automatically or timed to start regulating the dissolved oxygen and pH parameters of the water body to maintain the stability of the water quality. The water quality data is analyzed through a data processing algorithm to identify the trend of water quality changes and potential problems. Based on the analysis results, the system can automatically issue control instructions.

[0019] Preferably, the crop growth monitoring module includes a crop growth image recognition unit and a crop growth trend prediction unit;

[0020] The crop growth image recognition unit is used to collect rice growth data in real time, including plant height, leaf color and photosynthesis efficiency, by deploying crop growth monitoring equipment, including sensors and cameras, and process image data from the camera through an image recognition algorithm to analyze the growth status of rice, identify the causes of leaf lesions, water shortage or fertilizer shortage, generate a specific growth status report, and provide farmers with real-time feedback on the growth status;

[0021] The crop growth trend prediction unit is used to perform trend prediction on the collected crop growth data based on data analysis and machine learning technology, and predict future growth trends and upcoming pest and disease threats by analyzing historical data and current growth status.

[0022] Preferably, the automatic irrigation module includes an automatic irrigation control unit and an irrigation effect feedback unit;

[0023] The automatic irrigation control unit is used to evaluate the irrigation needs of crops based on the soil moisture and climate change data provided by the environmental monitoring module and the rice growth status, and automatically control the start and stop status, irrigation duration, irrigation water volume and irrigation frequency of the irrigation system;

[0024] The irrigation effect feedback unit is used to evaluate the irrigation effect through sensors and monitoring equipment, and feed back to the control system to ensure the accuracy and efficiency of the irrigation system operation. During the irrigation process, if it is found that the soil moisture does not meet the preset standard, the automatic irrigation control unit will automatically adjust the irrigation strategy to ensure that the irrigation effect meets the target requirements.

[0025] Preferably, the benefit analysis module includes a data preprocessing unit and a data modeling unit;

[0026] The data preprocessing unit is used to organize the data from the environment monitoring module, the crop growth monitoring module and the water quality control module, perform preprocessing work such as data cleaning, denoising and missing value filling, filter out abnormal data, and standardize the data formats obtained by different sensors;

[0027] The data modeling unit is used to analyze the integrated data using statistical methods, machine learning models or deep learning models, and calculate multiple key indicators by establishing mathematical models and prediction algorithms, including the crop growth index CGR, the land productivity index LPI, the irrigation efficiency index IEI and the rice field benefit index TGI.

[0028] Preferably, the crop growth index CGR is calculated by the following formula:

[0029]

[0030] Where H represents the current height of the crop, H ref represents the reference height, L represents the crop leaf area, L ref represents the reference leaf area, P represents the current photosynthesis efficiency, P max Represents the maximum photosynthesis efficiency.

[0031] Preferably, the land productivity index LPI is calculated by the following formula:

[0032]

[0033] In the formula, Y represents the current rice field yield, Y max Indicates the maximum output, T soil represents soil quality index, T soil,ref Denotes the reference soil quality index.

[0034] Preferably, the irrigation efficiency index IEI is calculated by the following formula:

[0035]

[0036] Where W needed Indicates the ideal amount of water required for crop growth, W actual Indicates the actual irrigation water volume, E rain Indicates precipitation, E ideal represents the ideal precipitation, W rain represents the irrigation water volume supplemented by the actual precipitation, λ1, λ2 and λ3 represent the weight factors of various parameters respectively;

[0037] The paddy field benefit index TGI is calculated by the following formula:

[0038] TGI=w1*CGR+w2*LPI+w3*IEI;

[0039] Where CGR stands for crop growth index, LPI stands for land productivity index, IEI stands for irrigation efficiency index, and TGI stands for rice field benefit index.

[0040] Preferably, the remote management module includes an early warning unit and a remote control unit;

[0041] The early warning unit is used to compare the rice field benefit index TGI with the preset first qualified threshold value A and second qualified threshold value B to obtain a grade evaluation scheme and issue an early warning;

[0042] When TGI>A, it means that the management and growth environment of the rice field are in an ideal state. At this time, there is no need for large-scale intervention. The rice field management status is qualified. Artificial intelligence and big data analysis can be used to further optimize the irrigation frequency and water volume to reduce consumption.

[0043] When A≥TGI>B, it means that the overall management and environmental conditions of the rice field are 20% away from the qualified level. Water quality control should be strengthened to avoid excessive fluctuations in water quality that affect rice growth. The automatic irrigation system needs to adjust the irrigation volume according to soil moisture and climate change. Image recognition technology should be used to detect abnormal plant growth in a timely manner to supplement pesticides or fertilizers.

[0044] When TGI≤B, it means that there are abnormalities in the management and growth environment of the rice field, and the growth of rice is affected. At this time, the rice field management is in poor condition and systemic intervention is urgently needed. There are serious water quality problems, stagnant crop growth or waste of resources. Aeration equipment should be activated, drainage system should be adjusted, and data analysis should be used to assess whether the crops are short of water and fertilizer. The amount of irrigation and fertilizer should be adjusted, the monitoring of crop diseases and pests should be strengthened, and pesticide spraying and pest control should be carried out regularly.

[0045] The remote control unit is used to allow farmers or managers to access rice field data at any location through a cloud platform or a remote management system, and to perform system settings and control operations, including adjusting the irrigation system and regulating water quality equipment, to ensure that managers can control the operating status of the rice fields anytime and anywhere.

[0046] The present invention provides an intelligent management system for rice field aquaculture based on the Internet of Things, which has the following beneficial effects:

[0047] (1) When the system is running, the Internet of Things sensor network is deployed to monitor and collect various environmental parameters in the rice fields in real time, monitor water quality changes and other factors of the farmland environment, regulate the water body of the rice fields by controlling the water quality regulation equipment, realize automatic regulation through the feedback control mechanism, respond to water quality changes in real time, install crop growth monitoring sensors and cameras, monitor the growth status of rice in real time to help identify abnormal growth problems, provide visual feedback on the growth status through image recognition technology and data analysis technology, control the water volume of the rice fields through the automatic irrigation system based on the data of the environmental monitoring module, and automatically adjust the irrigation frequency and water volume. Using data mining and statistical analysis technology, the comprehensive benefit index of the rice field TGI is calculated, the environmental parameters of the rice fields and the growth status of the crops are monitored in real time, abnormal situations are identified and early warnings are issued in time, prompting farmers to take timely measures, and fault diagnosis is performed through data analysis to optimize the rice planting and management process.

[0048] (2) Through the six modules of the paddy field aquaculture intelligent management system based on the Internet of Things, the management method of paddy fields has been comprehensively upgraded to intelligent ones. The collaborative work of the environmental monitoring module, crop growth monitoring module, water quality control module, automatic irrigation module, benefit analysis module and remote management module realizes the comprehensive monitoring and precise control of the paddy field environment, crop growth and water quality. The seamless connection of data between modules makes paddy field management more precise. Farmers can understand the environmental changes and crop status of paddy fields in real time. The system automatically responds to changes in environmental parameters, avoiding delays and errors caused by human operations. This intelligent and automated management method not only improves management efficiency, but also greatly reduces manpower input, realizing precise breeding and resource optimization of paddy fields.

[0049] (3) Compared with the traditional rice field aquaculture management method, the intelligent management system based on the Internet of Things solves many of the shortcomings of the traditional model. In the past, rice field management relied on manual experience and regular inspections, environmental monitoring methods lagged behind, and lacked a real-time feedback mechanism, resulting in many potential problems not being discovered and solved in a timely manner. However, through real-time data collection and feedback, this system can timely identify and adjust key issues such as water quality fluctuations and soil moisture changes, avoiding common problems such as excessive or insufficient irrigation and soil quality degradation. Especially in terms of water quality regulation, the system automatically adjusts the dissolved oxygen and pH value of the water body, effectively avoiding the negative impact of unstable water quality on rice growth. In addition, the introduction of automatic irrigation and crop growth monitoring modules not only reduces the waste of water resources, but also ensures that rice can grow under optimal environmental conditions, thereby improving crop yield and quality.

[0050] (4) Through the benefit analysis module, the system can integrate data from various modules and accurately calculate the comprehensive benefit index TGI of rice fields, thereby providing farmers with a scientific basis for decision-making. This data-driven decision-making method improves the scientificity and efficiency of management, helping farmers adjust management strategies based on real-time data and optimize resource allocation. In addition, the system can issue real-time warnings and intervene through the remote management module to ensure that timely measures are taken when abnormalities occur, effectively avoiding large-scale losses. Compared with traditional methods, this management method based on the Internet of Things and intelligent technology not only improves crop yield and quality, but also reduces negative impacts on the environment and promotes the sustainable development of rice field aquaculture. Through these improvements, the management level and production efficiency of rice fields have been significantly improved, farmers' income has grown steadily, and it has also provided strong support for the development of agricultural science and technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a block diagram of an intelligent management system for rice field aquaculture based on the Internet of Things of the present invention; DETAILED DESCRIPTION

[0052] 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.

[0053] Example 1

[0054] The present invention provides an intelligent management system for rice field aquaculture based on the Internet of Things. Figure 1 , including environmental monitoring module, water quality control module, crop growth monitoring module, automatic irrigation module, benefit analysis module and remote management module;

[0055] The environmental monitoring module is used to monitor and collect various environmental parameters in the rice fields in real time by deploying an Internet of Things sensor network, monitoring water quality changes and other factors of the farmland environment;

[0056] The water quality control module is used to control the water quality regulating equipment to regulate the water body in the paddy field based on the real-time data collected by the environmental monitoring module, so as to ensure the stability of water quality and meet the needs of rice growth. It realizes automatic regulation through the feedback control mechanism, responds to water quality changes in real time, and avoids sharp fluctuations in water quality.

[0057] The crop growth monitoring module is used to monitor the growth status of rice in real time by installing crop growth monitoring sensors and cameras, including plant height, leaf color and photosynthesis efficiency indicators, to help identify abnormal growth, water or fertilizer shortage problems, and to provide visual feedback on the growth status through image recognition technology and data analysis technology;

[0058] The automatic irrigation module is used to control the amount of water in the rice field through an automatic irrigation system based on the data of the environmental monitoring module, and automatically adjust the irrigation frequency and water amount according to soil moisture, climate change and crop demand;

[0059] The benefit analysis module is used to integrate the data of the environment monitoring module, the crop growth monitoring module and the water quality control module, including the crop growth index, land productivity and irrigation efficiency, and calculate the rice field comprehensive benefit index TGI reflecting the rice growth and breeding benefits by using data mining and statistical analysis techniques;

[0060] The remote management module is used to monitor paddy field environmental parameters and crop growth status in real time, identify abnormal situations and issue early warnings in a timely manner, prompt farmers to take timely measures, perform fault diagnosis through data analysis, and optimize the rice planting and management process.

[0061] In this embodiment, by deploying an Internet of Things sensor network, various environmental parameters in the rice field are monitored and collected in real time, water quality changes and other factors of the farmland environment are monitored, the water body of the rice field is regulated by controlling the water quality regulating equipment, and automatic regulation is achieved through a feedback control mechanism to respond to water quality changes in real time. By installing crop growth monitoring sensors and cameras, the growth status of rice is monitored in real time to help identify abnormal growth problems. Through image recognition technology and data analysis technology, visual feedback on the growth status is provided. Based on the data of the environmental monitoring module, the water volume of the rice field is controlled by an automatic irrigation system, and the irrigation frequency and water volume are automatically adjusted. Using data mining and statistical analysis technology, the comprehensive benefit index TGI of the rice field is calculated, the environmental parameters of the rice field and the growth status of the crop are monitored in real time, abnormal situations are identified and early warnings are issued in time, prompting farmers to take timely measures, and fault diagnosis is performed through data analysis to optimize the rice planting and management process.

[0062] Example 2

[0063] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the environmental monitoring module includes a soil environment monitoring unit, a crop growth health monitoring unit and a crop environment regulation ability monitoring unit;

[0064] The soil environment monitoring unit is used to monitor the moisture, pH, temperature and nutrient content of the soil in real time through soil moisture sensors, pH sensors, temperature sensors and nutrient sensors, measure soil quality, fertilization conditions and crop yields, and obtain: current rice field yield Y, maximum yield Y max , soil quality index T soil and reference soil quality index T soil,ref ;

[0065] The crop growth health monitoring unit is used to monitor the growth status, leaf color, photosynthesis and growth rate of crops using cameras and sensors, and obtain: the current height H of the crop, the reference height H ref , crop leaf area L, reference leaf area L ref , current photosynthesis efficiency P and maximum photosynthesis efficiency P max ;

[0066] The crop environment regulation capability monitoring unit is used to monitor and adjust the environmental response time, accuracy, frequency and energy efficiency through the irrigation system and environmental control equipment, including the automatic ventilation and temperature control system in the greenhouse, to obtain: the ideal amount of water W required for crop growth needed , actual irrigation water volume W actual , the amount of irrigation water supplemented by actual precipitation W rain , precipitation E rain and ideal precipitation E ideal.

[0067] The water quality control module includes a water quality parameter monitoring unit and a water quality adjustment unit;

[0068] The water quality parameter monitoring unit is used to configure a group of water quality sensors in the paddy field water body to monitor the key water quality indicators of the water body in real time, including pH value, dissolved oxygen, ammonia nitrogen content and temperature;

[0069] The water quality regulation unit is used to manage and control water quality regulation equipment, including aerators and dosing equipment. According to the water quality data fed back by the environmental monitoring module, the equipment is automatically or timed to start regulating the dissolved oxygen and pH parameters of the water body to maintain the stability of the water quality. The water quality data is analyzed through a data processing algorithm to identify the trend of water quality changes and potential problems. Based on the analysis results, the system can automatically issue control instructions.

[0070] In this embodiment, the combination of the environmental monitoring module and the water quality control module realizes the precise monitoring and dynamic adjustment of the paddy field environment. Through the real-time data collection of the soil environment monitoring unit, the crop growth health monitoring unit and the crop environment control ability monitoring unit, the system can comprehensively evaluate the soil condition, crop growth health and environmental control ability of the paddy field. For example, the monitoring data of soil moisture, pH value, temperature and nutrient content can accurately evaluate the health of the soil, help optimize fertilization strategies and improve soil utilization; the crop growth monitoring unit provides accurate growth status feedback by tracking data such as crop height, leaf area, photosynthesis efficiency, etc., so as to facilitate the timely detection of problems such as pests and diseases, lack of water or fertilizer, and take targeted measures.

[0071] Example 3

[0072] This embodiment is explained in Example 1, please refer to Figure 1 , specifically: the crop growth monitoring module includes a crop growth image recognition unit and a crop growth trend prediction unit;

[0073] The crop growth image recognition unit is used to collect rice growth data in real time, including plant height, leaf color and photosynthesis efficiency, by deploying crop growth monitoring equipment, including sensors and cameras, and process image data from the camera through an image recognition algorithm to analyze the growth status of rice, identify the causes of leaf lesions, water shortage or fertilizer shortage, generate a specific growth status report, and provide farmers with real-time feedback on the growth status;

[0074] The crop growth trend prediction unit is used to perform trend prediction on the collected crop growth data based on data analysis and machine learning technology, and predict future growth trends and upcoming pest and disease threats by analyzing historical data and current growth status.

[0075] The automatic irrigation module includes an automatic irrigation control unit and an irrigation effect feedback unit;

[0076] The automatic irrigation control unit is used to evaluate the irrigation needs of crops based on the soil moisture and climate change data provided by the environmental monitoring module and the rice growth status, and automatically control the start and stop status, irrigation duration, irrigation water volume and irrigation frequency of the irrigation system;

[0077] The irrigation effect feedback unit is used to evaluate the irrigation effect through sensors and monitoring equipment, and feed back to the control system to ensure the accuracy and efficiency of the irrigation system operation. During the irrigation process, if it is found that the soil moisture does not meet the preset standard, the automatic irrigation control unit will automatically adjust the irrigation strategy to ensure that the irrigation effect meets the target requirements.

[0078] In this embodiment, the coordinated work of the crop growth monitoring module and the automatic irrigation module greatly improves the monitoring accuracy and irrigation efficiency during the growth of rice. The crop growth image recognition unit collects growth data such as plant height, leaf color, photosynthesis efficiency, etc. in real time, and uses image recognition technology to analyze these data, which can accurately determine the growth status of rice and promptly discover problems such as leaf lesions, lack of water or lack of fertilizer. This intelligent monitoring not only reduces the workload of manual inspection, but also identifies potential risks in advance, provides farmers with accurate growth status reports, helps to formulate reasonable management plans, and improves the stability and yield of rice production. In addition, the crop growth trend prediction unit analyzes and predicts the historical growth data and current status of crops based on big data analysis and machine learning algorithms, and can accurately determine future growth trends and possible risks of pests and diseases. Through this trend prediction, farmers can take preventive measures in advance to avoid losses caused by environmental changes or pests and diseases, and ensure that the growth cycle of rice is smooth. Crop health management becomes more efficient and forward-looking, providing more intelligent decision support for rice field management.

[0079] Example 4

[0080] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the benefit analysis module includes a data preprocessing unit and a data modeling unit;

[0081] The data preprocessing unit is used to organize the data from the environment monitoring module, the crop growth monitoring module and the water quality control module, perform preprocessing work such as data cleaning, denoising and missing value filling, filter out abnormal data, and standardize the data formats obtained by different sensors;

[0082] The data modeling unit is used to analyze the integrated data using statistical methods, machine learning models or deep learning models, and calculate multiple key indicators by establishing mathematical models and prediction algorithms, including the crop growth index CGR, the land productivity index LPI, the irrigation efficiency index IEI and the rice field benefit index TGI.

[0083] The crop growth index CGR is calculated by the following formula:

[0084]

[0085] Where H represents the current height of the crop, H ref represents the reference height, L represents the crop leaf area, L ref represents the reference leaf area, P represents the current photosynthesis efficiency, P max Represents the maximum photosynthesis efficiency.

[0086] The land productivity index LPI is calculated by the following formula:

[0087]

[0088] In the formula, Y represents the current rice field yield, Y max Indicates the maximum output, T soil represents soil quality index, T soil,ref Denotes the reference soil quality index.

[0089] The irrigation efficiency index IEI is calculated by the following formula:

[0090]

[0091] Where W needed Indicates the ideal amount of water required for crop growth, W actual Indicates the actual irrigation water volume, E rain Indicates precipitation, E ideal represents the ideal precipitation, W rain represents the amount of irrigation water supplemented by actual precipitation, λ1, λ2 and λ3 represent the weight factors of various parameters respectively;

[0092] The paddy field benefit index TGI is calculated by the following formula:

[0093] TGI=w1*CGR+w2*LPI+w3*IEI;

[0094] Where CGR stands for crop growth index, LPI stands for land productivity index, IEI stands for irrigation efficiency index, and TGI stands for rice field benefit index.

[0095] In this embodiment, the introduction of the benefit analysis module provides a data-driven decision support system for rice field management, which can comprehensively evaluate the production benefits of rice fields and provide farmers with accurate optimization suggestions. The data preprocessing unit ensures the accuracy and consistency of the data by cleaning, denoising and standardizing the data from the environmental monitoring module, the crop growth monitoring module and the water quality control module. This process effectively avoids errors caused by sensor failure or data anomalies, thereby laying a solid foundation for subsequent benefit analysis. The data modeling unit uses advanced statistical and machine learning methods to integrate and analyze the processed data, establish a mathematical model and calculate multiple key indicators, including the crop growth index CGR, the land productivity index LPI, the irrigation efficiency index IEI and the rice field benefit index TGI. These indicators can fully reflect the production status of rice fields, from the growth of crops to the productivity of land, and then to the efficiency of the irrigation system, providing a quantitative benefit evaluation. For example, through the calculation of the crop growth index CGR, the system can reflect the health status of crops in real time, while the land productivity index LPI helps farmers understand the utilization efficiency and improvement space of the soil. The irrigation efficiency index IEI provides effective feedback for water resource management and ensures the accuracy of irrigation.

[0096] Example 5

[0097] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the remote management module includes an early warning unit and a remote control unit;

[0098] The early warning unit is used to compare the rice field benefit index TGI with the preset first qualified threshold value A and second qualified threshold value B to obtain a grade evaluation scheme and issue an early warning;

[0099] When TGI>A, it means that the management and growth environment of the rice field are in an ideal state. At this time, there is no need for large-scale intervention. The rice field management status is qualified. Artificial intelligence and big data analysis can be used to further optimize the irrigation frequency and water volume to reduce consumption.

[0100] When A≥TGI>B, it means that the overall management and environmental conditions of the rice field are 20% away from the qualified level. Water quality control should be strengthened to avoid excessive fluctuations in water quality that affect rice growth. The automatic irrigation system needs to adjust the irrigation volume according to soil moisture and climate change. Image recognition technology should be used to detect abnormal plant growth in a timely manner to supplement pesticides or fertilizers.

[0101] When TGI≤B, it means that there are abnormalities in the management and growth environment of the rice field, and the growth of rice is affected. At this time, the rice field management is in poor condition and systemic intervention is urgently needed. There are serious water quality problems, stagnant crop growth or waste of resources. Aeration equipment should be activated, drainage system should be adjusted, and data analysis should be used to assess whether the crops are short of water and fertilizer. The amount of irrigation and fertilizer should be adjusted, the monitoring of crop diseases and pests should be strengthened, and pesticide spraying and pest control should be carried out regularly.

[0102] The remote control unit is used to allow farmers or managers to access rice field data at any location through a cloud platform or a remote management system, and to perform system settings and control operations, including adjusting the irrigation system and regulating water quality equipment, to ensure that managers can control the operating status of the rice fields anytime and anywhere.

[0103] In this embodiment, the remote management module greatly improves the efficiency and response speed of paddy field management through the cooperation of the early warning unit and the remote control unit. The early warning unit can evaluate the management status of paddy fields in real time based on the comparison of the paddy field benefit index TGI with the preset threshold, and provide corresponding early warning and management strategies according to different TGI levels. This early warning mechanism ensures that managers can grasp the operation status of paddy fields at the first time, whether they are in an ideal state or have problems, and can accurately formulate response plans, reduce unnecessary interventions and prevent excessive management, thereby optimizing resource utilization. In particular, when the TGI is lower than the threshold, the early warning system can respond quickly and trigger automated intervention measures, such as enabling aeration equipment, adjusting water quality, irrigation amount and fertilization strategies, and timely handling of crop diseases and insect pests. Through the cloud platform or remote management system, farmers or managers can view data and remotely control equipment at any time, avoiding the drawbacks of traditional paddy field management relying on manual inspections. No matter where they are, managers can adjust the irrigation system and water quality equipment through the remote control unit to ensure the efficiency and flexibility of paddy field management, thereby significantly improving the accuracy and effectiveness of paddy field management, and avoiding losses caused by problems such as water quality fluctuations and stagnant crop growth.

[0104] 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 management system for rice field aquaculture based on the Internet of Things, characterized by: It includes environmental monitoring module, water quality control module, crop growth monitoring module, automatic irrigation module, benefit analysis module and remote management module; The environmental monitoring module is used to monitor and collect various environmental parameters in the rice fields in real time by deploying an Internet of Things sensor network, monitoring water quality changes and other factors of the farmland environment; The water quality control module is used to control the water quality regulating equipment to regulate the water body in the paddy field based on the real-time data collected by the environmental monitoring module, so as to ensure the stability of water quality and meet the needs of rice growth. It realizes automatic regulation through the feedback control mechanism, responds to water quality changes in real time, and avoids sharp fluctuations in water quality. The crop growth monitoring module is used to monitor the growth status of rice in real time by installing crop growth monitoring sensors and cameras, including plant height, leaf color and photosynthesis efficiency indicators, to help identify abnormal growth, water or fertilizer shortage problems, and to provide visual feedback on the growth status through image recognition technology and data analysis technology; The automatic irrigation module is used to control the amount of water in the rice field through an automatic irrigation system based on the data of the environmental monitoring module, and automatically adjust the irrigation frequency and water amount according to soil moisture, climate change and crop demand; The benefit analysis module is used to integrate the data of the environment monitoring module, the crop growth monitoring module and the water quality control module, including the crop growth index, land productivity and irrigation efficiency, and calculate the rice field comprehensive benefit index TGI reflecting the rice growth and breeding benefits by using data mining and statistical analysis techniques; The remote management module is used to monitor paddy field environmental parameters and crop growth status in real time, identify abnormal situations and issue early warnings in a timely manner, prompt farmers to take timely measures, perform fault diagnosis through data analysis, and optimize the rice planting and management process.

2. According to the Internet of Things-based rice field aquaculture intelligent management system according to claim 1, it is characterized by: The environmental monitoring module includes a soil environment monitoring unit, a crop growth health monitoring unit and a crop environment regulation ability monitoring unit; The soil environment monitoring unit is used to monitor the moisture, pH, temperature and nutrient content of the soil in real time through soil moisture sensors, pH sensors, temperature sensors and nutrient sensors, measure soil quality, fertilization conditions and crop yields, and obtain: current rice field yield Y, maximum yield Y max , soil quality index T soil and reference soil quality index T soil,ref ; The crop growth health monitoring unit is used to monitor the growth status, leaf color, photosynthesis and growth rate of crops using cameras and sensors, and obtain: the current height H of the crop, the reference height H ref , crop leaf area L, reference leaf area L ref , current photosynthesis efficiency P and maximum photosynthesis efficiency P max ; The crop environment regulation capability monitoring unit is used to monitor and adjust the environmental response time, accuracy, frequency and energy efficiency through the irrigation system and environmental control equipment, including the automatic ventilation and temperature control system in the greenhouse, to obtain: the ideal amount of water W required for crop growth needed , actual irrigation water volume W actual , the amount of irrigation water supplemented by actual precipitation W rain , precipitation E rain and ideal precipitation E ideal .

3. The intelligent management system for rice field aquaculture based on the Internet of Things according to claim 1 is characterized by: The water quality control module includes a water quality parameter monitoring unit and a water quality adjustment unit; The water quality parameter monitoring unit is used to configure a group of water quality sensors in the paddy field water body to monitor the key water quality indicators of the water body in real time, including pH value, dissolved oxygen, ammonia nitrogen content and temperature; The water quality regulation unit is used to manage and control water quality regulation equipment, including aerators and dosing equipment. According to the water quality data fed back by the environmental monitoring module, the equipment is automatically or timed to start regulating the dissolved oxygen and pH parameters of the water body to maintain the stability of the water quality. The water quality data is analyzed through a data processing algorithm to identify the trend of water quality changes and potential problems. Based on the analysis results, the system can automatically issue control instructions.

4. The intelligent management system for rice field aquaculture based on the Internet of Things according to claim 1 is characterized by: The crop growth monitoring module includes a crop growth image recognition unit and a crop growth trend prediction unit; The crop growth image recognition unit is used to collect rice growth data in real time, including plant height, leaf color and photosynthesis efficiency, by deploying crop growth monitoring equipment, including sensors and cameras, and process image data from the camera through an image recognition algorithm to analyze the growth status of rice, identify the causes of leaf lesions, water shortage or fertilizer shortage, generate a specific growth status report, and provide farmers with real-time feedback on the growth status; The crop growth trend prediction unit is used to perform trend prediction on the collected crop growth data based on data analysis and machine learning technology, and predict future growth trends and upcoming pest and disease threats by analyzing historical data and current growth status.

5. The intelligent management system for rice field aquaculture based on the Internet of Things according to claim 1 is characterized by: The automatic irrigation module includes an automatic irrigation control unit and an irrigation effect feedback unit; The automatic irrigation control unit is used to evaluate the irrigation needs of crops based on the soil moisture and climate change data provided by the environmental monitoring module and the rice growth status, and automatically control the start and stop status, irrigation duration, irrigation water volume and irrigation frequency of the irrigation system; The irrigation effect feedback unit is used to evaluate the irrigation effect through sensors and monitoring equipment, and feed back to the control system to ensure the accuracy and efficiency of the irrigation system operation. During the irrigation process, if it is found that the soil moisture does not meet the preset standard, the automatic irrigation control unit will automatically adjust the irrigation strategy to ensure that the irrigation effect meets the target requirements.

6. The intelligent management system for rice field aquaculture based on the Internet of Things according to claim 1 is characterized by: The benefit analysis module includes a data preprocessing unit and a data modeling unit; The data preprocessing unit is used to organize the data from the environment monitoring module, the crop growth monitoring module and the water quality control module, perform preprocessing work such as data cleaning, denoising and missing value filling, filter out abnormal data, and standardize the data formats obtained by different sensors; The data modeling unit is used to analyze the integrated data using statistical methods, machine learning models or deep learning models, and calculate multiple key indicators by establishing mathematical models and prediction algorithms, including the crop growth index CGR, the land productivity index LPI, the irrigation efficiency index IEI and the rice field benefit index TGI.

7. The intelligent management system for rice field aquaculture based on the Internet of Things according to claim 2 is characterized by: The crop growth index CGR is calculated by the following formula: Where H represents the current height of the crop, H ref represents the reference height, L represents the crop leaf area, L ref represents the reference leaf area, P represents the current photosynthesis efficiency, P max Represents the maximum photosynthesis efficiency.

8. The intelligent management system for rice field aquaculture based on Internet of Things according to claim 2 is characterized by: The land productivity index LPI is calculated by the following formula: In the formula, Y represents the current rice field yield, Y max Indicates the maximum output, T soil represents soil quality index, T soil,ref Denotes the reference soil quality index.

9. The intelligent management system for rice field aquaculture based on Internet of Things according to claim 2 is characterized by: The irrigation efficiency index IEI is calculated by the following formula: Where W needed Indicates the ideal amount of water required for crop growth, W actual Indicates the actual irrigation water volume, E rain Indicates precipitation, E ideal represents the ideal precipitation, W rain represents the amount of irrigation water supplemented by actual precipitation, λ1, λ2 and λ3 represent the weight factors of various parameters respectively; The paddy field benefit index TGI is calculated by the following formula: TGI=w1*CGR+w2*LPI+w3*IEI; Where CGR stands for crop growth index, LPI stands for land productivity index, IEI stands for irrigation efficiency index, and TGI stands for rice field benefit index.

10. The intelligent management system for rice field aquaculture based on Internet of Things according to claim 1, characterized in that: The remote management module includes an early warning unit and a remote control unit; The early warning unit is used to compare the rice field benefit index TGI with the preset first qualified threshold value A and second qualified threshold value B to obtain a grade evaluation scheme and issue an early warning; When TGI>A, it means that the management and growth environment of the rice field are in an ideal state. At this time, there is no need for large-scale intervention. The rice field management status is qualified. Artificial intelligence and big data analysis can be used to further optimize the irrigation frequency and water volume to reduce consumption. When A≥TGI>B, it means that the overall management and environmental conditions of the rice field are 20% away from the qualified level. Water quality control should be strengthened to avoid excessive fluctuations in water quality that affect rice growth. The automatic irrigation system needs to adjust the irrigation volume according to soil moisture and climate change. Image recognition technology should be used to detect abnormal plant growth in a timely manner to supplement pesticides or fertilizers. When TGI≤B, it means that there are abnormalities in the management and growth environment of the rice field, and the growth of rice is affected. At this time, the rice field management is in poor condition and systemic intervention is urgently needed. There are serious water quality problems, stagnant crop growth or waste of resources. Aeration equipment should be activated, drainage system should be adjusted, and data analysis should be used to assess whether the crops are short of water and fertilizer. The amount of irrigation and fertilizer should be adjusted, the monitoring of crop diseases and pests should be strengthened, and pesticide spraying and pest control should be carried out regularly. The remote control unit is used to allow farmers or managers to access rice field data at any location through a cloud platform or a remote management system, and to perform system settings and control operations, including adjusting the irrigation system and regulating water quality equipment, to ensure that managers can control the operating status of the rice fields anytime and anywhere.

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