System and method for breeding black-spotted frog in rice field

Abstract

The present invention relates to the technical field of breeding systems, and specifically to a system and method for breeding black-spotted frogs in rice fields. A system for breeding black-spotted frogs in rice fields is composed of a multi-level ecological rice field construction module, an intelligent environment monitoring system module, an ecological cycle agricultural model module, a black-spotted frog behavior analysis system module based on AR technology, a breeding prediction model module based on data analysis, and a health monitoring and microbial regulation system module. In the present invention, a suitable living and breeding environment is provided for black-spotted frogs through a multi-level ecological rice field construction module, an intelligent environment monitoring system monitors and adjusts the rice field environment in real time, improves the survival rate and growth rate of black-spotted frogs, an ecological cycle agricultural model reduces the use of fertilizers and pesticides, and improves the quality of rice, a prediction model based on AR technology and data analysis provides breeders with accurate breeding advice and predictions, reduces breeding risks, and a health monitoring and microbial regulation system monitors the health status of black-spotted frogs in real time and optimizes water quality.

Description

Technical Field

[0001] The invention relates to the technical field of breeding systems, and in particular to a system and method for breeding black-spotted frogs in rice fields. Background Art

[0002] Aquaculture system refers to a specific organization and management system established for the production of agricultural products. It includes facilities and equipment, aquaculture technology, management methods, and related production links and processes. The purpose of the aquaculture system is to provide a suitable environment and conditions so that the farmed animals or plants can grow and reproduce healthily, while maximizing output and benefits. Different forms and technologies can be adopted according to different aquaculture objects and specific needs. Among them, the black-spotted frog aquaculture system is a set of organizations and management systems designed and established specifically for the breeding of black-spotted frogs. The black-spotted frog is a common amphibian widely distributed in Asia, especially in southern China. The purpose of the black-spotted frog aquaculture system is to promote the good growth, reproduction and healthy development of black-spotted frogs by providing suitable environmental conditions and management measures, so as to obtain stable output and economic benefits.

[0003] In the actual use of the breeding system, the existing rice field breeding systems are often too simple and single. They often lack in-depth simulation of the ecological environment, resulting in the inability of black-spotted frogs to obtain sufficient growth and reproduction conditions. In addition, the existing system lacks intelligent environmental monitoring and regulation functions, which may cause environmental factors such as water quality and temperature to become unstable, increasing the risk of breeding. The existing breeding system also lacks quantitative analysis of material flow and energy conversion, resulting in resource waste and environmental pollution in the breeding process. In addition, the existing system is relatively rudimentary in terms of behavior analysis and breeding prediction, and cannot provide accurate decision-making support for breeders. In general, the existing rice field breeding system has major deficiencies in ecological simulation, environmental regulation, resource utilization and data analysis, and it is difficult to meet the needs of efficient and sustainable development of modern breeding industry. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art and to propose a system and method for breeding black-spotted frogs in rice fields.

[0005] In order to achieve the above-mentioned object, the present invention adopts the following technical scheme: a system for breeding black-spotted frogs in rice fields is composed of a multi-level ecological rice field construction module, an intelligent environment monitoring system module, an ecological cycle agriculture model module, a black-spotted frog behavior analysis system module based on AR technology, a breeding prediction model module based on data analysis, and a health monitoring and microbial regulation system module;

[0006] The multi-level ecological rice field construction module uses ecological modeling technology to simulate the species interaction in the natural ecosystem, design the three-layer ecological structure of the rice field, and generate an ecological rice field configuration plan;

[0007] The intelligent environment monitoring system module is based on the Internet of Things technology, monitors water quality, temperature, and humidity parameters in real time, and combines the deep learning prediction model and PID control algorithm to make real-time environmental adjustments according to the ecological rice field configuration plan and generate a real-time environmental adjustment instruction report;

[0008] The ecological cycle agricultural model module analyzes material flow and energy conversion according to the real-time environmental adjustment instruction report and the ecological cycle model, so that a complementary relationship is formed between the rice field and the black-spotted frog, and generates suggestions for optimizing the agricultural cycle;

[0009] The black-spotted frog behavior analysis system module based on AR technology uses AR technology and image recognition technology to observe and analyze the behavior patterns of black-spotted frogs and generate a behavior response optimization plan based on the suggestions for optimizing agricultural cycles;

[0010] The breeding prediction model module based on data analysis and historical data, real-time monitoring data and behavioral response optimization scheme uses machine learning algorithms to establish a correlation model between breeding parameters and the growth effect of black-spotted frogs, and outputs a breeding effect prediction report;

[0011] The health monitoring and microbial regulation system module uses sound recognition technology to monitor the health status of black-spotted frogs based on the breeding effect prediction report, and introduces microbial strains to regulate water quality and ecological environment to generate a comprehensive health and ecological adjustment plan.

[0012] As a further solution of the present invention, the multi-level ecological rice field construction module includes a species interaction submodule, a three-layer ecological structure submodule, and an ecological configuration scheme submodule;

[0013] The intelligent environment monitoring system module includes a water quality monitoring submodule, a climate monitoring submodule, and an environment adjustment instruction submodule;

[0014] The ecological cycle agriculture model module includes a material flow analysis submodule, an energy conversion analysis submodule, and an agricultural cycle optimization submodule.

[0015] As a further solution of the present invention, the black-spotted frog behavior analysis system module based on AR technology includes an AR observation submodule, an image recognition submodule, and a behavior response optimization submodule;

[0016] The aquaculture prediction model module based on data analysis includes a historical data analysis submodule, a real-time monitoring data analysis submodule, and an aquaculture effect prediction submodule;

[0017] The health monitoring and microbial regulation system module includes a health monitoring submodule, a microbial regulation submodule, and a comprehensive health and ecological adjustment submodule.

[0018] A method for culturing black-spotted frogs in rice fields is implemented based on a system for culturing black-spotted frogs in rice fields, comprising the following steps:

[0019] Through ecological modeling technology, a food chain dynamic balance algorithm is used to simulate the species interaction in the natural ecosystem, generate an ecological balance plan, and design a three-layer ecological structure of the rice field and generate an ecological rice field configuration plan based on the ecological balance plan;

[0020] Based on the Internet of Things technology and deep learning prediction model, the PID control algorithm is used to adjust the environmental parameters in real time. According to the ecological balance plan, the water quality, temperature and humidity are monitored and adjusted in real time to generate an environmental optimization instruction report;

[0021] Applying the material energy balance analysis method and the agricultural cycle model to analyze, according to the environmental optimization instruction report, analyzing the material flow and energy conversion, so as to realize the complementary relationship between rice fields and black-spotted frogs, and generate an agricultural cycle optimization plan;

[0022] Using AR technology and convolutional neural networks for image recognition, observing and analyzing the behavior patterns of black-spotted frogs according to the agricultural cycle optimization scheme, and generating a behavior response optimization report;

[0023] A support vector machine machine learning algorithm is used to establish a model for associating breeding parameters with growth effects, and a breeding effect prediction report is output by combining the behavior response optimization report, historical data, and real-time monitoring data;

[0024] By analyzing the sound recognition technology and the ecological regulation algorithm of microbial strains, the health status of black-spotted frogs is monitored according to the breeding effect prediction report, and the water quality and ecological environment are controlled through microbial regulation to generate a comprehensive health and ecological adjustment plan.

[0025] As a further solution of the present invention, the interactive relationship between species in the natural ecosystem is simulated by using the food chain dynamic balance algorithm through ecological modeling technology to generate an ecological balance plan. According to the ecological balance plan, the three-layer ecological structure of the rice field is designed and the steps of generating the ecological rice field configuration plan are specifically as follows:

[0026] Use the food chain dynamic balance algorithm to simulate the interaction between species and establish a species interaction model;

[0027] Based on the species interaction model, the optimization algorithm is used to design the rice field ecology and generate an ecological balance plan;

[0028] According to the ecological balance scheme, the ecological zoning technology is applied to construct the three-layer ecological structure of rice fields and generate a three-layer ecological structure sketch;

[0029] Based on the three-layer ecological structure sketch, the model is evaluated and the configuration is output to construct an ecological rice field configuration plan.

[0030] As a further solution of the present invention, based on the Internet of Things technology and the deep learning prediction model, the PID control algorithm is used to adjust the environmental parameters in real time. According to the ecological balance scheme, the water quality, temperature and humidity are monitored and adjusted in real time. The steps of generating the environmental optimization instruction report are as follows:

[0031] Use IoT technology sensors to collect water quality, temperature, and humidity data in real time and integrate the original environmental data set;

[0032] Applying a deep learning prediction model to predict future environmental trends of the original environmental data set and generating an environmental trend prediction report;

[0033] According to the environmental trend forecast report, the environmental parameters are adjusted in real time using a PID control algorithm to generate an environmental adjustment plan;

[0034] The environmental adjustment plan is integrated with the real-time environmental data to generate a detailed report as an environmental optimization instruction report.

[0035] As a further solution of the present invention, the material energy balance analysis method and the agricultural cycle model are used for analysis, and the material flow and energy conversion are analyzed according to the environmental optimization instruction report to achieve the complementary relationship between the rice field and the black-spotted frog. The steps of generating the agricultural cycle optimization plan are specifically as follows:

[0036] Based on the flow conservation principle, analyze the material flow in the environmental optimization instruction report and generate material flow analysis results;

[0037] Using an energy balance algorithm to evaluate the energy conversion efficiency in the material flow analysis results, and generating an energy conversion efficiency report;

[0038] Combine the energy conversion efficiency report with the material flow analysis results to establish a preliminary ecological cycle model;

[0039] Optimize the preliminary ecological cycle model, strengthen the complementary relationship between rice fields and black-spotted frogs, and establish an agricultural cycle optimization plan.

[0040] As a further solution of the present invention, the steps of using AR technology and convolutional neural network for image recognition, observing and analyzing the behavior pattern of black-spotted frog according to the agricultural cycle optimization scheme, and generating a behavior response optimization report are specifically as follows:

[0041] Use AR technology to collect real-time image data of black-spotted frogs in rice fields and integrate the original image data set;

[0042] Applying a convolutional neural network to analyze the original image data set, identifying the behavior pattern of the black-spotted frog, and generating a behavior pattern analysis report;

[0043] Based on the behavior pattern analysis report, an optimization algorithm is applied to adjust the breeding environment to optimize the black-spotted frog behavior and generate a behavior optimization plan;

[0044] Integrate the behavior optimization plan with the behavior pattern analysis report to form a behavior response optimization report.

[0045] As a further solution of the present invention, a support vector machine machine learning algorithm is used to establish a breeding parameter and growth effect association model, and the steps of outputting a breeding effect prediction report in combination with the behavior response optimization report, historical data, and real-time monitoring data are specifically as follows:

[0046] Integrate behavioral response optimization reports, historical farming data and real-time monitoring data to establish a comprehensive farming data set;

[0047] The comprehensive breeding data set is analyzed using a support vector machine algorithm to establish a correlation model between breeding parameters and growth effects, which is named a growth effect correlation model;

[0048] The growth effect correlation model is used to predict future breeding effects and generate a breeding effect prediction report.

[0049] As a further solution of the present invention, the steps of analyzing by sound recognition technology and ecological regulation algorithm of microbial strains, monitoring the health status of black-spotted frogs and controlling water quality and ecological environment through microbial regulation according to the breeding effect prediction report, and generating a comprehensive health and ecological adjustment plan are specifically as follows:

[0050] Analyze the health status of black-spotted frogs through sound recognition technology and generate frog health status reports;

[0051] Apply ecological control algorithms to analyze the ability of microbial strains to improve water quality and generate water quality regulation analysis reports;

[0052] Design an ecological environment optimization design plan based on the frog health status report and water quality regulation analysis report;

[0053] According to the ecological environment optimization design plan, actual operations are carried out to adjust the entire rice field ecosystem and generate a comprehensive health and ecological adjustment plan.

[0054] Compared with the prior art, the advantages and positive effects of the present invention are:

[0055] In the present invention, a rice field ecological environment closer to nature is simulated through a multi-level ecological rice field construction module, providing a more suitable living and breeding environment for black-spotted frogs. The intelligent environment monitoring system module monitors and adjusts the rice field environment in real time to ensure that it is always in the best condition, thereby improving the survival rate and growth rate of black-spotted frogs. The ecological cycle agriculture model module enables rice fields and black-spotted frogs to form a complementary closed-loop ecosystem, which not only reduces the use of fertilizers and pesticides, but also improves the quality of rice. In addition, the prediction model based on AR technology and data analysis provides breeders with more accurate breeding suggestions and predictions, reducing breeding risks. Finally, the health monitoring and microbial regulation system module can monitor the health status of black-spotted frogs in real time, and optimize water quality through microbial regulation, further ensuring the success rate of breeding. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A system flow chart of a system and method for breeding black-spotted frogs in rice fields is provided for the present invention;

[0057] Figure 2 The present invention provides a system and method for breeding black-spotted frogs in rice fields;

[0058] Figure 3 The present invention proposes a system and method for breeding black-spotted frogs in rice fields, step 1, a detailed flow chart;

[0059] Figure 4 The present invention proposes a system and method for breeding black-spotted frogs in rice fields, and a detailed flowchart of step 2;

[0060] Figure 5 The present invention proposes a system and method for breeding black-spotted frogs in rice fields, and a detailed flowchart of step 3;

[0061] Figure 6 The present invention proposes a system and method for breeding black-spotted frogs in rice fields, and a detailed flowchart of step 4;

[0062] Figure 7 The present invention proposes a system and method for breeding black-spotted frogs in rice fields, and a detailed flowchart of step 5;

[0063] Figure 8 The present invention proposes a system and method for breeding black-spotted frogs in rice fields, and a detailed flowchart of step 6 DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0065] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0066] Embodiment 1

[0067] See also Figure 1 , the present invention provides a technical solution: a system for breeding black-spotted frogs in rice fields is composed of a multi-level ecological rice field construction module, an intelligent environment monitoring system module, an ecological cycle agriculture model module, a black-spotted frog behavior analysis system module based on AR technology, a breeding prediction model module based on data analysis, and a health monitoring and microbial regulation system module;

[0068] The multi-level ecological rice field construction module uses ecological modeling technology to simulate the species interaction in the natural ecosystem, design the three-layer ecological structure of the rice field, and generate the ecological rice field configuration plan;

[0069] The intelligent environment monitoring system module is based on the Internet of Things technology, which monitors water quality, temperature, and humidity parameters in real time. It also combines deep learning prediction models and PID control algorithms to make real-time environmental adjustments based on the ecological rice field configuration plan and generate real-time environmental adjustment instruction reports.

[0070] The ecological cycle agricultural model module adjusts the instruction report and ecological cycle model according to the real-time environment, analyzes the material flow and energy conversion, forms a complementary relationship between rice fields and black-spotted frogs, and generates suggestions for optimizing the agricultural cycle;

[0071] The black-spotted frog behavior analysis system module based on AR technology uses AR technology and image recognition technology to observe and analyze the behavior patterns of black-spotted frogs according to the suggestions for optimizing agricultural cycles, and generates a behavior response optimization plan;

[0072] Based on the breeding prediction model module and historical data, real-time monitoring data and behavioral response optimization plan based on data analysis, the machine learning algorithm is used to establish the correlation model between breeding parameters and the growth effect of black-spotted frogs, and the breeding effect prediction report is output;

[0073] The health monitoring and microbial regulation system module uses sound recognition technology to monitor the health status of black-spotted frogs based on the breeding effect prediction report, and introduces microbial species to regulate water quality and ecological environment to generate a comprehensive health and ecological adjustment plan.

[0074] First, through ecological modeling technology and ecological rice field configuration scheme generation, species interactions in natural ecosystems are simulated to promote biodiversity and ecological balance. Secondly, the intelligent environment monitoring system module monitors environmental parameters in real time based on the Internet of Things technology, and adjusts the environment according to the deep learning prediction model and PID control algorithm to provide a stable growth environment and effective pest and disease control. In addition, the ecological cycle agriculture model module analyzes material flow and energy conversion, so that rice fields and black-spotted frogs form a complementary relationship and improve the efficiency and sustainability of the agricultural system. The black-spotted frog behavior analysis system module based on AR technology uses image recognition technology to observe and analyze the behavior patterns of black-spotted frogs, and provides behavioral response optimization solutions to improve feeding effects and production performance. In addition, the breeding prediction model module based on data analysis uses machine learning algorithms to establish a correlation model between breeding parameters and black-spotted frog growth effects, providing breeders with accurate breeding effect predictions to help them make corresponding adjustments and improve economic benefits. Finally, the health monitoring and microbial regulation system module monitors the health status of black-spotted frogs through sound recognition technology, regulates the microbial species of water quality and ecological environment, provides a comprehensive health and ecological adjustment plan, increases the health and growth of black-spotted frogs, and improves the stability and sustainability of the breeding system.

[0075] See also Figure 1 ,The multi-level ecological rice field construction module includes the species interaction submodule, the three-layer ecological structure submodule, and the ecological configuration scheme submodule;

[0076] The intelligent environment monitoring system module includes a water quality monitoring submodule, a climate monitoring submodule, and an environment adjustment instruction submodule;

[0077] The ecological cycle agriculture model module includes a material flow analysis submodule, an energy conversion analysis submodule, and an agricultural cycle optimization submodule;

[0078] The black-spotted frog behavior analysis system module based on AR technology includes AR observation submodule, image recognition submodule, and behavior response optimization submodule;

[0079] The aquaculture prediction model module based on data analysis includes a historical data analysis submodule, a real-time monitoring data analysis submodule, and an aquaculture effect prediction submodule;

[0080] The health monitoring and microbial regulation system module includes a health monitoring sub-module, a microbial regulation sub-module, and a comprehensive health and ecological adjustment sub-module.

[0081] First, through the species interaction submodule of the multi-level ecological rice field construction module, the species interaction relationship in the natural ecosystem can be simulated to promote biodiversity and ecological balance. This helps to reduce the occurrence of pests and diseases, improve the disease resistance of crops, and reduce dependence on chemical pesticides. Secondly, the water quality monitoring submodule and climate monitoring submodule of the intelligent environment monitoring system module can monitor the water quality and climate conditions of the rice field in real time. Through timely monitoring and analysis of data, accurate environmental information can be provided to help farmers to accurately regulate water quality and climate, thereby providing a stable growth environment and optimizing crop growth. The material flow analysis submodule and energy conversion analysis submodule of the ecological cycle agriculture model module can help farmers understand the flow of materials and energy in the breeding process. This helps to optimize the resource utilization efficiency of the breeding system, reduce the generation of waste in the breeding process, and achieve sustainable development of the agricultural system. The AR observation submodule and image recognition submodule of the black-spotted frog behavior analysis system module based on AR technology can help farmers observe and analyze the behavior patterns of black-spotted frogs. By understanding the behavioral characteristics of black-spotted frogs, farmers can adopt corresponding behavioral response optimization strategies to improve feeding effects and production performance. The historical data analysis submodule and real-time monitoring data analysis submodule of the breeding prediction model module based on data analysis can analyze and mine the data in the breeding process. By establishing a correlation model between breeding parameters and the growth effect of black-spotted frogs, the breeding effect can be predicted, helping farmers make corresponding adjustments and improving the economic benefits of breeding. The health monitoring submodule and microbial regulation submodule of the health monitoring and microbial regulation system module can monitor the health status of black-spotted frogs in real time and regulate the microbial species of water quality and ecological environment. Through a comprehensive health and ecological adjustment plan, the health and growth of black-spotted frogs can be improved, and the stability and sustainability of the breeding system can be increased.

[0082] See also Figure 2 A method for breeding black-spotted frogs in rice fields is based on the system for breeding black-spotted frogs in rice fields, comprising the following steps:

[0083] Through ecological modeling technology, the food chain dynamic balance algorithm is used to simulate the species interaction in the natural ecosystem, generate an ecological balance plan, and design the three-layer ecological structure of the rice field and generate an ecological rice field configuration plan based on the ecological balance plan;

[0084] Based on the Internet of Things technology and deep learning prediction model, the PID control algorithm is used to adjust environmental parameters in real time. According to the ecological balance plan, water quality, temperature, and humidity are monitored and adjusted in real time to generate environmental optimization instruction reports;

[0085] Apply material energy balance analysis and agricultural cycle model to analyze material flow and energy conversion according to the environmental optimization instruction report to achieve the complementary relationship between rice fields and black-spotted frogs and generate agricultural cycle optimization plans;

[0086] Using AR technology and convolutional neural networks for image recognition, we observed and analyzed the behavior patterns of black-spotted frogs and generated a behavior response optimization report based on the agricultural cycle optimization plan;

[0087] Use support vector machine machine learning algorithm to establish a model for associating breeding parameters with growth effects, combine behavioral response optimization reports, historical data, and real-time monitoring data to output breeding effect prediction reports;

[0088] Through the analysis of sound recognition technology and the ecological regulation algorithm of microbial strains, according to the breeding effect prediction report, the health status of black-spotted frogs is monitored, and the water quality and ecological environment are controlled through microbial regulation to generate a comprehensive health and ecological adjustment plan.

[0089] First, the species interaction relationship is simulated through ecological modeling technology and food chain dynamic balance algorithm to generate ecological balance scheme and three-layer ecological structure configuration scheme of rice fields. This helps to create an environment closer to nature and promote biodiversity and ecological balance. Secondly, based on the Internet of Things technology and deep learning prediction model, as well as the PID control algorithm, the water quality, temperature, humidity and other parameters of the rice fields are monitored and adjusted in real time, and the environment is optimized according to the ecological balance scheme. This can provide a stable growth environment, meet the needs of black-spotted frogs to the greatest extent, and prevent diseases and other adverse effects. Next, the material energy balance analysis method and agricultural cycle model are applied to analyze the material flow and energy conversion between rice fields and black-spotted frogs to achieve a complementary relationship between them. This helps to improve resource utilization efficiency, reduce waste generation, and promote the sustainable development of agricultural systems. Image recognition is performed through AR technology and convolutional neural network, the behavior patterns of black-spotted frogs are observed and analyzed, and a behavior response optimization report is generated. This helps to understand the behavioral habits of black-spotted frogs, further optimize the breeding environment and management strategies, and improve breeding effects and production capacity. The support vector machine machine learning algorithm is used to establish an association model between breeding parameters and growth effects, and a breeding effect prediction report is provided in combination with the behavior response optimization report, historical data and real-time monitoring data. This helps farmers understand the expected effects of breeding, make corresponding adjustments and decisions, and improve economic benefits and management effects. Finally, through sound recognition technology and ecological regulation algorithms of microbial strains, the health status of black-spotted frogs is monitored, and water quality and ecological environment are regulated to generate a comprehensive health and ecological adjustment plan. This helps maintain the health of black-spotted frogs, reduce the occurrence of diseases, and maintain the balance and stability of the rice field ecosystem.

[0090] See also Figure 3,Through ecological modeling technology, the food chain dynamic balance algorithm is used to simulate the species interaction in the natural ecosystem and generate an ecological balance plan. According to the ecological balance plan, the three-layer ecological structure of the rice field is designed and the steps of generating the ecological rice field configuration plan are as follows:

[0091] Use the food chain dynamic balance algorithm to simulate the interaction between species and establish a species interaction model;

[0092] Based on the species interaction model, the optimization algorithm is used to design the rice field ecology and generate an ecological balance plan;

[0093] According to the ecological balance scheme, the ecological zoning technology is applied to construct the three-layer ecological structure of rice fields and generate a three-layer ecological structure sketch;

[0094] Based on the three-layer ecological structure sketch, the model is evaluated and the configuration is output to construct an ecological rice field configuration plan.

[0095] The three-layer ecological structure includes:

[0096] Vegetation layer: This is the first layer of the rice field ecosystem. The vegetation layer mainly refers to rice plants, such as rice. The growth of rice provides an environment for habitat, foraging and reproduction, and provides food and shelter for other species.

[0097] Food chain layer: This is the second layer of the rice field ecosystem. The food chain layer includes various organisms that feed on rice, such as pests, snails, voles, etc. They constitute the lower links in the food chain and contribute to the balance and operation of the food chain.

[0098] Animal layer: This is the third layer of the rice field ecosystem. The animal layer includes animal groups in the rice field, such as frogs, fish, birds, etc. They feed on organisms in the food chain layer, and as upper predators in the food chain, they interact with organisms in other layers to maintain the balance of the ecosystem.

[0099] First, this method can promote biodiversity, maintain and increase species diversity in rice field aquaculture systems, and create a healthier and more stable ecosystem. Secondly, by optimizing the ecological balance, reducing the occurrence of pests and diseases, and improving the disease resistance of black-spotted frogs, the dynamic balance of the ecosystem can be achieved. In addition, by optimizing the three-layer ecological structure of rice fields, suitable habitats and resource supply are provided, the growth environment of black-spotted frogs is improved, and the breeding effect and yield are improved. Finally, this method helps to improve the health and stability of rice fields, reduce unnecessary losses and environmental pressures, and achieve sustainable development of breeding systems. In summary, by integrating ecological modeling technology and food chain dynamic balance algorithms and designing the three-layer ecological structure of rice fields, biodiversity promotion, ecological balance improvement, breeding effect optimization, and sustainable development can be achieved.

[0100] See also Figure 4 Based on the Internet of Things technology and deep learning prediction model, the PID control algorithm is used to adjust the environmental parameters in real time. According to the ecological balance plan, the water quality, temperature and humidity are monitored and adjusted in real time. The specific steps for generating the environmental optimization instruction report are as follows:

[0101] Use IoT technology sensors to collect water quality, temperature, and humidity data in real time and integrate the original environmental data set;

[0102] Apply deep learning prediction models to predict future environmental trends on original environmental data sets and generate environmental trend prediction reports;

[0103] According to the environmental trend forecast report, use the PID control algorithm to adjust environmental parameters in real time and generate environmental adjustment plans;

[0104] Comprehensive environmental adjustment plan and real-time environmental data to generate detailed report as environmental optimization instruction report.

[0105] First, real-time environmental monitoring can provide timely understanding of key parameters such as water quality, temperature and humidity in rice fields, help discover anomalies and problems, and take timely measures to protect the stability of the ecosystem. Secondly, through deep learning prediction models, environmental trends can be predicted, and future environmental change trends can be understood in advance, providing accurate basis and guidance for the adjustment of environmental parameters and improving the effect of adjustments. Furthermore, through real-time environmental adjustment through PID control algorithms, parameters such as water quality, temperature and humidity in rice fields can be adjusted quickly and accurately to maintain or restore the balance of the ecosystem. Finally, through comprehensive environmental adjustment plans and detailed reports and instructions generated by real-time environmental data, the optimization and sustainable development of the rice field environment can be promoted, breeding efficiency can be improved, and resource waste and environmental pressure can be reduced.

[0106] See also Figure 5 , applying the material energy balance analysis method and agricultural cycle model for analysis, analyzing the material flow and energy conversion according to the environmental optimization instruction report to achieve the complementary relationship between rice fields and black-spotted frogs, and generating the agricultural cycle optimization plan in the following steps:

[0107] Based on the flow conservation principle, analyze the material flow in the environmental optimization instruction report and generate material flow analysis results;

[0108] Use energy balance algorithm to evaluate energy conversion efficiency in material flow analysis results and generate energy conversion efficiency report;

[0109] Combine the energy conversion efficiency report with the material flow analysis results to establish a preliminary ecological cycle model;

[0110] Optimize the preliminary ecological cycle model, strengthen the complementary relationship between rice fields and black-spotted frogs, and establish an agricultural cycle optimization plan.

[0111] First, through material flow analysis and energy balance assessment, we can improve resource utilization efficiency, reduce resource waste and environmental load, and promote the sustainable development of aquaculture systems. Secondly, by establishing a preliminary ecological cycle model and optimization scheme, we can enhance the interaction and complementary relationship between rice fields and black-spotted frogs, and improve the stability and anti-interference ability of the ecosystem. Furthermore, by reducing external inputs and dependencies and optimizing the productivity of the ecosystem, we can improve the economic benefits and output quality of the agricultural system. Finally, through the cycle optimization scheme, we can achieve efficient operation and sustainable development of the agricultural system, which can make a positive contribution to environmental protection and agricultural sustainability.

[0112] See also Figure 6 , using AR technology and convolutional neural network for image recognition, observing and analyzing the behavior patterns of black-spotted frogs according to the agricultural cycle optimization plan, and generating a behavior response optimization report are as follows:

[0113] Use AR technology to collect real-time image data of black-spotted frogs in rice fields and integrate the original image data set;

[0114] Apply convolutional neural networks to analyze the original image dataset, identify the behavior patterns of black-spotted frogs, and generate behavior pattern analysis reports;

[0115] Based on the behavior pattern analysis report, the optimization algorithm is applied to adjust the breeding environment to optimize the black-spotted frog behavior and generate a behavior optimization plan;

[0116] Integrate behavior optimization solutions and behavior pattern analysis reports to form behavior response optimization reports.

[0117] First, with the precise recognition and classification capabilities of AR technology and convolutional neural networks, we can gain an in-depth understanding of the behavior patterns of black-spotted frogs, provide detailed behavior pattern analysis reports, and help breeders better understand the needs and habits of black-spotted frogs. Secondly, according to the guidance in the behavior pattern analysis report, the breeding environment is optimized to promote the normal and healthy development of black-spotted frog behavior and improve breeding efficiency. Adjusting environmental parameters such as water quality, temperature, and humidity in rice fields to provide black-spotted frogs with more suitable habitat conditions will help optimize the breeding environment. Furthermore, the behavior optimization program provides specific behavior optimization suggestions, provides guidance and implementation directions for breeders, and helps them adjust management measures to better meet the needs of black-spotted frogs. Finally, the implementation of the behavior response optimization report promotes the coordinated development of black-spotted frogs and rice field ecosystems, promotes the health and sustainable development of rice field ecosystems, and at the same time improves breeding efficiency and reduces resource waste.

[0118] See also Figure 7, the support vector machine machine learning algorithm is used to establish a model for the association between breeding parameters and growth effects, and the steps for outputting a breeding effect prediction report are as follows:

[0119] Integrate behavioral response optimization reports, historical farming data and real-time monitoring data to establish a comprehensive farming data set;

[0120] The support vector machine algorithm was used to analyze the comprehensive breeding data set and establish a correlation model between breeding parameters and growth effects, which was named the growth effect correlation model;

[0121] The growth effect correlation model is used to predict future breeding effects and generate a breeding effect prediction report.

[0122] First, by establishing a growth effect correlation model, the breeding effect can be accurately predicted, providing a scientific basis for decision-making by breeding owners and managers. Secondly, the breeding effect prediction report provides prediction results and trend analysis, providing guidance for breeding owners to formulate breeding strategies and optimize management. This helps to improve breeding benefits and resource utilization efficiency. Furthermore, by reasonably adjusting breeding parameters and environmental conditions during the breeding process, the breeding effect can be predicted in advance, further improving growth effects and economic benefits, while reducing resource waste and environmental risks. Finally, by embedding machine learning algorithms and prediction models, the breeding industry can be promoted to develop in the direction of intelligent and sustainable development.

[0123] See also Figure 8 , through the analysis of sound recognition technology and the ecological regulation algorithm of microbial strains, according to the breeding effect prediction report, the health status of black-spotted frogs is monitored and the water quality and ecological environment are controlled through microbial regulation. The specific steps for generating a comprehensive health and ecological adjustment plan are as follows:

[0124] Analyze the health status of black-spotted frogs through sound recognition technology and generate frog health status reports;

[0125] Apply ecological control algorithms to analyze the ability of microbial strains to improve water quality and generate water quality regulation analysis reports;

[0126] Design an ecological environment optimization plan based on the frog health status report and water quality regulation analysis report;

[0127] Based on the ecological environment optimization design plan, practical operations are carried out to adjust the entire rice field ecosystem and generate a comprehensive health and ecological adjustment plan.

[0128] First, through sound recognition technology and frog health status reports, black-spotted frog health problems can be captured in a timely manner, which helps maintain a good breeding state and healthy development. Secondly, through ecological regulation algorithms and water quality regulation analysis reports, the water quality of rice fields can be optimized, the environment can be purified, suitable ecological conditions can be provided, and the healthy growth of black-spotted frogs can be promoted. In addition, according to the comprehensive health and ecological adjustment plan, the rice field ecosystem is adjusted to provide the best growth environment and protection for black-spotted frogs, improve breeding benefits and sustainable development of rice fields. Through the comprehensive use of sound recognition technology, microbial regulation and ecological optimization, the efficient, environmentally friendly and sustainable development of the breeding industry can be achieved, while promoting the healthy growth of black-spotted frogs and the balance and health of the rice field ecosystem.

[0129] Working principle: The multi-level ecological rice field construction module uses ecological modeling technology to simulate the species interaction relationship in the natural ecosystem, design the three-layer ecological structure of the rice field, and generate the ecological rice field configuration plan. This module promotes the healthy development of the rice field ecosystem by optimizing the species interaction and the generated configuration plan.

[0130] The intelligent environmental monitoring system module monitors water quality, temperature, humidity and other environmental parameters in real time based on the Internet of Things technology, and combines deep learning prediction models and PID control algorithms to make real-time environmental adjustments. According to the ecological rice field configuration plan, the module generates a real-time environmental adjustment instruction report to guide environmental adjustment operations.

[0131] The Eco-cycle Agricultural Model module adjusts the instruction report and the ecological cycle model according to the real-time environment, analyzes the material flow and energy conversion, and generates optimized agricultural cycle suggestions by ensuring the complementary relationship between rice fields and black-spotted frogs, promoting the recycling of resources and sustainable agricultural development.

[0132] The black-spotted frog behavior analysis system module based on AR technology uses AR technology and image recognition technology to observe and analyze the behavior patterns of black-spotted frogs. According to the optimized agricultural cycle suggestions, the module generates a behavioral response optimization plan to optimize the behavior and breeding results of black-spotted frogs.

[0133] The breeding prediction model module based on data analysis establishes a correlation model between breeding parameters and the growth effect of black-spotted frogs through machine learning algorithms. This module integrates behavioral response optimization solutions, historical data and real-time monitoring data to generate a breeding effect prediction report to help breeders understand future breeding results.

[0134] The health monitoring and microbial regulation system module uses sound recognition technology to monitor the health of black-spotted frogs based on the breeding effect prediction report. At the same time, the module introduces microbial strains to regulate water quality and ecological environment, and generates a comprehensive health and ecological adjustment plan, which helps maintain the health of black-spotted frogs and the balance of rice field ecosystems.

[0135] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A system for breeding black-spotted frogs in rice fields, characterized by: The system for breeding black-spotted frogs in rice fields is composed of a multi-level ecological rice field construction module, an intelligent environment monitoring system module, an ecological cycle agriculture model module, a black-spotted frog behavior analysis system module based on AR technology, a breeding prediction model module based on data analysis, and a health monitoring and microbial regulation system module; The multi-level ecological rice field construction module uses ecological modeling technology to simulate the species interaction in the natural ecosystem, design the three-layer ecological structure of the rice field, and generate an ecological rice field configuration plan; The intelligent environment monitoring system module is based on the Internet of Things technology, monitors water quality, temperature, and humidity parameters in real time, and combines the deep learning prediction model and PID control algorithm to make real-time environmental adjustments according to the ecological rice field configuration plan and generate a real-time environmental adjustment instruction report; The ecological cycle agricultural model module analyzes material flow and energy conversion according to the real-time environmental adjustment instruction report and the ecological cycle model, so that a complementary relationship is formed between the rice field and the black-spotted frog, and generates suggestions for optimizing the agricultural cycle; The black-spotted frog behavior analysis system module based on AR technology uses AR technology and image recognition technology to observe and analyze the behavior patterns of black-spotted frogs and generate a behavior response optimization plan based on the suggestions for optimizing agricultural cycles; The breeding prediction model module based on data analysis and historical data, real-time monitoring data and behavioral response optimization scheme uses machine learning algorithms to establish a correlation model between breeding parameters and the growth effect of black-spotted frogs, and outputs a breeding effect prediction report; The health monitoring and microbial regulation system module uses sound recognition technology to monitor the health status of black-spotted frogs based on the breeding effect prediction report, and introduces microbial strains to regulate water quality and ecological environment to generate a comprehensive health and ecological adjustment plan.

2. The system for breeding black-spotted frogs in rice fields according to claim 1, characterized in that: The multi-level ecological rice field construction module includes a species interaction submodule, a three-layer ecological structure submodule, and an ecological configuration scheme submodule; The intelligent environment monitoring system module includes a water quality monitoring submodule, a climate monitoring submodule, and an environment adjustment instruction submodule; The ecological cycle agriculture model module includes a material flow analysis submodule, an energy conversion analysis submodule, and an agricultural cycle optimization submodule.

3. The system for breeding black-spotted frogs in rice fields according to claim 1, characterized in that: The black-spotted frog behavior analysis system module based on AR technology includes an AR observation submodule, an image recognition submodule, and a behavior response optimization submodule; The aquaculture prediction model module based on data analysis includes a historical data analysis submodule, a real-time monitoring data analysis submodule, and an aquaculture effect prediction submodule; The health monitoring and microbial regulation system module includes a health monitoring submodule, a microbial regulation submodule, and a comprehensive health and ecological adjustment submodule.

4. A method for breeding black-spotted frogs in rice fields, characterized in that: The method for breeding black-spotted frogs in rice fields is performed based on the system for breeding black-spotted frogs in rice fields according to any one of claims 1 to 3, and comprises the following steps: Through ecological modeling technology, a food chain dynamic balance algorithm is used to simulate the species interaction in the natural ecosystem, generate an ecological balance plan, and design a three-layer ecological structure of the rice field and generate an ecological rice field configuration plan based on the ecological balance plan; Based on the Internet of Things technology and deep learning prediction model, the PID control algorithm is used to adjust the environmental parameters in real time. According to the ecological balance plan, the water quality, temperature and humidity are monitored and adjusted in real time to generate an environmental optimization instruction report; Applying the material energy balance analysis method and the agricultural cycle model to analyze, according to the environmental optimization instruction report, analyzing the material flow and energy conversion, so as to realize the complementary relationship between rice fields and black-spotted frogs, and generate an agricultural cycle optimization plan; Using AR technology and convolutional neural networks for image recognition, observing and analyzing the behavior patterns of black-spotted frogs according to the agricultural cycle optimization scheme, and generating a behavior response optimization report; A support vector machine machine learning algorithm is used to establish a model for associating breeding parameters with growth effects, and a breeding effect prediction report is output by combining the behavior response optimization report, historical data, and real-time monitoring data; By analyzing the sound recognition technology and the ecological regulation algorithm of microbial strains, the health status of black-spotted frogs is monitored according to the breeding effect prediction report, and the water quality and ecological environment are controlled through microbial regulation to generate a comprehensive health and ecological adjustment plan.

5. The method for breeding black-spotted frogs in rice fields according to claim 4, characterized in that: Through ecological modeling technology, the food chain dynamic balance algorithm is used to simulate the species interaction in the natural ecosystem and generate an ecological balance plan. According to the ecological balance plan, the three-layer ecological structure of the rice field is designed and the steps of generating the ecological rice field configuration plan are as follows: Use the food chain dynamic balance algorithm to simulate the interaction between species and establish a species interaction model; Based on the species interaction model, the optimization algorithm is used to design the rice field ecology and generate an ecological balance plan; According to the ecological balance scheme, the ecological zoning technology is applied to construct the three-layer ecological structure of rice fields and generate a three-layer ecological structure sketch; Based on the three-layer ecological structure sketch, the model is evaluated and the configuration is output to construct an ecological rice field configuration plan.

6. The method for breeding black-spotted frogs in rice fields according to claim 4, characterized in that: Based on the Internet of Things technology and deep learning prediction model, the PID control algorithm is used to adjust the environmental parameters in real time. According to the ecological balance plan, the water quality, temperature and humidity are monitored and adjusted in real time. The specific steps of generating the environmental optimization instruction report are as follows: Use IoT technology sensors to collect water quality, temperature, and humidity data in real time and integrate the original environmental data set; Applying a deep learning prediction model to predict future environmental trends of the original environmental data set and generating an environmental trend prediction report; According to the environmental trend forecast report, the environmental parameters are adjusted in real time using a PID control algorithm to generate an environmental adjustment plan; The environmental adjustment plan is integrated with the real-time environmental data to generate a detailed report as an environmental optimization instruction report.

7. The method for breeding black-spotted frogs in rice fields according to claim 4, characterized in that: Apply the material energy balance analysis method and the agricultural cycle model to analyze, analyze the material flow and energy conversion according to the environmental optimization instruction report, so as to achieve the complementary relationship between rice fields and black-spotted frogs, and generate the agricultural cycle optimization plan in the following steps: Based on the flow conservation principle, analyze the material flow in the environmental optimization instruction report and generate material flow analysis results; Using an energy balance algorithm to evaluate the energy conversion efficiency in the material flow analysis results, and generating an energy conversion efficiency report; Combine the energy conversion efficiency report with the material flow analysis results to establish a preliminary ecological cycle model; Optimize the preliminary ecological cycle model, strengthen the complementary relationship between rice fields and black-spotted frogs, and establish an agricultural cycle optimization plan.

8. The method for breeding black-spotted frogs in rice fields according to claim 4, characterized in that: The steps of using AR technology and convolutional neural network for image recognition, observing and analyzing the behavior patterns of black-spotted frogs according to the agricultural cycle optimization scheme, and generating a behavior response optimization report are as follows: Use AR technology to collect real-time image data of black-spotted frogs in rice fields and integrate the original image data set; Applying a convolutional neural network to analyze the original image data set, identifying the behavior pattern of the black-spotted frog, and generating a behavior pattern analysis report; Based on the behavior pattern analysis report, an optimization algorithm is applied to adjust the breeding environment to optimize the black-spotted frog behavior and generate a behavior optimization plan; Integrate the behavior optimization plan with the behavior pattern analysis report to form a behavior response optimization report.

9. The method for breeding black-spotted frogs in rice fields according to claim 4, characterized in that: The support vector machine machine learning algorithm is used to establish a model for associating breeding parameters with growth effects. The steps for outputting a breeding effect prediction report are as follows, combining the behavior response optimization report, historical data, and real-time monitoring data: Integrate behavioral response optimization reports, historical farming data and real-time monitoring data to establish a comprehensive farming data set; The comprehensive breeding data set is analyzed using a support vector machine algorithm to establish a correlation model between breeding parameters and growth effects, which is named a growth effect correlation model; The growth effect correlation model is used to predict future breeding effects and generate a breeding effect prediction report.

10. The method for breeding black-spotted frogs in rice fields according to claim 4, characterized in that: The steps of analyzing the health of black-spotted frogs by using sound recognition technology and the ecological regulation algorithm of microbial strains, monitoring the health of black-spotted frogs and controlling the water quality and ecological environment through microbial regulation according to the breeding effect prediction report, and generating a comprehensive health and ecological adjustment plan are as follows: Analyze the health status of black-spotted frogs through sound recognition technology and generate frog health status reports; Apply ecological control algorithms to analyze the ability of microbial strains to improve water quality and generate water quality regulation analysis reports; Design an ecological environment optimization design plan based on the frog health status report and water quality regulation analysis report; According to the ecological environment optimization design plan, actual operations are carried out to adjust the entire rice field ecosystem and generate a comprehensive health and ecological adjustment plan.

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