Precise feeding intelligent management system based on rice and shrimp feeding

Abstract

The invention relates to the technical field of aquaculture, in particular to an accurate feeding intelligent management system based on rice and shrimp feeding, which comprises a data acquisition module, a rice growth and water quality state coupling analysis module, an accurate feeding module, an automatic feeding execution module and a water quality and water level regulation and control module. A multi-point sensor network is deployed to collect rice growth data and water quality parameters in real time, a coupling relation model between rice growth and a water quality state is utilized to analyze the influence of a rice growth stage on the water quality, and a mapping relation of water quality change on the feeding demand of the crayfish is further calculated; the feeding opportunity and quantity are optimized by combining the precise feeding module, so that the feeding efficiency and the water quality stability are improved, the feeding amount and frequency are dynamically adjusted through the automatic feeding execution module, and the water quality and water level regulation module monitors and regulates water level and water quality parameters, so that the intelligent level of the system is improved, manual intervention is reduced, and the feeding efficiency is improved. And the overall economic benefit is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of aquaculture, and in particular to a precise feeding intelligent management system based on rice-shrimp feeding. Background Art

[0002] With the integrated development of agriculture and aquaculture, the rice-shrimp symbiosis model, as an ecological agricultural model, is being used more and more widely. Rice and crayfish grow and are raised together in the same water body. Through reasonable water quality management and precise feeding system, it can not only improve the utilization efficiency of aquatic resources in rice fields, but also promote the growth of rice and the breeding benefits of crayfish.

[0003] The existing management of the rice-shrimp symbiosis model mainly relies on manual experience and regular water quality testing, lacking systematic and accurate monitoring and regulation methods. Traditional water quality management and feeding strategies are often based on rough time nodes and water quality parameters, but the growth cycle of rice and the feeding needs of crayfish show a complex nonlinear relationship with changes in water quality. Existing management methods cannot accurately capture these changes, resulting in delayed water quality regulation, inaccurate feeding amounts, and even water quality deterioration, overfeeding or underfeeding. In addition, traditional methods often ignore the impact of rice growth on water quality changes, resulting in a lack of precision in the coupled management of rice and water quality, which affects the efficiency and stability of the entire rice-shrimp symbiosis system. Summary of the invention

[0004] The present invention provides a precise feeding intelligent management system based on rice-shrimp feeding.

[0005] The precision feeding intelligent management system based on rice-shrimp feeding includes a data acquisition module, a rice growth and water quality state coupling analysis module, a precision feeding module, an automatic feeding execution module and a water quality and water level control module, among which;

[0006] The data acquisition module collects rice growth data and water quality parameters in real time through a multi-point sensor network deployed in the rice field;

[0007] The rice growth and water quality state coupling analysis module analyzes the impact of rice growth stages on water quality by establishing a coupling relationship model between rice growth and water quality states, and estimates the relationship between water quality changes and crayfish feeding needs;

[0008] The precision feeding module combines the rice growth stage with the water quality changes and optimizes the feeding time of crayfish based on the coupling analysis results;

[0009] The automatic feeding execution module automatically executes the feeding task according to the output of the precise feeding prediction and optimization module;

[0010] The water quality and water level control module ensures the stability and suitability of the symbiotic environment of rice and crayfish by adjusting the water level and water quality parameters of the rice field.

[0011] Optionally, the data acquisition module includes:

[0012] Sensor network deployment: Multiple sensors are arranged in the rice fields, and a multi-point sensor network is formed through wireless transmission technology to ensure full coverage of the entire rice field area and collect rice growth data and water quality parameters in real time. The sensors include laser ranging sensors and water quality monitoring sensors. The laser ranging sensors are used to monitor the growth status of rice, and the water quality monitoring sensors are used to collect water quality parameters.

[0013] Rice growth data collection: The growth height of rice is collected through a laser ranging sensor, and the growth stage of rice is calculated based on the growth height of rice. The growth stage of rice includes the seedling stage, tillering stage and heading stage.

[0014] Water quality parameter collection: Water quality monitoring sensors collect water quality parameters in rice fields in real time, including ammonia nitrogen concentration, phosphorus concentration, dissolved oxygen and pH value.

[0015] Integration of water quality and rice growth stage data: Integrate the collected rice growth data with water quality parameters to form an integrated data set.

[0016] Optionally, the impact of the rice growth stage on water quality includes:

[0017] Construction of coupling model between rice growth stage and water quality status: Based on the collected rice growth data and water quality parameters, an artificial neural network algorithm is used to establish a coupling relationship model between rice growth and water quality status, and analyze the impact of different rice growth stages on water quality;

[0018] Analysis of rice absorption and water quality impact: Based on the established coupling relationship model between rice growth and water quality, by analyzing the impact of rice growth stages on water quality, the absorption of nutrients (such as nitrogen, phosphorus, etc.) in water by rice at different growth stages is estimated, and its impact on water quality changes is analyzed.

[0019] Optionally, the relationship between the water quality change and the crayfish feeding needs includes:

[0020] Analysis of the relationship between water quality changes and crayfish feeding needs: Based on the impact of rice growth stages on water quality, the relationship between water quality changes (such as dissolved oxygen, ammonia nitrogen concentration, etc.) and crayfish feeding needs was estimated. A mapping model of water quality changes to crayfish feeding needs was established using the support vector regression algorithm.

[0021] Model training: By collecting crayfish feeding data under different water quality conditions, we train a model to map the effects of water quality changes on crayfish feeding needs and optimize model parameters.

[0022] Optionally, the precision feeding module includes:

[0023] Acquisition of analysis results: receiving the coupling analysis results of the coupling model of rice growth stage and water quality status, as well as the mapping relationship between water quality changes and crayfish feeding demand mapping model;

[0024] Judgment of feeding timing: Based on the analysis results obtained, combined with the rice growth stage (such as seedling stage, tillering stage, heading stage) and water quality changes (such as dissolved oxygen, ammonia nitrogen concentration, etc.), determine the best time to feed crayfish.

[0025] Optionally, the precision feeding module further includes:

[0026] Feeding quantity: Based on the mapping relationship, the feeding requirements of crayfish under the current water quality conditions are estimated, and the feeding quantity is determined in combination with the changes in the rice growth stage;

[0027] Feeding strategy optimization: Generate a comprehensive and precise feeding strategy based on feeding timing and feeding quantity.

[0028] Optionally, the automatic feeding execution module includes:

[0029] Feeding task reception: receiving the precise feeding strategy generated by the precise feeding module. The precise feeding strategy includes feeding timing and feeding quantity;

[0030] Feeding equipment control: Control the automatic feeding equipment to feed according to the received precise feeding strategy;

[0031] Real-time monitoring and adjustment: During the feeding process, the feeding situation and water quality parameters of crayfish are monitored in real time, and the feeding amount and frequency are dynamically adjusted according to the monitoring data;

[0032] Feedback on feeding task execution: After the feeding task is completed, the feeding results will be fed back to the precision feeding module to evaluate the feeding effect, including the changes in crayfish food intake and water quality parameters.

[0033] Optionally, the water quality and water level control module includes:

[0034] Water level monitoring and regulation: Water level monitoring sensors are deployed to monitor the water level data in the rice fields in real time. Based on the water level data, the water level in the rice fields is adjusted through water pumps or water diversion devices to ensure that the water level meets the optimal conditions for rice growth and crayfish habitat.

[0035] Water quality parameter monitoring and adjustment: Water quality monitoring sensors are used to monitor water quality parameters in rice fields in real time, including ammonia nitrogen concentration, phosphorus concentration, dissolved oxygen and pH value. Based on the monitored water quality parameters, it is determined whether the water quality meets the conditions for the symbiosis of rice and crayfish.

[0036] Optionally, the water quality and water level control module further includes:

[0037] Dynamic control: Based on water level data and water quality parameters, combined with changes in rice growth in rice fields and crayfish feeding needs, a dynamic control strategy is formulated.

[0038] Beneficial effects of the present invention:

[0039] The present invention, by constructing a coupling relationship model between rice growth and water quality status, can accurately capture the impact of rice growth stages on water quality changes, and optimize water quality control strategies based on the model. By real-time monitoring and analyzing the needs of rice at different growth stages, the system can dynamically adjust water levels and water quality parameters according to the growth characteristics of rice, ensuring the optimal water quality conditions required for rice growth, effectively improving the accuracy and responsiveness of rice and water quality management, avoiding the lag and inadaptability in traditional management methods, and enhancing the stability of the ecosystem.

[0040] The present invention establishes a mapping relationship model between water quality changes and crayfish feeding needs, and uses a support vector regression algorithm to accurately describe the relationship between water quality changes (such as dissolved oxygen, ammonia nitrogen concentration, etc.) and crayfish feeding needs (such as food intake, activity, etc.). Through this model, the system can monitor water quality changes in real time, accurately predict crayfish feeding needs, and optimize feeding strategies. This method avoids the errors in traditional feeding methods based on experience, makes feeding more accurate, reduces over- or under-feeding, and improves the efficiency and sustainability of crayfish farming.

[0041] The present invention forms a comprehensive and accurate environment and feeding management system by combining the coupling relationship model between rice growth and water quality status and the mapping relationship model between water quality changes and crayfish feeding needs. According to the output results of these two models, the system realizes automatic regulation of water quality, water level and feeding amount, thereby improving the growth efficiency and breeding output value of crayfish while ensuring the stability and suitability of the symbiosis of rice and crayfish. It improves the intelligence level of the system, reduces human intervention, improves operating efficiency, reduces resource waste, and enhances overall economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A schematic diagram of a system flow of an embodiment of the present invention;

[0044] Figure 2 Schematic diagram of the precision feeding module process of an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0046] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0047] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0048] like Figure 1-Figure 2 As shown, the precise feeding intelligent management system based on rice-shrimp feeding includes a data acquisition module, a rice growth and water quality state coupling analysis module, a precise feeding module, an automatic feeding execution module and a water quality and water level control module, wherein;

[0049] The data acquisition module collects rice growth data and water quality parameters in real time through a multi-point sensor network deployed in the rice fields;

[0050] The coupling analysis module of rice growth and water quality status establishes a coupling relationship model between rice growth and water quality status, analyzes the impact of rice growth stage on water quality, and estimates the relationship between water quality changes and crayfish feeding needs;

[0051] The precision feeding module combines the rice growth stage with water quality changes and optimizes the feeding time of crayfish based on the coupling analysis results;

[0052] The automatic feeding execution module automatically executes the feeding task according to the output of the precise feeding prediction and optimization module;

[0053] The water quality and water level control module ensures the stability and suitability of the symbiotic environment of rice and crayfish by adjusting the water level and water quality parameters of the rice fields.

[0054] The data acquisition module includes:

[0055] Sensor network deployment: Multiple sensors are arranged in the rice fields, and a multi-point sensor network is formed through wireless transmission technology to ensure full coverage of the entire rice field area and collect rice growth data and water quality parameters in real time. The sensors include laser ranging sensors and water quality monitoring sensors. The laser ranging sensors are used to monitor the growth status of rice, and the water quality monitoring sensors are used to collect water quality parameters. Through this sensor network, efficient data collection and transmission are ensured to support subsequent analysis and decision-making.

[0056] Rice growth data collection: The growth height of rice is collected through a laser ranging sensor, and the growth stage of rice is calculated based on the growth height of rice. The growth stage of rice includes the seedling stage, tillering stage and heading stage.

[0057] Water quality parameter collection: Water quality monitoring sensors collect water quality parameters in rice fields in real time, including ammonia nitrogen concentration, phosphorus concentration, dissolved oxygen and pH value;

[0058] Water quality monitoring sensors include ion selective electrode sensors, polarographic dissolved oxygen sensors, and glass electrodes;

[0059] The ion selective electrode sensor converts the concentration of ammonia ions into a voltage signal through an electrochemical reaction, thereby determining the concentration of ammonia nitrogen in water;

[0060] Polarographic dissolved oxygen sensors measure dissolved oxygen concentration in water through electrochemical reactions;

[0061] The glass electrode is composed of a layer of ion-selective glass membrane. When the electrode is immersed in water, hydrogen ions (H + ) generates a potential difference with the glass membrane, and the potential difference is proportional to the pH value of the water, thereby realizing the measurement of the pH value;

[0062] The phosphorus concentration is measured by colorimetry, which is based on the reaction of phosphorus in the water sample with a certain reagent to form a colored substance. The absorbance after the reaction is positively correlated with the phosphorus concentration;

[0063] Ammonia nitrogen concentration reflects the pollution level of nitrogen sources in water bodies, phosphorus concentration reflects the eutrophication level of water bodies, dissolved oxygen reflects the oxygen content of water bodies, and pH value reflects the acidity and alkalinity of water bodies. Real-time collection of these data provides a basis for subsequent water quality monitoring and optimization, and can effectively predict the feeding needs and activity of crayfish.

[0064] Integration of water quality and rice growth stage data: Integrate the collected rice growth data with water quality parameters to form an integrated data set. By synchronizing the time of rice growth stages and water quality parameters, it is ensured that the relationship between rice growth stages and water quality changes can be accurately reflected, providing high-quality data support for subsequent coupled analysis of rice growth and water quality status.

[0065] Impacts of rice growth stages on water quality include:

[0066] Construction of coupling model between rice growth stage and water quality status: Based on the collected rice growth data and water quality parameters, an artificial neural network algorithm is used to establish a coupling relationship model between rice growth and water quality status, and analyze the impact of different rice growth stages on water quality, including:

[0067] Artificial neural network algorithm, to establish the coupling relationship model between rice growth and water quality, including:

[0068] Using a multi-layer perceptron network structure, the input layer is rice growth data, and the output layer is water quality change parameters (such as ammonia nitrogen concentration, dissolved oxygen, etc.);

[0069] Input layer: rice growth stages, including seedling stage, tillering stage and heading stage;

[0070] Output layer: parameters related to water quality changes, such as ammonia nitrogen concentration, dissolved oxygen, pH value, etc.;

[0071] Hidden layer: one or more hidden layers, which perform nonlinear mapping through activation functions.

[0072] The model expression is expressed as:

[0073] Y water =f(X rice ,θ);

[0074] Among them, Y water Indicates changes in water quality parameters (such as changes in dissolved oxygen concentration), X rice is the rice growth stage, θ is the weight and bias of the model, and f(·) is the activation function (such as ReLU, Sigmoid, etc.);

[0075] Analysis of rice absorption and water quality impact: Based on the established coupling relationship model between rice growth and water quality, by analyzing the impact of rice growth stages on water quality, the absorption of nutrients (such as nitrogen, phosphorus, etc.) in water by rice at different growth stages is estimated, and its impact on water quality changes is analyzed.

[0076] Relationships between water quality changes and crayfish feeding requirements include:

[0077] Analysis of the relationship between water quality changes and crayfish feeding needs: Based on the impact of rice growth stages on water quality, the relationship between water quality changes (such as dissolved oxygen, ammonia nitrogen concentration, etc.) and crayfish feeding needs was estimated. A mapping model of water quality changes to crayfish feeding needs was established using the support vector regression algorithm.

[0078] Model training: By collecting crayfish feeding data under different water quality conditions, we train a model to map the effect of water quality changes on crayfish feeding needs and optimize model parameters;

[0079] The support vector regression algorithm was used to establish a mapping model of water quality changes on crayfish feeding requirements, including:

[0080] Input parameters: changes in water quality parameters (such as dissolved oxygen, ammonia nitrogen concentration, etc.);

[0081] Output parameters: crayfish feeding requirements (such as food intake, activity level, etc.).

[0082] The model expression is expressed as:

[0083] y 需求 =K(Y water ,θ1);

[0084] Among them, y 需求 is the feeding requirement of crayfish, Y water represents the change of water quality parameters (such as ammonia nitrogen concentration, dissolved oxygen concentration, etc.), θ1 is the parameter of the model, and K(·) is the decision function of the support vector regression algorithm;

[0085] The optimization objective is expressed as:

[0086]

[0087] in, <w,x i >+b is the decision function of the model, ∈ is the regression accuracy, w is the weight vector, x i is the input feature (such as dissolved oxygen concentration, ammonia nitrogen concentration, etc.), y 需求 is the target output (i.e., the feeding demand of crayfish).

[0088] The precision feeding module includes:

[0089] Acquisition of analysis results: receiving the coupling analysis results of the coupling model of rice growth stage and water quality status, as well as the mapping relationship between water quality changes and crayfish feeding demand mapping model;

[0090] Judgment of feeding timing: Based on the analysis results obtained, combined with the rice growth stage (such as seedling stage, tillering stage, heading stage) and water quality changes (such as dissolved oxygen, ammonia nitrogen concentration, etc.), determine the best feeding time for crayfish. The feeding timing is optimized according to the impact of rice on water quality and the impact of water quality on the feeding needs of crayfish, to ensure that feeding behavior does not aggravate water quality deterioration or poor growth of crayfish.

[0091] The precision feeding module also includes:

[0092] Feeding quantity: Based on the mapping relationship, the feeding demand of crayfish under the current water quality conditions is estimated, and the feeding quantity is determined in combination with the changes in the rice growth stage. The feeding quantity is dynamically adjusted according to water quality parameters (such as ammonia nitrogen concentration, dissolved oxygen concentration) and the feeding demand of crayfish to avoid overfeeding or underfeeding;

[0093] Feeding strategy optimization: Generate a comprehensive and precise feeding strategy based on feeding timing and feeding quantity. The feeding strategy ensures that crayfish receive the best nutrient supply at different rice growth stages and water quality conditions, optimizes their growth rate and reproductive performance, and avoids excessive water pollution;

[0094] The precision feeding module optimizes the timing and quantity of crayfish feeding by combining the analysis results of rice growth stage and water quality changes, ensuring that feeding behavior matches environmental conditions, improving crayfish feeding effects and breeding benefits, while reducing the risk of water pollution.

[0095] The automatic feeding execution module includes:

[0096] Feeding task reception: receiving the precise feeding strategy generated by the precise feeding module. The precise feeding strategy includes feeding timing and feeding quantity;

[0097] Feeding equipment control: Control the automated feeding equipment (such as feeders, feeding devices, etc.) to feed according to the received precise feeding strategy;

[0098] Real-time monitoring and adjustment: During the feeding process, the feeding situation and water quality parameters of crayfish are monitored in real time. According to the monitoring data, the feeding amount and frequency are dynamically adjusted to ensure that the feeding task meets the actual needs, avoid over- or under-feeding, and ensure that the water quality is not affected;

[0099] Feedback on feeding task execution: After the feeding task is completed, the feeding results will be fed back to the precision feeding module to evaluate the feeding effect, including the changes in crayfish food intake and water quality parameters. The feedback information is used to further optimize future feeding strategies and improve the intelligence level of the system.

[0100] The automated feeding execution module accurately controls the feeding equipment to perform the feeding task according to the feeding strategy provided by the precision feeding module, and monitors the crayfish's feeding situation and water quality parameters in real time during the feeding process to ensure that the feeding amount and frequency meet actual needs and avoid adverse effects on water quality. After the feeding task is completed, the system will feed back the results to the precision feeding module, and continuously optimize future feeding strategies through data feedback, realizing an intelligent and precise rice-shrimp symbiotic farming model and improving the system's management efficiency and production benefits.

[0101] The water quality and water level control module includes:

[0102] Water level monitoring and regulation: Water level monitoring sensors are deployed to monitor the water level data in the rice fields in real time. Based on the water level data, the water level in the rice fields is adjusted through water pumps or water diversion devices to ensure that the water level meets the optimal conditions for rice growth and crayfish habitat.

[0103] Monitoring and adjustment of water quality parameters: Water quality monitoring sensors are used to monitor water quality parameters in rice fields in real time, including ammonia nitrogen concentration, phosphorus concentration, dissolved oxygen and pH value. Based on the monitored water quality parameters, it is determined whether the water quality meets the conditions for the symbiosis of rice and crayfish. When the water quality does not meet the standards, the water quality parameters are automatically adjusted, for example, by adding oxygen equipment, adding regulators, etc.

[0104] The water quality and water level control module also includes:

[0105] Dynamic control: Based on water level data and water quality parameters, combined with the changes in rice growth and crayfish feeding needs in the rice fields, a dynamic control strategy is formulated. By analyzing the relationship between different stages of rice growth and water quality, real-time control decisions are generated to optimize the control of water level and water quality, and ensure the best environmental conditions for rice growth and crayfish habitat, including:

[0106] Analyze the impact of rice growth on water quality changes based on different stages of rice growth. For example, different rice growth stages may have different demands or impacts on water quality parameters such as ammonia nitrogen concentration and dissolved oxygen content in water.

[0107] Combined with water quality changes, analyze the impact of water quality parameters (such as dissolved oxygen and ammonia nitrogen concentration) on crayfish's feeding needs, and consider the changes in crayfish's feeding needs under different water quality conditions. For example, low dissolved oxygen or high ammonia nitrogen concentrations may lead to reduced crayfish activity or reduced food intake;

[0108] Based on the dynamic changes of rice growth stages and water quality parameters, as well as the real-time changes in crayfish feeding needs, specific environmental regulation strategies are generated, including:

[0109] (1) Water level control strategy: Based on the real-time data of the water level in the rice field and the growth requirements of the rice, decide whether to adjust the water level through a water diversion device or a water pump to ensure the best environment for rice growth;

[0110] (2) Water quality control strategy: Based on the water quality monitoring data, a plan for regulating water quality is generated. For example, if the ammonia nitrogen concentration is too high, the oxygenation equipment is started; if the dissolved oxygen is too low, it may be necessary to adjust the pH value or add a water quality regulator.

[0111] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0112] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A precise feeding intelligent management system based on rice-shrimp feeding, characterized in that: It includes data acquisition module, rice growth and water quality status coupling analysis module, precision feeding module, automatic feeding execution module and water quality and water level control module, among which; The data acquisition module collects rice growth data and water quality parameters in real time through a multi-point sensor network deployed in the rice field; The rice growth and water quality state coupling analysis module analyzes the impact of rice growth stages on water quality by establishing a coupling relationship model between rice growth and water quality states, and estimates the relationship between water quality changes and crayfish feeding needs; The precision feeding module combines the rice growth stage with the water quality changes and optimizes the feeding time of crayfish based on the coupling analysis results; The automatic feeding execution module automatically executes the feeding task according to the output of the precise feeding prediction and optimization module; The water quality and water level control module ensures the stability and suitability of the symbiotic environment of rice and crayfish by adjusting the water level and water quality parameters of the rice field.

2. The precise feeding intelligent management system based on rice-shrimp feeding according to claim 1 is characterized in that: The data acquisition module comprises: Sensor network deployment: Multiple sensors are arranged in the rice fields to form a multi-point sensor network through wireless transmission technology to collect rice growth data and water quality parameters in real time. The sensors include laser ranging sensors and water quality monitoring sensors. The laser ranging sensors are used to monitor the growth status of rice, and the water quality monitoring sensors are used to collect water quality parameters. Rice growth data collection: The growth height of rice is collected through a laser ranging sensor, and the growth stage of rice is calculated based on the growth height of rice. The growth stage of rice includes the seedling stage, tillering stage and heading stage. Water quality parameter collection: Water quality monitoring sensors collect water quality parameters in rice fields in real time, including ammonia nitrogen concentration, phosphorus concentration, dissolved oxygen and pH value; Integration of water quality and rice growth stage data: Integrate the collected rice growth data with water quality parameters to form an integrated data set.

3. The precise feeding intelligent management system based on rice-shrimp feeding according to claim 2 is characterized in that: The impacts of the rice growth stages on water quality include: Construction of coupling model between rice growth stage and water quality status: Based on the collected rice growth data and water quality parameters, an artificial neural network algorithm is used to establish a coupling relationship model between rice growth and water quality status, and analyze the impact of different rice growth stages on water quality; Analysis of rice absorption and water quality impact: Based on the established coupling relationship model between rice growth and water quality, by analyzing the impact of rice growth stages on water quality, the absorption of nutrients in water by rice at different growth stages is estimated, and its impact on water quality changes is analyzed.

4. The precise feeding intelligent management system based on rice-shrimp feeding according to claim 3 is characterized in that: The associations between water quality changes and crayfish feeding requirements include: Analysis of the relationship between water quality changes and crayfish feeding needs: The relationship between water quality changes and crayfish feeding needs was estimated by the effect of rice growth stages on water quality. A model mapping water quality changes to crayfish feeding needs was established using the support vector regression algorithm. Model training: By collecting crayfish feeding data under different water quality conditions, we train a model to map the effects of water quality changes on crayfish feeding needs and optimize model parameters.

5. The precise feeding intelligent management system based on rice-shrimp feeding according to claim 4 is characterized in that: The precision feeding module comprises: Acquisition of analysis results: receiving the coupling analysis results of the coupling model of rice growth stage and water quality status, as well as the mapping relationship between water quality changes and crayfish feeding demand mapping model; Feeding timing judgment: Based on the analysis results obtained, combined with the rice growth stage and water quality changes, determine the best time to feed crayfish.

6. The precise feeding intelligent management system based on rice-shrimp feeding according to claim 5 is characterized in that: The precision feeding module also includes: Feeding quantity: Based on the mapping relationship, the feeding requirements of crayfish under the current water quality conditions are estimated, and the feeding quantity is determined in combination with the changes in the rice growth stage; Feeding strategy optimization: Generate a comprehensive and precise feeding strategy based on feeding timing and feeding quantity.

7. The precise feeding intelligent management system based on rice-shrimp feeding according to claim 6 is characterized in that: The automatic feeding execution module comprises: Feeding task reception: receiving the precise feeding strategy generated by the precise feeding module. The precise feeding strategy includes feeding timing and feeding quantity; Feeding equipment control: Control the automatic feeding equipment to feed according to the received precise feeding strategy; Real-time monitoring and adjustment: During the feeding process, the feeding situation and water quality parameters of crayfish are monitored in real time, and the feeding amount and frequency are dynamically adjusted according to the monitoring data; Feedback on feeding task execution: After the feeding task is completed, the feeding results will be fed back to the precision feeding module to evaluate the feeding effect, including the changes in crayfish food intake and water quality parameters.

8. The precise feeding intelligent management system based on rice-shrimp feeding according to claim 7 is characterized in that: The water quality and water level control module comprises: Water level monitoring and regulation: Water level monitoring sensors are deployed to monitor the water level data in the rice fields in real time. Based on the water level data, water pumps or water diversion devices are used to adjust the water level in the rice fields. Water quality parameter monitoring and adjustment: Water quality monitoring sensors are used to monitor water quality parameters in rice fields in real time, including ammonia nitrogen concentration, phosphorus concentration, dissolved oxygen and pH value. Based on the monitored water quality parameters, it is determined whether the water quality meets the conditions for the symbiosis of rice and crayfish.

9. The precise feeding intelligent management system based on rice-shrimp feeding according to claim 8, characterized in that: The water quality and water level control module also includes: Dynamic control: Based on water level data and water quality parameters, combined with changes in rice growth in rice fields and crayfish feeding needs, a dynamic control strategy is formulated.

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