Intelligent livestock and poultry epidemic disease early warning and partitioned prevention and control management system and method

Abstract

The invention relates to the technical field of livestock and poultry epidemic disease management, and particularly discloses an intelligent livestock and poultry epidemic disease early warning and partitioned prevention and control management system and method, and the method comprises the steps: laying an integrated intelligent sensor network in a livestock and poultry farm, and collecting environment parameters, feeding management data and animal health data in real time through a low-power-consumption Internet of Things technology; transmitting the collected multi-dimensional data to a cloud platform by utilizing an edge computing technology; based on multi-dimensional data, a deep learning and multi-source data fusion analysis technology is adopted, a dynamic disease risk prediction model is constructed, and generalization ability of an adversarial generative network optimization model in a complex environment is introduced; by arranging the integrated intelligent sensor network, environmental parameters, feeding management data and animal health data in the livestock and poultry farm are comprehensively collected in real time, the early monitoring capability of epidemic diseases is remarkably improved, potential epidemic disease risks are found in time, and precious time is won for subsequent prevention and control work.

Description

Technical Field

[0001] The present invention belongs to the technical field of livestock and poultry disease management, and specifically relates to an intelligent livestock and poultry disease early warning and zoning prevention and control management system and method. Background Art

[0002] With the continuous expansion and centralization of the livestock and poultry farming industry, disease prevention and control has become a key issue in ensuring its healthy development. In high-density farming environments, the risk of disease transmission increases significantly. Traditional manual inspections and empirical judgments are unable to meet prevention and control needs, and are prone to delays, errors, and even the spread of large-scale epidemics.

[0003] Existing livestock and poultry disease prevention and control technologies typically rely on environmental monitoring, human intervention, and localized isolation. While these technologies can control the spread of diseases to a certain extent, they have numerous limitations. Existing monitoring systems typically focus on limited environmental factors, such as temperature, humidity, and gas concentrations, and are unable to comprehensively collect and analyze multi-dimensional data such as the breeding environment, animal health, and feeding management. This results in weak early warning capabilities for diseases and makes it difficult to detect potential risks in a timely manner. Current disease prediction methods often rely on simple statistical models or historical data, failing to fully utilize multi-source data for comprehensive analysis. The resulting predictions are less accurate and lack the ability to adapt to the multiple factors in complex environments. Furthermore, traditional prevention and control measures often lack flexibility and are difficult to dynamically adjust to real-time changes. Once an epidemic occurs, a timely and effective response is often impossible, leading to its spread and economic losses.

[0004] Therefore, it is necessary to propose an intelligent livestock and poultry disease early warning and zoning prevention and control management system and method to solve the problem of how to improve the accuracy of early disease monitoring and prediction and prevent the spread of disease in the existing technology.

[0005] The above information disclosed in this background technology is only for enhancing understanding of the background technology of the present invention and therefore it may contain information that does not constitute the prior art that is already known to a person of ordinary skill in the art. Summary of the Invention

[0006] The purpose of the present invention is to provide an intelligent livestock and poultry disease early warning and zoning prevention and control management system and method to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] An intelligent livestock and poultry disease early warning and zoning prevention and control management method, comprising:

[0009] Deploy an integrated intelligent sensor network in livestock and poultry farms, collect environmental parameters, feeding management data, and animal health data in real time through low-power IoT technology, and use edge computing technology to transmit the collected multi-dimensional data to the cloud platform;

[0010] Based on the multidimensional data, a dynamic disease risk prediction model is constructed using deep learning and multi-source data fusion analysis technology, and the generalization ability of the adversarial generative network optimization model in complex environments is introduced;

[0011] Utilize the dynamic disease risk prediction model to make real-time predictions of current epidemic risks, simulate possible transmission paths, and generate prediction results;

[0012] Based on the prediction results, the livestock and poultry breeding area is dynamically divided into multiple prevention and control zones using an intelligent clustering algorithm, and each zone generates a customized prevention and control strategy based on regional setting factors;

[0013] Deploy an intelligent automated control system, implement the aforementioned prevention and control measures, and monitor changes in environmental parameters within the farm in real time. Evaluate the effectiveness of the implementation based on the real-time monitoring data and generate evaluation results.

[0014] A closed-loop feedback optimization mechanism is established to adjust the parameters of the dynamic disease risk prediction model by continuously updating the assessment results, combining the economic loss assessment model, and performing adaptive optimization of disease early warning and the prevention and control strategy.

[0015] Preferably, the integrated intelligent sensor network includes at least three of a high-precision temperature and humidity sensor, a multi-gas component air quality sensor, a contactless body temperature monitoring instrument, a panoramic video monitoring device, and a feed consumption and water quality monitoring device, adopts a self-powered design and the data is encrypted and transmitted through blockchain technology.

[0016] Preferably, the dynamic disease risk prediction model combines historical epidemic data, real-time environmental data, animal behavior data and a cross-regional epidemic data sharing platform to perform real-time epidemic risk prediction and transmission path simulation, and introduces an attention mechanism to focus on characteristics that affect disease risk.

[0017] Preferably, the intelligent automatic control system integrates a dynamic control module based on a reinforcement learning algorithm, an automated disinfection robot, and an intelligent feeding and drinking water management module, and adjusts equipment parameters every 15 minutes according to real-time monitoring data and the prevention and control strategy.

[0018] Preferably, the method also establishes a knowledge map of livestock and poultry disease prevention and control, integrating global disease research progress, prevention and control cases and expert experience to provide comprehensive knowledge support and decision-making assistance to managers.

[0019] Preferably, the method further comprises an intelligent information push platform;

[0020] The intelligent information push platform uses natural language processing technology to push disease warning information, prevention and control recommendations, and decision-making support tools to managers at different levels in a personalized and interactive manner;

[0021] The prevention and control recommendations are pushed in a multi-level display mode:

[0022] (1) On-site operators receive equipment operation instructions;

[0023] (2) Technical consultants obtain simulated maps of pathogen transmission paths;

[0024] (3) The regional manager reviews the prevention and control cost-benefit analysis report.

[0025] An intelligent livestock and poultry disease early warning and zoning prevention and control management system, including:

[0026] The sensor network module is used to deploy an integrated intelligent sensor network in livestock and poultry farms to collect environmental parameters, feeding management data, and animal health data in real time to generate multidimensional data sets;

[0027] The data transmission module is used to transmit the collected multidimensional data sets to the cloud platform or local edge server for preliminary processing using low-power IoT technology and edge computing technology;

[0028] The disease risk prediction module uses deep learning and multi-source data fusion analysis technology to build a dynamic disease risk prediction model. It also introduces the generalization capability of the adversarial generative network optimization model to perform disease risk prediction and transmission path simulation.

[0029] The prevention and control strategy generation module is used to dynamically divide the prevention and control zones based on the prediction results using an intelligent clustering algorithm, and to generate customized prevention and control strategies for each zone;

[0030] An automated control module, used to implement the aforementioned prevention and control measures and monitor changes in environmental parameters within the farm in real time, and adjust equipment parameters based on real-time monitoring data and implementation results;

[0031] The information push module uses natural language processing technology to push disease warning information, prevention and control strategies, and decision-making support tools to managers at different levels in a personalized and interactive manner;

[0032] The feedback optimization module is used to establish a closed-loop feedback optimization mechanism, continuously monitor the prevention and control effects and adjust the model parameters, while establishing a knowledge map for livestock and poultry disease prevention and control to provide knowledge support and decision-making assistance.

[0033] Preferably, the automation control module is integrated with a fault prediction and health management submodule, and the fault prediction and health management submodule is used to predict equipment failures in advance and arrange maintenance plans.

[0034] Preferably, the feedback optimization module analyzes the behavior of farm staff through machine learning algorithms, identifies potential operational errors or violations, and provides real-time corrective suggestions.

[0035] Compared with the prior art, the present invention has the following beneficial effects:

[0036] The present invention deploys an integrated intelligent sensor network to collect environmental parameters, feeding management data and animal health data in livestock and poultry farms in real time and comprehensively, significantly improving the early monitoring capabilities of epidemics, timely discovering potential epidemic risks, and gaining valuable time for subsequent prevention and control work; it uses deep learning and multi-source data fusion analysis technology to construct a dynamic disease risk prediction model, and introduces the generalization ability of the adversarial generative network optimization model in complex environments, fully considering the impact of multiple factors on the spread of epidemics, and effectively preventing prevention and control errors caused by inaccurate predictions.

[0037] Furthermore, the deployed intelligent automated control system monitors changes in on-farm environmental parameters in real time. A closed-loop feedback optimization mechanism is also established to continuously monitor prevention and control effectiveness and adjust model parameters, implementing adaptive optimization of disease early warning and prevention and control strategies. This allows for continuous optimization of prevention and control effectiveness, better adapting to changes in the on-farm environment, and increasing the flexibility and effectiveness of prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 This is a flow chart of the intelligent livestock and poultry disease early warning and zoning prevention and control management method of the present invention;

[0039] Figure 2 This is a framework diagram of the intelligent livestock and poultry disease early warning and zoning prevention and control management system of the present invention. DETAILED DESCRIPTION

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0041] Example 1:

[0042] See also Figure 1 As shown, an intelligent livestock and poultry disease early warning and zoning prevention and control management method includes:

[0043] Deploy an integrated intelligent sensor network in livestock and poultry farms, collect environmental parameters, feeding management data, and animal health data in real time through low-power IoT technology, and use edge computing technology to transmit the collected multi-dimensional data to the cloud platform;

[0044] The integrated intelligent sensor network includes at least three of the following: high-precision temperature and humidity sensors, multi-gas composition air quality sensors, contactless body temperature monitoring instruments, panoramic video monitoring equipment, and feed consumption and water quality monitoring equipment. It is self-powered and its data is encrypted and transmitted using blockchain technology.

[0045] Furthermore, by combining low-power IoT technology with edge computing, real-time monitoring and data collection of livestock and poultry farm environments, feeding management, and animal health are possible. This technology not only improves farm management efficiency and reduces labor costs, but also provides data support for accurate decision-making, thereby promoting the intelligent and sustainable development of the livestock and poultry industry.

[0046] Based on multidimensional data, we use deep learning and multi-source data fusion analysis technology to build a dynamic disease risk prediction model, and introduce the generalization ability of the adversarial generative network optimization model in complex environments;

[0047] Use dynamic disease risk prediction models to make real-time predictions of current epidemic risks, simulate possible transmission paths, and generate prediction results;

[0048] The dynamic disease risk prediction model combines historical epidemic data, real-time environmental data, animal behavior data, and a cross-regional epidemic data sharing platform to conduct real-time epidemic risk prediction and transmission path simulation, and introduces an attention mechanism to focus on characteristics that affect disease risk;

[0049] Furthermore, through deep learning and multi-source data fusion analysis, combined with historical epidemic data, real-time environmental data, and animal behavior data, the model predicts disease risks in livestock and poultry farms in real time and simulates possible transmission paths. The introduction of a generative adversarial network optimization model and an attention mechanism enhances the model's generalization capabilities in complex environments and its focus on key risk factors. This technology not only improves the accuracy of disease warnings but also helps farms implement timely prevention and control measures, reducing the risk of disease spread and enhancing the industry's epidemic prevention capabilities and response efficiency.

[0050] Based on the prediction results, the livestock and poultry breeding area is dynamically divided into multiple prevention and control zones using an intelligent clustering algorithm. Each zone generates a customized prevention and control strategy based on regional setting factors.

[0051] Deploy intelligent automated control systems, implement prevention and control measures, and monitor changes in environmental parameters within the farm in real time. Evaluate the effectiveness of implementation based on real-time monitoring data and generate evaluation results.

[0052] The intelligent automated control system integrates a dynamic control module based on reinforcement learning algorithms, an automated disinfection robot, and an intelligent feeding and drinking water management module. It adjusts equipment parameters every 15 minutes based on real-time monitoring data and prevention and control strategies.

[0053] Furthermore, intelligent clustering algorithms dynamically divide livestock and poultry breeding areas and customize prevention and control strategies, effectively achieving precise disease prevention and control. Combined with a dynamic control module based on a reinforcement learning algorithm and an automated control system, this system monitors environmental parameter changes in real time and automatically adjusts equipment, improving the accuracy and responsiveness of prevention and control measures. Automated disinfection robots and intelligent feeding and water management modules optimize resource utilization, improve farm management efficiency, reduce human intervention, and ensure environmental and biosafety, ultimately enhancing disease prevention and control effectiveness and the overall farm operation.

[0054] Establish a closed-loop feedback optimization mechanism to continuously update assessment results, combine with the economic loss assessment model, adjust the parameters of the dynamic disease risk prediction model, and perform adaptive optimization of disease early warning and prevention and control strategies;

[0055] Establish a knowledge map for livestock and poultry disease prevention and control, integrate global disease research progress, prevention and control cases and expert experience, and provide managers with comprehensive knowledge support and decision-making assistance.

[0056] The intelligent information push platform uses natural language processing technology to push disease warning information, prevention and control recommendations, and decision-making support tools to managers at different levels in a personalized and interactive manner;

[0057] Prevention and control recommendations are pushed using a multi-level display mode:

[0058] (1) On-site operators receive equipment operation instructions;

[0059] (2) Technical consultants obtain simulated maps of pathogen transmission paths;

[0060] (3) The regional manager reviews the prevention and control cost-benefit analysis report.

[0061] Furthermore, by establishing a closed-loop feedback optimization mechanism, combined with an economic loss assessment model and a dynamic disease risk prediction model, adaptive optimization of disease early warning and prevention strategies can be achieved. This multi-level display model helps different roles quickly access the information they need, improving prevention and control efficiency, reducing costs, and enhancing the scientific nature and coordination of the decision-making process.

[0062] Example 2:

[0063] See also Figure 2 As shown, an intelligent livestock and poultry disease early warning and zoning prevention and control management system includes:

[0064] The sensor network module is used to deploy an integrated intelligent sensor network in livestock and poultry farms to collect environmental parameters, feeding management data, and animal health data in real time to generate multidimensional data sets;

[0065] The data transmission module is used to transmit the collected multidimensional data sets to the cloud platform or local edge server for preliminary processing using low-power IoT technology and edge computing technology;

[0066] The disease risk prediction module uses deep learning and multi-source data fusion analysis technology to build a dynamic disease risk prediction model. It also introduces the generalization capability of the adversarial generative network optimization model to perform disease risk prediction and transmission path simulation.

[0067] The prevention and control strategy generation module is used to dynamically divide the prevention and control zones based on the prediction results using an intelligent clustering algorithm, and to generate customized prevention and control strategies for each zone;

[0068] The automated control module is used to implement prevention and control measures and monitor changes in environmental parameters within the farm in real time, adjusting equipment parameters based on real-time monitoring data and implementation results;

[0069] Integrated with fault prediction and health management submodule, the fault prediction and health management submodule is used to predict equipment failures in advance and arrange maintenance plans;

[0070] The information push module uses natural language processing technology to push disease warning information, prevention and control strategies, and decision-making support tools to managers at different levels in a personalized and interactive manner;

[0071] Feedback optimization module, used to establish a closed-loop feedback optimization mechanism, continuously monitor the prevention and control effects and adjust model parameters, while also building a knowledge map for livestock and poultry disease prevention and control to provide knowledge support and decision-making assistance;

[0072] Machine learning algorithms are used to analyze farm worker behavior, identify potential operational errors or violations, and provide real-time corrective suggestions.

[0073] Application example: Refined application of intelligent livestock and poultry disease early warning and zoning prevention and control management system in a large chicken farm

[0074] A large chicken farm, serving as the region's primary egg-laying chicken supplier, boasts multiple modern breeding sheds housing tens of thousands of laying hens. With increasing stocking density and intensified market competition, disease prevention and control has become crucial for ensuring the farm's stable operations and product quality. Traditional manual inspections and empirical judgments are no longer sufficient for efficient and accurate disease prevention and control. To address this, the farm decided to introduce an intelligent livestock and poultry disease early warning and zoning control management system, leveraging technology to enhance the intelligence and sophistication of disease prevention and control.

[0075] 1. System Deployment and Application

[0076] (1) Integrated intelligent sensor network deployment

[0077] High-precision sensor layout: Each chicken house is meticulously equipped with high-precision temperature and humidity sensors, multi-gas air quality sensors (monitoring the concentration of harmful gases such as ammonia and carbon dioxide), contactless infrared body temperature monitoring equipment, panoramic HD video surveillance equipment, and intelligent feed consumption and water quality monitors. These sensors are self-powered and utilize low-power wide area network technology for efficient data transmission, ensuring real-time and accurate data.

[0078] Blockchain encrypted transmission: To ensure data security, all sensor data are encrypted using blockchain technology during transmission to prevent data tampering and leakage, providing a reliable data basis for disease early warning.

[0079] (2) Disease risk prediction and transmission path simulation

[0080] Multi-source data fusion analysis: Based on collected multidimensional data, the system combines deep learning algorithms with multi-source data fusion technology to build a dynamic disease risk prediction model. This model not only considers historical epidemic data and real-time environmental data, but also incorporates animal behavior pattern analysis (such as changes in feed intake and activity level) and information from cross-regional epidemic data sharing platforms to achieve a comprehensive risk assessment.

[0081] Attention mechanism and generative adversarial network: The attention mechanism is introduced to focus on features that have a significant impact on disease risk. At the same time, the generative adversarial network is used to optimize the model's generalization ability in complex environments, thereby improving the accuracy and robustness of predictions.

[0082] Three-dimensional transmission path simulation: Using GIS (geographic information system) technology and fluid dynamics models, the system can simulate the three-dimensional transmission path of the disease in the breeding house, providing a scientific basis for precise prevention and control.

[0083] (3) Generation and implementation of zoning prevention and control strategies

[0084] Intelligent Clustering and Dynamic Zoning: Based on the prediction results, the system uses an intelligent clustering algorithm, taking into account factors such as the physical layout of the farm, flock density, and environmental conditions, to dynamically divide the chicken farm into multiple prevention and control zones. Each zone generates customized prevention and control strategies based on its specific circumstances, such as adjusting ventilation strategies, optimizing stocking density, and implementing local disinfection.

[0085] Intelligent Automation Control System: This system integrates a dynamic control module based on reinforcement learning algorithms, an automated disinfection robot, and intelligent feeding and water management modules. Based on real-time monitoring data and prevention and control strategies, the system automatically adjusts equipment parameters every 10 minutes (more frequently than the original 15 minutes) to ensure the timeliness and effectiveness of prevention and control measures.

[0086] Real-time monitoring and control of environmental parameters: The system monitors the temperature, humidity, air quality and other environmental parameters in the breeding house in real time, and automatically adjusts ventilation, heating, cooling and other equipment through intelligent control modules to maintain the most suitable breeding environment and reduce the risk of disease.

[0087] (4) Closed-loop feedback optimization mechanism

[0088] Implementation effectiveness evaluation: The system continuously monitors changes in environmental parameters within the farm, animal health, and disease outbreaks, providing real-time assessments of the effectiveness of prevention and control measures. Results include reductions in disease risk, improvements in the farming environment, and reductions in economic losses.

[0089] Dynamic Adjustment of Model Parameters: Based on the assessment results, the system dynamically adjusts the parameters of the dynamic disease risk prediction model in conjunction with the economic loss assessment model, enabling adaptive optimization of disease early warning and prevention and control strategies. This closed-loop feedback mechanism ensures that prevention and control measures are continuously improved and optimized based on actual conditions.

[0090] (5) Intelligent information push and decision support

[0091] Personalized Information Push: The system uses natural language processing technology to deliver disease warning information, prevention and control recommendations, and decision-making support tools to managers at all levels in a personalized, interactive manner. On-site operators receive detailed equipment operating instructions and real-time data feedback; technical advisors access simulated pathogen transmission path maps and prevention and control effectiveness analysis reports; and regional managers review prevention and control cost-benefit analysis reports and strategic decision-making recommendations.

[0092] Livestock and Poultry Disease Prevention and Control Knowledge Graph: The system has established a knowledge graph for livestock and poultry disease prevention and control, integrating global disease research progress, successful prevention and control cases, and expert experience to provide managers with comprehensive knowledge support and decision-making assistance. Managers can use the knowledge graph to quickly find relevant information, improving the scientific nature and accuracy of their decisions.

[0093] 2. Application Effect

[0094] The accuracy of epidemic prediction has been significantly improved: through the application of multi-source data fusion analysis and deep learning algorithms, the system's prediction accuracy for epidemics has increased by more than 30% compared with traditional methods, effectively reducing the false alarm and missed alarm rates.

[0095] Improved refinement of zoning management: The implementation of intelligent clustering algorithms and dynamic zoning strategies has made prevention and control measures more precise and effective, reducing the risk of disease transmission by more than 50%.

[0096] Both management efficiency and economic benefits are improved: the application of intelligent automation control systems reduces manual intervention and improves management efficiency; at the same time, through precise prevention and control, it reduces disease losses and medical costs, bringing significant economic benefits to chicken farms.

[0097] In summary, the refined application of the intelligent livestock and poultry disease early warning and zoning prevention and control management system in a large chicken farm has achieved remarkable results. It not only improves the intelligence level of disease prevention and control, but also provides a strong guarantee for the stable operation and sustainable development of the chicken farm.

[0098] Example 3:

[0099] The present invention also provides a computer-readable storage medium, which stores a program for the intelligent livestock and poultry disease early warning and zoning prevention and control management system described above. When the program is executed by a processor, the various processes of the aforementioned prevention and control management system embodiment are implemented, and the same technical effects are achieved. To avoid repetition, the details are not described here. The computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0100] In the description of this specification, the reference terms "one embodiment", "some embodiments", "examples", "specific examples" or "some examples" mean that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0101] The drawings of the embodiments disclosed in the present invention only involve structures related to the embodiments disclosed in the present invention. Other structures may refer to common designs. In the absence of conflicts, the same embodiment and different embodiments of the present invention may be combined with each other.

[0102] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0103] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent livestock and poultry disease early warning and zoning prevention and control management method, characterized in that: include: Deploy an integrated intelligent sensor network in livestock and poultry farms, collect environmental parameters, feeding management data, and animal health data in real time through low-power IoT technology, and use edge computing technology to transmit the collected multi-dimensional data to the cloud platform; Based on the multidimensional data, a dynamic disease risk prediction model is constructed using deep learning and multi-source data fusion analysis technology, and the generalization ability of the adversarial generative network optimization model in complex environments is introduced; Utilize the dynamic disease risk prediction model to make real-time predictions of current epidemic risks, simulate possible transmission paths, and generate prediction results; Based on the prediction results, the livestock and poultry breeding area is dynamically divided into multiple prevention and control zones using an intelligent clustering algorithm, and each zone generates a customized prevention and control strategy based on regional setting factors; Deploy an intelligent automated control system, implement the aforementioned prevention and control measures, and monitor changes in environmental parameters within the farm in real time. Evaluate the effectiveness of the implementation based on the real-time monitoring data and generate evaluation results. A closed-loop feedback optimization mechanism is established to adjust the parameters of the dynamic disease risk prediction model by continuously updating the assessment results, combining the economic loss assessment model, and performing adaptive optimization of disease early warning and the prevention and control strategy.

2. The intelligent livestock and poultry disease early warning and zoning prevention and control management method according to claim 1 is characterized by: The integrated intelligent sensor network includes at least three of the following: high-precision temperature and humidity sensors, multi-gas component air quality sensors, contactless body temperature monitoring instruments, panoramic video monitoring equipment, and feed consumption and water quality monitoring equipment. It adopts a self-powered design and data is encrypted and transmitted using blockchain technology.

3. The intelligent livestock and poultry disease early warning and zoning prevention and control management method according to claim 2 is characterized by: The dynamic disease risk prediction model combines historical epidemic data, real-time environmental data, animal behavior data and a cross-regional epidemic data sharing platform to perform real-time epidemic risk prediction and transmission path simulation, and introduces an attention mechanism to focus on characteristics that affect disease risk.

4. The intelligent livestock and poultry disease early warning and zoning prevention and control management method according to claim 3 is characterized by: The intelligent automated control system integrates a dynamic control module based on a reinforcement learning algorithm, an automated disinfection robot, and an intelligent feeding and drinking water management module, and adjusts equipment parameters every 15 minutes based on real-time monitoring data and the prevention and control strategy.

5. The intelligent livestock and poultry disease early warning and zoning prevention and control management method according to claim 4 is characterized in that: The method also establishes a knowledge map for livestock and poultry disease prevention and control, integrating global disease research progress, prevention and control cases, and expert experience to provide managers with comprehensive knowledge support and decision-making assistance.

6. The intelligent livestock and poultry disease early warning and zoning prevention and control management method according to claim 5 is characterized in that: The method also includes an intelligent information push platform; The intelligent information push platform uses natural language processing technology to push disease warning information, prevention and control recommendations, and decision-making support tools to managers at different levels in a personalized and interactive manner; The prevention and control recommendations are pushed in a multi-level display mode: (1) On-site operators receive equipment operation instructions; (2) Technical consultants obtain simulated maps of pathogen transmission paths; (3) The regional manager reviews the prevention and control cost-benefit analysis report.

7. An intelligent livestock and poultry disease early warning and zoning prevention and control management system, characterized by: include: The sensor network module is used to deploy an integrated intelligent sensor network in livestock and poultry farms to collect environmental parameters, feeding management data, and animal health data in real time to generate multidimensional data sets; The data transmission module is used to transmit the collected multidimensional data sets to the cloud platform or local edge server for preliminary processing using low-power IoT technology and edge computing technology; The disease risk prediction module uses deep learning and multi-source data fusion analysis technology to build a dynamic disease risk prediction model. It also introduces the generalization capability of the adversarial generative network optimization model to perform disease risk prediction and transmission path simulation. The prevention and control strategy generation module is used to dynamically divide the prevention and control zones based on the prediction results using an intelligent clustering algorithm, and to generate customized prevention and control strategies for each zone; An automated control module, used to implement the aforementioned prevention and control measures and monitor changes in environmental parameters within the farm in real time, and adjust equipment parameters based on real-time monitoring data and implementation results; The information push module uses natural language processing technology to push disease warning information, prevention and control strategies, and decision-making support tools to managers at different levels in a personalized and interactive manner; The feedback optimization module is used to establish a closed-loop feedback optimization mechanism, continuously monitor the prevention and control effects and adjust the model parameters, while establishing a knowledge map for livestock and poultry disease prevention and control to provide knowledge support and decision-making assistance.

8. The intelligent livestock and poultry disease early warning and zoning prevention and control management system according to claim 7 is characterized by: The automation control module is integrated with a fault prediction and health management submodule, which is used to predict equipment failures in advance and arrange maintenance plans.

9. The intelligent livestock and poultry disease early warning and zoning prevention and control management system according to claim 8 is characterized by: The feedback optimization module analyzes the behavior of farm workers through machine learning algorithms, identifies potential operational errors or violations, and provides real-time corrective suggestions.

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