Animal epidemic prevention supervision system

Abstract

The invention provides an animal epidemic prevention supervision system, and relates to the technical field of Internet of Things information. The animal epidemic prevention supervision system comprises a data acquisition module which acquires body temperature, activity amount and diet condition of animals and data of environmental parameters and specific positions in real time through a sensor and a camera, and transmits the data to a data processing module; the data processing module is used for storing, analyzing and processing the collected data, identifying abnormal data and marking the abnormal data; the early warning module is used for judging whether the animal possibly suffers from diseases or not according to an analysis result of the data processing module; and the supervision control module is used for automatically or manually starting corresponding epidemic prevention measures according to the early warning information. By adopting an automatic and intelligent monitoring and early warning mechanism, the dependence on manpower is reduced, the labor intensity is reduced, and the working efficiency is improved, so that management personnel can put more energy into key epidemic prevention decision and execution.

Description

Technical Field

[0001] The present invention relates to the field of Internet of Things information technology, and in particular to an animal epidemic prevention and control system. Background Art

[0002] In the context of the rapid development of animal husbandry and animal breeding, the prevention and control of animal diseases is particularly important. With the deepening of globalization, the speed and scope of the spread of animal diseases are increasing, posing severe challenges to animal health, food safety and public health. Traditional animal epidemic prevention and control mainly relies on manual monitoring and experience judgment, which has problems such as low efficiency, slow response and limited data processing capabilities. In addition, with the expansion of breeding scale, the difficulty and cost of manual supervision have also increased significantly.

[0003] However, the existing animal epidemic prevention and control system still has certain limitations in terms of technical implementation, data processing, early warning mechanism and control strategy. For example, some systems need to be improved in terms of the comprehensiveness, real-time and accuracy of data collection; in terms of data processing and analysis, more efficient algorithms are needed to handle massive data and improve the accuracy of analysis; in terms of early warning mechanism, more intelligent algorithms are needed to improve the accuracy and timeliness of early warning; in terms of control strategy, more flexible execution mechanisms are needed to adapt to different breeding environments and disease types.

[0004] Therefore, developing a new animal epidemic prevention and control system and solving the above problems through technological innovation has important practical significance and application value for improving animal disease prevention and control capabilities and ensuring animal health and food safety. Summary of the invention

[0005] 1. Technical issues to be resolved

[0006] In view of the deficiencies of the prior art, the present invention provides an animal epidemic prevention and control system, which solves the problem that the existing control methods are inefficient and difficult to achieve comprehensive coverage.

[0007] (II) Technical solution

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an animal epidemic prevention and control system, comprising:

[0009] The data acquisition module uses sensors and cameras to collect data on the animal's body temperature, activity level, diet, environmental parameters, and specific location in real time, and transmits the data to the data processing module;

[0010] The data processing module stores, analyzes and processes the collected data, identifies abnormal data, and marks the abnormal data;

[0011] The early warning module determines whether the animal may have a disease based on the analysis results of the data processing module. It involves an early warning algorithm. According to the analysis results of the data processing module, the early warning threshold is set. When the monitored data exceeds the early warning threshold, the system automatically triggers the early warning mechanism and notifies the management personnel through SMS, email and APP push;

[0012] In the supervision and control module, according to the early warning information, the system automatically or manually initiates corresponding epidemic prevention measures, isolates suspected diseased animals, disinfects them, and adjusts the breeding environment, so as to record the implementation of epidemic prevention measures and evaluate their effects, and provide data support for subsequent disease prevention and control.

[0013] Preferably, the data acquisition module comprises the following steps:

[0014] S1. Sensor deployment and configuration: deploy various sensors in animal breeding areas, transport vehicles and quarantine areas, including but not limited to temperature sensors, humidity sensors, motion sensors and image acquisition devices, initialize the sensors, set the sampling frequency and data transmission protocol parameters, and ensure that the sensors can collect data stably and accurately;

[0015] S2. Data collection: temperature sensors monitor animal body temperature and ambient temperature in real time, and transmit data to the data processing module wirelessly or wiredly; humidity sensors monitor ambient humidity and also transmit data to the data processing module; motion sensors monitor animal activity and behavior patterns, and judge their health status by analyzing animal transportation data; image acquisition equipment captures animal activity images in real time, and analyzes animal behavior characteristics, including standing, lying, and eating, through image recognition technology;

[0016] S3. Data preprocessing: preliminary processing of the collected raw data to improve data quality, compress and encode image data, reduce data transmission volume, and maintain image quality;

[0017] S4. Data transmission: The processed data is transmitted to the data processing module through a wireless network or a wired network to ensure the security and stability of data transmission and avoid data loss or tampering;

[0018] S5. Data storage and management: set up a database in the data processing module to store the collected data, design a reasonable data storage structure to facilitate subsequent data query, analysis and processing, implement data backup and recovery mechanism, and ensure data security and integrity;

[0019] S6. Abnormal detection and alarm: Set the normal range of health parameters. When the collected data exceeds the normal range, the system automatically marks it as abnormal. The abnormal data triggers the alarm mechanism and notifies the management personnel through the system interface, SMS or email;

[0020] S7. System maintenance and upgrade: regularly check the working status of sensors to ensure the accuracy of data collection, and perform necessary maintenance and upgrades on the system according to actual application needs and technological development.

[0021] Preferably, the data processing module comprises the following steps:

[0022] S1. Data reception and integration. The data processing module receives real-time data from the data acquisition module through the network interface and integrates the received data, including timestamp, data source and data type information, to ensure the integrity and consistency of the data;

[0023] S2. Data cleaning: pre-processing the received data, including removing invalid data, filling missing values ​​and correcting erroneous data, applying data cleaning algorithms, using outlier detection or data smoothing to improve data quality;

[0024] S3. Data storage: storing the cleaned data in a database to facilitate subsequent query and analysis, involving a reasonable data storage structure and setting up a time series database to support efficient data retrieval and analysis;

[0025] S4. Data processing and analysis: Apply statistical analysis methods to make a preliminary assessment of the health status of the animals, and use a decision tree algorithm to establish a prediction model for the health status of the animals. The decision tree classification algorithm includes the following steps:

[0026] S4.1. Model selection, select the root node of the book and choose the best segmentation point based on the features;

[0027] S4.2. Recursive segmentation: recursively segment each child node until the stopping condition is met;

[0028] S4.3. Pruning, in order to prevent overfitting, the tree can be pruned to remove some unimportant nodes;

[0029] S4.4. Prediction, starting from the root node, traverse down the tree according to the feature value of the sample until reaching the leaf node, the category of the leaf node is the prediction result;

[0030] S5. Health status assessment: Based on the analysis results, the health status of the animal is assessed, including parameters of body temperature, activity level and diet, and health thresholds are set. When the health indicators of the animal exceed the thresholds, they are marked as abnormal.

[0031] S6. Generate early warning information. Generate early warning information based on the health status assessment results. The early warning information should include the animal's identification information, abnormal indicators and possible health problems. The early warning information should be readable so that managers can quickly understand and take corresponding measures.

[0032] S7. Data visualization can use charts and dashboards as visualization tools to intuitively display analysis results and warning information to users, and design user-friendly interfaces so that managers can easily view and understand data;

[0033] S8. Data sharing and interaction: Share processed data with other systems or services through API or other data exchange protocols, and support data interaction with other systems;

[0034] S9. System maintenance and optimization: regularly maintain the data processing module, including software updates and performance optimization, and continuously optimize the data processing process and algorithm based on user feedback and system operation;

[0035] S10. Security and privacy protection: ensure that the data processing process complies with relevant laws and regulations on data security and privacy protection, implement security measures such as data encryption and access control, and protect sensitive information of animals and farms.

[0036] Preferably, the early warning module comprises the following steps:

[0037] S1. Receiving health assessment results, the early warning module receives the analyzed animal health assessment results from the data processing module, which include the health status, abnormal indicators and health trends of the animals;

[0038] S2. Rule matching and threshold judgment: The early warning module matches and judges the health assessment results according to the preset rules and thresholds. These rules and thresholds are formulated based on the professional knowledge and historical data of animal disease prevention and control to identify potential health risks.

[0039] S3. Generate warning information. When the health assessment results trigger the preset rules or thresholds, the warning module will generate warning information, which includes the animal's identification information, abnormal indicators, possible health problems and recommended countermeasures;

[0040] S4. Warning information formatting. Warning information needs to be formatted in a clear and simplified way so that the recipient can understand it quickly. This includes text information, graphics and sound alerts.

[0041] S5. Warning information transmission: the warning module sends the formatted warning information to relevant personnel through multiple channels;

[0042] S6. Receiver confirmation: the warning module requires the receiver to confirm that the warning information has been received and has taken corresponding measures, which helps to ensure that the warning information is responded to in a timely manner;

[0043] S7. Recording and tracking, the early warning module records the sending of all early warning information and the response of the receiver, so as to facilitate subsequent tracking and analysis;

[0044] S8. Feedback and learning, the early warning module collects user feedback on early warning information, analyzes the effectiveness and accuracy of the early warning, and makes adjustments and optimizations based on the feedback;

[0045] S9. Warning level management: the warning module analyzes the effectiveness and accuracy of warnings based on the feedback of warning information, and makes adjustments and optimizations based on the feedback;

[0046] S10. System integration and linkage: the early warning module is integrated with other systems of the farm to achieve automated linkage response;

[0047] S11. Maintenance and upgrade of the early warning module. Regularly maintain and upgrade the early warning module to ensure that it can adapt to new epidemic situations and management needs.

[0048] Preferably, the supervision control module comprises the following steps:

[0049] S1. Receiving warning information: the supervision and control module receives warning information issued by the warning module, which includes the animal's identification information, health status and recommended epidemic prevention measures;

[0050] S2. Information analysis and confirmation: analyze warning information, confirm the urgency of the warning and the recommended epidemic prevention measures, and ensure the accuracy and completeness of the information;

[0051] S3. Execution strategy selection: select the appropriate execution strategy according to the urgency of the warning information and the recommended measures;

[0052] S4. Automatic execution of measures: For low-risk or routine warnings, the supervision and control module can automatically execute predetermined epidemic prevention measures;

[0053] S5. Manual confirmation and execution: For warnings with medium risk or requiring further evaluation, the supervision and control module will send the warning information to the management personnel and wait for the management personnel’s confirmation before executing the corresponding epidemic prevention measures;

[0054] S6. Execute epidemic prevention measures. According to the selected execution strategy, the supervision and control module executes corresponding epidemic prevention measures, including isolating suspected sick animals, starting disinfection procedures and adjusting the feeding environment;

[0055] S7. Execution record and feedback: record the implemented epidemic prevention measures and feed back the execution results to the early warning module and data processing module for subsequent analysis and evaluation;

[0056] S8. Execution effect evaluation: The supervision and control module evaluates the effectiveness of the implementation measures, including whether the isolation measures are effective and whether the disinfection is thorough, and adjusts the implementation strategy based on the evaluation results;

[0057] S9. System linkage: the supervision and control module may be linked with other systems in the farm to achieve comprehensive epidemic prevention management;

[0058] S10. User interaction interface, providing a user-friendly interactive interface so that managers can easily view warning information, execution status and system logs;

[0059] S11. System maintenance and upgrade: Regularly maintain and upgrade the supervision and control module to ensure that it can adapt to new epidemic prevention needs and management strategies;

[0060] S12. Security and authority management: Implement strict security measures and authority management to ensure that only authorized personnel can perform key epidemic prevention measures.

[0061] (III) Beneficial effects

[0062] The present invention provides an animal epidemic prevention and control system, which has the following beneficial effects:

[0063] 1. In the present invention, by integrating sensors and Internet of Things technology, the system can monitor the health status and environmental parameters of animals in real time, realize early detection and early warning of animal diseases, and effectively reduce the risk of disease outbreaks. At the same time, by using big data analysis and artificial intelligence algorithms, the system can extract valuable information from massive data, provide scientific decision-making support for managers, and improve the pertinence and effectiveness of epidemic prevention measures.

[0064] 2. In the present invention, by adopting an automated and intelligent monitoring and early warning mechanism, the dependence on manual labor is reduced, the labor intensity is reduced, and the work efficiency is improved, so that managers can devote more energy to key epidemic prevention decision-making and execution.

[0065] 3. In the present invention, the system design has good scalability and flexibility, can adapt to farms of different sizes and types, and meet the epidemic prevention needs in different breeding environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic diagram of the overall system flow of the present invention;

[0067] Figure 2 It is a flow chart of the data acquisition module in the present invention;

[0068] Figure 3 It is a flow chart of the data processing module in the present invention;

[0069] Figure 4 It is a schematic diagram of the process of the early warning module in the present invention;

[0070] Figure 5 It is a flow chart of the supervision and control module in the present invention. DETAILED DESCRIPTION

[0071] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0072] Embodiment 1:

[0073] like Figure 1-5 As shown, an embodiment of the present invention provides an animal epidemic prevention and control system, including:

[0074] The data acquisition module uses sensors and cameras to collect data on the animal's body temperature, activity level, diet, environmental parameters, and specific location in real time, and transmits the data to the data processing module;

[0075] The data processing module stores, analyzes and processes the collected data, identifies abnormal data, and marks the abnormal data;

[0076] The early warning module determines whether the animal may have a disease based on the analysis results of the data processing module. It involves an early warning algorithm. According to the analysis results of the data processing module, the early warning threshold is set. When the monitored data exceeds the early warning threshold, the system automatically triggers the early warning mechanism and notifies the management personnel through SMS, email and APP push;

[0077] In the supervision and control module, according to the early warning information, the system automatically or manually initiates corresponding epidemic prevention measures, isolates suspected diseased animals, disinfects them, and adjusts the breeding environment, so as to record the implementation of epidemic prevention measures and evaluate their effects, and provide data support for subsequent disease prevention and control.

[0078] The data acquisition module includes the following steps:

[0079] S1. Sensor deployment and configuration: deploy various sensors in animal breeding areas, transport vehicles and quarantine areas, including but not limited to temperature sensors, humidity sensors, motion sensors and image acquisition devices, initialize the sensors, set the sampling frequency and data transmission protocol parameters, and ensure that the sensors can collect data stably and accurately;

[0080] S2. Data collection: temperature sensors monitor animal body temperature and ambient temperature in real time, and transmit data to the data processing module wirelessly or wiredly; humidity sensors monitor ambient humidity and also transmit data to the data processing module; motion sensors monitor animal activity and behavior patterns, and judge their health status by analyzing animal transportation data; image acquisition equipment captures animal activity images in real time, and analyzes animal behavior characteristics, including standing, lying, and eating, through image recognition technology;

[0081] S3. Data preprocessing: preliminary processing of the collected raw data to improve data quality, compress and encode image data, reduce data transmission volume, and maintain image quality;

[0082] S4. Data transmission: The processed data is transmitted to the data processing module through a wireless network or a wired network to ensure the security and stability of data transmission and avoid data loss or tampering;

[0083] S5. Data storage and management: set up a database in the data processing module to store the collected data, design a reasonable data storage structure to facilitate subsequent data query, analysis and processing, implement data backup and recovery mechanism, and ensure data security and integrity;

[0084] S6. Abnormal detection and alarm: Set the normal range of health parameters. When the collected data exceeds the normal range, the system automatically marks it as abnormal. The abnormal data triggers the alarm mechanism and notifies the management personnel through the system interface, SMS or email;

[0085] S7. System maintenance and upgrade: regularly check the working status of sensors to ensure the accuracy of data collection, and perform necessary maintenance and upgrades on the system according to actual application needs and technological development.

[0086] The data processing module includes the following steps:

[0087] S1. Data reception and integration. The data processing module receives real-time data from the data acquisition module through the network interface and integrates the received data, including timestamp, data source and data type information, to ensure the integrity and consistency of the data;

[0088] S2. Data cleaning: pre-processing the received data, including removing invalid data, filling missing values ​​and correcting erroneous data, applying data cleaning algorithms, using outlier detection or data smoothing to improve data quality;

[0089] S3. Data storage: storing the cleaned data in a database to facilitate subsequent query and analysis, involving a reasonable data storage structure and setting up a time series database to support efficient data retrieval and analysis;

[0090] S4. Data processing and analysis: Apply statistical analysis methods to make a preliminary assessment of the health status of the animals, and use a decision tree algorithm to establish a prediction model for the health status of the animals. The decision tree classification algorithm includes the following steps:

[0091] S4.1. Model selection, select the root node of the book and choose the best segmentation point based on the features;

[0092] S4.2. Recursive segmentation: recursively segment each child node until the stopping condition is met;

[0093] S4.3. Pruning, in order to prevent overfitting, the tree can be pruned to remove some unimportant nodes;

[0094] S4.4. Prediction, starting from the root node, traverse down the tree according to the feature value of the sample until reaching the leaf node, the category of the leaf node is the prediction result;

[0095] S5. Health status assessment: Based on the analysis results, the health status of the animal is assessed, including parameters of body temperature, activity level and diet, and health thresholds are set. When the health indicators of the animal exceed the thresholds, they are marked as abnormal.

[0096] S6. Generate early warning information. Generate early warning information based on the health status assessment results. The early warning information should include the animal's identification information, abnormal indicators and possible health problems. The early warning information should be readable so that managers can quickly understand and take corresponding measures.

[0097] S7. Data visualization can use charts and dashboards as visualization tools to intuitively display analysis results and warning information to users, and design user-friendly interfaces so that managers can easily view and understand data;

[0098] S8. Data sharing and interaction: Share processed data with other systems or services through API or other data exchange protocols, and support data interaction with other systems;

[0099] S9. System maintenance and optimization: regularly maintain the data processing module, including software updates and performance optimization, and continuously optimize the data processing process and algorithm based on user feedback and system operation;

[0100] S10. Security and privacy protection: ensure that the data processing process complies with relevant laws and regulations on data security and privacy protection, implement security measures such as data encryption and access control, and protect sensitive information of animals and farms.

[0101] The early warning module includes the following steps:

[0102] S1. Receiving health assessment results, the early warning module receives the analyzed animal health assessment results from the data processing module, which include the health status, abnormal indicators and health trends of the animals;

[0103] S2. Rule matching and threshold judgment: The early warning module matches and judges the health assessment results according to the preset rules and thresholds. These rules and thresholds are formulated based on the professional knowledge and historical data of animal disease prevention and control to identify potential health risks.

[0104] S3. Generate warning information. When the health assessment results trigger the preset rules or thresholds, the warning module will generate warning information, which includes the animal's identification information, abnormal indicators, possible health problems and recommended countermeasures;

[0105] S4. Warning information formatting. Warning information needs to be formatted in a clear and simplified way so that the recipient can understand it quickly. This includes text information, graphics and sound alerts.

[0106] S5. Warning information transmission: the warning module sends the formatted warning information to relevant personnel through multiple channels;

[0107] S6. Receiver confirmation: the warning module requires the receiver to confirm that the warning information has been received and has taken corresponding measures, which helps to ensure that the warning information is responded to in a timely manner;

[0108] S7. Recording and tracking, the early warning module records the sending of all early warning information and the response of the receiver, so as to facilitate subsequent tracking and analysis;

[0109] S8. Feedback and learning, the early warning module collects user feedback on early warning information, analyzes the effectiveness and accuracy of the early warning, and makes adjustments and optimizations based on the feedback;

[0110] S9. Warning level management: the warning module analyzes the effectiveness and accuracy of warnings based on the feedback of warning information, and makes adjustments and optimizations based on the feedback;

[0111] S10. System integration and linkage: the early warning module is integrated with other systems of the farm to achieve automated linkage response;

[0112] S11. Maintenance and upgrade of the early warning module. Regularly maintain and upgrade the early warning module to ensure that it can adapt to new epidemic situations and management needs.

[0113] The supervisory control module includes the following steps:

[0114] S1. Receiving warning information: the supervision and control module receives warning information issued by the warning module, which includes the animal's identification information, health status and recommended epidemic prevention measures;

[0115] S2. Information analysis and confirmation: analyze warning information, confirm the urgency of the warning and the recommended epidemic prevention measures, and ensure the accuracy and completeness of the information;

[0116] S3. Execution strategy selection: select the appropriate execution strategy according to the urgency of the warning information and the recommended measures;

[0117] S4. Automatic execution of measures: For low-risk or routine warnings, the supervision and control module can automatically execute predetermined epidemic prevention measures;

[0118] S5. Manual confirmation and execution: For warnings with medium risk or requiring further evaluation, the supervision and control module will send the warning information to the management personnel and wait for the management personnel’s confirmation before executing the corresponding epidemic prevention measures;

[0119] S6. Execute epidemic prevention measures. According to the selected execution strategy, the supervision and control module executes corresponding epidemic prevention measures, including isolating suspected sick animals, starting disinfection procedures and adjusting the feeding environment;

[0120] S7. Execution record and feedback: record the implemented epidemic prevention measures and feed back the execution results to the early warning module and data processing module for subsequent analysis and evaluation;

[0121] S8. Execution effect evaluation: The supervision and control module evaluates the effectiveness of the implementation measures, including whether the isolation measures are effective and whether the disinfection is thorough, and adjusts the implementation strategy based on the evaluation results;

[0122] S9. System linkage: the supervision and control module may be linked with other systems in the farm to achieve comprehensive epidemic prevention management;

[0123] S10. User interaction interface, providing a user-friendly interactive interface so that managers can easily view warning information, execution status and system logs;

[0124] S11. System maintenance and upgrade: Regularly maintain and upgrade the supervision and control module to ensure that it can adapt to new epidemic prevention needs and management strategies;

[0125] S12. Security and authority management: Implement strict security measures and authority management to ensure that only authorized personnel can perform key epidemic prevention measures.

[0126] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An animal epidemic prevention and control system, characterized in that: include: The data acquisition module uses sensors and cameras to collect data on the animal's body temperature, activity level, diet, environmental parameters, and specific location in real time, and transmits the data to the data processing module; The data processing module stores, analyzes and processes the collected data, identifies abnormal data, and marks the abnormal data; The early warning module determines whether the animal may have a disease based on the analysis results of the data processing module. It involves an early warning algorithm. According to the analysis results of the data processing module, the early warning threshold is set. When the monitored data exceeds the early warning threshold, the system automatically triggers the early warning mechanism and notifies the management personnel through SMS, email and APP push; In the supervision and control module, according to the early warning information, the system automatically or manually initiates corresponding epidemic prevention measures, isolates suspected diseased animals, disinfects them, and adjusts the breeding environment, so as to record the implementation of epidemic prevention measures and evaluate their effects, and provide data support for subsequent disease prevention and control.

2. The animal epidemic prevention and control system according to claim 1, characterized in that: The data acquisition module comprises the following steps: S1. Sensor deployment and configuration: deploy various sensors in animal breeding areas, transport vehicles and quarantine areas, including but not limited to temperature sensors, humidity sensors, motion sensors and image acquisition devices, initialize the sensors, set the sampling frequency and data transmission protocol parameters, and ensure that the sensors can collect data stably and accurately; S2. Data collection: temperature sensors monitor animal body temperature and ambient temperature in real time, and transmit data to the data processing module wirelessly or wiredly; humidity sensors monitor ambient humidity and also transmit data to the data processing module; motion sensors monitor animal activity and behavior patterns, and judge their health status by analyzing animal transportation data; image acquisition equipment captures animal activity images in real time, and analyzes animal behavior characteristics, including standing, lying, and eating, through image recognition technology; S3. Data preprocessing: preliminary processing of the collected raw data to improve data quality, compress and encode image data, reduce data transmission volume, and maintain image quality; S4. Data transmission: The processed data is transmitted to the data processing module through a wireless network or a wired network to ensure the security and stability of data transmission and avoid data loss or tampering; S5. Data storage and management: set up a database in the data processing module to store the collected data, design a reasonable data storage structure to facilitate subsequent data query, analysis and processing, implement data backup and recovery mechanism, and ensure data security and integrity; S6. Abnormal detection and alarm: Set the normal range of health parameters. When the collected data exceeds the normal range, the system automatically marks it as abnormal. The abnormal data triggers the alarm mechanism and notifies the management personnel through the system interface, SMS or email; S7. System maintenance and upgrade: regularly check the working status of sensors to ensure the accuracy of data collection, and perform necessary maintenance and upgrades on the system according to actual application needs and technological development.

3. The animal epidemic prevention and control system according to claim 1, characterized in that: The data processing module comprises the following steps: S1. Data reception and integration. The data processing module receives real-time data from the data acquisition module through the network interface and integrates the received data, including timestamp, data source and data type information, to ensure the integrity and consistency of the data; S2. Data cleaning: pre-processing the received data, including removing invalid data, filling missing values ​​and correcting erroneous data, applying data cleaning algorithms, using outlier detection or data smoothing to improve data quality; S3. Data storage: storing the cleaned data in a database to facilitate subsequent query and analysis, involving a reasonable data storage structure and setting up a time series database to support efficient data retrieval and analysis; S4. Data processing and analysis: Apply statistical analysis methods to make a preliminary assessment of the health status of the animals, and use a decision tree algorithm to establish a prediction model for the health status of the animals. The decision tree classification algorithm includes the following steps: S4.

1. Model selection, select the root node of the book and choose the best segmentation point based on the features; S4.

2. Recursive segmentation: recursively segment each child node until the stopping condition is met; S4.

3. Pruning, in order to prevent overfitting, the tree can be pruned to remove some unimportant nodes; S4.

4. Prediction, starting from the root node, traverse down the tree according to the feature value of the sample until reaching the leaf node, the category of the leaf node is the prediction result; S5. Health status assessment: Based on the analysis results, the health status of the animal is assessed, including parameters of body temperature, activity level and diet, and health thresholds are set. When the health indicators of the animal exceed the thresholds, they are marked as abnormal. S6. Generate early warning information. Generate early warning information based on the health status assessment results. The early warning information should include the animal's identification information, abnormal indicators and possible health problems. The early warning information should be readable so that managers can quickly understand and take corresponding measures. S7. Data visualization can use charts and dashboards as visualization tools to intuitively display analysis results and warning information to users, and design user-friendly interfaces so that managers can easily view and understand data; S8. Data sharing and interaction: Share processed data with other systems or services through API or other data exchange protocols, and support data interaction with other systems; S9. System maintenance and optimization: regularly maintain the data processing module, including software updates and performance optimization, and continuously optimize the data processing process and algorithm based on user feedback and system operation; S10. Security and privacy protection: ensure that the data processing process complies with relevant laws and regulations on data security and privacy protection, implement security measures such as data encryption and access control, and protect sensitive information of animals and farms.

4. The animal epidemic prevention and control system according to claim 1, characterized in that: The early warning module comprises the following steps: S1. Receiving health assessment results, the early warning module receives the analyzed animal health assessment results from the data processing module, which include the health status, abnormal indicators and health trends of the animals; S2. Rule matching and threshold judgment: The early warning module matches and judges the health assessment results according to the preset rules and thresholds. These rules and thresholds are formulated based on the professional knowledge and historical data of animal disease prevention and control to identify potential health risks. S3. Generate warning information. When the health assessment results trigger the preset rules or thresholds, the warning module will generate warning information, which includes the animal's identification information, abnormal indicators, possible health problems and recommended countermeasures; S4. Warning information formatting. Warning information needs to be formatted in a clear and simplified way so that the recipient can understand it quickly. This includes text information, graphics and sound alerts. S5. Warning information transmission: the warning module sends the formatted warning information to relevant personnel through multiple channels; S6. Receiver confirmation: the warning module requires the receiver to confirm that the warning information has been received and has taken corresponding measures, which helps to ensure that the warning information is responded to in a timely manner; S7. Recording and tracking, the early warning module records the sending of all early warning information and the response of the receiver, so as to facilitate subsequent tracking and analysis; S8. Feedback and learning, the early warning module collects user feedback on early warning information, analyzes the effectiveness and accuracy of the early warning, and makes adjustments and optimizations based on the feedback; S9. Warning level management: the warning module analyzes the effectiveness and accuracy of warnings based on the feedback of warning information, and makes adjustments and optimizations based on the feedback; S10. System integration and linkage: the early warning module is integrated with other systems of the farm to achieve automated linkage response; S11. Maintenance and upgrade of the early warning module. Regularly maintain and upgrade the early warning module to ensure that it can adapt to new epidemic situations and management needs.

5. The animal epidemic prevention and control system according to claim 1, characterized in that: The supervisory control module comprises the following steps: S1. Receiving warning information: the supervision and control module receives warning information issued by the warning module, which includes the animal's identification information, health status and recommended epidemic prevention measures; S2. Information analysis and confirmation: analyze warning information, confirm the urgency of the warning and the recommended epidemic prevention measures, and ensure the accuracy and completeness of the information; S3. Execution strategy selection: select the appropriate execution strategy according to the urgency of the warning information and the recommended measures; S4. Automatic execution of measures: For low-risk or routine warnings, the supervision and control module can automatically execute predetermined epidemic prevention measures; S5. Manual confirmation and execution: For warnings with medium risk or requiring further evaluation, the supervision and control module will send the warning information to the management personnel and wait for the management personnel’s confirmation before executing the corresponding epidemic prevention measures; S6. Execute epidemic prevention measures. According to the selected execution strategy, the supervision and control module executes corresponding epidemic prevention measures, including isolating suspected sick animals, starting disinfection procedures and adjusting the feeding environment; S7. Execution record and feedback: record the implemented epidemic prevention measures and feed back the execution results to the early warning module and data processing module for subsequent analysis and evaluation; S8. Execution effect evaluation: The supervision and control module evaluates the effectiveness of the implementation measures, including whether the isolation measures are effective and whether the disinfection is thorough, and adjusts the implementation strategy based on the evaluation results; S9. System linkage: the supervision and control module may be linked with other systems in the farm to achieve comprehensive epidemic prevention management; S10. User interaction interface, providing a user-friendly interactive interface so that managers can easily view warning information, execution status and system logs; S11. System maintenance and upgrade: Regularly maintain and upgrade the supervision and control module to ensure that it can adapt to new epidemic prevention needs and management strategies; S12. Security and authority management: Implement strict security measures and authority management to ensure that only authorized personnel can perform key epidemic prevention measures.

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