Waterfowl disease intelligent diagnosis, prevention and treatment system and method thereof

Abstract

The invention relates to the technical field of waterfowl breeding, in particular to an intelligent waterfowl disease diagnosis, prevention and treatment system and method. The data acquisition module is used for collecting images, videos, physiology and environment data of waterfowls through a camera and a sensor; the data transmission module transmits data to the data analysis module in real time, and a multi-dimensional analysis result is generated after cleaning, mining and fusion. The intelligent diagnosis module uses a deep learning model to automatically identify and analyze waterfowl diseases, and outputs disease types and severity. And the prevention processing module matches a prevention scheme according to the diagnosis result or prompts experts to intervene. And the data visualization module presents a diagnosis result and a prevention and treatment scheme in a graphical mode, and provides an interactive interface for a user to query and view historical data. According to the system, through multi-source data fusion, intelligent algorithm analysis and personalized scheme recommendation, the accuracy, timeliness and prevention and treatment effectiveness of waterfowl disease diagnosis are remarkably improved, and a comprehensive solution is provided for waterfowl health management.

Description

Technical Field

[0001] The present invention relates to the technical field of waterfowl breeding, in particular to an intelligent diagnosis and prevention system for waterfowl diseases and its method. Background Art

[0002] In recent years, with the rapid development of the waterfowl breeding industry, disease prevention and control has become a key factor affecting the sustainable development of the industry. Traditional methods for diagnosing and preventing waterfowl diseases mainly rely on manual observation and empirical judgment. Although this method is simple and direct, it has many limitations. First, it is difficult to achieve all-weather and comprehensive monitoring through manual observation, and early symptoms of diseases are easily missed. Second, the accuracy of empirical judgment is often limited by personal ability and it is difficult to cope with complex or rare disease situations. In addition, the efficiency of manual diagnosis is low, and it is difficult to detect and handle all potential health problems in large-scale farms in a timely manner.

[0003] To overcome the deficiencies of manual diagnosis, some farms have started to adopt simple automatic monitoring systems based on fixed thresholds. Such systems trigger alarms when monitoring data exceeds the set warning thresholds for some basic parameters (such as body temperature, water intake, etc.). Although this method improves the automation of monitoring, its diagnostic accuracy and flexibility are still significantly insufficient. Fixed thresholds are difficult to adapt to the individual differences of waterfowl of different breeds and different growth stages, easily resulting in false alarms or missed alarms. At the same time, this simple judgment method cannot identify complex disease patterns and is also difficult to provide early warnings.

[0004] With the development of information technology, some researchers have tried to apply technologies such as image recognition and machine learning to waterfowl disease diagnosis. These methods have made certain progress in the identification of some specific diseases, but there are still many problems. First, most existing systems only focus on a single data source (such as images or a certain physiological index) and are difficult to comprehensively reflect the health status of waterfowl. Second, these systems are often designed for specific diseases and lack the generality of diagnosing multiple diseases. Moreover, existing systems mostly stay at the diagnostic stage and lack an organic combination with prevention and control measures, making it difficult to form a complete disease prevention and control closed loop.

[0005] In addition, existing waterfowl disease diagnosis systems generally have problems such as insufficient data processing capabilities, poor interpretability of diagnostic results, and inability to predict disease trends. These limitations seriously affect the application effect of the system in the actual breeding environment and are difficult to meet the urgent needs of modern and large-scale waterfowl breeding industries for intelligent disease diagnosis and prevention. Summary of the Invention

[0006] In view of this, it is urgent to develop an intelligent diagnosis and prevention system for waterfowl diseases that can comprehensively utilize multi-source data, has intelligent diagnosis capabilities, can predict disease trends, and can provide personalized prevention and treatment plans, so as to improve the accuracy, timeliness and efficiency of waterfowl disease prevention and control, and promote the healthy and sustainable development of the waterfowl breeding industry.

[0007] The present invention provides an intelligent diagnosis and prevention system for waterfowl diseases, including:

[0008] A data acquisition module, configured to:

[0009] Collect image and video data of waterfowl through a camera;

[0010] Collect physiological data of waterfowl and environmental parameters through a variety of sensors;

[0011] A data transmission module, communicatively connected to the data acquisition module, configured to:

[0012] Receive the image, video, physiological and environmental data sent by the data acquisition module;

[0013] Transmit the data to the data analysis module in real time through a mobile communication network;

[0014] A data analysis module, communicatively connected to the data transmission module, configured to:

[0015] Receive the multi-source heterogeneous data sent by the data transmission module;

[0016] Clean, mine and fuse the multi-source heterogeneous data;

[0017] Generate multi-dimensional data analysis results;

[0018] An intelligent diagnosis module, communicatively connected to the data analysis module, configured to:

[0019] Based on the multi-dimensional data analysis results, use a deep learning model to automatically identify and analyze waterfowl diseases;

[0020] Output the type and severity of waterfowl diseases;

[0021] A prevention and treatment module, communicatively connected to the intelligent diagnosis module, configured to:

[0022] Receive the disease diagnosis results output by the intelligent diagnosis module;

[0023] Match corresponding prevention and treatment plans from the prevention and treatment plan database according to the disease diagnosis results;

[0024] When no suitable plan can be matched, generate a prompt for expert intervention;

[0025] A data visualization module, communicatively connected to the prevention and control processing module and the intelligent diagnosis module, is configured to:

[0026] Receive the disease diagnosis results from the intelligent diagnosis module and the prevention and control solutions from the prevention and control processing module;

[0027] Present the disease diagnosis results and prevention and control solutions in a graphical manner;

[0028] Provide an interactive interface for users to input query conditions and view historical data.

[0029] Preferably, the data acquisition module includes:

[0030] An image acquisition unit for acquiring high-definition images and video data of waterfowl;

[0031] A physiological data acquisition unit for acquiring physiological parameters such as the body temperature, heart rate, and respiratory rate of waterfowl;

[0032] An environmental parameter acquisition unit for acquiring parameters such as the temperature, humidity, light intensity, and water quality of the waterfowl breeding environment;

[0033] Among them, the image acquisition unit, the physiological data acquisition unit, and the environmental parameter acquisition unit are all communicatively connected to the data transmission module for real-time transmission of the acquired data to the data transmission module.

[0034] Preferably, the data analysis module includes:

[0035] A data preprocessing unit for cleaning, denoising, and normalizing the received multi-source heterogeneous data;

[0036] A feature extraction unit for extracting features such as the body surface color, activity status, behavior pattern, limb condition, and feather status of waterfowl from the preprocessed data;

[0037] A data fusion unit for performing multi-dimensional fusion of the extracted features with physiological data and environmental parameters;

[0038] A time series analysis unit for performing time series analysis on the fused data to identify abnormal patterns and trends.

[0039] Preferably, the intelligent diagnosis module includes:

[0040] A deep learning model, including a convolutional neural network (CNN) and a fully convolutional neural network (FCN), for feature learning and disease identification of waterfowl images, videos, and fused data;

[0041] A disease classifier for determining the specific type of waterfowl disease based on the output results of the deep learning model;

[0042] A severity evaluator for evaluating the severity of a disease based on disease characteristics and physiological indicators;

[0043] A dynamic prediction unit for predicting the development trend of a disease based on historical data and the current state.

[0044] Preferably, the training process of the deep learning model includes:

[0045] Performing pre-training using a large-scale labeled waterfowl disease image dataset;

[0046] Adopting a transfer learning method and performing fine-tuning using the actual data collected by the system;

[0047] Through data augmentation techniques, improving the adaptability of the model to different lighting conditions and shooting angles;

[0048] Regularly updating the model using newly collected data to improve the diagnostic accuracy.

[0049] Preferably, the prevention and control processing module includes:

[0050] A scheme matching unit for retrieving the most highly matched prevention and control scheme from a prevention and control scheme database according to the disease type and severity;

[0051] A personalized recommendation unit for making personalized adjustments to the matched prevention and control scheme according to the individual characteristics of waterfowl and environmental conditions;

[0052] An expert system interface for connecting to an external expert system to obtain professional advice when the system cannot provide a satisfactory scheme;

[0053] A scheme execution monitoring unit for tracking the implementation situation and effect of the prevention and control scheme and feeding back to the data analysis module for scheme optimization.

[0054] Preferably, the data visualization module includes:

[0055] A disease diagnosis visualization unit for visually displaying the disease type and severity in the form of a chart;

[0056] A prevention and control scheme display unit for displaying the recommended prevention and control scheme in the form of a flowchart or a list of steps;

[0057] A trend analysis chart unit for generating a dynamic chart of the disease development trend and prediction results;

[0058] An interactive query interface allowing users to flexibly query historical data and statistical information according to conditions such as time, disease type, severity, etc.

[0059] Preferably, it further includes:

[0060] An alarm module, communicatively connected to the intelligent diagnosis module and the data visualization module, is configured to:

[0061] Set alarm thresholds at different levels according to the disease diagnosis results and prediction trends;

[0062] When an abnormal situation is detected, promptly notify the breeding personnel through acoustic and optical signals and mobile device push;

[0063] Provide quick handling suggestions and emergency response plans.

[0064] Preferably, it further includes:

[0065] An adaptive optimization module, communicatively connected to the data analysis module, the intelligent diagnosis module and the prevention and control processing module, is configured to:

[0066] Collect system operation data and user feedback information;

[0067] Regularly evaluate the performance and accuracy of each module;

[0068] Automatically adjust algorithm parameters and update the model to improve the overall performance of the system;

[0069] Generate an optimization report for the reference of the system administrator.

[0070] A method for intelligent diagnosis and prevention of waterfowl diseases, applied to the waterfowl disease intelligent diagnosis and prevention system, includes the following steps:

[0071] S1. Data acquisition: Collect images, videos, physiological data and environmental parameters of waterfowl through cameras and various sensors;

[0072] S2. Data transmission: Use the mobile communication network to transmit the collected multi-source heterogeneous data to the data analysis module in real time;

[0073] S3. Data analysis: Clean, mine and fuse the received data to generate multi-dimensional data analysis results;

[0074] S4. Intelligent diagnosis: Based on the multi-dimensional data analysis results, use a deep learning model to automatically identify and analyze waterfowl diseases, and output the disease type and severity;

[0075] S5. Dynamic prediction: Predict the development trend of the disease according to historical data and the current state;

[0076] S6. Prevention and control processing: According to the disease diagnosis results and prediction trends, match and customize the prevention and control plan from the prevention and control plan database, and prompt expert intervention if necessary;

[0077] S7. Solution Execution: Execute the recommended prevention and control solutions and continuously monitor the execution effects;

[0078] S8. Data Visualization: Present the disease diagnosis results, prevention and control solutions, and trend predictions in a graphical manner;

[0079] S9. Alarm Triggering: When abnormal situations are detected, send out alarms in a timely manner and provide quick handling suggestions;

[0080] S10. Adaptive Optimization: Regularly evaluate the system performance, automatically adjust the algorithm parameters and update the model to improve the diagnostic accuracy and prevention and control effects.

[0081] The intelligent diagnosis and prevention and control system for waterfowl diseases of the present invention is an innovative solution proposed precisely for the above-mentioned technical problems. Through the organic combination of a series of advanced technologies such as multi-source data collection, deep learning analysis, intelligent diagnosis, and personalized prevention and control, the system has achieved a comprehensive upgrade of waterfowl disease diagnosis and prevention and control.

[0082] From a macroscopic perspective, the present invention constructs a complete intelligent diagnosis and prevention and control ecosystem for waterfowl diseases. The system not only solves the problem of manual dependence in traditional methods but also overcomes the limitations of simple automated systems, realizing a paradigm shift from passive response to active prevention. Through the real-time collection and intelligent analysis of multi-dimensional data, the system can comprehensively and continuously monitor the health status of waterfowl, greatly increasing the possibility of early disease detection.

[0083] In terms of system architecture, the present invention adopts a modular design. The various functional modules cooperate closely to form an efficient and flexible workflow. The multi-source heterogeneous data of the data collection module provides a rich information basis for subsequent analysis. The data analysis module realizes the in-depth mining and fusion of data through advanced algorithms, providing reliable data support for intelligent diagnosis. The intelligent diagnosis module uses deep learning technology, not only improving the diagnostic accuracy but also being able to identify complex disease patterns. The prevention and control processing module converts the diagnosis results into actual prevention and control solutions and continuously optimizes the diagnosis and prevention and control strategies through a feedback mechanism.

[0084] From a microscopic level, the present invention has achieved innovative breakthroughs at multiple key technical points. For example, in data collection, the system not only considers traditional physiological indicators but also introduces behavioral data and environmental parameters to comprehensively capture the factors affecting the health of waterfowl. In data analysis, advanced algorithms such as deep learning are adopted, which can extract valuable features from massive data to achieve accurate identification of diseases. In the diagnostic model, the system adopts a sustainable learning mechanism that can continuously learn from new data to improve the adaptability and accuracy of the model.

[0085] A significant advantage of the present invention is its predictability and forward-looking nature. Through the comprehensive analysis of historical data and real-time data, the system can predict the development trend of diseases and provide early warning information for breeders. This forward-looking disease management method greatly improves the initiative and effectiveness of prevention and control. At the same time, the personalized recommendation function of the system takes into account the characteristics of different waterfowl individuals and the specific breeding environment, ensuring the pertinence and feasibility of the prevention and control plan.

[0086] In addition, the present invention also has good scalability and adaptability. Through modular design and standardized interfaces, the system can be easily integrated with new sensing devices or analysis algorithms to adapt to the ever-developing technology. At the same time, the system's adaptive learning mechanism enables it to continuously accumulate experience from actual applications and improve the effectiveness of diagnosis and prevention and control.

[0087] Generally speaking, the intelligent diagnosis and prevention and control system for waterfowl diseases of the present invention comprehensively improves the accuracy, timeliness of waterfowl disease diagnosis and the effectiveness of prevention and control through the organic combination of innovative technologies such as multi-source data fusion, intelligent algorithm analysis, and personalized plan recommendation. This can not only significantly reduce the economic losses in waterfowl breeding, but also promote the sustainable development of the breeding industry, and is also of great significance for improving food safety and ensuring public health. With the continuous optimization and popularization of the system, it is expected to bring a revolutionary change to the waterfowl breeding industry and promote the entire industry to develop in a more intelligent, efficient, and sustainable direction. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 is the overall logical block diagram of the system of the present invention;

[0089] Figure 2 is the logical block diagram of the data acquisition module of the present invention;

[0090] Figure 3 is the logical block diagram of the data analysis module of the present invention;

[0091] Figure 4 is the logical block diagram of the intelligent diagnosis module of the present invention;

[0092] Figure 5 is the logical block diagram of the prevention and control processing module of the present invention;

[0093] Figure 6 is the logical block diagram of the data visualization module of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0094] Please refer to the attached Figure 1-6 , the present invention provides an intelligent diagnosis and prevention and control system and method for waterfowl diseases, aiming to improve the accuracy and timeliness of waterfowl disease diagnosis through advanced information technology means and provide scientific prevention and control plans for breeders. The technical solutions of the present invention will be described in detail below.

[0095] An intelligent diagnosis and prevention system for waterfowl diseases, including a data acquisition module 1, a data transmission module 2, a data analysis module 3, an intelligent diagnosis module 4, a prevention and control processing module 5, and a data visualization module 6. These modules work together to form a complete closed-loop system for the intelligent diagnosis and prevention of waterfowl diseases.

[0096] The data acquisition module 1 is used to collect various data of waterfowl. Specifically, this module collects image and video data of waterfowl through a camera, and these visual data are crucial for observing aspects such as the appearance and behavior of waterfowl. For example, through a high-definition camera, subtle changes in the feathers of waterfowl or abnormal walking postures can be captured. At the same time, the data acquisition module 1 also collects physiological data and environmental parameters of waterfowl through a variety of sensors. Here, the physiological data may include body temperature, heart rate, respiratory rate, etc., while the environmental parameters may involve temperature, humidity, light intensity, water quality, etc. These multi-dimensional data provide a comprehensive information basis for subsequent disease diagnosis.

[0097] The data transmission module 2 is communicatively connected to the data acquisition module 1, and its main function is to receive the image, video, physiological, and environmental data sent by the data acquisition module 1 and transmit these data to the data analysis module 3 in real time through a mobile communication network. In practical applications, the data transmission module 2 may adopt 5G or Internet of Things technology to ensure the real-time and stability of data transmission. This real-time transmission is crucial for the timely discovery and treatment of waterfowl diseases.

[0098] The data analysis module 3 is communicatively connected to the data transmission module 2, and its main task is to process the received multi-source heterogeneous data. First, this module cleans these data to remove possible noise or error data. Then, through data mining techniques, valuable information is extracted from the massive data. Finally, the data from different sources are fused and processed to generate multi-dimensional data analysis results. This process may involve complex algorithms, such as principal component analysis (PCA) or independent component analysis (ICA), etc.

[0099] Specifically, the data analysis module 3 may adopt the following algorithm for data fusion:

[0100]

[0101] Among them, F is the fused result, n is the number of data sources, w i is the weight of the i-th data source, f i is the characteristic function of the i-th data source, x i is the original data of the i-th data source. By adjusting the weight w i , the dynamic adjustment of the importance of different data sources can be achieved.

[0102] The intelligent diagnosis module 4 is communicatively connected to the data analysis module 3 and is the core part of this system. Based on the multi-dimensional data analysis results generated by the data analysis module 3, this module uses a deep learning model for the automatic identification and analysis of waterfowl diseases. In a preferred embodiment of the present invention, the intelligent diagnosis module 4 adopts an improved convolutional neural network (CNN) structure, and its network architecture is as follows:

[0103] y = softmax(W2·ReLU(W1·CNN(x)+b1)+b2),

[0104] where x is the input multi-dimensional data, CNN() represents the operation of the convolutional neural network, W1 and W2 are weight matrices, b1 and b2 are bias vectors, ReLU() is the activation function, and softmax() is the activation function of the output layer. Through the training with a large number of labeled data, this model can accurately output the types and severities of waterfowl diseases.

[0105] The prevention and treatment processing module 5 is communicatively connected to the intelligent diagnosis module 4, and its main function is to provide corresponding prevention and treatment plans according to the diagnosis results. This module first receives the disease diagnosis results output by the intelligent diagnosis module 4, and then matches the corresponding prevention and treatment plans from the pre-established prevention and treatment plan database according to these results. It should be noted that when the system cannot match a suitable plan, the prevention and treatment processing module 5 will generate a prompt for expert intervention, which reflects the intelligence and flexibility of this system.

[0106] The data visualization module 6 is communicatively connected to the prevention and treatment processing module 5 and the intelligent diagnosis module 4, and its role is to present complex data to users in an intuitive way. Specifically, this module receives the disease diagnosis results of the intelligent diagnosis module 4 and the prevention and treatment plans of the prevention and treatment processing module 5, and presents this information in a graphical way. For example, a pie chart may be used to show the distribution of different types of diseases, and a line chart may be used to show the change trend of disease severity, etc. In addition, the data visualization module 6 also provides an interactive interface, allowing users to input query conditions and view historical data, which greatly improves the usability of the system.

[0107] In another embodiment of the present invention, the data acquisition module 1 includes an image acquisition unit 11, a physiological data acquisition unit 12, and an environmental parameter acquisition unit 13. The image acquisition unit 11 is specifically used for acquiring high-definition images and video data of waterfowl. For example, it may use a camera with a 4K resolution to capture the subtle changes in the appearance of waterfowl. The physiological data acquisition unit 12 is responsible for acquiring physiological parameters such as the body temperature, heart rate, and respiratory rate of waterfowl. Non-contact sensors may be used here to reduce interference with waterfowl. The environmental parameter acquisition unit 13 mainly acquires various indicators of the waterfowl breeding environment, such as temperature, humidity, light intensity, water quality, etc. These units are all in communication connection with the data transmission module 2 to ensure that the acquired data can be transmitted in real time.

[0108] This modular design makes the system highly scalable. For example, if new types of sensors are developed in the future, they can be easily integrated into the corresponding acquisition units without changing the overall architecture of the system.

[0109] The intelligent diagnosis and prevention system for waterfowl diseases of the present invention realizes the early detection, accurate diagnosis, and scientific prevention and treatment of waterfowl diseases by integrating multi-source data and applying advanced artificial intelligence algorithms. This not only improves the breeding efficiency but also provides technical support for the sustainable development of the waterfowl breeding industry. In practical applications, this system can be appropriately adjusted and optimized according to the specific breeding environment and waterfowl breeds to achieve the best diagnosis and prevention and treatment effects.

[0110] In a preferred embodiment of the present invention, the intelligent diagnosis module 4 includes a deep learning model 41, a disease classifier 42, a severity evaluator 43, and a dynamic prediction unit 44. This structural design enables the system to diagnose waterfowl diseases more precisely and predict the development trend of diseases.

[0111] The deep learning model 41 is the core component of the intelligent diagnosis module 4. It includes a convolutional neural network (CNN) and a fully convolutional neural network (FCN). These two network structures perform excellently in image recognition and semantic segmentation and are very suitable for processing waterfowl images, videos, and fusion data. In practical applications, CNN is mainly used to extract high-level features of waterfowl images, while FCN is used to accurately locate and classify different parts of the waterfowl body.

[0112] Specifically, the CNN structure adopted in the present invention can be expressed as:

[0113] f(x)=softmax(W n ·ReLU(W n -1·...ReLU(W1·x+b1)...+b n -1)+b n ),

[0114] Among them, x is the input data, and W i and b i are the weight matrix and bias vector of the i-th layer respectively, ReLU() is the activation function, and softmax() is the activation function of the output layer. This multi-layer structure can effectively extract complex features in waterfowl images.

[0115] Based on the output result of the deep learning model 41, the disease classifier 42 determines the specific type of waterfowl disease. In an embodiment of the present invention, the disease classifier 42 adopts the support vector machine (SVM) algorithm, and its decision function can be expressed as:

[0116]

[0117] Among them, x is the input feature vector, x i is the support vector, y i is the class label, α i is the Lagrange multiplier, K(x i , x) is the kernel function, and b is the bias term. By selecting an appropriate kernel function, SVM can effectively handle classification problems in high-dimensional feature spaces.

[0118] The severity evaluator 43 evaluates the severity of the disease according to the disease characteristics and physiological indicators. In the present invention, the severity evaluator 43 adopts a fuzzy logic system to map each index to a severity score of 0-100. For example, for a certain waterfowl respiratory disease, indicators such as respiratory rate, body temperature, and activity level may be considered, and a comprehensive score is obtained through fuzzy rules.

[0119] The dynamic prediction unit 44 is an innovation point of the present invention. It predicts the development trend of the disease based on historical data and the current state. In a preferred embodiment, the dynamic prediction unit 44 adopts a long short-term memory network (LSTM), and its structure can be expressed as:

[0120] f t =σ(W f ·[h t-1 , x t +b f ),

[0121] i t =σ(W i ·[h t-1 , x t +b i ),

[0122]

[0123]

[0124] o t = σ(W o · [h t-1 , x t + b o ),

[0125] h t = o t * tanh(C t ),

[0126] where f t , i t , o t are the forget gate, input gate, and output gate respectively, C t is the cell state, h t is the hidden state, W and b are the weight matrix and bias vector, and σ is the sigmoid function. This structure can effectively capture the long-term dependencies in the disease development.

[0127] Another feature of the present invention is the training process of the deep learning model. First, the system uses a large-scale labeled waterfowl disease image dataset for pre-training. This dataset contains typical symptom images of various common waterfowl diseases, covering waterfowl of different breeds and different ages. The cross-entropy loss function is used in the pre-training process:

[0128]

[0129] where y i is the true label, is the model prediction value.

[0130] After the pre-training is completed, the present invention adopts the transfer learning method and uses the actual data collected by the system for fine-tuning. This method can quickly adapt to the characteristics of a specific breeding environment and improve the accuracy of diagnosis. During the fine-tuning process, a relatively small learning rate, such as 0.0001, is adopted to retain the general characteristics of the pre-trained model while adapting to the characteristics of the new data.

[0131] To further improve the robustness of the model, the system of the present invention also adopts data augmentation techniques. Specifically, operations such as random rotation (±15°), translation (±10%), scaling (0.9 - 1.1 times), and brightness adjustment (±20%) are performed on the original images to generate more training samples. This method can improve the adaptability of the model to different lighting conditions and shooting angles.

[0132] In practical applications, the system of the present invention regularly updates the model using newly collected data. The update frequency can be adjusted according to actual circumstances, such as once a week or once a month. This dynamic update mechanism ensures that the diagnostic model can continuously improve its accuracy and adapt to potential changes in disease patterns.

[0133] The prevention and control processing module 5 of the present invention includes a scheme matching unit 51, a personalized recommendation unit 52, an expert system interface 53, and a scheme execution monitoring unit 54. This modular design enables the system to provide comprehensive and accurate prevention and control schemes.

[0134] The scheme matching unit 51 retrieves the prevention and control scheme with the highest matching degree from the prevention and control scheme database according to the disease type and severity. In an embodiment of the present invention, the matching process uses the cosine similarity algorithm:

[0135]

[0136] where A and B respectively represent the current disease feature vector and the scheme feature vector in the database. The system selects the scheme with the highest similarity as the initial recommended scheme.

[0137] The personalized recommendation unit 52 then makes personalized adjustments to the matched prevention and control scheme according to the individual characteristics of the waterfowl and environmental conditions. For example, considering factors such as the age, weight, and past medical history of the waterfowl, the drug dosage or treatment method is adjusted. This personalized recommendation greatly improves the pertinence and effectiveness of the prevention and control scheme.

[0138] The expert system interface 53 is a highlight of the present invention. When the system cannot provide a satisfactory scheme, it connects to an external expert system to obtain professional advice. This combination of human and machine can handle complex or rare disease situations, reflecting the flexibility and scalability of the system.

[0139] The scheme execution monitoring unit 54 is used to track the execution situation and effect of the prevention and control scheme, and feedback the information to the data analysis module 3 for scheme optimization. This forms a closed-loop optimization process to ensure that the prevention and control scheme can be continuously improved and perfected.

[0140] In a preferred embodiment of the present invention, the data visualization module 6 includes a disease diagnosis visualization unit 61, a prevention and control scheme display unit 62, a trend analysis chart unit 63, and an interactive query interface 64. These units work together to provide users with an intuitive and comprehensive information display.

[0141] The disease diagnosis visualization unit 61 visually displays the disease type and severity in the form of a chart. For example, a heat map may be used to show the health status of each part of the waterfowl's body, or a dashboard-style chart may be used to display the overall health score.

[0142] The prevention and control plan display unit 62 displays the recommended prevention and control plans in the form of flowcharts or step lists. This visual way can clearly show each step of the treatment and precautions, facilitating the understanding and implementation by the breeding personnel.

[0143] The trend analysis chart unit 63 generates dynamic charts of the disease development trend and prediction results. For example, a line chart may be used to show the spread trend of a certain disease in the group, or a prediction interval chart may be used to display the possible change range of the disease severity in a future period.

[0144] The interactive query interface 64 allows users to flexibly query historical data and statistical information according to conditions such as time, disease type, severity, etc. This interactive design greatly improves the usability of the system, enabling the breeding personnel to quickly obtain the required information.

[0145] The intelligent diagnosis and prevention and control system for waterfowl diseases of the present invention realizes the full-process intelligent processing from data collection, analysis, diagnosis to prevention and control plan recommendation through these carefully designed modules and units. The modular design of the system not only improves the accuracy and efficiency of diagnosis and prevention and control, but also provides convenience for future function expansion and performance optimization. In practical applications, the system can be appropriately adjusted and optimized according to the specific breeding environment and waterfowl varieties to achieve the best diagnosis and prevention and control effects.

[0146] In another embodiment of the present invention, the system further includes an alarm module 7, which is communicatively connected to the intelligent diagnosis module 4 and the data visualization module 6. The design of the alarm module 7 aims to further improve the real-time response ability of the system to ensure that the breeding personnel can timely learn and handle abnormal situations.

[0147] The main function of the alarm module 7 is to set alarm thresholds at different levels according to the disease diagnosis results and prediction trends. For example, for a highly contagious disease, a relatively low alarm threshold may be set to trigger an alarm in the early stage of the disease. In a preferred embodiment of the present invention, the alarm levels can be divided into three levels: low-level alarm, medium-level alarm, and high-level alarm. Each level corresponds to different processing procedures and emergency levels.

[0148] When the system detects an abnormal situation, the alarm module 7 will notify the breeding personnel in a timely manner through various means. These means include but are not limited to sound and light signals and mobile device push. The sound and light signals can quickly spread information within the farm, while the mobile device push ensures that the breeding personnel can learn the situation in a timely manner even when they are not on site. This multi-channel alarm mechanism greatly improves the practicability and reliability of the system.

[0149] In addition to issuing alerts, the alert module 7 can also provide quick handling suggestions and emergency response plans. These suggestions and plans are generated based on the system's diagnostic results and historical data, and can provide timely and effective guidance for breeding personnel. For example, for the early symptoms of avian influenza detected, the system may recommend immediately isolating the suspected cases and provide detailed isolation operation steps.

[0150] The system of the present invention further includes an adaptive optimization module 8, which is communicatively connected to the data analysis module 3, the intelligent diagnosis module 4, and the prevention and control processing module 5. The introduction of the adaptive optimization module 8 enables the system to have the ability of self-learning and continuous improvement, which is another important innovation point of the present invention.

[0151] The primary task of the adaptive optimization module 8 is to collect system operation data and user feedback information. These data include but are not limited to diagnostic accuracy, effectiveness of prevention and control plans, user operation logs, etc. By analyzing these data, the system can comprehensively evaluate its own performance.

[0152] Based on the collected data, the adaptive optimization module 8 will regularly evaluate the performance and accuracy of each module. For example, it may calculate the changing trend of the diagnostic accuracy of the intelligent diagnosis module 4 over time, or analyze the actual effect of the recommended plans of the prevention and control processing module 5. This regular evaluation mechanism ensures that the system can timely discover and solve potential problems.

[0153] Based on the evaluation results, the adaptive optimization module 8 can automatically adjust algorithm parameters and update the model to improve the overall performance of the system. For example, if it is found that the diagnostic accuracy of a certain disease has decreased, the system may automatically increase the weight of relevant training data, or adjust the structure of the deep learning model. This adaptive optimization process can be expressed as:

[0154]

[0155] where θ t represents the current model parameters, α is the learning rate, and L(θ t ) is the loss function. Through iterative optimization, the system can continuously improve its performance.

[0156] In addition, the adaptive optimization module 8 will also generate optimization reports for the reference of system administrators. These reports detail the changing trends of system performance, the effects of optimization measures, and possible future improvement directions. This not only helps with the continuous improvement of the system, but also provides valuable data support for future research and development.

[0157] Finally, the present invention also provides a method for intelligent diagnosis and prevention of waterfowl diseases, which can be applied to the waterfowl disease intelligent diagnosis and prevention system described above. This method includes the following steps:

[0158] First, the camera and various sensors of the data acquisition module 1 collect images, videos, physiological data, and environmental parameters of waterfowl. This step ensures that the system can obtain comprehensive and real-time data.

[0159] Next, the mobile communication network of the data transmission module 2 is used to transmit the collected multi-source heterogeneous data to the data analysis module 3 in real time. This real-time transmission mechanism is the basis for the system's rapid response.

[0160] In the data analysis module 3, the system cleans, mines, and fuses the received data to generate multi-dimensional data analysis results. This step lays the foundation for subsequent intelligent diagnosis.

[0161] Subsequently, based on the multi-dimensional data analysis results, the intelligent diagnosis module 4 uses a deep learning model to automatically identify and analyze waterfowl diseases, and outputs the disease type and severity. This is the core step of this method, reflecting the intelligent level of the system.

[0162] Meanwhile, the system also predicts the development trend of diseases according to historical data and the current status. This prediction function enables the system to have the ability of proactive prevention and control.

[0163] Based on the diagnosis results and prediction trends, the prevention and control treatment module 5 matches and adjusts the prevention and control plan personalized from the prevention and control plan database. If no suitable plan can be found, the system will prompt the need for expert intervention.

[0164] After determining the prevention and control plan, the system will execute the recommended plan and continuously monitor the implementation effect. This closed-loop management ensures the effectiveness of the prevention and control measures.

[0165] Throughout the whole process, the data visualization module 6 presents the disease diagnosis results, prevention and control plans, and trend predictions in a graphical way, which is convenient for users to understand and make decisions.

[0166] When an abnormal situation is detected, the alarm module 7 will issue an alarm in time and provide quick handling suggestions to ensure that the breeding personnel can respond to potential risks in time.

[0167] Finally, the adaptive optimization module 8 will regularly evaluate the system performance, automatically adjust the algorithm parameters and update the model, and continuously improve the diagnostic accuracy and prevention and control effect.

[0168] Through this series of steps, the method of the present invention realizes the intelligent diagnosis and prevention of waterfowl diseases, providing an efficient and reliable disease management solution for the waterfowl breeding industry. This method can not only significantly improve the accuracy and timeliness of disease diagnosis, but also reduce breeding costs and enhance breeding efficiency. In practical applications, this method can be flexibly adjusted according to the specific breeding environment and waterfowl varieties to achieve the best diagnosis and prevention effects.

[0169] To verify the effectiveness and superiority of the intelligent diagnosis and prevention system for waterfowl diseases of the present invention, a set of simulation experiments were designed to simulate the actual operation of a large waterfowl farm. The farm raises about 10,000 ducks, and the experiment lasts for 3 months. We compared the system of the present invention (Example 1) with two traditional methods (Comparative Example 1 and Comparative Example 2).

[0170] Example 1: The intelligent diagnosis and prevention system for waterfowl diseases of the present invention was adopted.

[0171] Comparative Example 1: The traditional method of regular manual inspection and empirical diagnosis was adopted.

[0172] Comparative Example 2: A simple automatic monitoring system based on fixed thresholds was adopted.

[0173] The experimental conditions were set as follows:

[0174] 1. Environmental parameters: temperature 20 - 25 °C, relative humidity 60 - 70%, light time 14 hours / day.

[0175] 2. Disease types: The occurrence and spread of three common waterfowl diseases, namely avian influenza, duck plague, and colibacillosis, were simulated.

[0176] 3. Data collection frequency: Data was collected once per hour for Example 1 and Comparative Example 2, and manual inspection was carried out once a day for Comparative Example 1.

[0177] We selected the following five key indicators to evaluate the system performance:

[0178] 1. Disease detection rate: The proportion of diseased waterfowl correctly identified.

[0179] 2. Misdiagnosis rate: The proportion of healthy waterfowl misjudged as diseased.

[0180] 3. Early detection time: The average time from the actual occurrence of the disease to the system detection.

[0181] 4. Mortality rate: The proportion of waterfowl that died due to diseases during the experiment.

[0182] 5. Prevention and control cost: Including drug, labor, and equipment operation and maintenance costs.

[0183] The detection methods for these indicators are as follows:

[0184] 1. Disease detection rate and misdiagnosis rate: All waterfowl are comprehensively examined by a professional veterinary team, which serves as the gold standard and is compared with the system diagnosis results.

[0185] 2. Early detection time: Microchips with temperature sensors are randomly implanted in waterfowl, and the starting time of abnormal body temperature is recorded and compared with the system alarm time.

[0186] 3. Mortality rate: The number of dead waterfowl is recorded daily, and the total mortality rate is calculated after the experiment ends.

[0187] 4. Prevention and control cost: All relevant expenditures are recorded in detail, including drug use, labor costs, and equipment maintenance costs.

[0188] The experimental results are shown in the following table:

[0189] Index Example 1 Comparative Example 1 Comparative Example 2 Disease detection rate 98.5％ 85.2％ 90.7％ Misdiagnosis rate 1.2％ 5.8％ 3.5％ Early detection time 6.5 28.3 15.7 Mortality rate 1.8％ 4.5％ 3.2％ Prevention and control cost (10,000 yuan) 12.3 18.6 15.9

[0190] Analysis and discussion:

[0191] 1. Disease detection rate: The system of the present invention (Example 1) shows obvious advantages, with a detection rate as high as 98.5%, far higher than traditional manual inspection (85.2%) and simple automatic systems (90.7%). This is mainly due to the deep learning model adopted by this system, which can extract complex features from multi-dimensional data and achieve more accurate disease recognition.

[0192] 2. Misdiagnosis rate: The misdiagnosis rate of Example 1 is only 1.2%, much lower than the other two methods. This not only reduces unnecessary treatments but also eases the psychological burden of farmers. The low misdiagnosis rate is mainly attributed to the multi-source data fusion and intelligent diagnosis algorithm of the system, which can comprehensively analyze the health status of waterfowl.

[0193] 3. Early detection time: The system of the present invention can detect abnormalities within an average of 6.5 hours after the onset of the disease, nearly 22 hours earlier than manual inspection and more than 9 hours earlier than simple automatic systems. This rapid response ability is crucial for controlling the spread of diseases and is also one of the main reasons for the reduction in the mortality rate.

[0194] 4. Mortality rate: The mortality rate of Example 1 (1.8%) is significantly lower than the other two methods. This directly reflects the advantages of this system in early disease detection and timely treatment, which is of great significance for improving breeding efficiency.

[0195] 5. Prevention and control costs: Although the initial investment in this system is relatively high, it shows obvious cost advantages in long-term operation. The total prevention and control cost of Example 1 is 33.9% lower than that of the traditional method and 22.6% lower than that of the simple automatic system. This is mainly because more accurate diagnosis reduces unnecessary medication and also lowers labor costs.

[0196] Through experimental data analysis, Example 1 is undoubtedly the best implementation plan. It shows significant advantages in all key indicators. Notably, the system not only improves the accuracy and timeliness of disease detection, but also significantly reduces the mortality rate and overall prevention and control costs. This fully demonstrates the great potential of this invention in practical applications.

[0197] The intelligent diagnosis and prevention system for waterfowl diseases of this invention realizes early and accurate diagnosis and scientific prevention and control of waterfowl diseases by integrating multi-source data and applying advanced artificial intelligence algorithms. The experimental results clearly show that compared with the traditional method and the simple automatic system, this system has significant advantages in improving the disease detection rate, reducing the misdiagnosis rate, shortening the detection time, reducing the mortality rate, and optimizing the prevention and control costs. These advantages can not only significantly improve the economic benefits of waterfowl farming, but also provide strong support for disease prevention and control and animal welfare. In the future, with the accumulation of data and the optimization of algorithms, the performance of this system is expected to be further improved, making greater contributions to the sustainable development of the waterfowl farming industry.

[0198] It should be noted that the above are only the preferred embodiments of this invention and are not intended to limit this invention. Any modifications, equivalent replacements, improvements, etc. made within the principles of this invention shall be included within the protection scope of this invention.

Claims

1. Intelligent diagnosis and prevention system for waterfowl diseases, characterized in that: include: Data acquisition module for: Collect images and video data of waterfowl through cameras; Collect waterfowl physiological data and environmental parameters through a variety of sensors; The data transmission module is connected to the data acquisition module for: Receiving images, videos, physiological and environmental data sent by the data acquisition module; Transmitting the data to the data analysis module in real time via a mobile communication network; A data analysis module is communicatively connected to the data transmission module and is used to: Receiving multi-source heterogeneous data sent by the data transmission module; Cleaning, mining and fusing the multi-source heterogeneous data; Generate multidimensional data analysis results; The intelligent diagnosis module is in communication with the data analysis module and is used to: Based on the multidimensional data analysis results, a deep learning model is used to automatically identify and analyze waterfowl diseases; Type and severity of diseases in exported waterfowl; The prevention and treatment module is connected to the intelligent diagnosis module for: Receiving the disease diagnosis result output by the intelligent diagnosis module; According to the disease diagnosis result, matching a corresponding prevention and treatment plan from a prevention and treatment plan database; When no suitable solution can be matched, an expert intervention prompt is generated; The data visualization module is in communication with the prevention and treatment module and the intelligent diagnosis module, and is used to: Receiving the disease diagnosis result of the intelligent diagnosis module and the prevention and treatment plan of the prevention and treatment module; Presenting the disease diagnosis results and prevention and treatment plans in a graphical manner; Provides an interactive interface for users to enter query conditions and view historical data.

2. The waterfowl disease intelligent diagnosis and prevention system according to claim 1 is characterized in that: The data acquisition module comprises: An image acquisition unit for collecting high-definition images and video data of waterfowl; Physiological data collection unit, used to collect physiological parameters such as body temperature, heart rate, and respiratory rate of waterfowl; Environmental parameter collection unit, used to collect parameters such as temperature, humidity, light intensity, water quality, etc. of waterfowl breeding environment; Wherein, the image acquisition unit, the physiological data acquisition unit and the environmental parameter acquisition unit are all communicatively connected with the data transmission module, and are used to transmit the acquired data to the data transmission module in real time.

3. The waterfowl disease intelligent diagnosis and prevention system according to claim 1 is characterized in that: The data analysis module includes: A data preprocessing unit, used to clean, denoise and standardize the received multi-source heterogeneous data; A feature extraction unit is used to extract features such as body color, activity status, behavior pattern, limb condition and feather status of waterfowl from the preprocessed data; A data fusion unit, used to fuse the extracted features with physiological data and environmental parameters in multiple dimensions; The time series analysis unit is used to perform time series analysis on the fused data to identify abnormal patterns and trends.

4. The waterfowl disease intelligent diagnosis and prevention system according to claim 1, characterized in that: The intelligent diagnosis module comprises: Deep learning models, including convolutional neural networks (CNNs) and fully convolutional neural networks (FCNs), for feature learning and disease recognition of waterfowl images, videos, and fused data; A disease classifier to identify the specific type of waterfowl disease based on the output of the deep learning model; A severity assessor, used to assess the severity of a disease based on disease characteristics and physiological indicators; The dynamic prediction unit is used to predict the development trend of the disease based on historical data and current status.

5. The waterfowl disease intelligent diagnosis and prevention system according to claim 4 is characterized in that: The training process of the deep learning model includes: Use a large-scale labeled waterfowl disease image dataset for pre-training; Adopt transfer learning methods and use the actual data collected by the system for fine-tuning; Improve the model's adaptability to different lighting conditions and shooting angles through data enhancement technology; Regularly update the model with newly acquired data to improve diagnostic accuracy.

6. The waterfowl disease intelligent diagnosis and prevention system according to claim 1, characterized in that: The prevention and treatment module comprises: A scheme matching unit, used for retrieving the most matching prevention and treatment scheme from the prevention and treatment scheme database according to the disease type and severity; Personalized recommendation unit, used to make personalized adjustments to the matching prevention and control programs according to the individual characteristics of waterfowl and environmental conditions; Expert system interface, used to connect to external expert systems to obtain professional advice when the system cannot provide a satisfactory solution; The program execution monitoring unit is used to track the implementation and effectiveness of the prevention and control program, and provide feedback to the data analysis module for program optimization.

7. The waterfowl disease intelligent diagnosis and prevention system according to claim 1, characterized in that: The data visualization module includes: Disease diagnosis visualization unit, used to visually display the disease type and severity in the form of charts; A control plan display unit is used to display the recommended control plan in the form of a flow chart or a step list; Trend analysis chart unit, used to generate dynamic charts of disease development trends and prediction results; The interactive query interface allows users to flexibly query historical data and statistical information based on time, disease type, severity, and other conditions.

8. The waterfowl disease intelligent diagnosis and prevention system according to claim 1, characterized in that: Also includes: An alarm module, in communication with the intelligent diagnosis module and the data visualization module, is used to: Set different levels of alarm thresholds based on disease diagnosis results and predicted trends; When an abnormal situation is detected, the farmers are notified promptly through sound and light signals and push notifications on mobile devices; Provide quick processing suggestions and emergency plans.

9. The waterfowl disease intelligent diagnosis and prevention system according to claim 1, characterized in that: Also includes: The adaptive optimization module is in communication with the data analysis module, the intelligent diagnosis module and the prevention and treatment module, and is used to: Collect system operation data and user feedback information; Regularly evaluate the performance and accuracy of each module; Automatically adjust algorithm parameters and update models to improve overall system performance; Generate optimization reports for system administrators' reference.

10. A method for intelligent diagnosis and prevention of waterfowl diseases, applied to the intelligent diagnosis and prevention system for waterfowl diseases as claimed in any one of claims 1 to 9, characterized in that: The following steps are involved: S1. Data collection: Collect images, videos, physiological data and environmental parameters of waterfowl through cameras and various sensors; S2. Data transmission: Use the mobile communication network to transmit the collected multi-source heterogeneous data to the data analysis module in real time; S3. Data analysis: Clean, mine and fuse the received data to generate multi-dimensional data analysis results; S4. Intelligent diagnosis: Based on the results of multidimensional data analysis, deep learning models are used to automatically identify and analyze waterfowl diseases and output the disease type and severity; S5. Dynamic prediction: predict the development trend of the disease based on historical data and current status; S6. Prevention and treatment: According to the disease diagnosis results and predicted trends, the prevention and treatment plan is matched and adjusted individually from the prevention and treatment plan database, and experts are prompted to intervene when necessary; S7. Plan implementation: Implement the recommended prevention and control plan and continuously monitor the implementation effect; S8. Data visualization: present disease diagnosis results, prevention and treatment plans, and trend forecasts in a graphical manner; S9. Alarm triggering: When an abnormal situation is detected, an alarm is issued in time and quick processing suggestions are provided; S10. Adaptive optimization: Regularly evaluate system performance, automatically adjust algorithm parameters and update models to improve diagnostic accuracy and prevention and treatment effects.

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