Method and system for identifying abnormal state of pet

Abstract

The invention discloses a method and a system for identifying an abnormal state of a pet, and aims to improve the intelligent level of pet monitoring. According to the system, real-time monitoring and analysis of pet behaviors are achieved through a high-definition camera installed indoors and machine vision algorithms such as a convolutional neural network (CNN). The system comprises the following steps that firstly, a camera captures a pet activity video and transmits data to a central processing unit; then, using a pre-trained deep learning model to extract and analyze behavior characteristics of the pet; next, the system detects anomalous behavior, such as frequent scratching, convulsion or long-time stagnation, and generates alert information. Alarms are transmitted to a user device through a cloud service platform, and a user can view pet activity and health reports through a client program. The system has the advantages of high intelligence, good user experience and high data security, and is suitable for various occasions such as families, pet hospitals and pet fostering centers. According to the invention, health or behavior problems of pets can be found in time, and the safety of the pets is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of intelligent pet monitoring, specifically to a method and system for identifying abnormal pet conditions using camera-based machine vision. Background Technology

[0002] As pets play an increasingly important role in people's daily lives, their health and safety have become a major concern for pet owners. Currently, most pet monitoring systems on the market are based on simple video surveillance and cannot effectively identify and assess abnormal behavior or health conditions in pets. This simple monitoring model requires owners to constantly monitor the video content and lacks proactive alerts, causing inconvenience to pet owners. Therefore, there is an urgent need for a method and system that can intelligently identify abnormal pet conditions in order to promptly notify owners to take appropriate measures. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for automatically identifying abnormal pet conditions using machine vision technology, so as to improve the level of intelligence in pet monitoring.

[0004] To achieve the above objectives, this invention proposes a recognition system based on a camera and machine vision algorithms. The specific steps are as follows: S1. Camera Installation: Install at least one high-definition camera in the pet's activity area to ensure full coverage. The camera is connected to the central processing unit (CPU) via a wireless network.

[0005] S2. Data Acquisition: The camera captures real-time video streams of the pet's activities and transmits them to the central processing unit.

[0006] S3. Model Training: A convolutional neural network (CNN) model is trained using deep learning techniques, based on a large number of video clips of pet behaviors labeled as normal or abnormal, to identify normal and abnormal behavior patterns in pets.

[0007] S4. Behavior Analysis: The central processing unit uses a pre-trained CNN model to analyze the received video stream and identify the pet's behavioral characteristics.

[0008] S5. Anomaly Detection: During the analysis process, the system automatically detects abnormal states of the pet, including but not limited to frequent scratching, prolonged immobility, and abnormal behavior patterns.

[0009] S6. Alarm Generation: Once abnormal behavior is detected, the system immediately generates an alarm message.

[0010] S7. Information Notification: Through the cloud service platform, alarm information is sent to the pet owner's smartphone or other smart devices, promptly notifying the owner to take appropriate measures.

[0011] S8. User Interaction: Provide a companion mobile application that allows users to view their pet's activities in real time and receive alerts for any abnormalities generated by the system.

[0012] The main advantages of this invention are that it improves the accuracy and real-time performance of pet abnormality identification, reduces pet owners' reliance on monitoring videos, improves overall life convenience, and provides an intelligent solution for pet monitoring.

[0013] A further improvement of this invention is that the camera has the function of automatically adjusting the focal length and angle to adapt to different indoor layouts and changes in the pet's activity range. Simultaneously, the camera has infrared night vision capabilities to ensure effective video capture even in low-light conditions.

[0014] A further improvement of this invention is that the data transmission process employs an encryption protocol to ensure that the video stream is not illegally intercepted or tampered with during transmission, thereby protecting user privacy and data security.

[0015] A further improvement of this invention is that the model training process employs transfer learning technology to reduce reliance on large-scale labeled data, improve the efficiency and accuracy of model training, and adapt to the behavioral characteristics of different types of pets.

[0016] A further improvement of this invention is that a real-time update mechanism is introduced during the behavior analysis process, allowing the model to continuously learn and optimize based on user feedback and new data, thereby improving recognition accuracy.

[0017] A further improvement of this invention is that the anomaly detection mechanism has a custom threshold function, allowing users to adjust the detection sensitivity according to the individual differences and health status of the pet.

[0018] A further improvement of this invention is that the alarm information of the alarm generation system includes video clips of abnormal behavior and analysis results, providing detailed reference information to help users make decisions.

[0019] A further improvement of this invention is that the notification system supports multi-channel push notifications, including SMS, email, and in-app push notifications, ensuring that users can receive alert information in a timely manner.

[0020] A further improvement of this invention is that the user-interactive mobile application (APP) integrates a visual health report function, allowing users to view their pet's historical activity data and health trend analysis, and to personalize and manage the pet through a user-friendly interface.

[0021] Compared with the prior art, the beneficial effects of the present invention are: 1. Enhanced intelligence: By introducing deep learning and machine vision technologies, this invention can automatically identify abnormal pet conditions, which not only improves the accuracy and real-time performance of identification, but also reduces reliance on manual monitoring, significantly enhancing the intelligence level of pet supervision.

[0022] 2. User Experience Optimization: Customizable thresholds and personalized settings allow users to adjust the system based on their pet's specific needs and behavioral characteristics, enhancing its applicability and flexibility. Furthermore, the mobile application provides visual health reports, offering users intuitive analysis of their pet's health status and trends.

[0023] 3. Data security and privacy protection: Encryption protocols are used for data transmission to ensure the security of video streams and user data, and to protect user privacy from infringement.

[0024] 4. High adaptability: By using transfer learning technology, the system can efficiently adapt to the behavioral characteristics of different types and breeds of pets, reducing the dependence on large-scale labeled data and lowering deployment and maintenance costs.

[0025] 5. Multi-channel information notification: The system supports multiple information notification methods to ensure that users can receive abnormal alerts in a timely manner under various circumstances, thereby improving the system's reliability and user satisfaction.

[0026] 6. Scalability and Customization: This invention provides flexible hardware and software configurations, which can be expanded and customized according to user needs, and is suitable for various application scenarios such as homes, pet hospitals, and pet boarding centers.

[0027] In summary, this invention not only represents significant technological innovation and breakthroughs, but also achieves comprehensive improvements in user experience, security, and applicability, thus possessing broad market application prospects and social value. Attached Figure Description

[0028] The accompanying drawings, which form part of this application, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of an embodiment of the present invention; Figure 2 This is a hardware and software framework diagram of the system in an embodiment of the present invention. Detailed Implementation

[0029] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0030] The term "and / or" simply describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. Additionally, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0031] Example 1 S1. Camera and Hardware Configuration S11. Camera Selection: The system uses a high-definition camera with a recommended resolution of at least 1080p. It should have the function of automatically adjusting the focus and angle to adapt to different indoor layouts.

[0032] S12. Installation Location: The camera should be installed in a location that fully covers the area where the pet is active, ensuring no blind spots. It is recommended to install it at a high position in the room or area where the pet frequently roams to obtain a wider field of view.

[0033] S13. Night vision function: The camera should have infrared night vision function to ensure effective monitoring under low light conditions.

[0034] S2. Data Acquisition and Transmission S21. Real-time video capture: The camera captures a real-time video stream of the pet's activities and transmits it to the central processing unit (CPU) via a wireless network.

[0035] S22. Data Encryption: During data transmission, SSL / TLS protocol is used for encryption to ensure data security and user privacy.

[0036] S3. Machine vision algorithms S31. Model Architecture: Convolutional Neural Network (CNN) is used as the core algorithm, and deep learning technology is used for model training. S32. Dataset Preparation: Utilize a large number of labeled pet behavior video clips to distinguish between normal and abnormal behaviors. The training dataset should include various pet species and behavioral patterns.

[0037] S33. Training and Learning: To improve the model's generalization ability and training efficiency, transfer learning techniques are adopted to adapt to the behavioral characteristics of different pets.

[0038] S4. Behavioral Analysis and Anomaly Detection S41. Behavioral Feature Extraction: Analyze the video stream using a pre-trained CNN model to extract the pet's behavioral features.

[0039] S42. Abnormal Behavior Recognition: The system identifies abnormal behaviors, such as frequent scratching or prolonged inactivity, and makes judgments based on set thresholds.

[0040] S43. User-defined: Allows users to adjust the detection sensitivity and abnormal behavior types according to the individual circumstances of their pets.

[0041] S5. Alarm Generation and Information Notification S51. Alarm Content: Once abnormal behavior is detected, the system generates alarm information, including video clips of the abnormal behavior and analysis results.

[0042] S52. Notification Mechanism: Through the cloud service platform, alarm information is sent to the user's smart device via various channels (such as SMS, email, and in-app push).

[0043] S6. User Interface and Interaction S61. Mobile Application: Provides an intuitive user interface, allowing users to view their pet's activities in real time and receive alerts for any abnormalities generated by the system.

[0044] S62. Health Report: Integrates health trend analysis functions, allowing users to view historical data and health reports to help better manage their pets' health status.

[0045] S63. Settings and Management: Provides user-friendly settings options, allowing users to personalize the system, including notification methods, detection sensitivity, alarm preferences, etc.

[0046] Through the above-described embodiments, this invention effectively improves the ability to identify abnormal pet conditions, promptly detects potential health or behavioral problems, and ensures the safety and health of pets. The system has good scalability and adaptability, and can meet the needs of different users.

[0047] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0048] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0049] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0050] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0051] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A method for identifying abnormal conditions in pets, characterized in that, This method uses artificial intelligence technology to identify pets from video data and analyze their behavior, including the following steps: S1. Capture pet activity video streams using a high-definition camera, which has the capability for continuous day and night acquisition; S2. Use convolutional neural networks (CNN) or other neural network models to preprocess the video data to extract pet features; S3. Use machine learning algorithms to analyze the extracted features, identify whether the objects in the video are pets, and perform behavioral pattern recognition and anomaly detection, including but not limited to: twitching, trembling, frequent scratching, prolonged stillness, etc. S4. Allows users to customize identification parameters and abnormal behavior types, which can be configured through the user interface; S5. Generate and send alarm information to the user device. When abnormal behavior is detected, the alarm information includes video clips of the abnormal behavior and analysis results.

2. The method according to claim 1, characterized in that, The artificial intelligence model is specially trained to identify different types of pets and their behavioral characteristics.

3. The method according to claim 1, characterized in that, The user interface allows users to view real-time activity and receive alerts for abnormal events via mobile applications, WeChat mini-programs, or desktop applications.

4. A system for implementing the method according to any one of claims 1 to 3, characterized in that, include:

1. A data processing unit configured to receive and process video streams from a camera; 2. An artificial intelligence model used to analyze video data and identify pet behavior; 3. Client program, used for user interaction, system configuration and data access.

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