Cat health management system and method

Abstract

The invention relates to a cat health management system and method, and belongs to the technical field of pet health management. The system aims at solving the problems that an existing pet nursing device is single in function, not matched in monitoring mode and lack of intelligent analysis ability. According to the technical scheme, a multi-sensor fusion module collects diet, activity and physiological data in a non-contact mode; the automatic feeding module executes precise feeding; the interaction module supports remote cat teasing and bidirectional audio-video interaction; the core control module processes data and controls the operation of each module; and the software system analyzes the data by using an AI algorithm and generates a health risk early warning. Functional integration, non-sensitive monitoring, intelligent management and remote care are realized, health abnormity can be found in advance, and scientific care guidance can be provided.

Description

Technical Field

[0001] This invention belongs to the field of pet health management technology, and relates to a health management system and method for cats. Background Technology

[0002] With socio-economic development and improved living standards, pet ownership rates continue to rise. As an important family companion animal, the health and well-being of cats are receiving increasing attention. The fast pace of modern life often forces pet owners to be separated from their pets for short periods, such as during business trips or travel, making remote health management and daily care for pets a pressing need.

[0003] To address this need, several technological solutions have emerged in the pet care equipment field. One type is the smart pet feeder, which typically uses a timer module and weighing sensors to automatically feed pets in measured quantities. However, its functionality is relatively limited, primarily focusing on providing food and lacking the ability to continuously monitor the pet's health. Another type is wearable devices, such as pet health monitoring collars, which can collect some physiological data such as the pet's activity level and heart rate. However, these devices must be worn around the pet's neck, which can easily cause resistance in naturally sensitive cats that dislike restraint, leading to the collar being pulled off or triggering a stress response. Furthermore, their functionality is not effectively integrated with remote interaction and automatic feeding. Additionally, there is a remote pet interaction device that allows users to control the device via a mobile application to emit laser dots for remote interaction. However, its functionality is fragmented and fails to integrate health monitoring with feeding and care.

[0004] In summary, existing technologies have significant shortcomings. First, they suffer from limited functionality; most devices can only perform one function, such as feeding, playing with the cat, or monitoring a single health indicator, failing to meet pet owners' comprehensive needs for all-round health management and remote care of their feline companions. Second, health monitoring methods are limited; wearable devices clash with cats' habits, resulting in poor comfort and low compliance; while non-wearable devices often lack the ability to accurately and synchronously collect multi-dimensional physiological and behavioral data. Most importantly, existing devices generally lack the analytical capabilities of Artificial Intelligence (AI), mostly remaining at the level of data collection and basic command execution, unable to deeply mine and intelligently analyze the acquired multi-source data, thus failing to provide early warning of potential health risks and offering precise data support for scientific pet care.

[0005] Therefore, there is an urgent need in this field for a comprehensive solution that integrates health monitoring, remote interaction, and automatic feeding. This solution should employ a non-wearable design to suit cat behavior, possess the ability to accurately collect multi-dimensional data, and incorporate intelligent algorithms for data analysis and risk warning. This would effectively address the technical problems of existing technologies, such as fragmented functionality, incompatible monitoring methods, and a lack of intelligent analysis capabilities. Summary of the Invention

[0006] In view of this, the purpose of this invention is to provide a health management system and method for cats. It addresses the technical problems of existing pet care devices, such as fragmented functions, health monitoring methods that are not suited to cats' habits, and a lack of intelligent data analysis capabilities. The system enables multi-dimensional data collection of cats' diet, activity, and physiological data without the need for wearing a device. It integrates remote video interaction, laser cat-playing, and automatic feeding functions, and uses AI algorithms for health analysis and risk warnings, providing cats with a comprehensive and accurate health management solution while meeting the comprehensive needs of pet owners for remote pet care.

[0007] A health management system for cats, comprising: A multi-sensor fusion module is built into the device body. The multi-sensor fusion module is used to collect the cat's diet data, activity data and physiological data in a non-contact manner. An automatic feeding module is connected to the multi-sensor fusion module, and the automatic feeding module is used to automatically feed food according to preset instructions or remote instructions; An interactive module is connected to the multi-sensor fusion module, and the interactive module is used for remote cat-teasing and two-way audio-visual interaction. The core control module is connected to the multi-sensor fusion module, the automatic feeding module, and the interaction module. The core control module is used to receive and process the data collected by the multi-sensor fusion module and control the operation of the automatic feeding module and the interaction module. The software system runs on the core control module and is used to perform AI analysis on the data collected by the multi-sensor fusion module to generate health risk warning information for the cat. The system connects to the backend server and mobile terminal via wireless data transmission.

[0008] Furthermore, the multi-sensor fusion module includes: Infrared sensors are used to detect the body temperature of a cat when it approaches. Weight sensors, deployed at the bottom of the food bowl, are used to collect data on the cat's food and water intake. The image acquisition module is used to capture videos of the cat's activities. The vibration sensor, in conjunction with the image acquisition module, is used to capture behavioral data on the cat's activity frequency and daily routine. The vibration sensor detects the minute vibrations caused by the cat's activities and fuses them with the data from the image acquisition module to jointly calculate the activity frequency. Temperature and humidity sensors are used to monitor parameters of a cat's living environment.

[0009] Furthermore, the core control module includes: The main control unit uses an STMicroelectronics 32-bit Microcontroller (STM32) for data reception, instruction execution, and information transmission. The AI ​​chip is used for local data preprocessing and running AI analysis algorithms in the software system.

[0010] Furthermore, the automatic feeding module is used to realize quantitative feeding and automatic water replenishment; the interactive module includes a laser module and a two-way audio and video module, the laser module is used to realize remote laser teasing of the cat, and the two-way audio and video module is used to realize remote real-time video observation and voice interaction.

[0011] The cat health management method based on the aforementioned management system includes the following steps: Collect multi-dimensional health characteristic data, including the cat's food intake, water intake, body surface temperature, activity frequency, and time spent in the food and water bowls; A personalized health baseline is generated, which is calculated based on the mean and standard deviation of the multi-dimensional health characteristic data over a preset 7-day historical monitoring period; mean and standard deviation Calculate dynamic threshold For example, when the average food intake is 120 grams and the standard deviation is 5 grams, the threshold is set to 110-130 grams.

[0012] The step of confirming a single feature anomaly includes calculating a dynamic threshold based on the personalized health baseline and identifying abnormal features in the multi-dimensional health feature data. A comprehensive health score is calculated based on the abnormal features and their preset weights. Determining the health risk warning level includes determining the corresponding health risk warning level based on the numerical range of the comprehensive health score.

[0013] Furthermore, the step of calculating the dynamic threshold based on the personalized health baseline specifically includes: For any health characteristic The formula for calculating its dynamic threshold is:

[0014] in, Health characteristics The upper or lower limit of the dynamic threshold. Health characteristics The average value of data within the historical monitoring period. Health characteristics Standard deviation of data within the historical monitoring period; when health characteristics The real-time monitoring value exceeds the dynamic threshold. At that time, determine health characteristics This is an abnormal characteristic.

[0015] Furthermore, the step of calculating the comprehensive health score specifically includes: The comprehensive health score is calculated using the following formula. :

[0016] in, For the comprehensive health score, The total number of the multi-dimensional health feature data. For the health characteristics Preset weights, Based on clinical data or veterinary guidelines, for example, the weighting of food intake is set at 0.3 (due to sensitivity to urinary tract diseases), activity frequency at 0.3, and water bowl time at 0.4, to meet the following requirements. .

[0017] Furthermore, the step of determining the health risk warning level specifically includes: when When the risk level is determined to be low, a health alert will be issued. when When the risk level is determined to be medium, a reminder message is issued advising the user to consult a veterinarian. when When a high-risk warning level is identified, an immediate reminder to consult a veterinarian is issued, and a confirmation request to automatically shut down the automatic feeding module is sent to the smart terminal. If the user does not respond in time or confirm the shutdown, the automatic feeding module is remotely shut down.

[0018] Furthermore, the step of collecting multi-dimensional health characteristic data specifically includes: the food intake data is collected through a weight sensor; the body surface temperature data is collected through an infrared sensor; the activity frequency data is collected through a vibration sensor and an image acquisition module; and the food and water bowl dwell time data is collected through an image acquisition module.

[0019] Furthermore, the multi-sensor fusion module adopts a non-wearable design; the software system runs the health management method to provide early warning of feline urinary system diseases.

[0020] The beneficial effects of this invention are as follows: (1) This invention breaks through the limitations of existing devices with single functions and organically integrates core functions such as health monitoring, remote interaction, automatic feeding and water replenishment into one. Users do not need to purchase multiple separate devices to achieve comprehensive care and health management for their cats, which significantly reduces economic costs and home space occupation, and provides a unified and convenient management experience.

[0021] (2) This invention adopts a completely non-contact multi-sensor fusion scheme, which fully meets the cat's natural dislike of restraint and effectively avoids stress reactions and escape problems caused by wearable devices. Through the coordinated work of multiple sensors such as infrared, weight, image, vibration and temperature and humidity, it can collect multi-dimensional data such as diet, activity, physiology and environment in a non-contact and accurate manner, providing a comprehensive and reliable data foundation for health analysis.

[0022] (3) This invention uses a built-in intelligent algorithm to perform in-depth analysis of the massive amounts of collected data, which can automatically establish a personalized health baseline for each cat and perform anomaly identification and comprehensive risk scoring based on dynamic thresholds and weight calculations. This enables the system not only to record data, but also to proactively discover potential health risks, achieving a leap from passive recording to proactive early warning, and providing pet owners with scientific and forward-looking health guidance.

[0023] (4) This invention supports real-time two-way audio and video calls, remote laser cat teasing, and precise timed and quantitative feeding control. This allows pet owners to interact with their pets at any time when they are away from home, meet their needs for food, water, and entertainment, effectively alleviate separation anxiety, and enhance the sense of presence, flexibility, and peace of mind of remote care.

[0024] (5) The core control module adopts an architecture that combines a high-performance microcontroller with a dedicated artificial intelligence chip, ensuring the efficiency and stability of data processing and instruction execution. The automatic feeding module achieves precise feeding control through a stepper motor, and the laser head of the interactive module can move flexibly over a wide range. Combined with a robust communication design, these features ensure the reliability of the device during long-term use and its high adaptability to cat behavior.

[0025] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0026] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a schematic diagram of the overall structure of the health management system provided in an embodiment of the present invention; Figure 2 This is a detailed schematic diagram of the multi-sensor fusion module in an embodiment of the present invention; Figure 3 This is a flowchart illustrating the algorithm logic of the health management method in this embodiment of the invention. Figure 4 This is a schematic diagram illustrating the deployment of the system in a home environment according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the hardware configuration of the core control module in an embodiment of the present invention; Figure 6 This is a schematic diagram of the display interface of the warning information on the user terminal in an embodiment of the present invention.

[0027] Figure reference numerals: Health Management System 100, Multi-Sensor Fusion Module 110, Infrared Sensor 111, Weight Sensor 112, Image Acquisition Module 113, Vibration Sensor 114, Temperature and Humidity Sensor 115, Automatic Feeding Module 120, Interaction Module 130, Core Control Module 140, Main Control Unit 141, AI Chip 142, Software System 150, Acquiring Multi-Dimensional Health Feature Data S310, Generating Personalized Health Baseline S320, Confirming Single Feature Abnormalities S330, Calculating Comprehensive Health Score S340, Determining Health Risk Warning Level S350. Detailed Implementation

[0028] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0029] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0030] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0031] Figure 1 This is a schematic diagram of the overall structure of a cat health management system provided in an embodiment of the present invention.

[0032] like Figure 1 As shown, the health management system 100 mainly includes a multi-sensor fusion module 110, an automatic feeding module 120, an interaction module 130, a core control module 140, and a software system 150. The multi-sensor fusion module 110 is built into the device body and is used for non-contact collection of various data from the cat. It transmits data in real time to the core control module 140 via electrical connection. The automatic feeding module 120 and the interaction module 130 are both connected to the multi-sensor fusion module 110 and are controlled by the core control module 140. The core control module 140, as the system's central hub, is responsible for processing all input and output signals. The software system 150 runs on top of the core control module 140 and carries the core algorithm logic of this invention. Data communication and command transmission between the modules are achieved via wired or wireless means.

[0033] Figure 2 This is a detailed schematic diagram of the multi-sensor fusion module in one embodiment of the present invention.

[0034] See Figure 2The multi-sensor fusion module 110 specifically includes an infrared sensor 111, a weight sensor 112, an image acquisition module 113, a vibration sensor 114, and a temperature and humidity sensor 115. The infrared sensor 111 is mounted on the front of the device to detect the cat's body temperature when it approaches. The weight sensor 112 is precisely positioned at the bottom of the food bowl to monitor changes in the bowl's weight in real time to calculate the cat's actual food intake. The image acquisition module 113 uses a wide-angle camera to capture video of the cat's activities around the device. The vibration sensor 114 works in conjunction with the image acquisition module 113 to detect minute vibrations caused by the cat's touch or activity, helping to capture the frequency of its activity. The vibration sensor detects the vibration frequency when the cat scratches the device, and combined with the image acquisition module, it determines abnormal behavior (e.g., excessive scratching may indicate anxiety). The temperature and humidity sensor 115 continuously monitors the environmental temperature and humidity parameters of the cat's living area. All the raw data collected by these sensors is sent to the core control module 140 for preprocessing and fusion.

[0035] Figure 3 This is an algorithm logic flowchart of a health management method in one embodiment of the present invention.

[0036] Figure 3 The core steps of the health management methodology executed by the software system 150 are demonstrated, including data acquisition S310, baseline generation S320, anomaly confirmation S330, score calculation S340, and risk warning S350. This process embodies a complete intelligent processing procedure from raw data to the final warning level.

[0037] Figure 4 This is a schematic diagram of the system deployment in a home environment according to one embodiment of the present invention.

[0038] like Figure 4 As shown in the diagram, this illustration depicts a practical application scenario of the health management system 100 in a typical home environment. The intelligent health management device is placed in the area where the cat is active, with its integrated multi-sensor fusion module 110 and automatic feeding module 120 facing the cat. The device connects to the home router via wireless communication methods such as Wi-Fi or Bluetooth, thereby accessing the internet and synchronizing data with a remote cloud server. Users can remotely access the system through a dedicated application on their personal mobile phones to view the cat's status in real time, receive health alerts, and issue interactive or feeding commands. This diagram clearly shows the complete data flow and control loop from the device to the cloud and then to the user.

[0039] Figure 5 This is a schematic diagram of the hardware configuration of the core control module in one embodiment of the present invention.

[0040] See Figure 5The core control module 140, acting as the central nervous system, primarily comprises a main control unit 141 and an AI chip 142. The main control unit 141, employing an STM32 series microcontroller, is responsible for overall system control and scheduling, instruction parsing, and basic communication with peripheral modules. The AI ​​chip 142 is dedicated to running complex machine learning algorithms, performing localized preprocessing and intelligent analysis on the massive amounts of data uploaded by the multi-sensor fusion module 110. The AI ​​chip is dedicated to running machine learning algorithms and processing sensor data in real time; the main control unit (STM32) is responsible for instruction scheduling, and the two operate in parallel via an internal bus. The main control unit 141 and the AI ​​chip 142 work collaboratively through the internal bus and are connected to various peripheral interfaces, including interfaces for connecting sensors, drive interfaces for driving the stepper motor of the automatic feeding module 120, Wi-Fi / Bluetooth module interfaces for wireless communication, and interfaces for processing audio and video encoding and decoding, thereby achieving unified and efficient management of all system hardware resources.

[0041] Figure 6 This is a schematic diagram of the warning information displayed on the user terminal interface in one embodiment of the present invention.

[0042] Figure 6 This simulation demonstrates the alert information interface displayed on a user's mobile application when the system detects a health anomaly. The interface clearly and prominently displays the core alert content, typically including: a high-risk health alert indicator, the name and timestamp of the cat that triggered the alert, a comprehensive health score and risk level derived from an algorithm, detailed lists of specific abnormal characteristics (such as abnormally increased water intake, significantly decreased activity levels, etc.), and action suggestions based on the analysis results. The bottom of the interface provides intuitive operation buttons, such as "Contact Veterinarian with One Click" and "View Detailed Report," enabling users to quickly respond to alerts and take appropriate measures. The accompanying diagram visually illustrates the invention's ability to transform complex backend data analysis results into user-friendly information on the front end.

[0043] Example 1: System Integration Operation and Routine Health Monitoring This embodiment demonstrates the integrated workflow of the health management system in everyday scenarios.

[0044] The user places the device in an area where the cat frequently roams and plugs it in. After the system starts up, the core control module 140 initializes each hardware unit. The multi-sensor fusion module 110 begins to work continuously: the weight sensor 112 records the initial weight of the food bowl; the image acquisition module 113 and the vibration sensor 114 begin to monitor the environment; the infrared sensor 111 and the temperature and humidity sensor 115 also enter standby mode.

[0045] When a cat approaches the device to eat, its body temperature is captured by infrared sensor 111. After the cat steps onto the food bowl, weight sensor 112 records the weight reduction curve. Combined with the cat's head movements captured by image acquisition module 113, the system can accurately calculate the amount of food consumed and the time spent in the water bowl. Simultaneously, the cat's walking and playing behavior around the device is recorded by vibration sensor 114 and image acquisition module 113, forming activity frequency data. All this data is transmitted in real time to the core control module 140.

[0046] The main control unit of the core control module 140 (using an STM32 series microcontroller) performs preliminary filtering and formatting of the data, which is then preprocessed locally by the AI ​​chip. The software system 150 runs in the background, establishing a personalized health baseline for the cat based on historical data over a period of time (e.g., seven days).

[0047] When users are away from home, they can remotely access the system via a mobile application. Users can activate the two-way audio and video function of the interactive module 130 to have real-time voice communication and video observation with their cats. Simultaneously, users can control the laser module to emit laser dots that move on the ground to entice the cats to move, thus achieving the purpose of remote companionship and encouraging exercise. When the temperature and humidity sensor detects that the ambient humidity is below the threshold, the core control module triggers the water pump to replenish water to the water bowl in a measured amount through the water pipe. Users can also manually trigger the automatic feeding module 120 to provide immediate food or water, or set a timed and measured feeding schedule.

[0048] During routine monitoring, the software system 150 continuously compares real-time data with a personalized health baseline. If a slight deviation is detected in any data point (such as a significant decrease in water intake), the system may send a "health reminder" to the user via the application, suggesting that the user pay attention to the cat's water intake. This is a low-risk warning. At this time, the overall system functionality remains unaffected, and the automatic feeding module 120 continues to operate as planned.

[0049] Example 2: Early warning of urinary system diseases This embodiment uses a specific case to illustrate in detail how the system achieves early disease warning through algorithms.

[0050] Let's assume a three-year-old male British Shorthair cat has used this system for a week, establishing its personalized health baseline. During the historical monitoring period, its average daily food intake was 120 grams, with a standard deviation of 5 grams; the average daily water bowl time was 20 minutes, with a standard deviation of 1 minute; and the average daytime activity frequency was 30 times per hour, with a standard deviation of 1 time per hour.

[0051] Starting from the third day, the system continuously acquired the following multi-dimensional health characteristic data in the data collection step S310: food intake decreased to 110 grams and 105 grams for two consecutive days; water basin stay time increased abnormally to 35 minutes and 40 minutes; activity frequency decreased to 25 times per hour and 22 times per hour.

[0052] In the baseline generation step S320, the system already possesses the cat's baseline parameters. Moving to the anomaly confirmation step S330, the software system 150 calculates a dynamic threshold for each feature based on the personalized health baseline. For the food intake feature, the upper limit of the dynamic threshold is 120 + 2 * 5 = 130 grams, and the lower limit is 120 - 2 * 5 = 110 grams. The real-time monitoring value of 105 grams is lower than the lower limit of 110 grams; therefore, this feature is marked as abnormal. F Set 1 to 1. Similarly, the dynamic threshold upper limit for the water basin dwell time is 20 + 2 * 1 = 22 minutes. The real-time value of 40 minutes far exceeds the upper limit and is marked as abnormal. F 2=1. The lower limit of the dynamic threshold for activity frequency is 30-2*1=28 times / hour. The real-time value of 22 times / hour is lower than the lower limit and is marked as abnormal. F 3 = 1.

[0053] In the scoring calculation step S340, the system calculates the comprehensive health score SH according to preset weights. Assume the weights for food intake, water bowl time, and activity frequency are respectively... W 1 = 0.3, W 2 = 0.4, W 3 = 0.3. Therefore... S H =(0.3*1)+(0.4*1)+(0.3*1)=1.0.

[0054] In risk warning step S350, the system makes a judgment. S H =1.0 is greater than 0.6, therefore it is determined to be a high-risk warning level. The software system 150 immediately sends a strong reminder message to the user through the application: "High-risk warning: It is recommended to consult a veterinarian immediately." At the same time, as a safety precaution, the system first sends a warning to the user's terminal, and after the user confirms, it shuts down the automatic feeding module 120 to prevent the risks of continuing to feed when the health cause is unknown. In addition, the AI ​​algorithm analyzes abnormal feature combinations (sudden increase in water intake accompanied by decreased activity and loss of appetite), and combines this with the knowledge base information that unneutered male cats of this breed are a high-risk group for urinary system diseases, indicating "high risk of suspected urinary system disease" in the warning message, which greatly improves the accuracy and guidance value of the warning. After receiving the warning, the user took the cat to the vet, and indeed found an early problem of urethral crystals. Because it was discovered in time, it could be controlled with medication, avoiding more serious consequences.

[0055] Example 3: Coordinated Control and Real-Time Response of Hardware Modules This embodiment focuses on demonstrating how the core control module 140 coordinates and controls various hardware modules to achieve precise real-time response.

[0056] When a user sends a remote command via a mobile app to "feed 15 grams immediately," the command is transmitted to the core control module 140 via Wi-Fi. The STM32 main control unit first parses the command, confirms its validity, and then immediately sends a control signal to the automatic feeding module 120. Upon receiving the signal, the stepper motor of the automatic feeding module 120 begins precise rotation, driving the feeding mechanism to dispense a measured amount of cat food. Simultaneously, the core control module 140 activates the image acquisition module 113 to begin recording a video stream of the feeding process.

[0057] During feeding, the weight sensor 112 monitors the weight change of the food bowl in real time. When it detects an increase of 15 grams, the weight sensor 112 sends a signal back to the core control module 140. The STM32 main control unit then sends a stop command to the stepper motor, completing a precise quantitative feeding. Throughout the process, the AI ​​chip may perform real-time analysis on the video transmitted back by the image acquisition module 113 to identify whether the cat has indeed come to eat and whether its eating state is normal.

[0058] On the other hand, when the vibration sensor 114 detects a continuous and rhythmic scratching vibration signal, it transmits it to the core control module 140. The algorithm of the software system 150 may interpret this behavior as a "play invitation" signal from the cat. The core control module 140 can then automatically activate the laser module of the interaction module 130, randomly moving the laser point within a preset safe area to attract the cat's attention and achieve intelligent interaction. Simultaneously, the core control module 140 collects ambient sound through a two-way audio-visual module. If it detects abnormal cat vocalizations (such as agitation), it records the event and may store it in the database as health reference data.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A health management system for cats, characterized in that: include: A multi-sensor fusion module is built into the device body. The multi-sensor fusion module is used to collect the cat's diet data, activity data and physiological data in a non-contact manner. An automatic feeding module is connected to the multi-sensor fusion module, and the automatic feeding module is used to automatically feed food according to preset instructions or remote instructions; An interactive module is connected to the multi-sensor fusion module, and the interactive module is used for remote cat-teasing and two-way audio-visual interaction. The core control module is connected to the multi-sensor fusion module, the automatic feeding module, and the interaction module. The core control module is used to receive and process the data collected by the multi-sensor fusion module and control the operation of the automatic feeding module and the interaction module. The software system runs on the core control module and is used to perform AI analysis on the data collected by the multi-sensor fusion module to generate health risk warning information for the cat. The system connects to the backend server and mobile terminal via wireless data transmission.

2. The cat health management system according to claim 1, characterized in that: The multi-sensor fusion module includes: Infrared sensors are used to detect the body temperature of a cat when it approaches. Weight sensors, deployed at the bottom of the food bowl, are used to collect data on the cat's food and water intake. The image acquisition module is used to capture videos of the cat's activities. A vibration sensor, in conjunction with the image acquisition module, is used to capture behavioral data on the cat's activity frequency and daily routine. Temperature and humidity sensors are used to monitor parameters of a cat's living environment.

3. The cat health management system according to claim 1, characterized in that: The core control module includes: The main control unit, using an SSTM32, is used for data reception, instruction execution, and information transmission. The AI ​​chip is used for local data preprocessing and running AI analysis algorithms in the software system.

4. The cat health management system according to claim 1, characterized in that: The automatic feeding module is used to achieve quantitative feeding and automatic water replenishment; the interactive module includes a laser module and a two-way audio and video module. The laser module is used to achieve remote laser cat teasing, and the two-way audio and video module is used to achieve remote real-time video observation and voice interaction.

5. A method for managing the health of cats based on the management system described in any one of claims 1 to 4, characterized in that: Includes the following steps: Collect multi-dimensional health characteristic data, including the cat's food intake, water intake, body surface temperature, activity frequency, and time spent in the food and water bowls; A personalized health baseline is generated, which is calculated based on the mean and standard deviation of the multi-dimensional health feature data within a historical monitoring period. The step of confirming a single feature anomaly includes calculating a dynamic threshold based on the personalized health baseline and identifying abnormal features in the multi-dimensional health feature data. A comprehensive health score is calculated based on the abnormal features and their preset weights. Determining the health risk warning level includes determining the corresponding health risk warning level based on the numerical range of the comprehensive health score.

6. The method for cat health management according to claim 5, characterized in that: The step of calculating the dynamic threshold based on the personalized health baseline specifically includes: For any health characteristic The formula for calculating its dynamic threshold is: in, Health characteristics The upper or lower limit of the dynamic threshold. Health characteristics The average value of data within the historical monitoring period. Health characteristics Standard deviation of data within the historical monitoring period; when health characteristics The real-time monitoring value exceeds the dynamic threshold. At that time, determine health characteristics This is an abnormal characteristic.

7. The method for cat health management according to claim 5, characterized in that: The steps for calculating the comprehensive health score specifically include: The comprehensive health score is calculated using the following formula. : in, For the comprehensive health score, The total number of the multi-dimensional health feature data. For the health characteristics Preset weights, For the health characteristics The abnormal marker, the In health characteristics When identified as an abnormal feature, the value is 1; in healthy features... When identified as a non-abnormal feature, the value is 0, and the... satisfy .

8. The method for cat health management according to claim 7, characterized in that: The steps for determining the health risk warning level specifically include: when When the risk level is determined to be low, a health alert will be issued. when When the risk level is determined to be medium, a reminder message is issued advising the user to consult a veterinarian. when When a high-risk warning level is identified, an immediate reminder to consult a veterinarian is issued, and a confirmation request to automatically shut down the automatic feeding module is sent to the smart terminal. If the user does not respond or confirm the shutdown in a timely manner, the automatic feeding module is remotely shut down.

9. The method for cat health management according to claim 5, characterized in that: The steps for collecting multi-dimensional health characteristic data specifically include: the food intake data is collected using a weight sensor; the body surface temperature data is collected using an infrared sensor; the activity frequency data is collected using a vibration sensor and an image acquisition module; and the time spent in the food and water bowl is collected using an image acquisition module.

10. The cat health management system according to claim 1, characterized in that: The multi-sensor fusion module adopts a non-wearable design; the software system runs the health management method to provide early warning of feline urinary system diseases.

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