Pet feeding health monitoring system and method

Abstract

The present application belongs to the technical field of smart home, and provides a pet feeding health monitoring system and method. The method comprises: periodically acquiring first monitoring data of a wearable device worn on a target pet, and analyzing whether the target pet has an abnormal trend according to the first monitoring data; if it is judged that the target pet has an abnormal trend, selecting a plurality of pet devices from a disposable list, controlling each pet device to execute a corresponding action, acquiring second monitoring data of the wearable device in the action stage, and evaluating an auxiliary factor based on the second monitoring data; using the auxiliary factor to adjust the abnormal trend to obtain an adjusted abnormal trend, and sending prompt information to a specified user terminal when the adjusted abnormal trend meets an abnormal condition. The present application can help users remotely and in real time master the abnormal health condition of pets.

Description

Technical Field

[0001] This invention relates to the field of smart home technology, and more specifically, to a pet feeding and health monitoring system and method. Background Technology

[0002] As people's living standards continue to improve, pets are playing an increasingly important role in families, and their health is of great concern to owners. Accurate and timely monitoring of pets' health is crucial for disease prevention and improving their quality of life.

[0003] Currently, common methods of pet health monitoring mainly rely on pet owners' daily observations. For example, owners make a preliminary judgment on their pet's health by observing its mental state, eating habits, and bowel movements. However, this method is highly subjective, and different owners have varying observational abilities and experience, which can easily lead to misjudgments or missed diagnoses of pet health problems. Some veterinary hospitals use regular checkups for health monitoring, including physical examinations, blood tests, and urine tests. However, checkups usually require pet owners to take their pets to the hospital, which is cumbersome, and the frequency of checkups is often low, making it impossible to achieve real-time, dynamic monitoring of the pet's health status. It is also difficult to detect and intervene promptly in cases of sudden illnesses or changes in condition.

[0004] Therefore, how to achieve convenient monitoring of pet health, especially remote monitoring, is a technical problem that urgently needs to be solved. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method, system, electronic device, computer storage medium, and computer program product for monitoring pet feeding health.

[0006] This invention discloses a method for monitoring the health of pets through feeding, the method comprising the following steps:

[0007] The system periodically acquires first monitoring data from a wearable device worn on a target pet, and analyzes whether the target pet exhibits any abnormal trends based on the first monitoring data. The first monitoring data includes first movement data and physiological indicator data.

[0008] If it is determined that the target pet has an abnormal trend, several pet devices are selected from the available list, each pet device is controlled to perform a corresponding action, and the second monitoring data of the wearable device during this action phase is obtained. An auxiliary factor is evaluated based on the second monitoring data; the second monitoring data includes second motion data.

[0009] The abnormal trend is adjusted using the auxiliary factor to obtain the adjusted abnormal trend. When the adjusted abnormal trend meets the abnormal conditions, a prompt message is sent to the designated user terminal.

[0010] The present invention also discloses a pet feeding health monitoring system, the system comprising a first communication module, an anomaly analysis module, and a second communication module;

[0011] The first communication module is used to periodically acquire first monitoring data from a wearable device worn on the target pet, and analyze whether the target pet has any abnormal trends based on the first monitoring data; the first monitoring data includes first movement data and physiological indicator data;

[0012] The anomaly analysis module is used to select several pet devices from the available list if it is determined that the target pet has an abnormal trend, control each pet device to perform a corresponding action, and obtain the second monitoring data of the wearable device during this action phase. Based on the second monitoring data, an auxiliary factor is evaluated and obtained. The second monitoring data includes second motion data. The abnormal trend is adjusted using the auxiliary factor to obtain an adjusted abnormal trend.

[0013] The second communication module is used to send a prompt message to a designated user terminal when the abnormal trend after adjustment meets the abnormal conditions.

[0014] The present invention also discloses an electronic device comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, the processor executing the computer program to implement the method as described in any of the preceding methods.

[0015] The present invention also discloses a computer storage medium storing a computer program that is executed by a processor to implement the methods described in any of the preceding methods.

[0016] The present invention also discloses a computer program product containing computer code, which, when executed by a processor of an electronic device, implements the method described in any of the preceding methods.

[0017] The beneficial effects of this invention are at least as follows:

[0018] The above-described solution of the present invention achieves real-time, dynamic tracking of pet health status by periodically acquiring first monitoring data from wearable devices, effectively overcoming the subjectivity and lag of traditional manual observation. When an abnormal trend is detected, second monitoring data is collected using pet devices from the available list to derive auxiliary factors, enabling a more accurate assessment of the pet's actual health status and avoiding misjudgments. Finally, based on the adjusted abnormal trend, a prompt message is sent to the user terminal, providing pet owners with convenient and timely health warnings. This greatly improves the comprehensiveness, accuracy, and timeliness of pet health monitoring, helping to prevent pet diseases and protect pet health. Attached Figure Description

[0019] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic flowchart of a pet feeding health monitoring method disclosed in an embodiment of the present invention;

[0021] Figure 2 This is a schematic diagram of the APP page of several manually added pet devices disclosed in an embodiment of the present invention;

[0022] Figure 3 This is a schematic diagram of the structure of a pet feeding health monitoring system disclosed in an embodiment of the present invention. Detailed Implementation

[0023] The following specific embodiments illustrate the implementation of this application. Those skilled in the art can easily understand other advantages and effects of this application from the content disclosed in this specification. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Furthermore, the technical features involved in the different embodiments of this application described below can be combined with each other as long as they do not conflict with each other.

[0025] like Figure 1 As shown, to address the aforementioned technical problems, this invention discloses a method for monitoring pet feeding health, the method comprising the following steps:

[0026] S1, periodically acquire first monitoring data from the wearable device worn on the target pet, and analyze whether the target pet has any abnormal trends based on the first monitoring data; the first monitoring data includes first movement data and physiological indicator data.

[0027] The solution of this invention is preferably implemented in the control device of a smart home system, such as a smart speaker or a smart home device. Multiple smart devices in the home (including various pet devices) are connected to the control device via Bluetooth or Wi-Fi, allowing the control device to obtain relevant data from these smart devices and control them. Simultaneously, the control device can also communicate with remote user terminals, such as smartphones, tablets, and computers. The user terminal can interact with the control device via a corresponding app to exchange data and commands.

[0028] The control device can receive the pet's initial monitoring data from the wearable device worn by the pet at preset intervals (such as every hour, every half hour, etc., the specific interval can be set according to the actual situation). The initial monitoring data includes initial movement data, such as the pet's movement distance, number of steps, running time, etc., which can reflect the pet's daily activity level; it also includes physiological indicator data, such as heart rate, body temperature, respiratory rate, etc.

[0029] The control equipment can determine whether there are abnormal trends in a pet's physiological signs according to preset abnormality judgment conditions. For example, the body temperature of a healthy adult cat is usually between 38℃ and 39.2℃. If a pet cat's body temperature is detected to reach 40℃ and it does not move for a long time, it can be determined that the pet cat has an abnormal trend, such as fever. There should be multiple abnormality judgment conditions, covering various pathological abnormalities in different pets, and the specific conditions are not limited.

[0030] S2, if it is determined that the target pet has an abnormal trend, select several pet devices from the available list, control each pet device to perform the corresponding action, and obtain the second monitoring data of the wearable device during this action phase. Based on the second monitoring data, evaluate and derive the auxiliary factor; the second monitoring data includes the second motion data.

[0031] like Figure 2 As shown, users can manually add various pet devices to the smart home system app's device interface in advance, creating a list of available devices. Pet devices include pet feeders, interactive toys, etc. Pet feeders include regular automatic feeders and catapult-type automatic feeders, etc. Interactive toys include pet balls, chew toys that can make sounds / light up, etc.

[0032] Once an abnormal trend is detected in the pet, the control device selects at least one suitable pet device from a pre-set list of available devices and then controls these devices to perform actions to attract the pet's attention. Simultaneously, a second set of monitoring data—referring to the pet's second movement data—is acquired from the wearable device during this action phase. By analyzing the pet's response to the actions of the pet devices, a corresponding auxiliary factor is derived. This auxiliary factor is used to assist in analyzing the authenticity of the pet's abnormal trend.

[0033] For example, if the selected pet device is an interactive toy, and it is controlled to perform specific actions (such as lighting or vibration), the second motion data generated by the wearable device during the interaction between the pet and the toy can be analyzed to determine the changes in the pet's movements during the interaction. These changes can be categorized as positive responses (e.g., the pet frequently chases or jumps) or negative responses (e.g., the pet shows no obvious movement changes, such as lying still). Based on the second motion data, corresponding auxiliary factors can be determined.

[0034] S3, use the auxiliary factor to adjust the abnormal trend to obtain the adjusted abnormal trend, and when the adjusted abnormal trend meets the abnormal conditions, send a prompt message to the designated user terminal.

[0035] The aforementioned auxiliary factors are used to correct the identified abnormal trends. For example, if the initial monitoring data indicated a persistent decline in pet activity and abnormal physiological indices, suggesting a potential health problem, the assessment of the abnormal trend can be re-evaluated by considering the pet's positive behavior (with a smaller auxiliary factor value, e.g., 0.6) or negative behavior (with a larger auxiliary factor value, e.g., 1.2) when interacting with interactive toys. This results in a adjusted abnormal trend. It is understood that in this invention, an abnormal trend can be defined as an abnormal assessment value higher than the abnormal trend threshold but lower than the abnormal determination threshold, or an abnormal determination threshold higher than the abnormal trend threshold.

[0036] If the adjusted abnormal trend reaches a preset abnormal condition (e.g., exceeding an abnormality threshold), the control device sends a notification to a pre-designated user terminal (such as the pet owner's mobile phone or tablet), informing the owner that the pet may have a health risk and reminding them to pay attention or take the pet to the vet. If the adjusted abnormal trend does not reach the preset abnormal condition (e.g., exceeding an abnormality threshold), the pet is determined to be normal; for example, the pet's temperature may be higher than the threshold due to lying in the sun, rather than having a fever.

[0037] The above-described solution of the present invention achieves real-time, dynamic tracking of pet health status by periodically acquiring first monitoring data from wearable devices, effectively overcoming the subjectivity and lag of traditional manual observation. When an abnormal trend is detected, second monitoring data is collected using pet devices from the available list to derive auxiliary factors, enabling a more accurate assessment of the pet's actual health status and avoiding misjudgments. Finally, based on the adjusted abnormal trend, a prompt message is sent to the user terminal, providing pet owners with convenient and timely health warnings. This greatly improves the comprehensiveness, accuracy, and timeliness of pet health monitoring, helping to prevent pet diseases and protect pet health.

[0038] Optionally, the periodic acquisition of first monitoring data from the wearable device worn on the target pet includes:

[0039] The system monitors whether a health concern instruction for the target pet is received. If it is received, a first monitoring cycle is determined based on the health concern instruction. If it is not received, a second monitoring cycle is set. The first monitoring cycle is shorter than the second monitoring cycle.

[0040] In this embodiment, the present invention sets the monitoring cycle of the control device for the target pet into a regular monitoring cycle and an irregular monitoring cycle. Specifically, the control device determines whether it has received a health concern instruction from a relevant user regarding the target pet. This health concern instruction refers to the instruction manually entered into the control device by the relevant user when they discover or suspect that the target pet has a health abnormality.

[0041] When the control device detects that the user has issued a health concern command for the target pet, it means that the user is quite worried about the pet's current health status. Based on this, the control device determines a shorter first monitoring cycle according to the health concern command, such as monitoring once every half hour, that is, to acquire high-frequency monitoring data of the target pet in order to achieve accurate and timely assessment of the pet's health status.

[0042] When the control device does not detect a health monitoring command, it indicates that the pet is in a relatively normal daily state and does not require a high frequency of data monitoring. At this time, the system will set a longer second monitoring cycle (which can also be a fixed value), such as monitoring once every 2 hours. This setting ensures continuous tracking of the pet's daily health status without consuming too much device power or creating a large amount of data storage pressure due to excessively frequent monitoring, thus achieving reasonable resource utilization while meeting basic monitoring needs.

[0043] Optionally, determining the first monitoring cycle based on the health attention instruction includes:

[0044] The health monitoring instructions that are in a valid state are selected, and the number of instructions and the number of instruction sources are counted. The first monitoring period is determined by matching the number of instructions and the number of instruction sources. The number of instructions and the number of instruction sources are both negatively correlated with the duration of the first monitoring period.

[0045] In this embodiment, when a user sends a health concern command to the control device, they set the validity period or time range of the command. If the health concern command exceeds the validity period or is outside the validity time range, it is determined that the health concern command has expired. The control device then stops filtering these expired health concern commands.

[0046] Next, the control device counts the number of valid health concern commands and the number of command sources. The number of commands represents the total number of times a user has issued a health concern command, while the number of command sources reflects the number of users who issued the commands (including pet owners and other family members of pet owners).

[0047] Then, the first monitoring cycle is determined based on the two statistical data points: the number of commands and the number of command sources. A higher number of health concern commands and from more different users indicate a greater level of user concern about their pet's health, and a stronger need for timely and frequent access to pet health information. In this case, the control device will correspondingly shorten the first monitoring cycle. For example, if only one user issues one health concern command, the first monitoring cycle might be set to once per hour; however, if three different users issue a total of five health concern commands, the first monitoring cycle might be shortened to once every 10 minutes to collect pet monitoring data more intensively and meet users' needs for close monitoring of their pet's health.

[0048] The duration of the first monitoring cycle is represented by a table showing the number of instructions and the number of sources of instructions, as shown in Table 1 below.

[0049] Table 1

[0050] Number of instructions Number of instruction sources Duration of the first monitoring cycle 1 1 1 hour 3 1 45 minutes 5 2 30 minutes 10 3 10 minutes

[0051] Optionally, selecting a plurality of pet devices from the available list and controlling each of the pet devices to perform a corresponding action includes:

[0052] Extract the functional attributes of each pet device in the available list, and classify each pet device into a first category of pet devices and a second category of pet devices based on the functional attributes. The first category of pet devices consists of pet devices with interactive capabilities, and the second category of pet devices consists of pet devices without interactive capabilities.

[0053] The interaction intensity of each pet device in the first type of pet device is determined, the action intensity of each pet device is determined according to the interaction intensity, and each pet device is controlled to execute the action intensity in descending order of the interaction intensity; the action intensity is negatively correlated with the interaction intensity.

[0054] In this embodiment, the control device analyzes the functional attributes of each pet device in the list of available devices. For example, smart toys (such as devices that can light up, make sounds, move, and attract pet interaction) and automatic cat teasers, which allow direct interaction with pets, are classified as Category 1 pet devices because they enable pets to actively participate in the interaction. Additionally, pet devices like automatic feeders, while not directly interacting with pets, still have moving parts internally. For example, the cat food agitator inside an automatic feeder makes a sound when it moves, allowing the cat to recognize that cat food has been dispensed, thus achieving indirect interaction. However, items like litter boxes and pet beds, which primarily provide basic necessities for pets and do not have the function of triggering active interaction from pets, are classified as Category 2 pet devices.

[0055] For the first category of interactive pet devices, the control device further evaluates the intensity of their interaction. This evaluation can be completed in advance or based on historical usage data of the pet devices, without limitation. For example, if statistical analysis of historical usage data of pet devices reveals that the total number of times and frequency of use of pet devices A and B are significantly higher than those of pet devices C and D, indicating that the target pet prefers to interact with pet devices A and B, then the interaction intensity of pet devices A and B is set higher than that of pet devices C and D.

[0056] Furthermore, the operating parameters of the pet device itself can be considered to adjust the interaction intensity estimated based on historical data. For example, for an interactive toy E that launches small balls, if it can launch the balls at high speed and frequency, keeping the pet continuously highly active, then its interaction intensity is high; while a simple light-up toy F may only occasionally attract the pet's brief attention, and its interaction intensity is relatively low. Additionally, evaluation models can be used to assess the operating parameters of these first-type pet devices. Specifically, the operating parameters of these first-type pet devices can be obtained through appropriate means (e.g., internet resources), analyzed and evaluated using a large language model (e.g., BERT), and the corresponding adjustment coefficients can be derived. These adjustment coefficients can then be used to adjust the interaction intensity estimated based on historical data.

[0057] After determining the potential interaction intensity of each pet device with the target pet, the control device sorts the first-category pet devices from highest to lowest interaction intensity. For pet devices at the top of the sorting queue, their action intensity is set lower than that of pet devices at the bottom of the queue. This is because first-category pet devices with higher interaction intensity only require slight movements to achieve the desired attraction effect, while first-category pet devices with lower interaction intensity require stronger movements. This setting allows different first-category pet devices to attract the target pet's attention with as similar an attraction effect as possible, thus enabling the analysis of secondary monitoring data.

[0058] It is understood that the action intensity in this invention has different meanings depending on the pet device, but multiple action intensity levels can be set for different types of pet devices, such as low, medium, and high. For a pet ball, a medium level of action intensity corresponds to medium-speed operation and medium-frequency triggering; for a chew toy, a high level of interaction intensity corresponds to rapidly flashing lights and high-frequency interactive sounds.

[0059] Finally, the control devices sequentially control the first-category pet devices to perform their respective actions in descending order of interaction intensity. This way, the most interactive devices are used first to stimulate the pet, their reactions are observed, and relevant data is collected. Then, devices with slightly lower interaction intensities are gradually activated to provide a more comprehensive and accurate understanding of the pet's state under different stimuli, offering richer data support for subsequent health assessments. Alternatively, the first-category pet devices can be controlled sequentially in ascending order of action intensity.

[0060] Optionally, the process of deriving auxiliary factors based on the second monitoring data includes:

[0061] The second monitoring data includes multiple sets of second monitoring sub-data, each set of second monitoring sub-data corresponding to each pet device in the first type of pet device;

[0062] A set of motion features is extracted from each of the second monitoring sub-data sets. The average value of the motion sub-features of the same type in each set of motion features is calculated to obtain the target motion feature.

[0063] The response intensity is analyzed based on the target motion characteristics, and the auxiliary factor is derived by matching the response intensity obtained from the analysis.

[0064] In this embodiment, during the movement of each type of pet device, a corresponding set of second monitoring sub-data is collected, from which a motion feature can be extracted, including running speed, jump height, turning frequency, etc. The same motion sub-features in different sets of motion features, such as running speed, are averaged to obtain target motion sub-features, and then these target motion sub-features are integrated into a target motion feature.

[0065] The control device assesses the intensity of a pet's response to the first type of pet device interactive stimuli based on calculated target motion characteristics. For example, if the target motion characteristics show high values ​​for the pet's running speed and jumping height, it indicates that the pet is responding positively to the device's interactive stimuli, with a high response intensity; conversely, the response intensity is low. Then, based on the analyzed response intensity, a matching process is performed within pre-defined rules or models to find corresponding auxiliary factors.

[0066] It is understood that there is a negative correlation between the auxiliary factor and the response intensity. This negative correlation can also be expressed by comparison data or conversion function, which will not be elaborated here.

[0067] like Figure 3 As shown in the figure, this embodiment of the invention also discloses a pet feeding health monitoring system, the system including a first communication module, an anomaly analysis module, and a second communication module;

[0068] The first communication module is used to periodically acquire first monitoring data from a wearable device worn on the target pet, and analyze whether the target pet has any abnormal trends based on the first monitoring data; the first monitoring data includes first movement data and physiological indicator data;

[0069] The anomaly analysis module is used to select several pet devices from the available list if it is determined that the target pet has an abnormal trend, control each pet device to perform a corresponding action, and obtain the second monitoring data of the wearable device during this action phase. Based on the second monitoring data, an auxiliary factor is evaluated and obtained. The second monitoring data includes second motion data. The abnormal trend is adjusted using the auxiliary factor to obtain an adjusted abnormal trend.

[0070] The second communication module is used to send a prompt message to a designated user terminal when the abnormal trend after adjustment meets the abnormal conditions.

[0071] Optionally, the first communication module is used for:

[0072] The system monitors whether a health concern instruction for the target pet is received. If it is received, a first monitoring cycle is determined based on the health concern instruction. If it is not received, a second monitoring cycle is set. The first monitoring cycle is shorter than the second monitoring cycle.

[0073] Optionally, the first communication module is used for:

[0074] The health monitoring instructions that are in a valid state are selected, and the number of instructions and the number of instruction sources are counted. The first monitoring period is determined by matching the number of instructions and the number of instruction sources. The number of instructions and the number of instruction sources are both negatively correlated with the duration of the first monitoring period.

[0075] Optionally, the anomaly analysis module is used for:

[0076] Extract the functional attributes of each pet device in the available list, and classify each pet device into a first category of pet devices and a second category of pet devices based on the functional attributes. The first category of pet devices consists of pet devices with interactive capabilities, and the second category of pet devices consists of pet devices without interactive capabilities.

[0077] The interaction intensity of each pet device in the first type of pet device is determined, the action intensity of each pet device is determined according to the interaction intensity, and each pet device is controlled to execute the action intensity in sequence from high to low according to the interaction intensity, and the action intensity changes from low to high.

[0078] Optionally, the anomaly analysis module is used for:

[0079] The second monitoring data includes multiple sets of second monitoring sub-data, each set of second monitoring sub-data corresponding to each pet device in the first type of pet device;

[0080] A set of motion features is extracted from each of the second monitoring sub-data sets. The average value of the motion sub-features of the same type in each set of motion features is calculated to obtain the target motion feature.

[0081] The response intensity is analyzed based on the target motion characteristics, and the auxiliary factor is derived by matching the response intensity obtained from the analysis.

[0082] This invention also discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, wherein the processor executes the computer program to implement the method described in the foregoing embodiments.

[0083] This invention also discloses a computer storage medium storing a computer program that is executed by a processor to implement the methods described in the foregoing embodiments.

[0084] This invention also discloses a computer program product containing computer code, which, when executed by a processor of an electronic device, implements the method described in the foregoing embodiments.

[0085] The aforementioned computer storage media include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or equipment, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0086] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0087] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for monitoring the health of pets through feeding, characterized in that: The method includes the following steps: The system periodically acquires first monitoring data from a wearable device worn on a target pet, and analyzes whether the target pet exhibits any abnormal trends based on the first monitoring data. The first monitoring data includes first movement data and physiological indicator data. If it is determined that the target pet has an abnormal trend, several pet devices are selected from the available list, each pet device is controlled to perform a corresponding action, and the second monitoring data of the wearable device during this action phase is obtained. An auxiliary factor is evaluated based on the second monitoring data; the second monitoring data includes second motion data. The abnormal trend is adjusted using the auxiliary factor to obtain the adjusted abnormal trend. When the adjusted abnormal trend meets the abnormal conditions, a prompt message is sent to the designated user terminal. The step of selecting several pet devices from the available list and controlling each pet device to perform a corresponding action includes: Extract the functional attributes of each pet device in the available list, and classify each pet device into a first category of pet devices and a second category of pet devices based on the functional attributes. The first category of pet devices consists of pet devices with interactive capabilities, and the second category of pet devices consists of pet devices without interactive capabilities. The interaction intensity of each pet device in the first type of pet device is determined, the action intensity of each pet device is determined according to the interaction intensity, and each pet device is controlled to execute the action intensity in descending order of the interaction intensity; the action intensity is negatively correlated with the interaction intensity. The auxiliary factors derived from the evaluation based on the second monitoring data include: The second monitoring data includes multiple sets of second monitoring sub-data, each set of second monitoring sub-data corresponding to each pet device in the first type of pet device; A set of motion features is extracted from each of the second monitoring sub-data sets. The average value of the motion sub-features of the same type in each set of motion features is calculated to obtain the target motion feature. The response intensity is analyzed based on the target motion characteristics, and the auxiliary factor is derived by matching the response intensity obtained from the analysis.

2. The pet feeding health monitoring method according to claim 1, characterized in that: The periodic acquisition of first monitoring data from the wearable device worn on the target pet includes: The system monitors whether a health concern instruction for the target pet is received. If it is received, a first monitoring cycle is determined based on the health concern instruction. If it is not received, a second monitoring cycle is set. The first monitoring cycle is shorter than the second monitoring cycle.

3. The pet feeding health monitoring method according to claim 2, characterized in that: The determination of the first monitoring cycle based on the health attention instruction includes: The health monitoring instructions that are in a valid state are selected, and the number of instructions and the number of instruction sources are counted. The first monitoring period is determined by matching the number of instructions and the number of instruction sources. The number of instructions and the number of instruction sources are both negatively correlated with the duration of the first monitoring period.

4. A pet feeding and health monitoring system, characterized in that: The system includes a first communication module, an anomaly analysis module, and a second communication module; The first communication module is used to periodically acquire first monitoring data from a wearable device worn on the target pet, and analyze whether the target pet has any abnormal trends based on the first monitoring data; the first monitoring data includes first movement data and physiological indicator data; The anomaly analysis module is used to select several pet devices from the available list if it is determined that the target pet has an abnormal trend, control each pet device to perform a corresponding action, and obtain the second monitoring data of the wearable device during this action phase. Based on the second monitoring data, an auxiliary factor is evaluated and obtained. The second monitoring data includes second motion data. The abnormal trend is adjusted using the auxiliary factor to obtain an adjusted abnormal trend. The second communication module is used to send a prompt message to a designated user terminal when the abnormal trend after adjustment meets the abnormal conditions; The anomaly analysis module is used for: Extract the functional attributes of each pet device in the available list, and classify each pet device into a first category of pet devices and a second category of pet devices based on the functional attributes. The first category of pet devices consists of pet devices with interactive capabilities, and the second category of pet devices consists of pet devices without interactive capabilities. Determine the interaction intensity corresponding to each pet device in the first type of pet devices, determine the action intensity of each pet device according to the interaction intensity, and control each pet device to execute the action intensity in order from high to low according to the interaction intensity, and the action intensity changes from low to high; The anomaly analysis module is used for: The second monitoring data includes multiple sets of second monitoring sub-data, each set of second monitoring sub-data corresponding to each pet device in the first type of pet device; A set of motion features is extracted from each of the second monitoring sub-data sets. The average value of the motion sub-features of the same type in each set of motion features is calculated to obtain the target motion feature. The response intensity is analyzed based on the target motion characteristics, and the auxiliary factor is derived by matching the response intensity obtained from the analysis.

5. A pet feeding health monitoring system according to claim 4, characterized in that: The first communication module is used for: The system monitors whether a health concern instruction for the target pet is received. If it is received, a first monitoring cycle is determined based on the health concern instruction. If it is not received, a second monitoring cycle is set. The first monitoring cycle is shorter than the second monitoring cycle.

6. An electronic device, comprising: At least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, characterized in that: the processor executes the computer program to implement the method as claimed in any one of claims 1-3.

7. A computer storage medium storing a computer program, characterized in that: The computer program is executed by a processor to implement the method as described in any one of claims 1-3.

8. A computer program product, characterized in that: The computer program product includes computer code, which, when executed by a processor of an electronic device, implements the method as described in any one of claims 1-3.

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