Wearable animal tracking devices and their control methods

Abstract

This application provides a wearable animal tracking device and its control method. The device includes: a data acquisition module, comprising a photoelectric sensing unit and an inertial sensing unit, wherein the photoelectric sensing unit is used to acquire photoelectric signals from the wearer, and the inertial sensing unit is used to acquire the wearer's motion data; an attribute determination module, used to determine the wearer's animal attribute information based on the data acquired by the data acquisition module and a preset animal characteristic database; and a data processing module, used to determine a physiological feature algorithm for processing the photoelectric signals and a motion feature algorithm for processing the motion data based on the wearer's animal attribute information, processing the photoelectric signals based on the physiological feature algorithm to obtain the wearer's physiological feature measurements, and processing the motion data based on the motion feature algorithm to obtain the wearer's motion feature measurements. This application improves the accuracy of animal tracking measurements.

Description

Technical Field

[0001] This application relates to the field of wearable device technology, specifically to a wearable animal tracking device and its control method. Background Technology

[0002] With the increasing demand for pet safety and health, wildlife protection, and livestock farming management, real-time location tracking and health monitoring of various animals, especially small animals, has become crucial. Traditional animal trackers are not only limited in function but also consume a lot of power, making them unsuitable for long-term, large-scale, and multi-dimensional animal location tracking and health status monitoring. Summary of the Invention

[0003] To address the problems in the prior art, the purpose of this application is to provide a wearable animal tracking device and its control method, which improves the accuracy of animal tracking and measurement.

[0004] This application provides a wearable animal tracking device, including:

[0005] The data acquisition module includes a photoelectric sensing unit and an inertial sensing unit. The photoelectric sensing unit is used to collect photoelectric signals from the wearer, and the inertial sensing unit is used to collect the wearer's motion data.

[0006] The attribute determination module is used to determine the wearer's animal attribute information based on the data collected by the data acquisition module and a preset animal characteristic database;

[0007] The data processing module is used to determine the physiological feature algorithm for processing the photoelectric signal and the motion feature algorithm for processing the motion data based on the wearer's animal attribute information. Based on the physiological feature algorithm, the photoelectric signal is processed to obtain the wearer's physiological feature measurement value, and based on the motion feature algorithm, the motion data is processed to obtain the wearer's motion feature measurement value.

[0008] In some embodiments, the data processing module is further configured to determine the sampling requirements of the data acquisition module based on the wearer's animal attribute information, generate a sampling control instruction based on the sampling requirements, and send it to the data acquisition module.

[0009] In some embodiments, a positioning module is also included for acquiring the wearer's location data;

[0010] The data processing module is also used to calculate the wearer's physical energy consumption based on the wearer's animal attribute information, physiological characteristic measurement values, movement characteristic measurement values, and location data, using a preset consumption algorithm.

[0011] In some embodiments, a communication module is further included for receiving control commands and sending data outwards;

[0012] The communication module includes at least two types of communication units, and the communication module is also used to select the communication unit with the highest priority and disable other communication units when at least two types of communication units are available.

[0013] In some embodiments, the data processing module is further configured to determine whether the current sleep conditions are met based on the motion data and the photoelectric signal; if so, to control the device to enter a sleep state.

[0014] In some embodiments, the animal feature database pre-stores feature determination conditions corresponding to various animal attributes;

[0015] The attribute determination module is used to determine the characteristic determination conditions that the wearer meets based on the photoelectric signal and the motion data, so as to determine the animal attribute corresponding to the wearer.

[0016] In some embodiments, the animal attributes include species attributes, body size attributes, and movement state attributes. The attribute determination module is used to determine the wearer's species attributes and body size attributes based on the animal feature database according to the movement data, and to determine the wearer's movement state attributes based on the wearer's species attributes, body size attributes, and movement data.

[0017] The data processing module is used to adjust the algorithm parameters in the preset physiological feature algorithm and the preset motion feature algorithm according to the species attribute, the body size attribute and the motion state attribute.

[0018] In some embodiments, the animal attributes include fur classification attributes, which include at least one of fur color attributes, skin color attributes, and hair density attributes;

[0019] The photoelectric sensing unit is used to collect photoelectric signals passing through the wearer under different signal channels, and the attribute determination module is used to determine the fur classification attribute of the wearer based on the photoelectric signals passing through the wearer under different signal channels.

[0020] The data processing module is used to adjust the algorithm parameters in the preset physiological feature algorithm according to the wearer's fur classification attributes.

[0021] This application also provides a control method for a wearable animal tracking device, used to control the wearable animal tracking device, the method comprising the following steps:

[0022] The photoelectric signals of the wearer are collected, and the wearer's motion data is collected through the inertial sensing unit;

[0023] Based on the photoelectric signals transmitted by the wearer and the wearer's motion data, and a preset animal characteristic database, the wearer's animal attribute information is determined;

[0024] Based on the wearer's animal attribute information, a physiological feature algorithm for processing the photoelectric signal and a motion feature algorithm for processing the motion data are determined;

[0025] The wearer's physiological characteristic measurement values ​​are obtained by processing the photoelectric signal based on the physiological characteristic algorithm, and the wearer's motion characteristic measurement values ​​are obtained by processing the motion data based on the motion characteristic algorithm.

[0026] In some embodiments, after processing the photoelectric signal based on the physiological feature algorithm to obtain the wearer's physiological feature measurement values, and processing the motion data based on the motion feature algorithm to obtain the wearer's motion feature measurement values, the following steps are further included:

[0027] Based on the wearer's animal attribute information, physiological characteristic measurement values, movement characteristic measurement values, and location data, the wearer's physical energy consumption is calculated using a preset consumption algorithm.

[0028] In some embodiments, the animal attributes include species attributes, body size attributes, movement status attributes, and fur classification attributes;

[0029] Based on the wearer's photoelectric signals and motion data, and a preset animal characteristic database, the wearer's animal attribute information is determined, including the following steps:

[0030] Based on the motion data, the wearer's species and body type are determined using the animal characteristic database.

[0031] The wearer's movement status attributes are determined based on the wearer's species attributes, body type attributes, and movement data.

[0032] The wearer's fur classification attributes are determined based on the photoelectric signals transmitted through different signal channels in different frequency bands.

[0033] The wearable animal tracking device and its control method provided in this application have the following advantages:

[0034] This application provides a wearable animal tracking device for tracking and measuring animals. Through a preset data processing algorithm, the data collected by the sensor can be processed to obtain the measurement value of the wearer. Furthermore, the wearer is classified through a preset animal characteristic database to determine the wearer's attribute information. Based on the attribute information, the preset data processing algorithm is adjusted to better suit the wearer's characteristics. Taking into account the influence of the wearer's own characteristics on the measurement value, the accuracy of animal tracking and measurement is further improved, which is conducive to realizing multi-dimensional and accurate health status monitoring of animals. Attached Figure Description

[0035] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0036] Figure 1 This is a structural block diagram of a wearable animal tracking device according to an embodiment of this application;

[0037] Figure 2 This is a flowchart of a control method for a wearable animal tracking device according to an embodiment of this application;

[0038] Figure 3 This is a flowchart illustrating the process of determining the animal attribute information of the wearer according to an embodiment of this application. Detailed Implementation

[0039] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and therefore repeated descriptions of them will be omitted. The words “or” and “or” in the specification may mean “and” or “or”.

[0040] To address the technical problems in existing technologies, this application provides a wearable animal tracking device. This device can be worn on the neck, chest, or other parts of an animal via a biocompatible material strap. The device housing is waterproof and dustproof, improving its versatility in various application scenarios. The tracking device may also be equipped with components such as speakers and indicator lights to facilitate assisted searching in the wild using sound and light.

[0041] like Figure 1As shown, this application provides a wearable animal tracking device. The tracking device includes a data acquisition module M100, an attribute determination module M200, and a data processing module M300. These modules can be housed inside the device casing, with the acquisition surface (the surface that transmits photoelectric signals) of the data acquisition module M100 facing the wearer. The data acquisition module M100 includes a photoelectric sensing unit M110 and an inertial sensing unit M120. The photoelectric sensing unit M110 employs a high-sensitivity photoelectric sensor module to acquire photoelectric signals transmitted through the wearer. Here, the photoelectric signal transmitted through the wearer refers to the photoelectric signal reflected back from the wearer after the photoelectric sensor module transmits a photoelectric signal to the wearer. The photoelectric sensing unit M110 can transmit multiple bands of photoelectric signals to the wearer as needed to detect differences in the wearer's response to different bands of photoelectric signals. The inertial sensing unit M120 is used to acquire the wearer's motion data. The inertial sensing unit M120 incorporates a three-axis inertial sensor, which can accurately collect animal motion data in real time. This data is used to calculate cadence, stride length, and running and jumping movements. Acceleration data can be used to calculate the animal's energy expenditure and analyze the risk of its movements. The wearer's motion data includes, but is not limited to, the wearer's speed, acceleration, and angular velocity.

[0042] The attribute determination module M200 is used to determine the wearer's animal attribute information based on the data collected by the data acquisition module M100 and a preset animal characteristic database. The animal attribute information corresponds to the specific category of each animal attribute. In this embodiment, the animal attributes may include one or more of the following: animal species attributes (e.g., rabbit, dog, cat), animal size attributes (e.g., large, medium, small), animal movement state attributes (e.g., running, jogging, walking, standing still, jumping), and animal fur classification attributes. The animal fur classification attributes further include one or more of the following: fur color attributes, skin color attributes, and hair density attributes. Different attributes possessed by an animal have a certain impact on the animal's step counting, heart rate calculation, and respiratory rate calculation.

[0043] The data processing module M300 is used to process the data acquisition module M100 according to a preset data processing algorithm. The data processing algorithm includes a physiological feature algorithm and a motion feature algorithm. The physiological feature algorithm calculates the animal's heart rate and respiratory rhythm based on the photoelectric signal processing. The motion feature algorithm calculates the animal's steps based on the motion data, tracks the animal's steps, and can further analyze whether the animal's movement posture is dangerous. For example, the physiological feature algorithm is the PPG algorithm. The motion feature algorithm, for example, analyzes the vibration amplitude based on the dynamic waveform of the motion data and counts steps based on the vibration peaks. Therefore, by using the wearable animal tracking device of this application, the data collected by the sensors can be processed through the preset data processing algorithm to obtain measurement values ​​for the wearer.

[0044] Furthermore, considering that different animal attributes can affect the accuracy of data processing algorithms to varying degrees, the data processing module M300 is also used to determine the physiological characteristic algorithm for processing the photoelectric signal and the motion characteristic algorithm for processing the motion data based on the wearer's animal attribute information. The determined physiological characteristic algorithm processes the photoelectric signal to obtain the wearer's physiological characteristic measurements, and the determined motion characteristic algorithm processes the motion data to obtain the wearer's motion characteristic measurements. Here, the physiological characteristic measurements include the wearer's heart rate, respiratory rhythm, etc., and the motion characteristic measurements include the wearer's step count, whether their movement posture is dangerous, etc. Therefore, by using the tracking device of this application and adjusting the preset data processing algorithm based on attribute information, the data processing algorithm is made more suitable for the wearer's characteristics. Considering the influence of the wearer's own characteristics on the measurement values, the accuracy of animal tracking and measurement is further improved, which is conducive to achieving multi-dimensional and accurate health status monitoring of animals.

[0045] In this embodiment, the data processing module M300 determines the physiological feature algorithm for processing the photoelectric signal and the motion feature algorithm for processing the motion data based on the wearer's animal attribute information. This can be achieved by pre-setting multiple physiological feature algorithms and multiple motion feature algorithms, establishing correspondences between different physiological feature algorithms and different animal attribute information, and selecting the appropriate physiological feature algorithm and motion feature algorithm based on the animal attribute information. Alternatively, a single physiological feature algorithm and a single motion feature algorithm can be fixed, but their algorithm parameters are adjustable. Multiple sets of physiological feature algorithm parameters and multiple sets of motion feature algorithm parameters can be set, establishing correspondences between different physiological feature algorithm parameters and different animal attribute information, and selecting the appropriate physiological feature algorithm parameters and motion feature algorithm parameters based on the animal attribute information. Adjustable physiological feature algorithm parameters may include, for example, waveform decomposition frequency range and amplitude thresholds.

[0046] In this embodiment, different data sampling requirements can be adopted for different animal attributes. These requirements include the selection of sampling channels for different wavelengths of the photoelectric sensor, the sensor's sampling frequency, and the length of the sensor's sampling time window. The data processing module M300 is also used to determine the sampling requirements of the data acquisition module based on the wearer's animal attribute information, generate sampling control commands based on the sampling requirements, and send them to the data acquisition module M100. The data acquisition module M100 controls the photoelectric sensing unit M110 and the inertial sensing unit M120 according to the sampling control commands.

[0047] Therefore, this application collects data through a combination of multiple sensors. Based on the collected data, animal attributes can be identified, enabling the classification of animals from multiple dimensions. Furthermore, the data collected by the sensors is corrected and analyzed a second time based on the animal attribute information, eliminating the influence of different animal classifications on the data analysis results and improving the accuracy and precision of data analysis for animals.

[0048] like Figure 1 As shown, the wearable animal tracking device also includes a positioning module M400, used to acquire the wearer's location data. The positioning module M400 supports GPS / GNSS / BeiDou satellite positioning signals, ensuring accurate real-time location tracking of animals in both outdoor and home environments.

[0049] The data processing module M300 is also used to calculate the wearer's energy expenditure based on the wearer's animal attribute information, physiological characteristic measurements, movement characteristic measurements, and location data using a preset energy expenditure algorithm. This preset energy expenditure algorithm can be a pre-defined formula for calculating energy expenditure, the solution of which represents the animal's energy expenditure. Variables include animal attribute information, physiological characteristic measurements, movement characteristic measurements, and changes in location data (movement distance). Since the energy expenditure of animals of different species, sizes, and movement states may differ even with the same number of steps, heart rate, and respiratory rhythm, this preset energy expenditure algorithm, based on existing methods for calculating energy expenditure, further incorporates animal attribute information as a factor. Animal attribute information can serve as a weighting coefficient for a variable in the energy expenditure calculation formula, influencing the final calculation of energy expenditure.

[0050] like Figure 1 As shown, in this embodiment, the wearable animal tracking device also includes a communication module M500 for receiving control commands and sending data. For example, the wearable animal tracking device can directly communicate with a cloud server, sending data collected by the data acquisition module M100 and data processed by the data processing module M300, and can receive control commands from the cloud server to control the operation of each module. The wearable animal tracking device can also communicate with a terminal device carried by staff, sending data and receiving control commands. The communication module M500 supports WiFi and Bluetooth communication modes and has built-in ultra-low power LoRA communication technology, supporting the tracking device to work over a wider range for longer periods. LoRA (Long Range Radio) is a low-power local area network wireless standard. Its biggest feature is that it can propagate farther than other wireless methods under the same power consumption conditions, achieving a balance between low power consumption and long distance. It extends the distance of traditional wireless radio frequency communication by 3-5 times under the same power consumption.

[0051] In this embodiment, different types of communication units can be further prioritized. The communication module M500 includes at least two types of communication units. When at least two types of communication units are available, the communication module M500 selects the communication unit with the highest priority and disables the others. For example, the priority of the WiFi / Bluetooth communication unit can be set higher than that of the LoRA communication unit. When only a LoRA connection can be established, the LoRA communication unit is used. When the WiFi / Bluetooth communication unit can function normally, the LoRA communication unit is disabled, thereby further reducing communication power consumption and increasing the usage time of the tracking device.

[0052] In this embodiment, the data processing module M300 is further configured to determine whether the current conditions for sleep mode are met based on the motion data and the photoelectric signal. If the conditions are met, the tracking device is controlled to enter a sleep state, thereby further reducing the energy consumption of the tracking device and ensuring that the tracking device can be used for a longer period of time over a wider range. For example, if the data processing module M300 detects that the wearer's acceleration value is less than a preset minimum acceleration threshold for a long time and the photoelectric sensor module does not receive a photoelectric signal, it determines that the current conditions for sleep mode are met, adaptively enters an unworn state, and performs sleep energy saving.

[0053] In this embodiment, the animal feature database is a pre-established database that associates different animal attributes with animal feature data. Animal feature data corresponding to different animal attributes is collected in advance, and this animal feature database can be constructed through data summarization and feature extraction.

[0054] For example, the following experimental data can be pre-collected and stored:

[0055] The numerical characteristic range of inertial sensing units for animals of different body sizes (weight, shoulder height, hip height, and length of fore and hind legs) under different movement characteristics (gait frequency range, stride range, gait speed range, number of steps) and movement states (running, jogging, walking, standing still, jumping).

[0056] Differences in routine heart rate, respiratory rhythm range, and stride range among different species and sizes of animals (e.g., small, medium, and large dogs, small, medium, and large cats, and small and medium rabbits).

[0057] The interference characteristics of animals under different movement states and gait frequency, stride length, and gait speed on respiratory rhythm and heart rate monitoring signals;

[0058] The interference characteristics of different fur color, skin color, and hair density on the photoelectric signals of photoelectric sensing units in different wavelength bands (intensity attenuation, angular deflection caused by scattering and reflection);

[0059] By aggregating, organizing, summarizing, and extracting features from the data collected in the above experiments, an animal feature database can be obtained.

[0060] In one embodiment, the animal characteristic database pre-stores characteristic determination conditions corresponding to various animal attributes. The attribute determination module M200 is used to determine the characteristic determination conditions that the wearer meets based on the photoelectric signal and the motion data, thereby determining the animal attribute corresponding to the wearer.

[0061] In this embodiment, the attribute determination module M200 is used to determine the wearer's species attribute and body type attribute based on the animal feature database according to the motion data, and to determine the wearer's motion state attribute based on the wearer's species attribute, body type attribute and motion data.

[0062] In this embodiment, the photoelectric sensing unit is used to collect photoelectric signals passing through the wearer under different frequency band signal channels, and the attribute determination module M200 is used to determine the fur classification attribute of the wearer based on the photoelectric signals passing through the wearer under the different frequency band signal channels.

[0063] For example, the criteria for determining species attributes include: when the animal's speed and acceleration are within a calibrated speed and acceleration range, the heart rate and respiratory rhythm ranges of various animal species are considered. The heart rate and respiratory rhythm detected by photoelectric signals are used to determine which species' heart rate and respiratory rhythm range the wearer falls into. As another example, the criteria for determining body size attributes are: when the animal's speed and acceleration are within a preset first speed and acceleration range, if the heart rate and respiratory rhythm detected by photoelectric signals both fall within a preset first heart rhythm and respiratory rhythm range, the wearer is determined to be a large animal. Similarly, when the animal's speed and acceleration are within a preset second speed and acceleration range, if the heart rate and respiratory rhythm detected by photoelectric signals both fall within a preset second heart rhythm and respiratory rhythm range, the wearer is determined to be a small animal. For example, the criteria for determining movement status attributes can be set as follows: for large animals, if the animal's acceleration is greater than a first acceleration threshold and its heart rate is higher than a first heart rate threshold, the wearer is determined to be running; if the animal's acceleration is less than a second acceleration threshold and its heart rate is lower than a second heart rate threshold, the wearer is determined to be walking slowly. Another example is that the photoelectric sensing unit collects photoelectric signals reflected from the wearer at multiple wavelengths, and compares the parameter differences of the photoelectric waveforms at multiple wavelengths with preset parameter difference ranges for various fur colors / skin colors / hair densities to determine the wearer's fur color / skin color / hair density.

[0064] In another alternative implementation, determining the wearer's animal attribute information based on the data collected by the data acquisition module M100 and a preset animal feature database can also be achieved by constructing a deep learning-based animal attribute classification model. Specifically, motion data and photoelectric signals of different animal attributes are collected experimentally. Features are extracted from the motion data and photoelectric signals to obtain animal sample features. Animal attribute labels are added to the animal sample features. Then, the animal sample features are used as input data for the animal attribute classification model. A loss function is calculated based on the output data of the animal attribute classification model and the animal attribute labels. The animal attribute labels are then iteratively optimized and trained in reverse. In application, the wearer's motion data and photoelectric information collected in real time are used to extract features and input into the animal attribute classification model to obtain the wearer's animal attribute information. When there are multiple different types of animal attributes, multiple branches can be set in the animal attribute classification model, such as a species classification branch, a monotypic classification branch, a coat color classification branch, etc., or a separate animal attribute classification model can be trained for each animal attribute.

[0065] The data processing module M300 is used to adjust the algorithm parameters in the preset physiological feature algorithm and the preset motion feature algorithm based on the species attribute, the body size attribute, the movement state attribute, and the fur classification attribute. For example, multiple sets of physiological feature algorithm parameters and multiple sets of motion feature algorithm parameters are set respectively, establishing a correspondence between different physiological feature algorithm parameters and different animal attribute information, and establishing a correspondence between different motion feature algorithm parameters and different animal attribute information. The corresponding physiological feature algorithm parameters and motion feature algorithm parameters are selected based on the animal attribute information. Adjustable physiological feature algorithm parameters may include, for example, the waveform decomposition frequency range and amplitude threshold.

[0066] In another alternative implementation, the physiological feature algorithm and the motion feature algorithm can also be implemented using deep learning models. That is, a physiological feature model is pre-trained, and the wearer's attribute information, motion data and photoelectric signals are used as inputs to the physiological feature model to obtain the physiological feature measurement values ​​output by the physiological feature model. Similarly, a motion feature model is pre-trained, and the wearer's attribute information, motion data and photoelectric signals are used as inputs to the motion feature model to obtain the motion feature measurement values ​​output by the motion feature model.

[0067] like Figure 2 As shown in the figure, this application embodiment also provides a control method for a wearable animal tracking device, used to control the wearable animal tracking device. The method can be specifically implemented using the data acquisition module, attribute judgment module, and data processing module described above, and the method includes the following steps:

[0068] S100: Collects photoelectric signals from the wearer and also collects the wearer's motion data;

[0069] S200: Based on the photoelectric signal transmitted by the wearer and the wearer's motion data and a preset animal characteristic database, determine the wearer's animal attribute information;

[0070] In this embodiment, the animal attributes include one or more of species attributes, body size attributes, movement status attributes, and fur classification attributes. However, this application is not limited to these. In other alternative embodiments, the animal attributes may also include other types of attributes, and are not limited to those listed herein.

[0071] S300: Determine the physiological feature algorithm for processing the photoelectric signal and the motion feature algorithm for processing the motion data based on the wearer's animal attribute information;

[0072] S400: Based on the physiological feature algorithm, process the photoelectric signal to obtain the wearer's physiological feature measurement value, and based on the motion feature algorithm, process the motion data to obtain the wearer's motion feature measurement value.

[0073] In this embodiment, after step S400—processing the photoelectric signal based on the physiological feature algorithm to obtain the wearer's physiological feature measurement values, and processing the motion data based on the motion feature algorithm to obtain the wearer's motion feature measurement values—the following steps are also included:

[0074] Based on the wearer's animal attribute information, physiological characteristic measurement values, movement characteristic measurement values, and location data, the wearer's physical energy consumption is calculated using a preset consumption algorithm.

[0075] like Figure 3 As shown, step S300: Based on the photoelectric signal transmitted by the wearer and the wearer's motion data and a preset animal characteristic database, determine the wearer's animal attribute information, including the following steps:

[0076] S310: Determine whether the wearer's species attributes, body type attributes, and fur classification attributes are pre-stored;

[0077] When a wearer consistently wears the same tracking device, it is only necessary to determine the species, size, and fur classification attributes when the wearer first wears the device. Since these attributes are relatively fixed, the initial determination can be used directly in subsequent wears. However, the movement attribute changes in real time depending on the animal's movement, so it needs to be determined each time. When a wearer does not consistently wear the same tracking device, or when the wearer is wearing a particular tracking device for the first time, it is necessary to determine the species, size, and fur classification attributes.

[0078] If not, proceed to step S320: Based on the motion data, determine the wearer's species and body type attributes using the animal characteristic database;

[0079] S330: Determine the wearer's movement status attributes based on the wearer's species attributes, body type attributes, and movement data;

[0080] S340: Determines the fur classification attribute of the wearer based on the photoelectric signal transmitted by the wearer under different frequency band signal channels;

[0081] If so, proceed to step S350: obtain the pre-stored species attributes, body type attributes, and fur classification attributes of the wearer, and proceed to step S360: determine the wearer's movement status attributes based on the wearer's species attributes, body type attributes, and movement data.

[0082] The wearable animal tracking device of this application can be applied to pet health monitoring, wildlife conservation and ecological research, and livestock management. For example, when applied to pet health monitoring, the device's location module can acquire location data to prevent pets from getting lost and facilitate rapid retrieval. Furthermore, monitoring heart rate, respiratory rhythm, and energy expenditure can improve the pet's quality of life. As another example, when applied to wildlife conservation and ecological research, the device's location module can track the activity trajectory of wild animals, especially endangered species, for extended periods, monitor their health status, and collect data on animal habits and activity ranges, providing valuable data for animal conservation and ecological research. Finally, when applied to livestock management, the device's location module can locate livestock on large ranches and collect movement and health data, improving livestock quality of life and management efficiency. This tracking device simultaneously features physiological characteristic monitoring, step counting, and satellite positioning, providing multi-dimensional management of animals. The data processing algorithm has been further optimized for various animal attributes, improving the accuracy and precision of data processing calculations. It can also select a low-power communication method based on its working status and enter a sleep state when it automatically detects that the device is not being worn, significantly reducing power consumption. With its simple structure, the device's size and weight are minimized, making it suitable for long-term, wide-range, and multi-dimensional animal location tracking and health monitoring, demonstrating broad application prospects and market potential.

[0083] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of this application and should not be construed as limiting the specific implementation of this application to these descriptions. For those skilled in the art, various simple deductions or substitutions can be made without departing from the concept of this application, and all such modifications or substitutions should be considered within the scope of protection of this application.

Claims

1. A wearable animal tracking device, characterized in that, include: The data acquisition module includes a photoelectric sensing unit and an inertial sensing unit. The photoelectric sensing unit is used to collect photoelectric signals from the wearer, and the inertial sensing unit is used to collect the wearer's motion data. The attribute determination module is used to determine the wearer's animal attribute information based on the data collected by the data acquisition module and a preset animal characteristic database; The data processing module is used to determine the physiological feature algorithm for processing the photoelectric signal and the motion feature algorithm for processing the motion data based on the wearer's animal attribute information; to process the photoelectric signal based on the physiological feature algorithm to obtain the wearer's physiological feature measurement value; and to process the motion data based on the motion feature algorithm to obtain the wearer's motion feature measurement value. The wearer's animal attributes include species attributes, body size attributes, movement status attributes, and fur classification attributes; the photoelectric sensing unit is used to collect photoelectric signals passing through the wearer under different frequency band signal channels; The attribute determination module is used to determine the wearer's species and body type attributes based on the motion data and the animal feature database, and to determine the wearer's motion state attributes based on the wearer's species, body type, and motion data; and to determine the wearer's fur classification attributes based on the photoelectric signals transmitted through the wearer under different frequency band signal channels.

2. The wearable animal tracking device according to claim 1, characterized in that, The data processing module is also used to determine the sampling requirements of the data acquisition module based on the wearer's animal attribute information, generate sampling control instructions based on the sampling requirements, and send them to the data acquisition module.

3. The wearable animal tracking device according to claim 1, characterized in that, It also includes a positioning module for obtaining the wearer's location data; The data processing module is also used to calculate the wearer's physical energy consumption based on the wearer's animal attribute information, physiological characteristic measurement values, movement characteristic measurement values, and location data, using a preset consumption algorithm.

4. The wearable animal tracking device according to claim 1, characterized in that, It also includes a communication module for receiving control commands and sending data outwards; The communication module includes at least two types of communication units, and the communication module is also used to select the communication unit with the highest priority and disable other communication units when at least two types of communication units are available.

5. The wearable animal tracking device according to claim 1, characterized in that, The data processing module is also used to determine whether the current sleep conditions are met based on the motion data and the photoelectric signal. If they are met, the module controls the device to enter a sleep state.

6. The wearable animal tracking device according to claim 1, characterized in that, The animal characteristic database pre-stores characteristic judgment conditions corresponding to various animal attributes; The attribute determination module is used to determine the characteristic determination conditions that the wearer meets based on the photoelectric signal and the motion data, so as to determine the animal attribute corresponding to the wearer.

7. A control method for a wearable animal tracking device, characterized in that, The method for controlling the wearable animal tracking device according to any one of claims 1 to 6, the method comprising the following steps: The photoelectric signals of the wearer are collected, and the wearer's motion data is collected through the inertial sensing unit; Based on the photoelectric signals transmitted by the wearer and the wearer's motion data, and a preset animal characteristic database, the wearer's animal attribute information is determined; Based on the wearer's animal attribute information, a physiological feature algorithm for processing the photoelectric signal and a motion feature algorithm for processing the motion data are determined; The wearer's physiological characteristic measurement values ​​are obtained by processing the photoelectric signal based on the physiological characteristic algorithm, and the wearer's motion characteristic measurement values ​​are obtained by processing the motion data based on the motion characteristic algorithm.

8. The control method for the wearable animal tracking device according to claim 7, characterized in that, After processing the photoelectric signal based on the physiological feature algorithm to obtain the wearer's physiological feature measurement values, and processing the motion data based on the motion feature algorithm to obtain the wearer's motion feature measurement values, the following steps are also included: Based on the wearer's animal attribute information, physiological characteristic measurement values, movement characteristic measurement values, and location data, the wearer's physical energy consumption is calculated using a preset consumption algorithm.

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