Biosensing system, method, apparatus and medium with end-side analysis

Abstract

The invention relates to the technical field of field biological monitoring, in particular to a biosensing system, method and equipment with end-side analysis capability and a medium, and the system comprises a sensing module which is used for obtaining behavior and physiological feature data of a monitored object; the processing module is used for generating a health state evaluation result; the management module is used for distributing electric energy and executing a hierarchical communication strategy; through a dual-core heterogeneous architecture and an intelligent event-driven mechanism, the system realizes dynamic balance between low-power-consumption conventional monitoring and high-performance deep analysis; by combining physiological-behavior coupling analysis and hierarchical communication strategies, while the field endurance time of equipment is effectively prolonged, accurate recognition, timely response and reliable reporting of key health abnormal events are ensured, and the monitoring efficiency and the data value are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of field biological monitoring technology, and in particular to biosensing systems, methods, devices and media with end-to-end analysis capabilities. Background Technology

[0002] With the deepening of ecological research, the use of wearable sensors for long-term tracking and monitoring of wild animals has become a key means of obtaining their survival status. These devices are usually deployed in the wild and rely entirely on their own limited power supply to work.

[0003] However, existing wildlife monitoring equipment faces a major technical challenge in practical applications: it is difficult to achieve real-time, high-precision edge analysis of the complex physiological and behavioral characteristics of monitored subjects while maintaining long-term battery life. Current equipment often struggles to balance low-power routine monitoring with high-energy-consuming in-depth analysis when processing multimodal data, making it difficult to support continuous health status assessments.

[0004] Furthermore, due to the lack of intelligent triggering mechanisms, existing technologies are usually unable to dynamically adjust their working modes based on the real-time activity status of the monitored objects. This results in the equipment wasting a lot of power when it does not need to perform in-depth analysis, or failing to respond in a timely manner when critical abnormal events occur. This contradiction between long battery life and high-performance sensing severely limits the ability to conduct refined health monitoring and timely early warning of wild animals. Summary of the Invention

[0005] The main objective of this invention is to provide a biosensing system with end-side analysis capabilities, aiming to solve the technical problem that high-energy-consuming end-side analysis biosensing systems are difficult to achieve effective endurance.

[0006] To achieve the above objectives, the present invention provides a biosensing system with end-side analysis capabilities, the system comprising: The sensing module is used to acquire behavioral and physiological characteristic data of the monitored object; The processing module includes a coprocessing unit and a main processing unit. The coprocessing unit is used to acquire the behavioral feature data when the main processing unit is in a sleep state, and to determine whether the behavioral feature data meets a preset event triggering condition. If it does, a wake-up command is generated. The main processing unit is used to acquire the wake-up command to switch from the sleep state to the working state; and in the working state, acquire the behavioral feature data and the physiological feature data, and execute the physiological-behavioral coupling analysis algorithm to generate the health status assessment result of the monitored object; The management module is used to allocate power to the sensing module and the processing module, and to execute a hierarchical communication strategy based on the health status assessment results.

[0007] Optionally, the sensing module includes a bio-radar unit, which is used to transmit electromagnetic pulses and receive reflected echoes modulated by the chest movement of the monitored object, and extract heart rate data and respiratory rate data from the reflected echoes as the physiological characteristic data.

[0008] Optionally, the system further includes a communication module, which includes a first communication unit and a second communication unit, wherein the power consumption and transmission distance of the second communication unit are higher than those of the first communication unit.

[0009] Optionally, the sensing module further includes a positioning unit. The system is encapsulated in a housing. A dielectric boss is provided at the geometric center of the top of the housing. The positioning unit is disposed in the dielectric boss, and the mounting plane of the positioning unit is higher than the circuit board plane where the processing module is located.

[0010] Optionally, the sensing module includes at least two differential acoustic units, the sound-receiving holes of the differential acoustic units are covered with a waterproof and sound-permeable membrane, the differential acoustic units are used to acquire ambient acoustic energy, and when the acoustic energy acquired by the coprocessing unit exceeds a preset sound pressure threshold, the wake-up command is generated to activate the main processing unit.

[0011] Optionally, the management module includes a first power supply unit and a second power supply unit, wherein the first power supply unit is electrically connected to the coprocessing unit and the second power supply unit is electrically connected to the main processing unit, and the management module is further configured to disconnect or connect the second power supply unit.

[0012] Optionally, the sensing module includes an inertial measurement unit; when the coprocessing unit determines whether the behavioral feature data meets the preset event triggering conditions, it obtains the overall dynamic body acceleration index within a preset time window based on the output data of the inertial measurement unit.

[0013] Optionally, the management module includes a power management unit, which is also used to monitor the remaining power of the system and send a functional constraint signal to the processing module when the remaining power is lower than a preset survival threshold; the main processing unit enters survival mode in response to the functional constraint signal; in survival mode, the management module only retains power supply to the positioning unit to control the system to enter a low-frequency positioning state.

[0014] Optionally, the system further includes an onboard storage unit; the onboard storage unit is used to: mark the current behavioral characteristic data and physiological characteristic data as high-value events for non-volatile storage when the health status assessment result contains priority alarm data and the communication module cannot establish a connection.

[0015] To achieve the above objectives, the present invention also provides a biosensing method with end-side analysis capability, the method comprising the following steps: Acquire behavioral and physiological characteristic data of the monitored objects; When the main processing unit is in a sleep state, the behavioral feature data is acquired, and it is determined whether the behavioral feature data meets the preset event triggering conditions. If it does, a wake-up command is generated. The system acquires the wake-up command to switch from a dormant state to a working state; and in the working state, it acquires the behavioral characteristic data and the physiological characteristic data, and executes a physiological-behavioral coupling analysis algorithm to generate a health status assessment result for the monitored object. Power is allocated to the sensing module and the processing module, and a hierarchical communication strategy is executed based on the health status assessment results.

[0016] Optionally, determining whether the behavioral feature data meets the preset event triggering conditions includes the following steps: The overall dynamic body acceleration index within a preset time window is obtained based on the output data of the inertial measurement unit. Determine whether the overall dynamic body acceleration index exceeds a preset behavior intensity threshold; if it does, determine that the event triggering condition is met and generate the wake-up command.

[0017] Optionally, the execution of the physiological-behavioral coupling analysis algorithm includes the following steps: Based on the behavioral characteristic data, the current movement pattern of the monitored object is identified; Call the preset baseline physiological zone corresponding to the exercise pattern; The acquired physiological characteristic data is compared with the preset benchmark physiological interval; If the physiological characteristic data deviates from the preset baseline physiological range and the degree of deviation exceeds the preset abnormality index, the monitored object is determined to be in an abnormal state, and a health status assessment result containing priority alarm data is generated.

[0018] Optionally, the step of executing the hierarchical communication strategy based on the health status assessment result includes the following steps: when the health status assessment result does not contain the priority alarm data, the first communication unit is activated to transmit data; when the health status assessment result contains the priority alarm data, the second communication unit is activated to transmit data.

[0019] To achieve the above objectives, the present invention also provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program and the processor executes the computer program to implement the above-described method.

[0020] To achieve the above objectives, the present invention also provides a computer-readable storage medium storing a computer program, wherein a processor executes the computer program to implement the above-described method.

[0021] The beneficial effects that this invention can achieve are as follows: This invention resolves the technical contradiction between long battery life and high-precision monitoring, significantly extending the lifespan of the device in the field. Unlike existing technologies that use a single processor for timed wake-up, this invention employs a dual-core heterogeneous architecture with a coprocessor unit and a main processing unit. Under normal conditions, only the milliwatt / microampere-level coprocessor unit operates, utilizing its low-power characteristics to continuously monitor the dynamic body acceleration overall index (ODBA) output by the inertial measurement unit or the environmental acoustic energy of the differential acoustic unit. Only when the detected behavior intensity exceeds a threshold or abnormal sound pressure is the high-performance main processing unit awakened via a hardware interrupt. This sentinel-commander event-driven mechanism avoids the energy waste caused by running high-energy-consuming algorithms during periods when the monitored object is stationary or inactive, while ensuring millisecond-level response to sudden abnormal events (such as hunting or escape). Thus, while ensuring real-time monitoring, the theoretical battery life of the device is extended from the traditional several months to several years.

[0022] This invention represents a leap from single-behavioral monitoring to physiological-behavioral coupled diagnosis, improving the accuracy of health assessment. It integrates a bio-radar unit (UWB radar) into the sensing module, utilizing its ability to emit nanosecond-level electromagnetic pulses and receive reflected echoes modulated by the chest cavity movement of the monitored object, achieving non-contact, fur-penetrating detection. Combined with a physiological-behavioral coupling analysis algorithm running on the main processing unit, the system can correlate heart rate / respiratory rate data extracted by the bio-radar unit with movement patterns extracted by the inertial measurement unit (e.g., identifying abnormal coupling patterns of "low activity + high heart rate"). This design overcomes the limitation of existing technologies that cannot obtain internal physiological indicators without removing fur, effectively identifying stress, injury, or disease states in animals, and providing deeper-dimensional health data support for wildlife conservation.

[0023] This invention optimizes communication resource allocation, achieving a balance between low-power daily transmission and high-reliability emergency alarms. The invention implements a tiered communication strategy based on health status assessment results through a management module, configuring a first communication unit (e.g., LoRa / BLE) and a second communication unit (e.g., satellite / cellular) with differences in power consumption and transmission distance. When the assessment result is normal, the system prioritizes the use of the low-power first communication unit for opportunistic transmission. Only when the assessment result contains priority alarm data (e.g., near-death state) is the high-power second communication unit for real-time reporting forcibly activated. This strategy avoids the high energy consumption and cost of indiscriminately using satellite communication, while ensuring that critical information is not lost due to network coverage issues during emergencies, achieving an optimal solution for energy efficiency and information timeliness.

[0024] This invention improves signal reception quality and positioning reliability in complex outdoor environments. Addressing the problem of severe signal obstruction in such environments, the invention optimizes the system structure by placing a dielectric protrusion at the geometric center of the top of the outer casing and encapsulating the positioning unit (GPS antenna) within this protrusion. This ensures the mounting plane of the protrusion is higher than the circuit board plane where the processing module is located. This physical structure design not only utilizes the wave-transmitting properties of the dielectric protrusion to reduce casing loss but also achieves spatial isolation between the antenna and the electromagnetic interference source on the PCB motherboard through the height difference, ensuring the antenna obtains a hemispherical omnidirectional radiation pattern. Combined with a waterproof and acoustically permeable membrane covering the surface of the differential acoustic unit, wind noise is effectively suppressed and water and dust intrusion is prevented, thereby significantly improving the positioning lock-in rate and acoustic event recognition rate in harsh environments such as forests, rain, and snow.

[0025] This invention constructs a closed-loop data protection mechanism under extreme operating conditions, ensuring the integrity and recyclability of critical data. Through the collaborative work of the survival mode logic integrated into the management module and the onboard storage unit, the invention enhances the system's robustness. When the power level falls below the survival threshold, the system automatically cuts off all loads except the positioning unit, retaining only the low-frequency positioning function, thereby maximizing the device's traceability time before power depletion and improving device recovery rate. Simultaneously, in communication blind spots where high-value events (priority alarms) occur but the communication module cannot establish a connection, the system automatically writes behavioral and physiological characteristic data into the non-volatile onboard storage unit. This mechanism ensures that critical data reflecting the end of life or major anomalies of the monitored object are not lost due to power outages or network interruptions, guaranteeing the scientific value of the monitoring data. Attached Figure Description

[0026] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0027] Figure 1 This is a structural block diagram of the system in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of the system hardware structure in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the internal structure of the system hardware in Embodiment 1 of the present invention; Figure 4 This is a logic flowchart of the method in Embodiment 9 of the present invention.

[0028] Figure label: 100-Ring, 200-Outer shell, 201-Upper shell, 202-PCB main circuit board, 203-Battery pack, 204-Lower shell, 205-Solar film, 206-Dielectric boss; 202A - Ceramic antenna, 202B - Main processing unit, 202C - Coprocessing unit, 202D - Power management unit, 202E - Inertial measurement unit, 202F - Differential acoustic unit, 202G - UWB antenna, 202H - UWB radar chip.

[0029] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0031] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0032] In this invention, unless otherwise explicitly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral part; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0033] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0034] Example 1

[0035] Please refer to the above as well. Figures 1 to 3 This embodiment provides a biosensing system with end-side analysis capabilities, the system comprising: The sensing module is used to acquire behavioral and physiological characteristic data of the monitored object; The processing module includes a coprocessing unit 202C and a main processing unit 202B. The coprocessing unit 202C is used to acquire the behavioral feature data when the main processing unit 202B is in a sleep state, and to determine whether the behavioral feature data meets the preset event triggering conditions. If it does, a wake-up command is generated. The main processing unit 202B is used to acquire the wake-up command to switch from the hibernation state to the working state; and in the working state, acquire the behavioral feature data and the physiological feature data, and execute the physiological-behavioral coupling analysis algorithm to generate the health status assessment result of the monitored object; The management module is used to allocate power to the sensing module and the processing module, and to execute a hierarchical communication strategy based on the health status assessment results.

[0036] It should be noted that traditional wildlife monitoring equipment, lacking an intelligent triggering mechanism, struggles to achieve a dynamic balance between low-power routine monitoring and high-energy-consuming in-depth analysis. Specifically, when the monitored object is stationary or inactive, the continuous operation of the high-precision analysis module leads to unnecessary power consumption. Conversely, if the device is in a dormant state when a critical abnormal event occurs, it cannot respond promptly. Furthermore, the coupled analysis process of behavioral and physiological characteristic data is forcibly decoupled. Routine monitoring only acquires basic behavioral data, while in-depth analysis requires additional activation of high-energy-consuming units, resulting in an unreasonable allocation of power. This contradiction further restricts the device's battery life and real-time analysis performance, significantly weakening the completeness and timeliness of health status assessments, ultimately limiting the ability to precisely track the survival status of wild animals.

[0037] To address the aforementioned issues, this embodiment provides a biosensing system with end-side analysis capabilities. This system includes a sensing module for acquiring behavioral and physiological characteristic data of the monitored object. The implementation of the sensing module can be varied. For example, an inertial measurement unit 202E (IMU nine-axis inertial measurement unit 202E) can be configured to acquire motion data of the monitored object, such as gait and activity intensity, as behavioral characteristic data. Simultaneously, a bio-radar unit (UWB bio-radar) can be configured to acquire the body surface temperature of the monitored object as physiological characteristic data. In another implementation, the sensing module may include at least one differential acoustic unit 202F (MEMS microphone array) for collecting environmental acoustic information and analyzing voiceprints to identify the activity patterns of the monitored object as behavioral characteristic data. These sensors can operate independently or collaboratively to provide multi-dimensional data input.

[0038] The system also includes a processing module comprising a coprocessor unit 202C and a main processing unit 202B. The coprocessor unit 202C is typically designed as a low-power microcontroller, such as the HC32L series low-power MCU from a certain semiconductor company, which possesses powerful computing and interrupt handling capabilities. The main processing unit 202B is typically designed as a high-performance application processor, such as the GD32L series MCU from another semiconductor company, which has stronger floating-point operation capabilities, larger memory, and richer interfaces, enabling it to support complex operating systems and algorithms. The coprocessor unit 202C and the main processing unit 202B can exchange data and transfer instructions through shared memory, interrupts, or dedicated communication interfaces.

[0039] The coprocessor unit 202C acquires the behavioral characteristic data while the main processing unit 202B is in sleep mode, and determines whether the behavioral characteristic data meets a preset event triggering condition. If it does, a wake-up command is generated. For example, the coprocessor unit 202C can continuously monitor the raw data output by the accelerometer and calculate the average acceleration value within a specific time window. The preset event triggering condition can be set so that when the average acceleration value exceeds a certain threshold, the monitored object is considered to be in an active state or has performed a specific behavior. Once the condition is met, the coprocessor unit 202C generates a wake-up command by sending an interrupt signal to the main processing unit 202B or writing to a specific register.

[0040] The main processing unit 202B receives the wake-up command to switch from sleep mode to working mode. In working mode, it acquires the behavioral and physiological characteristic data and executes a physiological-behavioral coupling analysis algorithm to generate a health status assessment result for the monitored object. Upon receiving the wake-up command, the main processing unit 202B's power management unit 202D is activated, causing the main processing unit 202B to transition from low-power mode to full-function operation mode. Subsequently, the main processing unit 202B activates more sophisticated sensors in the sensing module to acquire higher sampling rates or more types of behavioral characteristic data (e.g., more detailed motion trajectory data) and physiological characteristic data (e.g., continuous heart rate waveform data). Next, the main processing unit 202B loads and executes a pre-stored physiological-behavioral coupling analysis algorithm. This algorithm can be a machine learning-based model used to analyze the correlation between behavioral patterns and physiological indicators, assessing whether the heart rate response at a specific exercise intensity meets expectations, thereby generating a health status assessment result for the monitored object.

[0041] The system also includes a management module for distributing power to the sensing and processing modules and executing tiered communication strategies based on the health status assessment results. The management module can integrate a power management unit 202D to achieve fine-grained power distribution to the sensing and processing modules by controlling the on / off state or voltage regulation of different power rails. For example, when the main processing unit 202B is in sleep mode, the management module can cut off power to the high-performance sensor, retaining power only to the co-processing unit 202C and the basic sensor (positioning unit). Regarding the tiered communication strategy, the management module can select different communication methods based on the urgency of the health status assessment results. For example, if the assessment results indicate that the monitored object is in a normal state, low-power short-range wireless communication (such as Bluetooth Low Energy) can be used to transmit a small amount of summary data to a nearby gateway device; if the assessment results indicate that the monitored object is in an abnormal state, high-power long-range wireless communication (such as cellular network or satellite communication) can be activated to send detailed alarm data to a remote monitoring center.

[0042] It should also be noted that, based on the above system architecture, by introducing a dual-processor architecture of coprocessor unit 202C and main processing unit 202B, the problem of balancing long-term device battery life and high-precision edge analysis in existing technologies is effectively solved. Existing devices often use a single processor, either continuously operating at high power consumption for in-depth analysis, resulting in short battery life, or operating at low power consumption for extended periods, but unable to respond to critical events in a timely manner. In this embodiment, coprocessor unit 202C performs low-power monitoring while main processing unit 202B is in sleep mode, and only wakes up main processing unit 202B to perform in-depth analysis when preset event triggering conditions are met, thereby achieving on-demand wake-up and fine-grained power management.

[0043] Secondly, the system has an intelligent event triggering mechanism. The coprocessing unit 202C can determine whether the preset event triggering conditions are met based on the behavioral characteristic data of the monitored object, and generate a wake-up command. This is in contrast to the lack of an intelligent triggering mechanism in the prior art, which leads to the waste of power when the device does not need in-depth analysis or the inability to respond in time when critical abnormal events occur. The triggering mechanism of this embodiment ensures that high-energy-consuming in-depth analysis is only started when necessary, avoiding unnecessary power consumption, while ensuring the ability to respond to critical events in real time.

[0044] Furthermore, the main processing unit 202B executes a physiological-behavioral coupling analysis algorithm in its working state, which can generate health status assessment results for the monitored objects. This coupling analysis method is more comprehensive and accurate than analyzing physiological or behavioral data alone. Existing technologies may only focus on single modality data, making it difficult to reveal the complex relationship between physiology and behavior, thus limiting the accuracy of health status assessment. This embodiment improves the reliability of the assessment results by combining data from two modalities for in-depth analysis.

[0045] Finally, the management module executes a tiered communication strategy based on the health status assessment results, further optimizing the system's energy efficiency and response efficiency. When the assessment result indicates a non-emergency situation, the system can use a low-power communication method to transmit small amounts of data; while when the assessment result includes priority alarm data, the system can quickly switch to a high-power, high-priority communication method for alarm transmission. This has significant advantages compared to existing technologies that may employ a single communication strategy, leading to wasted energy in non-emergency situations or untimely communication in emergency situations. This tiered strategy ensures that data transmission is performed in the most optimized way under different scenarios, improving the overall system performance.

[0046] Example 2: The sensing module includes a bio-radar unit, which is used to emit electromagnetic pulses and receive reflected echoes modulated by the chest movement of the monitored object, and extract heart rate data and respiratory rate data from the reflected echoes as the physiological characteristic data.

[0047] Understandably, in this embodiment, by integrating a bio-radar unit based on ultra-wideband (UWB) technology into the sensing module, the system can utilize the strong penetrating characteristics of nanosecond-level electromagnetic pulses to penetrate the thick fur and epidermal tissue of the monitored object, reaching the chest cavity interface. It then receives reflected echoes modulated by the minute mechanical displacements of the chest cavity caused by heartbeats and lung respiration, thereby accurately capturing vital signs signals without any contact. This effectively solves the technical problem of existing wildlife monitoring equipment, which can only acquire macroscopic external behavioral data such as displacement or posture, but cannot acquire key internal physiological indicators such as heart rate and respiratory rate without removing fur or using conductive coupling agents. It achieves undisturbed, long-term physiological health monitoring of wild animals in their natural, unrestrained environment, filling the data gap in mapping external behavior to internal physiological states.

[0048] Furthermore, the main processing unit 202B performs complex time-frequency domain signal processing on the reflected echoes, including using bandpass filtering to remove high-amplitude motion artifacts caused by body swaying, and employing Fast Fourier Transform (FFT) or wavelet transform to perform spectral analysis on the signal to extract characteristic peaks, thereby successfully separating and calculating high-precision real-time heart rate and respiratory rate data. This process not only achieves accurate extraction of physiological characteristic data, but also achieves a perfect balance between high-precision physiological sensing and extreme power consumption control through deep collaboration with the system's dual-core architecture—that is, only activating the radar for intermittent sampling when the co-processing unit 202C determines through the IMU that the animal is in a stationary or low-activity state. This avoids data invalidation caused by noise interference during the animal's vigorous movement and maximizes the device's field endurance.

[0049] In a preferred embodiment, the antenna of the bio-radar unit is integrated with the device housing 200 and worn close to the animal's body. In terms of working logic, the LPC (acting as a sentinel) activates the radar module with a low duty cycle (e.g., once per minute, measuring for 15 seconds) when it detects that the animal is stationary. After the MCU (acting as the commander) is awakened, it synchronously acquires a high-resolution data stream, filters out noise through algorithms and performs spectrum analysis, and finally accurately calculates vital signs from the time delay and phase changes of the echo, thereby realizing a quantitative assessment of the animal's stress level or health status at the end.

[0050] Example 3: In this embodiment, the system further includes a communication module, which includes a first communication unit and a second communication unit. The power consumption and transmission distance of the second communication unit are higher than those of the first communication unit.

[0051] In this embodiment, by configuring a heterogeneous communication architecture including a first communication unit and a second communication unit, and limiting the power consumption and transmission distance of the second communication unit to be higher than those of the first communication unit, the system can utilize the differences in the technical characteristics of different communication modules to solve the technical problem that a single communication mode cannot simultaneously achieve low-power operation and wide-area coverage in complex field environments. This overcomes the drawback of relying solely on long-distance communication such as satellites, which leads to excessive energy consumption and shortens equipment lifespan. It also compensates for the shortcomings of relying solely on short-distance communication, which results in monitoring blind spots or untimely data transmission, providing a hardware foundation for the system to achieve long-term stable operation under resource-constrained conditions.

[0052] Furthermore, in conjunction with the hierarchical communication strategy implemented by the management module, the above scheme achieves adaptive optimization of communication links. The system is configured to prioritize the use of the low-power first communication unit for non-urgent data transmission under normal conditions to reduce average operating current; only when the physiological-behavioral coupling analysis indicates that the monitored object is in an abnormal state and generates priority alarm data, or when the first communication unit link is unavailable, is the high-power, long-distance second communication unit forcibly activated. This technical logic ensures that critical alarm information can be transmitted in real time through highly reliable links, optimizing the allocation of communication resources and significantly improving the system's energy efficiency ratio while ensuring information timeliness.

[0053] The first communication unit is preferably used for low-energy offloading of large amounts of data when the monitored object approaches a preset gateway (such as an infrared camera monitoring point); the second communication unit preferably uses a Beidou satellite communication module or a cellular network module for daily routine data reporting and real-time alarms for emergency events. In specific application scenarios, when the main processing unit 202B determines that a "health alarm" has occurred and there is no LoRa network coverage, the communication decision algorithm will control the system to start the satellite communication module to report key data (such as location and heart rate curves), while in non-emergency situations it will wait for a short-range communication window, thereby effectively supporting the device to achieve a theoretical battery life of up to several years.

[0054] Example 4: In this embodiment, the sensing module further includes a positioning unit. The system is encapsulated in a housing 200. A dielectric boss 206 is provided at the geometric center of the top of the housing 200. The positioning unit is disposed in the dielectric boss 206, and the mounting plane of the positioning unit is higher than the circuit board plane where the processing module is located.

[0055] In this embodiment, by integrating a positioning unit into the sensing module and encapsulating this positioning unit within a dielectric protrusion 206 at the top geometric center of the housing 200, and ensuring that its mounting plane is physically higher than the circuit board plane where the processing module is located, the system employs an optimized spatial hierarchy layout strategy. This unique structural design effectively solves the technical challenges of high-frequency positioning signals being highly susceptible to electromagnetic interference from the internal main processing unit 202B and power circuit during the high integration process of compact biosensing devices, as well as the weakening of satellite signal reception due to obstruction by the housing 200 structure. This provides a reliable physical guarantee for the system to acquire high-precision location information in complex field environments.

[0056] Furthermore, the above solution utilizes the wave transmission characteristics of the dielectric boss 206 and the raised mounting plane to achieve spatial isolation and decoupling between the positioning antenna and the main interference source (processing module PCB). This design not only effectively attenuates electromagnetic radiation interference from the underlying circuit board by utilizing the height difference, but also ensures that the positioning antenna is at the geometric high point of the device, thereby obtaining the optimal hemispherical omnidirectional radiation pattern and the widest possible field of view. This achieves the technical effect of maintaining a high positioning lock-on rate and accuracy even in weak signal scenarios such as dense forests or deep valleys, preventing the loss of spatial dimension of monitoring data due to location loss, and significantly improving the system's environmental adaptability and data integrity.

[0057] Furthermore, the antenna of the positioning unit is preferably a high-gain ceramic patch antenna, placed on a specially designed raised structure (i.e., dielectric boss 206) at the highest geometric point of the device housing 200. This aligns with the grounding area of ​​the PCB below, thus forming a good signal radiation pattern. This structure ensures that regardless of the animal's movement causing the device to flip, the antenna has the widest possible field of view, minimizing signal obstruction. Combined with the low-frequency positioning strategy in Embodiment 1, this effectively supports the position tracking function in survival mode.

[0058] Example 5: In this embodiment, the sensing module includes at least two differential acoustic units 202F. The sound receiving port of the differential acoustic unit 202F is covered with a waterproof and sound-permeable membrane. The differential acoustic unit 202F is used to acquire ambient acoustic energy. When the acoustic energy acquired by the coprocessing unit 202C exceeds a preset sound pressure threshold, the wake-up command is generated to activate the main processing unit 202B.

[0059] Understandably, this embodiment constructs a low-power acoustic sensing subsystem with environmental adaptability by configuring at least two differential acoustic units 202F in the sensing module and covering the sound-absorbing hole with a waterproof and sound-permeable membrane, in conjunction with the sound pressure threshold wake-up mechanism of the coprocessing unit 202C. This effectively solves the technical problems of low signal-to-noise ratio and easy damage of a single microphone signal in complex outdoor sound field environments (such as wind noise and friction noise interference), as well as the high system power consumption caused by continuous high sampling rate audio monitoring. It not only ensures the reliability of acoustic devices in humid and dusty environments from a physical perspective through the sound-permeable membrane, but also achieves effective suppression of background noise through the hardware characteristics of the differential array, ensuring the accuracy and robustness of the system in capturing specific acoustic events (such as animal calls, gunshots, etc.).

[0060] Furthermore, this embodiment utilizes the coprocessor unit 202C to continuously monitor the energy of the differential acoustic signal in low-power mode. The system only determines that a valid acoustic event has occurred and generates a wake-up command to activate the main processing unit 202B when it detects that the ambient acoustic energy exceeds a preset sound pressure threshold. This "acoustic sentinel" mechanism complements the aforementioned "motion sentinel" mechanism, enabling the system to trigger in-depth analysis even when the monitored object is stationary but emits sound (such as a roar or mating call while lying still). This greatly expands the dimensions of event capture and avoids the energy waste caused by the main processing unit 202B continuously performing complex audio analysis, achieving an organic combination of all-weather acoustic monitoring and long battery life.

[0061] Example 6: In this embodiment, the management module includes a first power supply unit and a second power supply unit. The first power supply unit is electrically connected to the coprocessing unit 202C, and the second power supply unit is electrically connected to the main processing unit 202B. The management module is also used to cut off or turn on the second power supply unit.

[0062] In this embodiment, by configuring independent first and second power supply units in the management module, and constructing a constant power supply loop for the coprocessor unit 202C and a controllable power supply loop for the main processing unit 202B respectively, the system implements a physical-level refined energy consumption management mechanism. This effectively solves the technical problem in existing dual-core or multi-core systems where the high-performance main processing unit 202B still exhibits significant static power consumption and leakage current even in software sleep mode. By physically disconnecting the second power supply unit during non-working periods, the system completely blocks the power consumption path of the main processing unit 202B and related high-power components, retaining only the microampere-level power consumption coprocessor unit 202C for monitoring. This reduces standby power consumption to physical limits while ensuring the system's responsiveness, significantly extending the device's long-term dwell time.

[0063] Furthermore, this embodiment utilizes the logic control capabilities within the management module (such as the power management unit 202DPMU) to establish a linkage mechanism between power supply status and system operating mode. The first power supply unit acts as a "resident" power source, ensuring that the coprocessor unit 202C continuously executes the task of acquiring and judging behavioral feature data; while the second power supply unit acts as an "on-demand" power source, which is in a de-circuit state by default and is only turned on when the coprocessor unit 202C issues a wake-up command or when the system is in a specific maintenance window. This hardware-level power gating strategy avoids the power loss in the traditional soft shutdown mode and ensures that limited battery energy is maximized for effective data acquisition and analysis.

[0064] Example 7: In this embodiment, the sensing module includes an inertial measurement unit 202E; when the coprocessing unit 202C determines whether the behavioral feature data meets the preset event triggering conditions, it obtains the overall dynamic body acceleration index within the preset time window based on the output data of the inertial measurement unit 202E.

[0065] In this embodiment, by configuring an inertial measurement unit 202E in the sensing module and utilizing a coprocessing unit 202C to execute the dynamic body acceleration overall index (ODBA) calculation logic based on a preset time window, the system constructs an intelligent triggering mechanism based on the statistical characteristics of motion energy. This effectively solves the technical problem that relying solely on instantaneous acceleration thresholds for judgment is easily affected by invalid signals such as environmental vibrations and brief collisions, leading to false wake-ups, or that insufficient sampling frequency results in missed detections of critical brief behaviors. By integrating or statistically processing the acceleration data over time, the system can effectively filter out high-frequency noise and occasional interference, accurately pinpointing the biologically significant continuous activity states of the monitored object.

[0066] Furthermore, this embodiment utilizes the coprocessor unit 202C to perform real-time calculation and threshold comparison of the ODBA index in low-power operation mode, realizing the quantitative classification of the intensity of the monitored object's behavior. Only when the calculated ODBA value indicates that the overall activity level of the monitored object exceeds the preset behavior intensity threshold is the event trigger condition determined and the main processing unit 202B is awakened. This processing logic ensures that the high-energy-consuming main processing unit 202B only intervenes when high-value behavioral events such as hunting, escaping, or fierce fighting occur, thereby significantly reducing the average operating power consumption of the system while ensuring the accuracy of behavior capture, and improving the energy efficiency performance of the equipment in long-term field monitoring.

[0067] Example 8: In this embodiment, the management module includes a power management unit 202D, which is also used to monitor the remaining power of the system and send a functional constraint signal to the processing module when the remaining power is lower than a preset survival threshold. The main processing unit 202B responds to the functional constraint signal and enters the survival mode. In the survival mode, the management module only retains the power supply to the positioning unit to control the system to enter a low-frequency positioning state.

[0068] In this embodiment, the system further includes an onboard storage unit; the onboard storage unit is used to: mark the current behavioral characteristic data and physiological characteristic data as high-value events for non-volatile storage when the health status assessment result contains priority alarm data and the communication module cannot establish a connection.

[0069] Understandably, in this embodiment, by configuring the power management unit 202D in the management module to monitor the remaining system power in real time and constructing a low-power response mechanism based on a survival threshold, the system implements an adaptive survival strategy for the device under extreme conditions. This effectively solves the technical problem of field monitoring equipment suddenly shutting down completely due to a lack of energy consumption hierarchical management when the battery is about to run out, leading to the loss of the device's location and inability to be recovered. By sending a functional constraint signal to the processing module when the power level is detected to be lower than the preset survival threshold, the system forces the main processing unit 202B to enter survival mode, physically cutting off the power supply circuits of non-core loads such as the bio-radar and acoustic unit, retaining only the power supply to the positioning unit and switching it to a low-frequency operating state. This strategy sacrifices unnecessary sensing functions to minimize system power consumption, thereby extending the traceable time window of the device before the power is exhausted, providing researchers with a final guarantee for recovering the device using the positioning signal.

[0070] Furthermore, this embodiment incorporates an onboard storage unit to construct a "black box" protection mechanism for critical data, effectively solving the technical challenge of risking loss of high-value physiological and behavioral data in communication dead zones or when communication modules are limited by low power. Specifically, when physiological-behavioral coupling analysis generates an evaluation result containing priority alarm data (e.g., detecting a lethal heart rate abnormality or quiescence after violent struggle), if the communication module cannot establish a valid connection at this time, the main processing unit 202B will not perform high-energy-consuming repeated reconnection operations, but will immediately mark the current physiological and behavioral characteristic data as "high-value events" and write them into non-volatile memory. This mechanism ensures that even under extreme communication conditions where real-time alarms are not possible, key evidence reflecting significant health abnormalities or the moment of death of the monitored subject can be completely preserved for subsequent traceability analysis after equipment recovery or network restoration, thus guaranteeing the integrity and scientific value of the monitoring data.

[0071] Specifically, the power management unit 202D preferably uses a power management chip (PMIC) with a battery level meter function. When the battery level is detected to be lower than a preset survival threshold (e.g., 10%), it actively triggers the interrupt pin of the main processing unit 202B. After responding, the main processing unit 202B shuts down the clocks and power supplies of all peripherals except the positioning unit, and controls the positioning unit (e.g., GPS) to enter a low-frequency mode (e.g., adjusting the positioning interval from seconds to minutes). Meanwhile, the onboard storage unit preferably uses embedded flash memory (e.g., NOR Flash). When a priority alarm such as "risk of sudden cardiac death" occurs and cannot be uploaded via Bluetooth or cellular network, the main processing unit 202B packages the relevant data, adds an "emergency alarm" tag, and stores it in the Flash memory, ensuring that the data is not lost even after power failure.

[0072] Example 9: To achieve the above objectives, this embodiment provides a biosensing method with end-side analysis capabilities, as shown in the attached figure. Figure 4 As shown, the method includes the following steps: Acquire behavioral and physiological characteristic data of the monitored objects; When the main processing unit 202B is in a sleep state, the behavioral feature data is acquired, and it is determined whether the behavioral feature data meets the preset event triggering conditions. If it does, a wake-up command is generated. The system acquires the wake-up command to switch from a dormant state to a working state; and in the working state, it acquires the behavioral characteristic data and the physiological characteristic data, and executes a physiological-behavioral coupling analysis algorithm to generate a health status assessment result for the monitored object. Power is allocated to the sensing module and the processing module, and a hierarchical communication strategy is executed based on the health status assessment results.

[0073] In this embodiment, determining whether the behavioral feature data meets the preset event triggering conditions includes the following steps: The overall dynamic body acceleration index within a preset time window is obtained based on the output data of the inertial measurement unit 202E. Determine whether the overall dynamic body acceleration index exceeds a preset behavior intensity threshold; if it does, determine that the event triggering condition is met and generate the wake-up command.

[0074] In this embodiment, the execution of the physiological-behavioral coupling analysis algorithm includes the following steps: Based on the behavioral characteristic data, the current movement pattern of the monitored object is identified; Call the preset baseline physiological zone corresponding to the exercise pattern; The acquired physiological characteristic data is compared with the preset benchmark physiological interval; If the physiological characteristic data deviates from the preset baseline physiological range and the degree of deviation exceeds the preset abnormality index, the monitored object is determined to be in an abnormal state, and a health status assessment result containing priority alarm data is generated.

[0075] In this embodiment, the step of executing the hierarchical communication strategy based on the health status assessment result includes the following steps: when the health status assessment result does not contain the priority alarm data, the first communication unit is activated to transmit data; when the health status assessment result contains the priority alarm data, the second communication unit is activated to transmit data.

[0076] It should be noted that in this embodiment, an event-driven workflow based on a dual-core heterogeneous architecture is first established to address the core contradiction between continuous monitoring and limited energy resources. This method does not continuously run high-energy-consuming analysis algorithms. Instead, it utilizes the coprocessor unit 202C, which is in a constantly running state, to perform low-power background monitoring while the main processing unit 202B is in sleep mode. Only when the behavioral characteristic data of the monitored object meets specific preset conditions does the system generate a wake-up command to activate the main processing unit 202B, completing the state switch from "silent sensing" to "deep diagnosis." This mechanism ensures that the system remains in a low-power state at the microampere level most of the time, only allocating computing resources when necessary, thus laying the methodological foundation for long-term endurance.

[0077] Furthermore, regarding the wake-up condition determination step, this embodiment employs a statistical feature extraction method based on inertial measurement data. The coprocessor unit 202C does not simply respond to instantaneous acceleration peaks, but continuously calculates the Dynamic Overall Body Acceleration Index (ODBA) within a preset time window. According to the technical disclosure, the coprocessor unit 202C can be set to collect data at a specific frequency (e.g., 50Hz) and calculate the ODBA value within a specific duration (e.g., 1 minute). Only when this index exceeds a preset behavioral intensity threshold (e.g., a sudden increase from a resting baseline of 0.2g to 2.5g) is a biologically significant active event (e.g., hunting or fleeing) determined to have occurred, thereby generating a wake-up command. This step effectively filters out false triggers caused by environmental micro-movements or brief collisions, ensuring that the main processing unit 202B is only woken up by "valid events."

[0078] During the analysis phase after the main processing unit 202B is activated, the method executes the core physiological-behavioral coupling analysis algorithm to achieve accurate assessment of the health status of the monitored object. This step breaks through the limitations of traditional single-dimensional monitoring. First, it identifies the current movement pattern of the monitored object (such as stillness, patrolling, or running) based on behavioral characteristic data, and then calls the preset benchmark physiological interval corresponding to this pattern. The system compares the real-time acquired physiological characteristic data (such as heart rate extracted by bio-radar) with this dynamic benchmark. If the physiological data is found to deviate significantly from the benchmark interval and the degree of deviation exceeds the preset abnormality index (for example, detecting an abnormal coupling pattern of "low activity" accompanied by "high resting heart rate"), it is determined that the monitored object is in an abnormal state such as injury or disease, and an assessment result containing priority alarm data is generated.

[0079] Subsequently, the method implements a tiered communication strategy based on the aforementioned evaluation results to address the balance between communication energy consumption and information timeliness. When the health status evaluation results do not include priority alarm data (i.e., the monitored object is in normal condition), the system activates a low-power, short-range first communication unit (such as LoRa or BLE) for opportunistic transmission of regular data. Conversely, when the evaluation results include priority alarm data indicating high risk, the system forcibly activates a high-power, long-range second communication unit (such as a satellite communication module), ensuring real-time reporting of alarm information regardless of energy consumption.

[0080] In summary, the method provided in this embodiment achieves intelligent monitoring of the vital signs of wild animals through a closed-loop logic of low-power sensing triggering, high-precision coupled diagnosis, and differentiated communication transmission. It not only improves the diagnostic value of the data at the algorithm level by eliminating motion artifacts and performing correlation analysis, but also ensures, at the system level, that in resource-constrained environments in the wild, it can both capture fleeting critical abnormal events and maintain the equipment's effective operating cycle for several years.

[0081] Example 10: To make the technical solution of the present invention clearer, this embodiment uses a snow leopard living in a high-altitude area as the monitoring object for specific explanation.

[0082] The overall structure and packaging of the present invention are as follows: Figure 2 As shown, the device adopts a collar-like shape. The outer shell 200 is made of high-strength, low-temperature resistant polycarbonate (PC) material and is injection molded in one piece. The PCB board inside is encapsulated with epoxy resin to achieve IP68 waterproof and dustproof rating.

[0083] The device has a streamlined overall shape with no protruding edges that could be easily dragged. The collar strap 100 is made of an anti-jamming composite material. A flexible CIGS solar film 205 is integrated into the upper surface of the collar for auxiliary charging. The highest point of the device is an arc-shaped protrusion (dielectric protrusion 206), which encapsulates the GPS ceramic antenna 202A.

[0084] Regarding the selection of core hardware for this invention: Main processing unit 202B (main controller (MCU)): The GD32L series MCU is selected, which has a good balance between high-performance computing and low-power mode.

[0085] Coprocessor Unit 202C (Low Power Coprocessor (LPC)): HC32L series low power MCU is selected. The HC32L series performs well in sensor-integrated coprocessor scenarios.

[0086] Bio-radar unit (UWB bio-radar): It uses the NK6000 series UWB chip, which has extremely low power consumption and high-precision ranging and sensing capabilities, and is paired with a customized microstrip antenna that conforms to the inner wall of the collar.

[0087] Inertial Measurement Unit 202E: Nine-axis motion sensor.

[0088] Differential acoustic unit 202F (microphone array): MEMS microphones, 1.5cm spacing.

[0089] First communication unit (short-range communication): BG77 series LoRa module, used to offload large amounts of data when the snow leopard approaches the preset infrared camera monitoring point.

[0090] The second communication unit (long-distance communication) uses a Beidou satellite communication module, which can be used for daily routine data reporting and real-time alarms for emergency events.

[0091] Battery pack 203: The main battery is a 10000mAh wide-temperature lithium battery, which is managed by the SGM41511 power management unit 202D (PMU).

[0092] The layout of the aforementioned core components on the PCB motherboard is as follows: Figure 3 As shown, each antenna region is placed at the top to reduce interference, and the IMU is placed at the geometric center to ensure measurement accuracy.

[0093] Regarding the workflow: Normal state (snow leopard sleeping or moving slowly): The MCU and satellite module are in deep sleep. The LPC continuously collects IMU data at a frequency of 50Hz and calculates the ODBA value within a 1-minute window. If the ODBA value is continuously lower than the threshold of 0.2g, the LPC maintains this state, and the power consumption of the whole machine is less than 50μA.

[0094] Event triggered: The snow leopard begins hunting the blue sheep. Its vigorous movement causes the ODBA value to surge to 2.5g within 10 seconds. The LPC event prediction algorithm detects this sudden change and immediately wakes up the MCU via the interrupt pin.

[0095] Data Acquisition and Processing: After the MCU is woken up, it immediately starts the GPS module for high-frequency positioning (1Hz), and at the same time obtains high-resolution IMU and audio data from LPC, and runs a complete machine learning anomaly recognition algorithm. The MCU judges the anomaly index of this hunting behavior to be 0.85 (which is a normal but violent behavior).

[0096] Physiological-behavioral coupling diagnostic scenario: A snow leopard's activity level (ODBA value) was detected by a collar for 48 consecutive hours as extremely low, far below its behavioral baseline. The LPC continuously monitored its resting heart rate via UWB radar, finding it abnormally elevated from the normal 40-50 bpm to 80 bpm, accompanied by rapid breathing. Upon activation of the MCU, the physiological-behavioral coupling analysis model was run, identifying this "low activity + high resting heart rate" pattern as a high-risk signal of "illness or injury." The communication decision algorithm marked this event as a highest-priority "health alarm," immediately activating the BeiDou satellite communication module to report the alarm information and key data (location, heart rate curve) to the research center, enabling timely rescue.

[0097] Data storage and communication decision-making: The MCU packages the detailed data of this hunting event (duration, trajectory, energy consumption, audio clips), marks it as a "high-value event," and stores it in the onboard flash memory. The communication decision algorithm determines that there is currently no LoRa network coverage, and the event index has not reached the set emergency alarm threshold of 0.98. Therefore, it decides not to immediately initiate satellite communication, but to wait for the next regular communication window.

[0098] Return to low-power state: After processing is complete, the MCU enters deep sleep again, and control is handed back to the LPC. The entire process lasts approximately 90 seconds.

[0099] Through this embodiment, the present invention extends the theoretical battery life of the device from a few months for traditional GPS collars to more than two years, and is able to collect behavioral detail data that traditional devices cannot obtain, fully demonstrating its technological advancement and practical value.

[0100] Example 11: This embodiment describes the application of the present invention in animal monitoring with a completely different morphology and application scenario, in order to highlight the scalability and adaptability of the solution.

[0101] Design goals differ: Unlike snow leopard monitoring, which pursues high precision for individual targets, marmot monitoring focuses on low-cost, large-scale deployment to study their group behavior and response to geological disasters.

[0102] Hardware selection adjustments: Appearance: The device takes the form of an ear tag or a back implant, and its size and weight are much smaller than a collar. Although the form has changed to an ear tag or implant, the IMU is still placed close to the animal's core or head to ensure that it can most effectively capture the animal's overall, high-frequency vibrational behavior caused by external stimuli.

[0103] Power supply: Due to size limitations, the solar thin film 205 was removed, relying solely on a small-capacity disposable lithium-ion battery, which made the requirements for extremely low power consumption even more stringent.

[0104] Communication: The expensive satellite communication module was removed, and only the LoRa module was retained. Data is transmitted opportunistically to multiple LoRa gateways deployed in the vicinity as the marmot moves near the burrow entrance.

[0105] Sensors: The core IMU is retained, but the microphone has been removed to further reduce cost and power consumption.

[0106] Adaptive adjustments to work patterns: Hibernation period: By continuously monitoring the tiny vibrations and temperature of the IMU, LPC can accurately determine the time when the marmot enters and exits hibernation. During the entire hibernation period, the MCU can remain completely unawakened for several months, and the power consumption of the whole machine reaches the limit of a few microamps.

[0107] Disaster precursor monitoring: LPC's event prediction algorithm threshold is set to be extremely sensitive to mass, synchronous, high-frequency vibrations (which may be caused by pre-earthquake ground sounds or ground micro-movements that trigger mass panic). Once such an event is detected, LPC will immediately wake up the MCU, record the precise time and motion pattern, and broadcast a high-priority warning packet to all gateways through the LoRa network.

[0108] This embodiment demonstrates that the core architecture of the present invention (especially the dual-core heterogeneous processing core) is not bound to a specific product form or sensor combination. It can be flexibly tailored and customized according to cost, size, power consumption and monitoring objectives, and can still exert its key technical advantages in very different application scenarios. This strongly supports the universality of the claims of the present invention.

[0109] Example 12: Based on the same inventive concept as the foregoing embodiments, this embodiment provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0110] Example 13: Based on the same inventive concept as the foregoing embodiments, this embodiment provides a computer-readable storage medium storing a computer program, and a processor executes the computer program to implement the above-described method.

[0111] In this embodiment, the computer-readable storage medium can be a memory such as FRAM, ROM, PROM, EPROM, EEPROM, flash memory, magnetic surface memory, optical disc, or CD-ROM; it can also be a device that includes one or any combination of the above-mentioned memories. The computer can be a variety of computing devices, including smart terminals and servers.

[0112] In this embodiment, the executable instructions may take the form of a program, software, software module, script, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including being deployed as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.

[0113] As an example, executable instructions may, but do not necessarily, correspond to files in a file system. They may be stored as part of a file that holds other programs or data, for example, in one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple collaborating files (e.g., a file that stores one or more modules, subroutines, or code sections).

[0114] As an example, executable instructions can be deployed to execute on a single computing device, or on multiple computing devices located in one location, or on multiple computing devices distributed across multiple locations and interconnected via a communication network.

[0115] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0116] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0117] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as read-only memory / random access memory, magnetic disk, optical disk) and includes several instructions to cause a multimedia terminal device (which may be a mobile phone, computer, television receiver, or network device, etc.) to execute the methods described in the various embodiments of the present invention.

[0118] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A biosensing system with end-side analysis capability, characterized in that, The system includes: The sensing module is used to acquire behavioral and physiological characteristic data of the monitored object; The processing module includes a coprocessing unit and a main processing unit. The coprocessing unit is used to acquire the behavioral feature data when the main processing unit is in a sleep state, and to determine whether the behavioral feature data meets a preset event triggering condition. If it does, a wake-up command is generated. The main processing unit is used to acquire the wake-up command to switch from the sleep state to the working state; and in the working state, acquire the behavioral feature data and the physiological feature data, and execute the physiological-behavioral coupling analysis algorithm to generate the health status assessment result of the monitored object; The management module is used to allocate power to the sensing module and the processing module, and to execute a hierarchical communication strategy based on the health status assessment results.

2. The biosensing system with end-side analysis capability as described in claim 1, characterized in that, The sensing module includes a multimodal sensing unit, which includes: The bio-radar unit is used to transmit electromagnetic pulses and receive reflected echoes modulated by the chest cavity movement of the monitored object, and extract heart rate data and respiratory rate data from the reflected echoes as the physiological characteristic data. The positioning unit is a system encapsulated in a housing. A dielectric boss is provided at the geometric center of the top of the housing. The positioning unit is disposed in the dielectric boss, and the mounting plane of the positioning unit is higher than the plane of the circuit board on which the processing module is located.

3. The biosensing system with end-side analysis capability as described in claim 1, characterized in that, The sensing module includes at least two differential acoustic units. The sound-receiving holes of the differential acoustic units are covered with a waterproof and sound-permeable membrane. The differential acoustic units are used to acquire ambient acoustic energy. When the acoustic energy acquired by the coprocessing unit exceeds a preset sound pressure threshold, the wake-up command is generated. The sensing module also includes an inertial measurement unit. The coprocessing unit obtains the overall dynamic body acceleration index within a preset time window based on the output data of the inertial measurement unit. When the overall dynamic body acceleration index exceeds a preset threshold, the wake-up command is generated.

4. The biosensing system with end-side analysis capability as described in claim 1, characterized in that, The management module includes a first power supply unit and a second power supply unit. The first power supply unit is electrically connected to the coprocessor unit, and the second power supply unit is electrically connected to the main processing unit. The management module is used to disconnect the second power supply unit in a sleep state. The management module also includes a power management unit, which monitors the remaining power of the system and sends a functional constraint signal to the processing module when the remaining power is lower than a preset survival threshold. The main processing unit responds to the functional constraint signal and enters survival mode. In survival mode, the management module only maintains power supply to the positioning unit to control the system to enter a low-frequency positioning state.

5. The biosensing system with end-side analysis capability as described in claim 1, characterized in that, The system further includes a first communication unit and a second communication unit, wherein the power consumption and transmission distance of the second communication unit are higher than those of the first communication unit; the management module activates the first communication unit to transmit data when the health status assessment result does not contain the priority alarm data, and activates the second communication unit to transmit data when the health status assessment result contains the priority alarm data; The system also includes an onboard storage unit, which is used to: mark the current behavioral characteristic data and physiological characteristic data as high-value events for non-volatile storage when the health status assessment result contains priority alarm data and the communication module cannot establish a connection.

6. A biosensing method with end-side analysis capability, characterized in that, The method is based on the biosensing system with end-side analysis capability as described in any one of claims 1 to 5, and the method includes the following steps: Acquire behavioral and physiological characteristic data of the monitored objects; When the main processing unit is in a sleep state, the coprocessing unit acquires the behavioral feature data and determines whether the behavioral feature data meets the preset event triggering conditions. If it does, a wake-up command is generated. The main processing unit acquires the wake-up command, switches from the sleep state to the working state, and acquires the behavioral feature data and the physiological feature data in the working state, and executes the physiological-behavioral coupling analysis algorithm to generate the health status assessment result of the monitored object. Power is allocated to the sensing module and the processing module, and a hierarchical communication strategy is executed based on the health status assessment results.

7. The biosensing method with end-side analysis capability as described in claim 6, characterized in that, The execution of the physiological-behavioral coupling analysis algorithm includes the following steps: Based on the behavioral characteristic data, the current movement pattern of the monitored object is identified; Call the preset baseline physiological zone corresponding to the exercise pattern; The acquired physiological characteristic data is compared with the preset benchmark physiological interval; If the physiological characteristic data deviates from the preset baseline physiological range and the degree of deviation exceeds the preset abnormality index, the monitored object is determined to be in an abnormal state, and a health status assessment result containing priority alarm data is generated.

8. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a computer program and the processor executes the computer program to implement the method as described in claim 6 or 7.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the processor executes the computer program to implement the method as described in claim 6 or 7.

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