Smart watch high-precision positioning method and system

Abstract

The application relates to the technical field of smart watch positioning, and provides a smart watch high-precision positioning method and system, which comprises the following steps: acquiring a satellite positioning signal of a global navigation satellite system; continuously monitoring signal quality of the satellite positioning signal and motion information of an inertial measurement unit in the smart watch; judging whether a wearer is about to enter a signal-limited environment according to the signal quality, the motion information and preset geographic information, and obtaining a signal-limited judgment result; and if the signal-limited judgment result indicates that the wearer is about to enter the signal-limited environment, adjusting a working mode of a positioning service. The application has the effect of improving the positioning precision and stability of the smart watch.

Description

Technical Field

[0001] This invention relates to the technical field of smartwatch positioning, and specifically to a high-precision positioning method and system for smartwatches. Background Technology

[0002] As an indispensable portable device in daily life, the high-precision positioning capability of smartwatches is crucial for user experience. However, in practical applications, the high-precision positioning function of smartwatches often faces severe challenges. For example, when a wearer moves through urban areas with tall buildings, these buildings create "urban canyons," severely affecting the satellite signals received by the smartwatch's built-in Global Navigation Satellite System (GNSS) receiving antenna, generating a large amount of multipath signals, which in turn causes distortion in the phase and amplitude of the signal.

[0003] The resulting complex technical dilemma is that in complex environments such as urban canyons transitioning to indoor spaces, the initial positioning caused by the wearer's daily activities is unstable, triggering the system to enter a high computational load state. Consequently, the "system overheat protection mechanism" weakens the real-time computing power required by the "motion trajectory estimation method based on inertial sensors." This causes the high-precision positioning method of smartwatches to completely fail during the most critical seamless switching phase due to the deterioration in the processing logic performance of fusing location information from different sources. As a result, it cannot provide continuous and accurate positioning trajectories, severely impacting the user's navigation experience and battery life.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] This application discloses a high-precision positioning method and system for smartwatches, aiming to solve the technical problems of limited positioning accuracy, frequent mode switching, increased power consumption, and positioning failure caused by system overheating protection in complex environments.

[0006] The technical solution of this application is as follows:

[0007] In a first aspect, this application discloses a high-precision positioning method for a smartwatch, including:

[0008] Obtain satellite positioning signals from the Global Navigation Satellite System;

[0009] Continuously monitor the signal quality of satellite positioning signals and the motion information of the inertial measurement unit in the smartwatch;

[0010] Based on signal quality, motion information, and preset geographical information, it determines whether the wearer is about to enter a signal-limited environment and obtains a signal-limited judgment result.

[0011] If the signal limitation assessment indicates that the wearer is about to enter a signal-limited environment, the location service will adjust its operating mode, including:

[0012] Switch the operating mode of the receiver module of the Global Navigation Satellite System to signal tracking mode;

[0013] Increase the data acquisition frequency and processing priority of the inertial measurement unit;

[0014] When the wearer is about to enter an environment with limited signal, the smartwatch will activate wireless assisted positioning scanning;

[0015] Real-time monitoring of the workload and temperature of the smartwatch's main processing chip;

[0016] When the workload or temperature of the main processing chip reaches a preset threshold, computing resources are prioritized for the processing tasks of the global navigation satellite system in signal tracking mode, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning scanning tasks, while reducing the operating frequency of the remaining tasks to achieve high-precision positioning of the smartwatch.

[0017] This technical solution effectively addresses the problems of decreased positioning accuracy, frequent mode switching, increased power consumption, and positioning failure caused by system overheating protection in smartwatches under complex signal environments. Through intelligent resource scheduling and mode adjustment, it ensures that continuous, accurate, and high-precision positioning services can still be provided in signal-limited environments.

[0018] Furthermore, based on signal quality, motion information, and preset geographical information, the steps to determine whether the wearer is about to enter a signal-limited environment and obtain the signal-limited judgment result include:

[0019] Wireless signal characteristics that monitor the wireless signals in the wearer's surrounding environment;

[0020] Based on signal quality, motion information, preset geographical information, and wireless signal characteristics, it determines whether the wearer is about to enter a signal-limited environment, and obtains the signal-limited judgment result.

[0021] This technical solution, by introducing the monitoring of the characteristics of wireless signals in the surrounding environment, can more comprehensively and accurately determine whether the wearer is about to enter a signal-limited environment, thereby providing a more reliable basis for subsequent adjustments to the positioning service and further improving the adaptability and robustness of the positioning method.

[0022] Based on the above, this application further proposes that when the workload or temperature of the main processing chip reaches a preset threshold, priority computing resources are allocated to the processing tasks of the global navigation satellite system in signal tracking mode, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning scanning tasks, while reducing the operating frequency of the remaining tasks, in order to achieve high-precision positioning of the smartwatch. The steps include:

[0023] For the processing tasks of the Global Navigation Satellite System in signal tracking mode, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning and scanning tasks, continuously monitor the actual processing frequency and data output accuracy of various tasks.

[0024] Determine whether the actual processing frequency is lower than the preset minimum guaranteed frequency, or whether the data output accuracy is consistently higher than the preset threshold, to obtain the task judgment result;

[0025] When the task judgment result indicates yes, some non-critical calculations are reduced for the processing tasks that have been upgraded in the inertial measurement unit, and compensation data points are generated using the latest effective data and the motion trend of the inertial measurement unit to realize the data interpolation compensation mechanism.

[0026] The processing task of the Global Navigation Satellite System in signal tracking mode is to extend the retention time of historical effective pseudorange information, and to perform extrapolation compensation in combination with the short-term prediction of the inertial measurement unit to make up for the interruption of data stream.

[0027] For wireless assisted positioning scanning tasks, priority is given to scanning beacons with the highest signal strength, and historical scan data is used for padding compensation to maintain the continuity of wireless assisted positioning data.

[0028] This technical solution ensures the continuity and accuracy of data for critical positioning tasks by using refined task performance monitoring and targeted data compensation strategies when the main processing chip exceeds the threshold for load or temperature. This effectively mitigates the impact of resource constraints on positioning performance and maintains high-precision positioning even under extreme conditions.

[0029] More specifically, in some preferred embodiments, the steps of continuously monitoring the actual processing frequency and data output accuracy of various tasks, including those in signal tracking mode for Global Navigation Satellite System processing tasks, those with increased priority in the Inertial Measurement Unit, and those for wireless assisted positioning scanning tasks, include:

[0030] When the workload or temperature of the main processing chip reaches a preset threshold, the hardware performance counter is activated to obtain the number of CPU cycles and instructions executed for the processing tasks of the global navigation satellite system, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning scanning tasks in signal tracking mode.

[0031] Based on the temperature and workload of the main processing chip, the monitoring frequency of the preset non-core positioning tasks is adjusted to reduce the overall computing resources occupied by the monitoring process.

[0032] For global navigation satellite system processing tasks in signal tracking mode, processing tasks with increased priority in inertial measurement unit, and wireless assisted positioning scanning tasks, a timestamp calibration mechanism is adopted to compensate for the monitoring data reporting delay caused by the load exceeding the threshold, so as to accurately reflect the real-time performance of the task.

[0033] After monitoring frequency adjustment and reporting latency compensation, consistency checks are performed on the continuously monitored number of CPU cycles and instruction executions, and potentially unreliable data is marked.

[0034] Based on the number of CPU cycles and instructions executed after filtering out potentially unreliable data, the actual processing frequency and data output accuracy of various tasks are obtained.

[0035] This technical solution enables more accurate and real-time acquisition of the actual processing frequency and data output accuracy of critical positioning tasks by enabling hardware performance counters, adjusting the monitoring frequency of non-core tasks, and adopting refined monitoring methods such as timestamp calibration and consistency verification. This provides a reliable performance evaluation basis for subsequent resource scheduling and compensation mechanisms, further improving the response speed and accuracy of the positioning system.

[0036] Based on the above, this application further proposes that, when the task judgment result indicates yes, the non-critical computational processing of the processing tasks that have been upgraded in the inertial measurement unit is reduced, and compensation data points are generated using the latest valid data and the motion trend of the inertial measurement unit to realize the data interpolation compensation mechanism. The steps include:

[0037] Before generating compensation data points, noise assessment and outlier detection are performed on the latest valid data to obtain the assessment and detection results of the latest valid data.

[0038] If the evaluation results indicate that the latest valid data contains noise or outliers, then when generating compensation data points, the motion trend of the inertial measurement unit is used for prediction, and historical data is combined for correction.

[0039] If the evaluation results indicate that the latest valid data does not contain noise or outliers, then when generating compensation data points, the latest valid data is used for interpolation, and smoothing is performed in conjunction with the motion trend of the inertial measurement unit.

[0040] This technical solution enables the generation of compensation data points more intelligently and robustly by performing noise assessment and outlier detection before data interpolation compensation, and selecting different compensation strategies based on the detection results. This effectively improves the continuity and accuracy of inertial measurement unit data under resource-constrained conditions, thereby enhancing the overall positioning stability.

[0041] Preferably, the steps of extending the retention time of historical effective pseudorange information for the processing task of the Global Navigation Satellite System in signal tracking mode, and combining short-term predictions from the inertial measurement unit for extrapolation compensation to compensate for data stream interruptions include:

[0042] The signal-to-noise ratio was evaluated and satellite visibility was analyzed based on historical effective pseudorange information to obtain the evaluation and analysis results.

[0043] Based on the evaluation and analysis results, the historical effective pseudorange information is initially screened to obtain the screened historical effective pseudorange information.

[0044] Based on the timestamp and geographic location information of the filtered historical valid pseudorange information, the filtered historical valid pseudorange information is stored in the pseudorange information cache area;

[0045] The historical valid pseudorange information in the pseudorange information buffer is weighted to obtain the weighted historical valid pseudorange information.

[0046] By using the velocity and direction information of the inertial measurement unit, the wearer's position change within a preset time period is determined, and position change information is obtained;

[0047] Based on the position change information, the weighted historical effective pseudorange information is corrected to obtain the corrected historical effective pseudorange information.

[0048] The corrected historical effective pseudorange information is processed by median filtering.

[0049] This technical solution, through multi-stage refined processing of historical effective pseudorange information, including evaluation, filtering, caching, weighting, position correction, and filtering, combined with extrapolation compensation based on short-term predictions from the inertial measurement unit, can significantly extend the effective utilization time of pseudorange information, effectively compensate for the positioning accuracy degradation caused by the interruption of data streams from the Global Navigation Satellite System, and ensure the continuity and accuracy of positioning in unstable signal environments.

[0050] In some preferred embodiments, the steps of prioritizing the scanning of beacons with the highest signal strength for the task of wireless assisted positioning (WAPM) scanning, and using historical scan data for padding compensation to maintain the continuity of WAPM data, include:

[0051] A preliminary scan of the surrounding environment is performed to obtain the signal strength of each beacon;

[0052] Based on the signal strength of the beacons, identify a set of beacons whose signal strength falls within a preset similarity range;

[0053] The signal stability of the beacons in the beacon set is analyzed, and the stability analysis results are obtained.

[0054] Based on the stability analysis results and the geographical location information of each beacon in the beacon set, the spatial distribution density is assessed.

[0055] Based on the combined signal strength, stability analysis results, and spatial distribution density, the scanning priority of each beacon in the beacon set is adjusted, prioritizing the scanning of beacons with higher scanning priority;

[0056] An interpolation method based on the spatial distribution characteristics of beacons is used to fill and compensate the scanning data corresponding to the priority beacons in order to maintain the continuity of wireless assisted positioning data.

[0057] This technical solution dynamically adjusts the priority of wireless assisted positioning scanning by comprehensively considering factors such as beacon signal strength, stability, and spatial distribution density, and uses an interpolation method based on spatial distribution characteristics for data filling compensation. This enables more efficient acquisition and utilization of wireless assisted positioning data, effectively maintaining data continuity, and thus providing more reliable assisted positioning information in signal-constrained environments.

[0058] Furthermore, the steps for continuously monitoring the signal quality of satellite positioning signals and the motion information of the inertial measurement unit in the smartwatch include:

[0059] The satellite positioning signal is received in real time, and the signal-to-noise ratio, pseudorange residual and multipath interference indicators are extracted to obtain the satellite positioning signal quality parameters.

[0060] The outputs of the accelerometer, gyroscope, and magnetometer of the inertial measurement unit are synchronously acquired to obtain raw motion data;

[0061] The original motion data is time-stamp aligned and coordinate system transformed to obtain motion information under a unified reference coordinate system.

[0062] By combining satellite positioning signal quality parameters with motion information, a continuous monitoring data stream is formed.

[0063] This technical solution enables the collection and processing of satellite positioning signal quality and inertial measurement unit motion information in a multi-dimensional, synchronous, and unified manner, thereby generating a high-quality continuous monitoring data stream. This provides comprehensive and accurate input for subsequent signal limitation judgment and positioning service adjustment, thus improving the overall performance and reliability of the positioning method.

[0064] Based on the above, this application further proposes that the steps for forming a continuous monitoring data stream by combining satellite positioning signal quality parameters and motion information include:

[0065] Time synchronization processing is performed on satellite positioning signal quality parameters and motion information to establish a unified time series;

[0066] Normalize the satellite positioning signal quality parameters and motion information;

[0067] Based on a pre-defined correlation model, the correlation coefficient between the normalized satellite positioning signal quality parameters and motion information is calculated, and data pairs that meet the correlation criteria are selected.

[0068] Data pairs that meet the correlation standards are fused to generate fused monitoring segments;

[0069] The continuous monitoring segments are spliced ​​together in chronological order to form a complete continuous monitoring data stream.

[0070] This technical solution generates high-quality, high-reliability continuous monitoring data streams by synchronizing, normalizing, filtering correlations, and fusing satellite positioning signal quality parameters and motion information over time. This effectively improves data utilization efficiency and the accuracy of information fusion, providing a more solid data foundation for high-precision positioning of smartwatches.

[0071] Secondly, this application also discloses a high-precision positioning system for smartwatches, used to perform high-precision positioning of smartwatches, including:

[0072] The positioning signal acquisition module is used to acquire satellite positioning signals from the Global Navigation Satellite System.

[0073] The continuous signal monitoring module is used to continuously monitor the signal quality of satellite positioning signals and the motion information of the inertial measurement unit in the smartwatch;

[0074] The signal limitation judgment module is used to determine whether the wearer is about to enter a signal-limited environment based on signal quality, motion information and preset geographical information, and obtain the signal limitation judgment result;

[0075] The location service adjustment module is used to adjust the operation mode of the location service if the signal limitation judgment indicates that the wearer is about to enter a signal-limited environment, including:

[0076] Switch the operating mode of the receiver module of the Global Navigation Satellite System to signal tracking mode;

[0077] Increase the data acquisition frequency and processing priority of the inertial measurement unit;

[0078] When the wearer is about to enter an environment with limited signal, the smartwatch will activate wireless assisted positioning scanning;

[0079] Real-time monitoring of the workload and temperature of the smartwatch's main processing chip;

[0080] When the workload or temperature of the main processing chip reaches a preset threshold, computing resources are prioritized for the processing tasks of the global navigation satellite system in signal tracking mode, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning scanning tasks, while reducing the operating frequency of the remaining tasks to achieve high-precision positioning of the smartwatch.

[0081] This application provides a high-precision positioning system for smartwatches through this technical solution. Through modular design, it can effectively realize the above-mentioned high-precision positioning method and support key functions such as signal acquisition, monitoring, judgment, adjustment and resource allocation at the hardware level, thereby providing users with stable and accurate positioning services in complex environments.

[0082] Beneficial Effects: The high-precision positioning method for smartwatches disclosed in this application acquires satellite positioning signals from the Global Navigation Satellite System (GNSS) and continuously monitors the signal quality of the satellite positioning signals and the motion information of the inertial measurement unit (IMU) in the smartwatch, enabling a comprehensive perception of the smartwatch's environment and the wearer's motion state. Based on this, and according to signal quality, motion information, and preset geographical information, it intelligently determines whether the wearer is about to enter a signal-limited environment, thus predicting potential positioning challenges in advance. When the determination indicates that the wearer is about to enter a signal-limited environment, this method can intelligently adjust the working mode of the positioning service, including switching the GNSS receiver module to signal tracking mode to enhance signal acquisition and tracking capabilities in weak signal environments; simultaneously, it increases the data acquisition frequency and processing task priority of the IMU to ensure that inertial navigation can provide more accurate short-term positioning support when satellite signals are limited; furthermore, it initiates wireless assisted positioning scanning of the smartwatch to utilize auxiliary positioning information such as Wi-Fi and Bluetooth to compensate for the deficiencies of the GNSS. More importantly, this method monitors the workload and temperature of the smartwatch's main processing chip in real time. When these reach a preset threshold, it prioritizes computing resources for tasks such as those using the Global Navigation Satellite System in signal tracking mode, tasks with increased priority in the Inertial Measurement Unit, and wireless assisted positioning scanning, while reducing the operating frequency of other tasks. This strategy effectively solves the problem of performance degradation of key positioning algorithms caused by system overheating protection mechanisms in existing technologies. It ensures that core positioning tasks still receive sufficient computing support even under extreme resource constraints, thereby avoiding positioning trajectory jumps and drifts. This enables smartwatches to achieve high-precision, continuous, and stable positioning in complex environments such as urban canyons and indoor spaces. Compared to existing technologies, the method in this application effectively overcomes technical challenges such as multipath effects, signal instability, frequent mode switching, increased power consumption, and system overheating protection, significantly improving the positioning accuracy, stability, and user experience of smartwatches, while also optimizing battery life. Attached Figure Description

[0083] Figure 1 This is a flowchart of a high-precision positioning method for a smartwatch according to one embodiment of the present invention;

[0084] Figure 2 This is a flowchart of a high-precision positioning method for a smartwatch according to another embodiment of the present invention;

[0085] Figure 3 This is a system block diagram of a high-precision positioning system for a smartwatch according to another embodiment of the present invention;

[0086] Explanation of reference numerals in the attached figures:

[0087] 1. High-precision positioning system for smartwatches; 11. Positioning signal acquisition module; 12. Continuous signal monitoring module; 13. Signal limitation judgment module; 14. Positioning service adjustment module. Detailed Implementation

[0088] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0089] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0090] Traditional high-precision positioning methods for smartwatches suffer significant accuracy degradation in complex environments such as urban canyons due to the susceptibility of Global Navigation Satellite System (GNSS) signals to multipath effects and signal blockage. Furthermore, wrist movements during daily activities exacerbate signal reception instability. To compensate for GNSS limitations, smartwatch systems frequently switch positioning modes and employ wireless-assisted positioning scans. This not only increases the computational load and temperature of the main processing chip but may also trigger overheat protection mechanisms, leading to a decline in positioning algorithm performance. Ultimately, this results in an inability to provide continuous and accurate positioning trajectories, severely impacting user experience and battery life.

[0091] To address this, this application proposes a high-precision positioning method for smartwatches, combining... Figure 1 As shown, it includes:

[0092] S1, acquire satellite positioning signals from the Global Navigation Satellite System;

[0093] S2 continuously monitors the signal quality of satellite positioning signals and the motion information of the inertial measurement unit in the smartwatch;

[0094] S3 determines whether the wearer is about to enter a signal-limited environment based on signal quality, motion information, and preset geographical information, and obtains the signal-limited judgment result;

[0095] S4. If the signal limitation assessment indicates that the wearer is about to enter a signal-limited environment, the location service will adjust its operating mode, including:

[0096] Switch the operating mode of the receiver module of the Global Navigation Satellite System to signal tracking mode;

[0097] Increase the data acquisition frequency and processing priority of the inertial measurement unit;

[0098] When the wearer is about to enter an environment with limited signal, the smartwatch will activate wireless assisted positioning scanning;

[0099] Real-time monitoring of the workload and temperature of the smartwatch's main processing chip;

[0100] When the workload or temperature of the main processing chip reaches a preset threshold, computing resources are prioritized for the processing tasks of the global navigation satellite system in signal tracking mode, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning scanning tasks, while reducing the operating frequency of the remaining tasks to achieve high-precision positioning of the smartwatch.

[0101] To better understand the high-precision positioning method for smartwatches proposed in this application, some key terms involved will be explained first.

[0102] A Global Navigation Satellite System (GNSS) is a satellite system that provides positioning, navigation, and timing services globally, such as the US GPS, Russia's GLONASS, Europe's Galileo, and China's BeiDou Navigation Satellite System. Smartwatches receive satellite signals through their built-in GNSS receiver modules to calculate their own location.

[0103] An Inertial Measurement Unit (IMU) is an integrated sensor that typically includes an accelerometer, gyroscope, and magnetometer to measure a device's acceleration, angular velocity, and attitude. IMU data can provide short-term position estimation capabilities when GNSS signals are limited.

[0104] "Signal-limited environment" refers to an environment where the quality of GNSS signal reception is poor, such as urban canyons, indoor spaces, and underground parking lots. These environments can cause satellite signals to be blocked, reflected, or attenuated, thereby affecting positioning accuracy.

[0105] "Signal tracking mode" is a working mode of the GNSS receiver module. In this mode, the receiver focuses on maintaining tracking of the acquired satellite signal. Even if the signal quality deteriorates, it can maintain signal lock as much as possible in order to obtain more pseudorange and carrier phase information.

[0106] "Wireless Assisted Positioning Scanning" refers to a smartwatch scanning surrounding Wi-Fi hotspots, Bluetooth beacons, or other wireless signal sources to obtain their location information, thereby assisting GNSS in positioning or providing positioning capabilities when GNSS signals are completely lost.

[0107] The core of the high-precision positioning method for smartwatches in this application lies in ensuring continuous and accurate positioning services even in complex environments through intelligent prediction and resource management.

[0108] Firstly, smartwatches can employ various methods to acquire satellite positioning signals from Global Navigation Satellite Systems (GNSS). For example, a standard GNSS receiver module can be configured to continuously receive signals from visible satellites in the L1, L2, or L5 frequency bands. These signals, after being received by the antenna, undergo down-conversion, digitization, and preliminary processing to extract raw observation data such as pseudorange, carrier phase, and Doppler shift. Alternatively, a smartwatch can integrate a multi-mode, multi-frequency GNSS receiver module, enabling it to simultaneously receive signals from multiple satellite systems such as GPS, GLONASS, BeiDou, and Galileo, and support L1 / L5 dual-frequency reception to improve signal availability and anti-interference capabilities.

[0109] Secondly, regarding the continuous monitoring of satellite positioning signal quality and motion information from the inertial measurement unit (IMU) in the smartwatch, the following methods can be employed. For example, the smartwatch can have a built-in GNSS signal quality monitor that analyzes the signal-to-noise ratio (SNR), pseudorange residuals, and multipath interference of the received satellite signals in real time. Simultaneously, the inertial measurement unit (IMU) in the smartwatch collects raw data from the accelerometer, gyroscope, and magnetometer at a preset frequency (e.g., 50Hz). After preliminary filtering and timestamp alignment, this data forms a continuous motion information stream.

[0110] Furthermore, in determining whether a wearer is about to enter a signal-limited environment based on signal quality, motion information, and preset geographical information, the following methods can be used to obtain a signal-limited judgment result. For example, a smartwatch can have a built-in environment judgment algorithm. This algorithm takes GNSS signal quality parameters (such as signal-to-noise ratio below a certain threshold), IMU motion information (such as drastic changes in vertical acceleration, which may indicate entering an elevator or stairs), and preset geographical information (such as the current location being near a known tall building area or underground parking lot entrance) as input. By analyzing this data, the algorithm can predict the likelihood of the wearer entering a signal-limited environment.

[0111] Furthermore, if the signal limitation assessment indicates that the wearer is about to enter a signal-limited environment, then the location service's operating mode needs to be adjusted.

[0112] Specifically, the following methods can be used to switch the operating mode of the GNSS receiver module to signal tracking mode. For example, when it is determined that the wearer is about to enter a signal-constrained environment, the smartwatch's main processing chip will send a command to the GNSS receiver module to switch its operating mode from the regular "positioning mode" to "signal tracking mode." In signal tracking mode, the GNSS receiver module will reduce the priority of acquiring new satellites and instead allocate more computing resources to maintaining the tracking of existing satellite signals, thus maintaining signal lock as much as possible even if the signal strength is weak or multipath interference exists.

[0113] To increase the data acquisition frequency and processing task priority of the inertial measurement unit (IMU), the following methods can be adopted. For example, the main processing chip of a smartwatch can send instructions to the IMU sensor to increase its data acquisition frequency from the default 50Hz to 100Hz or higher to obtain more intensive motion data. At the same time, the priority of IMU data processing tasks (such as attitude calculation and trajectory estimation) in the operating system will be increased to ensure that these tasks can obtain more CPU time slices and memory resources, thereby reducing data processing latency.

[0114] When the wearer is about to enter an environment with limited signal, the smartwatch can activate its wireless-assisted positioning scan in the following ways. For example, the smartwatch will immediately activate its Wi-Fi and Bluetooth modules and begin scanning for high-frequency wireless signals. The Wi-Fi module will scan for nearby Wi-Fi hotspots, recording their MAC addresses, signal strength (RSSI), and other information; the Bluetooth module will scan for nearby Bluetooth beacons (such as iBeacon), obtaining their IDs and signal strength. This scanning data will be used for assisted positioning.

[0115] To monitor the workload and temperature of a smartwatch's main processing chip in real time, the following methods can be used. For example, the smartwatch operating system may have a built-in performance monitoring module that continuously collects workload metrics such as CPU utilization, memory usage, and I / O throughput of the main processing chip. Simultaneously, the smartwatch's internal temperature sensor will monitor the surface and core temperatures of the main processing chip in real time. This monitoring data will be periodically reported to the system resource manager.

[0116] Finally, when the workload or temperature of the main processing chip reaches a preset threshold, computing resources are prioritized for GNSS signal tracking tasks, priority-adjusted tasks in the inertial measurement unit, and wireless assisted positioning scanning tasks. The operating frequency of other tasks is reduced to achieve high-precision positioning for the smartwatch. For example, when CPU utilization exceeds 80% or the core temperature exceeds 60°C, the system resource manager initiates a resource scheduling strategy. This strategy prioritizes allocating CPU cores, memory bandwidth, and cache resources to GNSS signal tracking tasks, IMU data processing tasks, and wireless assisted positioning scanning tasks. Simultaneously, the operating frequency of other non-critical tasks, such as background application updates, notification pushes, and screen refreshes, is temporarily reduced or even suspended to free up computing resources and ensure the stable operation of the core positioning tasks.

[0117] Optional, combined Figure 2 As shown, S3 determines whether the wearer is about to enter a signal-limited environment based on signal quality, motion information, and preset geographical information. The steps to obtain the signal-limited judgment result include:

[0118] S31, monitoring the wireless signal characteristics of the wireless signal in the wearer's surrounding environment;

[0119] S32 determines whether the wearer is about to enter a signal-limited environment based on signal quality, motion information, preset geographical information, and wireless signal characteristics, and obtains the signal-limited judgment result.

[0120] Specifically, monitoring the wireless signal characteristics of the wearer's surrounding environment can be understood as the smartwatch actively or passively scanning and collecting information about various wireless signals present in the surrounding environment through its built-in wireless communication modules (such as Wi-Fi, Bluetooth, and cellular communication modules). These wireless signal characteristics may include, but are not limited to, the Service Set Identifier (SSID) of Wi-Fi hotspots, Media Access Control (MAC) addresses, Signal Strength Indicator (RSSI), the name, address, and signal strength of Bluetooth devices, and the Cell ID and signal strength (RSRP / RSRQ) of cellular base stations. The purpose is to obtain local radio environment information about the wearer's current location, serving as an important auxiliary basis for judging signal-constrained environments.

[0121] The determination of whether a wearer is about to enter a signal-limited environment, based on signal quality, motion information, preset geographical information, and wireless signal characteristics, involves comprehensively analyzing and fusing the monitored wireless signal characteristics with the signal quality of the Global Navigation Satellite System (GNSS), the motion information of the Inertial Measurement Unit (INS), and the preset geographical information. For example, if the GNSS signal quality begins to decline, the INS indicates that the wearer is continuously moving, and the preset geographical information indicates that the wearer is approaching a known signal-blocked area (such as an entrance to a building), further monitoring of a significant increase in the number of Wi-Fi hotspots or a sharp decrease in cellular signal strength in the surrounding environment can more accurately determine that the wearer is about to enter a signal-limited environment.

[0122] As a specific implementation method, a concrete example is given below. Suppose a wearer is walking from an outdoor street into a large shopping mall. As the wearer approaches the mall entrance, the quality of the GPS satellite positioning signal may begin to fluctuate slightly or decrease. Simultaneously, the inertial measurement unit (IMU) in the smartwatch continuously records the wearer's walking motion information, and preset geographic information may indicate that the area is a large building complex. Based on this, the solution of this application further monitors the wireless signal characteristics of the wearer's surrounding environment. Specifically, the smartwatch will scan for a large number of new Wi-Fi hotspots (such as shop Wi-Fi inside the mall, public Wi-Fi, etc.), and these Wi-Fi signals may have high strength. At the same time, the strength of cellular signals may decrease to some extent. When the system comprehensively analyzes the declining trend of GPS signal quality, the continuous movement of the IMU, the building areas indicated by the preset geographic information, and the wireless signal characteristics such as the large number of newly added Wi-Fi signals and the attenuation of cellular signals, it can more accurately and timely determine that the wearer is about to enter a signal-limited indoor environment. Therefore, smartwatches can initiate corresponding location service adjustment measures in advance, such as switching the working mode of the global navigation satellite system receiver module to signal tracking mode and increasing the data acquisition frequency and processing priority of the inertial measurement unit. This allows them to maintain high-precision positioning before the wearer is fully inside the shopping mall and the global navigation satellite system signal is severely blocked.

[0123] Optionally, when the workload or temperature of the main processing chip reaches a preset threshold, priority is given to allocating computing resources to the processing tasks of the Global Navigation Satellite System in signal tracking mode, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning scanning tasks, while reducing the operating frequency of the remaining tasks. The steps to achieve high-precision positioning of the smartwatch include:

[0124] For the processing tasks of the Global Navigation Satellite System in signal tracking mode, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning and scanning tasks, continuously monitor the actual processing frequency and data output accuracy of various tasks.

[0125] Determine whether the actual processing frequency is lower than the preset minimum guaranteed frequency, or whether the data output accuracy is consistently higher than the preset threshold, to obtain the task judgment result;

[0126] When the task judgment result indicates yes, some non-critical calculations are reduced for the processing tasks that have been upgraded in the inertial measurement unit, and compensation data points are generated using the latest effective data and the motion trend of the inertial measurement unit to realize the data interpolation compensation mechanism.

[0127] The processing task of the Global Navigation Satellite System in signal tracking mode is to extend the retention time of historical effective pseudorange information, and to perform extrapolation compensation in combination with the short-term prediction of the inertial measurement unit to make up for the interruption of data stream.

[0128] For wireless assisted positioning scanning tasks, priority is given to scanning beacons with the highest signal strength, and historical scan data is used for padding compensation to maintain the continuity of wireless assisted positioning data.

[0129] Specifically, continuous monitoring of the actual processing frequency and data output accuracy of various tasks refers to the system tracking the operational status of the Global Navigation Satellite System (GNSS) processing tasks, Inertial Measurement Unit (INS) processing tasks, and Wireless Assisted Positioning (WAAP) scanning tasks in real time when the main processing chip's workload or temperature reaches a preset threshold. The actual processing frequency can be understood as the number of computation cycles or data update rate completed by the task per unit time, while data output accuracy reflects the accuracy or error level of the task results. For example, for GNSS tasks, pseudorange measurement errors can be monitored; for INS tasks, the drift in attitude or position estimation can be monitored; and for WAP tasks, the error range of positioning results can be monitored. The purpose is to promptly detect performance degradation of critical positioning tasks under extreme conditions.

[0130] The process of determining whether the actual processing frequency is lower than the preset minimum guaranteed frequency, or whether the data output accuracy consistently exceeds the preset threshold, to obtain the task judgment result, refers to the system comparing the monitored actual performance indicators with the preset performance baseline. The preset minimum guaranteed frequency is the minimum processing rate required to ensure the normal operation of the positioning system's basic functions, while the preset threshold is the maximum acceptable data output error. When the actual processing frequency of any critical task falls below its minimum guaranteed frequency, or its data output accuracy continuously deteriorates and exceeds the preset threshold, the performance of that task is considered to have been severely affected, requiring the activation of further compensation mechanisms.

[0131] In practical applications, when the task judgment result indicates yes, some non-critical computational processing is reduced for tasks that have been prioritized in the inertial measurement unit (IMU). Compensation data points are generated using the latest valid data and the IMU's motion trend to implement a data interpolation compensation mechanism. This means that when IMU data processing faces resource constraints, non-core but computationally intensive processing steps such as high-order filtering and redundancy checks can be temporarily reduced to ensure the timely output of core motion data. Simultaneously, to compensate for potential data loss or accuracy degradation, the system generates virtual compensation data points based on the latest valid readings from IMU sensors (such as accelerometers and gyroscopes), combined with the wearer's historical motion patterns and current motion trends (e.g., uniform linear motion, turning), using prediction algorithms (such as Kalman filtering and spline interpolation). These virtual compensation data points fill gaps in the data stream, maintaining the continuity and smoothness of motion information.

[0132] Furthermore, in signal tracking mode, the processing tasks of the Global Navigation Satellite System (GNSS) extend the retention time of historical valid pseudorange information and combine it with short-term predictions from the inertial measurement unit (IMU) for extrapolation compensation to mitigate data stream interruptions. When GNSS signal limitations or processing resource constraints lead to pseudorange data interruptions, the system no longer immediately discards old valid pseudorange information but retains it in the cache for a longer period. When new pseudorange data cannot be acquired in a timely manner, the system utilizes short-term high-frequency motion prediction information provided by the IMU (e.g., changes in position, velocity, and attitude within the next few hundred milliseconds), combined with this extended historical pseudorange information, to estimate the current pseudorange value using extrapolation algorithms (such as linear extrapolation, polynomial extrapolation, etc.). This, to some extent, compensates for GNSS data stream interruptions and maintains the continuity of positioning calculations.

[0133] Furthermore, for wireless assisted positioning (WAPM) scanning tasks, priority is given to scanning beacons with the highest signal strength, and historical scan data is used for padding and compensation to maintain the continuity of WAPM data. During WAPM scanning, when system resources are limited, comprehensive beacon scanning is no longer performed; instead, intelligent selective scanning is used. Specifically, the system prioritizes scanning beacons with the highest signal strength, which usually also means the closest or best signal quality, to ensure that the most reliable WAPM data can be obtained quickly. Simultaneously, to address potential interruptions in scan data, the system utilizes previously successfully scanned historical beacon data (e.g., beacon ID, location, signal strength), combined with the wearer's movement trends, to pad and compensate for currently missing scan data using interpolation or prediction algorithms. This ensures the continuity of WAPM data and provides stable auxiliary information for overall high-precision positioning.

[0134] In some preferred embodiments, a specific example is given below. Suppose the wearer is engaged in strenuous exercise, causing the main processing chip of the smartwatch to experience a continuous increase in workload and temperature, reaching a preset threshold. At this point, the system immediately activates the optimization mechanism of this embodiment. First, the system continuously monitors the actual processing frequency and data output accuracy of three core tasks: the Global Navigation Satellite System (GNSS), the Inertial Measurement Unit (INS), and wireless assisted positioning. For example, if the data output frequency of the INS is detected to be lower than the preset minimum guaranteed frequency of 100Hz per second, or if the pseudorange measurement error of the GNSS is continuously higher than the preset threshold of 5 meters, the system will determine that compensation needs to be initiated. Specifically, for the INS task, the system will temporarily suspend some non-critical sensor calibration or background filtering calculations to free up computing resources. Simultaneously, using the latest accelerometer and gyroscope data, combined with the wearer's current movement trend (e.g., judging from historical data that the wearer is running), a Kalman filter-based prediction model is used to generate compensation data points for the missing time period, ensuring the continuity of the movement trajectory. For Global Navigation Satellite System (GNSS) missions, the system extends the retention of all valid pseudorange information from the past 30 seconds in the cache, instead of the usual 10 seconds. When new GNSS data is delayed due to processing latency, the system uses short-term motion predictions from the inertial measurement unit (IMU) for the next second (e.g., predicting the wearer will continue moving at the current speed and direction) and combines this extended historical pseudorange information with a linear extrapolation algorithm to estimate the pseudorange value at the current moment, thus providing a continuous positioning reference during data gaps. For wireless assisted positioning (WAMS) scanning missions, the system prioritizes scanning Wi-Fi hotspots or Bluetooth beacons with the strongest signal strength around the wearer; for example, it prioritizes beacons with signal strength exceeding -60 dBm. If scanning data for a high-priority beacon is not acquired in time, the system uses the beacon's historical location and signal strength data, combined with the wearer's current motion state, to fill in the missing scan data using a distance-weighted inverse range-weighted interpolation method to maintain the continuity of WAMS data. Through the above series of refined monitoring and compensation measures, even under extreme conditions such as high load or high temperature of the main processing chip, the smartwatch can still continuously output high-precision and high-continuity positioning information, ensuring that the wearer's positioning experience is not affected in complex environments.

[0135] Optionally, for processing tasks of the Global Navigation Satellite System in signal tracking mode, processing tasks with increased priority in the inertial measurement unit, and wireless assisted positioning scanning tasks, the steps to continuously monitor the actual processing frequency and data output accuracy of various tasks include:

[0136] When the workload or temperature of the main processing chip reaches a preset threshold, the hardware performance counter is activated to obtain the number of CPU cycles and instructions executed for the processing tasks of the global navigation satellite system in signal tracking mode, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning scanning tasks.

[0137] Among them, the hardware performance counter is a dedicated hardware module integrated inside the processor. It can record the number of times the processor executes specific events, such as CPU cycles and instruction executions, in real time with very low overhead. By enabling this counter, the actual running status of critical location tasks on the main processing chip can be obtained, providing raw and accurate data for subsequent performance evaluation.

[0138] Based on the temperature and workload of the main processing chip, the monitoring frequency of preset non-core positioning tasks is adjusted to reduce the overall computing resource consumption of the monitoring process.

[0139] Specifically, when the main processing chip's temperature or workload is high, the system dynamically reduces the monitoring frequency of non-core tasks that have little impact on real-time positioning accuracy, such as user interface updates and background data synchronization. This aims to optimize system resource allocation and ensure that, when resources are scarce, the monitoring of core positioning tasks does not further increase the system burden due to its own overhead.

[0140] For tasks of the Global Navigation Satellite System in signal tracking mode, tasks with increased priority in the inertial measurement unit, and tasks of wireless assisted positioning and scanning, a timestamp calibration mechanism is adopted to compensate for the delay in monitoring data reporting caused by the load exceeding the threshold, so as to accurately reflect the real-time performance of the task.

[0141] Under high load or high temperature conditions on the main processing chip, task execution and data reporting may be delayed. The timestamp calibration mechanism records the actual start and end times of task execution and compares them with the reporting time to accurately calculate and compensate for these delays, ensuring that the acquired performance data accurately reflects the task's running status at a specific point in time.

[0142] After monitoring frequency adjustments and reporting latency compensation, consistency checks are performed on the continuously monitored CPU cycle count and instruction execution count, and potentially unreliable data is marked.

[0143] Consistency checks aim to identify and eliminate anomalies in monitoring data caused by transient interference, data transmission errors, or system malfunctions. For example, it can check whether the rate of change between consecutive data points is within a reasonable range, or whether there are significant deviations from historical data patterns. Data marked as unreliable will not be used in subsequent performance evaluations, thus ensuring the accuracy of the evaluation results.

[0144] Based on the number of CPU cycles and instructions executed after filtering out potentially unreliable data, the actual processing frequency and data output accuracy of various tasks are obtained.

[0145] By calculating and analyzing verified CPU cycle counts and instruction execution counts, the actual processing frequency of each critical positioning task can be precisely quantified, such as the number of data packets processed per second or the number of calculation iterations. Simultaneously, by combining the task's output results, the accuracy of its data output can be evaluated, such as the root mean square value of the positioning error or the noise level of the inertial data. These quantitative indicators are crucial for subsequently determining whether the task performance meets the standards and whether a compensation mechanism needs to be activated.

[0146] Optionally, when the task judgment result indicates yes, the steps of reducing some non-critical computational processing for the processing tasks that have been upgraded in the inertial measurement unit, and generating compensation data points using the latest valid data and the motion trend of the inertial measurement unit to realize the data interpolation compensation mechanism include:

[0147] Before generating compensation data points, noise assessment and outlier detection are performed on the latest valid data to obtain the assessment and detection results of the latest valid data.

[0148] If the evaluation results indicate that the latest valid data contains noise or outliers, then when generating compensation data points, the motion trend of the inertial measurement unit is used for prediction, and historical data is combined for correction.

[0149] If the evaluation results indicate that the latest valid data does not contain noise or outliers, then when generating compensation data points, the latest valid data is used for interpolation, and smoothing is performed in conjunction with the motion trend of the inertial measurement unit.

[0150] Specifically, before generating compensation data points, the "latest valid data" output by the inertial measurement unit (IMU) is first subjected to "noise assessment and outlier detection." "Latest valid data" refers to the most recent, considered valid data point that the system can acquire when the main processing chip's workload or temperature exceeds a threshold, and the IMU's processing performance is substandard. "Noise assessment" aims to identify random, irregular fluctuations in the data; for example, this can be quantified by calculating the standard deviation, root mean square error, or using wavelet analysis. "Outlier detection" aims to identify data points that significantly deviate from other data points in the dataset; this can be achieved using statistical methods (such as Z-score, IQR), machine learning methods (such as Isolation Forest, One-Class SVM), or distance-based methods (such as LOF). Through these assessments and detections, the "assessment and detection results of the latest valid data" are obtained, indicating the current data quality status.

[0151] Furthermore, based on the evaluation and testing results, two different compensation strategies are adopted. On the one hand, if the evaluation and testing results indicate that the latest valid data contains noise or outliers, this means that the current data quality is poor, and direct interpolation may introduce errors. In this case, when generating compensation data points, the motion trend of the inertial measurement unit (IMU) will be used for prediction, and historical data will be combined for correction. The "motion trend of the IMU" can be obtained by analyzing long-term data from the accelerometer and gyroscope to establish a motion model (e.g., Kalman filtering, extended Kalman filtering, etc.), which can predict the wearer's motion trajectory over a short period. "Combining historical data for correction" means using high-quality IMU data from a previous period to calibrate and optimize the prediction results to reduce prediction errors and ensure the rationality of the compensation data. On the other hand, if the evaluation and testing results indicate that the latest valid data does not contain noise or outliers, this means that the current data quality is good and can be directly used for interpolation. In this case, when generating compensation data points, the latest valid data will be used for interpolation, and the motion trend of the IMU will be combined for smoothing. The process of "interpolating using the latest valid data" can employ methods such as linear interpolation, spline interpolation, or polynomial interpolation to estimate missing data based on the latest valid data points and their preceding and following data points. Combining this with "smoothing the motion trend of the inertial measurement unit" refers to smoothing the interpolation results using the motion model of the inertial measurement unit after interpolation. This eliminates minor fluctuations that may occur during the interpolation process, making the compensated data more consistent with the actual motion trajectory and improving the continuity and accuracy of the data.

[0152] Optionally, the steps for extending the retention time of historical effective pseudorange information in the processing tasks of the Global Navigation Satellite System in signal tracking mode, and combining it with short-term predictions from the inertial measurement unit for extrapolation compensation to compensate for data stream interruptions, include:

[0153] The signal-to-noise ratio was evaluated and satellite visibility was analyzed based on historical effective pseudorange information to obtain the evaluation and analysis results.

[0154] Based on the evaluation and analysis results, the historical effective pseudorange information is initially screened to obtain the screened historical effective pseudorange information.

[0155] Based on the timestamp and geographic location information of the filtered historical valid pseudorange information, the filtered historical valid pseudorange information is stored in the pseudorange information cache area;

[0156] The historical valid pseudorange information in the pseudorange information buffer is weighted to obtain the weighted historical valid pseudorange information.

[0157] By using the velocity and direction information of the inertial measurement unit, the wearer's position change within a preset time period is determined, and position change information is obtained;

[0158] Based on the position change information, the weighted historical effective pseudorange information is corrected to obtain the corrected historical effective pseudorange information.

[0159] The corrected historical effective pseudorange information is processed by median filtering.

[0160] Specifically, historical valid pseudorange information refers to pseudorange measurement data that was normally received and preliminarily processed by the Global Navigation Satellite System (GNSS) receiver module before the onset of signal-limited environments or before the main processing chip's workload became high. This data typically includes the satellite ID, reception time, pseudorange value, and preliminary signal quality indicators.

[0161] The signal-to-noise ratio (SNR) assessment aims to quantify the reliability of pseudorange information. By analyzing the ratio of received satellite signal strength to noise level, it identifies pseudorange data with less interference and higher quality. Satellite visibility analysis determines which satellites are visible at a specific time and geographical location, and their geometric distribution. This helps to eliminate unreliable pseudorange data caused by satellite obstruction or low elevation angles. These two assessments yield a comprehensive evaluation result to guide subsequent data processing.

[0162] In practical applications, historically valid pseudorange information is initially screened based on the evaluation and analysis results. The purpose is to eliminate low-quality, unreliable, or inconsistent pseudorange data to ensure the high usability of data in subsequent processing. For example, a signal-to-noise ratio threshold can be set, and pseudorange data below this threshold is discarded. At the same time, pseudorange data provided by theoretically invisible satellites is excluded by combining satellite ephemeris data with a rough estimate of the smartwatch's current location.

[0163] Furthermore, the timestamps and geographic locations of the filtered historical pseudorange information are key attributes used for ordered storage and rapid retrieval within the pseudorange information cache. The pseudorange information cache can be a circular buffer or a time-indexed database for efficient management and access to historical pseudorange data.

[0164] The process involves weighting historical valid pseudorange information within the pseudorange information buffer. This weighting is based on factors such as the timeliness, quality (e.g., signal-to-noise ratio), and relevance to the current positioning scenario. For example, pseudorange data that is more recent and has a higher signal-to-noise ratio receives a greater weight, allowing it to play a more significant role in subsequent compensation and correction.

[0165] Furthermore, the velocity and orientation information from the inertial measurement unit, such as the velocity vector and attitude angle calculated by the fusion of accelerometers and gyroscopes, can accurately reflect the wearer's motion state over a short period of time. This information can be used to predict the wearer's position changes within a preset timeframe (e.g., a few seconds to tens of seconds), providing crucial position change information for pseudorange extrapolation compensation in dynamic scenarios.

[0166] Therefore, position correction is performed on the weighted historical effective pseudorange information based on position change information. The purpose is to compensate for the wearer's positional movement between the pseudorange data acquisition time and the current time, so that the historical pseudorange data can more accurately reflect the satellite-receiver distance relationship at the current time. For example, the historical pseudorange values ​​can be adjusted incrementally in terms of geometric distance based on predicted position changes.

[0167] Finally, median filtering is applied to the corrected historical pseudorange information to further eliminate random noise and anomalous jumps in the data, smooth the data sequence, improve the continuity and stability of the pseudorange data, and provide more reliable input for subsequent positioning calculations. Median filtering has a good suppression effect on impulse noise and can effectively preserve the edge features of the data.

[0168] Optionally, for wireless assisted positioning scanning tasks, the steps of prioritizing scanning beacons with the highest signal strength and using historical scan data for padding compensation to maintain the continuity of wireless assisted positioning data include:

[0169] A preliminary scan of the surrounding environment is performed to obtain the signal strength of each beacon;

[0170] Based on the signal strength of the beacons, identify a set of beacons whose signal strength falls within a preset similarity range;

[0171] The signal stability of the beacons in the beacon set is analyzed, and the stability analysis results are obtained.

[0172] Based on the stability analysis results and the geographical location information of each beacon in the beacon set, the spatial distribution density is assessed.

[0173] Based on the combined signal strength, stability analysis results, and spatial distribution density, the scanning priority of each beacon in the beacon set is adjusted, prioritizing the scanning of beacons with higher scanning priority;

[0174] An interpolation method based on the spatial distribution characteristics of beacons is used to fill and compensate the scanning data corresponding to the priority beacons in order to maintain the continuity of wireless assisted positioning data.

[0175] Specifically, during the initial scan of beacons in the surrounding environment, the smartwatch can actively send probe signals and receive responses from Wi-Fi hotspots, Bluetooth beacons, or other wireless access points to obtain the signal strength of each beacon. Signal strength is typically expressed as a Received Signal Strength Indicator (RSSI) value.

[0176] Furthermore, based on the signal strength of the beacons, a set of beacons with signal strength within a preset similar range is identified. The purpose is to filter out beacons with potential positioning value and avoid unnecessary processing of isolated beacons with excessively weak or strong signals. For example, a signal strength threshold range can be set, and all beacons with RSSI values ​​within this range can be grouped into one category.

[0177] Analyzing the signal stability of beacons within a beacon set to obtain stability analysis results involves statistically analyzing the changes in beacon signal strength over a period of time, such as calculating the variance, standard deviation, or fluctuation frequency of the signal strength. Beacons with high signal stability typically provide more reliable positioning data.

[0178] In practical applications, based on the stability analysis results and the geographical location information of each beacon in the beacon set, the spatial distribution density is evaluated. The purpose is to ensure that the selected beacons not only have stable signals but also good spatial coverage and distinguishability. For example, the distance between each beacon in the beacon set can be calculated, or its distribution uniformity within a specific area can be assessed. Geographical location information can be obtained through preset map data, historical positioning data, or other auxiliary positioning methods.

[0179] Therefore, by combining signal strength, stability analysis results, and spatial distribution density, the scanning priority of each beacon in the beacon set is adjusted, prioritizing the scanning of beacons with higher scanning priority. This means that the scanning priority no longer depends solely on signal strength, but comprehensively considers the reliability (stability) of the signal and the geometric distribution (spatial distribution density) of the beacons, thereby selecting the beacons most conducive to high-precision positioning for scanning.

[0180] Specifically, an interpolation method based on the spatial distribution characteristics of beacons is used to fill in and compensate the scan data corresponding to the priority beacons, thereby maintaining the continuity of wireless assisted positioning data. When some beacon data is missing or of poor quality, missing data points can be generated using methods such as Kriging interpolation, inverse distance-weighted interpolation, or spline interpolation, based on the geographical location of surrounding beacons and existing scan data, thus ensuring the continuity of the positioning data stream.

[0181] In some preferred embodiments, a specific example is given below. Suppose the wearer is moving within a large shopping mall, where Wi-Fi hotspots and Bluetooth beacons are densely and complexly distributed. When the smartwatch's main processing chip reaches a preset threshold in workload or temperature, and the system enters resource optimization mode, the location service adjustment module initiates wireless assisted positioning scanning.

[0182] First, the smartwatch performs a preliminary scan of the surrounding Wi-Fi hotspots and Bluetooth beacons to obtain their RSSI values. For example, beacons A, B, C, and D are detected, with RSSI values ​​of -50dBm, -52dBm, -60dBm, and -45dBm, respectively.

[0183] Next, the system identifies a set of beacons with signal strengths within a preset similar range. For example, beacons A, B, C, and D with RSSI values ​​between -40dBm and -65dBm are included in the set.

[0184] The system then began analyzing the signal stability of these beacons. For example, continuous monitoring revealed that the RSSI value of beacon A fluctuated by only ±2 dBm over the past 30 seconds, while that of beacon D fluctuated by ±8 dBm, indicating that beacon A was more stable than beacon D.

[0185] Simultaneously, by combining pre-set map information of the shopping mall's interior, the system assesses the spatial distribution density of these beacons. For example, it was found that beacons A, B, and C form a relatively uniform triangular coverage area near the wearer's current location, while beacon D, although having a high signal strength, is relatively isolated and far from other beacons.

[0186] After comprehensively considering signal strength, stability analysis results, and spatial distribution density, the system adjusts the scanning priority. For example, although beacon D has the highest signal strength, its priority may be reduced due to its poor stability and isolated spatial distribution. Beacons A and B, on the other hand, have higher scanning priorities because of their high signal strength, good stability, and reasonable spatial distribution.

[0187] Ultimately, the system prioritizes scanning beacons A and B to obtain their latest positioning data. If, at some point, data from beacon A is temporarily missing, the system uses an interpolation method based on the spatial distribution characteristics of the beacons, combined with real-time data from beacons B and C and their relative positions to beacon A, to generate a compensation data point to fill the gap in beacon A's data. This ensures the continuity of wireless assisted positioning data, maintaining high-precision positioning services even when main processing chip resources are limited.

[0188] Optionally, the steps of continuously monitoring the signal quality of satellite positioning signals and the motion information of the inertial measurement unit in the smartwatch include:

[0189] The satellite positioning signal is received in real time, and the signal-to-noise ratio, pseudorange residual and multipath interference indicators are extracted to obtain the satellite positioning signal quality parameters.

[0190] The outputs of the accelerometer, gyroscope, and magnetometer of the inertial measurement unit are synchronously acquired to obtain raw motion data;

[0191] The original motion data is time-stamp aligned and coordinate system transformed to obtain motion information under a unified reference coordinate system.

[0192] By combining satellite positioning signal quality parameters with motion information, a continuous monitoring data stream is formed.

[0193] Specifically, real-time reception of satellite positioning signals aims to ensure the acquired positioning signal data is up-to-date. Based on this, the quality of satellite positioning signals can be comprehensively evaluated by extracting signal-to-noise ratio (SNR), pseudorange residual, and multipath interference (MDI) metrics. SNR reflects the relative relationship between signal strength and background noise; pseudorange residual indicates the deviation between the measured pseudorange and the true pseudorange; and MDI quantifies the error caused by reflection and other effects during signal propagation. These metrics, taken together, form the satellite positioning signal quality parameters, used to characterize the current availability and reliability of GNSS signals.

[0194] Simultaneously, the outputs of the accelerometer, gyroscope, and magnetometer of the inertial measurement unit are acquired to obtain the wearer's raw motion data. The accelerometer measures the smartwatch's linear acceleration in three-dimensional space, the gyroscope measures angular velocity, and the magnetometer senses the direction of the Earth's magnetic field. This simultaneous acquisition ensures the consistency of data from these different sensors over time, providing a reliable foundation for subsequent motion analysis.

[0195] Furthermore, the raw motion data undergoes timestamp alignment and coordinate system transformation to precisely match data collected by different sensors at different times, and to transform it from its respective sensor coordinate system to a unified reference coordinate system, such as the smartwatch's own coordinate system or the Earth's coordinate system. This provides motion information under a unified reference coordinate system, ensuring the spatial consistency and comparability of the motion data.

[0196] Finally, by combining satellite positioning signal quality parameters with motion information, a continuous monitoring data stream is formed. This means that the pre-processed and standardized GNSS signal quality parameters are effectively correlated and integrated with IMU motion information to construct a data sequence that is continuous in time and complementary in content.

[0197] Optionally, the steps of combining satellite positioning signal quality parameters and motion information to form a continuous monitoring data stream include:

[0198] Time synchronization processing is performed on satellite positioning signal quality parameters and motion information to establish a unified time series;

[0199] Normalize the satellite positioning signal quality parameters and motion information;

[0200] Based on a pre-defined correlation model, the correlation coefficient between the normalized satellite positioning signal quality parameters and motion information is calculated, and data pairs that meet the correlation criteria are selected.

[0201] Data pairs that meet the correlation standards are fused to generate fused monitoring segments;

[0202] The continuous monitoring segments are spliced ​​together in chronological order to form a complete continuous monitoring data stream.

[0203] Specifically, time synchronization processing of satellite positioning signal quality parameters and motion information refers to ensuring that signal quality parameters (such as signal-to-noise ratio, pseudorange residuals, and multipath interference indicators) from the Global Navigation Satellite System (GNSS) and motion information (such as acceleration, angular velocity, and magnetic field strength) from the Inertial Measurement Unit (IMU) are consistent on the time axis through a precise timestamp alignment mechanism. The purpose is to eliminate time deviations caused by different sensor sampling frequencies or data transmission delays, laying the foundation for subsequent data fusion.

[0204] Normalizing satellite positioning signal quality parameters and motion information can be understood as standardizing data with different dimensions or numerical ranges to bring them to a uniform scale. For example, signal-to-noise ratio, pseudorange residuals, and acceleration data can be mapped to a range of 0 to 1 or -1 to 1 using linear transformations or Z-score standardization. The aim is to eliminate dimensional differences between different data types, prevent certain features with larger values ​​from dominating subsequent processing, and ensure that all features have equal importance during data fusion.

[0205] In practical applications, based on a pre-defined correlation model, the correlation coefficient between the normalized satellite positioning signal quality parameters and motion information is calculated. Data pairs meeting the correlation criteria are then selected. Specifically, statistical methods (such as Pearson correlation coefficient and mutual information) are used to assess the degree of correlation between changes in the global navigation satellite system signal quality and changes in the motion state of the inertial measurement unit. The pre-defined correlation model can be trained and optimized based on actual application scenarios and empirical data. Its purpose is to identify and retain data that are physically correlated and jointly reflect the characteristics of the wearer's environment, while eliminating noisy or irrelevant data, thus improving the effectiveness of data fusion.

[0206] Furthermore, data fusion of data pairs that meet the correlation standards to generate fused monitoring segments refers to integrating satellite positioning signal quality parameters and motion information, after time synchronization, normalization, and correlation screening, using specific fusion algorithms (such as Kalman filtering, extended Kalman filtering, particle filtering, or machine learning-based fusion models). The aim is to comprehensively utilize the advantages of both sensors to generate a more comprehensive and robust description of the environment and motion state, overcoming the limitations of single-sensor data.

[0207] Therefore, splicing continuous fusion monitoring segments in chronological order to form a complete continuous monitoring data stream refers to arranging and connecting the aforementioned fusion monitoring segments at different time points in an orderly manner according to their timestamps. The purpose is to construct a continuous, complete, and highly reliable data sequence, providing continuous and accurate input for subsequent judgments regarding signal-constrained environments.

[0208] This application also discloses a high-precision positioning system for smartwatches, used to perform high-precision positioning of smartwatches, combined with... Figure 3 As shown, the high-precision positioning system 1 for smartwatches includes:

[0209] Positioning signal acquisition module 11 is used to acquire satellite positioning signals from the Global Navigation Satellite System;

[0210] The signal continuous monitoring module 12 is used to continuously monitor the signal quality of satellite positioning signals and the motion information of the inertial measurement unit in the smartwatch;

[0211] The signal limitation judgment module 13 is used to determine whether the wearer is about to enter a signal-limited environment based on signal quality, motion information and preset geographical information, and to obtain the signal limitation judgment result.

[0212] Location service adjustment module 14 is used to adjust the working mode of the location service if the signal limitation judgment result indicates that the wearer is about to enter a signal-limited environment, including:

[0213] Switch the operating mode of the receiver module of the Global Navigation Satellite System to signal tracking mode;

[0214] Increase the data acquisition frequency and processing priority of the inertial measurement unit;

[0215] When the wearer is about to enter an environment with limited signal, the smartwatch will activate wireless assisted positioning scanning;

[0216] Real-time monitoring of the workload and temperature of the smartwatch's main processing chip;

[0217] When the workload or temperature of the main processing chip reaches a preset threshold, computing resources are prioritized for the processing tasks of the global navigation satellite system in signal tracking mode, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning scanning tasks, while reducing the operating frequency of the remaining tasks to achieve high-precision positioning of the smartwatch.

[0218] Specifically, the positioning signal acquisition module is configured to acquire satellite positioning signals from the Global Navigation Satellite System (GNSS). One implementation approach is that this module can be a standalone GNSS receiver chip, which receives and initially processes satellite signals via a built-in antenna. Another approach is that the module can be a software-defined radio module integrated into the smartwatch's main processing chip, using software algorithms to acquire and demodulate satellite signals.

[0219] The continuous signal monitoring module is configured to continuously monitor the signal quality of satellite positioning signals and the motion information of the inertial measurement unit (IMU) in the smartwatch. For example, this module could be a dedicated signal processing unit responsible for real-time analysis of multiple metrics such as the signal-to-noise ratio and pseudorange residuals of the GNSS signals, while simultaneously receiving raw data from the IMU. Alternatively, this module's functionality can be implemented by the smartwatch's main processing chip running specific monitoring software that periodically reads data from the GNSS receiver module and the IMU and performs preliminary analysis.

[0220] The signal limitation judgment module is configured to determine whether the wearer is about to enter a signal-limited environment based on the aforementioned signal quality, motion information, and preset geographical information, and to obtain a signal limitation judgment result. This module can be a built-in decision engine that analyzes monitoring data through a preset set of rules or a machine learning model to predict the likelihood of signal limitation. For example, when the signal-to-noise ratio of the Global Navigation Satellite System signal is continuously below a certain threshold, and the inertial measurement unit data shows that the wearer is making rapid vertical movement (such as riding an elevator), and the map data indicates that the current location is in a densely populated area of ​​tall buildings, the module will output a judgment result that the wearer is about to enter a signal-limited environment.

[0221] The location service adjustment module is configured to adjust the location service's operating mode if the signal limitation judgment indicates that the wearer is about to enter a signal-limited environment. This module can be a system-level scheduler, responsible for sending instructions to other hardware modules or software services to make corresponding adjustments. Specifically, the module's functions include:

[0222] Switching the operating mode of the Global Navigation Satellite System (GNSS) receiver module to signal tracking mode. For example, this can be done by sending specific control commands to the GNSS receiver chip, causing it to switch from its regular positioning mode to a mode focused on signal tracking.

[0223] Increase the data acquisition frequency and processing task priority of the inertial measurement unit (IMU). For example, increase its data output rate by configuring the IMU sensor interface and adjust the scheduling priority of the IMU data processing process in the operating system.

[0224] When the wearer is about to enter an environment with limited signal, the smartwatch's wireless-assisted positioning scan is activated. For example, the smartwatch's built-in Wi-Fi and Bluetooth modules are activated, causing it to begin scanning for surrounding wireless signal sources at a high frequency.

[0225] Real-time monitoring of the smartwatch's main processing chip's workload and temperature. For example, by reading data from the main processing chip's internal performance counters and temperature sensors to obtain real-time operating status information.

[0226] When the workload or temperature of the main processing chip reaches a preset threshold, computing resources are prioritized for the processing tasks of the global navigation satellite system in the aforementioned signal tracking mode, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning scanning tasks. The operating frequency of other tasks is reduced to achieve high-precision positioning for the smartwatch. For example, this module can be a dynamic resource manager that, based on a preset resource scheduling strategy, adjusts the CPU time slices, memory bandwidth, and cache allocation for different tasks when it detects that the chip load or temperature exceeds limits, ensuring that computing resources for critical positioning tasks are prioritized.

[0227] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A high-precision positioning method for a smartwatch, characterized in that, include: Obtain satellite positioning signals from the Global Navigation Satellite System; Continuously monitor the signal quality of satellite positioning signals and the motion information of the inertial measurement unit in the smartwatch; Based on the signal quality, the motion information, and the preset geographical information, it is determined whether the wearer is about to enter a signal-limited environment, and a signal-limited judgment result is obtained. If the signal limitation determination result indicates that the wearer is about to enter a signal-limited environment, the location service will adjust its operating mode, including: Switch the operating mode of the global navigation satellite system's receiver module to signal tracking mode; Increase the data acquisition frequency and processing priority of the inertial measurement unit; When the wearer is about to enter an environment with limited signal, the smartwatch will activate wireless assisted positioning scanning; Real-time monitoring of the workload and temperature of the smartwatch's main processing chip; When the workload or temperature of the main processing chip reaches a preset threshold, computing resources are allocated preferentially to the processing tasks of the global navigation satellite system in the signal tracking mode, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning scanning tasks, and the running frequency of the remaining tasks is reduced, so as to achieve high-precision positioning of the smartwatch. The steps for prioritizing the allocation of computing resources to the global navigation satellite system processing tasks in signal tracking mode, the high-priority processing tasks in the inertial measurement unit, and the wireless assisted positioning scanning tasks when the workload or temperature of the main processing chip reaches a preset threshold, and reducing the operating frequency of the remaining tasks to achieve high-precision positioning of the smartwatch include: For the processing tasks of the Global Navigation Satellite System in the signal tracking mode, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning and scanning tasks, the actual processing frequency and data output accuracy of each task are continuously monitored. Determine whether the actual processing frequency is lower than the preset minimum guaranteed frequency, or determine whether the data output accuracy is consistently higher than the preset threshold, to obtain the task judgment result; When the task judgment result indicates yes, the non-critical calculation processing of the processing tasks that have been upgraded in the inertial measurement unit is reduced, and compensation data points are generated using the latest effective data and the motion trend of the inertial measurement unit to realize the data interpolation compensation mechanism. The processing task of the global navigation satellite system in the signal tracking mode is extended by extending the retention time of historical effective pseudorange information, and extrapolation compensation is performed in combination with the short-term prediction of the inertial measurement unit to make up for the interruption of data stream. For wireless assisted positioning scanning tasks, priority is given to scanning beacons with the highest signal strength, and historical scan data is used for padding compensation to maintain the continuity of wireless assisted positioning data.

2. The high-precision positioning method for a smartwatch according to claim 1, characterized in that, The step of determining whether the wearer is about to enter a signal-limited environment based on the signal quality, the motion information, and the preset geographical information, and obtaining the signal-limited judgment result, includes: Wireless signal characteristics that monitor the wireless signals in the wearer's surrounding environment; Based on the signal quality, motion information, preset geographical information, and wireless signal characteristics, it is determined whether the wearer is about to enter a signal-limited environment, and a signal-limited judgment result is obtained.

3. The high-precision positioning method for a smartwatch according to claim 1, characterized in that, The step of continuously monitoring the actual processing frequency and data output accuracy of the global navigation satellite system processing tasks, the priority-upgraded processing tasks in the inertial measurement unit, and the wireless assisted positioning and scanning tasks in the signal tracking mode includes: When the workload or temperature of the main processing chip reaches a preset threshold, the hardware performance counter is activated to obtain the number of CPU cycles and instruction executions of the processing tasks of the global navigation satellite system, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning scanning tasks in the signal tracking mode. Based on the temperature and workload of the main processing chip, the monitoring frequency of the preset non-core positioning tasks is adjusted to reduce the overall computing resources occupied by the monitoring process. For the processing tasks of the Global Navigation Satellite System in the signal tracking mode, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning and scanning tasks, a timestamp calibration mechanism is adopted to compensate for the monitoring data reporting delay caused by the load exceeding the threshold, so as to accurately reflect the real-time performance of the tasks. After monitoring frequency adjustment and reporting latency compensation, consistency checks are performed on the continuously monitored number of CPU cycles and instruction executions, and potentially unreliable data is marked. Based on the number of CPU cycles and instructions executed after filtering out potentially unreliable data, the actual processing frequency and data output accuracy of various tasks are obtained.

4. The high-precision positioning method for a smartwatch according to claim 1, characterized in that, When the task judgment result indicates yes, the steps of reducing some non-critical computational processing for the processing tasks that have been upgraded in the inertial measurement unit, and generating compensation data points using the latest effective data and the motion trend of the inertial measurement unit to realize the data interpolation compensation mechanism include: Before generating compensation data points, noise assessment and outlier detection are performed on the latest valid data to obtain the assessment and detection results of the latest valid data. If the evaluation and detection results indicate that the latest valid data contains noise or outliers, then when generating the compensation data points, the motion trend of the inertial measurement unit is used for prediction, and historical data is combined for correction. If the evaluation and detection results indicate that the latest valid data does not contain noise or outliers, then when generating the compensation data points, the latest valid data is used for interpolation, and smoothing is performed in conjunction with the motion trend of the inertial measurement unit.

5. The high-precision positioning method for a smartwatch according to claim 1, characterized in that, The steps of extending the retention time of historical effective pseudorange information in the processing task of the Global Navigation Satellite System in the signal tracking mode, and extrapolating and compensating for data stream interruptions by combining short-term predictions from the inertial measurement unit, include: The signal-to-noise ratio was evaluated and satellite visibility was analyzed based on historical effective pseudorange information to obtain the evaluation and analysis results. Based on the evaluation and analysis results, the historical effective pseudorange information is initially screened to obtain the screened historical effective pseudorange information. Based on the timestamp and geographic location information of the filtered historical valid pseudorange information, the filtered historical valid pseudorange information is stored in the pseudorange information cache area; The historical valid pseudorange information in the pseudorange information buffer is weighted to obtain the weighted historical valid pseudorange information. By using the velocity and direction information of the inertial measurement unit, the wearer's position change within a preset time period is determined, and position change information is obtained; Based on the position change information, the weighted historical effective pseudorange information is corrected to obtain the corrected historical effective pseudorange information. The corrected historical effective pseudorange information is processed by median filtering.

6. The high-precision positioning method for a smartwatch according to claim 1, characterized in that, The steps of prioritizing scanning beacons with the highest signal strength and using historical scan data for padding compensation to maintain the continuity of wireless assisted positioning data include: A preliminary scan of the surrounding environment is performed to obtain the signal strength of each beacon; Based on the signal strength of the beacons, identify a set of beacons whose signal strength falls within a preset similarity range; The signal stability of the beacons in the beacon set was analyzed, and the stability analysis results were obtained. Based on the stability analysis results, and combined with the geographical location information of each beacon in the beacon set, the spatial distribution density is assessed. Based on the combined signal strength, stability analysis results, and spatial distribution density, the scanning priority of each beacon in the beacon set is adjusted, prioritizing the scanning of beacons with higher scanning priority; An interpolation method based on the spatial distribution characteristics of beacons is used to fill and compensate the scanning data corresponding to the priority beacons in order to maintain the continuity of wireless assisted positioning data.

7. A high-precision positioning method for a smartwatch according to claim 1, characterized in that, The steps for continuously monitoring the signal quality of satellite positioning signals and the motion information of the inertial measurement unit in the smartwatch include: The satellite positioning signal is received in real time, and the signal-to-noise ratio, pseudorange residual, and multipath interference index are extracted to obtain the satellite positioning signal quality parameters. The outputs of the accelerometer, gyroscope, and magnetometer of the inertial measurement unit are synchronously acquired to obtain raw motion data; The original motion data is time-stamp aligned and coordinate system transformed to obtain motion information under a unified reference coordinate system. By combining the satellite positioning signal quality parameters with the motion information, a continuous monitoring data stream is formed.

8. A high-precision positioning method for a smartwatch according to claim 7, characterized in that, The step of combining the satellite positioning signal quality parameters with the motion information to form a continuous monitoring data stream includes: Time synchronization processing is performed on satellite positioning signal quality parameters and motion information to establish a unified time series; Normalize the satellite positioning signal quality parameters and motion information; Based on the preset correlation model, the correlation coefficient between the normalized satellite positioning signal quality parameters and motion information is calculated, and data pairs that meet the correlation standard are selected. Data pairs that meet the correlation standards are fused to generate fused monitoring segments; The continuous monitoring segments are spliced ​​together in chronological order to form a complete continuous monitoring data stream.

9. A high-precision positioning system for a smartwatch, used to execute the high-precision positioning method for a smartwatch as described in any one of claims 1 to 8, characterized in that, include: The positioning signal acquisition module is used to acquire satellite positioning signals from the Global Navigation Satellite System. The continuous signal monitoring module is used to continuously monitor the signal quality of satellite positioning signals and the motion information of the inertial measurement unit in the smartwatch; The signal limitation judgment module is used to determine whether the wearer is about to enter a signal-limited environment based on the signal quality, the motion information and the preset geographical information, and to obtain the signal limitation judgment result. The location service adjustment module is used to adjust the operating mode of the location service if the signal limitation judgment result indicates that the wearer is about to enter a signal-limited environment, including: Switch the operating mode of the global navigation satellite system's receiver module to signal tracking mode; Increase the data acquisition frequency and processing priority of the inertial measurement unit; When the wearer is about to enter an environment with limited signal, the smartwatch will activate wireless assisted positioning scanning; Real-time monitoring of the workload and temperature of the smartwatch's main processing chip; When the workload or temperature of the main processing chip reaches a preset threshold, computing resources are prioritized for the processing tasks of the global navigation satellite system in the signal tracking mode, the processing tasks with increased priority in the inertial measurement unit, and the wireless assisted positioning scanning tasks, while reducing the operating frequency of the remaining tasks to achieve high-precision positioning of the smartwatch.

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