Smart watch adaptive positioning method and system, and storage medium

Abstract

This invention relates to the field of positioning technology, and provides an adaptive positioning method, system, and storage medium for smartwatches. The method includes: receiving a positioning request and simultaneously activating Wi-Fi scanning and GPS modules; scanning to obtain a list of BSSIDs of surrounding Wi-Fi hotspots; matching with a local BSSID location information database; if successful, directly outputting the corresponding location; if unsuccessful, obtaining network positioning and GPS positioning, comparing their accuracy, and outputting the optimal result, and associating the current BSSID list with the final positioning result in a local database. This invention, through simultaneous activation, priority matching in the local database, real-time accuracy optimization, and a self-learning update mechanism, achieves progressive optimization, including second-level positioning in familiar scenarios, high-precision output in unfamiliar scenarios, and increasing accuracy with use. This effectively solves the problems of slow positioning, unstable accuracy, and limited scenarios in existing smartwatches, and is particularly suitable for all-weather monitoring of elderly smartwatches, improving positioning efficiency and reliability.

Description

Technical Field

[0001] This invention belongs to the field of intelligent positioning technology, and in particular relates to an adaptive positioning method, system and storage medium for smartwatches. Background Technology

[0002] With the increasing aging of the population, smartwatches for the elderly, as important devices for real-time monitoring, are receiving increasing attention for their positioning capabilities. Currently, most smartwatches on the market employ multi-mode fusion positioning technology, typically combining GPS (Global Navigation Satellite System), Wi-Fi, and location-based services (LBS) to address positioning needs in different environments. However, existing solutions still have significant shortcomings in practical applications.

[0003] First, GPS positioning suffers from long cold start times (usually over 30 seconds) in environments with weak satellite signals, such as indoors, underground parking garages, and densely populated high-rise buildings, and the positioning accuracy drops significantly, sometimes even failing to acquire a location. This means that when elderly people enter indoor environments such as stairwells or shopping malls, the monitoring device may be unable to update its location for an extended period, posing a safety hazard.

[0004] Secondly, while Wi-Fi positioning can quickly locate individuals indoors, it relies on pre-stored Wi-Fi hotspot location information. In traditional solutions, this information is often a static database or requires manual deployment, making it unsuitable for adapting to the frequently changing daily activity patterns of the elderly. When seniors enter unfamiliar environments (such as new residential areas or parks), the lack of corresponding Wi-Fi fingerprints often forces the system to revert to slow and unstable GPS or cell tower positioning, resulting in a poor positioning experience.

[0005] Furthermore, while network-based location services (such as Google Geolocator) can quickly return a location using nearby Wi-Fi hotspots, their accuracy fluctuates significantly due to network quality and hotspot density, and they cannot operate without network coverage. Existing technologies, although offering solutions with multiple location sources, typically employ fixed priorities (such as prioritizing GPS) or simple switching, failing to dynamically select the best option based on real-time accuracy, resulting in location results that are not optimal at the current time.

[0006] Furthermore, existing positioning systems mostly perform one-time queries and lack the ability to learn and remember the user's usual environment. Each positioning requires repeating the above process, making it impossible to utilize historical information to speed up subsequent positioning, resulting in wasted power consumption and time.

[0007] Therefore, achieving fast, accurate, and adaptive positioning for smartwatches across all scenarios has become a pressing technical problem. This invention addresses this issue by proposing an adaptive positioning method and system for smartwatches. Through mechanisms such as synchronous startup, priority matching from a local fingerprint database, real-time accuracy optimization, and database self-learning updates, positioning efficiency and reliability are significantly improved. Summary of the Invention

[0008] The purpose of this invention is to provide a smartwatch adaptive positioning method, system, and storage medium, aiming to solve the problem that existing technologies cannot provide an effective adaptive positioning method for smartwatches, resulting in the need to repeat complex processes for each positioning, the inability to utilize historical information to accelerate subsequent positioning, and the resulting waste of power consumption and time.

[0009] On one hand, the present invention provides an adaptive positioning method for smartwatches, the method comprising the following steps: Upon receiving a location request, simultaneously activate the Wi-Fi scanning function and the GPS module; Obtain a list of BSSIDs for nearby Wi-Fi hotspots by scanning for Wi-Fi. The BSSID list is matched with the locally stored BSSID location information database; If a match is successful, the corresponding location information is read from the local library and output as the location result. If the match fails, then: The BSSID list is sent to the network location service to obtain the first location information; Obtain the second location information obtained from the GPS module. Compare the accuracy of the first location information and the second location information, and select the one with higher accuracy as the final positioning result output. The BSSID list is associated with the final location result and stored in the local BSSID location information database.

[0010] The adaptive positioning method for smartwatches described in this invention, wherein the simultaneous activation of the Wi-Fi scanning function and the GPS module specifically involves: upon receiving a positioning request, the Wi-Fi scanning module is triggered to begin scanning and the GPS module is triggered to begin satellite search, so as to utilize the GPS cold start time window to complete the Wi-Fi scanning and local matching.

[0011] The adaptive positioning method for smartwatches described in this invention includes, in which each record in the local BSSID location information database includes at least: BSSID, latitude and longitude of the corresponding location information, estimated location accuracy, and last update timestamp.

[0012] The adaptive positioning method for smartwatches described in this invention, wherein when multiple BSSIDs in the BSSID list successfully match the local database, a weighted average is performed based on the estimated location accuracy of each matching record and the last update timestamp, or the record with the highest accuracy and the latest information is selected as the positioning result output.

[0013] The adaptive positioning method for smartwatches according to the present invention further includes, in part, storing the BSSID list and the final positioning result in a local library: performing a storage or update operation only when the accuracy estimate of the final positioning result is better than a preset threshold.

[0014] The adaptive positioning method for smartwatches according to the present invention further includes: when the storage capacity of the local BSSID location information database reaches its limit, removing at least one old record according to the least recently used (LRU) strategy or the timestamp-based stale record eviction strategy.

[0015] The adaptive positioning method for smartwatches according to the present invention includes, wherein comparing the accuracy of the first location information and the second location information comprises: extracting the horizontal accuracy factor HDOP or estimated error value carried in the first location information, extracting the positioning accuracy dilution factor PDOP or estimated horizontal error value carried in the second location information, and selecting the location with the smaller error estimate as the final positioning result.

[0016] On the other hand, the present invention also provides a smartwatch that applies the smartwatch adaptive positioning method described above.

[0017] On the other hand, the present invention also provides a computer control system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method described above.

[0018] On the other hand, the present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method described above.

[0019] The beneficial effects of this invention are as follows: By simultaneously activating Wi-Fi scanning and GPS modules, and utilizing the GPS cold start time window for Wi-Fi scanning and local matching, overall positioning latency is reduced. When a local database match is successful, the location is output directly, achieving second-level rapid positioning in familiar scenarios, improving user experience and reducing power consumption. If matching fails, both network and GPS locations are acquired simultaneously, and the best result is output, ensuring that the positioning result in unfamiliar scenarios is always the most accurate among available sources. Furthermore, by associating the BSSID of unfamiliar scenarios with the final positioning result and storing it in the local database, a self-learning mechanism is implemented, allowing the system to continuously expand its familiarity with the area as it is used, resulting in faster and more accurate positioning in subsequent uses. This progressive optimization, which improves accuracy with use, effectively solves the problems of slow positioning, unstable accuracy, and limited scenarios in existing smartwatches, making it particularly suitable for all-weather monitoring of elderly smartwatches, improving positioning efficiency and reliability. Attached Figure Description

[0020] Figure 1 This is a flowchart illustrating the implementation of the adaptive positioning method for smartwatches provided in Embodiment 1 of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0022] The specific implementation of the present invention will be described in detail below with reference to specific embodiments: Example 1: This embodiment provides an adaptive positioning method for a smartwatch, which can be executed by the processor in the smartwatch. The specific process is as follows: Figure 1 As shown, it includes the following steps: Step S101: Receive location request and simultaneously start Wi-Fi scanning function and GPS module.

[0023] When a smartwatch's operating system or application (such as a senior monitoring app) initiates a location query request, the processor immediately triggers the Wi-Fi scanning module to begin scanning for nearby Wi-Fi hotspots, while simultaneously triggering the GPS module to acquire satellite signals. This synchronous startup design fully utilizes the time window during which GPS cold starts typically require more than 30 seconds. During this period, Wi-Fi scanning can be completed within 1-2 seconds, creating conditions for rapid subsequent positioning and avoiding the additional delay of waiting for GPS to become ready in traditional serial startup methods.

[0024] Step S102: Obtain a list of BSSIDs of nearby Wi-Fi hotspots by scanning for Wi-Fi.

[0025] The Wi-Fi scanning module collects all Wi-Fi access points in the current environment, extracts the unique identifier (BSSID) of each access point (such as the MAC address), and forms a list L={BSSID1, BSSID2, …, BSSID}. }

[0026] Step S103: Match the BSSID list with the locally stored BSSID location information database. The local BSSID location information database is stored in the watch's non-volatile memory. Each record contains at least: BSSID, latitude and longitude of the corresponding location information, estimated location accuracy (such as the radius of the error circle), last update timestamp, and number of hits. The system compares each BSSID in list L with the records in the database one by one.

[0027] Step S104: Determine whether the match is successful.

[0028] If at least one BSSID in list L matches the local database, it is determined that the current scenario is familiar and proceeds to step S105; otherwise, it is determined to be an unfamiliar scenario and proceeds to step S106.

[0029] Step S105: Read the corresponding location information from the local database and output it as the positioning result.

[0030] If a single BSSID is matched, the location information of that record is read directly. If multiple BSSIDs are matched, a weighted average can be calculated based on the estimated location accuracy of each record and its last update timestamp, or the record with the highest accuracy and most recent information can be selected as the positioning result. The result is then returned to the requesting client, and the process ends. This step achieves second-level positioning in familiar scenarios (average measured time of 0.8 seconds), significantly better than the more than 30 seconds of traditional GPS, significantly reducing power consumption and improving user experience.

[0031] Step S106: Send the BSSID list to the network location service to obtain the first location information.

[0032] In unfamiliar scenarios, the system uploads the list L to the Google Geolocator server (or other network location services) via mobile network. The server returns estimated location data P_net based on the Wi-Fi fingerprint database, including latitude and longitude and horizontal precision factor (HDOP) or estimation error value.

[0033] Step S107: Obtain the second location information obtained by the GPS module.

[0034] Once the GPS module completes positioning (usually takes about 30 seconds, but has already started synchronously in step S101), obtain the GPS location data P_gps, including latitude and longitude and positioning accuracy dilution factor (PDOP) or estimated horizontal error value.

[0035] Step S108: Compare the accuracy of the first location information and the second location information, and select the one with higher accuracy as the final positioning result.

[0036] The system extracts the error estimates carried by P_net and P_gps respectively, and selects the location with the smaller error estimate as the final result. For example, if the HDOP of P_net corresponds to an error of 15 meters and the PDOP of P_gps corresponds to an error of 25 meters, then P_net is selected; and vice versa. This selection mechanism ensures that the output result is always the most accurate among the currently available positioning sources. In actual tests in unfamiliar city streets, it can reduce the positioning error from 18.3 meters with GPS alone to 11.2 meters.

[0037] Step S109: Associate the BSSID list with the final location result and store it in the local BSSID location information database.

[0038] After successfully obtaining the final location result, the system associates all unfamiliar BSSIDs detected in this scan with the result and writes them to the local database. Before writing, it checks whether the accuracy estimate of the final location result is better than a preset threshold (e.g., 20 meters). Storage or update operations are only performed if the condition is met to avoid introducing low-quality fingerprints. If the database capacity has reached its limit (e.g., 500 records), the least used record is evicted according to the LRU policy, or the record with the oldest timestamp is deleted to ensure the timeliness of data in the database and the effective use of storage resources.

[0039] Through step S109, the local database achieves self-learning capability: when a user enters an unfamiliar environment (such as a new community) for the first time, the system obtains the location through GPS or network positioning and stores the Wi-Fi fingerprint of that environment in the database; thereafter, when the user enters the environment again, a successful match can be achieved in step S104, enjoying second-level positioning, truly realizing "the more you use it, the more accurate it becomes".

[0040] The method described in this embodiment is particularly suitable for smartwatches for the elderly. It can operate automatically throughout the entire process without the need for elderly intervention. The location results can be pushed to the monitoring platform or family members in real time, providing reliable protection for the safety of the elderly when traveling.

[0041] Example 2: This embodiment provides a smartwatch that uses the adaptive positioning method described in Embodiment 1 to achieve positioning functionality. The smartwatch includes the following main execution modules: Location request receiving module: Used to receive location requests from the operating system or applications and trigger other modules to work together.

[0042] Wi-Fi scanning module: Controlled by the processor, it starts immediately when a location request is triggered, scans for nearby Wi-Fi hotspots and extracts a list of BSSIDs.

[0043] GPS module: Used to capture satellite signals and calculate GPS location data. This module starts synchronously with the Wi-Fi scanning module to take advantage of the GPS cold start time window.

[0044] Local storage module: Stores the BSSID location information database, including fields such as BSSID, location, precision, and timestamp, and supports data reading, writing, updating, and phasing out.

[0045] Matching Decision Module: Responsible for matching the scanned BSSID list with the local database. If a match is found, the location is output directly; if no match is found, network positioning and GPS positioning are activated, and the accuracy is compared to select the best location.

[0046] Network communication module: Used to send the BSSID list to a network location server (such as Google Geolocator) in unfamiliar scenarios and receive the returned location information.

[0047] Accuracy Comparison Module: Extracts accuracy parameters from network positioning and GPS positioning, and selects the more accurate result by comparing error estimates.

[0048] Library update module: After successful localization in an unfamiliar scene, the BSSID list scanned this time is associated with the final localization result, and the local library is updated according to preset rules (accuracy threshold, elimination strategy).

[0049] Results output module: Returns the final location results to the requesting end and can also push them to the monitoring platform.

[0050] The various modules communicate via the watch's internal bus and are uniformly coordinated and controlled by the central processing unit. The watch can adopt a low-power design, enabling only Wi-Fi scanning and local pairing in familiar scenarios, without needing to activate GPS and network communication, significantly extending battery life; in unfamiliar scenarios, it automatically activates multi-source positioning to ensure accuracy.

[0051] In practical use, watches may face the following challenges, which are effectively addressed in this embodiment through the above module design: Weak GPS signal: such as indoors or underground parking garages, where the GPS module cannot locate or takes too long, network positioning can quickly provide the location. The accuracy comparison module automatically selects the best network positioning result to avoid no result being returned.

[0052] Network unavailable: If the watch is in an area without network coverage, step S106 cannot be executed. In this case, the system can wait for the GPS positioning result or directly output the GPS data to avoid the process getting stuck.

[0053] Local database pollution: If errors occur in the database due to certain anomalies (such as Wi-Fi drift), the database quality can be gradually corrected by using the database entry accuracy threshold and the elimination strategy.

[0054] Multiple BSSID conflicts: When multiple BSSIDs are matched and their locations are inconsistent, the matching decision module adopts a weighted average or optimal selection strategy to reduce the impact of individual erroneous fingerprints.

[0055] Example 3: This embodiment provides a computer control system, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the adaptive positioning method as described in Embodiment 1.

[0056] Specifically, this system can be deployed in the control chip of a smartwatch, or it can serve as a standalone positioning server (such as the backend of an elderly monitoring platform) to process positioning requests reported by the watch or assist in positioning calculations. The system includes: Memory: Used to store computer programs, local BSSID location information database, temporary data, etc. Flash memory, RAM, etc., can be used.

[0057] Processor: Coupled with memory, it executes computer programs and implements instructions that provide the following functional modules: Synchronous start control unit: In response to a location request, it triggers the Wi-Fi scan and GPS module in parallel.

[0058] Matching unit: Compares the scanned list of BSSIDs with the local library in memory.

[0059] Location data acquisition unit: acquires first location information from network location services and second location information from GPS module.

[0060] Precision comparison unit: compares the precision parameters of two position information and selects the better one.

[0061] Library Management Unit: Updates the local library based on the location results, including judging the entry conditions and executing the eviction policy.

[0062] Communication interface: Used for data exchange with Wi-Fi scanning hardware, GPS hardware, network communication modules, etc.

[0063] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Therefore, the scope of protection of this embodiment is not limited to a specific hardware form.

[0064] Example 4: This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the adaptive positioning method as described in Embodiment 1.

[0065] The computer-readable storage medium can be any tangible medium that contains or stores a program, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk, etc., any medium capable of storing program code. When the computer program is executed by the smartwatch's processor or server processor, the device is able to perform the following operations: Upon receiving a location request, simultaneously activate the Wi-Fi scanning and GPS modules; Scan to obtain the BSSID list and match it with the local database; If a match is successful, output the position directly. If a match fails, obtain network location and GPS location, select the best one, and update the local database.

[0066] This embodiment enables cross-platform reuse of positioning methods, facilitating deployment in different models of smartwatches or monitoring systems.

[0067] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An adaptive positioning method for a smartwatch, characterized in that, The method includes the following steps: Upon receiving a location request, simultaneously activate the Wi-Fi scanning function and the GPS module; Obtain a list of BSSIDs for nearby Wi-Fi hotspots by scanning for Wi-Fi. The BSSID list is matched with the locally stored BSSID location information database; If a match is successful, the corresponding location information is read from the local library and output as the location result. If the match fails, then: The BSSID list is sent to the network location service to obtain the first location information; Obtain the second location information obtained from the GPS module. Compare the accuracy of the first location information and the second location information, and select the one with higher accuracy as the final positioning result output. The BSSID list is associated with the final location result and stored in the local BSSID location information database.

2. The smartwatch adaptive positioning method as described in claim 1, characterized in that, The simultaneous activation of the Wi-Fi scanning function and the GPS module specifically involves: upon receiving a positioning request, the Wi-Fi scanning module is triggered to begin scanning and the GPS module is triggered to begin satellite search, so as to utilize the GPS cold start time window to complete the Wi-Fi scanning and local matching.

3. The adaptive positioning method for smartwatches as described in claim 1, characterized in that, Each record in the local BSSID location information database includes at least: BSSID, latitude and longitude of the corresponding location information, estimated location accuracy, and last update timestamp.

4. The adaptive positioning method for smartwatches as described in claim 3, characterized in that, When multiple BSSIDs in the BSSID list successfully match the local database, a weighted average is calculated based on the estimated location accuracy of each matching record and the last update timestamp, or the record with the highest accuracy and the latest information is selected as the location result output.

5. The adaptive positioning method for smartwatches as described in claim 3, characterized in that, The step of associating the BSSID list with the final positioning result and storing it in the local library further includes: performing the storage or update operation only when the accuracy estimate of the final positioning result is better than a preset threshold.

6. The adaptive positioning method for smartwatches as described in claim 5, characterized in that, The method further includes: when the storage capacity of the local BSSID location information database reaches its limit, removing at least one old record according to the least recently used LRU policy or the timestamp-based old record eviction policy.

7. The adaptive positioning method for smartwatches as described in claim 1, characterized in that, The comparison of the accuracy of the first location information and the second location information includes: extracting the horizontal accuracy factor HDOP or the estimated error value carried in the first location information, extracting the positioning accuracy dilution factor PDOP or the estimated horizontal error value carried in the second location information, and selecting the location with the smaller error estimate as the final positioning result.

8. A smartwatch, characterized in that, The adaptive positioning method for smartwatches as described in any one of claims 1 to 7 is applied.

9. A computer control system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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