A positioning method and system based on wearable device

Abstract

The present invention is applicable to the field of wearable device positioning technology, and provides a positioning method and system based on wearable devices, the method comprising: when the wearable device starts the Bluetooth positioning function, determining whether the wearable device is located at the edge of the target area, and if so, identifying the currently used target signal source and the initial signal matching degree between it and the wearable device, and obtaining the historical behavior data of the wearable device within a predetermined time period. The present invention innovatively introduces the comparison between the historical movement direction change rate and the preset standard rate to intelligently evaluate the impact of the user's movement state on the positioning accuracy. It is possible to determine whether the user is in an unstable movement state based on the rate of change of the user's movement, and dynamically adjust the matching degree of the signal source accordingly, thereby ensuring that the signal remains stable in the edge area and improving the positioning accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of wearable device positioning, and in particular relates to a positioning method and system based on a wearable device. Background Art

[0002] Existing wearable device positioning technology, especially systems based on Bluetooth positioning, has been applied in many fields, such as sports tracking, health monitoring, smart home, etc. At present, the existing wearable device positioning technology mainly relies on Bluetooth signal strength (RSSI) and signal source coverage to achieve positioning function. Traditional positioning methods are based on measuring the distance between the device and the signal source, and infer the location of the device through triangulation positioning, fingerprint positioning and other technologies. However, although these methods can work normally in some ideal environments, the positioning accuracy is greatly challenged in complex environments, especially at the edge of the area. There are usually problems of signal strength attenuation and environmental interference in the edge areas, which makes traditional positioning technology unstable in these areas. What's more serious is that the user's movement behavior, especially the change of direction, further aggravates the instability of the signal and affects the accuracy and stability of positioning.

[0003] Traditional positioning systems often use fixed signal matching algorithms when facing edge areas, without considering the dynamic changes of users during movement. For example, when a user makes a large change in direction in an edge area, the system may fail to adjust the signal source matching degree in time, resulting in interference in signal transmission and a significant decrease in positioning accuracy. In these scenarios, the limitations of traditional technologies are particularly prominent, and they cannot effectively deal with the combined effects of signal strength attenuation and motion changes. In addition, existing technologies cannot flexibly adapt to the user's motion state, especially in a dynamic environment, and cannot accurately judge the impact of changes in motion direction on positioning accuracy, resulting in inaccurate signal matching and unstable positioning results. Summary of the invention

[0004] The purpose of the present invention is to provide a positioning method and system based on a wearable device, aiming to solve the problems raised in the background technology.

[0005] The present invention is implemented in this way: a positioning method based on a wearable device, the method comprising:

[0006] When the wearable device activates the Bluetooth positioning function, it is determined whether the wearable device is located at the edge of the target area. If so, the target signal source currently in use and the initial signal matching degree between the target signal source and the wearable device are identified, and the historical behavior data of the wearable device within a predetermined time period is obtained;

[0007] Based on historical behavior data, analyze the motion characteristics of the wearable device and calculate its historical motion direction change rate within a predetermined time period;

[0008] Retrieve a preset motion direction change rate benchmark model of the target area, determine the specified specific edge zone where the wearable device is located according to the current Bluetooth positioning service, input the target signal source and the specified specific edge zone into the preset motion direction change rate benchmark model, and extract the corresponding signal stable motion direction change rate threshold;

[0009] Determine whether the historical motion direction change rate is greater than the signal stable motion direction change rate threshold. If so, dynamically optimize the initial signal matching degree based on the difference between the two.

[0010] As a further limitation of the technical solution of the embodiment of the present invention, the step of analyzing the motion characteristics of the wearable device based on the historical behavior data and calculating the historical motion direction change rate within a predetermined time period includes:

[0011] Parse the historical behavior data of the wearable device within a predetermined time period, extract the motion trajectory and related timestamp information, analyze the motion trajectory and calculate the direction change at each time point, and record the angle and time interval of each turn;

[0012] Based on the motion trajectory, the change amplitude of the motion direction between every two adjacent time points, that is, the turning angle of the motion path, is calculated, and the turning rate is calculated by the time difference;

[0013] The average of all turning rates is calculated and used as the historical movement direction change rate of the wearable device.

[0014] As a further limitation of the technical solution of the embodiment of the present invention, the preset motion direction change rate benchmark model refers to a motion direction change rate reference model based on different environmental characteristics and interference factors of the target area. The preset motion direction change rate benchmark model defines the signal stable motion direction change rate threshold corresponding to each signal source within different edge areas according to the signal coverage of different signal sources in the target area;

[0015] The signal stable motion direction change rate threshold means that, within the target area, for a specific signal source, when the motion direction change rate is lower than the signal stable motion direction change rate threshold, even if the wearable device and the signal source are Bluetooth positioned and are in the corresponding edge area, the signal transmission between the signal source and the wearable device will not be affected by the degradation of the motion direction change.

[0016] As a further limitation of the technical solution of the embodiment of the present invention, the step of retrieving a preset motion direction change rate benchmark model of the target area, determining the specified specific edge zone where the wearable device is located according to the current Bluetooth positioning service, inputting the target signal source and the specified specific edge zone into the preset motion direction change rate benchmark model, and extracting the corresponding signal stable motion direction change rate threshold comprises:

[0017] Retrieving a preset motion direction change rate reference model corresponding to the target area;

[0018] Determine the real-time location of the wearable device based on the current Bluetooth positioning service, and determine the specific edge zone where the wearable device is located based on the real-time location;

[0019] The target signal source and the specified specific edge zone are input into a preset motion direction change rate reference model, the corresponding signal source is matched, and the signal stable motion direction change rate threshold of the signal source in the specified specific edge zone is extracted.

[0020] As a further limitation of the technical solution of the embodiment of the present invention, it is determined whether the historical motion direction change rate is greater than the signal stable motion direction change rate threshold, and if so, the step of dynamically optimizing the initial signal matching degree according to the difference between the two includes:

[0021] Determine whether the historical motion direction change rate is greater than the signal stable motion direction change rate threshold;

[0022] If the historical motion direction change rate is not greater than the signal stable motion direction change rate threshold, the initial signal matching degree is kept unchanged;

[0023] If the historical motion direction change rate is greater than the signal stable motion direction change rate threshold, the preset signal matching degree optimization formula is called, and the initial signal matching degree is adjusted based on the difference between the historical motion direction change rate and the signal stable motion direction change rate threshold.

[0024] As a further limitation of the technical solution of the embodiment of the present invention, the signal matching degree optimization formula is:

[0025] ;

[0026] Among them, M optimized Refers to the optimized signal matching degree, M initial Refers to the initial signal matching, V history Refers to the rate of change of historical motion direction, V reference Refers to the threshold value of the rate of change of the signal's stable motion direction. It refers to the difference between the historical motion direction change rate and the signal stable motion direction change rate threshold, and K refers to the adjustment coefficient.

[0027] A positioning system based on a wearable device, the system comprises: a data acquisition module, a historical direction change rate calculation module, a stable motion rate threshold matching module and a signal matching degree adjustment module, wherein:

[0028] A data acquisition module is used to determine whether the wearable device is located at the edge of the target area when the wearable device activates the Bluetooth positioning function. If so, it identifies the target signal source currently in use and the initial signal matching degree between the target signal source and the wearable device, and obtains the historical behavior data of the wearable device within a predetermined time period;

[0029] A historical direction change rate calculation module is used to analyze the motion characteristics of the wearable device based on the historical behavior data and calculate its historical motion direction change rate within a predetermined time period;

[0030] The stable motion rate threshold matching module is used to retrieve the preset motion direction change rate benchmark model of the target area, and determine the specified specific edge zone where the wearable device is located according to the current Bluetooth positioning service, input the target signal source and the specified specific edge zone into the preset motion direction change rate benchmark model, and extract the corresponding signal stable motion direction change rate threshold;

[0031] The preset motion direction change rate benchmark model refers to a motion direction change rate reference model based on different environmental characteristics and interference factors of the target area. The preset motion direction change rate benchmark model defines the signal stable motion direction change rate threshold corresponding to each signal source in different edge areas according to the signal coverage of different signal sources in the target area;

[0032] The signal stable motion direction change rate threshold means that, in the target area, for a specific signal source, when the motion direction change rate is lower than the signal stable motion direction change rate threshold, even if the wearable device and the signal source are Bluetooth positioned and are in the corresponding edge area, the signal transmission between the signal source and the wearable device will not be affected by the degradation of the motion direction change;

[0033] The signal matching degree adjustment module is used to determine whether the historical motion direction change rate is greater than the signal stable motion direction change rate threshold. If so, the initial signal matching degree is dynamically optimized according to the difference between the two.

[0034] As a further limitation of the technical solution of the embodiment of the present invention, the historical direction change rate calculation module specifically includes:

[0035] A data analysis unit, used to analyze the historical behavior data of the wearable device within a predetermined time period, extract the motion trajectory and related timestamp information, analyze the motion trajectory and calculate the direction change at each time point, and record the angle and time interval of each turn;

[0036] A turning rate calculation unit, used to calculate the change amplitude of the motion direction between every two adjacent time points, that is, the turning angle of the motion path, based on the motion trajectory, and calculate the turning rate through the time difference;

[0037] The historical motion direction change rate calculation unit is used to calculate the average value of all turning rates and use it as the historical motion direction change rate of the wearable device.

[0038] As a further limitation of the technical solution of the embodiment of the present invention, the stable motion rate threshold matching module specifically includes:

[0039] A model retrieving unit, used to retrieve a preset motion direction change rate reference model corresponding to the target area;

[0040] A designated specific edge zone determining unit, configured to determine a real-time location of the wearable device according to a current Bluetooth positioning service, and determine a designated specific edge zone where the wearable device is located based on the real-time location;

[0041] The signal stable motion direction change rate threshold determination unit is used to input the target signal source and the specified specific edge zone into a preset motion direction change rate reference model, match the corresponding signal source, and extract the signal stable motion direction change rate threshold of the signal source in the specified specific edge zone.

[0042] As a further limitation of the technical solution of the embodiment of the present invention, the signal matching adjustment module specifically includes:

[0043] A rate size judgment unit, used to judge whether the historical motion direction change rate is greater than the signal stable motion direction change rate threshold;

[0044] A first signal matching degree adjustment unit, configured to keep the initial signal matching degree unchanged if the historical motion direction change rate is not greater than the signal stable motion direction change rate threshold;

[0045] A second signal matching degree adjustment unit is used to retrieve a preset signal matching degree optimization formula if the historical motion direction change rate is greater than the signal stable motion direction change rate threshold, and adjust the initial signal matching degree based on the difference between the historical motion direction change rate and the signal stable motion direction change rate threshold;

[0046] The signal matching optimization formula is: ;

[0047] Among them, M optimized Refers to the optimized signal matching degree, M initial Refers to the initial signal matching, V history Refers to the rate of change of the historical motion direction, Vreference Refers to the threshold value of the rate of change of the signal's stable motion direction. It refers to the difference between the historical motion direction change rate and the signal stable motion direction change rate threshold, and K refers to the adjustment coefficient.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] First, the present invention innovatively introduces the comparison between the historical movement direction change rate and the preset reference rate to intelligently evaluate the impact of the user's movement state on positioning accuracy. In marginal areas, due to the serious attenuation of signal strength and environmental interference, traditional positioning technology is susceptible to motion instability, resulting in unstable signal transmission or reduced positioning accuracy. Through the present invention, the system can determine whether the user is in an unstable motion state based on the rate of change of the user's movement, and dynamically adjust the matching degree of the signal source accordingly, thereby ensuring that the signal remains stable in marginal areas and improving positioning accuracy.

[0050] When the historical movement direction change rate is small and the device is in the edge area, the system maintains a high signal matching degree to avoid unnecessary adjustments and improve the system response efficiency. When the movement rate is large, the system dynamically optimizes the matching degree according to the difference between the change rate and the reference rate, and even selects other signal sources with higher signal strength to ensure that the wearable device can still obtain accurate positioning in the edge area.

[0051] Therefore, the present invention provides a flexible and accurate dynamic signal matching mechanism by targeting the special needs of edge areas, significantly improving the stability and accuracy of the positioning system in complex environments, solving the problem of unstable signals in edge areas in the prior art, and having good practical application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A flowchart of a method provided by an embodiment of the present invention;

[0053] Figure 2 A flow chart of calculating the historical movement direction change rate of a wearable device in the method provided in an embodiment of the present invention;

[0054] Figure 3 A flow chart of determining a threshold value of a change rate of a signal stable motion direction corresponding to a wearable device in a method provided in an embodiment of the present invention;

[0055] Figure 4 A flow chart of optimizing the initial signal matching degree in the method provided in an embodiment of the present invention;

[0056] Figure 5 An application architecture diagram of a system provided by an embodiment of the present invention;

[0057] Figure 6 A structural block diagram of a historical direction change rate calculation module in a system provided by an embodiment of the present invention;

[0058] Figure 7 A structural block diagram of a stable motion rate threshold matching module in a system provided by an embodiment of the present invention;

[0059] Figure 8 This is a structural block diagram of a signal matching degree adjustment module in a system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0061] Figure 1 A flow chart of a method provided by an embodiment of the present invention is shown.

[0062] Specifically, a positioning method based on a wearable device comprises the following steps:

[0063] Step S100, when the wearable device starts the Bluetooth positioning function, it is determined whether the wearable device is located at the edge of the target area. If so, the currently used target signal source and the initial signal matching degree between it and the wearable device are identified, and the historical behavior data of the wearable device within a predetermined time period is obtained.

[0064] In an embodiment of the present invention, the target area may be various types of places, such as shopping malls, airports, subway stations, exhibition halls, hospitals, office buildings, etc. These places usually have more complex environmental characteristics, such as dense crowds, complex signal coverage, etc., and there are specific signal sources (such as Bluetooth base stations, Wi-Fi access points, etc.) in these areas, which can provide positioning services for wearable devices. These target areas are pre-defined and set in the system. Usually, when the system is initialized, a regional model is formed based on the specific characteristics of the place (such as the size and structure of the building, the layout of the signal source, etc.). When the system detects that certain areas meet the preset target area characteristics, the positioning service method of the present invention can be started to start the positioning of the wearable device.

[0065] When determining whether the wearable device is located at the edge of the target area, the following steps can be taken: First, based on the Bluetooth positioning function currently activated on the wearable device, the system can obtain the real-time location of the device. This location can be roughly estimated by the strength of the Bluetooth signal (RSSI) and distance calculation, and the approximate location of the device can be determined. Then, the system compares the location information of the device with the map data of the target area to determine whether the location is close to the edge of the target area. The edge of the target area can be defined by the preset boundary in the map, or further confirmed based on relevant information such as signal strength. Generally, if the device is located outside the target area and close to the boundary of the area, it can be considered that the device is located at the edge of the target area.

[0066] The initial signal matching degree between the target signal source and the wearable device is usually calculated by analyzing multiple factors such as the receiving strength (RSSI) of the Bluetooth signal, the relative distance between the device and the signal source, and the signal stability. Existing technical means, such as RSSI-based positioning algorithms, are already able to evaluate the matching degree between the signal source and the wearable device. Specifically, the system calculates the distance between the wearable device and each signal source based on the signal strength, thereby inferring a signal matching degree. The matching degree usually reflects the positioning reliability between the signal source and the device. The initial matching degree is calculated based on the current signal source situation when the device starts to locate, and serves as the basis for subsequent dynamic adjustments.

[0067] The basis for setting the predetermined time period is usually determined by the system according to the needs of the target application scenario. For example, it can be set to a specific time period after the device turns on the Bluetooth positioning function, or a certain period of time when the device continuously uses the Bluetooth positioning function, or a certain period of time before the Bluetooth positioning function is turned on, as a reference for analyzing historical behavior data. Historical behavior data refers to all positioning data of the device during this period, including the device's movement trajectory, positioning point information, and movement status. The core content of historical behavior data usually includes the device's movement path, positioning information at each time point, and dynamic change characteristics related to time. By analyzing these data, the system can extract information such as the device's movement characteristics and behavior patterns, thereby providing a basis for subsequent dynamic signal matching adjustments.

[0068] When the system analyzes historical behavior data, it can include analysis of motion trajectory, rate of change of motion direction, motion frequency, change of motion state, etc. This information helps to determine the motion characteristics of the target user in the target area and provide data support for subsequent signal source matching optimization. For example, if the device's motion direction changes drastically over a period of time, it may mean that the user is in a more complex motion state (such as brisk walking, turning, etc.), and this type of information will have an important impact on the subsequent adjustment of signal matching.

[0069] Furthermore, the positioning method based on the wearable device also includes the following steps:

[0070] Step S200: Analyze the motion characteristics of the wearable device based on the historical behavior data, and calculate the historical motion direction change rate within a predetermined time period.

[0071] Specifically, Figure 2 A flow chart for calculating the historical movement direction change rate of a wearable device is shown.

[0072] The process of analyzing the motion characteristics of the wearable device based on the historical behavior data and calculating the rate of change of the historical motion direction of the wearable device within a predetermined time period specifically includes the following steps:

[0073] Step S201, parsing the historical behavior data of the wearable device within a predetermined time period, extracting the motion trajectory and related timestamp information, analyzing the motion trajectory and calculating the direction change at each time point, and recording the angle and time interval of each turn;

[0074] Step S202, based on the motion trajectory, calculating the change amplitude of the motion direction between every two adjacent time points, that is, the turning angle of the motion path, and calculating the turning rate through the time difference;

[0075] Step S203: Calculate the average of all turning rates and use it as the historical movement direction change rate of the wearable device.

[0076] In an embodiment of the present invention, historical behavior data is first obtained from a wearable device, and the data content includes the location coordinates of the device within a predetermined time period and the corresponding timestamp. These data are derived from sensors built into the device, such as accelerometers, gyroscopes, and GPS modules. The motion trajectory of the device can be reconstructed by the location information and timestamps obtained from these sensors. At this point, the data can be cleaned and preprocessed using existing data processing technology to ensure the accuracy of the coordinate and time information, and to provide a basis for subsequent analysis.

[0077] Then, by analyzing these trajectory data, the direction of movement of the device at each time point can be calculated. The change in movement direction is achieved by calculating the angle change between adjacent position points. For example, the angle between the line formed by two adjacent coordinate points and the horizontal line can be calculated to obtain the direction of movement of the device. By continuously performing such angle calculations on consecutive coordinate points, the direction of movement of the device at each time node can be obtained, and the angle of each turn and the time interval between its occurrence can be recorded. In this process, the techniques used include coordinate geometry calculations and time series analysis.

[0078] Subsequently, the magnitude of each turn is further calculated based on the calculated change in the direction of motion at every two adjacent time points. The rate of device turning, that is, the magnitude of the direction change per unit time, can be calculated through the time difference. This calculation helps to quantify the turning frequency and the rate of change of the turning angle during the device's motion. Finally, all the turning rates are averaged to obtain the historical rate of change of the device's motion direction within a predetermined time period. This rate reflects the movement characteristics of the device within the time period, especially its turning changes on the trajectory.

[0079] When analyzing and calculating the rate of change of the direction of motion, existing technical means are mainly used in the acquisition of sensor data, synchronization of timestamps, geometric calculation of trajectory points, etc. Existing accelerometers, gyroscopes and GPS modules can provide accurate location information for wearable devices, and the calculation of direction changes based on this is based on the use of coordinate geometry and vector analysis techniques. These technologies have been widely used in smart wearable devices, and through effective data filtering and processing methods, the accuracy of the motion trajectory can be ensured.

[0080] The innovation of this technical solution for the study of the rate of change of the direction of motion, especially in the edge areas of the target area, is that it can dynamically adjust the positioning service based on the motion characteristics of the device. In the edge areas, the signal reception of the device may be subject to multiple interferences, and the traditional signal source matching method often cannot effectively distinguish the change of the device motion state and the signal strength. By analyzing the historical rate of change of the direction of motion, it is possible to identify the situations in which the device motion has a significant impact on the signal transmission, thereby avoiding the incorrect optimization of the signal matching degree.

[0081] The problem with the existing technology in this scenario is that the positioning signal quality in the edge area is usually poor, and the user's movement behavior (such as fast turns, sudden stops, etc.) can easily cause signal fluctuations. Therefore, the traditional static signal source matching algorithm cannot effectively deal with the signal fluctuations caused by changes in motion state. This problem is particularly prominent in the edge areas of the target area, because the signals in these areas are weak and the signal quality varies greatly. If you rely solely on static signal strength to judge the positioning effect, it is easy to have positioning errors or inaccuracies.

[0082] Furthermore, the positioning method based on the wearable device also includes the following steps:

[0083] Step S300, call up the preset motion direction change rate benchmark model of the target area, and determine the specified specific edge zone where the wearable device is located based on the current Bluetooth positioning service, input the target signal source and the specified specific edge zone into the preset motion direction change rate benchmark model, and extract the corresponding signal stable motion direction change rate threshold.

[0084] The preset motion direction change rate benchmark model refers to a motion direction change rate reference model based on different environmental characteristics and interference factors of the target area. The preset motion direction change rate benchmark model defines the signal stable motion direction change rate threshold corresponding to each signal source in different edge areas according to the signal coverage of different signal sources in the target area;

[0085] The signal stable motion direction change rate threshold means that, within the target area, for a specific signal source, when the motion direction change rate is lower than the signal stable motion direction change rate threshold, even if the wearable device and the signal source are Bluetooth positioned and are in the corresponding edge area, the signal transmission between the signal source and the wearable device will not be affected by the degradation of the motion direction change.

[0086] Specifically, Figure 3 A flow chart for determining a threshold value of a change rate of a signal stable motion direction corresponding to a wearable device is shown.

[0087] The preset motion direction change rate benchmark model of the target area is retrieved, and the specified specific edge zone where the wearable device is located is determined according to the current Bluetooth positioning service, and the target signal source and the specified specific edge zone are input into the preset motion direction change rate benchmark model, and the corresponding signal stable motion direction change rate threshold is extracted, which specifically includes the following steps:

[0088] Step S301, calling a preset motion direction change rate reference model corresponding to the target area;

[0089] Step S302, determining the real-time location of the wearable device according to the current Bluetooth positioning service, and determining the designated specific edge zone where the wearable device is located based on the real-time location;

[0090] Step S303, inputting the target signal source and the specified specific edge zone into a preset motion direction change rate reference model, matching the corresponding signal source, and extracting the signal stable motion direction change rate threshold of the signal source in the specified specific edge zone.

[0091] In an embodiment of the present invention, the preset motion direction change rate benchmark model is a reference model constructed based on parameters such as the environmental characteristics of the target area, signal source distribution, and interference factors. The core function of this model is to provide standardized motion direction change rate values ​​for different types of signal sources in the edge areas of the target area, ensuring that during the movement of the device, appropriate signal transmission optimization can be made based on the actual motion characteristics and characteristics of the signal source. Specifically, the preset motion direction change rate benchmark model not only takes into account the signal coverage range of different signal sources in the target area, but also sets corresponding motion direction change rate standards based on the performance of the signal source in the edge areas, so as to guide the Bluetooth positioning service system on how to adapt to different motion states.

[0092] In the process of setting the preset motion direction change rate benchmark model, it is necessary to conduct detailed surveys and simulations on the geographical features of the target area, the radio signal propagation characteristics, and the layout of the signal source. By measuring the coverage of different signal sources in real time and the impact of various interference sources on signal transmission, the signal transmission characteristics of different signal sources in the edge area can be obtained. These data are used to set the signal stable motion direction change rate threshold in the model. The benchmark rate included in the model reflects whether the signal transmission will be significantly affected under specific motion conditions. This process relies on existing radio signal propagation theory, signal strength assessment, and complex environmental modeling techniques such as environmental monitoring, geographic information system (GIS) technology, and signal interference analysis methods.

[0093] The construction of a preset motion direction change rate benchmark model relies on a deep understanding of the target area. First, the infrastructure in the area needs to be surveyed to ensure that the number, type, and deployment method of the signal sources are clearly known. Secondly, use radio measurement tools to conduct signal tests in different scenarios to obtain signal strength and coverage data of different signal sources in the target area, and establish corresponding mathematical models based on these data to evaluate the impact of changes in motion direction on signal transmission. In this way, a set of signal stable motion direction change rate thresholds can be set for each signal source. These rates reflect the stability of signal transmission between the signal source and the wearable device in a specific edge area.

[0094] In the specific application process, when the rate of change of the motion direction is lower than the set signal stable motion direction change rate threshold, even if the wearable device is at the edge of the target area, the signal transmission between the signal source and the wearable device will not be significantly affected by degradation. This is because when designing the signal stable motion direction change rate threshold, the impact of the device's steering change amplitude and frequency during movement on signal transmission is taken into account. When the rate of change of the motion direction is lower than the baseline rate, it means that the device's motion trajectory is relatively smooth, the steering frequency is low, and the change in motion direction will not cause significant interference to the signal. At this time, the stability and reliability of signal transmission are relatively high, and the system does not need to adjust the matching degree of the signal source. This phenomenon is based on the signal propagation model and kinematic theory, indicating that the interference of motion on signal transmission is minimal when the device moves smoothly, so the signal quality between the signal source and the device can remain stable.

[0095] The technical effect of this design is that it can intelligently evaluate the impact of changes in motion direction on signal transmission by dynamically analyzing the motion characteristics of the device and the coverage of the signal source, thereby optimizing the signal source matching. This process solves the problems of signal quality fluctuations and reduced positioning accuracy faced by traditional Bluetooth positioning systems in marginal areas, and improves the accuracy and stability of positioning services through more detailed and personalized motion analysis.

[0096] Throughout the implementation process, existing technical means applied include: sensor data collection (such as accelerometers, gyroscopes, GPS, etc.), signal strength analysis, synchronous processing of location and time data, geographic information system (GIS) for environmental modeling and interference assessment of target areas, and radio signal propagation models, etc. These technical means enable the system to obtain motion characteristics in real time in a dynamic environment, perform efficient data processing, and optimize signal source matching in combination with models, ultimately improving the accuracy and stability of positioning services.

[0097] Furthermore, the positioning method based on the wearable device also includes the following steps:

[0098] Step S400, determining whether the historical motion direction change rate is greater than the signal stable motion direction change rate threshold, and if so, dynamically optimizing the initial signal matching degree according to the difference between the two.

[0099] Specifically, Figure 4 A flow chart for optimizing the initial signal matching degree is shown.

[0100] Among them, judging whether the historical motion direction change rate is greater than the signal stable motion direction change rate threshold, if so, dynamically optimizing the initial signal matching degree according to the difference between the two specifically includes the following steps:

[0101] Step S401, determining whether the historical motion direction change rate is greater than the signal stable motion direction change rate threshold;

[0102] Step S402, if the historical motion direction change rate is not greater than the signal stable motion direction change rate threshold, then the initial signal matching degree is kept unchanged;

[0103] Step S403, if the historical motion direction change rate is greater than the signal stable motion direction change rate threshold, the preset signal matching degree optimization formula is called, and the initial signal matching degree is adjusted based on the difference between the historical motion direction change rate and the signal stable motion direction change rate threshold.

[0104] The signal matching optimization formula is: ; where M optimized Refers to the optimized signal matching degree, M initial Refers to the initial signal matching, V history Refers to the rate of change of the historical motion direction, V reference Refers to the threshold value of the rate of change of the signal's stable motion direction. It refers to the difference between the historical motion direction change rate and the signal stable motion direction change rate threshold, and K refers to the adjustment coefficient.

[0105] In an embodiment of the present invention, it is of great practical significance to determine whether the historical rate of change of the direction of motion is greater than the threshold value of the rate of change of the direction of motion for stable signal, and to dynamically optimize the initial signal matching degree according to the difference between the two. First of all, this method can intelligently evaluate the impact of the user's motion state on the positioning accuracy, especially in the marginal areas of the target area. If the historical rate of change of the direction of motion is large, it means that the user's motion is unstable, which may cause unstable signal reception. Therefore, it is necessary to adjust the matching degree of the signal source according to the size of the motion change rate, so as to ensure the accuracy of the positioning service in this case. If the historical rate of change of the direction of motion is lower than the standard value, it means that the user's motion is relatively stable. At this time, the signal transmission between the signal source and the device will not be significantly disturbed, and the initial signal matching degree can remain unchanged, which can reduce unnecessary calculations and improve the efficiency of the system.

[0106] The advantage of this strategy is that it can not only adapt to different user movement behaviors, but also dynamically adjust according to the specific movement environment, greatly improving the stability and accuracy of the positioning system in edge areas. Existing technologies generally face the problem of signal strength attenuation and reduced positioning accuracy in edge areas. This method can more intelligently determine whether the matching degree of the signal source needs to be adjusted by comparing the historical movement change rate with the benchmark rate, thereby avoiding excessive adjustment or unnecessary optimization in traditional methods.

[0107] Unlike traditional static methods, this dynamic adjustment mechanism shows higher adaptability when facing complex and changing environments. For example, if a user is relatively stationary in an edge area, or only makes slight movements, the rate of change of the historical movement direction will be less than the standard value, and the signal matching degree will not be affected. However, if the user turns frequently and the rate of change of the movement direction is large, the system will make corresponding optimization adjustments based on this change to ensure that the signal transmission quality is not affected. At the same time, when it is detected that the rate of change of the historical movement direction is large, if the matching degree of the current signal source decreases, the system will not only adjust the matching degree, but also intelligently select a more suitable signal source, so as to ensure that the device is always connected to the signal source with a higher matching degree to ensure signal stability.

[0108] In addition to this difference-based dynamic optimization method, other calculation methods can also be used to adjust the initial signal matching degree. For example, you can consider using the weighted average method to conduct a comprehensive evaluation based on the rate of change of the device's motion direction, the signal source coverage range, signal strength and other factors. In addition, you can also introduce machine learning algorithms to predict the need for adjustment of signal source matching under different motion states through training models, thereby achieving a more intelligent optimization process.

[0109] Let's take an example to illustrate the specific implementation of this process: suppose the target area is a large shopping mall, and the Bluetooth positioning system in this area has configured multiple signal sources for wearable devices. Based on the map in the mall and the Bluetooth positioning service, the system preliminarily determines that the user is located at the edge of the mall. At this time, the system first retrieves the preset signal source and the corresponding signal matching degree, and determines the user's real-time location through the Bluetooth positioning function. Next, the system obtains the initial signal matching degree of the signal source in the edge area based on the preset model of the target area, assuming that the matching degree is 0.85.

[0110] After the system obtains the user's historical motion data for the past 10 minutes, it analyzes and calculates the historical rate of change of motion direction, assuming it is 0.25 degrees / second. Based on the Bluetooth positioning model, the system determines that the threshold of the signal stable motion direction change rate at the edge of the target area is 0.2 degrees / second. Because the historical rate of change of motion direction is greater than the baseline rate, the system will start the signal matching optimization mechanism. During the optimization process, the system will calculate the difference between the historical rate of change of motion direction and the threshold of the signal stable motion direction change rate. Assuming that the difference between the two is 0.05 degrees / second, the system dynamically adjusts the initial signal matching degree based on this difference. Through the optimization formula calculation, the system derives the optimized signal matching degree as 0.75, which means that due to the large motion changes, the system reduces the signal matching degree to cope with possible signal interference.

[0111] In this example, the system dynamically adjusts the signal matching degree to avoid the problem of unstable signal transmission when the motion state changes greatly, ensuring the accuracy of positioning services. This process solves the problem of reduced positioning accuracy caused by unstable signals in edge areas in the existing technology, and effectively improves the user experience.

[0112] In general, the core innovation of the present invention is to dynamically adjust the matching degree of the signal source to cope with the change of the motion state of the wearable device in the edge area of ​​the target area. By analyzing the difference between the historical motion direction change rate and the preset reference rate in real time, the signal source matching degree is intelligently optimized to ensure the improvement of signal transmission quality when the user's motion is unstable. When the matching degree decreases, the system can automatically switch to a signal source with a higher matching degree, thereby ensuring that the device can still maintain a stable positioning service in a high motion rate environment, solving the problem of signal attenuation and reduced positioning accuracy in the edge area of ​​traditional technology.

[0113] Furthermore, Figure 5 The application architecture diagram of the system provided by the embodiment of the present invention is shown.

[0114] Among them, in another preferred embodiment provided by the present invention, a positioning system based on a wearable device includes:

[0115] The data acquisition module 100 is used to determine whether the wearable device is located at the edge of the target area when the wearable device activates the Bluetooth positioning function. If so, it identifies the currently used target signal source and the initial signal matching degree between it and the wearable device, and obtains the historical behavior data of the wearable device within a predetermined time period.

[0116] In an embodiment of the present invention, the target area may be various types of places, such as shopping malls, airports, subway stations, exhibition halls, hospitals, office buildings, etc. These places usually have more complex environmental characteristics, such as dense crowds, complex signal coverage, etc., and there are specific signal sources (such as Bluetooth base stations, Wi-Fi access points, etc.) in these areas, which can provide positioning services for wearable devices. These target areas are pre-defined and set in the system. Usually, when the system is initialized, a regional model is formed based on the specific characteristics of the place (such as the size and structure of the building, the layout of the signal source, etc.). When the system detects that certain areas meet the preset target area characteristics, the positioning service method of the present invention can be started to start the positioning of the wearable device.

[0117] When determining whether the wearable device is located at the edge of the target area, the following steps can be taken: First, based on the Bluetooth positioning function currently activated on the wearable device, the system can obtain the real-time location of the device. This location can be roughly estimated by the strength of the Bluetooth signal (RSSI) and distance calculation, and the approximate location of the device can be determined. Then, the system compares the location information of the device with the map data of the target area to determine whether the location is close to the edge of the target area. The edge of the target area can be defined by the preset boundary in the map, or further confirmed based on relevant information such as signal strength. Generally, if the device is located outside the target area and close to the boundary of the area, it can be considered that the device is located at the edge of the target area.

[0118] The initial signal matching degree between the target signal source and the wearable device is usually calculated by analyzing multiple factors such as the receiving strength (RSSI) of the Bluetooth signal, the relative distance between the device and the signal source, and the signal stability. Existing technical means, such as RSSI-based positioning algorithms, are already able to evaluate the matching degree between the signal source and the wearable device. Specifically, the system calculates the distance between the wearable device and each signal source based on the signal strength, thereby inferring a signal matching degree. The matching degree usually reflects the positioning reliability between the signal source and the device. The initial matching degree is calculated based on the current signal source situation when the device starts to locate, and serves as the basis for subsequent dynamic adjustments.

[0119] The basis for setting the predetermined time period is usually determined by the system according to the needs of the target application scenario. For example, it can be set to a specific time period after the device turns on the Bluetooth positioning function, or a certain period of time when the device continuously uses the Bluetooth positioning function, or a certain period of time before the Bluetooth positioning function is turned on, as a reference for analyzing historical behavior data. Historical behavior data refers to all positioning data of the device during this period, including the device's movement trajectory, positioning point information, and movement status. The core content of historical behavior data usually includes the device's movement path, positioning information at each time point, and dynamic change characteristics related to time. By analyzing these data, the system can extract information such as the device's movement characteristics and behavior patterns, thereby providing a basis for subsequent dynamic signal matching adjustments.

[0120] When the system analyzes historical behavior data, it can include analysis of motion trajectory, rate of change of motion direction, motion frequency, change of motion state, etc. This information helps to determine the motion characteristics of the target user in the target area and provide data support for subsequent signal source matching optimization. For example, if the device's motion direction changes drastically over a period of time, it may mean that the user is in a more complex motion state (such as brisk walking, turning, etc.), and this type of information will have an important impact on the subsequent adjustment of signal matching.

[0121] Furthermore, the positioning system based on the wearable device also includes:

[0122] The historical direction change rate calculation module 200 is used to analyze the motion characteristics of the wearable device based on the historical behavior data, and calculate its historical motion direction change rate within a predetermined time period.

[0123] Specifically, Figure 6 The structure block diagram of the historical direction change rate calculation module 200 in the system provided by the embodiment of the present invention is shown.

[0124] Among them, in the preferred implementation manner provided by the present invention, the historical direction change rate calculation module 200 specifically includes:

[0125] The data analysis unit 201 is used to analyze the historical behavior data of the wearable device within a predetermined time period, extract the motion trajectory and related timestamp information, analyze the motion trajectory and calculate the direction change at each time point, and record the angle and time interval of each turn;

[0126] A turning rate calculation unit 202 is used to calculate the change amplitude of the motion direction between every two adjacent time points, that is, the turning angle of the motion path, based on the motion trajectory, and calculate the turning rate through the time difference;

[0127] The historical motion direction change rate calculation unit 203 is used to calculate the average value of all turning rates and use it as the historical motion direction change rate of the wearable device.

[0128] In an embodiment of the present invention, historical behavior data is first obtained from a wearable device, and the data content includes the location coordinates of the device within a predetermined time period and the corresponding timestamp. These data are derived from sensors built into the device, such as accelerometers, gyroscopes, and GPS modules. The motion trajectory of the device can be reconstructed by the location information and timestamps obtained from these sensors. At this point, the data can be cleaned and preprocessed using existing data processing technology to ensure the accuracy of the coordinate and time information, and to provide a basis for subsequent analysis.

[0129] Then, by analyzing these trajectory data, the direction of movement of the device at each time point can be calculated. The change in movement direction is achieved by calculating the angle change between adjacent position points. For example, the angle between the line formed by two adjacent coordinate points and the horizontal line can be calculated to obtain the direction of movement of the device. By continuously performing such angle calculations on consecutive coordinate points, the direction of movement of the device at each time node can be obtained, and the angle of each turn and the time interval between its occurrence can be recorded. In this process, the techniques used include coordinate geometry calculations and time series analysis.

[0130] Subsequently, the magnitude of each turn is further calculated based on the calculated change in the direction of motion at every two adjacent time points. The rate of device turning, that is, the magnitude of the direction change per unit time, can be calculated through the time difference. This calculation helps to quantify the turning frequency and the rate of change of the turning angle during the device's motion. Finally, all the turning rates are averaged to obtain the historical rate of change of the device's motion direction within a predetermined time period. This rate reflects the movement characteristics of the device within the time period, especially its turning changes on the trajectory.

[0131] When analyzing and calculating the rate of change of the direction of motion, existing technical means are mainly used in the acquisition of sensor data, synchronization of timestamps, geometric calculation of trajectory points, etc. Existing accelerometers, gyroscopes and GPS modules can provide accurate location information for wearable devices, and the calculation of direction changes based on this is based on the use of coordinate geometry and vector analysis techniques. These technologies have been widely used in smart wearable devices, and through effective data filtering and processing methods, the accuracy of the motion trajectory can be ensured.

[0132] The innovation of this technical solution for the study of the rate of change of the direction of motion, especially in the edge areas of the target area, is that it can dynamically adjust the positioning service based on the motion characteristics of the device. In the edge areas, the signal reception of the device may be subject to multiple interferences, and the traditional signal source matching method often cannot effectively distinguish the change of the device motion state and the signal strength. By analyzing the historical rate of change of the direction of motion, it is possible to identify the situations in which the device motion has a significant impact on the signal transmission, thereby avoiding the incorrect optimization of the signal matching degree.

[0133] The problem with the existing technology in this scenario is that the positioning signal quality in the edge area is usually poor, and the user's movement behavior (such as fast turns, sudden stops, etc.) can easily cause signal fluctuations. Therefore, the traditional static signal source matching algorithm cannot effectively deal with the signal fluctuations caused by changes in motion state. This problem is particularly prominent in the edge areas of the target area, because the signals in these areas are weak and the signal quality varies greatly. If you rely solely on static signal strength to judge the positioning effect, it is easy to have positioning errors or inaccuracies.

[0134] Furthermore, the positioning system based on the wearable device also includes:

[0135] The stable motion rate threshold matching module 300 is used to retrieve the preset motion direction change rate benchmark model of the target area, and determine the specified specific edge zone where the wearable device is located based on the current Bluetooth positioning service, input the target signal source and the specified specific edge zone into the preset motion direction change rate benchmark model, and extract the corresponding signal stable motion direction change rate threshold.

[0136] The preset motion direction change rate benchmark model refers to a motion direction change rate reference model based on different environmental characteristics and interference factors of the target area. The preset motion direction change rate benchmark model defines the signal stable motion direction change rate threshold corresponding to each signal source in different edge areas according to the signal coverage of different signal sources in the target area;

[0137] The signal stable motion direction change rate threshold means that, within the target area, for a specific signal source, when the motion direction change rate is lower than the signal stable motion direction change rate threshold, even if the wearable device and the signal source are Bluetooth positioned and are in the corresponding edge area, the signal transmission between the signal source and the wearable device will not be affected by the degradation of the motion direction change.

[0138] Specifically, Figure 7 It shows a structural block diagram of the stable motion rate threshold matching module 300 in the system provided by an embodiment of the present invention.

[0139] In a preferred embodiment of the present invention, the stable motion rate threshold matching module 300 specifically includes:

[0140] A model retrieving unit 301 is used to retrieve a preset motion direction change rate reference model corresponding to a target area;

[0141] The designated specific edge zone determining unit 302 is used to determine the real-time location of the wearable device according to the current Bluetooth positioning service, and determine the designated specific edge zone where the wearable device is located based on the real-time location;

[0142] The signal stable motion direction change rate threshold determination unit 303 is used to input the target signal source and the specified specific edge zone into a preset motion direction change rate reference model, match the corresponding signal source, and extract the signal stable motion direction change rate threshold of the signal source in the specified specific edge zone.

[0143] In an embodiment of the present invention, the preset motion direction change rate benchmark model is a reference model constructed based on parameters such as the environmental characteristics of the target area, signal source distribution, and interference factors. The core function of this model is to provide standardized motion direction change rate values ​​for different types of signal sources in the edge areas of the target area, ensuring that during the movement of the device, appropriate signal transmission optimization can be made based on the actual motion characteristics and characteristics of the signal source. Specifically, the preset motion direction change rate benchmark model not only takes into account the signal coverage range of different signal sources in the target area, but also sets corresponding motion direction change rate standards based on the performance of the signal source in the edge areas, so as to guide the Bluetooth positioning service system on how to adapt to different motion states.

[0144] In the process of setting the preset motion direction change rate benchmark model, it is necessary to conduct detailed surveys and simulations on the geographical features of the target area, the radio signal propagation characteristics, and the layout of the signal source. By measuring the coverage of different signal sources in real time and the impact of various interference sources on signal transmission, the signal transmission characteristics of different signal sources in the edge area can be obtained. These data are used to set the signal stable motion direction change rate threshold in the model. The benchmark rate included in the model reflects whether the signal transmission will be significantly affected under specific motion conditions. This process relies on existing radio signal propagation theory, signal strength assessment, and complex environmental modeling techniques such as environmental monitoring, geographic information system (GIS) technology, and signal interference analysis methods.

[0145] The construction of a preset motion direction change rate benchmark model relies on a deep understanding of the target area. First, the infrastructure in the area needs to be surveyed to ensure that the number, type, and deployment method of the signal sources are clearly known. Secondly, use radio measurement tools to conduct signal tests in different scenarios to obtain signal strength and coverage data of different signal sources in the target area, and establish corresponding mathematical models based on these data to evaluate the impact of changes in motion direction on signal transmission. In this way, a set of signal stable motion direction change rate thresholds can be set for each signal source. These rates reflect the stability of signal transmission between the signal source and the wearable device in a specific edge area.

[0146] In the specific application process, when the rate of change of the motion direction is lower than the set signal stable motion direction change rate threshold, even if the wearable device is at the edge of the target area, the signal transmission between the signal source and the wearable device will not be significantly affected by degradation. This is because when designing the signal stable motion direction change rate threshold, the impact of the device's steering change amplitude and frequency during movement on signal transmission is taken into account. When the rate of change of the motion direction is lower than the baseline rate, it means that the device's motion trajectory is relatively smooth, the steering frequency is low, and the change in motion direction will not cause significant interference to the signal. At this time, the stability and reliability of signal transmission are relatively high, and the system does not need to adjust the matching degree of the signal source. This phenomenon is based on the signal propagation model and kinematic theory, indicating that the interference of motion on signal transmission is minimal when the device moves smoothly, so the signal quality between the signal source and the device can remain stable.

[0147] The technical effect of this design is that it can intelligently evaluate the impact of changes in motion direction on signal transmission by dynamically analyzing the motion characteristics of the device and the coverage of the signal source, thereby optimizing the signal source matching. This process solves the problems of signal quality fluctuations and reduced positioning accuracy faced by traditional Bluetooth positioning systems in marginal areas, and improves the accuracy and stability of positioning services through more detailed and personalized motion analysis.

[0148] Throughout the implementation process, existing technical means applied include: sensor data collection (such as accelerometers, gyroscopes, GPS, etc.), signal strength analysis, synchronous processing of location and time data, geographic information system (GIS) for environmental modeling and interference assessment of target areas, and radio signal propagation models, etc. These technical means enable the system to obtain motion characteristics in real time in a dynamic environment, perform efficient data processing, and optimize signal source matching in combination with models, ultimately improving the accuracy and stability of positioning services.

[0149] Furthermore, the positioning system based on the wearable device also includes:

[0150] The signal matching degree adjustment module 400 is used to determine whether the historical motion direction change rate is greater than the signal stable motion direction change rate threshold, and if so, dynamically optimize the initial signal matching degree according to the difference between the two.

[0151] Specifically, Figure 8 It shows a structural block diagram of the signal matching degree adjustment module 400 in the system provided by the embodiment of the present invention.

[0152] In a preferred embodiment of the present invention, the signal matching adjustment module 400 specifically includes:

[0153] A rate size determination unit 401 is used to determine whether the historical motion direction change rate is greater than a signal stable motion direction change rate threshold;

[0154] A first signal matching degree adjustment unit 402, configured to keep the initial signal matching degree unchanged if the historical motion direction change rate is not greater than the signal stable motion direction change rate threshold;

[0155] The second signal matching degree adjustment unit 403 is used to call a preset signal matching degree optimization formula if the historical motion direction change rate is greater than the signal stable motion direction change rate threshold, and adjust the initial signal matching degree based on the difference between the historical motion direction change rate and the signal stable motion direction change rate threshold;

[0156] The signal matching optimization formula is: ; where M optimized Refers to the optimized signal matching degree, M initial Refers to the initial signal matching, V history Refers to the rate of change of the historical motion direction, V reference Refers to the threshold value of the rate of change of the signal's stable motion direction. It refers to the difference between the historical motion direction change rate and the signal stable motion direction change rate threshold, and K refers to the adjustment coefficient.

[0157] In an embodiment of the present invention, it is of great practical significance to determine whether the historical rate of change of the direction of motion is greater than the threshold value of the rate of change of the direction of motion for stable signal, and to dynamically optimize the initial signal matching degree according to the difference between the two. First of all, this method can intelligently evaluate the impact of the user's motion state on the positioning accuracy, especially in the marginal areas of the target area. If the historical rate of change of the direction of motion is large, it means that the user's motion is unstable, which may cause unstable signal reception. Therefore, it is necessary to adjust the matching degree of the signal source according to the size of the motion change rate, so as to ensure the accuracy of the positioning service in this case. If the historical rate of change of the direction of motion is lower than the standard value, it means that the user's motion is relatively stable. At this time, the signal transmission between the signal source and the device will not be significantly disturbed, and the initial signal matching degree can remain unchanged, which can reduce unnecessary calculations and improve the efficiency of the system.

[0158] The advantage of this strategy is that it can not only adapt to different user movement behaviors, but also dynamically adjust according to the specific movement environment, greatly improving the stability and accuracy of the positioning system in edge areas. Existing technologies generally face the problem of signal strength attenuation and reduced positioning accuracy in edge areas. This method can more intelligently determine whether the matching degree of the signal source needs to be adjusted by comparing the historical movement change rate with the benchmark rate, thereby avoiding excessive adjustment or unnecessary optimization in traditional methods.

[0159] Unlike traditional static methods, this dynamic adjustment mechanism shows higher adaptability when facing complex and changing environments. For example, if a user is relatively stationary in an edge area, or only makes slight movements, the rate of change of the historical movement direction will be less than the standard value, and the signal matching degree will not be affected. However, if the user turns frequently and the rate of change of the movement direction is large, the system will make corresponding optimization adjustments based on this change to ensure that the signal transmission quality is not affected. At the same time, when it is detected that the rate of change of the historical movement direction is large, if the matching degree of the current signal source decreases, the system will not only adjust the matching degree, but also intelligently select a more suitable signal source, so as to ensure that the device is always connected to the signal source with a higher matching degree to ensure signal stability.

[0160] In general, the core innovation of the present invention is to dynamically adjust the matching degree of the signal source to cope with the change of the motion state of the wearable device in the edge area of ​​the target area. By analyzing the difference between the historical motion direction change rate and the preset reference rate in real time, the signal source matching degree is intelligently optimized to ensure the improvement of signal transmission quality when the user's motion is unstable. When the matching degree decreases, the system can automatically switch to a signal source with a higher matching degree, thereby ensuring that the device can still maintain a stable positioning service in a high motion rate environment, solving the problem of signal attenuation and reduced positioning accuracy in the edge area of ​​traditional technology.

[0161] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

Claims

1. A positioning method based on a wearable device, characterized in that: The method comprises: When the wearable device activates the Bluetooth positioning function, it is determined whether the wearable device is located at the edge of the target area. If so, the target signal source currently in use and the initial signal matching degree between the target signal source and the wearable device are identified, and the historical behavior data of the wearable device within a predetermined time period is obtained; Based on historical behavior data, analyze the motion characteristics of the wearable device and calculate its historical motion direction change rate within a predetermined time period; Retrieve a preset motion direction change rate benchmark model of the target area, determine the specified specific edge zone where the wearable device is located according to the current Bluetooth positioning service, input the target signal source and the specified specific edge zone into the preset motion direction change rate benchmark model, and extract the corresponding signal stable motion direction change rate threshold; Determine whether the historical motion direction change rate is greater than the signal stable motion direction change rate threshold. If so, dynamically optimize the initial signal matching degree based on the difference between the two.

2. The positioning method based on a wearable device according to claim 1, characterized in that: The steps of analyzing the motion characteristics of the wearable device based on the historical behavior data and calculating the rate of change of the historical motion direction of the wearable device within a predetermined time period include: Parse the historical behavior data of the wearable device within a predetermined time period, extract the motion trajectory and related timestamp information, analyze the motion trajectory and calculate the direction change at each time point, and record the angle and time interval of each turn; Based on the motion trajectory, the change amplitude of the motion direction between every two adjacent time points, that is, the turning angle of the motion path, is calculated, and the turning rate is calculated by the time difference; The average of all turning rates is calculated and used as the historical movement direction change rate of the wearable device.

3. The positioning method based on a wearable device according to claim 1, characterized in that: The preset motion direction change rate benchmark model refers to a motion direction change rate reference model based on different environmental characteristics and interference factors of the target area. The preset motion direction change rate benchmark model defines the signal stable motion direction change rate threshold corresponding to each signal source in different edge areas according to the signal coverage of different signal sources in the target area; The signal stable motion direction change rate threshold means that, within the target area, for a specific signal source, when the motion direction change rate is lower than the signal stable motion direction change rate threshold, even if the wearable device and the signal source are Bluetooth positioned and are in the corresponding edge area, the signal transmission between the signal source and the wearable device will not be affected by the degradation of the motion direction change.

4. The positioning method based on a wearable device according to claim 3, characterized in that: The steps of retrieving a preset motion direction change rate benchmark model of the target area, determining the designated specific edge zone where the wearable device is located according to the current Bluetooth positioning service, inputting the target signal source and the designated specific edge zone into the preset motion direction change rate benchmark model, and extracting the corresponding signal stable motion direction change rate threshold include: Retrieving a preset motion direction change rate reference model corresponding to the target area; Determine the real-time location of the wearable device based on the current Bluetooth positioning service, and determine the specific edge zone where the wearable device is located based on the real-time location; The target signal source and the specified specific edge zone are input into a preset motion direction change rate reference model, the corresponding signal source is matched, and the signal stable motion direction change rate threshold of the signal source in the specified specific edge zone is extracted.

5. The positioning method based on a wearable device according to claim 1, characterized in that: Determine whether the historical motion direction change rate is greater than the signal stable motion direction change rate threshold. If so, the steps of dynamically optimizing the initial signal matching degree according to the difference between the two include: Determine whether the historical motion direction change rate is greater than the signal stable motion direction change rate threshold; If the historical motion direction change rate is not greater than the signal stable motion direction change rate threshold, the initial signal matching degree is kept unchanged; If the historical motion direction change rate is greater than the signal stable motion direction change rate threshold, the preset signal matching degree optimization formula is called, and the initial signal matching degree is adjusted based on the difference between the historical motion direction change rate and the signal stable motion direction change rate threshold.

6. The positioning method based on a wearable device according to claim 5, characterized in that: The signal matching optimization formula is: ; Among them, M optimized Refers to the optimized signal matching degree, M initial Refers to the initial signal matching, V history Refers to the rate of change of the historical motion direction, V reference Refers to the threshold value of the rate of change of the signal's stable motion direction. It refers to the difference between the historical motion direction change rate and the signal stable motion direction change rate threshold, and K refers to the adjustment coefficient.

7. A positioning system based on a wearable device, characterized in that: The system comprises: a data acquisition module, a historical direction change rate calculation module, a stable motion rate threshold matching module and a signal matching degree adjustment module, wherein: A data acquisition module is used to determine whether the wearable device is located at the edge of the target area when the wearable device activates the Bluetooth positioning function. If so, it identifies the target signal source currently in use and the initial signal matching degree between the target signal source and the wearable device, and obtains the historical behavior data of the wearable device within a predetermined time period; A historical direction change rate calculation module is used to analyze the motion characteristics of the wearable device based on the historical behavior data and calculate its historical motion direction change rate within a predetermined time period; The stable motion rate threshold matching module is used to retrieve the preset motion direction change rate benchmark model of the target area, and determine the specified specific edge zone where the wearable device is located according to the current Bluetooth positioning service, input the target signal source and the specified specific edge zone into the preset motion direction change rate benchmark model, and extract the corresponding signal stable motion direction change rate threshold; The preset motion direction change rate benchmark model refers to a motion direction change rate reference model based on different environmental characteristics and interference factors of the target area. The preset motion direction change rate benchmark model defines the signal stable motion direction change rate threshold corresponding to each signal source in different edge areas according to the signal coverage of different signal sources in the target area; The signal stable motion direction change rate threshold means that, in the target area, for a specific signal source, when the motion direction change rate is lower than the signal stable motion direction change rate threshold, even if the wearable device and the signal source are Bluetooth positioned and are in the corresponding edge area, the signal transmission between the signal source and the wearable device will not be affected by the degradation of the motion direction change; The signal matching degree adjustment module is used to determine whether the historical motion direction change rate is greater than the signal stable motion direction change rate threshold. If so, the initial signal matching degree is dynamically optimized according to the difference between the two.

8. The positioning system based on a wearable device according to claim 7, characterized in that: The historical direction change rate calculation module specifically includes: A data analysis unit, used to analyze the historical behavior data of the wearable device within a predetermined time period, extract the motion trajectory and related timestamp information, analyze the motion trajectory and calculate the direction change at each time point, and record the angle and time interval of each turn; A turning rate calculation unit, used to calculate the change amplitude of the motion direction between every two adjacent time points, that is, the turning angle of the motion path, based on the motion trajectory, and calculate the turning rate through the time difference; The historical motion direction change rate calculation unit is used to calculate the average value of all turning rates and use it as the historical motion direction change rate of the wearable device.

9. The positioning system based on a wearable device according to claim 8, characterized in that: The stable motion rate threshold matching module specifically includes: A model retrieving unit, used to retrieve a preset motion direction change rate reference model corresponding to the target area; A designated specific edge zone determining unit, configured to determine a real-time location of the wearable device according to a current Bluetooth positioning service, and determine a designated specific edge zone where the wearable device is located based on the real-time location; The signal stable motion direction change rate threshold determination unit is used to input the target signal source and the specified specific edge zone into a preset motion direction change rate reference model, match the corresponding signal source, and extract the signal stable motion direction change rate threshold of the signal source in the specified specific edge zone.

10. The positioning system based on wearable device according to claim 9, characterized in that: The signal matching adjustment module specifically includes: A rate size judgment unit, used to judge whether the historical motion direction change rate is greater than the signal stable motion direction change rate threshold; A first signal matching degree adjustment unit, configured to keep the initial signal matching degree unchanged if the historical motion direction change rate is not greater than the signal stable motion direction change rate threshold; A second signal matching degree adjustment unit is used to retrieve a preset signal matching degree optimization formula if the historical motion direction change rate is greater than the signal stable motion direction change rate threshold, and adjust the initial signal matching degree based on the difference between the historical motion direction change rate and the signal stable motion direction change rate threshold; The signal matching optimization formula is: ; Among them, M optimized Refers to the optimized signal matching degree, M initial Refers to the initial signal matching, V history Refers to the rate of change of the historical motion direction, V reference Refers to the threshold value of the rate of change of the signal's stable motion direction. It refers to the difference between the historical motion direction change rate and the signal stable motion direction change rate threshold, and K refers to the adjustment coefficient.

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