A high-precision positioning method for smart wearable devices in complex environments

Abstract

This invention relates to the field of positioning and navigation technology, specifically to a high-precision positioning method for smart wearable devices in complex environments. The method includes: collecting the wearer's motion data and acquiring satellite signal strength; determining the thrust fluctuation value and thrust reversal anomaly degree for each cycle based on the motion data within each cycle; using the thrust fluctuation value and thrust reversal anomaly degree of the current cycle to determine whether the current cycle is a normal walking state; if it is determined to be a normal walking state, obtaining a step length reference value based on the distribution characteristics of thrust fluctuation values ​​in historical normal walking cycles; determining the environmental health score for the current cycle based on the satellite signal strength corresponding to the current cycle and the number of satellites searched by the smart wearable device; if the environmental health score is lower than a preset safety threshold, updating the wearer's position based on the step length reference value and real-time azimuth angle. This invention improves the positioning accuracy of smart wearable devices in complex environments.

Description

Technical Field

[0001] This invention relates to the field of positioning and navigation technology, and specifically to a high-precision positioning method for smart wearable devices in complex environments. Background Technology

[0002] With the popularization of the Internet of Things (IoT) and space intelligence technologies, smart wearable devices have been widely used in scenarios such as personnel inspection and navigation. In real life, wearers often need to move between different environments, such as walking from a wide outdoor road into a commercial street lined with high-rise buildings, or entering a large shopping mall or underground parking garage with glass curtain walls. In these environments, not only are satellite signals severely blocked by buildings, but people themselves are also constantly moving. Currently, most smart wearable devices calculate their location by combining satellite positioning modules and built-in motion sensors. When in open outdoor areas, they primarily follow the guidance of satellites. When satellite signals weaken, they utilize the built-in motion sensors.

[0003] Existing positioning methods have certain shortcomings when dealing with environmental changes or human activities. For example, when a person walks to the entrance of a shopping mall or next to a tall building, satellite signals will bounce off glass walls. The signal received by the device is bent and refracted. If the device blindly trusts this signal, the positioning icon on the map will instantly "fly" to the next street dozens of meters away, causing a serious change in location. At the same time, if the satellite signal is completely blocked, and the device relies entirely on the built-in motion sensor for calculation, since smart wearable devices are worn on the body, if a person stands still and waves their hand violently or moves violently, the sensor will sense a huge acceleration. It will then treat the force generated by waving or moving as the driving force for forward motion and continuously accumulate it in the calculation, resulting in a situation where the person is standing still, but the positioning trajectory moves abnormally on the map. Summary of the Invention

[0004] To address the inaccurate positioning issues of existing methods when locating smart wearable devices in complex environments, the present invention aims to provide a high-precision positioning method for smart wearable devices in complex environments. The specific technical solution adopted is as follows: This invention provides a high-precision positioning method for smart wearable devices in complex environments, the method comprising the following steps: The system utilizes smart wearable devices to collect the wearer's motion data and obtain satellite signal strength. Based on the action data within each cycle, determine the thrust fluctuation value and the degree of thrust reversal anomaly in each cycle; use the thrust fluctuation value and the degree of thrust reversal anomaly in the current cycle to determine whether the current cycle is a normal walking state; If the walking state is determined to be normal, the step length reference value is obtained based on the distribution characteristics of thrust fluctuation values ​​in historical normal walking cycles; the environmental health score for the current cycle is determined based on the satellite signal strength corresponding to the current cycle and the number of satellites searched by the smart wearable device. If the environmental health score is lower than the preset safety threshold, the wearer's position is updated based on the step size reference value and the real-time deviation angle.

[0005] Preferably, obtaining the thrust fluctuation value includes: Obtain the maximum and minimum thrust values ​​from the action data within the candidate period, and use the difference between the maximum and minimum thrust values ​​as the thrust fluctuation value of the candidate period; The candidate period can be any period.

[0006] Preferably, the acquisition of the degree of thrust reversal anomaly includes: The number of thrust direction reversals within a candidate period is counted as the thrust reversal count of the candidate period, and the thrust reversal count is used as the thrust reversal anomaly degree of the candidate period.

[0007] Preferably, the step of determining whether the current cycle is a normal travel state by utilizing the thrust fluctuation value and the degree of thrust reversal anomaly in the current cycle includes: If the thrust fluctuation value of the current cycle is within the preset thrust fluctuation value range and the abnormality of thrust reversal in the current cycle is within the preset thrust reversal range, then it is determined to be a normal walking state. If the thrust fluctuation value of the current cycle is not within the preset thrust fluctuation value range and / or the abnormality of thrust reversal in the current cycle is not within the preset thrust reversal range, it is determined to be an abnormal walking state.

[0008] Preferably, obtaining the step length reference value based on the distribution characteristics of thrust fluctuation values ​​during historical normal walking cycles includes: The thrust fluctuation values ​​of a preset number of historical normal walking cycles are curve fitted to obtain the normal walking thrust fluctuation curve. The horizontal axis of the normal walking thrust fluctuation curve is time, and the vertical axis is the thrust fluctuation value. In the normal travel thrust fluctuation curve, each peak and its adjacent trough form a combination. Obtain the first difference between the thrust fluctuation values ​​between the peaks and troughs in each combination; The first difference is converted using the wearer's walking characteristic conversion coefficient to obtain the step length reference value; wherein, the walking characteristic conversion coefficient is pre-calibrated by the wearer walking a known distance in an open area and counting the number of steps.

[0009] Preferably, determining the environmental health score for the current period based on the satellite signal strength and the number of satellites detected by the smart wearable device for the current period includes: The degree of satellite signal discrepancy is evaluated based on the differences in satellite signal strength between different satellite signals from smart wearable devices within the current period. The environmental health score for the current period is obtained by combining the average signal strength of all satellite signals received by the smart wearable device, the ratio of the number of satellites searched in the current period to the maximum number of searchable satellites, and the second difference between the preset upper limit of contradiction and the degree of contradiction of the satellite signals. The average value, the ratio, and the second difference are all positively correlated with the environmental health score.

[0010] Preferably, the step of evaluating the degree of satellite signal contradiction based on the difference in satellite signal strength between different satellite signals from smart wearable devices within the current period includes: Calculate the first difference between the satellite signal strengths of each pair of satellite signals; The average of all the first differences is determined as the degree of satellite signal discrepancy.

[0011] Preferably, updating the wearer's position based on the step size reference value and the real-time deviation angle includes: Based on the coordinates of the safe position, the step length reference value, and the sine and cosine values ​​of the real-time deviation angle, the position changes in the longitude and latitude directions are calculated respectively to obtain the step prediction coordinates. Obtain the wearer's satellite coordinates in real time based on satellite communication data; Based on the predicted gait coordinates and the wearer's satellite coordinates, the updated position coordinates are obtained.

[0012] Preferably, obtaining the updated position coordinates based on the predicted gait coordinates and the wearer's satellite coordinates includes: The difference between the horizontal coordinate in the Cartesian coordinate system converted from the wearer's satellite coordinates and the longitude coordinate in the predicted gait coordinates is taken as the longitude coordinate deviation value; the difference between the vertical coordinate in the Cartesian coordinate system converted from the wearer's satellite coordinates and the latitude coordinate in the predicted gait coordinates is taken as the latitude coordinate deviation value. By combining the longitude coordinate deviation value, the latitude coordinate deviation value, and the step prediction coordinates, the updated position coordinates are obtained.

[0013] Preferably, if the walking state is determined to be abnormal, the current location data will not be updated.

[0014] The present invention has at least the following beneficial effects: This invention collects the wearer's motion data and satellite signal strength, and comprehensively utilizes motion information and satellite positioning information to achieve high-precision updates of the wearer's position in complex environments. It determines whether the wearer is in a normal walking state based on the thrust fluctuation value and the degree of thrust reversal anomaly within a single cycle, avoiding abnormal drift in the positioning trajectory caused by non-walking states such as limb swaying in place, thus improving positioning reliability. It obtains a step length reference value based on the distribution characteristics of thrust fluctuation values ​​in historical normal walking cycles, and determines an environmental health score by combining the satellite signal strength and number of satellites in the current cycle. This accurately reflects the quality of positioning conditions in the wearer's environment. When the environmental health score is lower than a preset safety threshold, the position is updated using the step length reference value and real-time skew angle. This effectively overcomes problems such as positioning jumps and failures caused by signal refraction, attenuation, or loss in complex environments with severe satellite signal obstruction or interference, such as streets with tall buildings, near glass curtain wall buildings, and underground parking garages. This improves the positioning accuracy and stability of smart wearable devices in complex environments. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating a high-precision positioning method for smart wearable devices in complex environments, provided as an embodiment of the present invention; Figure 2 This is a structural block diagram of a high-precision positioning system for smart wearable devices in complex environments, provided by an embodiment of the present invention. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by the present invention to achieve the intended purpose, the following detailed description of a high-precision positioning method for smart wearable devices in complex environments, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0019] The following description, in conjunction with the accompanying drawings, details a specific scheme for a high-precision positioning method for smart wearable devices in complex environments provided by the present invention.

[0020] An embodiment of a high-precision positioning method for smart wearable devices in complex environments: This embodiment proposes a high-precision positioning method for smart wearable devices in complex environments, such as... Figure 1 As shown, a high-precision positioning method for smart wearable devices in complex environments according to this embodiment includes the following steps: Step S1: Use smart wearable devices to collect the wearer's motion data and obtain satellite signal strength.

[0021] First, a smart wearable device is worn by the wearer. This device can be a smartwatch or a smart bracelet, and includes sensing components, control components, and data acquisition components. The sensing components include motion sensors and a satellite receiving antenna. The control component is the chip processor inside the smart wearable device, responsible for filtering data and calculating location. The data acquisition component reads data such as satellite signal strength and number of satellites from the satellite receiving antenna, as well as the wearer's motion data, including thrust and real-time yaw angle, where thrust is acceleration. In this embodiment, the data acquisition frequency is once every 10 milliseconds; in specific applications, the implementer can set this frequency according to specific circumstances.

[0022] Step S2: Based on the action data in each cycle, determine the thrust fluctuation value and thrust reversal anomaly degree of each cycle; use the thrust fluctuation value and thrust reversal anomaly degree of the current cycle to determine whether the current cycle is a normal walking state.

[0023] Considering that smart wearable devices are usually worn on the body, when the wearer waves their hand vigorously or moves vigorously in place, the sensors will sense a huge acceleration, and then treat the force generated by waving or moving as the driving force for forward movement, continuously accumulating and calculating, resulting in a situation where the person is standing still, but the positioning trajectory moves abnormally on the map. Therefore, it is first necessary to determine the degree of local shaking of the user's limbs by acquiring body motion data.

[0024] The entire monitoring cycle can be divided into multiple cycles with a preset duration as one cycle. In this embodiment, the preset duration is 1 minute. In specific applications, the implementer can set it according to the specific situation.

[0025] The following embodiment uses one cycle as an example for explanation. Other cycles can be processed using the method provided in this embodiment.

[0026] Specifically, any given period is designated as a candidate period. The maximum and minimum thrust values ​​from the action data within each candidate period are obtained. The difference between the maximum and minimum thrust values ​​is taken as the thrust fluctuation value for the candidate period. If the wearer is stationary, the difference between the maximum and minimum thrust values ​​is small, meaning the thrust fluctuation value is small. However, if the wearer is walking, vigorously swinging their arms, or washing their hands, the difference between the maximum and minimum thrust values ​​is larger, meaning the thrust fluctuation value is larger. The thrust fluctuation value is a measure of acceleration.

[0027] Considering that the swing of the user's arms is regular when walking, the direction of the force will not change many times within a cycle, at most two or three times. Therefore, it is necessary to analyze the changes in the direction of the thrust within a cycle to determine the degree of abnormality of thrust reversal within the corresponding cycle.

[0028] A change in thrust direction from positive to negative or vice versa indicates a thrust direction reversal. The number of thrust direction reversals within a candidate period is counted and used as the thrust reversal count for that candidate period. This count is then used to determine the degree of thrust reversal anomaly in the candidate period.

[0029] Using the above methods, we can obtain the thrust fluctuation value and the degree of thrust reversal anomaly for each cycle.

[0030] The current cycle is defined as the current period. If the thrust fluctuation value of the current period is within a preset thrust fluctuation value range and the abnormal degree of thrust reversal in the current period is within a preset thrust reversal range, then the wearer is walking normally, and this is determined to be a normal walking state. If the thrust fluctuation value of the current period is not within the preset thrust fluctuation value range and / or the abnormal degree of thrust reversal in the current period is not within the preset thrust reversal range, then the wearer is experiencing excessive localized abnormal limb shaking within the current period, and this is determined to be an abnormal walking state. In this embodiment, the preset thrust fluctuation value range is... The preset thrust reversal range is In practical applications, implementers can configure the settings according to specific circumstances. The method described above is also used to determine whether other cycles besides the current one represent a normal walking state.

[0031] Step S3: If the walking state is determined to be normal, the step length reference value is obtained based on the distribution characteristics of thrust fluctuation values ​​in historical normal walking cycles; the environmental health score for the current cycle is determined based on the satellite signal strength corresponding to the current cycle and the number of satellites searched by the smart wearable device.

[0032] When a user walks in an open plaza, with no obstructions overhead, the smart wearable device receives a pure signal directly from satellites, ensuring accurate location updates based on preset rules. However, when the user enters indoors or in areas with significant obstructions, such as glass curtain walls, the signal is refracted multiple times before reaching the smart wearable device, potentially leading to seriously erroneous coordinates. In such cases, it's necessary to promptly lock the safe location when the external signal weakens. Specifically, if the current period is determined to be an abnormal walking state, the wearer's current location data will not be updated. If the current period is determined to be a normal walking state, further assessment is needed to determine whether adjustments to the current coordinates are necessary.

[0033] First, by using the method of determining whether the current cycle belongs to the normal walking state in step S2, it is determined whether the historical cycles before the current cycle belong to the normal walking state. Based on the determination result, a preset number of historical cycles with the closest time interval to the current cycle are selected as historical normal walking cycles. In this embodiment, the preset number is 500. In specific applications, the implementer can set it according to the specific situation.

[0034] Then, curve fitting is performed on the thrust fluctuation values ​​of all historical normal walking cycles obtained above to obtain the normal walking thrust fluctuation curve. The horizontal axis of the normal walking thrust fluctuation curve is time, and the vertical axis is the thrust fluctuation value. Curve fitting is existing technology and will not be elaborated further here. When a person walks, their arms swing regularly, so the obtained normal walking thrust fluctuation curve has a certain degree of regularity. Based on this, peaks and troughs are detected on the normal walking thrust fluctuation curve. Each peak and its adjacent trough in the normal walking thrust fluctuation curve constitute a combination, that is, multiple pairs of combinations can be obtained. Each combination contains a peak point and a trough point. A combination represents one walking behavior of the wearer, that is, one step. Peak and trough detection on the curve is also existing technology and will not be elaborated further here. The difference between the thrust fluctuation value of the peak and the thrust fluctuation value of the trough in each combination is calculated, and this difference is recorded as the first difference value.

[0035] Furthermore, the first difference is converted using the wearer's walking characteristic conversion coefficient to obtain the wearer's step length reference value; wherein, the walking characteristic conversion coefficient is pre-calibrated by the wearer walking a known distance in an open area and counting the steps, and in this embodiment, the walking characteristic conversion coefficient is [missing value]. .

[0036] As a concrete example, the specific formula for calculating the wearer's step length reference value is given. The wearer's step length reference value can be expressed as: In the formula, This indicates the wearer's stride reference value. This indicates the number of combinations of peaks and troughs in the normal travel thrust fluctuation curve. This represents the thrust fluctuation value of the peak in the x-th combination. This represents the thrust fluctuation value at the trough in the x-th combination. This represents the walking feature conversion coefficient.

[0037] In particular, when hour, The value is set directly to 0.65 meters, which represents the normal stride length of an adult.

[0038] During the current cycle, it is necessary to monitor in real time whether the wearer's walking process is in a normal environment. Smart wearable devices determine the wearer's location information through signals from multiple satellites. If there are significant differences in the signal strength information between different satellites, it indicates a higher degree of discrepancy between the signals, and the satellite signals in the wearer's current environment may have been refracted or subjected to other abnormalities.

[0039] Based on the above characteristics, the degree of satellite signal contradiction will be evaluated according to the differences in satellite signal strength between different satellite signals of smart wearable devices in the current period.

[0040] As a specific example, the degree of satellite signal conflict can be determined by calculating the absolute value of the difference between the signal strengths of each pair of satellite signals, and denoteing this absolute value as the first difference; the average of all first differences is then determined as the degree of satellite signal conflict.

[0041] The more satellites a smart wearable device can detect, the stronger the signal, and the less conflicting the satellite signals, the higher the wearer's environmental health score for the current period.

[0042] Therefore, the environmental health score for the current period is obtained by combining the average signal strength of all satellite signals received by the smart wearable device in the current period, the ratio of the number of satellites searched in the current period to the maximum number of searchable satellites, and the second difference between the preset upper limit of contradiction and the degree of contradiction of satellite signals. The above average value, the above ratio and the second difference are all positively correlated with the environmental health score.

[0043] As a concrete example, the specific formula for calculating the environmental health score is given. The environmental health score for the current period can be expressed as: in, This indicates the environmental health score for the current period. This represents the average signal strength of all satellite signals received by the smart wearable device in the current period. This indicates the number of satellites detected in the current search period. Indicates the maximum number of searchable satellites. This indicates the preset upper limit of the degree of contradiction. Indicates the degree of contradiction in satellite signals. This represents the normalization function.

[0044] The preset upper limit of the contradiction degree is determined based on the measured maximum deviation value of the equipment under standard multipath interference environment. In this embodiment, the preset upper limit of the contradiction degree is set to 30. In specific applications, the implementer can set it according to the specific situation.

[0045] In this embodiment, the maximum and minimum value normalization method is used when normalizing the data. The maximum and minimum values ​​are obtained based on historical data statistics. As other implementation methods, other existing data normalization methods can also be used for processing.

[0046] The average signal strength of all satellite signals received by the smart wearable device in the current period is used to characterize the overall signal strength of the satellite signals received by the smart wearable device in the current period. The larger the value, the stronger the overall signal strength of the received satellite signals. This represents the percentage of satellites found in the current period; the larger the value, the higher the percentage of satellites found. The second difference value reflects the relative degree of contradiction in satellite signals during the current period. The smaller the value, the greater the relative degree of contradiction in satellite signals during the current period. The stronger the overall signal strength of satellite signals received by the smart wearable device in the current period, the higher the proportion of satellites detected, and the smaller the degree of contradiction in satellite signals, the less likely the satellite signals in the wearer's current environment are to have undergone abnormal conditions such as refraction, i.e., the higher the environmental health score for the current period.

[0047] Step S4: If the environmental health score is lower than the preset safety threshold, the wearer's position is updated based on the step length reference value and the real-time deviation angle.

[0048] In this embodiment, the environmental health score for the current period is obtained in step S3. The lower the environmental health score for the current period, the more likely the wearer is in an environment with strong refraction or poor satellite signal. Therefore, if the environmental health score for the current period is less than a preset safety threshold, the wearer's current location coordinates need to be updated; if the environmental health score for the current period is greater than or equal to the preset safety threshold, the wearer's current location coordinates do not need to be updated. In this embodiment, the preset safety threshold is 0.3.

[0049] When it is necessary to update the wearer's current location coordinates, the historical period with the lowest environmental health score (greater than the preset safety threshold) and the shortest time interval between the current period is selected as the target historical period, and the location corresponding to the wearer's latitude and longitude coordinates at the last moment within the target historical period is recorded as the safe location.

[0050] When users are in environments with poor external signals or continue to go deeper, satellite signals usually do not disappear, but become very unstable due to refraction and other reasons. Therefore, the confidence level of satellite data is set to 0.3, and the confidence level of motion sensor data is set to 0.7.

[0051] Furthermore, the wearer's predicted stride coordinates are first obtained based on motion sensors. Specifically, the changes in position in the longitude and latitude directions are calculated based on the coordinates of the safe position, the reference value of the stride length, and the sine and cosine values ​​of the real-time deviation angle, to obtain the predicted stride coordinates.

[0052] As a concrete example, the wearer's real-time location coordinates (latitude and longitude) are first converted to a Cartesian coordinate system, with the origin of the Cartesian coordinate system set as the starting point. The wearer's predicted longitude and latitude coordinates based on their gait can be expressed as follows: ; ; in, This indicates the wearer's predicted longitude coordinates based on their stride. This indicates the longitude coordinates corresponding to the safe location. Indicates the step size reference value. Indicates the wearer's real-time deflection angle. This represents the function for finding the sine value. This represents the function for finding the cosine value. The wearer's stride indicates the predicted latitude and position coordinates. This indicates the latitude coordinates corresponding to the safe location.

[0053] The specific method for obtaining the wearer's real-time deflection angle is as follows: the raw data from the motion sensor is processed by PCA principal component analysis or coordinate axis alignment algorithm to obtain the deflection angle that is consistent with the direction of human movement, which is the wearer's real-time deflection angle.

[0054] Furthermore, the wearer's satellite coordinates, determined in real time based on satellite communication data, are obtained. Based on the step prediction coordinates and the wearer's current satellite coordinates, the satellite coordinates are converted to Cartesian coordinates to obtain the wearer's updated position coordinates at the current moment.

[0055] Specifically, firstly, the difference between the horizontal coordinate in the wearer's converted Cartesian coordinates and the longitude coordinate in the predicted gait coordinates is taken as the longitude coordinate deviation value; the difference between the vertical coordinate in the wearer's converted Cartesian coordinates and the latitude coordinate in the predicted gait coordinates is taken as the latitude coordinate deviation value. Then, by combining the longitude coordinate deviation value, the latitude coordinate deviation value, and the predicted gait coordinates, the updated position coordinates are obtained.

[0056] As a concrete example, given the wearer's updated coordinates at the current moment, the wearer's updated longitude and latitude coordinates can be represented as follows: in, This indicates the wearer's updated longitude coordinates at the current moment. This indicates the wearer's updated latitude and longitude coordinates at the current moment. This indicates the wearer's predicted longitude coordinates based on their stride. This represents the wearer's current abscissa in Cartesian coordinates after satellite coordinate transformation. The wearer's stride indicates the predicted latitude and position coordinates. It represents the vertical coordinate in the Cartesian coordinate system of the plane after the satellite coordinates of the wearer are transformed at the current moment.

[0057] The above method allows for updates driven by the wearer's regular walking movements, even in environments with weakened external signals. Simultaneously, using satellite communication data to obtain position coordinates as a guide helps prevent trajectory deviations. Since satellite positioning errors do not accumulate and diverge indefinitely over time, they do not disrupt the overall trajectory smoothness, thus limiting the lower bound of coordinate errors.

[0058] This indicates the deviation value of the longitude coordinates. This represents the latitude coordinate deviation value. Specifically, if... If the value is greater than the preset maximum correction threshold, then let The value is equal to the preset maximum correction threshold; similarly, if If the value is greater than the preset maximum correction threshold, then let The value is equal to the preset maximum correction threshold. In this embodiment, the preset maximum correction threshold is 2 meters. If If the value is less than the preset minimum correction threshold, then let The value is equal to the preset minimum correction threshold; similarly, if If the value is less than the preset maximum correction threshold, then let The value is equal to the preset minimum correction threshold. In this embodiment, the preset minimum correction threshold is -2 meters.

[0059] When the wearer leaves an obstructed environment (such as a shopping mall) and enters an open outdoor area, to prevent frequent oscillations in the system control logic caused by fluctuating signal strength at the mall's edge, the system continuously monitors the environmental health score. When the environmental health score exceeds a preset safety threshold for a second consecutive preset number of cycles, it is determined that the wearer has left the satellite communication blind zone. After confirming that the wearer has left the blind zone, the system obtains the absolute coordinates of the currently restored high-quality satellites and updates the location according to preset map update rules. The preset second number can be 5.

[0060] Thus, the method provided in this embodiment achieves high-precision positioning of wearers of smart wearable devices in complex environments.

[0061] This embodiment collects the wearer's motion data and obtains satellite signal strength, enabling high-precision updates of the wearer's position in complex environments by comprehensively utilizing motion information and satellite positioning information. It determines whether the wearer is in a normal walking state based on thrust fluctuation values ​​and the degree of thrust reversal anomalies within a single cycle, avoiding abnormal drift in positioning trajectory caused by non-walking states such as limb swaying in place, thus improving positioning reliability. It obtains step length reference values ​​based on the distribution characteristics of thrust fluctuation values ​​in historical normal walking cycles, and determines the environmental health score by combining the satellite signal strength and number of satellites in the current cycle. This accurately reflects the quality of positioning conditions in the wearer's environment. When the environmental health score is lower than a preset safety threshold, the position is updated using the step length reference value and real-time skew angle. This effectively overcomes problems such as positioning jumps and failures caused by signal refraction, attenuation, or loss in complex environments with severe satellite signal obstruction or interference, such as streets with tall buildings, near glass curtain wall buildings, and underground parking garages. This improves the positioning accuracy and stability of smart wearable devices in complex environments.

[0062] An embodiment of a high-precision positioning system for smart wearable devices in complex environments: See Figure 2 The diagram illustrates a structural block diagram of a high-precision positioning system for smart wearable devices in complex environments, provided by an embodiment of the present invention. The system may include an acquisition module, a judgment module, an evaluation module, and an update module.

[0063] The acquisition module is used to collect the wearer's motion data using smart wearable devices and to acquire satellite signal strength. The judgment module is used to determine the thrust fluctuation value and the degree of thrust reversal anomaly in each cycle based on the action data in each cycle; and to determine whether the current cycle is a normal walking state based on the thrust fluctuation value and the degree of thrust reversal anomaly in the current cycle. The evaluation module is used to obtain a step length reference value based on the distribution characteristics of thrust fluctuation values ​​in historical normal walking cycles if the condition is determined to be normal walking state; and to determine the environmental health score for the current cycle based on the satellite signal strength corresponding to the current cycle and the number of satellites searched by the smart wearable device. The update module is used to update the wearer's position based on the step size reference value and the real-time deviation angle if the environmental health score is lower than a preset safety threshold.

[0064] It should be understood that Figure 2 The structural block diagram and modules of a high-precision positioning system for smart wearable devices in complex environments shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of both. The hardware portion can be implemented using dedicated logic; the software portion can be stored in memory and executed by appropriate instructions, such as a microprocessor or dedicated hardware. Those skilled in the art will understand that the above-described methods and systems can be implemented using computer-executable instructions and / or included in processor control code, for example, on a carrier medium such as a disk, CD, or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The systems and modules of this specification can be implemented not only by hardware circuits such as very large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field-programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, or by a combination of the above-described hardware circuits and software (e.g., firmware).

[0065] For more details about the above modules, please refer to other parts of this manual; they will not be repeated here.

[0066] It should be noted that 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, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A high-precision positioning method for smart wearable devices in complex environments, characterized in that, The method includes the following steps: The system utilizes smart wearable devices to collect the wearer's motion data and obtain satellite signal strength. Based on the action data within each cycle, determine the thrust fluctuation value and the degree of thrust reversal anomaly in each cycle; use the thrust fluctuation value and the degree of thrust reversal anomaly in the current cycle to determine whether the current cycle is a normal walking state; If the walking state is determined to be normal, the step length reference value is obtained based on the distribution characteristics of thrust fluctuation values ​​in historical normal walking cycles; the environmental health score for the current cycle is determined based on the satellite signal strength corresponding to the current cycle and the number of satellites searched by the smart wearable device. If the environmental health score is lower than the preset safety threshold, the wearer's position is updated based on the step size reference value and the real-time deviation angle. The acquisition of the thrust fluctuation value includes: Obtain the maximum and minimum thrust values ​​from the action data within the candidate period, and use the difference between the maximum and minimum thrust values ​​as the thrust fluctuation value of the candidate period; The candidate period can be any period; Determining the environmental health score for the current period includes: The degree of satellite signal discrepancy is evaluated based on the differences in satellite signal strength between different satellite signals from smart wearable devices within the current period. The environmental health score for the current period is obtained by taking the average signal strength of all satellite signals received by the smart wearable device, the ratio of the number of satellites searched in the current period to the maximum number of searchable satellites, and the second difference between the preset upper limit of contradiction and the degree of contradiction of the satellite signals. The average value, the ratio, and the second difference are all positively correlated with the environmental health score. The method of determining whether the current cycle is a normal travel state by utilizing the thrust fluctuation value and the degree of thrust reversal anomaly in the current cycle includes: If the thrust fluctuation value of the current cycle is within the preset thrust fluctuation value range and the abnormality of thrust reversal in the current cycle is within the preset thrust reversal range, then it is determined to be a normal walking state. If the thrust fluctuation value of the current cycle is not within the preset thrust fluctuation value range and / or the abnormality of thrust reversal in the current cycle is not within the preset thrust reversal range, it is determined to be an abnormal walking state.

2. The high-precision positioning method for intelligent wearable devices in complex environments according to claim 1, characterized in that, The acquisition of the degree of thrust reversal anomaly includes: The number of thrust direction reversals within a candidate period is counted as the thrust reversal count of the candidate period, and the thrust reversal count is used as the thrust reversal anomaly degree of the candidate period.

3. The high-precision positioning method for intelligent wearable devices in complex environments according to claim 1, characterized in that, The step length reference value is obtained based on the distribution characteristics of thrust fluctuation values ​​during historical normal walking cycles, including: The thrust fluctuation values ​​of a preset number of historical normal walking cycles are curve fitted to obtain the normal walking thrust fluctuation curve. The horizontal axis of the normal walking thrust fluctuation curve is time, and the vertical axis is the thrust fluctuation value. In the normal travel thrust fluctuation curve, each peak and its adjacent trough form a combination. Obtain the first difference between the thrust fluctuation values ​​between the peaks and troughs in each combination; The first difference is converted using the wearer's walking characteristic conversion coefficient to obtain the step length reference value; wherein, the walking characteristic conversion coefficient is pre-calibrated by the wearer walking a known distance in an open area and counting the number of steps.

4. The high-precision positioning method for intelligent wearable devices in complex environments according to claim 1, characterized in that, The evaluation of the degree of satellite signal discrepancy based on the differences in satellite signal strength between different satellite signals from smart wearable devices within the current period includes: Calculate the first difference between the satellite signal strengths of each pair of satellite signals; The average of all the first differences is determined as the degree of satellite signal discrepancy.

5. The high-precision positioning method for intelligent wearable devices in complex environments according to claim 1, characterized in that, The step size reference value and real-time skew angle are used to update the wearer's position, including: Based on the coordinates of the safe position, the step length reference value, and the sine and cosine values ​​of the real-time deviation angle, the position changes in the longitude and latitude directions are calculated respectively to obtain the step prediction coordinates. Obtain the wearer's satellite coordinates in real time based on satellite communication data; Based on the predicted gait coordinates and the wearer's satellite coordinates, the updated position coordinates are obtained.

6. The high-precision positioning method for intelligent wearable devices in complex environments according to claim 5, characterized in that, The process of obtaining updated position coordinates based on the predicted gait coordinates and the wearer's satellite coordinates includes: The difference between the horizontal coordinate in the Cartesian coordinate system converted from the wearer's satellite coordinates and the longitude coordinate in the predicted gait coordinates is taken as the longitude coordinate deviation value; the difference between the vertical coordinate in the Cartesian coordinate system converted from the wearer's satellite coordinates and the latitude coordinate in the predicted gait coordinates is taken as the latitude coordinate deviation value. By combining the longitude coordinate deviation value, the latitude coordinate deviation value, and the step prediction coordinates, the updated position coordinates are obtained.

7. The high-precision positioning method for intelligent wearable devices in complex environments according to claim 1, characterized in that, If the walking state is determined to be abnormal, the current location data will not be updated.

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