Track correction method based on multiple sensors, intelligent wearable device and readable storage medium

Abstract

The invention provides a trajectory correction method and device based on multiple sensors, intelligent wearable equipment and a readable storage medium, and the method comprises the steps: obtaining the current weather information, and collecting the infrared intensity, ultraviolet index and visible light intensity of the current ambient light when the intelligent wearable equipment is outdoor; determining an illumination scene where the intelligent wearable device is located according to the weather information, the infrared intensity of the current ambient light, the ultraviolet index and the visible light intensity; when the intelligent wearable device is in a non-sunshine scene, satellite navigation data and nine-axis inertial data are collected; according to the satellite navigation data and the nine-axis inertial data, the trajectory of the intelligent wearable device is rectified, and the unsmoothness and drifting phenomena frequently occurring in the positioning trajectory can be effectively solved.

Description

Technical Field

[0001] This invention relates to the field of navigation and positioning technology, and in particular to a trajectory correction method based on multiple sensors, a smart wearable device, and a readable storage medium. Background Technology

[0002] With the development of IoT technology and the popularization of sports and health concepts, smart wearable devices are increasingly integrated into people's daily exercise and travel. Among them, outdoor positioning functions, such as recording running and cycling routes, have become one of the core application scenarios for this type of device.

[0003] Currently, such functions mainly rely on global navigation satellite systems, such as GPS (Global Positioning System), BDS (BeiDou Navigation Satellite System), and GLONASS (Global Navigation Satellite System). However, due to limitations inherent in wearable devices and factors in the actual application environment such as signal obstruction, their positioning trajectories often exhibit significant unevenness and drift, directly impacting user experience and data accuracy. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a multi-sensor-based trajectory correction method, a smart wearable device, and a readable storage medium, which can effectively solve the unsmoothness and drift phenomena that often occur in positioning trajectories.

[0005] The technical solution provided by this invention is as follows:

[0006] On one hand, the present invention provides a trajectory correction method based on multiple sensors, applicable to smart wearable devices, the trajectory correction method comprising:

[0007] When the smart wearable device is outdoors, it acquires the current weather information and collects the infrared intensity, ultraviolet index and visible light intensity of the current ambient light.

[0008] The lighting scene in which the smart wearable device is located is determined based on the weather information, the infrared intensity of the current ambient light, the ultraviolet index, and the visible light intensity.

[0009] When the smart wearable device is in a non-sunlight environment, it collects satellite navigation data and nine-axis inertial data;

[0010] The trajectory of the smart wearable device is corrected based on the satellite navigation data and the nine-axis inertial data.

[0011] In some embodiments, determining the lighting scene of the smart wearable device based on the weather information, the infrared intensity of the current ambient light, the ultraviolet index, and the visible light intensity includes:

[0012] Based on the weather information, determine the outdoor scene where the smart wearable device is located;

[0013] The infrared intensity, the ultraviolet index, and the visible light intensity are each normalized.

[0014] The lighting scene in which the smart wearable device is located is determined based on the normalized ratio of ultraviolet index to visible light intensity, the ratio of infrared intensity to visible light intensity, and the outdoor scene.

[0015] In some embodiments, determining the lighting scene of the smart wearable device based on the normalized ratio of ultraviolet index to visible light intensity, the ratio of infrared intensity to visible light intensity, and the outdoor scene includes:

[0016] When the outdoor scene is sunny, and the ratio of the ultraviolet index to the visible light intensity is less than or equal to a first preset ratio, and the ratio of the infrared intensity to the visible light intensity is less than or equal to a second preset ratio, the smart wearable device is determined to be in a non-sunlight exposure scene.

[0017] In some embodiments, determining the lighting scene of the smart wearable device based on the weather information, the infrared intensity of the current ambient light, the ultraviolet index, and the visible light intensity includes:

[0018] Obtain sunrise and sunset information and time information from smart wearable devices;

[0019] The lighting scene in which the smart wearable device is located is determined based on the sunrise and sunset information, the time information of the smart wearable device, the weather information, the infrared intensity of the current ambient light, the ultraviolet index, and the visible light intensity.

[0020] In some embodiments, determining the lighting scene of the smart wearable device based on the sunrise / sunset information, the time information of the smart wearable device, the weather information, the infrared intensity of the current ambient light, the ultraviolet index, and the visible light intensity includes:

[0021] The time period of the outdoor area is determined based on the sunrise and sunset information and the time information of the smart wearable device;

[0022] Based on the weather information and the time information of the smart wearable device, the outdoor scene in which the smart wearable device is located is determined;

[0023] The infrared intensity, the ultraviolet index, and the visible light intensity are each normalized.

[0024] The lighting scene of the smart wearable device is determined based on the ratio of the normalized ultraviolet index to the visible light intensity, the ratio of the infrared intensity to the visible light intensity, the time period of the outdoor environment, and the outdoor scene.

[0025] In some embodiments, the lighting scene of the smart wearable device is determined based on the ratio of the normalized ultraviolet index to the visible light intensity, the ratio of the infrared intensity to the visible light intensity, the time period of the outdoor activity, and the outdoor scene, including:

[0026] When the outdoor time period is between sunrise and sunset, the outdoor scene is sunny, and the ratio of ultraviolet index to visible light intensity is less than or equal to a first preset ratio, and the ratio of infrared intensity to visible light intensity is less than or equal to a second preset ratio, the smart wearable device is determined to be in a non-sunlight exposure scene.

[0027] In some embodiments, when the smart wearable device is outdoors, it is determined that the smart wearable device is outdoors if multiple of the following conditions are met, based on current weather information and the infrared intensity, ultraviolet index, and visible light intensity of the current ambient light:

[0028] Map service providers use the geographical coordinates provided by the Wi-Fi information around smart wearable devices to indicate that they are outdoors;

[0029] The number of satellites used within the first preset time period is greater than the preset satellite threshold;

[0030] The standard deviation of air pressure within the second preset time period is greater than the preset standard deviation threshold.

[0031] The intensity of the collected visible light is greater than the preset ambient light intensity threshold.

[0032] In some embodiments, after determining the lighting scene in which the smart wearable device is located based on the weather information, the infrared intensity of the current ambient light, the ultraviolet index, and the visible light intensity, the method further includes:

[0033] When the smart wearable device is in a sunlight-exposed environment, it collects satellite navigation data and performs orientation determination and trajectory drawing based on the satellite navigation data.

[0034] On the other hand, the present invention provides a trajectory correction device based on multiple sensors, applicable to smart wearable devices, comprising:

[0035] The data acquisition module is used to acquire current weather information and collect the current ambient light IR intensity, UVI and visible light intensity when the smart wearable device is outdoors;

[0036] The lighting scene determination module, connected to the data acquisition module, is used to determine the lighting scene in which the smart wearable device is located based on the weather information, the infrared intensity of the current ambient light, the ultraviolet index, and the visible light intensity.

[0037] The trajectory correction module, connected to the lighting scene determination module, is used to collect satellite navigation data and nine-axis inertial data when the smart wearable device is in a non-sunlight scene; and to correct the trajectory of the smart wearable device based on the satellite navigation data and the nine-axis inertial data.

[0038] In another aspect, the present invention provides a smart wearable device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described multi-sensor-based trajectory correction method.

[0039] In another aspect, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described multi-sensor-based trajectory correction method.

[0040] This invention provides a multi-sensor-based trajectory correction method, a smart wearable device, and a readable storage medium. When the smart wearable device is outdoors, and the outdoor scene is determined to be sunny based on weather information, the illumination scene of the smart wearable device is further confirmed by collecting the infrared intensity, ultraviolet index, and visible light intensity of the current ambient light. If it is in a non-sunlight environment, i.e., the illumination conditions are poor, the device prioritizes nine-axis inertial navigation processing based on data provided by the GNSS chip to draw the trajectory. This process effectively smooths the trajectory path, reduces glitches caused by data fluctuations, and improves the stability and accuracy of the trajectory output. Attached Figure Description

[0041] The preferred embodiments will now be described in a clear and easy-to-understand manner, with reference to the accompanying drawings, to further explain the above-mentioned characteristics, technical features, advantages, and implementation methods.

[0042] Figure 1 This is a schematic flowchart of one embodiment of the trajectory correction method in this invention;

[0043] Figure 2 This is a schematic diagram illustrating the process of determining the lighting scene in which the smart wearable device is located in this invention.

[0044] Figure 3This is a schematic flowchart of another embodiment of the trajectory correction method in this invention;

[0045] Figure 4 This is a schematic flowchart of one embodiment of the trajectory correction device in this invention;

[0046] Figure 5 This is a schematic diagram of the structure of the smart wearable device in this invention.

[0047] Figure label:

[0048] 100 - Trajectory correction device; 110 - Data acquisition module; 120 - Illumination scene determination module; 130 - Trajectory correction module; 200 - Smart wearable device; 210 - Processor; 220 - Memory; 221 - Computer program. Detailed Implementation

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without any creative effort.

[0050] The first embodiment of the present invention provides a trajectory correction method based on multiple sensors, applied to smart wearable devices, such as... Figure 1 As shown, trajectory correction methods include:

[0051] When the smart wearable device is outdoors, the S10 obtains the current weather information and collects the infrared intensity, ultraviolet index and visible light intensity of the current ambient light.

[0052] The S20 determines the lighting scene of the smart wearable device based on weather information, the infrared intensity, ultraviolet index, and visible light intensity of the current ambient light.

[0053] When the smart wearable device is in a non-sunlight environment, the S30 collects satellite navigation data and nine-axis inertial data;

[0054] The S40 uses satellite navigation data and nine-axis inertial data to correct the trajectory of smart wearable devices.

[0055] The trajectory correction method in this embodiment is applied to smart wearable devices to reduce trajectory unevenness and drift problems that occur when smart wearable devices are positioned outdoors. Smart wearable devices are conventional intelligent wearable products developed using wearable technology, such as smartwatches, smart bracelets, and smart glasses. To achieve the purpose of this embodiment, the internal components may include, but are not limited to, conventional memory, processor, and other components, as well as a UV (ultraviolet) sensor for collecting infrared intensity, ultraviolet index, and visible light intensity; an inertial measurement unit (IMU) for collecting nine-axis inertial data; and a GNSS navigation chip for collecting satellite navigation data (GNN data).

[0056] During operation, when the smart wearable device is outdoors, it immediately sends a request to the cloud to obtain the current weather information, facilitating confirmation of the outdoor weather conditions, such as sunny, cloudy, or rainy. Simultaneously, it controls the UV sensor to collect the infrared (IR) intensity, ultraviolet index (UVI), and visible light intensity of the current ambient light. Visible light intensity can be represented by LUX, also known as illumination intensity, which refers to the energy of visible light received per unit area.

[0057] After data collection is completed, step S20 proceeds to determine the lighting scenario of the smart wearable device based on weather information, the infrared intensity, ultraviolet index, and visible light intensity of the current ambient light. Specifically, as... Figure 2 As shown, this step includes:

[0058] S21 determines the outdoor scene where the smart wearable device is located based on weather information;

[0059] S22 normalizes the infrared intensity, ultraviolet index, and visible light intensity respectively;

[0060] S23 determines the lighting scene of the smart wearable device based on the normalized ratio of ultraviolet index to visible light intensity, the ratio of infrared intensity to visible light intensity, and the outdoor scene.

[0061] Since the application scenario in this embodiment is a sunny day, the judgment process first determines whether the outdoor scene where the smart wearable device is located is sunny based on the acquired weather information. If the acquired weather information shows that the current outdoor weather is cloudy or rainy, the subsequent steps are not performed, and the trajectory correction method of this embodiment is skipped. The smart wearable device still uses the conventional outdoor positioning method for outdoor positioning and trajectory drawing. If the acquired weather information shows that the current outdoor weather is sunny, the collected IR intensity, UVI, and visible light intensity are normalized respectively, and then the lighting scene where the smart wearable device is located is further determined based on the normalized IR intensity, UVI, and visible light intensity. The acquired IR intensity, UVI, and visible light intensity can be data acquired in a single instance or an average over a period of time; there is no limitation here.

[0062] In the normalization process, the maximum UVI and maximum IR intensity values ​​collected over a period of time in an outdoor scene are first obtained. Then, the current UVI is divided by the maximum UVI value to obtain the normalized UVI value. Similarly, the current IR intensity is divided by the maximum IR intensity value to obtain the normalized IR intensity value. The normalization process for visible light intensity is as follows: after obtaining the visible light intensity over a period of time in an outdoor scene, each visible light intensity is first converted using log10 to obtain the corresponding log value = log... 10 (Visible light intensity), obtain the maximum and minimum log values; then calculate the normalized value of the current visible light intensity according to the formula = (log value of current visible light intensity - minimum log value) / (maximum log value - minimum log value).

[0063] According to the judgment rules, if the ratio of UVI to visible light intensity is high, the smart wearable device can be judged to be in a sunlight-exposed scene (sunny side); otherwise, it is judged to be in a non-sunlight-exposed scene (shaded side), such as in the shadow of a building, under a tree, or indoors. Similarly, if the ratio of IR intensity to visible light intensity is high, the smart wearable device can also be judged to be in a sunlight-exposed scene; conversely, if the ratio is low, it is judged to be in a non-sunlight-exposed scene (shaded side). Therefore, this embodiment limits the judgment to the following: when the ratio of UVI to visible light intensity is less than or equal to a first preset ratio, and the ratio of IR intensity to visible light intensity is less than or equal to a second preset ratio, the smart wearable device is judged to be in a non-sunlight-exposed scene; correspondingly, when the ratio of UVI to visible light intensity is greater than the first preset ratio, and the ratio of IR intensity to visible light intensity is greater than the second preset ratio, the smart wearable device is judged to be in a sunlight-exposed scene. The values ​​of the first and second preset ratios can be adjusted according to the actual application scenario, and are not specifically limited here. In one example, the first preset threshold is set to 0.015, and the second preset threshold is set to 2. That is, when UVI / visible light intensity > 0.015 and IR intensity / visible light intensity > 2, the smart wearable device is determined to be in a sunlight-exposed scene; otherwise, it is determined to be in a non-sunlight-exposed scene. In other embodiments, the first preset threshold can also be set to 0.01, 0.02, etc., and the second preset threshold can be set to 1.5, 2.5, etc.

[0064] When the smart wearable device is in sunlight, it collects GNSS data and performs orientation determination and trajectory drawing based on the GNSS data, achieving stable and accurate trajectory output using conventional methods. However, when the smart wearable device is in non-sunlight conditions, signal loss can easily occur, significantly reducing the positioning accuracy of the GNSS chip and even rendering navigation and positioning impossible. Therefore, in this embodiment, for non-sunlight scenarios, orientation determination and trajectory drawing are performed by fusing GNSS data and nine-axis inertial data. For example, absolute position and velocity information provided by GPS are used to correct the IMU's calculation results. During this process, the system quickly switches the core of navigation calculation from a GNSS chip that relies on absolute position information to a relative motion calculation mode dominated by the IMU. The algorithm first uses the reliable fused position and heading from the previous moment as an initial reference, then reads high-frequency data from the IMU in real time. The gyroscope in the IMU provides accurate angular velocity data, which, after integration, yields the continuous change in the device's orientation. The accelerometer, after removing the gravity component, calculates the travel distance through double integration, correcting pitch and roll drift. Meanwhile, the magnetometer provides an absolute compass reference in environments with minimal interference, assisting in correcting gyroscope drift and serving as an absolute reference to correct heading angle drift. Furthermore, the algorithm uses a Kalman filter to weightedly fuse the relative displacement calculated by the IMU with GNSS position information (GNSS data): when GNSS data is reliable, such as when GPS signals are strong and stable, the Kalman filter gain favors GNSS measurements, using new observation data to correct and fuse predicted states; when GNSS data is unreliable, such as when GPS signals are lost, the Kalman filter gain favors nine-axis inertial data, maintaining system state estimation based on its short-term motion extrapolation algorithm. This allows smart wearable devices in non-sunlight environments to still draw continuous, smooth, and reasonably oriented predicted trajectories based on their motion inertia and attitude changes.

[0065] This embodiment is obtained by improving the above embodiments. In this embodiment, as follows: Figure 3 As shown, trajectory correction methods include:

[0066] When the smart wearable device is outdoors, the S10 obtains the current weather information and collects the infrared intensity, ultraviolet index and visible light intensity of the current ambient light.

[0067] S24 obtains sunrise and sunset information and time information from smart wearable devices;

[0068] S25 determines the lighting scene of the smart wearable device based on sunrise and sunset information, time information of the smart wearable device, weather information, infrared intensity, ultraviolet index and visible light intensity of the current ambient light;

[0069] When the smart wearable device is in a non-sunlight environment, the S30 collects satellite navigation data and nine-axis inertial data;

[0070] The S40 uses satellite navigation data and nine-axis inertial data to correct the trajectory of smart wearable devices.

[0071] In this embodiment, when the smart wearable device is outdoors, it immediately sends a request to the cloud to obtain current weather information, sunrise and sunset information, and the time information of the smart wearable device, so as to confirm the time period and the outdoor scene in which the smart wearable device is located. At the same time, it controls the UV sensor to collect the current ambient light IR intensity, UVI, and visible light intensity.

[0072] Since the application scenario in this embodiment is a sunny daytime period (the time between sunrise and sunset), the determination process first uses sunrise and sunset information and the time information of the smart wearable device to determine the outdoor time period. Common sense dictates that daytime typically refers to the time between sunrise and sunset, i.e., the time with sunlight. Therefore, after obtaining the sunrise and sunset information and the smart wearable device's time information, it can be confirmed whether the current time is between sunrise and sunset. If it is determined that the current time is not daytime, subsequent steps are not performed, and the trajectory correction method of this embodiment is skipped. The smart wearable device still uses conventional outdoor positioning methods for outdoor positioning and trajectory drawing. After confirming that the current time is daytime, the outdoor scene where the smart wearable device is located is further determined based on weather information and the smart wearable device's time information. Generally, weather information may vary slightly at different times; for example, 10:00-11:00 AM is sunny, while 11:00 AM-12:00 PM is cloudy. Therefore, based on the weather information and the smart wearable device's time information, it is possible to accurately determine whether the current time is sunny outdoors. If the acquired weather information indicates that the current outdoor weather is cloudy or rainy, or even if it is sunny but the current time period is before sunrise or after sunset, the subsequent steps will not be continued, and the trajectory correction method of this embodiment will be skipped. The smart wearable device will still use the conventional outdoor positioning method for outdoor positioning and trajectory drawing. If the acquired weather information indicates that it is currently daytime and the outdoor weather is sunny, the collected IR intensity, UVI, and visible light intensity will be normalized respectively. Then, based on the normalized ratio of UVI to visible light intensity and the ratio of IR intensity to visible light intensity, the lighting scene in which the smart wearable device is located will be further determined.

[0073] In the normalization process, the maximum UVI and maximum IR intensity values ​​collected over a period of time in an outdoor scene are first obtained. Then, the current UVI is divided by the maximum UVI value to obtain the normalized UVI value. Similarly, the current IR intensity is divided by the maximum IR intensity value to obtain the normalized IR intensity value. The normalization process for visible light intensity is as follows: after obtaining the visible light intensity over a period of time in an outdoor scene, each visible light intensity is first converted using log10 to obtain the corresponding log value = log... 10 (Visible light intensity), obtain the maximum and minimum log values; then calculate the normalized value of the current visible light intensity according to the formula = (log value of current visible light intensity - minimum log value) / (maximum log value - minimum log value).

[0074] According to the judgment rules, if the ratio of UVI to visible light intensity is high, the smart wearable device can be judged to be in a sunlight-exposed scene (sunny side); otherwise, it is judged to be in a non-sunlight-exposed scene (shaded side), such as in the shadow of a building, under a tree, or indoors. Similarly, if the ratio of IR intensity to visible light intensity is high, the smart wearable device can also be judged to be in a sunlight-exposed scene; conversely, if the ratio is low, it is judged to be in a non-sunlight-exposed scene (shaded side). Therefore, this embodiment limits the judgment to the following: when the ratio of UVI to visible light intensity is less than or equal to a first preset ratio, and the ratio of IR intensity to visible light intensity is less than or equal to a second preset ratio, the smart wearable device is judged to be in a non-sunlight-exposed scene; correspondingly, when the ratio of UVI to visible light intensity is greater than the first preset ratio, and the ratio of IR intensity to visible light intensity is greater than the second preset ratio, the smart wearable device is judged to be in a sunlight-exposed scene. The values ​​of the first and second preset ratios can be adjusted according to the actual application scenario, and are not specifically limited here.

[0075] This embodiment is an improvement on the above embodiment. In this embodiment, in step S10, when the smart wearable device is outdoors, the current weather information is obtained, and the current ambient light IR intensity, UVI, and visible light intensity are collected. The smart wearable device is determined to be outdoors if multiple of the following conditions are met:

[0076] A. The geographic coordinates fed back by the map service provider based on the Wi-Fi information around the smart wearable device are considered to be outdoors;

[0077] B. The number of satellites used within the first preset time period is greater than the preset satellite threshold;

[0078] C. The standard deviation of air pressure within the second preset time period is greater than the preset standard deviation threshold;

[0079] D. The intensity of the collected visible light is greater than the preset ambient light intensity threshold.

[0080] Since the application scenario in this embodiment is outdoors, it is necessary to determine whether the smart wearable device is outdoors before performing trajectory correction. To improve the accuracy of the determination, this embodiment defines four preset rules, and the device is determined to be outdoors only if multiple of these rules are met simultaneously. To further improve the accuracy of the determination, it can be further defined that the device is determined to be outdoors only if three of these rules are met simultaneously.

[0081] In Rule A, when a smart wearable device is locating itself, it scans for nearby Wireless Fidelity (Wi-Fi) information and reports the scanned Wi-Fi information to a map service provider, such as Baidu Maps or Gaode Maps. Upon receiving the information, the map service provider returns the smart wearable device's geographic coordinates and detailed location (province, city, district, street, etc.) based on the received Wi-Fi information. If the smart wearable device confirms it is outdoors based on the received geographic coordinates and detailed location, then Rule A is satisfied; otherwise, it is not.

[0082] In Rule B, when a smart wearable device is outdoors, the number of satellites that the GNSS navigation chip can detect will significantly increase. The first preset time period and preset satellite threshold mentioned in the rule can be set according to the actual situation, such as setting the first preset time period to 5 seconds, 10 seconds, etc., and the preset satellite threshold to 8, 10, 12, etc. No specific limitations are made here. In one example, if the number of satellites that can be detected within 5 seconds is greater than or equal to 8, it is determined that the device is outdoors; if the number of satellites that can be detected within 5 seconds is less than 8, it is determined that the device is indoors (typically, the number of satellites that can be detected indoors is less than or equal to 5). It should be understood that the GNSS navigation chip will also provide positioning coordinates, which can be combined with Rule B to further improve accuracy.

[0083] In Rule C, when a smart wearable device moves from indoors to outdoors, or from outdoors to indoors, the air pressure value will fluctuate from stable to slightly fluctuating. Air pressure values ​​are collected over a second preset time period, and the standard deviation is calculated and compared to a preset standard deviation threshold. If the standard deviation is greater than the preset standard deviation threshold, it is determined that a transition from indoors to outdoors has occurred. In application, the value of the preset standard deviation threshold can be set according to the actual situation; no specific limitation is made here, such as setting it between 0.5 and 5 hPa (hPa).

[0084] Rule D states that the intensity of visible light collected by the UV sensor differs significantly between indoors and outdoors. Therefore, this condition can be used to determine whether a smart wearable device is outdoors. In applications, a preset ambient light intensity threshold can also be set based on actual conditions. For example, in one instance, the preset ambient light intensity threshold is set to 1000 LUX. If the collected visible light intensity is greater than 1000 LUX, it is determined that the device is currently outdoors.

[0085] The present invention also provides a trajectory correction device 100 based on multiple sensors, which can be applied to smart wearable devices, such as... Figure 4 As shown, the trajectory correction device 100 includes: a data acquisition module 110, used to acquire current weather information and collect the IR intensity, UVI, and visible light intensity of the current ambient light when the smart wearable device is outdoors; a lighting scene determination module 120, connected to the data acquisition module 110, used to determine the lighting scene where the smart wearable device is located based on the weather information, the infrared intensity, ultraviolet index, and visible light intensity of the current ambient light; and a trajectory correction module 130, connected to the lighting scene determination module 120, used to collect satellite navigation data and nine-axis inertial data when the smart wearable device is in a non-sunlight scene; and to correct the trajectory of the smart wearable device based on the satellite navigation data and nine-axis inertial data.

[0086] The trajectory correction device 100 in this embodiment is applied to a smart wearable device to reduce trajectory unevenness and drift problems that occur when the smart wearable device is positioned outdoors. The smart wearable device is a conventional intelligent wearable product developed using wearable technology, such as a smartwatch, smart bracelet, or smart glasses. To achieve the purpose of this embodiment, in addition to conventional components such as a memory and processor, it may also be equipped with a UV sensor for collecting infrared intensity, ultraviolet index, and visible light intensity, an IMU for collecting nine-axis inertial data, and a GNSS navigation chip for collecting GNSS data.

[0087] During operation, when the smart wearable device is outdoors, it immediately sends a request to the cloud to obtain the current weather information, making it easy to confirm the current outdoor weather conditions, such as sunny, cloudy, or rainy. At the same time, it controls the UV sensor to collect the current ambient light IR intensity, UVI, and visible light intensity. Visible light intensity can be represented by LUX, which can also be called illumination intensity, referring to the energy of visible light received per unit area.

[0088] The illumination scene determination module 120 includes: an outdoor scene determination unit, used to determine the outdoor scene where the smart wearable device is located based on weather information; a normalization unit, connected to the outdoor scene determination unit, used to normalize the infrared intensity, ultraviolet index, and visible light intensity respectively when the outdoor scene determination unit determines that the outdoor scene is sunny; and an illumination scene determination unit, connected to the normalization unit, used to determine the illumination scene where the smart wearable device is located based on the ratio of the normalized ultraviolet index to the visible light intensity, the ratio of the infrared intensity to the visible light intensity, and the outdoor scene.

[0089] Since the application scenario in this embodiment is a sunny day, the judgment process first determines whether the outdoor scene where the smart wearable device is located is sunny based on the acquired weather information. If the acquired weather information indicates that the current outdoor weather is cloudy or rainy, the smart wearable device still uses the conventional outdoor positioning method for outdoor positioning and trajectory drawing. If the acquired weather information indicates that the current outdoor weather is sunny, the collected IR intensity, UVI, and visible light intensity are normalized respectively, and then the lighting scene where the smart wearable device is located is further determined based on the normalized IR intensity, UVI, and visible light intensity. The acquired IR intensity, UVI, and visible light intensity can be data acquired in a single instance or an average over a period of time; there is no limitation here.

[0090] In the normalization unit, the maximum UVI and maximum IR intensity values ​​collected over a period of time in an outdoor scene are first obtained. Then, the current UVI is divided by the maximum UVI value to obtain the normalized UVI value. Similarly, the current IR intensity is divided by the maximum IR intensity value to obtain the normalized IR intensity value. The normalization process for visible light intensity is as follows: after obtaining the visible light intensity over a period of time in an outdoor scene, each visible light intensity is first converted using log10 to obtain the corresponding log value = log... 10 (Visible light intensity), obtain the maximum and minimum log values; then calculate the normalized value of the current visible light intensity according to the formula = (log value of current visible light intensity - minimum log value) / (maximum log value - minimum log value).

[0091] According to the judgment rules, if the ratio of UVI to visible light intensity is high, the smart wearable device can be determined to be in a sunny scene (sunny side); otherwise, it is determined to be in a non-sunny scene (shaded side), such as in the shadow of a building, under a tree, or indoors. Similarly, if the ratio of IR intensity to visible light intensity is high, the smart wearable device can also be determined to be in a sunny scene; otherwise, it is determined to be in a non-sunny scene (shaded side). Based on this, the illumination scene determination unit in this embodiment further specifies that when the ratio of UVI to visible light intensity is less than or equal to a first preset ratio, and the ratio of IR intensity to visible light intensity is less than or equal to a second preset ratio, the smart wearable device is determined to be in a non-sunny scene; correspondingly, when the ratio of UVI to visible light intensity is greater than the first preset ratio, and the ratio of IR intensity to visible light intensity is greater than the second preset ratio, the smart wearable device is determined to be in a sunny scene. The values ​​of the first and second preset ratios can be adjusted according to the actual application scenario, and are not specifically limited here.

[0092] When the smart wearable device is in sunlight, it collects GNSS data and performs orientation determination and trajectory drawing based on the GNSS data, achieving stable and accurate trajectory output using conventional methods. However, when the smart wearable device is in non-sunlight conditions, signal loss can easily occur, significantly reducing the positioning accuracy of the GNSS chip and even rendering navigation and positioning impossible. Therefore, in this embodiment, the trajectory correction module 130, for non-sunlight scenarios, performs orientation determination and trajectory drawing by fusing GNSS data and nine-axis inertial data. For example, it uses absolute position and velocity information provided by GPS to correct the IMU's calculation results. During this process, the trajectory correction module 130 quickly switches the core of navigation calculation from a GNSS chip that relies on absolute position information to a relative motion calculation mode dominated by the IMU. The algorithm first uses the reliable fused position and heading from the previous moment as an initial reference. Then, it reads high-frequency data from the IMU in real time. The gyroscope in the IMU provides precise angular velocity data, which, after integration, yields the continuous change in the device's orientation. The accelerometer, after removing the gravitational component, calculates the travel distance through double integration, correcting for pitch and roll drift. Simultaneously, the magnetometer provides an absolute compass reference in a low-interference environment, assisting in correcting gyroscope drift and serving as an absolute reference to correct heading drift. Furthermore, the algorithm uses a Kalman filter to weightedly fuse the relative displacement calculated by the IMU with the GNSS position information (GNSS data): when the GNSS data is reliable, such as when the GPS signal is strong and stable, the Kalman filter gain tends to favor the GNSS measurement value, using new observation data to correct and fuse the predicted state; when the GNSS data is unreliable, such as when the GPS signal is lost, the Kalman filter gain tends to favor the nine-axis inertial data, maintaining the estimation of the system state based on its short-term motion extrapolation algorithm, so that smart wearable devices in non-sunlight scenarios can still draw continuous, smooth and reasonably oriented predicted trajectories based on their motion inertia and attitude changes.

[0093] This embodiment is obtained by improving the above embodiment. In this embodiment, the lighting scene determination module 120 is also used to obtain sunrise and sunset information and time information of the smart wearable device; and to determine the lighting scene of the smart wearable device based on the sunrise and sunset information, time information of the smart wearable device, weather information, infrared intensity, ultraviolet index and visible light intensity of the current ambient light.

[0094] In this embodiment, when the smart wearable device is in the process of determining the outdoor lighting scene, the lighting scene determination module 120 immediately acquires sunrise and sunset information and the time information of the smart wearable device to facilitate confirmation of the current time period and the outdoor scene in which the smart wearable device is located. At the same time, it controls the UV sensor to collect the IR intensity, UVI, and visible light intensity of the current ambient light.

[0095] Since the application scenario in this embodiment is a sunny daytime period (the time between sunrise and sunset), the lighting scene determination module 120 first determines the outdoor time period based on sunrise and sunset information and the time information of the smart wearable device during the judgment process. Common sense dictates that daytime typically refers to the time between sunrise and sunset, i.e., the time with sunlight. Therefore, after obtaining the sunrise and sunset information and the smart wearable device's time information, it can be confirmed whether the current time is between sunrise and sunset. If it is determined that the current time is not daytime, the smart wearable device still uses conventional outdoor positioning methods for outdoor positioning and trajectory drawing. After confirming that the current time is daytime, the outdoor scene where the smart wearable device is located is further determined based on weather information and the smart wearable device's time information. Generally, weather information may vary slightly at different times; for example, 10:00-11:00 AM is sunny, while 11:00 AM-12:00 PM is cloudy. Therefore, based on the weather information and the smart wearable device's time information, it is possible to accurately determine whether the current time is sunny outdoors. If the acquired weather information indicates that the current outdoor weather is cloudy or rainy, or even if it is sunny but the current time is before sunrise or after sunset, the smart wearable device will still use the conventional outdoor positioning method for outdoor positioning and trajectory mapping. If the acquired weather information indicates that the current time is daytime and the outdoor weather is sunny, the collected IR intensity, UVI, and visible light intensity will be normalized respectively, and then the lighting scene in which the smart wearable device is located will be further determined based on the normalized ratio of UVI to visible light intensity and the ratio of IR intensity to visible light intensity.

[0096] In the normalization process, the maximum UVI and maximum IR intensity values ​​collected over a period of time in an outdoor scene are first obtained. Then, the current UVI is divided by the maximum UVI value to obtain the normalized UVI value. Similarly, the current IR intensity is divided by the maximum IR intensity value to obtain the normalized IR intensity value. The normalization process for visible light intensity is as follows: after obtaining the visible light intensity over a period of time in an outdoor scene, each visible light intensity is first converted using log10 to obtain the corresponding log value = log... 10 (Visible light intensity), obtain the maximum and minimum log values; then calculate the normalized value of the current visible light intensity according to the formula = (log value of current visible light intensity - minimum log value) / (maximum log value - minimum log value).

[0097] According to the judgment rules, if the ratio of UVI to visible light intensity is high, the smart wearable device can be judged to be in a sunlight-exposed scene (sunny side); otherwise, it is judged to be in a non-sunlight-exposed scene (shaded side), such as in the shadow of a building, under a tree, or indoors. Similarly, if the ratio of IR intensity to visible light intensity is high, the smart wearable device can also be judged to be in a sunlight-exposed scene; conversely, if the ratio is low, it is judged to be in a non-sunlight-exposed scene (shaded side). Therefore, this embodiment limits the judgment to the following: when the ratio of UVI to visible light intensity is less than or equal to a first preset ratio, and the ratio of IR intensity to visible light intensity is less than or equal to a second preset ratio, the smart wearable device is judged to be in a non-sunlight-exposed scene; correspondingly, when the ratio of UVI to visible light intensity is greater than the first preset ratio, and the ratio of IR intensity to visible light intensity is greater than the second preset ratio, the smart wearable device is judged to be in a sunlight-exposed scene. The values ​​of the first and second preset ratios can be adjusted according to the actual application scenario, and are not specifically limited here.

[0098] This embodiment is obtained by improving the above embodiment. In this embodiment, the smart wearable device is determined to be outdoors when multiple of the following conditions are met in the data acquisition module 110:

[0099] A. The geographic coordinates fed back by the map service provider based on the Wi-Fi information around the smart wearable device are considered to be outdoors;

[0100] B. The number of satellites used within the first preset time period is greater than the preset satellite threshold;

[0101] C. The standard deviation of air pressure within the second preset time period is greater than the preset standard deviation threshold;

[0102] D. The intensity of the collected visible light is greater than the preset ambient light intensity threshold.

[0103] Since the application scenario in this embodiment is outdoors, it is necessary to determine whether the smart wearable device is outdoors before performing trajectory correction. To improve the accuracy of the determination, this embodiment defines four preset rules, and the device is determined to be outdoors only if multiple of these rules are met simultaneously. To further improve the accuracy of the determination, it can be further defined that the device is determined to be outdoors only if three of these rules are met simultaneously.

[0104] In Rule A, when a smart wearable device is locating itself, it scans for nearby Wireless Fidelity (Wi-Fi) information and reports the scanned Wi-Fi information to a map service provider, such as Baidu Maps or Gaode Maps. Upon receiving the information, the map service provider returns the smart wearable device's geographic coordinates and detailed location (province, city, district, street, etc.) based on the received Wi-Fi information. If the smart wearable device confirms it is outdoors based on the received geographic coordinates and detailed location, then Rule A is satisfied; otherwise, it is not.

[0105] In Rule B, when a smart wearable device is outdoors, the number of satellites that the GNSS navigation chip can detect will significantly increase. The first preset time period and preset satellite threshold mentioned in the rule can be set according to the actual situation, such as setting the first preset time period to 5 seconds, 10 seconds, etc., and the preset satellite threshold to 8, 10, 12, etc. No specific limitations are made here. In one example, if the number of satellites that can be detected within 5 seconds is greater than or equal to 8, it is determined that the device is outdoors; if the number of satellites that can be detected within 5 seconds is less than 8, it is determined that the device is indoors (typically, the number of satellites that can be detected indoors is less than or equal to 5). It should be understood that the GNSS navigation chip will also provide positioning coordinates, which can be combined with Rule B to further improve accuracy.

[0106] In Rule C, when a smart wearable device moves from indoors to outdoors, or from outdoors to indoors, the air pressure value will fluctuate from stable to slightly fluctuating. Air pressure values ​​are collected over a second preset time period, and the standard deviation is calculated and compared to a preset standard deviation threshold. If the standard deviation is greater than the preset standard deviation threshold, it is determined that a transition from indoors to outdoors has occurred. In application, the value of the preset standard deviation threshold can be set according to the actual situation; no specific limitation is made here, such as setting it between 0.5 and 5 hPa (hPa).

[0107] Rule D states that the intensity of visible light collected by the UV sensor differs significantly between indoors and outdoors. Therefore, this condition can be used to determine whether a smart wearable device is outdoors. In applications, a preset ambient light intensity threshold can also be set based on actual conditions. For example, in one instance, the preset ambient light intensity threshold is set to 1000 LUX. If the collected visible light intensity is greater than 1000 LUX, it is determined that the device is currently outdoors.

[0108] In another embodiment, such as Figure 5 As shown, the present invention provides a smart wearable device 200, including a processor 210 and a memory 220, wherein the memory 220 is used to store a computer program 221; the processor 210 is used to execute the computer program 221 stored in the memory 220 to implement the method in the above-described trajectory correction method embodiment.

[0109] The smart wearable device 200 may include, but is not limited to, a processor 210 and a memory 220. Those skilled in the art will understand that... Figure 5This is merely an example of a smart wearable device 200 and does not constitute a limitation on the smart wearable device 200. It may include more or fewer components than illustrated, or combine certain components, or use different components. For example, the smart wearable device 200 may also include an input / output interface, a display device, a network access device, a communication bus, and a communication interface. The communication interface and communication bus may further include an input / output interface, wherein the processor 210, memory 220, input / output interface, and communication interface communicate with each other through the communication bus. The memory 220 stores a computer program 221, and the processor 210 executes the computer program 221 stored in the memory 220 to implement the methods in the corresponding method embodiments described above.

[0110] The processor 210 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0111] The memory 220 can be an internal storage unit of the smart wearable device 200, such as a hard drive or RAM. The memory can also be an external storage device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory 220 can include both internal and external storage units of the smart wearable device 200. The memory 220 is used to store the computer program 221 and other programs and data required by the smart wearable device 200. The memory can also be used to temporarily store data that has been output or will be output.

[0112] A communication bus is a circuit that connects the described elements and enables transmission between these elements. For example, processor 210 receives commands from other elements via the communication bus, decrypts the received commands, and performs calculations or data processing based on the decrypted commands. Memory 220 may include program modules, for example, a kernel, middleware, an application programming interface (API), and applications. The program module may consist of software, firmware, or hardware, or at least two of these. Input / output interfaces forward commands or data input by the user through input / output interfaces (for example, sensors, keyboards, touchscreens). Communication interfaces connect the smart wearable device 200 to other network devices, user equipment, and networks. For example, the communication interface may connect to a network via wired or wireless means to connect to other external network devices or user equipment. Wireless communication may include at least one of the following: Wi-Fi, Bluetooth (BT), Near Field Communication (NFC), Global Positioning System (GPS), and cellular communication, etc. Wired communication can include at least one of the following: Universal Serial Bus (USB), High Definition Multimedia Interface (HDMI), Asynchronous Transfer Standard (RS-232), etc. The network can be a telecommunications network or a communication network. The communication network can be a computer network, the Internet, the Internet of Things (IoT), or a telephone network. The smart wearable device 200 can connect to the network via a communication interface. The protocol used by the smart wearable device 200 to communicate with other network devices can be supported by at least one of the following: application programming interface (API), middleware, kernel, and communication interface.

[0113] In another embodiment, the present invention also provides a storage medium storing at least one instruction, which is loaded and executed by a processor to perform the operations described in the corresponding embodiments above. Exemplarily, the storage medium may be a read-only memory (ROM), a random access memory (RAM), a read-only optical disc (CD-ROM), a magnetic tape, a floppy disk, or an optical data storage device, etc.

[0114] These can be implemented using computer-executable program code, thus allowing them to be stored in a storage device for execution by a computing device, or fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Therefore, this invention is not limited to any particular hardware and software combination.

[0115] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0116] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0117] In the embodiments provided in this application, it should be understood that the disclosed devices / smart wearable devices and methods can be implemented in other ways. For example, the device / smart wearable device embodiments described above are merely illustrative; the division of modules or units is merely a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interface; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0118] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0119] Furthermore, the functional units in the various embodiments of this application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The integrated unit described above can be implemented in hardware or as a software functional unit.

[0120] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a storage medium. Based on this understanding, the present invention can implement all or part of the processes in the methods of the above embodiments, or it can be accomplished by sending instructions from a computer program 221 to related hardware. The computer program 221 can be stored in a storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program 221 can be in the form of source code, object code, executable file, or some intermediate form. The storage medium can include: any entity or device capable of carrying the computer program 221, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content included in the storage medium can be appropriately added or removed according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electrical carrier signals and telecommunication signals.

[0121] It should be noted that the above embodiments can be freely combined as needed. The above are merely preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A trajectory correction method based on multiple sensors, characterized in that, The trajectory correction method, applied to smart wearable devices, includes: When the smart wearable device is outdoors, it acquires the current weather information and collects the infrared intensity, ultraviolet index and visible light intensity of the current ambient light. The lighting scene in which the smart wearable device is located is determined based on the weather information, the infrared intensity of the current ambient light, the ultraviolet index, and the visible light intensity. When the smart wearable device is in a non-sunlight environment, it collects satellite navigation data and nine-axis inertial data; The trajectory of the smart wearable device is corrected based on the satellite navigation data and the nine-axis inertial data.

2. The trajectory correction method as described in claim 1, characterized in that, Based on the weather information, the infrared intensity of the current ambient light, the ultraviolet index, and the visible light intensity, the lighting scene in which the smart wearable device is located is determined, including: Based on the weather information, determine the outdoor scene where the smart wearable device is located; The infrared intensity, the ultraviolet index, and the visible light intensity are each normalized. The lighting scene in which the smart wearable device is located is determined based on the normalized ratio of ultraviolet index to visible light intensity, the ratio of infrared intensity to visible light intensity, and the outdoor scene.

3. The trajectory correction method as described in claim 2, characterized in that, Based on the normalized ratios of ultraviolet index to visible light intensity, infrared intensity to visible light intensity, and the outdoor scene, the lighting scene in which the smart wearable device is located is determined, including: When the outdoor scene is sunny, and the ratio of the ultraviolet index to the visible light intensity is less than or equal to a first preset ratio, and the ratio of the infrared intensity to the visible light intensity is less than or equal to a second preset ratio, the smart wearable device is determined to be in a non-sunlight exposure scene.

4. The trajectory correction method as described in claim 1, characterized in that, Based on the weather information, the infrared intensity of the current ambient light, the ultraviolet index, and the visible light intensity, the lighting scene in which the smart wearable device is located is determined, including: Obtain sunrise and sunset information and time information from smart wearable devices; The lighting scene in which the smart wearable device is located is determined based on the sunrise and sunset information, the time information of the smart wearable device, the weather information, the infrared intensity of the current ambient light, the ultraviolet index, and the visible light intensity.

5. The trajectory correction method as described in claim 4, characterized in that, Based on the sunrise and sunset information, the time information of the smart wearable device, the weather information, the infrared intensity of the current ambient light, the ultraviolet index, and the visible light intensity, the lighting scene in which the smart wearable device is located is determined, including: The time period of the outdoor area is determined based on the sunrise and sunset information and the time information of the smart wearable device; Based on the weather information and the time information of the smart wearable device, the outdoor scene in which the smart wearable device is located is determined; The infrared intensity, the ultraviolet index, and the visible light intensity are each normalized. The lighting scene of the smart wearable device is determined based on the ratio of the normalized ultraviolet index to the visible light intensity, the ratio of the infrared intensity to the visible light intensity, the time period of the outdoor environment, and the outdoor scene.

6. The trajectory correction method as described in claim 5, characterized in that, Based on the normalized ratio of ultraviolet index to visible light intensity, the ratio of infrared intensity to visible light intensity, the time period outdoors, and the outdoor scene, the lighting scene of the smart wearable device is determined, including: When the outdoor time period is between sunrise and sunset, the outdoor scene is sunny, and the ratio of ultraviolet index to visible light intensity is less than or equal to a first preset ratio, and the ratio of infrared intensity to visible light intensity is less than or equal to a second preset ratio, the smart wearable device is determined to be in a non-sunlight exposure scene.

7. The trajectory correction method according to any one of claims 1-6, characterized in that, When the smart wearable device is outdoors, it is determined that the device is outdoors if multiple of the following conditions are met, based on current weather information and the infrared intensity, ultraviolet index, and visible light intensity of the current ambient light: Map service providers use the geographical coordinates provided by the Wi-Fi information around smart wearable devices to indicate that they are outdoors; The number of satellites used within the first preset time period is greater than the preset satellite threshold; The standard deviation of air pressure within the second preset time period is greater than the preset standard deviation threshold. The intensity of the collected visible light is greater than the preset ambient light intensity threshold.

8. The trajectory correction method as described in claim 1 or 2, characterized in that, After determining the lighting scene of the smart wearable device based on the weather information, the infrared intensity of the current ambient light, the ultraviolet index, and the visible light intensity, the method further includes: When the smart wearable device is in a sunlight-exposed environment, it collects satellite navigation data and performs orientation determination and trajectory drawing based on the satellite navigation data.

9. A smart wearable device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor runs the computer program, it implements the steps of the multi-sensor-based trajectory correction method as described in any one of claims 1-8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the multi-sensor-based trajectory correction method as described in any one of claims 1-8.

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