Wind resistance detection method and device, electronic equipment, storage medium and program product

Abstract

The invention discloses a wind resistance detection method and device, electronic equipment, a storage medium and a program product, and belongs to the technical field of electronics. The method is applied to a wearable device system, and the wearable device system comprises a first wearable device and at least two second wearable devices. In the movement process of a user wearing the first wearable device and the at least two second wearable devices, the first wearable device obtains at least three wind noise values and at least three first distances; the first wearable device determines a first wind resistance value according to the at least three wind noise values and the at least three first distances, and the first wind resistance value represents wind resistance borne by the user in the movement process; wherein the at least three wind noise values comprise a wind noise value detected by the first wearable device and wind noise values detected by the at least two second wearable devices; each first distance is the distance between every two wearable devices in the wearable device system.

Description

Technical Field

[0001] The present application belongs to the field of electronic technology, and specifically relates to a wind resistance detection method, device, electronic equipment, storage medium and program product. Background Art

[0002] When riding a bicycle, the faster the rider rides, the greater the air resistance (i.e. wind resistance) the rider will bear. In high-speed riding scenarios, most of the rider's physical energy is used to fight against wind resistance, so reducing wind resistance is the key to improving riding efficiency.

[0003] When riding, users can query the weather information of the current geographical area through electronic devices, such as wind direction, wind level, temperature and other information, and then judge the size of wind resistance when riding in the current geographical area based on this information, and adjust the riding formation or riding posture to reduce wind resistance, reduce physical exertion and improve riding efficiency.

[0004] However, the weather information queried through the electronic device is comprehensive weather information for the current geographical area, and the electronic device cannot obtain the wind resistance in a specific riding environment, so the existing solution cannot accurately detect the actual wind resistance during riding and other sports. Summary of the invention

[0005] The purpose of the embodiments of the present application is to provide a wind resistance detection method, device, electronic device, storage medium and program product, which can accurately detect the actual wind resistance during sports such as cycling.

[0006] In a first aspect, an embodiment of the present application provides a wind resistance detection method, which is applied to a wearable device system, including: a first wearable device and at least two second wearable devices; during the exercise of a user wearing the first wearable device and the at least two second wearable devices, the first wearable device obtains at least three wind noise values ​​and at least three first distances; the first wearable device determines a first wind resistance value based on the at least three wind noise values ​​and the at least three first distances, and the first wind resistance value represents the wind resistance encountered by the user during the exercise; wherein the at least three wind noise values ​​include the wind noise value detected by the first wearable device and the wind noise value detected by the at least two second wearable devices; each first distance is the distance between every two wearable devices in the above-mentioned wearable device system.

[0007] In a second aspect, an embodiment of the present application provides a wind resistance detection device, which is applied to a wearable device system including a first wearable device and at least two second wearable devices, and the wind resistance detection device includes: an acquisition module and a determination module. The acquisition module is used to acquire at least three wind noise values ​​and at least three first distances during the movement of a user wearing the first wearable device and the at least two second wearable devices; the determination module is used to determine a first wind resistance value based on the at least three wind noise values ​​acquired by the acquisition module and the at least three first distances acquired by the acquisition module, and the first wind resistance value represents the wind resistance encountered by the user during the movement; wherein the at least three wind noise values ​​include the wind noise value detected by the first wearable device and the wind noise value detected by the at least two second wearable devices; each first distance is the distance between every two wearable devices in the wearable device system.

[0008] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor and a memory, wherein the memory stores programs or instructions that can be run on the processor, and when the program or instructions are executed by the processor, the steps of the method described in the first aspect are implemented.

[0009] In a fourth aspect, an embodiment of the present application provides a readable storage medium, on which a program or instruction is stored, and when the program or instruction is executed by a processor, the steps of the method described in the first aspect are implemented.

[0010] In a fifth aspect, an embodiment of the present application provides a chip, comprising a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run a program or instruction to implement the method described in the first aspect.

[0011] In a sixth aspect, an embodiment of the present application provides a computer program product, which is stored in a storage medium and is executed by at least one processor to implement the method described in the first aspect.

[0012] In an embodiment of the present application, during the exercise of a user wearing a first wearable device and at least two second wearable devices, the first wearable device can obtain at least three wind noise values ​​and at least three first distances, and then determine a first wind resistance value based on the at least three wind noise values ​​and the at least three first distances, wherein the first wind resistance value represents the wind resistance experienced by the user during the exercise, wherein the at least three wind noise values ​​include the wind noise value detected by the first wearable device and the wind noise value detected by the at least two second wearable devices, and each first distance is the distance between each two wearable devices in the wearable device system. In this solution, the first wearable device can obtain the strength of the wind during the exercise in combination with the wind noise values ​​detected by the multiple wearable devices worn by the user, and can obtain the range of the wind resistance experienced by the user during the exercise in combination with the distances between the multiple wearable devices worn by the user, thereby determining the size of the wind resistance currently experienced by the user based on the strength of the wind and the range of the wind resistance experienced by the user, thereby realizing accurate detection of wind resistance during exercise by the wearable device. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is one of the flow charts of the wind resistance detection method provided in the embodiment of the present application;

[0014] Figure 2 This is the second flow chart of the wind resistance detection method provided in the embodiment of the present application;

[0015] Figure 3 is a schematic diagram of calculating the distance between wearable devices provided in an embodiment of the present application;

[0016] Figure 4 This is the third flow chart of the wind resistance detection method provided in the embodiment of the present application;

[0017] Figure 5 This is the fourth flow chart of the wind resistance detection method provided in the embodiment of the present application;

[0018] Figure 6 This is the fifth flow chart of the wind resistance detection method provided in the embodiment of the present application;

[0019] Figure 7 is a schematic diagram of determining the position of a hand by symmetry provided in an embodiment of the present application;

[0020] Figure 8 is a schematic diagram of calculating the area of ​​a quadrilateral provided in an embodiment of the present application;

[0021] Fig. 9 This is the sixth flow chart of the wind resistance detection method provided in the embodiment of the present application;

[0022] Fig.10 is a schematic diagram of a specific embodiment of the wind resistance detection method provided in an embodiment of the present application;

[0023] Fig.11 is a schematic diagram of a wind resistance detection device provided in an embodiment of the present application;

[0024] Fig.12 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0025] Fig.13 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present application to clearly describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments in the present application belong to the scope of protection of this application.

[0027] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0028] The terms "at least one (item)", "at least one of" and the like in this application refer to any one, any two or a combination of more than two of the objects included therein. For example, at least one (item) of a, b, and c can be represented by: "a", "b", "c", "a and b", "a and c", "b and c" and "a, b and c", where a, b, and c can be single or multiple. Similarly, "at least two (items)" refers to two or more, and its meaning is similar to that of "at least one (item)".

[0029] The wind resistance detection method, device, electronic device, storage medium and program product provided in the embodiments of the present application are described in detail below in conjunction with the accompanying drawings through specific embodiments and their application scenarios.

[0030] The embodiments of the present application can be applied to detecting the wind resistance of a user in a sports scene, which may include but is not limited to: cycling, sprinting, skating, skiing, bobsleigh, etc.

[0031] For example, taking a cycling sports scene as an example, when riding a bicycle, the wind resistance experienced by the rider increases with the square of the speed, so the impact of wind resistance is particularly significant when riding at high speed. When riding, the rider needs to adjust the riding formation and riding posture according to the size of the wind resistance to reduce the wind resistance and improve riding efficiency.

[0032] For example, taking the skiing scene as an example, when skiing, skiers often need to jump from a diving platform, fly in the air, and adjust their body position to control the flight trajectory and posture, and finally land safely and keep gliding. When flying in the air, the wind resistance will affect the athlete's flight distance and stability. Therefore, when skiing, athletes need to adjust their body position according to the size of the wind resistance to reduce the wind resistance and increase the flight distance and stability.

[0033] Currently, when exercising, users can use electronic devices to query weather information in the current geographical area, such as wind direction, wind force level, temperature, etc., to determine the wind resistance in the current geographical area, and adjust exercise posture, exercise trajectory, exercise formation, etc. to minimize the impact of wind resistance. However, the weather information queried by electronic devices is comprehensive weather information for the current geographical area, and electronic devices cannot obtain wind resistance in a specific sports environment, so existing solutions cannot accurately detect the actual wind resistance during sports such as cycling.

[0034] The embodiments of the present application provide a wind resistance detection method, apparatus, electronic device, storage medium and program product. The first wearable device can combine the wind noise values ​​detected by multiple wearable devices worn by the user to obtain the strength of the wind during exercise, and can combine the distance between the multiple wearable devices worn by the user to obtain the range of wind resistance the user is exposed to during exercise, so as to determine the size of the wind resistance currently exposed to the user based on the strength of the wind and the range of wind resistance the user is exposed to, thereby realizing accurate detection of wind resistance during exercise through wearable devices.

[0035] The wind resistance detection method provided in the embodiment of the present application may be executed by a wind resistance detection device, which may be an electronic device, or a functional module or functional entity in an electronic device. The technical solution provided in the embodiment of the present application is described below using an electronic device as an example.

[0036] Figure 1 A flow chart of a wind resistance detection method provided in an embodiment of the present application is shown. Figure 1 As shown, the wind resistance detection method provided in the embodiment of the present application may include the following steps 201 and 202.

[0037] Step 201: During exercise of a user wearing a first wearable device and at least two second wearable devices, the first wearable device obtains at least three wind noise values ​​and at least three first distances.

[0038] In the embodiment of the present application, the first wearable device and the at least two second wearable devices are multiple wearable devices worn by the user and in a connected state. Each of the at least three wind noise values ​​represents the intensity of ambient noise detected by a wearable device. Each of the at least three first distances is the distance between each two wearable devices.

[0039] Optionally, in the embodiment of the present application, the wearable device may be a device including at least one of an acceleration sensor, a gyroscope and a microphone module. For example, the wearable device may include but is not limited to at least one of the following: a smart watch, a headset device, smart glasses, a smart ring, etc.

[0040] Exemplarily, the first wearable device may be a smart watch worn by the user, and the at least two second wearable devices may be two headphone devices worn by the user. Alternatively, the first wearable device may be a headphone device worn by the user, and the at least two second wearable devices may be two smart watches and another headphone device worn by the user.

[0041] It should be noted that the above-mentioned user movement process of wearing the first wearable device and at least two second wearable devices refers to: the user wears the first wearable device and at least two second wearable devices, and the first wearable device detects whether the user is currently in a state of motion. In addition, the above-mentioned first wearable device and the above-mentioned at least two second wearable devices both perform the operation of detecting whether the user is currently in a state of motion. The embodiment of the present application is described from the perspective of the first wearable device, and the solution of the embodiment of the present application can also be executed by any second wearable device.

[0042] Optionally, in an embodiment of the present application, the wearable device can detect whether the wearable device is in motion through an acceleration sensor or a gyroscope in the wearable device. The acceleration sensor or the gyroscope detects the motion state of the wearable device by measuring the acceleration change of the wearable device in three spatial dimensions. When the acceleration of the electronic device changes, the mass block inside the sensor will be displaced, and this displacement is converted into an electrical signal through a change in capacitance or a piezoelectric effect, so that the wearable device can obtain the signal strength of these electrical signals by performing data analysis on these electrical signals, and judge that the wearable device is in motion when the signal strength is greater than a threshold.

[0043] Optionally, in the embodiment of the present application, the above-mentioned sports may include but are not limited to any of the following situations: cycling, sprinting, skating, skiing, bobsleigh, etc.

[0044] It should be noted that the first wearable device and at least two second wearable devices being in a connected state means that: the first wearable device is connected to at least two second wearable devices respectively; or any two of these wearable devices are in a connected state.

[0045] Optionally, in an embodiment of the present application, the above-mentioned wearable device can enter a connection state and transmit information through a variety of wireless communication technologies, and the connection state can be achieved by including but not limited to any one of the following technologies: Bluetooth technology, near field communication technology (Near Field Communication, NFC), wireless fidelity technology (Wireless Fidelity, Wi-Fi), cellular network technology (such as 3G, 4G and 5G networks), satellite communication technology, etc.

[0046] Optionally, in the embodiment of the present application, combined with Figure 1 ,like Figure 2 As shown, in the above step 201, "the first wearable device obtains at least three wind noise values ​​and at least three first distances" can be implemented by the following steps 201a and 201b.

[0047] Step 201a: The first wearable device detects a first wind noise value through a microphone module of the first wearable device, and receives at least two second wind noise values ​​from at least two second wearable devices to obtain at least three wind noise values.

[0048] In the embodiment of the present application, each of the above-mentioned second wind noise values ​​is a wind noise value detected by a microphone module of a second wearable device.

[0049] It should be noted that, usually when a wearable device is in motion, friction and collision will occur between the wearable device and the air, thereby causing vibrations of the wearable device. These vibrations further cause vibrations of the surrounding air, generating sound waves, i.e., environmental noise. In an embodiment of the present application, these noises can be captured by the microphone module of the wearable device and converted into electrical signals for analysis and processing to obtain wind noise values ​​that characterize the intensity of the wind. Moreover, the more intense the friction and collision between the wearable device and the air, the greater the intensity of the vibration, and the higher the intensity of the environmental noise generated, so that the wearable device can capture environmental noise through the microphone module when in motion, and determine the size of the wind noise value according to the intensity of the environmental noise, for example, 40 decibels (dB).

[0050] It should be noted that the sensitivity and physical structure of the microphone modules of different wearable devices (such as headphone devices, smart watches, etc.) may be different, and the sensitivity and physical structure of the microphone modules between the same but different brands of wearable devices (such as multiple smart watches of different brands) may also be different. Even the microphone modules between two wearable devices with exactly the same brand and model may have different sensitivities due to errors and other reasons. Therefore, in the embodiment of the present application, at least three wind noise values ​​can be obtained respectively by at least three wearable devices worn by the user during exercise. Compared with obtaining a single wind noise value, the advantage of obtaining multiple wind noise values ​​is that multiple wind speed values ​​can be obtained according to multiple wind noise values, thereby reducing the wind noise value error caused by the difference between the sensitivity and physical structure of the microphone modules of different wearable devices. In addition, since different wearable devices are worn on different parts of the user's body, the wind speed of different parts of the user's body may be different, that is, the detected wind noise value may be different, so that the wind noise values ​​of multiple wearable devices are used for measurement, which can improve the credibility of the measured wind noise value. A wind noise test can be performed on each wearable device, which is used to record the wind noise value of the device at different wind speed values, and organize and save these data as a wind speed set.

[0051] Step 201b: The first wearable device obtains at least three first distances through positioning and ranging technology.

[0052] In the embodiment of the present application, the above-mentioned positioning and ranging technology includes at least one of the following: Angle of Departure (AOD) technology, Angle of Arrival (AOA) technology, and Ultra-Wideband (UWB) technology.

[0053] It should be noted that the above-mentioned departure angle technology is a high-precision positioning technology based on Bluetooth 5.1, which is mainly used for the positioning of Bluetooth devices. It determines the angle of the object relative to the device by measuring the angle at which the signal is emitted. This technology can transmit signals through an electronic device with an antenna array (such as a positioning base station), and the receiving device (such as a smartphone or tag) can calculate the departure direction of the signal, that is, the departure angle, by analyzing the phase difference between different antennas of the signal transmitted from the antenna array.

[0054] The above-mentioned arrival angle technology is a positioning technology based on the signal arrival angle. This technology determines the position of the object by measuring the angle of signal arrival, and is usually used in radio wave direction-finding positioning systems. This technology uses a single transmitter to transmit signals, and the receiving end is equipped with a multi-antenna array. This technology can be achieved through a receiving array with multiple antenna elements. When the signal arrives at the array from a certain direction, different antenna elements will receive signals with different phases and amplitudes. By analyzing these phase and amplitude differences, the direction of arrival of the signal, that is, the arrival angle, can be calculated. This technology can be used in conjunction with the trigger angle technology to further improve the accuracy of positioning.

[0055] The above-mentioned ultra-wideband technology can use extremely short pulse signals to transmit data within a wide frequency band. It has the characteristics of high speed, low power consumption, and high-precision positioning. It is suitable for short-distance high-speed data transmission and precise positioning. The working principles of this technology mainly include two methods: Time of Flight (ToF) and Time Difference of Arrival (TDoA). ToF calculates the distance by measuring the time required for the signal to be transmitted and received, while TDoA calculates the position by receiving the signal at multiple base stations at the same time and comparing the time difference between the signals received by each base station.

[0056] The ToF technology can transmit a signal (such as a light pulse or a radio signal) through an electronic device, and the signal is reflected after reaching the target or received and reflected by the target electronic device, so that the electronic device can measure the total time from the transmission to the reception of the signal. Since the speed of light or the speed of electromagnetic waves is a known constant, the distance between the device and the target can be calculated by dividing the total time by 2 (taking into account the round-trip time).

[0057] The above TDoA technology requires at least three or more receiving devices. When a signal is sent from a transmitting device at an unknown location, the time it takes for the signal to reach different receiving devices will be different. By measuring these time differences and combining them with the known location information of the receiving device, algorithms such as triangulation or solving overdetermined linear equations can be used to calculate the location of the transmitting device.

[0058] Optionally, in an embodiment of the present application, the first wearable device can measure the angle between the above-mentioned at least three wearable devices through the departure angle technology or the arrival angle technology, and measure the distance between the first wearable device and each of the above-mentioned at least two second wearable devices according to the ultra-wideband technology, so that the first wearable device can determine the position (i.e., angle and distance) of the first wearable device and each of the above-mentioned at least two second wearable devices, and determine the distance between each two second wearable devices based on the position.

[0059] For example, Figure 3 As shown, taking three wearable devices as an example, including a smart watch 10, a headphone device 11, and a headphone device 12, the smart watch 10 measures the angle between the smart watch 10 and the headphone device 11 by using the departure angle technology or the arrival angle technology, and measures the angle between the smart watch 10 and the headphone device 12, and measures the distance between the smart watch 10 and the headphone device 11 by using the ultra-wideband technology, and measures the distance between the smart watch 10 and the headphone device 12. Then, the smart watch 10 can establish a plane rectangular coordinate system 13 with itself as the origin O on the plane where the smart watch 10, the headphone device 11, and the headphone device 12 are located, and determine the coordinates (x1, y1) of the headphone device 11 according to the angle and distance between the origin O and the headphone device 11, and determine the coordinates (x2, y2) of the headphone device 12 according to the angle and distance between the origin O and the headphone device 12, so as to calculate the distance D between the headphone device 11 and the headphone device 12 using the coordinates of the headphone device 11 and the coordinates of the headphone device 12, that is,

[0060]

[0061] In this way, the first wearable device can detect the first wind noise value through its own microphone module, and receive the second wind noise value detected by each second wearable device through its own microphone module, so as to accurately know the wind strength during exercise by combining the wind noise values ​​detected by multiple wearable devices worn by the user. In addition, the first wearable device can measure the distance between each two wearable devices through wireless ranging technology to determine the range of wind resistance to which the user is exposed.

[0062] Step 202: The first wearable device determines a first wind resistance value according to at least three wind noise values ​​and at least three first distances.

[0063] In the embodiment of the present application, the first wind resistance value represents the wind resistance encountered by the user during exercise.

[0064] It should be noted that, for a specific implementation scheme in which the first wearable device determines the first wind resistance value according to the wind noise value and the distance, please refer to the description in the following embodiment, which will not be repeated here.

[0065] An embodiment of the present application provides a wind resistance detection method, in which a first wearable device can combine the wind noise values ​​detected by multiple wearable devices worn by the user to obtain the strength of the wind during exercise, and can combine the distance between the multiple wearable devices worn by the user to obtain the range of wind resistance the user is exposed to during exercise, thereby determining the magnitude of wind resistance the user is currently exposed to based on the strength of the wind and the range of wind resistance the user is exposed to, thereby achieving accurate detection of wind resistance during exercise through wearable devices.

[0066] Optionally, in the embodiment of the present application, combined with Figure 1 ,like Figure 4 As shown, the above step 202 can be implemented through the following steps 202a to 202c.

[0067] Step 202a: The first wearable device determines a first wind speed value according to at least three wind noise values.

[0068] In the embodiment of the present application, the first wind speed value is the actual wind speed value of the wind force that the user is subjected to during exercise.

[0069] It should be noted that due to different air flow speeds, the friction generated by the air and the wearable device is different, so the noise intensity generated by the friction detected by the microphone module of the wearable device is different, that is, the wind noise value is different. Therefore, the embodiment of the present application can determine the wind speed value by detecting the wind noise value.

[0070] Optionally, in the embodiment of the present application, different wind noise intervals correspond to different wind speed values, and the first wearable device can determine the first wind speed value according to the correspondence between the wind noise value and the wind speed value. Figure 4 ,like Figure 5 As shown, the above step 202a can be implemented through the following steps 202a1 and 202a2.

[0071] Step 202a1: The first wearable device determines a wind speed value corresponding to each wind noise value from a wind speed set according to at least three wind noise values, to obtain at least one wind speed value.

[0072] In the embodiment of the present application, the above-mentioned wind speed set includes at least one wind noise interval and a wind speed value corresponding to each wind noise interval.

[0073] In the embodiment of the present application, for each of the at least three wind noise values, the first wearable device can determine the wind noise interval to which a wind noise value belongs in the wind speed set, and determine the wind speed value corresponding to the wind noise interval as the wind speed value corresponding to the one wind noise value, thereby obtaining the at least one wind speed value. It can be understood that when the wind noise intervals to which the at least three wind noise values ​​belong are different, the wind speed values ​​corresponding to the at least three wind noise values ​​are at least three wind speed values. When the wind noise intervals to which the at least three wind noise values ​​belong are the same, the wind speed values ​​corresponding to the at least three wind noise values ​​are the same wind speed value.

[0074] For example, assuming that the at least three wind noise values ​​are 20dB, 22dB and 24dB, and the wind speed set includes wind noise intervals [0,10], (10,20], (20,30]. Assuming that the wind speed value corresponding to the wind noise interval [0,10] is 3m / s, the wind speed value corresponding to (10,20] is 3.5m / s, and the wind speed value corresponding to (20,30] is 4m / s, then the first wearable device can determine that the wind speed value corresponding to the above 20dB is 3.5m / s, the wind speed value corresponding to 22dB is 4m / s, and the wind speed value corresponding to 24dB is 4m / s.

[0075] It should be noted that the wearable device can perform a wind noise test, which is used to record the wind noise value of the device at different wind speed values, and organize and save these data as a wind speed set.

[0076] Optionally, in the embodiment of the present application, the manner of obtaining the above wind speed set includes but is not limited to any of the following:

[0077] (1) Experimental measurement: In a wind tunnel experiment or outdoor conditions, use an anemometer and noise measurement equipment to simultaneously record wind noise values ​​at different wind speeds. By collecting a large amount of data, the relationship between wind speed and wind noise can be established, and then the wind noise interval can be divided and the wind speed value corresponding to each interval can be determined.

[0078] (2) Data analysis and modeling: Statistical analysis is performed on the collected data on wind speed and wind noise values, and the wind noise interval is divided and a mathematical model is established to predict the wind speed corresponding to a specific wind noise value.

[0079] (3) Physical model: Based on aerodynamics and acoustics theory, a physical model is established to predict the relationship between wind speed and wind noise. For example, fluid dynamics equations and acoustic wave equations are used to simulate wind noise when wind speed changes.

[0080] (4) Environmental monitoring: Long-term monitoring of wind speed and wind noise values ​​at specific locations. By accumulating a large amount of data, analyzing the patterns between wind speed changes and wind noise values, and forming a wind speed set.

[0081] Step 202a2: The first wearable device calculates a first wind speed value based on at least one wind speed value.

[0082] Optionally, in the embodiment of the present application, the first wearable device may use the average value of the at least one wind speed value as the first wind speed value. Alternatively, the first wearable device may also calculate the weighted average of the corresponding wind speed values ​​according to the weight of each wearable device to obtain the first wind speed value.

[0083] For example, taking the average wind speed value of three wearable devices as the first wind speed value, the wearable devices are a smart watch and two headphone devices, where the wind speed value of the smart watch is 5m / s, the wind speed value of headphone device A is 4.8m / s, and the wind speed value of headphone device B is 5.2m / s. The first wind speed value is (5+4.8+5.2)÷3=5 meters per second.

[0084] As another example, taking the weighted average calculation of each wind speed value according to the weight of each wearable device, the wearable devices are a smart watch and two headphone devices, where the wind speed value of the smart watch is 5 m / s and the weight is 60%, the wind speed value of headphone device A is 4.8 m / s and the weight is 20%, and the wind speed value of headphone device B is 5.2 m / s and the weight is 20%. The first wind speed value is 5*0.6+4.8*0.2+5.2*0.2=5 meters per second.

[0085] In this way, the first wearable device can determine the corresponding wind speed value through the wind noise value of each wearable device, and average or weighted average all wind speed values ​​to determine the first wind speed value, thereby reducing the measurement error of a single wearable device and improving the accuracy and reliability of the measurement results.

[0086] Step 202b: The first wearable device determines a first wind resistance area according to at least three first distances.

[0087] In the embodiment of the present application, the first wind resistance area is a unit resistance area of ​​the wind resistance encountered by the user during exercise.

[0088] In the embodiment of the present application, when a user wears multiple wearable devices, the positions of the multiple wearable devices can form a graph, and the at least three first distances are the lengths of the sides and diagonals of the graph. The first wearable device can calculate the area of ​​the graph based on the at least three first distances, and thus determine the first wind resistance area based on the area of ​​the graph.

[0089] Optionally, in the embodiment of the present application, combined with Figure 4 ,like Figure 6 As shown, the above step 202b can be implemented through the following steps 202b1 and 202b2.

[0090] Step 202b1: The first wearable device calculates the area of ​​the first graphic according to at least three first distances.

[0091] In the embodiment of the present application, the area of ​​the first graphic is the area of ​​a graphic formed based on the positions of the first wearable device and at least two second wearable devices.

[0092] Optionally, in the embodiment of the present application, when the number of the wearable devices is greater than or equal to four, the number of sides of the first figure may be less than or equal to the number of the wearable devices. For example, when the number of the wearable devices is 5, the first figure may be a triangle, a quadrilateral, or a pentagon.

[0093] Optionally, in an embodiment of the present application, when the number of the above-mentioned wearable devices is greater than or equal to four, the above-mentioned multiple wearable devices can constitute at least one quadrilateral, and the above-mentioned quadrilateral can be the quadrilateral with the largest area among the at least one quadrilateral, or, the above-mentioned quadrilateral can be the quadrilateral with the largest perimeter among the at least one quadrilateral.

[0094] Optionally, in the embodiment of the present application, when the number of the wearable devices is three, the multiple wearable devices may form a triangle.

[0095] Optionally, in an embodiment of the present application, when the number of the above-mentioned wearable devices is three, such as a left earphone device, a right earphone device, and a left hand smartwatch, the first wearable device can determine the position of the right hand based on the triangle formed by the wearable devices by the principle of symmetry, and obtain a quadrilateral based on the position and the positions of the left earphone device, the right earphone device, and the left hand smartwatch, and use the quadrilateral as the first figure. In this way, when the number of the above-mentioned wearable devices is three, the fourth position can be determined by the principle of symmetry to simulate the situation where the user wears four wearable devices, thereby improving the credibility of the area of ​​the first figure.

[0096] For example, Figure 7 As shown, the wearable devices are a smart watch 10, an earphone device 11 and an earphone device 12, and a plane 13 can be determined by the positions of the three. Then, through the positions of the earphone device 11 and the earphone device 12, a symmetry axis 14 can be determined in the plane 13, so that according to the position of the smart watch 10 and the symmetry axis 14, the position 15 of the user's other hand can be determined. In this way, the quadrilateral composed of the smart watch 10, the earphone device 11, the earphone device 12, and the position 15 can be used as the area of ​​the quadrilateral formed by the positions of the smart watch 10, the earphone device 11, and the earphone device 12.

[0097] Optionally, in an embodiment of the present application, when the number of sides of the above-mentioned first figure is greater than or equal to four, the first wearable device may divide the first figure into at least one triangle, and the first wearable device may divide the above-mentioned first figure into at least one triangle along a diagonal line, and calculate the area of ​​the first figure by respectively calculating the area of ​​the at least one triangle.

[0098] It should be noted that for a triangle, if the lengths of the three sides of the triangle are all known, the area of ​​the triangle can be calculated using Heron's method. Assuming that the lengths of the sides of the triangle are x, y, and z, the semi-perimeter p = (x+y+z) / 2, and the area formula of the triangle can be obtained as:

[0099]

[0100] For example, Figure 8 As shown, the lengths of the sides of the quadrilateral are a, b, c, d, and e respectively, and the area of ​​the quadrilateral is the sum of the area of ​​triangle S2 and the area of ​​triangle S3. If the length of each side of S2 and S3 is known, the area of ​​each triangle can be calculated using Heron's formula.

[0101] Among them, the area of ​​triangle S2 is:

[0102]

[0103] The area of ​​triangle S3 is:

[0104]

[0105] Then the area S of the quadrilateral is: S=S2+S3.

[0106] Step 202b2: The first wearable device determines a first wind resistance area based on the first graphic area.

[0107] Optionally, in an embodiment of the present application, the first wearable device may determine, based on the first graphic area, a wind resistance area corresponding to the first graphic area from a wind resistance area set as the first wind resistance area.

[0108] In the embodiment of the present application, the above-mentioned wind resistance area set includes at least one first graphic area interval and a wind resistance area corresponding to each first graphic area interval.

[0109] In the embodiment of the present application, the above-mentioned wind resistance area set is used to determine the wind resistance area corresponding to the first graphic area according to the first graphic area.

[0110] Optionally, in an embodiment of the present application, a method for acquiring the above-mentioned wind resistance area set includes but is not limited to any of the following: experimental measurement, data analysis and modeling, etc.

[0111] In this way, the first wearable device can determine the first graphic area according to at least three first distances, and then determine the wind resistance area corresponding to the first graphic area from the wind resistance area set according to the first graphic area.

[0112] Optionally, in the embodiment of the present application, combined with Figure 6 ,like Fig. 9 As shown, the above step 202b2 can be implemented through the following step 301.

[0113] Step 301: The first wearable device determines, based on the first graphic area and user characteristic information of the user, a wind resistance area corresponding to the first graphic area and the user characteristic information from a wind resistance area set to obtain a first wind resistance area.

[0114] In the embodiment of the present application, the above-mentioned wind resistance area set includes at least one user characteristic information, at least one graphic area interval and at least one wind resistance area, and each wind resistance area corresponds to one user characteristic information and one graphic area interval.

[0115] Optionally, in the embodiment of the present application, the above-mentioned user characteristic information may include but is not limited to: the user's height, the user's weight, the user's body shape, the user's hair information (such as hair density), etc.

[0116] In the embodiment of the present application, the first wearable device can determine the first wind resistance area from the wind resistance area set according to the first graphic area and the user characteristic information of the user.

[0117] In this way, the first wearable device can determine the first wind resistance area from the wind resistance area set based on the first graphic area and the user's characteristic information, thereby being able to determine the wind resistance area when the user's characteristic information is different, thereby improving the accuracy of the wind resistance area.

[0118] Step 202c: The first wearable device determines a first wind resistance value according to the first wind speed value and the first wind resistance area.

[0119] In the embodiment of the present application, the first wearable device can substitute the first wind speed value and the first wind resistance area into the wind resistance formula to calculate the above-mentioned first wind resistance value.

[0120] It should be noted that the above wind resistance formula can be:

[0121] F=(1 / 2)×C×ρ×S 风阻 ×V 2

[0122] Where F is the wind resistance value. C is the air resistance coefficient, which is usually an experimental value and is related to the wind resistance area, smoothness and overall shape of the object. Taking cycling as an example, in cycling, this value is approximately between 0.7 and 1.0. ρ is the air density, which depends on temperature, air pressure and humidity, etc., and is usually 1.293g / l. In special cases (such as higher altitudes), it can be monitored on site. S 风阻 is the wind resistance area. V is the relative speed between the object and the air (i.e. the wind speed value).

[0123] In this way, the first wearable device can obtain at least three wind noise values ​​of multiple wearable devices in motion and the distance between each two wearable devices, and then determine the current wind speed according to the at least three wind noise values, and determine the current wind resistance area according to the distance between the wearable devices. That is to say, during the movement, the first wearable device can obtain the relative wind speed between the wearable device and the air in the motion state, and the force area of ​​the first figure surrounded by the multiple wearable devices that is subject to wind resistance. In this way, the first wearable device can calculate the current wind resistance according to the relative wind speed between the wearable device and the air in the motion state and the force area of ​​the wind resistance. And the above-mentioned multiple wearable devices are worn by the same user, so that the above-mentioned force area can represent the wind resistance area of ​​the user in the motion state. Therefore, the first wearable device can calculate the wind resistance of the user wearing these wearable devices in the motion state through at least three wind noise values ​​of the multiple wearable devices in the motion state and the distance between each two wearable devices, thereby realizing accurate detection of the actual wind resistance during sports such as cycling.

[0124] The wind resistance detection method provided in the embodiment of the present application is described below through a specific example implementation process.

[0125] Combined with the above Figure 7 ,like Fig.10As shown, taking a user riding a bicycle as an example, assuming that the user wears three wearable devices while riding, namely, a smart watch 10, an earphone device 11, and an earphone device 12, and the first wearable device is the smart watch 10, then the smart watch 10 can detect the current first wind noise value, for example, 42dB, through the microphone module of the smart watch 10, and the earphone device 11 and the earphone device 12 can respectively detect the current two second wind noise values ​​through the microphone modules of the earphone device 11 and the earphone device 12, for example, the wind noise value of the earphone device 11 is 40dB, and the wind noise value of the earphone 12 is 38dB, and then the earphone device 11 and the earphone device 12 can send the two second wind noise values ​​to the smart watch. 10 is sent so that the smart watch 10 obtains two second wind noise values, so that the smart watch 10 can obtain three wind noise values; at the same time, the smart watch 10 measures the angle between the smart watch 10 and the headphone device 11 through the departure angle technology or the arrival angle technology, and measures the angle between the smart watch 10 and the headphone device 12, assuming that the angle between the line segment formed by the smart watch 10 and the headphone device 11 and the line segment formed by the smart watch 10 and the headphone device 12 is 30°, and the distance between the smart watch 10 and the headphone device 11 is measured by ultra-wideband technology, assuming it is 40cm, and the distance between the smart watch 10 and the headphone device 12 is measured, assuming it is 50cm. Then, the smart watch 10 can establish a plane rectangular coordinate system with itself as the origin O in the plane where the smart watch 10, the headphone device 11, and the headphone device 12 are located, and determine the coordinates (x1, y1) of the headphone device 11 according to the angle and distance between the origin O and the headphone device 11, assuming it is (0, 40), and determine the coordinates (x2, y2) of the headphone device 12 according to the angle and distance between the origin O and the headphone device 12, which is (30, 40), so that the distance D between the headphone device 11 and the headphone device 12 is 30 cm, so that the smart watch 10 can obtain three first distances; after obtaining the three first distances, After the wind noise values ​​and the three first distances are determined, the smart watch 10 can determine the wind speed value corresponding to each wind noise value from the wind speed set. For example, the wind speed corresponding to the wind noise interval (35, 38] in the wind speed set is 4.9 m / s, the wind speed corresponding to (38, 41] is 5 m / s, and the wind speed corresponding to (41, 44] is 5.1 m / s. The wind speed corresponding to the first wind noise value 42dB is 5.1 m / s. Among the second wind noise values, the wind speed corresponding to 40dB is 5 m / s, and the wind speed corresponding to 38dB is 4.9 m / s. The average of the three wind speed values ​​is calculated to obtain the first wind speed value of 5 m / s.At the same time, the smart watch 10 can determine the symmetry axis 14 of the quadrilateral, that is, x=15 in the above coordinate axis, from the triangle formed by the smart watch 10, the headphone device 11 and the headphone device 12 in the above coordinate axis through the coordinates (0,40) and (30,40) of the headphone device 11 and the headphone device 12, so as to determine the position 15 of the user's other hand and its coordinates, that is, (30,0) according to the smart watch 10, thereby determining the above quadrilateral and calculating the area of ​​the quadrilateral, that is, 1500 square centimeters (cm). 2 ), then the smart watch 10 can determine the quadrilateral area interval corresponding to the quadrilateral area according to the area of ​​the quadrilateral, and then determine the corresponding wind resistance area from the wind resistance area set according to the area interval and the user's characteristic information. Assuming that the user's characteristic information is 60kg in weight and 170cm in height, in the wind resistance area set, the quadrilateral area intervals corresponding to the characteristic information include (1400, 1450], (1450, 1500], (1450, 1550], then the above 1500cm 2 The corresponding wind resistance area is in the interval (1450, 1500]. Assume that the wind resistance area corresponding to this area is 1 square meter (m 2 ), the smart watch 10 can be based on the first wind speed value 5m / s and the wind resistance area 1m 2 Substituting the above wind resistance value formula, taking the air resistance coefficient C as 1.0 and the air density as 1.293g / l as an example, the wind resistance value F is calculated as:

[0126]

[0127] That is, F=16.1625 Newton (N).

[0128] Each of the above-mentioned method embodiments, or various possible implementation methods in each method embodiment can be executed separately, or any two or more of them can be executed in combination with each other. The specific implementation method can be determined according to actual usage requirements, and the embodiments of the present application do not limit this.

[0129] The wind resistance detection method provided in the embodiment of the present application can be executed by a wind resistance detection device. In the embodiment of the present application, the wind resistance detection device provided in the embodiment of the present application is described by taking the wind resistance detection method executed by the wind resistance detection device as an example.

[0130] Fig.11 FIG. 1 shows a possible structural diagram of a wind resistance detection device involved in some embodiments of the present application. Fig.11 As shown, the wind resistance detection device 70 may include: an acquisition module 71 and a determination module 72 .

[0131] The acquisition module 71 is used to acquire at least three wind noise values ​​and at least three first distances during the movement of a user wearing the first wearable device and at least two second wearable devices;

[0132] A determination module 72, configured to determine a first wind resistance value according to at least three wind noise values ​​obtained by the acquisition module 71 and at least three first distances obtained by the acquisition module 71, wherein the first wind resistance value represents the wind resistance experienced by the user during the exercise;

[0133] Among them, the above-mentioned at least three wind noise values ​​include the wind noise value detected by the above-mentioned first wearable device and the wind noise value detected by the above-mentioned at least two second wearable devices; each first distance is the distance between every two wearable devices in the above-mentioned wearable device system.

[0134] In one possible implementation, the acquisition module 71 is specifically used to: detect a first wind noise value through a microphone module of a first wearable device, and receive at least two second wind noise values ​​from the at least two second wearable devices to obtain at least three wind noise values, each second wind noise value being a wind noise value detected by a microphone module of a second wearable device; and, through positioning and ranging technology, obtain the at least three first distances.

[0135] In a possible implementation, the determination module 72 is specifically used to: determine a first wind speed value based on the above-mentioned at least three wind noise values ​​obtained by the acquisition module 71, and the first wind speed value is the actual wind speed value of the wind force to which the user is subjected during exercise; determine a first wind resistance area based on the above-mentioned at least three first distances obtained by the acquisition module 71, and the first wind resistance area represents the unit resistance area of ​​the wind resistance to which the user is subjected during exercise; determine the first wind resistance value based on the first wind speed value and the first wind resistance area.

[0136] In one possible implementation, the determination module 72 is specifically used to: determine the wind speed value corresponding to each wind noise value from the wind speed set according to the at least three wind noise values ​​obtained by the acquisition module 71, and obtain at least one wind speed value, wherein the wind speed set includes at least one wind noise interval and the wind speed value corresponding to each wind noise interval; based on the at least one wind speed value, calculate the first wind speed value.

[0137] In a possible implementation, the determination module 72 is specifically used to: calculate the first graphic area based on the at least three first distances obtained by the acquisition module 71, and the first graphic area is the area of ​​the first graphic formed based on the positions of the first wearable device and at least two second wearable devices; based on the first graphic area, determine the first wind resistance area.

[0138] In a possible implementation, the determination module 72 is specifically used to: determine, based on the first graphic area and the user characteristic information of the user, the wind resistance area corresponding to the first graphic area and the user characteristic information from the wind resistance area set to obtain the first wind resistance area; wherein the wind resistance area set includes at least one user characteristic information, at least one graphic area interval and at least one wind resistance area, each wind resistance area corresponds to one user characteristic information and one graphic area interval, and the user characteristic information includes at least one of the following: height information, weight information, body shape information, and hair information.

[0139] A wind resistance detection device is provided in an embodiment of the present application. The wind resistance detection device can combine the wind noise values ​​detected by multiple wearable devices worn by the user to obtain the strength of the wind during exercise, and can combine the distance between the multiple wearable devices worn by the user to obtain the range of wind resistance the user is exposed to during exercise. Based on the strength of the wind and the range of wind resistance the user is exposed to, the size of the wind resistance the user is currently exposed to is determined, thereby realizing accurate detection of wind resistance during exercise through wearable devices.

[0140] The wind resistance detection device in the embodiment of the present application can be an electronic device, or a component in the electronic device, such as an integrated circuit or a chip. The electronic device can be a terminal, or other devices other than a terminal. Exemplarily, the electronic device can be a mobile phone, a tablet computer, a laptop computer, a PDA, a vehicle-mounted electronic device, a mobile Internet device (Mobile Internet Device, MID), an augmented reality (augmented reality, AR) / virtual reality (virtual reality, VR) device, a robot, a wearable device, an ultra-mobile personal computer (ultra-mobile personal computer, UMPC), a netbook or a personal digital assistant (personal digital assistant, PDA), etc., and can also be a server, a network attached storage (Network Attached Storage, NAS), a personal computer (personal computer, PC), a television (television, TV), a teller machine or a self-service machine, etc., which is not specifically limited in the embodiment of the present application.

[0141] The wind resistance detection device in the embodiment of the present application may be a device having an operating system. The operating system may be an Android operating system, an iOS operating system, or other possible operating systems, which are not specifically limited in the embodiment of the present application.

[0142] The wind resistance detection device provided in the embodiment of the present application can implement each process implemented in the above method embodiment, and will not be described again here to avoid repetition.

[0143] Alternatively, if Fig.12 As shown, an embodiment of the present application further provides an electronic device 1000, including a processor 1001 and a memory 1002, wherein the memory 1002 stores programs or instructions that can be executed on the processor 1001, and when the program or instructions are executed by the processor 1001, the various steps of the above-mentioned wind resistance detection method embodiment are implemented, and the same technical effect can be achieved. To avoid repetition, they are not described here.

[0144] It should be noted that the electronic devices in the embodiments of the present application include the mobile electronic devices and non-mobile electronic devices mentioned above.

[0145] Fig.13 A schematic diagram of the hardware structure of an electronic device to implement an embodiment of the present application.

[0146] The electronic device 100 includes but is not limited to components such as a radio frequency unit 101, a network module 102, an audio output unit 103, an input unit 104, a sensor 105, a display unit 106, a user input unit 107, an interface unit 108, a memory 109, and a processor 110.

[0147] Those skilled in the art will appreciate that the electronic device 100 may also include a power source (such as a battery) for supplying power to various components, and the power source may be logically connected to the processor 110 through a power management system, thereby implementing functions such as managing charging, discharging, and power consumption management through the power management system. Fig.13 The electronic device structure shown in the figure does not constitute a limitation on the electronic device. The electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently, which will not be described in detail here.

[0148] The radio frequency unit 101 and the sensor 105 are used to obtain at least three wind noise values ​​and at least three first distances during the movement of a user wearing the first wearable device and at least two second wearable devices;

[0149] The processor 110 is configured to determine a first wind resistance value according to at least three wind noise values ​​and at least three first distances, where the first wind resistance value represents the wind resistance experienced by the user during exercise;

[0150] Among them, the above-mentioned at least three wind noise values ​​include the wind noise value detected by the above-mentioned first wearable device and the wind noise value detected by the above-mentioned at least two second wearable devices; each first distance is the distance between every two wearable devices in the above-mentioned wearable device system.

[0151] Optionally, the sensor 105 is specifically used to: detect a first wind noise value through a microphone module of a first wearable device, and receive at least two second wind noise values ​​from the at least two second wearable devices to obtain at least three wind noise values, each second wind noise value being a wind noise value detected by a microphone module of a second wearable device; and, through positioning and ranging technology, obtain the at least three first distances.

[0152] Optionally, the processor 110 is specifically used to: determine a first wind speed value based on the above-mentioned at least three wind noise values, where the first wind speed value is an actual wind speed value of the wind force to which the user is subjected during exercise; determine a first wind resistance area based on the above-mentioned at least three first distances, where the first wind resistance area represents a unit resistance area of ​​the wind resistance to which the user is subjected during exercise; determine the first wind resistance value based on the first wind speed value and the first wind resistance area.

[0153] Optionally, the processor 110 is specifically used to: determine, based on at least three wind noise values, a wind speed value corresponding to each wind noise value from a wind speed set to obtain at least one wind speed value, wherein the wind speed set includes at least one wind noise interval and a wind speed value corresponding to each wind noise interval; and calculate a first wind speed value based on the at least one wind speed value.

[0154] Optionally, the processor 110 is specifically used to: calculate a first graphic area based on the above-mentioned at least three first distances, where the first graphic area is the area of ​​a first graphic formed based on the positions of the above-mentioned first wearable device and at least two second wearable devices; and determine the first wind resistance area based on the first graphic area.

[0155] Optionally, the processor 110 is specifically used to: determine, based on the first graphic area and user characteristic information of the user, a wind resistance area corresponding to the first graphic area and the user characteristic information from a wind resistance area set to obtain a first wind resistance area; wherein the wind resistance area set includes at least one user characteristic information, at least one graphic area interval and at least one wind resistance area, each wind resistance area corresponds to one user characteristic information and one graphic area interval, and the user characteristic information includes at least one of the following: height information, weight information, body shape information, and hair information.

[0156] An electronic device is provided in an embodiment of the present application. A first wearable device can combine the wind noise values ​​detected by multiple wearable devices worn by the user to obtain the strength of the wind during exercise, and can combine the distance between the multiple wearable devices worn by the user to obtain the range of wind resistance the user is exposed to during exercise. Based on the strength of the wind and the range of wind resistance the user is exposed to, the magnitude of the wind resistance the user is currently exposed to is determined, thereby achieving accurate detection of wind resistance during exercise through wearable devices.

[0157] The electronic device provided in the embodiment of the present application can implement each process implemented in the above method embodiment, and can achieve the same technical effect. To avoid repetition, it is not repeated here. The beneficial effects of various implementations in this embodiment can be specifically referred to the beneficial effects of the corresponding implementations in the above method embodiment. To avoid repetition, it is not repeated here.

[0158] It should be understood that in the embodiment of the present application, the input unit 104 may include a graphics processor (Graphics Processing Unit, GPU) 1041 and a microphone 1042, and the graphics processor 1041 processes the image data of a static picture or video obtained by an image capture device (such as a camera) in a video capture mode or an image capture mode. The display unit 106 may include a display panel 1061, and the display panel 1061 may be configured in the form of a liquid crystal display, an organic light emitting diode, etc. The user input unit 107 includes a touch panel 1071 and at least one of other input devices 1072. The touch panel 1071 is also called a touch screen. The touch panel 1071 may include two parts: a touch detection device and a touch controller. Other input devices 1072 may include, but are not limited to, a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, and a joystick, which will not be repeated here.

[0159] The memory 109 can be used to store software programs and various data. The memory 109 may mainly include a first storage area for storing programs or instructions and a second storage area for storing data, wherein the first storage area may store an operating system, an application program or instructions required for at least one function (such as a sound playback function, an image playback function, etc.), etc. In addition, the memory 109 may include a volatile memory or a non-volatile memory, or the memory 109 may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), a static random access memory (SRAM), a dynamic random access memory (DRAM), a synchronous dynamic random access memory (SDRAM), a double data rate synchronous dynamic random access memory (DDRSDRAM), an enhanced synchronous dynamic random access memory (ESDRAM), a synchronous link dynamic random access memory (SLDRAM) and a direct memory bus random access memory (DRRAM). The memory 109 in the embodiment of the present application includes but is not limited to these and any other suitable types of memory.

[0160] The processor 110 may include one or more processing units; optionally, the processor 110 integrates an application processor and a modem processor, wherein the application processor mainly processes operations related to an operating system, a user interface, and application programs, and the modem processor mainly processes wireless communication signals, such as a baseband processor. It is understandable that the modem processor may not be integrated into the processor 110.

[0161] An embodiment of the present application also provides a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by a processor, each process of the above-mentioned wind resistance detection method embodiment is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0162] The processor is the processor in the electronic device described in the above embodiment. The readable storage medium includes a computer readable storage medium, such as a computer read-only memory ROM, a random access memory RAM, a magnetic disk or an optical disk.

[0163] An embodiment of the present application further provides a chip, which includes a processor and a communication interface, wherein the communication interface is coupled to the processor, and the processor is used to run programs or instructions to implement the various processes of the above-mentioned wind resistance detection method embodiment, and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0164] It should be understood that the chip mentioned in the embodiments of the present application can also be called a system-level chip, a system chip, a chip system or a system-on-chip chip, etc.

[0165] An embodiment of the present application provides a computer program product, which is stored in a storage medium. The program product is executed by at least one processor to implement the various processes of the above-mentioned wind resistance detection method embodiment and can achieve the same technical effect. To avoid repetition, it will not be repeated here.

[0166] It should be noted that, in this article, the terms "comprise", "include" or any other variant thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise one..." do not exclude the presence of other identical elements in the process, method, article or device including the element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in reverse order according to the functions involved, for example, the described method may be performed in an order different from that described, and various steps may also be added, omitted, or combined. In addition, the features described with reference to certain examples may be combined in other examples.

[0167] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a computer software product, which is stored in a storage medium (such as ROM / RAM, a disk, or an optical disk), and includes a number of instructions for a terminal (which can be a mobile phone, a computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0168] The embodiments of the present application are described above in conjunction with the accompanying drawings, but the present application is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present application, ordinary technicians in this field can also make many forms without departing from the purpose of the present application and the scope of protection of the claims, all of which are within the protection of the present application.

Claims

1. A wind resistance detection method, characterized in that: Applied to wearable device systems, including: a first wearable device and at least two second wearable devices; During the movement of the user wearing the first wearable device and the at least two second wearable devices, the first wearable device acquires at least three wind noise values ​​and at least three first distances; The first wearable device determines a first wind resistance value according to the at least three wind noise values ​​and the at least three first distances, wherein the first wind resistance value represents the wind resistance encountered by the user during exercise; The at least three wind noise values ​​include the wind noise value detected by the first wearable device and the wind noise value detected by the at least two second wearable devices; each first distance is the distance between every two wearable devices in the wearable device system.

2. The method according to claim 1, characterized in that The obtaining of at least three wind noise values ​​and at least three first distances includes: Detecting a first wind noise value through the microphone module of the first wearable device, and receiving at least two second wind noise values ​​from the at least two second wearable devices, to obtain the at least three wind noise values, each second wind noise value being a wind noise value detected by the microphone module of a second wearable device; The at least three first distances are acquired through positioning and ranging technology.

3. The method according to claim 1 or 2, characterized in that: The determining the first wind resistance value according to the at least three wind noise values ​​and the at least three first distances includes: Determine a first wind speed value according to the at least three wind noise values, where the first wind speed value is an actual wind speed value of the wind force to which the user is subjected during exercise; Determine a first wind resistance area according to the at least three first distances, where the first wind resistance area represents a unit resistance area of ​​wind resistance experienced by the user during exercise; The first wind resistance value is determined according to the first wind speed value and the first wind resistance area.

4. The method according to claim 3, characterized in that: The determining the first wind speed value according to the at least three wind noise values ​​comprises: According to the at least three wind noise values, determine a wind speed value corresponding to each wind noise value from a wind speed set to obtain at least one wind speed value, wherein the wind speed set includes at least one wind noise interval and a wind speed value corresponding to each wind noise interval; Based on the at least one wind speed value, the first wind speed value is calculated.

5. The method according to claim 3, characterized in that: The determining the first wind resistance area according to the at least three first distances includes: Calculate a first graphic area according to the at least three first distances, where the first graphic area is an area of ​​a graphic formed based on the positions of the first wearable device and the at least two second wearable devices; The first wind resistance area is determined based on the first graphic area.

6. The method according to claim 5, characterized in that The determining the first wind resistance area based on the first graphic area includes: Based on the first graphic area and the user characteristic information of the user, determine the wind resistance area corresponding to the first graphic area and the user characteristic information from a wind resistance area set to obtain the first wind resistance area; The wind resistance area set includes at least one user characteristic information, at least one graphic area interval and at least one wind resistance area, each wind resistance area corresponds to one user characteristic information and one graphic area interval, and the user characteristic information includes at least one of the following: height information, weight information, body shape information, and hair information.

7. A wind resistance detection device, characterized in that: Applied to a wearable device system including a first wearable device and at least two second wearable devices, the apparatus includes: an acquisition module and a determination module; The acquisition module is used to acquire at least three wind noise values ​​and at least three first distances during the movement of a user wearing the first wearable device and the at least two second wearable devices; The determining module is configured to determine a first wind resistance value according to the at least three wind noise values ​​obtained by the obtaining module and the at least three first distances obtained by the obtaining module, wherein the first wind resistance value represents the wind resistance experienced by the user during exercise; The at least three wind noise values ​​include the wind noise value detected by the first wearable device and the wind noise value detected by the at least two second wearable devices; each first distance is the distance between every two wearable devices in the wearable device system.

8. The device according to claim 7, characterized in that The acquisition module is specifically used to: detect a first wind noise value through the microphone module of the first wearable device, and receive at least two second wind noise values ​​from the at least two second wearable devices to obtain the at least three wind noise values, each second wind noise value is a wind noise value detected by the microphone module of a second wearable device; and obtain the at least three first distances through positioning and ranging technology.

9. The device according to claim 7 or 8, characterized in that The determining module is specifically used for: Determine a first wind speed value according to the at least three wind noise values ​​acquired by the acquisition module, where the first wind speed value is an actual wind speed value of the wind force to which the user is subjected during exercise; Determine a first wind resistance area according to the at least three first distances acquired by the acquisition module, where the first wind resistance area represents a unit resistance area of ​​wind resistance experienced by the user during exercise; The first wind resistance value is determined according to the first wind speed value and the first wind resistance area.

10. The device according to claim 9, characterized in that The determining module is specifically used for: According to the at least three wind noise values ​​obtained by the obtaining module, determine the wind speed value corresponding to each wind noise value from the wind speed set to obtain at least one wind speed value, wherein the wind speed set includes at least one wind noise interval and the wind speed value corresponding to each wind noise interval; Based on the at least one wind speed value, the first wind speed value is calculated.

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