Skiing data processing method and related device

Abstract

The embodiment of the present application provides a skiing data processing method and related devices, wherein the method includes: obtaining the user's historical skiing data; determining the user's safe skiing speed based on the historical skiing data; obtaining the plantar force information and action posture data; predicting the user's target skiing speed; determining the target deceleration action required by the user; determining the target deceleration reminder message based on the target deceleration action and action posture data; and sending the deceleration reminder message to the display device and the voice broadcast device respectively. The embodiment of the present application is used to perform skiing speed control simulation training on the user through the human motion quantification training system, which is conducive to improving the intelligence of the skiing speed control training of the computing device in the human motion quantification training system.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and specifically relates to a skiing data processing method and related devices. Background Art

[0002] When skiing, users are affected by gravity and their speed on the slope will become faster and faster. The faster the speed, the more difficult it is to control. If skiers fail to slow down in time, they will rush through the buffer area or hit the side fence, and the risk of injury is very high. Therefore, speed control training is a key consideration for users during skiing training. However, the current skiing simulation system only provides users with a way to experience simulated skiing, and fails to actually combine users' skiing data to provide users with targeted skiing assistance training, especially simulated training in skiing speed control, and fails to help users improve their skiing skills while improving skiing safety. Summary of the invention

[0003] The embodiments of the present application provide a skiing data processing method and related devices, which perform skiing speed control simulation training on users through a human motion quantification training system, which is beneficial to improving the intelligence of skiing speed control training of computing equipment in the human motion quantification training system.

[0004] In a first aspect, an embodiment of the present application provides a skiing data processing method, which is applied to a computing device in a human motion quantification training system, wherein the human motion quantification training system comprises the computing device, a six-degree-of-freedom motion platform, a display device, a voice broadcast device, and a motion capture device, wherein the six-degree-of-freedom motion platform comprises a snowboard force measuring platform, and the computing device is respectively connected to the snowboard force measuring platform, the display device, the voice broadcast device, and the motion capture device; the method comprises:

[0005] Acquire historical skiing data of a user on a preset simulated snow track, wherein the preset simulated snow track includes a simulated downhill ski track and a simulated buffer platform;

[0006] Determine the safe skiing speed of the user according to the historical skiing data, wherein the safe skiing speed is used to represent the maximum skiing speed of the user sliding through the simulated downhill ski slope and stopping on the simulated buffer platform;

[0007] Acquiring plantar force information from the snowboard force measuring platform, and acquiring motion posture data from the motion capture device;

[0008] Predicting the target skiing speed of the user according to the current position, current speed, plantar force information and action posture data of the user on the simulated ski slope, wherein the target skiing speed is used to represent the maximum skiing speed that the user can achieve on the simulated ski slope while maintaining the current sliding state;

[0009] If it is determined that the target skiing speed is greater than the safe skiing speed, determining the target deceleration action required by the user according to the plantar force information, the current position, the current speed, the slope of the simulated ski slope, the action posture data and the type of skis; and

[0010] Determining a target deceleration reminder message for the user according to the target deceleration action and the action posture data; and

[0011] The deceleration reminder message is sent to the display device and the voice broadcast device respectively.

[0012] In a second aspect, an embodiment of the present application provides a skiing data processing device, which is applied to a computing device in a human motion quantification training system, wherein the human motion quantification training system includes the computing device, a six-degree-of-freedom motion platform, a display device, a voice broadcast device, and a motion capture device, wherein the six-degree-of-freedom motion platform includes a snowboard force measuring platform, and the computing device is respectively connected to the snowboard force measuring platform, the display device, the voice broadcast device, and the motion capture device; the device includes an acquisition unit, a determination unit, and a prediction unit, wherein:

[0013] The acquisition unit is used to acquire the historical skiing data of the user on a preset simulated snow track, wherein the preset simulated snow track includes a simulated downhill ski track and a simulated buffer platform;

[0014] The determining unit is used to determine the safe skiing speed of the user according to the historical skiing data, wherein the safe skiing speed is used to represent the maximum skiing speed of the user when sliding across the simulated downhill ski slope and stopping on the simulated buffer platform;

[0015] The acquisition unit is further used to acquire the plantar force information from the snowboard force measuring platform, and acquire the motion posture data from the motion capture device;

[0016] The prediction unit is used to predict the target skiing speed of the user according to the current position, current speed, plantar force information and action posture data of the user on the simulated ski slope, wherein the target skiing speed is used to represent the maximum skiing speed that the user can achieve on the simulated ski slope while maintaining the current sliding state;

[0017] The determination unit is further used to determine the target deceleration action required by the user according to the plantar force information, the current position, the current speed, the slope of the simulated ski slope, the action posture data and the type of ski if it is determined that the target skiing speed is greater than the safe skiing speed; and, based on the target deceleration action and the action posture data, determine the target deceleration reminder message for the user; and, respectively, send the deceleration reminder message to the display device and the voice broadcast device.

[0018] A third aspect of the present application provides a computing device, comprising: a processor and a memory; and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for some or all of the steps described in the first aspect.

[0019] A fourth aspect of an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium is used to store a computer program, wherein the computer program enables a computer to execute instructions of some or all of the steps described in the first aspect of the embodiment of the present application.

[0020] A fifth aspect of the embodiments of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0021] It can be seen that in the embodiment of the present application, the computing device in the human motion quantification training system can first obtain the user's historical skiing data on the preset simulated snow track, and then determine the user's safe skiing speed based on the historical skiing data, and then obtain the plantar force information from the snowboard force measuring platform, and obtain the motion posture data from the motion capture device, and further, predict the user's target skiing speed based on the user's current position, current speed, plantar force information and motion posture data on the simulated ski track, and further, if it is determined that the target skiing speed is greater than the safe skiing speed, determine the target deceleration action required by the user based on the plantar force information, current position, current speed and snowboard type, and determine the user's target deceleration reminder message based on the target deceleration action and action posture data; and send the deceleration reminder message to the display device and the voice broadcast device respectively. When the predicted target skiing speed is greater than the safe skiing speed, the target deceleration action required by the user is determined, and a deceleration reminder message is determined in combination with the user's current action posture. The deceleration reminder message is then displayed on a display device and broadcast through a voice broadcast device to help the user change his skiing action posture to decelerate and achieve skiing speed control. This is beneficial to improving the user's skiing speed control training experience and to improving the intelligence of skiing speed control training of computing equipment in a human motion quantification training system. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1A It is a schematic diagram of the architecture of a human motion quantification training system provided in an embodiment of the present application;

[0024] Figure 1B It is a schematic diagram of the architecture of another human motion quantification training system provided in an embodiment of the present application;

[0025] Figure 1C It is a schematic diagram of the architecture of another human motion quantification training system provided in an embodiment of the present application;

[0026] Figure 2 is a flowchart of a skiing data processing method provided in an embodiment of the present application;

[0027] Figure 3A is a schematic diagram of a simulated ski slope provided in an embodiment of the present application;

[0028] Figure 3B is a schematic diagram of another simulated ski slope provided in an embodiment of the present application;

[0029] Figure 4 is a schematic diagram of the structure of a computing device provided in an embodiment of the present application;

[0030] Figure 5 It is a block diagram of the functional units for skiing data processing provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0033] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0034] In the embodiments of the present application, "and / or" describes the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B can represent the following three situations: A exists alone; A and B exist at the same time; B exists alone. Among them, A and B can be singular or plural.

[0035] In the embodiment of the present application, the symbol " / " can indicate that the objects associated with each other are in an "or" relationship. In addition, the symbol " / " can also indicate a division sign, that is, performing a division operation. For example, A / B can indicate A divided by B.

[0036] In the embodiments of the present application, "at least one item" or similar expressions refer to any combination of these items, including any combination of single items or plural items, and refer to one or more, and multiple refers to two or more. For example, at least one item of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0037] In the embodiments of the present application, "equal to" can be used in conjunction with greater than, and is applicable to the technical solution adopted when greater than, and can also be used in conjunction with less than, and is applicable to the technical solution adopted when less than. When equal to is used in conjunction with greater than, it is not used in conjunction with less than; when equal to is used in conjunction with less than, it is not used in conjunction with greater than.

[0038] In order to better understand the solutions of the embodiments of the present application, the terminal devices, related concepts and backgrounds that may be involved in the embodiments of the present application are first introduced below.

[0039] The terminal device of the embodiment of the application is a device with a wireless communication function, which can be called a terminal, user equipment (UE), mobile station (MS), mobile terminal (MT), access terminal equipment, vehicle-mounted terminal equipment, industrial control terminal equipment, UE unit, UE station, mobile station, remote station, remote terminal equipment, mobile equipment, UE terminal equipment, wireless communication equipment, UE agent or UE device, etc. The terminal device can be fixed or mobile. It should be noted that the terminal device can support at least one wireless communication technology, such as LTE, new radio (NR), wideband code division multiple access (WCDMA), etc. For example, the terminal device may be a mobile phone, a tablet computer (pad), a desktop computer, a laptop computer, an all-in-one computer, a vehicle-mounted terminal, a virtual reality (VR) terminal device, an augmented reality (AR) terminal device, a wireless terminal in industrial control, a wireless terminal in self driving, a wireless terminal in remote medical surgery, a wireless terminal in a smart grid, a wireless terminal in transportation safety, a wireless terminal in a smart city, a wireless terminal in a smart home, a cellular phone, a cordless phone, a session initiation protocol (SIP) phone, a wireless local loop (WLL) station, a personal digital assistant (PDA), a handheld device with wireless communication function, a computing device or other processing device connected to a wireless modem, a wearable device, a terminal device in a future mobile communication network, or a terminal device in a future evolved public mobile land network (PLMN), etc. The terminal device may be a computing device in a human motion quantification training system.

[0040] See also Figure 1A , Figure 1A Schematic diagram of the structure of a human motion quantitative training system provided by an embodiment of the present application. Figure 1AAs shown, the human motion quantitative training system 1 includes a computing device 10, a six-degree-of-freedom motion platform 20, a display device 30, a voice broadcasting device 40 and a motion capture device 50. The six-degree-of-freedom motion platform 20 includes a snowboard force measuring platform 21, and the computing device 10 is respectively connected to the snowboard force measuring platform 21, the display device 30, the voice broadcasting device 40 and the motion capture device 50.

[0041] The computing device 10 may be a server, a terminal device, a host computer of a user simulation training site, etc., which is not limited here. The computing device may create a simulated snow track based on real data modeling of an actual ski resort. For example, the length of the created simulated snow track is 200m, the width is 50m, and the slope is 7°.

[0042] The snowboard force measuring platform 21 includes an inclined platform 212, a snowboard 211, and a pressure sensor and / or a six-dimensional force measuring platform installed under the snowboard, which can collect the pressure data of the user's foot when the user is on the snowboard. There can be two snowboards 211. The snowboard 211 can be installed on the inclined platform 212 through a fixing device. Figure 1B , Figure 1B It is a schematic diagram of the architecture of another human motion quantification training system provided in an embodiment of the present application. The six-degree-of-freedom motion platform 20 has a built-in ski 211 and an inclined platform 212 , and the ski 211 is installed on the inclined platform 212 .

[0043] Among them, see Figure 1C , Figure 1C This is another schematic diagram of the structure of a human motion quantitative training system provided by an embodiment of the present application. The tilting platform 212 in the six-degree-of-freedom motion platform 20 can be tilted at a certain angle to simulate a ski slope with a certain slope, and the ski board 211 is tilted at a certain angle accordingly. For example, if the slope of the simulated ski slope is 12.6°, the tilting platform can be tilted 12.6° to give the user a real experience.

[0044] The display device 30 can play the scene of a simulated character created by the user skiing on a simulated snow track. The simulated character can be a digital person, which is not limited here. The actions of the simulated character are synchronized with the actions of the user. The voice broadcast device 40 can play audio associated with the scene of the simulated character skiing on the simulated snow track, such as the sound of the skis rubbing against the snow.

[0045] The user may install a motion capture device 50 on the body, which may capture the user's motion gestures, convert the motion gestures into digital data, and transmit the digital data to the computing device 10 .

[0046] In one possible example, the computing device 10 in the human motion quantification training system 1 may first obtain the user's historical skiing data on a preset simulated ski slope, and the computing device 10 may then determine the user's safe skiing speed based on the historical skiing data. The computing device 10 may then obtain the plantar force information from the ski board force measuring platform 21, and obtain the motion posture data from the motion capture device 50. Further, the computing device 10 may predict the user's target skiing speed based on the user's current position, current speed, plantar force information, and motion posture data on the simulated ski slope. Further, if the computing device 10 determines that the target skiing speed is greater than the safe skiing speed, the computing device 10 may determine the target deceleration action required by the user based on the plantar force information, current position, current speed, and ski type, and may determine the user's target deceleration reminder message based on the target deceleration action and motion posture data; and may send the deceleration reminder message to the display device and the voice broadcast device, respectively. When the predicted target skiing speed is greater than the safe skiing speed, the target deceleration action required by the user is determined, and a deceleration reminder message is determined in combination with the user's current action posture. The deceleration reminder message is displayed through the display device 30 and the deceleration reminder message is broadcast through the voice broadcast device 40 to help the user change the skiing action posture to decelerate and achieve skiing speed control, which is beneficial to improving the user's skiing speed control training experience and is beneficial to improving the intelligence of the skiing speed control training of the computing device 10 in the human motion quantification training system 1.

[0047] See also Figure 2 , Figure 2 It is a flow chart of a skiing data processing method provided by an embodiment of the present application, which is applied to a computing device in a human motion quantification training system, wherein the human motion quantification training system comprises the computing device, a six-degree-of-freedom motion platform, a display device, a voice broadcast device, and a motion capture device, wherein the six-degree-of-freedom motion platform comprises a snowboard force measuring platform, and the computing device is respectively connected to the snowboard force measuring platform, the display device, the voice broadcast device, and the motion capture device. The method comprises:

[0048] Step S201, obtaining historical skiing data of a user on a preset simulated ski track, wherein the preset simulated ski track includes a simulated downhill ski track and a simulated buffer platform.

[0049] Among them, the preset simulated snow track can be constructed by modeling real data such as the length, width, slope, softness of snow, etc. of a real ski resort.

[0050] Among them, the historical skiing data includes the ski track information of the preset simulated ski track that the user has skied on, the user's real-time skiing speed, plantar pressure data, skiing time, the position where the user starts buffering and deceleration to prepare to enter the simulated buffer platform, etc., which are not limited here; the ski track information includes the first length, first width and slope of the simulated ski track in the preset simulated snow track, and the second length and second width of the simulated buffer platform.

[0051] When users are doing skiing training, they are trained to ski on a simulated down-ski slope. Due to physical reasons or ski slope level restrictions, they need to stop on a simulated buffer platform to rest to prevent users from rushing out of the simulated buffer platform and entering the next down-ski slope. In reality, the ski slope levels of two adjacent down-ski slopes are not exactly the same. For example, the first down-ski slope and the second down-ski slope are connected by a buffer platform. The first down-ski slope is a novice beginner slope with a slope of 8°, and the second down-ski slope is an intermediate slope with a slope of 20°.

[0052] Step S202: determining the safe skiing speed of the user based on the historical skiing data, wherein the safe skiing speed is used to represent the maximum skiing speed of the user when skiing down the simulated ski slope and stopping on the simulated buffer platform.

[0053] Among them, when the user is skiing, when the ski slope is about to end, the user has already started to buffer and decelerate, and continues to decelerate to enter the buffer platform and stop on the buffer platform, or starts to buffer and decelerate to stop after entering the buffer platform. If the user's speed is too high when the buffer and deceleration starts, the user is at risk of hitting the side guardrail of the buffer platform, or rushing out of the buffer platform and entering the next ski slope. The user may not be able to continue skiing due to lack of physical strength, which increases the risk of injury, or it is not suitable to enter a higher-level ski slope, which increases the risk of injury. Therefore, the maximum speed at which the user can stably stop on the buffer platform after buffering and deceleration can be used as the safe skiing speed. When the speed at which the user starts to buffer and decelerate is less than or equal to the safe skiing speed, the user can stop on the ski platform.

[0054] Step S203, obtaining the plantar force information from the snowboard force measuring platform, and obtaining the motion posture data from the motion capture device.

[0055] Among them, when the user uses dual skis to ski, the ski board force measurement platform can collect the user's left foot plantar force data and right foot plantar force data in real time, that is, plantar force information.

[0056] Among them, the motion capture device can actually capture the user's motion posture data and send the motion posture data to the computing device, which analyzes the motion posture data to obtain the user's current body movements, for example, based on the motion posture data, the user's upper body and knees are slightly tilted forward, the legs are parallel, the hips are tilted forward, the arms are drooped, etc. The user's center of gravity can also be estimated through the motion posture data, for example, based on the motion posture data, the user's center of gravity is biased toward the front end of the center of the snowboard.

[0057] Step S204, predicting the target skiing speed of the user based on the current position, current speed, plantar force information and movement posture data of the user on the simulated ski slope, wherein the target skiing speed is used to represent the maximum skiing speed that the user can achieve on the simulated ski slope while maintaining the current sliding state.

[0058] Among them, when the user remains in the accelerated gliding state, the user's skiing speed continues to increase until the user is ready to enter the buffer platform on the simulated descent of the ski slope or starts buffering deceleration at the end point of the simulated descent of the ski slope. At this time, the user's skiing speed reaches the maximum, and thus the target skiing speed can be the maximum skiing speed reached by the user on the simulated descent of the ski slope.

[0059] Step S205, if it is determined that the target skiing speed is greater than the safe skiing speed, then determining the target deceleration action required by the user based on the plantar force information, the current position, the current speed, the slope of the simulated ski slope, the action posture data and the type of skis; and, determining the target deceleration reminder message of the user based on the target deceleration action and the action posture data; and, sending the deceleration reminder message to the display device and the voice broadcast device, respectively.

[0060] Among them, the display device can be used to display the deceleration reminder message, and the voice broadcasting device can be used to broadcast the deceleration reminder message.

[0061] Among them, the type of skis can be a double ski or a single ski; for example: if the user uses a double ski, the user's position on the snow track is biased to the right, the current speed is high, the user's current feet are parallel and the legs are evenly loaded, and the predicted target deceleration action can be toes slightly close together and the right leg is loaded, then the reminder message is: please slowly twist your ankles to gradually move closer to your toes, be careful not to move too much, keep your center of gravity in the middle of the body or slightly behind, increase the weight on your right leg, and decelerate. If the user uses a double ski, the user's position on the snow track is biased to the left, the current speed is moderate, the user's current feet are parallel and the legs are evenly loaded, and the predicted target deceleration action is to toes close together and the left leg is loaded, then the reminder message is: please slowly twist your ankles to gradually move closer to your toes, keep your center of gravity in the middle of the body or slightly behind, increase the weight on your left leg, and decelerate.

[0062] It can be seen that in the embodiment of the present application, the computing device in the human motion quantification training system can first obtain the user's historical skiing data on the preset simulated snow track, and then determine the user's safe skiing speed based on the historical skiing data, and then obtain the plantar force information from the snowboard force measuring platform, and obtain the motion posture data from the motion capture device, and further, predict the user's target skiing speed based on the user's current position, current speed, plantar force information and motion posture data on the simulated ski track, and further, if it is determined that the target skiing speed is greater than the safe skiing speed, determine the target deceleration action required by the user based on the plantar force information, current position, current speed and snowboard type, and determine the user's target deceleration reminder message based on the target deceleration action and action posture data; and send the deceleration reminder message to the display device and the voice broadcast device respectively. When the predicted target skiing speed is greater than the safe skiing speed, the target deceleration action required by the user is determined, and a deceleration reminder message is determined in combination with the user's current action posture. The deceleration reminder message is displayed on a display device and broadcast through a voice broadcast device to help the user change his skiing action posture to decelerate and achieve skiing speed control. This is beneficial to improving the user's skiing speed control training experience and to improving the intelligence of skiing speed control training of computing equipment in a human motion quantification training system.

[0063] In one possible example, in terms of predicting the user's target skiing speed based on the user's current position, current speed, the plantar force information and the action posture data on the simulated down-piste, the above method may include the following steps: determining a reference skiing buffer distance of the user on the simulated down-piste based on the historical skiing data; predicting the first acceleration of the user's current sliding on the simulated down-piste based on the plantar force information, the slope and the action posture data; determining a target sliding distance based on the current position, the first length and the reference skiing buffer distance; and determining the target skiing speed of the user after sliding over the target sliding distance based on the current speed, the first acceleration and the target sliding distance.

[0064] The reference skiing buffer distance is the reference distance for users to buffer and decelerate on the simulated ski slope. Figure 3A , Figure 3AIt is a schematic diagram of a simulated ski slope provided in an embodiment of the present application, wherein the simulated ski slope includes a simulated lower ski slope and a simulated buffer platform, the simulated lower ski slope has a certain slope, and the slope of the simulated buffer platform is 0, and the reference ski buffer distance is a certain distance before the end point of the simulated lower ski slope. For example, if the user starts buffering and deceleration 3 meters before the end point of the simulated lower ski slope and enters the simulated buffer platform, then the reference ski buffer distance is 3 meters.

[0065] Among them, the user's center of gravity distribution, the symmetry of the force of the legs, the plantar pressure of the left and right feet on the ski board, and the overall balance of the user's skiing are all factors that affect the acceleration of the user's sliding on the downhill ski slope. If the center of gravity is too backward, the user will fall. The user's center position is low and relatively forward, which helps to increase the stability and acceleration of skiing; the higher the symmetry of the force of the legs, the more stable and accelerated the skiing; users with good overall balance can better control the ski board and effectively convert force into acceleration; at appropriate angles and speeds, greater plantar pressure and more uniform pressure distribution help to generate higher acceleration. The symmetry of the force of the legs and the plantar pressure of the left and right feet on the ski board can be determined by the plantar force information, and the overall balance of the user's skiing can be obtained by artificial pre-professional evaluation taking into account factors such as plantar pressure, symmetry of the force of the legs, and center of gravity distribution. For example, the range of the balanced evaluation value of the overall balance is [0, 1], and the overall balance of the user is 0.7. The center distribution position of the user can be determined based on the user's action posture data.

[0066] For details, please refer to Figure 3B , Figure 3B A schematic diagram of another simulated ski slope provided in an embodiment of the present application determines the first sliding distance that the user has traveled based on the user's current position, and the target sliding distance = the first length - the first sliding distance - the reference ski buffer distance.

[0067] It can be seen that in this example, the influence of the user's center of gravity distribution, the symmetry of the left and right legs' force, the plantar pressure of the left and right feet on the ski board and the overall balance of the user's skiing on the skiing acceleration can be comprehensively considered to determine the first acceleration of the user's current sliding state, and further determine the target skiing speed, which is conducive to the accuracy of the computing device in determining the target skiing speed.

[0068] In one possible example, the method may include the following steps of predicting the first acceleration of the user currently skiing on the simulated ski slope based on the plantar force information, the slope and the action posture data: obtaining a pre-stored balance evaluation value, wherein the balance evaluation value is used to characterize the degree of balance of the user skiing on the preset simulated ski slope; determining the plantar pressure value of the left foot and the plantar pressure value of the right foot of the user based on the plantar pressure value of the left foot and the plantar pressure value of the right foot; determining the left-right force symmetry of the user's legs based on the left-right force value; determining the center of gravity distribution characterization value of the user based on the action posture data, wherein the center of gravity distribution characterization value is used to characterize the position of the user's center of gravity relative to the center of the ski board; correcting the initial acceleration using the balance evaluation value, the left-foot plantar pressure value, the right-foot plantar pressure value, the center of gravity distribution characterization value and the left-right force symmetry of the legs to obtain a corrected target acceleration, and using the target acceleration as the first acceleration.

[0069] Among them, the initial acceleration does not directly take into account factors such as plantar pressure, symmetry of the user's legs, user's center of gravity distribution, overall balance, etc., but is only an acceleration calculated based on gravity and the friction between the ski board and the snow. It is a theoretical value without considering the skier's specific movements and postures.

[0070] Specifically, the initial acceleration is: ,in, is the initial acceleration, is the acceleration due to gravity, To simulate the slope of the ski slope.

[0071] The position of the center of the snowboard is set in advance. The range of the symmetry of the left and right legs is [0,1], and the larger the value, the more symmetrical it is. The center of gravity distribution representation value is used to represent the position of the user's center of gravity relative to the center of the snowboard.

[0072] When the gravity center distribution characterization value is positive, the user's gravity center is biased toward the front of the center of the snowboard; when the gravity center distribution characterization value is negative, the user's gravity center is biased toward the rear of the center of the snowboard. For example, if the gravity center distribution characterization value is 0.02, it means that the user's gravity center is 0.02m forward of the center of the snowboard; if the gravity center distribution characterization value is -0.015, it means that the user's gravity center is 0.015m backward of the center of the snowboard.

[0073] Among them, the symmetry of the force applied by both legs = .

[0074] Among them, the correction formula of initial acceleration is expressed as follows:

[0075] .

[0076] in, is the corrected target acceleration, is the plantar pressure correction coefficient, which is used to adjust the influence of plantar pressure on acceleration. is the correction coefficient of the center of gravity distribution, which adjusts the influence of the center of gravity distribution on the acceleration. To ensure the symmetry of the force applied to both legs. Evaluate the value for balance. represents the position of the center of the ski, Indicates the maximum possible range of user center of gravity distribution, represents the centroid distribution characterization value, Indicates the plantar pressure of the user's left foot on the snowboard, Indicates the plantar pressure of the user's right foot on the snowboard, Indicates the smaller value of the left and right plantar pressures. Indicates the larger value of the left and right plantar pressures.

[0077] in, It may be pre-determined, for example half the length of the ski. and It’s all pre-set.

[0078] Specifically, the balance evaluation value, the left foot plantar pressure value, the right foot plantar pressure value, the center of gravity distribution characterization value, the left and right leg force symmetry, the plantar pressure correction coefficient, the center distribution correction coefficient and the initial acceleration are substituted into the correction formula to obtain the corrected target acceleration, and the target acceleration is used as the first acceleration.

[0079] It can be seen that in this example, the influence of the user's center of gravity distribution, the symmetry of the left and right legs' force, the plantar pressure of the left and right feet on the ski board and the overall balance of the user's skiing on the skiing acceleration can be comprehensively considered to determine the first acceleration of the user in the current sliding state, which is conducive to the computing device determining the accuracy of the target skiing speed.

[0080] In one possible example, in terms of determining a reference skiing buffer distance for the user on the simulated down-piste based on the historical skiing data, the method may include the following steps: determining a plurality of historical buffer deceleration distances for buffering and deceleration of the user on the simulated down-piste based on the historical skiing data; determining an average of the plurality of historical buffer deceleration distances to obtain an average buffer deceleration distance, and using the average buffer deceleration distance as the reference skiing buffer distance.

[0081] The position where the user starts buffering and decelerating on the simulated ski slope each time can be analyzed based on historical skiing data, and the historical buffering and deceleration distance of the user on the simulated ski slope can be determined according to the position.

[0082] Among them, when the user starts to decelerate after entering the simulated buffer platform, the user's reference ski buffer distance is 0.

[0083] Optionally, the reference ski buffer distance may also be a simulated buffer distance on a ski slope based on big data analysis, or may be a default value of the system, which is not limited here.

[0084] It can be seen that in this example, the computing device can derive the user's accustomed buffer deceleration position based on the user's historical skiing data, and further determine the reference skiing buffer distance, which is conducive to improving the accuracy of the computing device in determining the safe skiing speed.

[0085] In one possible example, in terms of determining the target deceleration action required by the user based on the plantar force information, the current position, the current speed, the slope of the simulated ski slope, the action posture data and the type of ski, the method may include the following steps: predicting the first acceleration of the user's current sliding on the simulated ski slope based on the plantar force information, the slope and the action posture data; inputting the current position, the current speed, the type of ski board, the first acceleration and the plantar force information into a trained skiing deceleration action prediction model to obtain the target deceleration action.

[0086] Among them, the deceleration action of skiing is different for different types of ski boards. The deceleration action of a single ski board can be to lean forward, make the board close to the snow, and then quickly rub the ski board to decelerate. The deceleration action of a double ski board can be to bring the toes together to make the double boards inward-facing, so as to increase the friction between the ski board and the snow surface to achieve deceleration. If the user's speed is too fast and the acceleration is greater, it is not suitable to change to the deceleration action too quickly to prevent the user from falling due to insufficient stability. The plantar pressure value of the left foot and the plantar pressure value of the right foot can be used to analyze the symmetry of the left and right use cases of the user's legs. Poor symmetry of the left and right use cases of the legs indicates that the user's legs bear inconsistent weights, and the user is actually prone to sliding biased. For example, if the left leg bears less weight than the right leg, the user will ski to the left.

[0087] Among them, the kinematic data of skiers during the deceleration process can be collected in advance, including real-time speed, skiing position, real-time acceleration, and plantar pressure data. The kinematic data is used as a training set to train the neural network model to obtain a trained ski deceleration action prediction model.

[0088] It can be seen that in this example, the computing device can use the skiing deceleration action prediction model to predict the deceleration action required by the user, which is helpful to improve the accuracy of determining the deceleration action.

[0089] In one possible example, the historical skiing data includes a first length, a first width and a slope of the simulated downhill ski slope, and a second length and a second width of the simulated buffer platform; in terms of determining the safe skiing speed of the user based on the historical skiing data, the method may include the following steps: determining a second acceleration at which the user decelerates on the simulated buffer platform based on the historical skiing data; determining a third acceleration at which the user decelerates on the downhill ski slope based on the historical skiing data; determining a first speed at which the user skis into the buffer platform based on the second length and the second acceleration; determining a maximum second speed allowed for the user's buffer deceleration based on the first speed, a reference skiing buffer distance, the slope and the third acceleration, and using the second speed as the safe skiing speed.

[0090] Among them, multiple first reference accelerations can be calculated based on the speed of the user skiing into the simulated buffer platform each time in the historical skiing data (i.e., the speed at the starting point of the simulated buffer platform) and the distance from deceleration to stop on the simulated buffer platform, and the second acceleration can be obtained by taking the average of the multiple first reference accelerations; multiple second reference accelerations on the downskiing slope can be analyzed based on the speed changes of the user within a certain distance on the downskiing slope each time in the historical skiing data, and the third acceleration can be obtained by taking the average of the multiple second reference accelerations.

[0091] Among them, it is assumed that the end point of the user's slide simulation buffer platform just stops, that is, the speed is 0. Then, at this time, the acceleration of the user's deceleration on the slide simulation buffer platform and the deceleration distance from deceleration to stop, that is, the second length, can be calculated. The first speed of the user skiing into the buffer platform can be calculated; and based on the first speed and the slope, the third speed of the user at the end point of the reference skiing buffer distance can be calculated. Further, based on the third speed, the reference skiing buffer distance and the third acceleration, the second speed of the user at the starting point of the reference skiing buffer distance can be calculated, and the second speed is used as the safe skiing speed.

[0092] It can be seen that in this example, the computing device can determine the safe skiing speed based on the user's historical skiing data, which is conducive to improving the accuracy of determining the safe skiing speed.

[0093] See also Figure 4 , Figure 4 : is a structural schematic diagram of a computing device provided in an embodiment of the present application, which is applied to a computing device in a human motion quantification training system, wherein the human motion quantification training system comprises the computing device, a six-degree-of-freedom motion platform, a display device, a voice broadcast device and a motion capture device, wherein the six-degree-of-freedom motion platform comprises a snowboard force measuring platform, and the computing device is respectively connected to the snowboard force measuring platform, the display device, the voice broadcast device and the motion capture device; Figure 4 As shown, the computing device includes a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs are configured to execute the following steps by the processor:

[0094] Acquire historical skiing data of a user on a preset simulated snow track, wherein the preset simulated snow track includes a simulated downhill ski track and a simulated buffer platform;

[0095] Determine the safe skiing speed of the user according to the historical skiing data, wherein the safe skiing speed is used to represent the maximum skiing speed of the user sliding through the simulated downhill ski slope and stopping on the simulated buffer platform;

[0096] Acquiring plantar force information from the snowboard force measuring platform, and acquiring motion posture data from the motion capture device;

[0097] Predicting the target skiing speed of the user according to the current position, current speed, plantar force information and action posture data of the user on the simulated ski slope, wherein the target skiing speed is used to represent the maximum skiing speed that the user can achieve on the simulated ski slope while maintaining the current sliding state;

[0098] If it is determined that the target skiing speed is greater than the safe skiing speed, determining the target deceleration action required by the user according to the plantar force information, the current position, the current speed, the slope of the simulated ski slope, the action posture data and the type of skis; and

[0099] Determining a target deceleration reminder message for the user according to the target deceleration action and the action posture data; and

[0100] The deceleration reminder message is sent to the display device and the voice broadcast device respectively.

[0101] It can be seen that in the embodiment of the present application, the computing device can first obtain the historical skiing data of the user on the preset simulated snow track, then determine the safe skiing speed of the user based on the historical skiing data, then obtain the plantar force information from the snowboard force measuring platform, and obtain the motion posture data from the motion capture device, further predict the user's target skiing speed based on the user's current position, current speed, plantar force information and motion posture data on the simulated ski track, and further, if it is determined that the target skiing speed is greater than the safe skiing speed, determine the target deceleration action required by the user based on the plantar force information, current position, current speed and snowboard type, and determine the user's target deceleration reminder message based on the target deceleration action and the motion posture data; and send the deceleration reminder message to the display device and the voice broadcast device respectively. By determining the target deceleration action required by the user when the predicted target skiing speed is greater than the safe skiing speed, determining the deceleration reminder message in combination with the user's current motion posture, displaying the deceleration reminder message through the display device and broadcasting the deceleration reminder message through the voice broadcast device, the user is helped to change the skiing posture to decelerate, and the skiing speed control is realized, which is conducive to improving the intelligence of the skiing speed control training of the computing device in the human motion quantitative training system.

[0102] In one possible example, in predicting the target skiing speed of the user based on the current position, current speed, plantar force information and action posture data of the user on the simulated ski slope, the program includes instructions for performing the following steps:

[0103] determining a reference skiing buffer distance of the user on the simulated downhill ski slope based on the historical skiing data;

[0104] Predicting a first acceleration of the user currently sliding on the simulated downhill ski slope according to the plantar force information, the slope and the action posture data;

[0105] determining a target skiing distance according to the current position, the first length and the reference ski buffer distance;

[0106] The target skiing speed after the user skis over the target skiing distance is determined according to the current speed, the first acceleration, and the target skiing distance.

[0107] In a possible example, in predicting the first acceleration of the user's current sliding on the simulated downhill ski slope based on the plantar force information, the slope, and the action posture data, the program includes instructions for executing the following steps:

[0108] Obtaining a pre-stored balance evaluation value, where the balance evaluation value is used to characterize the degree of balance of the user skiing on the preset simulated snow track;

[0109] Determine the plantar pressure value of the left foot and the plantar pressure value of the right foot of the user according to the plantar force information;

[0110] Determining the symmetry of the force exerted by the user's legs according to the left foot plantar pressure value and the right foot plantar pressure value;

[0111] Determine, according to the action posture data, a gravity center distribution characterization value of the user, wherein the gravity center distribution characterization value is used to characterize the position of the gravity center of the user relative to the center of the snowboard;

[0112] The initial acceleration is corrected using the balance evaluation value, the left foot plantar pressure value, the right foot plantar pressure value, the center of gravity distribution characterization value, and the left-right force symmetry of both legs to obtain a corrected target acceleration, and the target acceleration is used as the first acceleration.

[0113] In a possible example, in determining a reference ski buffer distance of the user on the simulated ski slope based on the historical ski data, the program includes instructions for performing the following steps:

[0114] Determining, based on the historical skiing data, a plurality of historical buffer deceleration distances of the user on the simulated downhill ski slope;

[0115] An average of the plurality of historical buffer deceleration distances is determined to obtain an average buffer deceleration distance, and the average buffer deceleration distance is used as the reference skiing buffer distance.

[0116] In a possible example, in determining the target deceleration action required by the user according to the plantar force information, the current position, the current speed, the slope of the simulated ski slope, the action posture data and the type of snowboard, the program further includes instructions for performing the following steps:

[0117] Predicting a first acceleration of the user currently sliding on the simulated downhill ski slope according to the plantar force information, the slope and the action posture data;

[0118] The current position, the current speed, the type of ski board, the first acceleration, and the plantar force information are input into a trained skiing deceleration action prediction model to obtain the target deceleration action.

[0119] In a possible example, the historical skiing data includes a first length, a first width and a slope of the simulated downhill ski run, and a second length and a second width of the simulated buffer platform; in determining the safe skiing speed of the user based on the historical skiing data, the program further includes instructions for executing the following steps:

[0120] determining a second acceleration at which the user decelerates on the simulated cushioning platform based on the historical skiing data;

[0121] determining, based on the historical skiing data, a third acceleration for buffering deceleration of the user on the downhill ski slope;

[0122] determining a first speed at which the user skis into the cushioning platform according to the second length and the second acceleration;

[0123] A maximum second speed allowed by the user buffer deceleration is determined according to the first speed, the reference ski buffer distance, the slope and the third acceleration, and the second speed is used as the safe ski speed.

[0124] The above mainly introduces the scheme of the embodiment of the present application from the perspective of the execution process on the method side. It is understandable that, in order to realize the above functions, the computing device includes a hardware structure and / or software module corresponding to the execution of each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiment provided in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0125] The embodiment of the present application can divide the computing device into functional units according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0126] In the case of dividing each functional module into corresponding functional modules, Figure 5 A functional unit block diagram for ski data processing is given, such as Figure 5As shown, a computing device used in a human motion quantification training system, the human motion quantification training system includes the computing device, a six-degree-of-freedom motion platform, a display device, a voice broadcast device and a motion capture device, the six-degree-of-freedom motion platform includes a snowboard force measuring platform, the computing device is respectively connected to the snowboard force measuring platform, the display device, the voice broadcast device and the motion capture device; the skiing data processing device includes: an acquisition unit 501, a determination unit 502 and a prediction unit 503, wherein,

[0127] The acquisition unit 501 is used to acquire the historical skiing data of the user on a preset simulated snow track, wherein the preset simulated snow track includes a simulated downhill ski track and a simulated buffer platform;

[0128] The determining unit 502 is used to determine the safe skiing speed of the user according to the historical skiing data, where the safe skiing speed is used to represent the maximum skiing speed of the user when sliding down the simulated ski slope and stopping on the simulated buffer platform;

[0129] The acquisition unit 501 is further used to acquire the force information of the sole of the foot from the snowboard force measuring platform, and acquire the motion posture data from the motion capture device;

[0130] The prediction unit 503 is used to predict the target skiing speed of the user according to the current position, current speed, plantar force information and action posture data of the user on the simulated ski slope, wherein the target skiing speed is used to represent the maximum skiing speed that the user can achieve on the simulated ski slope while maintaining the current sliding state;

[0131] The determination unit 502 is also used to determine the target deceleration action required by the user according to the plantar force information, the current position, the current speed, the slope of the simulated ski slope, the action posture data and the type of ski if it is determined that the target skiing speed is greater than the safe skiing speed; and, based on the target deceleration action and the action posture data, determine the target deceleration reminder message of the user; and, respectively, send the deceleration reminder message to the display device and the voice broadcast device.

[0132] It can be seen that the skiing data processing device described in the embodiment of the present application can first obtain the user's historical skiing data on a preset simulated ski slope, and then determine the user's safe skiing speed based on the historical skiing data, and then obtain the plantar force information from the ski board force measuring platform, and obtain the motion posture data from the motion capture device, and further, predict the user's target skiing speed based on the user's current position, current speed, plantar force information and motion posture data on the simulated ski slope, and further, if it is determined that the target skiing speed is greater than the safe skiing speed, determine the target deceleration action required by the user based on the plantar force information, current position, current speed and ski type, and determine the user's target deceleration reminder message based on the target deceleration action and motion posture data; and send the deceleration reminder message to the display device and the voice broadcast device respectively. When the predicted target skiing speed is greater than the safe skiing speed, the target deceleration action required by the user is determined, and a deceleration reminder message is determined in combination with the user's current action posture. The deceleration reminder message is displayed on a display device and broadcast through a voice broadcast device to help the user change his skiing action posture to decelerate, thereby achieving skiing speed control, which is beneficial to improving the intelligence of skiing speed control training of computing equipment in the human motion quantification training system.

[0133] In a possible example, in predicting the target skiing speed of the user according to the current position, current speed, plantar force information and action posture data of the user on the simulated ski slope, the determining unit 502 is specifically used to:

[0134] determining a reference skiing buffer distance of the user on the simulated downhill ski slope based on the historical skiing data;

[0135] Predicting a first acceleration of the user currently sliding on the simulated downhill ski slope according to the plantar force information, the slope and the action posture data;

[0136] determining a target skiing distance according to the current position, the first length and the reference ski buffer distance;

[0137] The target skiing speed after the user skis over the target skiing distance is determined according to the current speed, the first acceleration, and the target skiing distance.

[0138] In a possible example, in predicting the first acceleration of the user currently sliding on the simulated downhill ski slope according to the plantar force information, the slope, and the action posture data, the prediction unit 503 is specifically used to:

[0139] Obtaining a pre-stored balance evaluation value, where the balance evaluation value is used to characterize the degree of balance of the user skiing on the preset simulated snow track;

[0140] Determine the plantar pressure value of the left foot and the plantar pressure value of the right foot of the user according to the plantar force information;

[0141] Determining the symmetry of the force exerted by the user's legs according to the left foot plantar pressure value and the right foot plantar pressure value;

[0142] Determine, according to the action posture data, a gravity center distribution characterization value of the user, wherein the gravity center distribution characterization value is used to characterize the position of the gravity center of the user relative to the center of the snowboard;

[0143] The initial acceleration is corrected using the balance evaluation value, the left foot plantar pressure value, the right foot plantar pressure value, the center of gravity distribution characterization value, and the left-right force symmetry of both legs to obtain a corrected target acceleration, and the target acceleration is used as the first acceleration.

[0144] In a possible example, in determining the reference ski buffer distance of the user on the simulated ski slope according to the historical ski data, the determining unit 502 is specifically configured to:

[0145] Determining, based on the historical skiing data, a plurality of historical buffer deceleration distances of the user on the simulated downhill ski slope;

[0146] An average of the plurality of historical buffer deceleration distances is determined to obtain an average buffer deceleration distance, and the average buffer deceleration distance is used as the reference skiing buffer distance.

[0147] In a possible example, in determining the target deceleration action required by the user according to the plantar force information, the current position, the current speed, the slope of the simulated ski slope, the action posture data, and the type of snowboard, the determining unit 502 is specifically configured to:

[0148] Predicting a first acceleration of the user currently sliding on the simulated downhill ski slope according to the plantar force information, the slope and the action posture data;

[0149] The current position, the current speed, the type of ski board, the first acceleration, and the plantar force information are input into a trained skiing deceleration action prediction model to obtain the target deceleration action.

[0150] In a possible example, the historical skiing data includes a first length, a first width and a slope of the simulated downhill ski run, and a second length and a second width of the simulated buffer platform; in determining the safe skiing speed of the user according to the historical skiing data, the determining unit 502 is further specifically configured to: determine a second acceleration of the user decelerating on the simulated buffer platform according to the historical skiing data;

[0151] determining, based on the historical skiing data, a third acceleration for buffering deceleration of the user on the downhill ski slope;

[0152] determining a first speed at which the user skis into the cushioning platform according to the second length and the second acceleration;

[0153] A maximum second speed allowed by the user buffer deceleration is determined according to the first speed, the reference ski buffer distance, the slope and the third acceleration, and the second speed is used as the safe ski speed.

[0154] It should be noted that all relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module and will not be repeated here.

[0155] The computing device provided in this embodiment is used to execute the above-mentioned skiing data processing method, and thus can achieve the same effect as the above-mentioned implementation method.

[0156] In the case of an integrated unit, the computing device may include a processing module, a storage module and a communication module. The processing module may be used to control and manage the actions of the computing device, for example, it may be used to support the computing device to execute the steps performed by the above functional units. The storage module may be used to support the computing device to execute stored program codes and data, etc. The communication module may be used to support the computing device to communicate with other devices.

[0157] Among them, the processing module can be a processor or a controller. It can implement or execute various exemplary logic boxes, modules and circuits described in conjunction with the disclosure of this application. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc. The storage module can be a memory. The communication module can specifically be a device that interacts with other computing devices, such as a radio frequency circuit, a Bluetooth chip, a Wi-Fi chip, etc.

[0158] An embodiment of the present application also provides a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes a computing device.

[0159] The present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes a control platform.

[0160] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0161] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0162] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0163] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0164] In addition, each functional unit in each embodiment of the present 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 above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0165] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the above-mentioned methods in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0166] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable memory, which can include: a flash drive, a read-only memory, a random access memory, a magnetic disk or an optical disk, etc.

[0167] The embodiments of the present application are introduced in detail above. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A skiing data processing method, characterized in that: A computing device used in a human motion quantitative training system, the human motion quantitative training system comprising the computing device, a six-degree-of-freedom motion platform, a display device, a voice broadcast device and a motion capture device, the six-degree-of-freedom motion platform comprising a snowboard force measuring platform, the computing device being respectively connected to the snowboard force measuring platform, the display device, the voice broadcast device and the motion capture device; the method comprising: Acquire historical skiing data of a user on a preset simulated snow track, wherein the preset simulated snow track includes a simulated lower ski track and a simulated buffer platform, and the historical skiing data includes a first length, a first width, and a slope of the simulated lower ski track, and a second length and a second width of the simulated buffer platform; Determine the safe skiing speed of the user according to the historical skiing data, wherein the safe skiing speed is used to represent the maximum skiing speed of the user sliding through the simulated downhill ski slope and stopping on the simulated buffer platform; Acquiring plantar force information from the snowboard force measuring platform, and acquiring motion posture data from the motion capture device; Predicting the target skiing speed of the user according to the current position, current speed, plantar force information and action posture data of the user on the simulated ski slope, wherein the target skiing speed is used to represent the maximum skiing speed that the user can achieve on the simulated ski slope while maintaining the current sliding state; If it is determined that the target skiing speed is greater than the safe skiing speed, determining the target deceleration action required by the user according to the plantar force information, the current position, the current speed, the slope of the simulated ski slope, the action posture data and the type of skis; and Determining a target deceleration reminder message for the user according to the target deceleration action and the action posture data; and Sending the deceleration reminder message to the display device and the voice broadcast device respectively; The predicting the target skiing speed of the user according to the current position, current speed, sole force information and action posture data of the user on the simulated ski slope includes: determining a reference skiing buffer distance of the user on the simulated downhill ski slope based on the historical skiing data; Predicting a first acceleration of the user currently sliding on the simulated downhill ski slope according to the plantar force information, the slope and the action posture data; determining a target skiing distance according to the current position, the first length and the reference ski buffer distance; The target skiing speed after the user skis over the target skiing distance is determined according to the current speed, the first acceleration, and the target skiing distance.

2. The method according to claim 1, characterized in that The predicting, based on the plantar force information, the slope and the action posture data, a first acceleration of the user currently sliding on the simulated downhill ski slope comprises: Obtaining a pre-stored balance evaluation value, where the balance evaluation value is used to characterize the degree of balance of the user skiing on the preset simulated snow track; Determine the plantar pressure value of the left foot and the plantar pressure value of the right foot of the user according to the plantar force information; Determining the symmetry of the force exerted by the user's legs according to the left foot plantar pressure value and the right foot plantar pressure value; Determine, according to the action posture data, a gravity center distribution characterization value of the user, wherein the gravity center distribution characterization value is used to characterize the position of the gravity center of the user relative to the center of the snowboard; The initial acceleration is corrected using the balance evaluation value, the left foot plantar pressure value, the right foot plantar pressure value, the center of gravity distribution characterization value, and the left-right force symmetry of both legs to obtain a corrected target acceleration, and the target acceleration is used as the first acceleration.

3. The method according to claim 2, characterized in that When the center of gravity distribution characterization value is a positive value, the center of gravity of the user is biased toward the front end of the center of the snowboard; When the center of gravity distribution characterization value is a negative value, the user's center of gravity is biased toward the rear end of the center of the snowboard.

4. The method according to claim 1, characterized in that: The step of determining a reference skiing buffer distance of the user on the simulated ski slope according to the historical skiing data comprises: Determining, based on the historical skiing data, a plurality of historical buffer deceleration distances of the user on the simulated downhill ski slope; An average of the plurality of historical buffer deceleration distances is determined to obtain an average buffer deceleration distance, and the average buffer deceleration distance is used as the reference skiing buffer distance.

5. The method according to claim 1, characterized in that The step of determining the target deceleration action required by the user according to the plantar force information, the current position, the current speed, the slope of the simulated ski slope, the action posture data and the type of ski board includes: Predicting a first acceleration of the user currently sliding on the simulated downhill ski slope according to the plantar force information, the slope and the action posture data; The current position, the current speed, the type of ski board, the first acceleration, and the plantar force information are input into a trained skiing deceleration action prediction model to obtain the target deceleration action.

6. The method according to claim 1, characterized in that Determining the safe skiing speed of the user according to the historical skiing data includes: determining a second acceleration at which the user decelerates on the simulated cushioning platform based on the historical skiing data; determining, based on the historical skiing data, a third acceleration for buffering deceleration of the user on the downhill ski slope; determining a first speed at which the user skis into the cushioning platform according to the second length and the second acceleration; A maximum second speed allowed by the user buffer deceleration is determined according to the first speed, the reference ski buffer distance, the slope and the third acceleration, and the second speed is used as the safe ski speed.

7. A ski data processing device, characterized in that: A computing device used in a human motion quantitative training system, the human motion quantitative training system comprising the computing device, a six-degree-of-freedom motion platform, a display device, a voice broadcast device and a motion capture device, the six-degree-of-freedom motion platform comprising a snowboard force measuring platform, the computing device being respectively connected to the snowboard force measuring platform, the display device, the voice broadcast device and the motion capture device; the device comprising an acquisition unit, a determination unit and a prediction unit; wherein, The acquisition unit is used to acquire historical skiing data of the user on a preset simulated snow track, wherein the preset simulated snow track includes a simulated lower ski track and a simulated buffer platform, and the historical skiing data includes a first length, a first width and a slope of the simulated lower ski track, and a second length and a second width of the simulated buffer platform; The determining unit is used to determine the safe skiing speed of the user according to the historical skiing data, wherein the safe skiing speed is used to represent the maximum skiing speed of the user when sliding across the simulated downhill ski slope and stopping on the simulated buffer platform; The acquisition unit is further used to acquire the plantar force information from the snowboard force measuring platform, and acquire the motion posture data from the motion capture device; The prediction unit is used to predict the target skiing speed of the user according to the current position, current speed, plantar force information and action posture data of the user on the simulated ski slope, wherein the target skiing speed is used to represent the maximum skiing speed that the user can reach on the simulated ski slope while maintaining the current sliding state; the prediction of the target skiing speed of the user according to the current position, current speed, plantar force information and action posture data of the user on the simulated ski slope comprises: determining a reference skiing buffer distance of the user on the simulated ski slope according to the historical skiing data; predicting a first acceleration of the user currently sliding on the simulated ski slope according to the plantar force information, the slope and the action posture data; determining a target sliding distance according to the current position, the first length and the reference skiing buffer distance; determining the target skiing speed of the user after sliding over the target sliding distance according to the current speed, the first acceleration and the target sliding distance; The determination unit is further used to determine the target deceleration action required by the user according to the plantar force information, the current position, the current speed, the slope of the simulated ski slope, the action posture data and the type of ski if it is determined that the target skiing speed is greater than the safe skiing speed; and, based on the target deceleration action and the action posture data, determine the target deceleration reminder message for the user; and, respectively, send the deceleration reminder message to the display device and the voice broadcast device.

8. A computing device, characterized in that The method comprises a processor and a memory, wherein the memory is used to store one or more programs and is configured to be executed by the processor, wherein the program comprises instructions for executing the steps in the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 6.

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