A method, system and storage medium for processing sports data for a racing boat

Abstract

The application relates to a sports data processing method, system and storage medium for racing boats, the processing method comprising: acquiring action data of a monitoring object in real time; generating action records in chronological order based on the action data; dividing a plurality of action data segments based on the action records, each action data segment corresponding to a complete rowing action period; inputting each action data segment into a preset action model for comparison to obtain deviation data; statistically processing the deviation data based on a preset data amount and generating evaluation information; judging whether the evaluation information meets the standard; if yes, increasing the statistical data amount; if not, resetting the statistical data amount to the preset data amount; and continuing to statistically process the remaining deviation data based on the adjusted statistical data amount after the judgment is completed. The application has the effects of filling the blank of science and technology empowering sports in the field of racing boats, digitally presenting the sports performance of a monitoring object, and providing effective reference data for the monitoring object to help improve the technical level.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of sports digital presentation, in particular to a method and system for processing sports data of rowing and a storage medium. BACKGROUND

[0002] In most competitive sports, real-time data of training or competition is of great significance for the improvement of athletes' skills, and the same is true for rowing sports. In recent years, technology has empowered sports, but there is still a blank in the field of rowing sports.

[0003] The correctness of the rowing posture is related to the maximum exertion of power. When doing rowing training, a relatively wide training water area is needed, but there is a lack of necessary information acquisition means when rowing sports are trained on water, making it difficult for coaches and athletes to obtain effective reference data and restricting the improvement of technical level. Therefore, the traditional training method mainly relies on shore simulation training devices to obtain training data for reference for athletes.

[0004] For the related technology in the above, the inventors believe that the simulation training mechanical device used on the shore lacks real water experience compared to water training, and is limited to single action simulation, and cannot fully cover complex actions and technical requirements in various water rowing environments, so the reference value of the training data obtained is limited. Therefore, a processing system is needed to effectively acquire real-time training information, research related parameter perception and acquisition methods, and solve the problem of obtaining sports performance information of athletes in rowing sports. SUMMARY

[0005] In order to fill the blank of technology empowering sports in the field of rowing, digitally present the sports performance of the monitoring object, and provide effective reference data to help improve the technical level, the present application provides a method and system for processing sports data of rowing and a storage medium.

[0006] The present application provides a method and system for processing sports data of rowing and a storage medium, which adopts the following technical solution:

[0007] In a first aspect, the present application provides a method for processing sports data of rowing, which adopts the following technical solution:

[0008] A method for processing sports data of rowing, the processing method comprising:

[0009] real-time acquisition of action data of a monitoring object;

[0010] generating action records in time sequence based on the action data;

[0011] Based on the action record, a plurality of action data segments are divided, and each of the action data segments corresponds to a complete rowing action cycle;

[0012] Each of the action data segments is input into a preset action model for comparison to obtain deviation data;

[0013] Based on the deviation data, a preset data amount is counted, and evaluation information is generated;

[0014] It is judged whether the evaluation information meets the standard. If it meets the standard, the statistical data amount is increased, and the previous statistical step is repeated based on the remaining deviation data. If it does not meet the standard, the statistical data amount is reset to the preset data amount, and the previous statistical step is repeated based on the remaining deviation data.

[0015] The correctness of the rowing posture is related to whether the maximum force can be exerted. By adopting the technical scheme, the action data of the monitoring object obtained is recorded in time sequence. Rowing is a periodical sport, and the rowing boat is driven forward by continuously repeating a set of actions. Then, the collected data is divided into a plurality of action data segments according to each complete rowing action cycle. The fluctuation of each action data segment relative to the preset action model is compared to obtain deviation data of each action data segment. The larger the deviation value is, the worse the action standard is. In this way, the standard of the rowing posture of the monitoring object is measured. However, there are about 30-45 rowing action cycles per minute in rowing sports, and the monitoring data amount is large, which is not convenient for intuitively understanding the sports performance. Therefore, a plurality of deviation data is divided into a group for unified judgment to obtain an evaluation information. If the evaluation meets the standard, it indicates that the standard of the action completed in this time period is high, and there may be individual incorrect actions, but the monitoring object does not need to pay attention to it. The number of the next group of deviation data is increased, and the number of the output evaluation information is further reduced. If the evaluation does not meet the standard, it indicates that the standard of the action completed in this time period is low, and there are many incorrect actions that need the attention of the monitoring object. The next group of deviation data is grouped in the preset data amount. In this way, the monitoring object obtains more concise and intuitive sports performance results, and can quickly locate the data position of the action completion not meeting the standard, helps the monitoring object to find the problems of himself, and thus improves the rowing technology level.

[0016] Preferably, the action data includes trajectory data of the oar, and the action record includes a pulling trajectory, a pressing trajectory, a returning trajectory and a lifting trajectory generated based on the trajectory data;

[0017] Based on the action record, a plurality of action data segments are divided, and each of the action data segments corresponds to a complete rowing action cycle, specifically, one of the pulling trajectory, the pressing trajectory, the returning trajectory and the lifting trajectory is divided into one of the action data segments.

[0018] Each rowing cycle includes the process of pulling and returning oar, in which the pressing and lifting stages are closely linked and each plays its own role. By adopting the technical scheme, the motion sensor can be arranged on the oar handle to obtain the trajectory data of the oar. Since the trajectory of the oar handle in a rowing cycle is approximately a rectangle, the actions of pulling, pressing, returning and lifting are each a side of the rectangle, which is obvious and low in difficulty of obtaining and distinguishing. According to the change of the direction of the trajectory data of the oar, the action record generated in time sequence can obtain the process of pulling, pressing, returning and lifting. A set of continuous pulling trajectory, pressing trajectory, returning trajectory and lifting trajectory is a rowing action cycle. The obtained large amount of data is divided into multiple complete action data segments, so as to reflect the completion quality of each rowing action cycle. The motion performance result obtained by the monitoring object is more concise and intuitive.

[0019] Preferably, the action data includes the oar blade posture.

[0020] When the returning trajectory is generated based on the trajectory data, the oar blade posture in the action record in each time period when the oar blade posture is in a horizontal state is respectively and one-to-one corresponding to the correction of each returning trajectory.

[0021] Correctly mastering the action and oar blade posture change in each stage is crucial to optimizing rowing technology, which can improve rowing efficiency, reduce energy consumption and help prevent sports injuries. In the returning stage of each oar, the oar blade should be kept in a horizontal state close to the water surface to reduce the air resistance of the oar blade. In the pulling stage, the oar blade needs to be kept in a vertical state to increase the resistance of the oar blade in the water, so that the oar blade generates power in the water resistance to form displacement work and push the boat in the backward direction. In the lifting and pressing stages, the oar blade should be kept in a vertical state when it is out of water and enters water to reduce water resistance. However, during the pressing stage, from when the oar blade is out of water to the beginning of the returning stage, and from the returning stage to when the oar blade enters water before the lifting stage, the oar handle has actually started to move horizontally, resulting in inaccurate division of the lifting trajectory, the returning trajectory and the pressing trajectory. By adopting the technical scheme, since the oar blade posture is obvious, the moment when the oar blade posture completely enters a horizontal state is determined as the end of the pressing stage, and the moment when the oar blade posture ends the horizontal state is determined as the beginning of the lifting stage, and then the action record corresponding to the oar blade posture in the horizontal state is the returning trajectory. The division of the returning trajectory in the action data segment is more accurate, the system error is smaller in the subsequent comparison with the preset action model, and effective reference data is provided for the monitoring object to help improve the technical level. The motion sensor can be arranged on the oar to obtain the oar blade posture in real time.

[0022] Preferably, the action data includes the position information of the sliding seat on the sliding rail, and the position information includes the leg bending position of the sliding seat at one end of the sliding rail.

[0023] The starting point of each of the pull stroke trajectories is corrected one by one according to the position information of the sliding seat leaving the bent leg position in the action record.

[0024] In actual rowing, at the moment when the pull stroke action makes the paddle blade catch water, the two legs quickly react, straighten up backward and upward, and push the sliding seat to move backward, that is, enter the pull stroke action. By adopting the technical scheme, in the straight leg action of the pull stroke action, the sliding seat will slide along the slide rail to the straight leg position far away from the feet. The straight leg action further includes the backward leaning and arm bending action, and together constitutes the pull stroke action. The starting point of the pull stroke trajectory is marked by the moment when the sliding seat leaves the bent leg position, so that the division of the pull stroke trajectory in the action data segment is more accurate, the system error is smaller in subsequent comparison with the preset action model, and effective reference data is provided for the monitoring object to help improve the technical level. The position of the sliding seat on the slide rail can be measured by setting a distance measuring sensor on the slide rail of the racing boat.

[0025] Preferably, the method further comprises the following steps after the action data segments are input into the preset action model for comparison to obtain deviation data:

[0026] The synchronous difference value between the moment when the sliding seat leaves the bent leg position and the moment when the paddle blade posture turns to the vertical state in the same action data segment is calculated. If the absolute value of the synchronous difference value is greater than the preset synchronous difference value, the corresponding action data segment is marked.

[0027] In the pull stroke action, the paddle blade pushes the water in the water to generate a reaction force to push the racing boat forward. The water resistance of the paddle blade in the vertical state is the largest, and the generated thrust is also the largest, which most efficiently converts the force applied by the monitoring object on the paddle into driving force for the racing boat. By adopting the technical scheme, when the position information leaves the bent leg position, the straight leg action starts. If the paddle blade has not yet assumed the vertical state at this time, it indicates that the paddle angle is incorrect in the previous pull stroke stage or the straight leg action is too early and the paddle blade has not yet completed the paddle turning and entering the water. The above two situations will reduce the work conversion efficiency of this paddle cycle. If the paddle blade has maintained the vertical state for a period of time before the position information leaves the bent leg position, it indicates that the straight leg action is too late, and the paddle blade in the vertical state increases the resistance of the boat in the water, which will cause the speed to decrease. When the absolute value of this synchronous difference value is greater than the preset synchronous difference value, it indicates that there is a large error in the synchronization of the hands and legs, and the corresponding action data segments are marked to provide a reference for the monitoring object, help the monitoring object find their own problems, and thus improve the racing boat technical level.

[0028] Preferably, the position information further includes the straight leg position of the sliding seat at the other end of the slide rail.

[0029] The method further comprises the following steps after inputting each action data segment into a preset action model for comparison and obtaining deviation data:

[0030] The method further comprises the following steps after inputting each action data segment into a preset action model for comparison and obtaining deviation data:

[0031] In the rowing action, the oar blade moves in the air and is hindered by the air force to affect the forward movement of the racing boat. When the oar blade is in a horizontal state, the cross-sectional area in the moving direction is the smallest, and the air resistance is also the smallest, thereby reducing the influence of the oar blade on the forward movement of the racing boat in the rowing process. Through the above technical scheme, the rowing action comprises a straight arm action, a forward leaning action, and a bent leg action. In the bent leg action, the sliding seat slides along the sliding rail to the bent leg position close to the feet. Therefore, the moment when the position information deviates from the straight leg position is the start of the bent leg action, and at this moment, the oar blade should be kept in a horizontal state to reduce air resistance. If the moment when the position information deviates from the straight leg position does not correspond to the rowing track corrected based on the oar blade being kept in a horizontal state, it indicates that the oar blade is kept in a horizontal state for a very short time during the rowing action, and the oar blade is kept at a certain angle with the moving direction of the oar blade during the rowing action, thereby generating a certain air resistance and reducing the speed of the racing boat.

[0032] Preferably, when the deviation data is counted based on the preset data amount, the deviation data does not include the deviation data corresponding to the action data segment with the mark.

[0033] Through the above technical scheme, when the deviation data is counted, the action data segment with the mark is removed, because the action data segment with the mark indicates that the monitoring object has a major mistake in the action data segment, and the mark is set to remind the monitoring object to pay attention. The deviation data corresponding to the action data segment with the mark is very large and has no reference value. If the deviation data is counted together with other deviation data, the generated evaluation information will lose representativeness and interfere with the monitoring object to quickly understand the movement performance. Therefore, the removal operation is set.

[0034] Preferably, the action data comprises a foot support pressure value, and the action record comprises a foot support pressure value change curve.

[0035] The method further comprises the following steps after inputting each action data segment into a preset action model for comparison and obtaining deviation data:

[0036] Based on the foot support pressure value change curve, the lower limb work value in the time period corresponding to the change of the sliding seat from the bent leg position to the straight leg position in each action data segment is calculated.

[0037] The rowing sport requires high strength and continuous and stable work. The rowing sport relies on monitoring the compression of the body, the leg lifting, the upper body leaning back to pull the paddle in the water resistance to form displacement work, and pushing the boat to move in the backward direction. By adopting the technical scheme, when the paddle is lifted, the body naturally curls forward, the center of gravity of the body is located at the front foot position on the foot pedal, and the hands are quickly moved upward, so that the paddle is quickly inserted into the water. Then, the straight leg action of pulling the paddle is performed, the front foot is forced to step on the foot pedal, the foot pedal is the main force point of the paddle, and a pressure sensor can be arranged on the foot pedal of the rowing boat. In the time period corresponding to the pulling trajectory, the foot pedal pressure value change curve reflects the size of the reaction force applied by the monitoring object to the boat paddle, and whether the force of the legs, abdomen and arms in the pulling stage is stable and balanced can be measured.

[0038] In the pulling action, the leg force is mainly relied on, then the back muscle, and finally the shoulder and arm force, and the force is also pulled in the order of leg, abdomen and shoulder arm to swing the paddle. By calculating the lower limb work value in the pulling stage in each rowing action cycle, the monitoring object can intuitively understand the lower limb endurance level and intuitively know the physical consumption.

[0039] In the second aspect, the application provides a sports data processing system for rowing, which adopts the following technical scheme:

[0040] A sports data processing system for rowing, the processing system comprises:

[0041] An acquisition module is configured to acquire action data of a monitoring object in real time, the action data comprising trajectory data of a paddle, a paddle attitude, and foot pedal pressure values of a foot pedal, and position information of a sliding seat on a sliding rail, the position information comprising a bent leg position and a straight leg position;

[0042] A recording module is configured to store the action data and generate action records in time sequence, the action records comprising pulling trajectories, pressure paddle trajectories, return paddle trajectories and lifting paddle trajectories generated based on the trajectory data, and foot pedal pressure value change curves generated based on the foot pedal pressure values;

[0043] An information processing module is configured to correct each return paddle trajectory in the action records in which the paddle attitude is in a horizontal state one by one corresponding to a plurality of time periods, correct the starting points of each pulling trajectory in the action records in which the sliding seat is away from the bent leg position one by one corresponding to a plurality of time points, and divide a plurality of action data segments by taking one corrected pulling trajectory, one pressure paddle trajectory, one return paddle trajectory and one lifting paddle trajectory as a group, and each action data segment corresponds to one rowing action cycle;

[0044] The information processing module is further configured to calculate, according to the change curve of the footrest pressure value, a lower limb work value corresponding to a time period in which the slide changes from the bent leg position to the straight leg position in each of the action data segments;

[0045] a model analysis module configured to input the action data segments into a preset action model one by one, obtain deviation data, and mark the action data segments whose absolute value of a synchronization difference value is greater than a preset synchronization difference value and the action data segments whose slide leaves the straight leg position at a time not corresponding to the rowing track, the synchronization difference value being a difference between a time at which the slide leaves the bent leg position and a time at which the paddle posture turns to a vertical state;

[0046] The model analysis module is further configured to count the deviation data to generate evaluation information, wherein the deviation data corresponding to the marked action data segments is not counted, and a preset data amount is used as a statistical data amount for the first time counting;

[0047] a judgment module configured to judge whether the evaluation information meets a standard, increase a statistical data amount of the model analysis module if the evaluation information meets the standard, and reset the statistical data amount of the model analysis module to the preset data amount if the evaluation information does not meet the standard;

[0048] an output module configured to output the lower limb work value, the evaluation information, and the action data segments containing the marks.

[0049] In a third aspect, the present application provides a storage medium, which adopts the following technical solution:

[0050] A storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by a processor to implement the above-mentioned method for processing sports data of a racing boat.

[0051] In summary, the present application has at least one of the following beneficial technical effects:

[0052] 1. A large amount of acquired data is divided into a plurality of complete action data segments, so as to reflect the completion quality of each rowing action period. The monitoring object can obtain more concise and intuitive sports performance results;

[0053] 2. A plurality of deviation data is divided into a group for unified judgment to obtain an evaluation information. If the evaluation meets a standard, the number of the next group of statistical deviation data is increased to further reduce the number of output evaluation information. If the evaluation does not meet the standard, a preset data amount is initially set to count the next group of deviation data. Thus, the monitoring object can obtain more concise and intuitive sports performance results, and can quickly locate the data position of an action completion not meeting a standard;

[0054] 3. The stages of each rowing cycle are corrected by the paddle posture and the position information of the slide on the slide rail, making the extraction of the motion data segment more accurate, and the system error smaller in the subsequent comparison with the preset motion model, providing effective reference data for the monitoring object to improve the technical level;

[0055] 4. The motion data segment that meets the conditions and is considered to be a serious motion error is marked, and the deviation data corresponding to the motion data segment containing the mark is skipped during statistics, avoiding the generated evaluation information from losing representativeness and interfering with the monitoring object to quickly understand the motion performance, providing the marked motion data segment to the monitoring object for reference, helping the monitoring object to find their own problems and improve them, thereby further improving the racing boat technical level. BRIEF DESCRIPTION OF DRAWINGS

[0056] Figure 1 is a motion data processing method block diagram for racing boats in the embodiments of the present application;

[0057] Figure 2 is a schematic diagram of boat paddle track data in the embodiments of the present application;

[0058] Figure 3 is an additional block diagram of step S3 in the embodiments of the present application;

[0059] Figure 4 is an additional block diagram of step S4 in the embodiments of the present application;

[0060] Figure 5 is a motion data processing system block diagram for racing boats in the embodiments of the present application.

[0061] BRIEF DESCRIPTION OF DRAWINGS DETAILED DESCRIPTION

[0062] To more clearly understand the purpose, technical solutions and advantages of the present application, the present application is described and explained below in conjunction with the drawings and embodiments. However, those of ordinary skill in the art should understand that the present application can be implemented without these details. In some cases, in order to avoid unnecessary description, the aspects of the present application become obscure. Well-known methods, processes, systems, components and / or circuits that have been described at a high level will not be described in detail. It is obvious to those of ordinary skill in the art that various changes can be made to the embodiments disclosed in the present application, and the general principles defined in the present application can be applied to other embodiments and application scenarios without deviating from the principles and scope of the present application. Therefore, the present application is not limited to the embodiments shown, but conforms to the broadest scope claimed in the present application.

[0063] In the description of the present application, the description of the terms "one embodiment", "some embodiments", "illustrative embodiment", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or more embodiments or examples in a combined manner.

[0064] In order for those skilled in the art to have a detailed understanding of the technical solutions provided by the present application, first, the specific terms and basic principles involved in the embodiments of the present application are briefly described.

[0065] Rowing is a cyclical sport, and each rowing cycle includes two stages of pulling and recovering.

[0066] The drive phase refers to the process of the paddle entering the water, the rower transmitting the weight of the body to the footboard through the leg force, and at the same time, the body is unfolded, the paddle is pulled in the water, and the boat is driven forward. The movement of the racing boat is to compress the body, and the leg is pulled back to pull the paddle in the water resistance to form displacement and do work, thereby pushing the boat to move in the backward direction. Among them, the biggest force source of the rower in the rowing movement comes from the leg.

[0067] The recovery phase refers to the process of the paddle out of the water, the rower pushing the paddle forward with both hands, when the arms are fully stretched, the paddle is pushed through the knee, the upper body is naturally inclined, the sliding seat is moved forward, and finally restored to the next paddle preparation posture. In this process, the paddle is always suspended in the water to ensure that the boat does not slow down due to resistance. The purpose of the recovery phase is to restore the body to the pull preparation posture while keeping the boat stable.

[0068] The paddle entering the water and the paddle out of the water are called lifting the paddle and pressing the paddle. Lifting the paddle, pulling the paddle, pressing the paddle, and recovering the paddle together form a complete rowing cycle, and each rowing cycle is continuous and uninterrupted.

[0069] The rower sits on the sliding seat which can slide on the sliding rail.

[0070] When pulling the paddle, the rower first kicks the leg to move the sliding seat to the end of the sliding rail away from the footrest, then leans back, and finally bends the arm to complete the pulling action. The pulling action should keep the paddle vertical to ensure that the paddle produces the maximum water resistance.

[0071] When pressing the paddle, the paddle should first be kept vertical out of the water to ensure that the paddle quickly exits the water with the smallest water resistance, and then turn the paddle to make the paddle horizontal.

[0072] During the return stroke, the rower first fully extends both arms, pushes the paddle handle past the knees, then naturally leans forward, bends the legs to move the slide forward to the end of the slide rail near the pedals, completing the return stroke. The return stroke should keep the paddle blades horizontal to minimize air resistance.

[0073] When lifting the paddle, rotate the paddle simultaneously to ensure that the blades are vertical before entering the water, thus minimizing water resistance and allowing the blades to enter the water quickly.

[0074] This application discloses a method for processing motion data for rowing.

[0075] like Figure 1 As shown, the processing methods include:

[0076] S1: Acquire real-time motion data of the monitored object.

[0077] The motion data includes paddle trajectory data, paddle attitude, pedal pressure value, and slide position information on the slide rail, including bent leg position and straight leg position.

[0078] For example, motion sensors can be installed on the paddle blades to obtain the paddle trajectory data and blade attitude in real time; pressure sensors can be installed on the pedals of the racing boat to obtain the pedal pressure value in real time; and distance sensors can be installed on the slide rails of the racing boat to obtain the position of the slide block on the slide rails in real time.

[0079] Interpretive data for the paddle includes: Paddle trajectory data, which is the relative coordinates of the motion sensor's position in a two-dimensional plane at a given moment, formed by the movement of the motion sensor on the paddle blade during stroke. Paddle blade attitude is the angle between the paddle blade and the water surface at a given moment; vertical means perpendicular to the water surface, and horizontal means parallel to the water surface. Paddle pedal pressure is the real-time positive pressure value of the paddler's shoe sole on the pedals. Position information is the distance of the slide block on the slide rail relative to a reference point; a bent-leg position indicates the slide block has moved to the end of the slide rail closest to the pedals, while a straight-leg position indicates the slide block has moved to the end of the slide rail furthest from the pedals.

[0080] S2: Generate action records in chronological order based on action data.

[0081] Interpretive, all the aforementioned types of motion data are recorded on the same timeline to generate their own separate motion records.

[0082] The motion records include the pull-up trajectory, push-up trajectory, return trajectory, and lift-up trajectory generated based on the trajectory data.

[0083] Explanatory, such as Figure 2As shown, the trajectory data of the paddle at each moment in an ideal paddle cycle will form a figure approximately resembling a rectangle in a two-dimensional plane. The figure can be roughly divided into four segments corresponding to the pulling, pressing, returning and lifting stages according to the characteristics of the figure. The horizontal part at the bottom of the figure is taken as the pulling trajectory, the horizontal part at the top of the figure is taken as the returning trajectory, the remaining part at the left side of the figure is taken as the pressing trajectory, and the remaining part at the right side of the figure is taken as the lifting trajectory. The pressing trajectory and the lifting trajectory are just kept vertical when passing through the water surface.

[0084] However, in actual paddling, the figure formed by the trajectory data in the action record will be deformed to some extent, causing deviation in the division of the action trajectories in the paddle cycle.

[0085] In this embodiment, each returning trajectory is corrected one by one according to the time period when the paddle attitude in the action record is in a horizontal state. For example, by comparing each action data in the action record, the starting time point of the returning trajectory is marked according to the position on the time axis when the position information in the action record is away from the bent leg position, instead of the position on the time axis corresponding to the horizontal part at the top of the figure formed by the trajectory data in the action record.

[0086] In this embodiment, the starting point of each pulling trajectory is also corrected one by one according to the time period when the position information in the action record is away from the bent leg position. For example, by comparing each action data in the action record, the starting point of the pulling trajectory is marked according to the position on the time axis when the position information in the action record is away from the bent leg position, instead of the position on the time axis corresponding to the intersection of the horizontal part at the bottom of the figure and the vertical part at the right side of the figure formed by the trajectory data in the action record.

[0087] This multi-dimensional division of the four stages in the paddle cycle makes the division of each stage more accurate, and the system error is smaller in the subsequent comparison with the preset action model.

[0088] The action record also includes a foot pedal pressure value change curve.

[0089] S3: Based on the action record, a plurality of action data segments are divided, and each action data segment corresponds to a complete paddle action cycle.

[0090] Explanatorily, an adjacent pulling trajectory, a pressing trajectory, a returning trajectory and a lifting trajectory formed after the correction of the trajectory data in the action record are divided together to form an action data segment.

[0091] In this way, a large amount of action data of various types obtained is divided into a plurality of complete action data segments, so as to reflect the completion quality of each paddle action cycle, and the movement performance result obtained by the monitoring object is more concise and intuitive.

[0092] After generating the plurality of action data segments, the following steps are further included:

[0093] As shown in Figure 3 S3-1: Based on the pedal force value change curve, the lower limb work value corresponding to the time period in which the sliding seat changes from the bent leg position to the straight leg position in each action data segment is calculated.

[0094] Explanatorily, by comparing each action data in the action record, in a certain action data segment, the interval on the time axis corresponding to the change of the sliding seat from the bent leg position to the straight leg position in the position information is used to intercept the pedal force value change curve, and the result obtained by integral operation of the intercepted part is the lower limb work value of the action data segment.

[0095] By calculating the lower limb work value in the rowing phase in each rowing action cycle, the monitoring object can intuitively understand the lower limb endurance level and intuitively know the physical exertion.

[0096] S4: Input each action data segment into a preset action model for comparison to obtain deviation data.

[0097] The preset action model is an action mode preset as a template or reference standard according to the idealized movement trajectory of the paddle in a single rowing cycle, which is used for analysis and comparison of each input action data segment, and based on the deviation of the action data segment from the preset action model, a deviation data representing the overall deviation of the action data segment is calculated. The preset action model can help the monitoring object to intuitively understand the similarities and differences between different rowing cycles, thereby improving the standard of the action. For the difference in the length of the single rowing cycle of each monitoring object, the preset action model can be scaled proportionally to match and then compared, thereby overcoming the difference in the length of the rowing cycle.

[0098] As shown in Figure 4 The following steps are further included:

[0099] S4-1: Calculate the synchronization difference value between the time when the sliding seat leaves the bent leg position and the time when the paddle attitude turns to the vertical state in the same action data segment; if the absolute value of the synchronization difference value is greater than the preset synchronization difference value, mark the corresponding action data segment.

[0100] Explanatorily, by comparing each action data in the action record, when the position information shows that the sliding seat leaves the bent leg position, the leg driving action starts, at this time, if the paddle blade has not been in the vertical state, it means that the paddle angle is not correct in the paddle turning stage or the leg driving action is too early and the paddle blade has not completed the paddle turning into water, both of the above two situations will reduce the work conversion efficiency of this paddle cycle, the calculated synchronization difference value is negative; if the paddle blade has been in the vertical state for a period of time before the position information leaves the bent leg position, it means that the leg driving action is too late, the paddle blade in the vertical state increases the resistance of the boat moving forward in water, which will cause the boat speed to decrease, the calculated synchronization difference value is positive. Set a synchronization difference preset value, when the absolute value of the calculated synchronization difference value is greater than the synchronization difference preset value, it is considered that there is a big mistake in the synchronization of hands and legs in this action data segment, mark it and prompt the monitoring object to pay attention.

[0101] S4-2: Determine whether the time when the sliding seat leaves the straight leg position in the same action data segment corresponds to the paddle turning trajectory; if not, mark the corresponding action data segment.

[0102] Explanatorily, the paddle turning action includes the straight arm and the forward leaning of the upper body, and then includes the bent leg action, which together constitutes the paddle turning action. When the bent leg action, the sliding seat will slide along the slide rail to the bent leg position near one end of the footrest. Therefore, by comparing each action data in the action record, when the position information shows that the sliding seat leaves the straight leg position, the bent leg action starts, at this time, the paddle blade should be kept in the horizontal state to reduce the air resistance. If the time when the position information leaves the straight leg position does not fall within the interval on the time axis corresponding to the corrected paddle turning trajectory, it is considered that the length of time when the paddle blade keeps in the horizontal state during the paddle turning action is far from enough, or there is a big mistake in the synchronization of hands and legs in this action data segment, mark the action data segment and prompt the monitoring object to pay attention.

[0103] S5: Based on the deviation data, a preset data amount is counted and evaluation information is generated.

[0104] Explanatorily, a preset data amount is set, and after the deviation data corresponding to each action data segment is grouped and counted according to the time occurrence order, a comprehensive evaluation is made to generate evaluation information. Among them, the statistical data amount of the first group is grouped according to the preset data amount.

[0105] Among them, the deviation data corresponding to the action data segment with the mark is not counted when grouping. The deviation data corresponding to the action data segment with the mark will be very large and has no reference value, if it is counted together with other deviation data, the generated evaluation information will lose its representativeness and interfere with the monitoring object to quickly understand their own movement performance, therefore, the removal operation is set.

[0106] S6: judging whether the evaluation information meets the standard; if yes, increasing the statistical data amount and repeating the previous statistical step based on the remaining deviation data; if not, resetting the statistical data amount to the preset data amount and repeating the previous statistical step based on the remaining deviation data.

[0107] For example, the evaluation information can be divided into three levels of A, B and C according to the size of the sum average of each deviation data in the same group, A is excellent, B is general, and C is not up to standard. When judging that the evaluation information meets the standard, that is, the evaluation information is A or B, the statistical data amount of the next group is increased, and the statistical data amount increased when the evaluation information is A can be more than that when the evaluation information is B; when judging that the evaluation information does not meet the standard, that is, the evaluation information is C, the statistical data amount of the next group is reset to the preset data amount. In this way, when the evaluation of the previous group meets the standard, it indicates that the action completion standard degree in the group is relatively high, and the action completion standard degree of the subsequent action data segment is also expected to be relatively high, the statistical data amount of the next group is increased, thereby reducing the number of grouped action data segments and the number of output evaluation information, and the movement performance result obtained by the monitoring object is more concise.

[0108] The application also discloses a movement data processing system for a racing boat.

[0109] As shown in Figure 5 , the processing system comprises:

[0110] An acquisition module 1 is configured to acquire action data of a monitoring object in real time, the action data comprising trajectory data of a paddle, a paddle blade attitude, and a footrest pressure value of a footrest, and position information of a sliding seat on a sliding rail, the position information comprising a bent leg position and a straight leg position;

[0111] A recording module 2 is configured to store the action data and generate action records in chronological order, the action records comprising a pulling trajectory, a pressing trajectory, a returning trajectory and a lifting trajectory of the paddle generated based on the trajectory data, and a footrest pressure value change curve generated based on the footrest pressure value;

[0112] An information processing module 3 is configured to correct each returning trajectory in the action records with the paddle blade attitude in a horizontal state one by one, correct the starting point of each pulling trajectory in the action records with the position information leaving the bent leg position one by one, and divide a plurality of action data segments with one pulling trajectory, one pressing trajectory, one returning trajectory and one lifting trajectory as a group after correction, each action data segment corresponding to one paddle stroke cycle;

[0113] The information processing module 3 is further configured to calculate a lower limb work value corresponding to a time period in which the position information changes from the bent leg position to the straight leg position in each action data segment according to the footrest pressure value change curve;

[0114] The model analysis module 4 is configured to sequentially input the action data segments into a preset action model, obtain deviation data, and mark the action data segments whose absolute value of a synchronization difference value is greater than a preset synchronization difference value and the action data segments whose position information deviates from a straight leg position and does not correspond to a back row trajectory.

[0115] The model analysis module 4 is further configured to count the deviation data, generate evaluation information, wherein the deviation data corresponding to the marked action data segments is not counted, and a preset data amount is used as a statistical data amount for the first time counting;

[0116] The judgment module 5 is configured to judge whether the evaluation information meets a standard, increase a statistical data amount of the model analysis module 4 if the evaluation information meets the standard, and reset the statistical data amount of the model analysis module 4 to the preset data amount if the evaluation information does not meet the standard.

[0117] The output module 6 is configured to output the lower limb work value, the evaluation information, and the action data segments containing the marks.

[0118] The functions performed by the above-mentioned obtaining module 1, recording module 2, information processing module 3, model analysis module 4, judgment module 5 and output module 6 and the technical details of each function are the same as or similar to the corresponding features in the above-described method for processing sports data of racing boats, and thus will not be described again here.

[0119] The embodiment of the application further discloses a storage medium

[0120] The storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the above-mentioned method for processing sports data of racing boats.

[0121] It should be understood that, although each step in the flowchart of the accompanying drawings is displayed in sequence according to the indication of the arrow, these steps are not necessarily executed in sequence according to the indication of the arrow. Unless explicitly stated herein, the execution of these steps is not strictly limited in sequence, and they can be executed in other sequences.

[0122] The above are the preferred embodiments of the application, which do not limit the protection scope of the application, and therefore: any equivalent changes made on the structure, shape, principle of the application should be covered within the protection scope of the application.

Claims

1. A method for processing motion data in rowing, characterized in that: The processing method includes: Real-time acquisition of action data of the monitored objects; Based on the action data, action records are generated in chronological order; Based on the motion record, multiple motion data segments are divided, and each motion data segment corresponds to a complete paddling motion cycle. Each of the aforementioned action data segments is input into a preset action model for comparison to obtain deviation data; After grouping and statistically analyzing the deviation data corresponding to each action data segment according to the order of occurrence, a comprehensive evaluation is performed to generate evaluation information. The statistical data volume of the first group is grouped according to the preset data volume. Determine whether the evaluation information meets the standard; if the previous group evaluation meets the standard, increase the statistical data volume of the next group, thereby reducing the number of groups of all action data segments, reducing the number of output evaluation information, and repeat the previous statistical step based on the remaining deviation data; if it does not meet the standard, reset the statistical data volume to the preset data volume, and repeat the previous statistical step based on the remaining deviation data.

2. The motion data processing method for rowing according to claim 1, characterized in that: The motion data includes paddle trajectory data, and the motion record includes a pull paddle trajectory, a push paddle trajectory, a return paddle trajectory, and a lift paddle trajectory generated based on the trajectory data. Based on the motion record, multiple motion data segments are divided, and each motion data segment corresponds to a complete paddling motion cycle. Specifically, an adjacent pull stroke trajectory, push stroke trajectory, return stroke trajectory, and lift stroke trajectory are divided into one motion data segment.

3. The motion data processing method for rowing according to claim 2, characterized in that: The motion data includes the blade attitude of the propeller; When generating the return trajectory based on the trajectory data, each return trajectory is corrected one by one according to multiple time periods in the motion record when the blade attitude is horizontal.

4. The motion data processing method for rowing according to claim 3, characterized in that: The motion data includes the position information of the slide block on the slide rail, and the position information includes the bent leg position of the slide block at one end of the slide rail; When generating the paddle trajectory based on the trajectory data, the starting point of each paddle trajectory is corrected one by one according to the multiple moments when the slide leaves the bent leg position in the action record.

5. The motion data processing method for rowing according to claim 4, characterized in that, The step of inputting each of the motion data segments into a preset motion model for comparison to obtain deviation data further includes the following steps: Calculate the synchronization difference between the moment the slide leaves the bent leg position and the moment the blade attitude changes to a vertical state within the same motion data segment; if the absolute value of the synchronization difference is greater than a preset synchronization difference value, mark the corresponding motion data segment.

6. The motion data processing method for rowing according to claim 4, characterized in that, The position information also includes the position of the straight leg of the slide block at the other end of the slide rail; The step of inputting each of the motion data segments into a preset motion model for comparison to obtain deviation data further includes the following steps: Determine whether the moment when the slide leaves the straight leg position in the same motion data segment corresponds to the return trajectory; if not, mark the corresponding motion data segment.

7. The motion data processing method for rowing according to claim 5 or 6, characterized in that: When performing statistics based on the deviation data with a preset data volume, the deviation data does not include the deviation data corresponding to the action data segment containing the label.

8. The motion data processing method for rowing according to claim 6, characterized in that: The motion data includes pedal frame pressure values, and the motion record includes a pedal frame pressure value change curve. Based on the motion record, multiple motion data segments are divided, each corresponding to a complete paddling motion cycle. The process then includes the following steps: Based on the footrest pressure value change curve, calculate the lower limb work value during the time period when the slide changes from the bent leg position to the straight leg position in each segment of the motion data.

9. A motion data processing system for rowing, characterized in that: The processing system includes: The acquisition module (1) is used to acquire the motion data of the monitored object in real time. The motion data includes the trajectory data of the paddle, the paddle attitude, the pressure value of the foot pedal, and the position information of the slide on the slide rail. The position information includes the bent leg position and the straight leg position. The recording module (2) is used to store the action data and generate action records in chronological order. The action records include the paddle pull trajectory, paddle press trajectory, paddle return trajectory and paddle lift trajectory generated based on the trajectory data, and the pedal pressure value change curve generated based on the pedal pressure value. The information processing module (3) is used to correct each of the return stroke trajectories in the action record according to the multiple time periods when the blade attitude is horizontal, and to correct the starting point of each of the pull stroke trajectories in the action record according to the multiple times when the slide leaves the bent leg position. The module also divides the corrected pull stroke trajectory, push stroke trajectory, return stroke trajectory and lift stroke trajectory into a group to form multiple action data segments, and each action data segment corresponds to a rowing action cycle. The information processing module (3) is also used to calculate the lower limb work value corresponding to the time period when the slide changes from the bent leg position to the straight leg position in each segment of the action data, based on the foot pedal pressure value change curve; The model analysis module (4) is used to input the motion data segments into the preset motion model in sequence, obtain the deviation data, and mark the motion data segments whose absolute value of the synchronization difference is greater than the preset value of the synchronization difference, and the motion data segments whose time when the slide leaves the straight leg position does not correspond to the return trajectory. The synchronization difference is the difference between the time when the slide leaves the bent leg position and the time when the blade attitude turns to the vertical state. The model analysis module (4) is also used to perform comprehensive evaluation after grouping and statistically analyzing the deviation data corresponding to each action data segment in the order of time occurrence, and to generate evaluation information. The statistical data volume of the first group is grouped according to the preset data volume. Judgment module (5): used to determine whether the evaluation information meets the standard; when the previous group evaluation meets the standard, the statistical data volume of the next group is increased, thereby reducing the number of groups of all action data segments and reducing the amount of output evaluation information; if it does not meet the standard, the statistical data volume of the model analysis module (4) is reset to the preset data volume; The output module (6) is used to output the work value of the lower limb, the evaluation information, and the action data segment containing the label.

10. A storage medium, characterized in that, The storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the motion data processing method for rowing as described in any one of claims 1-8.

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