CN112933573B - Indoor snow skating game control method and system and readable storage medium - Google Patents
Indoor snow skating game control method and system and readable storage medium Download PDFInfo
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B69/00—Training appliances or apparatus for special sports
- A63B69/18—Training appliances or apparatus for special sports for skiing
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B71/00—Games or sports accessories not covered in groups A63B1/00 - A63B69/00
- A63B71/06—Indicating or scoring devices for games or players, or for other sports activities
- A63B71/0605—Decision makers and devices using detection means facilitating arbitration
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B71/00—Games or sports accessories not covered in groups A63B1/00 - A63B69/00
- A63B71/06—Indicating or scoring devices for games or players, or for other sports activities
- A63B71/0619—Displays, user interfaces and indicating devices, specially adapted for sport equipment, e.g. display mounted on treadmills
- A63B71/0622—Visual, audio or audio-visual systems for entertaining, instructing or motivating the user
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B71/00—Games or sports accessories not covered in groups A63B1/00 - A63B69/00
- A63B71/06—Indicating or scoring devices for games or players, or for other sports activities
- A63B71/0619—Displays, user interfaces and indicating devices, specially adapted for sport equipment, e.g. display mounted on treadmills
- A63B71/0669—Score-keepers or score display devices
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- A—HUMAN NECESSITIES
- A63—SPORTS; GAMES; AMUSEMENTS
- A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
- A63B71/00—Games or sports accessories not covered in groups A63B1/00 - A63B69/00
- A63B71/06—Indicating or scoring devices for games or players, or for other sports activities
- A63B71/0619—Displays, user interfaces and indicating devices, specially adapted for sport equipment, e.g. display mounted on treadmills
- A63B2071/0647—Visualisation of executed movements
Abstract
According to the indoor snow sliding game control method, the indoor snow sliding game control system and the readable storage medium, the position adjustment schemes of different sensors are obtained according to the obtained user identification and the game content information, so that the sensors can be located at the optimal positions, and the obtained user data are more accurate. In addition, the invention also utilizes big data, and can acquire the body state information of the user from a third-party resource end, thereby analyzing the motion state of the user. When the invention is used for analyzing the pre-state, the neural network model is also utilized, and the pre-state value can be more accurately analyzed. Through accurate analysis of user data, the position of a sensor of the snowplow simulator can be adjusted more accurately, and acquired data are more accurate.
Description
Technical Field
The present application relates to the field of sensor data processing, and more particularly, to a method, a system, and a readable storage medium for controlling an indoor snowslide game.
Background
In recent years, indoor snowboarder machines which are not limited by environmental factors such as regions, seasons, climate and the like are favored by more and more skiers, and the snowboarder machines are produced at the same time, but can not be deeply matched with the functions and characteristics of the snowboarder machines.
At present, the competition on the snowplow mostly adopts different snowplows to set the specified gradient and speed, the flag gate of the traditional snowfield competition is virtualized by boundary ropes or correlation sensor devices at two sides of a snow blanket, and a judge counts manually according to the competition rule of timing counting to judge the competition score.
In the existing indoor snowmobile simulation competition, 1, a timed counting method is adopted to count competition scores, and as the timed counting method is low in precision, the competition scores of players are approximate, the overlapping rate is high, and the phenomenon that a plurality of players have the same name is easy to occur in the competition; 2. limited by the physical characteristics (size of the snowmobile and length and width of a snow blanket) of the model of the snowmobile, when the competition rules are designed according to parameters such as virtual flagpoles, slopes, speeds and the like, the indoor simulation snowmobile competition currently has a single competition type and does not have a uniform and fixed competition rule; 3. the existing events are played in a snowmobile training venue, and a large snowmobile training venue only has about 6-10 indoor snowmobiles simulated by a small quantity, so that the large-scale events are difficult to organize; 4. based on the commercial operation mode of the current indoor snowmobile simulation training venue, the frequency of organizing the events is low, the settling period is long, and the cost of manpower and material resources is high; 5. in the existing indoor snowmobile simulation competition, the competition rules are difficult to unify, so that a data information acquisition system for the competition of skiers, such as data of different ages, different sexes, different skiing levels, completed training courses and the like, is lacked; 6. the competition can not provide the supporting data of a system for deeply teaching and developing the indoor snowplow training center, can not fully utilize the characteristics of the indoor snowplow, is organically combined with an indoor skiing simulation teaching and research system, helps a skier to learn and promote more efficiently, and causes the relative disjunction of the existing competition and the indoor snowplow teaching and research system. In the prior art, how to collect the action information of a user through a plurality of sensors and control the position of the sensors is urgent to solve at present.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides an indoor snowslide game control method, an indoor snowslide game control system and a readable storage medium.
The invention provides a control method for an indoor snow sliding game in a first aspect, which comprises the following steps:
acquiring user identification information and match content information;
determining the motion state of the user according to the user identification information;
inputting the user motion state into a motor neural network model to obtain pre-state information;
determining a position adjusting scheme of each category sensor according to the pre-state information and the match content information to obtain position adjusting information;
and sending the position adjustment information to an adjusting device to adjust the position of each category of sensor.
In this scheme, the determining the user motion state according to the user identification information includes:
sending the user identification information to a server side;
the server side searches a database according to the user identification information to obtain the basic user identification information;
sending the basic identification information of the user to a third-party resource end to obtain body state information of the user;
and analyzing the body state information to obtain the motion state of the user.
In this scheme, the inputting the user motion state into a motor neural network model to obtain pre-state information specifically includes:
carrying out feature calculation on the motion state of the user to obtain feature information;
quantizing the characteristic information to obtain a quantized value corresponding to the characteristic information;
judging the range of the quantization value corresponding to the characteristic information, and determining the characteristic quantization information;
and inputting the characteristic quantitative information into a motor neural network model to obtain pre-state information.
In this scheme, the training method of the motor neural network model specifically includes:
acquiring historical motion information of a plurality of users;
carrying out feature classification on a plurality of users to obtain different feature user groups;
preprocessing the characteristic values and the historical movement information of different characteristic user groups to obtain training data;
and training the training data to obtain a motor neural network model.
In this embodiment, the determining a position adjustment scheme for each category sensor according to the pre-state information and the game content information specifically includes:
determining a pre-running track of a user according to the pre-state information and the competition content information;
and determining the optimal position of each category sensor in the next time period through the pre-running track to obtain a position adjusting scheme.
In this scheme, each type sensor is one or more of laser sensor, correlation sensor, sonar sensor, switch sensor.
A second aspect of the present invention provides an indoor snowslide game control system, including a memory and a processor, where the memory includes an indoor snowslide game control program, and the indoor snowslide game control program, when executed by the processor, implements the following steps:
acquiring user identification information and match content information;
determining the motion state of the user according to the user identification information;
inputting the user motion state into a motor neural network model to obtain pre-state information;
determining a position adjusting scheme of each category sensor according to the pre-state information and the match content information to obtain position adjusting information;
and sending the position adjustment information to an adjusting device to adjust the position of each category of sensor.
In this scheme, the determining the user motion state according to the user identification information includes:
sending the user identification information to a server side;
the server side searches a database according to the user identification information to obtain the basic user identification information;
sending the basic identification information of the user to a third-party resource end to obtain the body state information of the user;
and analyzing the body state information to obtain the motion state of the user.
In this scheme, the inputting the user motion state into a motor neural network model to obtain pre-state information specifically includes:
carrying out feature calculation on the motion state of the user to obtain feature information;
quantizing the characteristic information to obtain a quantized value corresponding to the characteristic information;
judging the range of the quantization value corresponding to the characteristic information, and determining the characteristic quantization information;
and inputting the characteristic quantitative information into a motor neural network model to obtain pre-state information.
In this scheme, the training method of the motor neural network model specifically includes:
acquiring historical motion information of a plurality of users;
carrying out feature classification on a plurality of users to obtain different feature user groups;
preprocessing the characteristic values and the historical movement information of different characteristic user groups to obtain training data;
and training the training data to obtain a motor neural network model.
In this scheme, the determining a position adjustment scheme for each category sensor according to the pre-state information and the game content information specifically includes:
determining a pre-running track of a user according to the pre-state information and the competition content information;
and determining the optimal position of each category sensor in the next time period through the pre-running track to obtain a position adjusting scheme.
In this scheme, each type sensor is one or more of laser sensor, correlation sensor, sonar sensor, switch sensor.
The third aspect of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a program of an indoor snow-skiing game control method, and when the program of the indoor snow-skiing game control method is executed by a processor, the method implements the steps of the indoor snow-skiing game control method as described in any one of the above.
According to the indoor snow sliding game control method, the indoor snow sliding game control system and the readable storage medium, the position adjustment schemes of different sensors are obtained according to the obtained user identification and the game content information, so that the sensors can be located at the optimal positions, and the obtained user data are more accurate. In addition, the invention also utilizes big data, and can acquire the body state information of the user from a third-party resource end, thereby analyzing the motion state of the user. When the invention is used for analyzing the pre-state, the neural network model is also utilized, and the pre-state value can be more accurately analyzed. Through accurate analysis of user data, the position of a sensor of the snowplow simulator can be adjusted more accurately, and acquired data are more accurate.
Drawings
FIG. 1 is a flow chart illustrating an indoor snowslide game control method of the present invention;
fig. 2 shows a block diagram of an indoor snow race control system of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flow chart of the control method of the indoor snowslide game.
As shown in fig. 1, the invention discloses a control method for an indoor snowslide game, which comprises the following steps:
s102, acquiring user identification information and match content information;
s104, determining the motion state of the user according to the user identification information;
s106, inputting the motion state of the user into a motor neural network model to obtain pre-state information;
s108, determining a position adjusting scheme of each category sensor according to the pre-state information and the match content information to obtain position adjusting information;
and S110, sending the position adjusting information to an adjusting device to adjust the position of each category of sensor.
The snowplow is generally of a cubic structure, and a plurality of types of sensors are provided in the snowplow, each type of sensor being one or more of a laser sensor, a correlation sensor, a sonar sensor, and a switch sensor. Wherein, laser sensor, correlation sensor, sonar sensor, switch sensor can set up in the position of difference as required. For example, 4 sonar sensors are arranged at 4 corners of the snowplow and are positioned more than 1 meter higher than the snow blanket; the correlation sensors are arranged on two sides of the snow blanket, the number of the correlation sensors can be 5-8 pairs, and the interval of each pair is 20-40 cm. The position of each sensor can be adjusted and changed, and it can be understood that each sensor can be provided with an adjusting device for adjusting the angle, a motor can be arranged in the adjusting device for driving, and each adjusting device is controlled and adjusted by a controller. In the invention, other kinds of sensors can be provided with corresponding adjusting devices so as to adjust the positions of different sensors. In the invention, user identification information and competition content information are firstly obtained, wherein the user identification information can be ID information of a user, such as identification number and other information, and is used for indicating and marking different users, and different competition modes exist in a ski competition, namely competition content information, such as big-turn and small-turn or straight-line racing and the like. It should be noted that the server or the local snowplow may be used to store the user's information, which may be input by the user in advance, or may be collected by the user on the snowplow on the spot. The user's motion state is then determined based on the user identification information, where the motion state may be the user's motion capability information, such as the user's reaction speed, movement speed, gliding posture, and the like. After the motion state is acquired, the motion state of the user is input into a motor neural network model to obtain the pre-state information. The pre-state information may be a motion track of the user to reflect track information of the user in a later period of time. If the pre-movement track is obtained, the position adjustment scheme of each category sensor can be determined according to the pre-state information and the match content information, and position adjustment information is obtained. And finally, sending the position adjustment information to an adjusting device to adjust the position of each category of sensor.
According to the embodiment of the present invention, the determining the user motion state according to the user identification information includes:
sending the user identification information to a server side;
the server side searches a database according to the user identification information to obtain the basic user identification information;
sending the basic identification information of the user to a third-party resource end to obtain the body state information of the user;
and analyzing the body state information to obtain the motion state of the user.
It should be noted that the user identifier may be sent to the server, and the server pre-stores a database of user identifier information. After receiving the user identification information, the server side can query the database to obtain the basic identification information of the user. The basic identification information is different from the user identification information and is the basic characteristic information of the user, such as the name, age, height, weight, work done, disease state and the like of the user. And then sending the basic identification information of the user to a third-party resource end to obtain the body state information of the user. The third-party resource end can be a medical system, an insurance system, a physical examination system and the like, and it is worth mentioning that any third-party resource end capable of acquiring the body state information of the user falls within the protection scope of the application. For example, if the third-party resource is a medical system, the basic identification information of the user, that is, the information of the name, age, height, weight, and the like of the user is sent to the medical system, and the physical state information of the user is searched, for example, the physical state is better, the reflection speed is faster, and the strength is stronger. The motion state of the user can be analyzed through the obtained body state information of the user, namely the information of the reaction speed, the moving speed, the sliding posture and the like of the user can be analyzed.
According to the embodiment of the present invention, the inputting the user motion state into the motor neural network model to obtain the pre-state information specifically includes:
carrying out feature calculation on the motion state of the user to obtain feature information;
quantizing the characteristic information to obtain a quantized value corresponding to the characteristic information;
judging the range of the quantization value corresponding to the characteristic information, and determining the characteristic quantization information;
and inputting the characteristic quantitative information into a motor neural network model to obtain pre-state information.
It should be noted that the present invention can also calculate the pre-state information through a neural network model. Firstly, the motion state of the user is subjected to characteristic calculation to obtain characteristic information. The feature of each user can be calculated through feature calculation, which is a common calculation means in the field, and the present invention is not repeated. And judging the range of the quantization value corresponding to the characteristic information, and determining the characteristic quantization information. In order to facilitate calculation, the characteristic information is quantized to obtain a quantized value corresponding to the characteristic information. Since the quantization values are large, in order to reduce the amount of calculation, a range value quantization determination method may be used, that is, quantization values falling within a preset range are all classified into a fixed quantization value. And inputting the characteristic quantitative information into a motor neural network model to obtain pre-state information. Because the neural network is trained by a large amount of user data, the pre-state information can be output only by inputting the characteristic quantization information.
According to the embodiment of the invention, the training method of the motor neural network model specifically comprises the following steps:
acquiring historical motion information of a plurality of users;
carrying out feature classification on a plurality of users to obtain different feature user groups;
preprocessing the characteristic values and the historical movement information of different characteristic user groups to obtain training data;
and training the training data to obtain a motor neural network model.
It should be noted that a more accurate result can be obtained by using the motor neural network model, but the motor neural network model needs to be trained by using a large amount of data. First, historical motion information of a plurality of users is acquired. When the neural network is trained, the larger the data quantity is, the better the data quantity is, and the more the data is, the higher the training accuracy is. Then, carrying out feature classification on each user, and classifying different users into different categories, wherein the categories can be preset categories, such as A-type users, which represent young users with strong exercise capacity and good reaction; and the class B users represent users with strong middle-aged capability and good responsiveness. And preprocessing the characteristic values and the historical motion information of different characteristic user groups to obtain training data. The preprocessing may be a training process that processes data for neural network models. For example, the feature values may be converted into vectors that facilitate data training. And finally, training the training data to obtain a motor neural network model.
According to the embodiment of the present invention, the determining the position adjustment scheme of each category sensor according to the pre-state information and the game content information specifically includes:
determining a pre-running track of a user according to the pre-state information and the competition content information;
and determining the optimal position of each category sensor in the next time period through the pre-running track to obtain a position adjusting scheme.
It should be noted that after the pre-state information is acquired, a pre-movement trajectory of the user may be calculated and determined, where the pre-movement trajectory represents a position trajectory of the user in the next time period, and the position trajectory may be represented by a three-dimensional coordinate point. The location trajectory of the user is determined, and the optimal location of the sensor for the next time period can be determined. For example, if the motion trajectory of the user at the next moment is from the middle point of the snowplow to the front 50 cm on the right, the sensors such as the opposite-emitting sensor and the laser sensor move according to the motion trajectory at the moment, so that the detection of the user at the next moment is more suitable, and the detection accuracy is improved.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring a match scoring mode;
obtaining scoring parameters according to the match scoring mode;
calculating according to the scoring parameters to obtain the competition scores of the users;
the game scoring mode is a timed counting mode or a fixed number counting mode.
It should be noted that the present invention can be used for scoring in different ways, and the scoring mode of the specific game is a timed counting or fixed number counting mode. The mode of timing counting can be that the number of devices passed by the user or the number of actions is counted and scored within a certain time. The larger the number, the higher the score, or the smaller the number, the higher the score, which can be specifically set by those skilled in the art according to actual needs. For example, the number of the piles wound by the user can be set, and in this mode, the score is higher when the number of the piles wound is larger in a certain time. The fixed number and timing mode can be that the time is counted after the user passes through the fixed number of equipment or finishes the fixed number of actions, and the scoring is carried out according to the time. For example, if the user spends 50 seconds after 10 stakes, the conversion score is 60 points; the user 40 seconds after the user passes 10 stakes, the conversion score is 80 points.
According to the embodiment of the invention, the position of the user, the coordinates of the sensor, the driving track of the user and the gradient of the snow blanket are calculated and recorded in a three-dimensional coordinate mode. The three-dimensional coordinates may be established by three xyz axes.
First, a three-dimensional coordinate system is established, and uniform xyz coordinates can be established in a predetermined area. Then, according to the data, position and angle information of each sensor, the position of the user can be calculated and mapped to a coordinate system to obtain corresponding coordinate values. Since the time point information is also acquired, the motion trajectory can be generated by the coordinate values and the time points. Through the motion trail, motion state data can be obtained. The motion state data may include information about the user's stride frequency, speed, orientation, etc.
Fig. 2 shows a block diagram of an indoor snow race control system of the present invention.
As shown in fig. 2, the present invention discloses an indoor ski game control system 2, which comprises a memory 21 and a processor 22, wherein the memory includes an indoor ski game control program, and the indoor ski game control program, when executed by the processor, implements the following steps:
acquiring user identification information and match content information;
determining the motion state of the user according to the user identification information;
inputting the user motion state into a motor neural network model to obtain pre-state information;
determining a position adjusting scheme of each category sensor according to the pre-state information and the match content information to obtain position adjusting information;
and sending the position adjustment information to an adjusting device to adjust the position of each category of sensor.
The snowplow is generally of a cubic structure, and a plurality of types of sensors are provided in the snowplow, each type of sensor being one or more of a laser sensor, a correlation sensor, a sonar sensor, and a switch sensor. Wherein, laser sensor, correlation sensor, sonar sensor, switch sensor can set up in the position of difference as required. For example, 4 sonar sensors are arranged at 4 corners of the snowplow and are positioned more than 1 meter higher than the snow blanket; the correlation sensors are arranged on two sides of the snow blanket, the number of the correlation sensors can be 5-8 pairs, and the interval of each pair is 20-40 cm. The position of each sensor can be adjusted and changed, and it can be understood that each sensor can be provided with an adjusting device for adjusting the angle, a motor can be arranged in the adjusting device for driving, and each adjusting device is controlled and adjusted by a controller. In the invention, other kinds of sensors can be provided with corresponding adjusting devices so as to adjust the positions of different sensors. In the invention, user identification information and competition content information are firstly acquired, wherein the user identification information can be ID information of a user, such as identification number and other information, and is used for indicating and marking different users, and different competition modes exist in a ski competition, namely competition content information, such as big-loop competition, small-loop competition or straight-line competition. It should be noted that the server or the local snowplow may be used to store the user's information, which may be input by the user in advance, or may be collected by the user on the snowplow on the spot. The user's motion state is then determined based on the user identification information, where the motion state may be the user's motion capability information, such as the user's reaction speed, movement speed, gliding posture, and the like. After the motion state is obtained, the motion state of the user is input into a motor neural network model to obtain pre-state information. The pre-state information may be a motion track of the user to reflect track information of the user in a later period of time. If the pre-movement track is obtained, the position adjustment scheme of each category sensor can be determined according to the pre-state information and the match content information, and position adjustment information is obtained. And finally, sending the position adjustment information to an adjusting device to adjust the position of each category of sensor.
According to the embodiment of the present invention, the determining the user motion state according to the user identification information includes:
sending the user identification information to a server side;
the server side searches a database according to the user identification information to obtain the basic user identification information;
sending the basic identification information of the user to a third-party resource end to obtain body state information of the user;
and analyzing the body state information to obtain the motion state of the user.
It should be noted that the user identifier may be sent to the server, and the server pre-stores a database of user identifier information. After receiving the user identification information, the server side can query the database to obtain the basic identification information of the user. The basic identification information is different from the user identification information and is the basic characteristic information of the user, such as the name, age, height, weight, work done, disease state and the like of the user. And then sending the basic identification information of the user to a third-party resource end to obtain the body state information of the user. The third-party resource end can be a medical system, an insurance system, a physical examination system and the like, and it is worth mentioning that any third-party resource end capable of acquiring the body state information of the user falls within the protection scope of the application. For example, if the third-party resource is a medical system, the basic identification information of the user, that is, the information of the name, age, height, weight, and the like of the user is sent to the medical system, and the physical state information of the user is searched, for example, the physical state is better, the reflection speed is faster, and the strength is stronger. The motion state of the user can be analyzed through the obtained body state information of the user, namely the information of the reaction speed, the moving speed, the sliding posture and the like of the user can be analyzed.
According to the embodiment of the present invention, the inputting the user motion state into the motor neural network model to obtain the pre-state information specifically includes:
carrying out feature calculation on the motion state of the user to obtain feature information;
quantizing the characteristic information to obtain a quantized value corresponding to the characteristic information;
judging the range of the quantization value corresponding to the characteristic information, and determining the characteristic quantization information;
and inputting the characteristic quantitative information into a motor neural network model to obtain pre-state information.
It should be noted that the present invention can also calculate the pre-state information through a neural network model. Firstly, the motion state of the user is subjected to characteristic calculation to obtain characteristic information. The feature of each user can be calculated through feature calculation, which is a common calculation means in the field, and the present invention is not repeated. And judging the range of the quantization value corresponding to the characteristic information, and determining the characteristic quantization information. In order to facilitate calculation, the characteristic information is quantized to obtain a quantized value corresponding to the characteristic information. Since the quantization value is large, in order to reduce the amount of calculation, a range value quantization determination method may be used, that is, quantization values falling within a preset range are all classified into a fixed quantization value. And inputting the characteristic quantitative information into a motor neural network model to obtain pre-state information. Because the neural network is trained by a large amount of user data, the pre-state information can be output only by inputting the characteristic quantization information.
According to the embodiment of the invention, the training method of the motor neural network model specifically comprises the following steps:
acquiring historical motion information of a plurality of users;
carrying out feature classification on a plurality of users to obtain different feature user groups;
preprocessing the characteristic values and the historical movement information of different characteristic user groups to obtain training data;
and training the training data to obtain a motor neural network model.
It should be noted that a more accurate result can be obtained by using the motor neural network model, but the motor neural network model needs to be trained by using a large amount of data. First, historical motion information of a plurality of users is acquired. When the neural network is trained, the larger the data quantity is, the better the data quantity is, and the more the data is, the higher the training accuracy is. Then, carrying out feature classification on each user, and classifying different users into different categories, wherein the categories can be preset categories, such as A-type users, which represent young users with strong exercise capacity and good reaction; and the class B users represent users with strong middle-aged capability and good responsiveness. And preprocessing the characteristic values and the historical motion information of different characteristic user groups to obtain training data. The preprocessing may be a training process that processes data for neural network models. For example, the feature values may be converted into vectors that facilitate data training. And finally, training the training data to obtain a motor neural network model.
According to the embodiment of the present invention, the determining the position adjustment scheme of each category sensor according to the pre-state information and the game content information specifically includes:
determining a pre-running track of a user according to the pre-state information and the competition content information;
and determining the optimal position of each category sensor in the next time period through the pre-running track to obtain a position adjusting scheme.
It should be noted that after the pre-state information is obtained, a pre-movement trajectory of the user may be calculated and determined, where the pre-movement trajectory represents a position trajectory of the user in the next time period, and the position trajectory may be represented by a three-dimensional coordinate point. The location trajectory of the user is determined, and the optimal location of the sensor for the next time period can be determined. For example, if the motion trajectory of the user at the next moment is from the middle point of the snowplow to the front 50 cm on the right, the sensors such as the opposite-emitting sensor and the laser sensor move according to the motion trajectory at the moment, so that the detection of the user at the next moment is more suitable, and the detection accuracy is improved.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring a match scoring mode;
obtaining scoring parameters according to the match scoring mode;
calculating according to the scoring parameters to obtain the competition score of the user;
the game scoring mode is a timed counting or fixed number counting mode.
It should be noted that the present invention can be used for scoring in different ways, and the scoring mode of the specific game is a timed counting or fixed number counting mode. The mode of timing counting can be that the number of devices passed by the user or the number of actions is counted and scored within a certain time. The larger the number, the higher the score, or the smaller the number, the higher the score, which can be specifically set by those skilled in the art according to actual needs. For example, the number of the piles wound by the user can be set, and in this mode, the score is higher when the number of the piles wound is larger in a certain time. The fixed number and timing mode can be that the time is counted after the user passes through the fixed number of equipment or finishes the fixed number of actions, and the scoring is carried out according to the time. For example, if the user spends 50 seconds after 10 stakes, the conversion score is 60 points; the user 40 seconds after the user passes 10 stakes, the conversion score is 80 points.
According to the embodiment of the invention, the position of the user, the coordinates of the sensor, the driving track of the user and the gradient of the snow blanket are calculated and recorded in a three-dimensional coordinate mode. The three-dimensional coordinates may be established by three xyz axes.
First, a three-dimensional coordinate system is established, and uniform xyz coordinates can be established in a predetermined area. Then, according to the data, position and angle information of each sensor, the position of the user can be calculated and mapped to a coordinate system to obtain corresponding coordinate values. Since the time point information is also acquired, the motion trajectory can be generated by the coordinate values and the time points. And obtaining motion state data through the motion trail. The motion state data may include information about the user's stride frequency, speed, orientation, etc.
The third aspect of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a program of an indoor snow-skiing game control method, and when the program of the indoor snow-skiing game control method is executed by a processor, the method implements the steps of the indoor snow-skiing game control method as described in any one of the above.
According to the indoor snow sliding game control method, the indoor snow sliding game control system and the readable storage medium, the position adjustment schemes of different sensors are obtained according to the obtained user identification and the game content information, so that the sensors can be located at the optimal positions, and the obtained user data are more accurate. In addition, the invention also utilizes big data, and can acquire the body state information of the user from a third-party resource end, thereby analyzing the motion state of the user. When the invention is used for analyzing the pre-state, the neural network model is also utilized, and the pre-state value can be more accurately analyzed. Through accurate analysis of user data, the position of a sensor of the snowplow simulator can be adjusted more accurately, and acquired data are more accurate.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (4)
1. A method of controlling an indoor snow skiing game, comprising:
acquiring user identification information and match content information;
determining the motion state of the user according to the user identification information;
inputting the user motion state into a motor neural network model to obtain pre-state information;
determining a position adjusting scheme of each category sensor according to the pre-state information and the match content information to obtain position adjusting information;
sending the position adjustment information to an adjusting device to adjust the position of each category of sensor;
the determining the user motion state according to the user identification information includes:
sending the user identification information to a server side;
the server side searches a database according to the user identification information to obtain the basic user identification information;
sending the basic identification information of the user to a third-party resource end to obtain body state information of the user;
analyzing the body state information to obtain the motion state of the user;
inputting the user motion state into a motor neural network model to obtain pre-state information, specifically:
carrying out feature calculation on the motion state of the user to obtain feature information;
quantizing the characteristic information to obtain a quantized value corresponding to the characteristic information;
judging the range of the quantization value corresponding to the characteristic information, and determining the characteristic quantization information;
inputting the characteristic quantitative information into a motor neural network model to obtain pre-state information;
the training method of the motor neural network model specifically comprises the following steps:
acquiring historical motion information of a plurality of users;
carrying out feature classification on a plurality of users to obtain different feature user groups;
preprocessing the characteristic values and the historical movement information of different characteristic user groups to obtain training data;
training the training data to obtain a motor neural network model;
the determining a position adjustment scheme of each category sensor according to the pre-state information and the match content information specifically comprises:
determining a pre-running track of a user according to the pre-state information and the competition content information;
and determining the optimal position of each category sensor in the next time period through the pre-running track to obtain a position adjusting scheme.
2. An indoor snow skating game control method according to claim 1, wherein each category sensor is one or more of a laser sensor, a correlation sensor, a sonar sensor and a switch sensor.
3. An indoor snow race control system, characterized by comprising a memory and a processor, wherein the memory includes an indoor ski race control program, and the indoor ski race control program when executed by the processor implements the following steps:
acquiring user identification information and match content information;
determining the motion state of the user according to the user identification information;
inputting the user motion state into a motor neural network model to obtain pre-state information;
determining a position adjusting scheme of each category sensor according to the pre-state information and the match content information to obtain position adjusting information;
sending the position adjustment information to an adjusting device to adjust the position of each category of sensor;
the determining the user motion state according to the user identification information includes:
sending the user identification information to a server side;
the server searches a database according to the user identification information to obtain the basic user identification information;
sending the basic identification information of the user to a third-party resource end to obtain body state information of the user;
analyzing the body state information to obtain the motion state of the user;
inputting the user motion state into a motor neural network model to obtain pre-state information, specifically:
carrying out feature calculation on the motion state of the user to obtain feature information;
quantizing the characteristic information to obtain a quantized value corresponding to the characteristic information;
judging the range of the quantization value corresponding to the characteristic information, and determining the characteristic quantization information;
inputting the characteristic quantitative information into a motor neural network model to obtain pre-state information;
the training method of the motor neural network model specifically comprises the following steps:
acquiring historical motion information of a plurality of users;
carrying out feature classification on a plurality of users to obtain different feature user groups;
preprocessing the characteristic values and the historical movement information of different characteristic user groups to obtain training data;
training the training data to obtain a motor neural network model;
the determining a position adjustment scheme of each category sensor according to the pre-state information and the match content information specifically comprises:
determining a pre-running track of a user according to the pre-state information and the competition content information;
and determining the optimal position of each category sensor in the next time period through the pre-running track to obtain a position adjusting scheme.
4. A computer-readable storage medium, characterized in that the computer-readable storage medium includes a program of an indoor snow race control method, and when the program of the indoor snow race control method is executed by a processor, the steps of the indoor snow race control method according to any one of claims 1 to 2 are implemented.
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