Comprehensive real-time assessment system and method for sports risk in field

Abstract

The invention discloses an in-site exercise risk comprehensive real-time assessment system and method, and relates to the technical field of exercise risk assessment, the system comprises a real-time monitoring module, a data fusion module, a data processing module, a risk assessment module and an optimization suggestion early warning module; the data fusion module processes the initial motion information collected by the real-time monitoring module to obtain target motion information, and the data processing module constructs a motion risk field based on the target motion information on the basis of four dimensions of a motion field terrain energy field, an athlete kinetic energy field, a ball kinetic energy field and an athlete behavior psychological field; according to the method and the system, a motion risk field is established, a motion risk assessment model is established through the motion risk field, the motion risk assessment model is solved to obtain a risk reliability index, and an alarm is given when the risk reliability index exceeds a threshold value, so that high-precision motion state monitoring, comprehensive risk assessment and real-time risk early warning can be realized.

Description

Technical Field

[0001] The present application relates to the technical field of sports risk assessment, and in particular to a comprehensive real-time assessment system and method for sports risks within a venue. Background Art

[0002] In the field of sports, with the advancement of science and technology, sports monitoring and risk management technologies have made significant progress. At present, traditional sports monitoring systems mainly rely on wearable devices (such as heart rate monitors and gait analyzers) and fixed sensors (such as cameras). These systems are able to collect athletes' physiological data and motion status, but they still have limitations in real-time data processing and multi-dimensional analysis. In addition, computer vision technologies such as OpenPose and YOLO, although they have made breakthroughs in pose estimation and object detection, may face challenges in real-time and computing resources when processing large-scale data streams. Existing risk assessment methods usually rely on static safety standards and rules of thumb, which are not flexible and accurate enough when dealing with complex and dynamic environments. Summary of the invention

[0003] The purpose of this application is to provide a comprehensive real-time assessment system and method for sports risks within a venue, which can achieve high-precision sports status monitoring, comprehensive risk assessment and real-time risk warning.

[0004] To achieve the above objectives, this application provides the following solutions:

[0005] In a first aspect, the present application provides a comprehensive real-time assessment system for sports risks in a venue, comprising:

[0006] A real-time monitoring module is used to collect real-time motion data of athletes when they are playing ball games; and calculate initial motion information based on the real-time motion data; the real-time motion data includes image data and motion state data, and the motion state data includes acceleration, angular velocity and posture change of the athletes; the initial motion information includes the position, speed, acceleration, motion mode of the athletes and the position, speed and acceleration of the ball;

[0007] A data fusion module is used to pre-process the initial motion information to obtain target motion information;

[0008] A data processing module is used to: construct a sports risk field based on the target sports information; the sports risk field is composed of the superposition of the sports field potential field, the athlete kinetic energy field, the ball kinetic energy field and the athlete behavior and psychology field; construct a risk response influence function based on the change of the athlete's sports state in the sports risk field; establish a sports risk assessment model based on the risk response influence function and the sports risk field;

[0009] A risk assessment module is used to solve the motion risk assessment model to obtain a motion risk boundary value; the motion risk boundary value includes an upper risk boundary and a lower risk boundary; and a risk reliability index is calculated according to the motion risk boundary value;

[0010] The optimization suggestion warning module is used to issue an alarm when the risk reliability index exceeds the motion risk threshold.

[0011] Optionally, the optimization suggestion warning module further includes a display and notification unit; the display and notification unit is used to visualize the athlete's real-time motion status data and risk reliability index.

[0012] Optionally, the display and notification unit is further used to: when the risk reliability index exceeds the motion risk threshold, send a warning notification to the user through visual, auditory and / or tactile means.

[0013] Optionally, the optimization suggestion warning module also includes a database and a history recording unit; the database and the history recording unit are used to: store the athlete's historical sports data and risk assessment records; the risk assessment records include the risk reliability index corresponding to the athlete's historical sports data.

[0014] Optionally, the optimization suggestion warning module is also used to: utilize an intelligent algorithm to obtain a trained risk prediction model based on historical sports data and risk assessment records; input the athlete's real-time sports data into the trained risk prediction model to predict a risk reliability index.

[0015] Optionally, the intelligent algorithm is a decision tree algorithm, a random forest algorithm or a convolutional neural network algorithm.

[0016] Optionally, the data processing module is used to: construct the risk intensity of the potential energy field of the sports venue, the risk intensity of the kinetic energy field of the athlete, the risk intensity of the kinetic energy field of the ball, and the risk intensity of the behavioral and psychological field of the athlete based on the target motion information; and perform weighted summation of the risk intensity of the potential energy field of the sports venue, the risk intensity of the kinetic energy field of the athlete, the risk intensity of the kinetic energy field of the ball, and the risk intensity of the behavioral and psychological field of the athlete to construct a sports risk field.

[0017] Optionally, the comprehensive real-time assessment system for sports risks within the venue also includes a communication module; the real-time monitoring module, the data fusion module, the data processing module, the risk assessment module and the optimization suggestion warning module are communicatively connected via the communication module.

[0018] Optionally, the preprocessing is Kalman filtering.

[0019] In a second aspect, the present application provides a method for comprehensive real-time assessment of sports risks in a venue, comprising:

[0020] When an athlete plays a ball game, real-time motion data of the athlete is collected; and initial motion information is calculated based on the real-time motion data; the real-time motion data includes image data and motion state data, and the motion state data includes acceleration, angular velocity and posture change of the athlete; the initial motion information includes the position, speed, acceleration, motion mode of the athlete and the position, speed and acceleration of the ball;

[0021] Preprocessing the initial motion information to obtain target motion information;

[0022] Constructing a sports risk field based on the target sports information; the sports risk field includes a sports field potential field, an athlete kinetic energy field, a ball kinetic energy field, and an athlete behavior and psychology field; constructing a risk response influence function based on changes in the athlete's sports state in the sports risk field; establishing a sports risk assessment model based on the risk response influence function and the sports risk field;

[0023] Solving the motion risk assessment model to obtain a motion risk boundary value; the motion risk boundary value includes an upper risk boundary and a lower risk boundary; calculating a risk reliability index according to the motion risk boundary value;

[0024] When the risk reliability index exceeds the motion risk threshold, an alarm is issued.

[0025] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0026] The present application provides a comprehensive real-time assessment system and method for sports risks within a venue. The system constructs a potential energy field of the sports venue, a kinetic energy field of the athletes, a kinetic energy field of the ball, and a behavioral and psychological field of the athletes through real-time sports data. Then, a sports risk field is constructed considering four dimensions, namely, the potential energy field of the sports venue, the kinetic field of the athletes, the kinetic field of the ball, and the behavioral and psychological field of the athletes. Then, a sports risk assessment model is established by considering the multi-dimensional sports risk field. The sports risk assessment model is solved to obtain an accurate sports risk boundary value. The risk reliability index calculated based on the sports risk boundary value has higher accuracy, which solves the problem that existing risk assessment methods usually rely on static safety standards and rules of thumb, and are not flexible and accurate enough when dealing with complex and dynamic environments. High-precision sports status monitoring, comprehensive risk assessment, and real-time risk warning are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0028] Figure 1 A schematic diagram of functional modules of a comprehensive real-time assessment system for sports risks in a venue provided by an embodiment of the present application;

[0029] Figure 2 A schematic diagram of the status of athletes and a ball on a sports field provided by an embodiment of the present application;

[0030] Figure 3 A flowchart of a comprehensive real-time assessment method for sports risks within a venue provided in one embodiment of the present application. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] Although existing technologies have made progress in motion monitoring and risk assessment, some technical problems still exist. First, the accuracy of traditional sensors and monitoring equipment is often insufficient, especially in dynamic and complex environments, which may lead to inaccurate data and affect the accuracy of risk assessment. Secondly, existing computer vision systems may cause delays and slow processing speeds when processing real-time data streams due to computing resource limitations or inefficient algorithms. This will affect the timeliness of real-time warnings and feedback. In addition, existing systems can usually only focus on a single risk factor and lack the ability to comprehensively analyze multi-dimensional and multi-source data. Finally, traditional early warning systems rely on fixed rules and thresholds, with low intelligence and adaptability, and may not be able to accurately identify and handle complex risk situations.

[0033] In response to the above problems, the present application provides a comprehensive real-time assessment system and method for sports risks in a venue. By introducing high-precision sensors, computer vision technology, data fusion algorithms and intelligent early warning systems, many problems in the prior art are effectively solved. The system improves the accuracy of the data through the combination of high-precision cameras and IMU devices, overcoming the problem of insufficient accuracy of traditional sensors. By utilizing efficient data stream processing and dynamic visualization technology, real-time feedback is achieved, solving the problem of poor real-time performance of traditional systems. Through the construction of multi-dimensional risk fields and comprehensive analysis methods, the comprehensive assessment capability of complex environments is enhanced, making up for the problem of insufficient comprehensive capabilities of existing methods. The application of intelligent early warning algorithms and multi-channel notification systems realizes intelligent and adaptive early warnings, improves the accuracy of risk identification and processing, and solves the problem of low intelligence level of traditional early warning systems. The system provides athletes and coaches with more accurate and timely risk management solutions.

[0034] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0035] In an exemplary embodiment, Figure 1 As shown, a comprehensive real-time assessment system for sports risks in a venue is provided, including a real-time monitoring module, a data fusion module, a data processing module, a risk assessment module, a communication module and an optimization suggestion warning module. The real-time monitoring module, the data fusion module, the data processing module, the risk assessment module and the optimization suggestion warning module are connected in communication via the communication module.

[0036] The real-time monitoring module is used to collect the real-time motion data of the athletes when they are playing ball sports; and calculate the initial motion information based on the real-time motion data; the real-time motion data includes image data and motion state data, and the motion state data includes the acceleration, angular velocity and posture change of the athletes; the initial motion information includes the position, speed, acceleration, motion mode of the athletes and the position, speed and acceleration of the ball.

[0037] The real-time monitoring module uses sensor units such as high-precision cameras and inertial measurement units (IMUs) to monitor the athlete's motion status in real time. The high-precision camera captures the athlete's image data and uses computer vision techniques such as object detection and posture estimation to track the athlete's position, speed, and movement pattern. By matching features in consecutive image frames, the high-precision camera can calculate the athlete's speed and acceleration and identify different movement patterns. The IMU measures the athlete's acceleration, angular velocity, and posture changes through accelerometers, gyroscopes, and magnetometers. Through filtering and integration operations, the athlete's speed, position, and acceleration are accurately calculated, and features are extracted from the acceleration and angular velocity data, such as the rate of change of linear acceleration, the rate of change of angular velocity, and the intensity of movement in each axis. The extracted features are classified using machine learning algorithms (such as support vector machines, decision trees, random forests, etc.) or deep learning algorithms (such as convolutional neural networks) to obtain movement patterns. Movement patterns include jumping, turning, sideways, and sprinting.

[0038] The data fusion module is used to pre-process the initial motion information to obtain the target motion information; the pre-processing is Kalman filtering. That is, the visual information provided by the high-precision camera and the sensor data of the IMU are comprehensively processed through the data fusion algorithm (such as the Kalman filtering algorithm), thereby improving the accuracy of position, speed and attitude estimation.

[0039] The data processing module is used to: construct a sports risk field based on the target motion information; the sports risk field is composed of the superposition of the sports field potential energy field, the athlete kinetic energy field, the ball kinetic energy field and the athlete's behavioral psychological field; construct a risk response influence function based on the changes in the athlete's motion state in the sports risk field; and establish a sports risk assessment model based on the risk response influence function and the sports risk field.

[0040] As an optional implementation, constructing a sports risk field based on the target motion information specifically includes: constructing the risk intensity of the sports venue potential energy field, the risk intensity of the athlete's kinetic energy field, the risk intensity of the ball kinetic energy field and the risk intensity of the athlete's behavioral and psychological field based on the target motion information; taking weighted summation of the risk intensity of the sports venue potential energy field, the risk intensity of the athlete's kinetic energy field, the risk intensity of the ball kinetic energy field and the risk intensity of the athlete's behavioral and psychological field to construct the sports risk field.

[0041] Based on the detected spatiotemporal position and speed of athletes and balls, a sports risk assessment method is constructed based on the concept of field. On the sports field, each component element such as field conditions and athletes will generate a potential field, the size of which is determined by the attributes of the elements themselves. In addition, the components will affect each other, and the superposition of multiple fields constitutes the change of the athlete's motion state, thus forming different risk points. According to the properties of the component elements, the sports risk field is divided into the sports field potential field, the athlete kinetic field, the ball kinetic field and the athlete's behavioral psychology field. The superposition of the four fields constructs the sports risk field. This application defines the sports risk field intensity at a certain point on the field as the weighted sum E of the field intensity generated by different elements, as shown in formula (1).

[0042] E=ω r E r +ω y E y +ω q E q +ω x E x (1).

[0043] Among them, E r is the risk intensity of the potential energy field of the sports venue; E y is the risk intensity of the athlete's kinetic field; E q is the risk intensity of the ball kinetic energy field; E x is the risk intensity of the athlete's behavioral psychological field; r ,ω r ,ω r ,ω r They represent the weight coefficients of different risk fields.

[0044] (1) Potential energy field of sports venues

[0045] When athletes are close to the boundaries of the field (such as the baseline and sideline), their movements require higher precision and the risk increases accordingly. When athletes move close to the baseline, they need to have higher control over their shots, because once a mistake is made, the ball is likely to go out of bounds, which is equivalent to a rapid increase in the potential energy field. Considering that most ball sports on the field are centrally symmetrical, the potential energy fields on the left and right sides are also different. Usually, athletes will choose to stand in the middle. Based on this principle, the risk intensity function of the potential energy field of the sports field is constructed, as shown in Equations (2), (3) and (4).

[0046]

[0047] Among them, E1 and E2 represent the potential energy of the sports field at the first angle and the potential energy of the sports field at the second angle, respectively. They are the potential energy sizes considered from different angles and characterize the sports risk. E1 is modeled based on a function with a large amplitude and a fast change rate to represent the rapid increase of the potential energy field; E2 uses a linear function as the basic risk, and superimposing it with E1 can reflect a more realistic sports risk situation; Z(t) is the real-time position of the athlete; Z l is the centerline position of one side of the field; d is the half width of one side of the field; ε i is the potential energy field coefficient of the unilateral center line of the site; i=1, 2 represent the left and right boundaries of the site respectively; Z b1 , Z b2 are the horizontal coordinates of the left and right boundaries of the field, respectively; Z is the horizontal coordinate of the athlete in the field, that is, the distance between the athlete and the left or right boundary of the field; is a boundary related parameter.

[0048] Summing equation (2) and equation (3), we get the potential energy field function E of the sports field: r .

[0049] E r =E1+E2 (4).

[0050] (2) Athlete kinetic field

[0051] In ball sports, factors such as the speed and acceleration of the athlete will affect the direction of movement. High-speed movement or sudden stop will affect the athlete's physical fitness and movement accuracy. Therefore, the athlete's kinetic energy field is constructed to analyze the level of sports risk, and its size is related to the athlete's weight, sports state, etc. The specific formula for calculating the risk intensity of the athlete's kinetic energy field is shown in formula (5).

[0052]

[0053] Among them, F(x,z) is a correction function, which is corrected considering that the kinetic energy of athletes at different positions has different effects on risk; δ0 is the kinetic energy field correction coefficient; m is the athlete's equivalent mass; v is the athlete's speed. The above correction function F(x,z) is expressed as formula (6).

[0054]

[0055] Among them, the correction function considers the most unfavorable situation, that is, the maximum value of the two situations in formula (6) is selected as the final correction value, x(t) is the distance between the athlete and the front side line of the field at time t; Z(t) is the distance between the athlete and the back side line of the field at time t; (x1, Z1) and (x2, Z2) are the coordinates of the center line of the front and back boundaries of the field respectively; δ1 and δ2 are the kinetic energy field adjustment coefficients of the athlete respectively.

[0056] (3) Ball kinetic energy field

[0057] The mass and speed of the ball will affect the risk of the athlete hitting the ball during the movement. It is related to the ball speed, mass, movement state and direction. Based on this, a ball kinetic energy risk field model is established. The calculation formula for the risk intensity of the ball kinetic energy field is shown in formula (7).

[0058]

[0059] Where: Q x (t) and Q y (t) are the horizontal and vertical coordinates of the ball respectively; δ3 is the adjustment coefficient of the kinetic energy field of the ball; a is the acceleration of the ball; θ is the angle formed between the ball and the athlete.

[0060] (4) Athletes’ behavioral psychology field

[0061] The athlete's behavior is related to the landing point and speed of the target ball, as shown in formula (8).

[0062]

[0063] Among them, Eb n is the basic field strength value formed by the target; v n is the speed of the target ball, which refers to the speed of the ball when the opponent hits the ball; L0 is the distance from the origin of the target ball along the target direction, v ε is the correction value.

[0064] The psychological field strength of athletes at any point on the field is calculated based on the behavioral psychological field model. Figure 2 As shown, OP x OP is the distance from the intersection of the equipotential line, the moving obstacle and the line connecting them to the target ball. y is the distance from the intersection of the equipotential line and the perpendicular line connecting the athlete and the moving obstacle to the target ball. x With OP y The ratio is α, and the value of α is related to the speed of the athlete.

[0065] ρ=L0-(1-α)L0|sinθ| (9).

[0066] Among them, ρ is the distance between the point on the equipotential line and the athlete's origin O.

[0067] The basic field strength calculation model of the psychological field formed by the athlete at any point in space is as follows: n (x,y) is the basic field strength at point (x,y).

[0068]

[0069] In the formula, ρ(x,y) is the distance from the point on the equipotential line to the athlete's origin O, Eb n (x,y) has no special meaning, it just introduces the coordinates of the point and then assigns it to E x .

[0070] The model based on the athlete's behavior psychological field should include two parts, namely, the description of the athlete's own psychological field and the description of the influence of the motion state and attributes of other objects within the psychological field. Therefore, the framework structure of the athlete's behavior model is constructed as shown in the following formula.

[0071] F n =E n (Eb n )·g n (F inf ,E inf ,P inf ,S inf ) (11).

[0072] Among them, F n E is the magnitude of the psychological force on the athlete; n It is the field strength of the behavioral field formed by athletes in the sports field, which is composed of the basic field strength Eb n Transformed from; F inf The psychological impact of the site facilities on pedestrians; inf The psychological field is the impact of the external environment on the athletes when they play, including noise and other influencing factors; inf Indicates the influence of the opponent on itself, S inf Represents the impact of social factors on athletes.

[0073] In order to further analyze the impact of external environmental factors in sports venues on athletes, an athlete behavior field model framework based on behavior field theory is established, as shown in formula (12).

[0074]

[0075] Among them, r j It is a variable adjustment coefficient that expresses the severity of the influence of the influencing factor on the athlete's behavior. Its size is determined by the membership function of each parameter and its weight in the psychological field; ΔE n is the amplitude of the field intensity change in the behavioral psychological field, which varies with the attributes of the influencing factors, the real-time environment, and the degree of psychological impact on the athlete; j = F inf ,E inf ,P inf ,S inf , that is, m=4.

[0076] (5) Risk response field construction

[0077] Based on the analysis of the changes in the athlete's motion state in the superposition state of multiple fields, the risk response influence function G(t) is constructed, thereby constructing the basic equation of the risk field as shown in formula (13).

[0078]

[0079] Where E(t) is the intensity of the motion risk field calculated by formula (1), is the gradient of the risk field, indicating how the risk varies with location; γ and β are the influence coefficients; y i (t) is the risk response function, which is determined by the changes of the athlete's speed, acceleration, etc. over time. In a specific example, i represents the athlete's speed, acceleration and position, then n = 3; m represents the initial time, T i Indicates the sampling time of the athlete's motion status data.

[0080] (6) Risk Assessment Model

[0081] Taking into account the relationship between risk response measures and sports risks, a sports risk assessment model D(t) is constructed.

[0082]

[0083] Among them, μ1 and μ2 are the weights of the risk response function and the athlete's risk field intensity, respectively. is the correction parameter, G(t) is the risk response function, E(t) is the athlete's risk field intensity, and its uncertainty domain is described by the generalized hyperellipsoid model:

[0084]

[0085] In the formula, G(t) c and E(t) c represents the midpoint of the function value interval; G(t) r and E(t) r is the radius of the function value interval, α is the adjustment coefficient; N1 and N2 are the parameters of the generalized hyperellipsoid model. In order to ensure that the model is a convex set and has more flexibility, they take different integer values ​​greater than 1.

[0086]

[0087] Among them, β is the risk reliability index, which reflects the importance of the impact of sports risk field and risk response measures on sports risk. When β is greater than 1, the sports risk is low, and when β is less than -1, the sports risk is extremely high; f c (G,E) and f r (G, E) are the mean and deviation of the function, respectively, fU (G,E) and f L (G, E) are the upper and lower risk boundaries respectively.

[0088] The risk assessment module is used to solve the motion risk assessment model to obtain a motion risk boundary value; the motion risk boundary value includes an upper risk boundary and a lower risk boundary; and the risk reliability index is calculated according to the motion risk boundary value.

[0089] By constructing the Lagrangian function to solve the sports risk assessment model, we can get the risk upper boundary f(G,E) U (G,E) and the lower risk boundary and f L (G, E), the constructed Lagrangian function is shown in formula (18).

[0090]

[0091] Where L(G(t), E(t), λ) represents the Lagrangian function, and λ represents the Lagrangian multiplier.

[0092] The optimization suggestions and real-time warning technologies provided by the optimization suggestion warning module are as follows:

[0093] Through sports risk assessment methods, a device is developed with integrated data acquisition and processing modules, which can provide accurate risk assessment and optimization suggestions through comprehensive analysis of athletes' real-time data.

[0094] (1) Rule engine: Set risk trigger conditions and automatically issue warning signals. Optimize the warning module to issue an alarm when the risk reliability index exceeds the sports risk threshold. Set the sports risk threshold based on the athlete's regular sports performance. When the athlete is in a high-risk sports state for a long time, an alarm will be issued to remind the athlete to stop exercising and reduce sports injuries.

[0095] (2) Multi-channel notification system: Send warnings through visual, auditory and tactile means, and convey information to relevant personnel through mobile applications, emails and text messages.

[0096] The optimization suggestion warning module also includes a display and notification unit and a database and history recording unit.

[0097] The display and notification unit is used to visualize the athlete's real-time motion status data and risk reliability index.

[0098] The database and history record unit is used to store the athlete's historical sports data and risk assessment records; the risk assessment records include the risk reliability index corresponding to the athlete's historical sports data.

[0099] (3) Intelligent warning algorithm: Based on real-time data and historical data analysis, the sports risk assessment model is combined with deep learning algorithms such as decision trees, random forests or neural networks to make risk judgments.

[0100] The optimization suggestion warning module is also used to: use intelligent algorithms to train historical sports data and risk assessment records to obtain a trained risk prediction model; input the athlete's real-time sports data into the trained risk prediction model to predict the risk reliability index. The intelligent algorithm is a decision tree algorithm, a random forest algorithm, or a convolutional neural network algorithm.

[0101] (4) API interface: used for data exchange and integration with other systems (such as training management systems and health monitoring systems).

[0102] (5) Real-time communication protocols: ensure real-time data synchronization between system components, such as WebSocket, MQTT, etc.

[0103] Based on key points, this application can accurately determine the sports risks of athletes. The above process does not involve the addition of any equipment that affects the sports state, and is not restricted by sports venue conditions. Based on the above, this application can accurately detect sports risks in real time and issue risk warning prompts.

[0104] The high-precision camera and IMU device are connected to the data acquisition module through a data cable. The high-precision camera is responsible for capturing the image data of the athlete, and the IMU device is responsible for measuring the acceleration, angular velocity and posture change of the athlete. The data acquisition module transmits the collected data to the data fusion module. The data acquisition module is responsible for preliminary processing of the data from the high-precision camera and IMU device. The data fusion module receives the data from the data acquisition module and passes the data to the real-time data processing module. The data fusion module uses data fusion algorithms (such as Kalman filtering) to comprehensively process the data to improve the accuracy of position, velocity and posture estimation. The data processing module transmits the processed data to the risk assessment module. The real-time data processing module is responsible for real-time analysis and processing of the motion state to provide accurate and reliable data support for risk assessment. The risk assessment module conducts a comprehensive analysis of the data and sends the assessment results to the display and notification unit and the database and history record unit. The risk assessment module constructs a sports risk field based on the comprehensive analysis results and generates a corresponding risk assessment report. The display and notification unit is responsible for real-time display of the risk assessment results and sends early warning notifications to the user through visual, auditory and tactile means. It ensures that the information is conveyed to the relevant personnel in a timely manner so that they can respond quickly. The database and history recording unit is responsible for storing historical data and risk assessment records. It supports long-term data analysis to help identify risk patterns and trends, optimize training plans and risk management strategies. Through the above module structures and their connection relationships, the comprehensive real-time assessment system for sports risks in the venue provided by this application can achieve high-precision sports status monitoring, comprehensive risk assessment and timely risk warning, providing athletes and coaches with efficient risk management solutions.

[0105] The sports risk assessment model can analyze the spatiotemporal position, speed and other data of athletes and balls, and the system constructs a comprehensive risk field including the potential field of the venue, the kinetic field of athletes, the kinetic field of the ball and the behavior field of athletes. This comprehensive risk assessment method not only takes into account a single factor, but also integrates the influence of multiple fields, provides a more comprehensive risk analysis, and can better identify potential risk points. This application can quickly display the athlete's motion status and risk area based on real-time processing of sensor data. Through dynamic identification and visualization, the real-time position of the athlete and its risk distribution in the sports field can be immediately seen, helping coaches and athletes to make adjustments quickly. This real-time feedback improves the instant understanding of the athlete's status and enhances the ability to respond to potential risks. The early warning system uses intelligent algorithms (such as machine learning models) to judge risks and issue early warnings based on real-time data and historical data. The trigger conditions set by the rule engine combined with the dynamic adjustment ability of the intelligent algorithm enable the early warning system to accurately identify and predict risks, reducing the possibility of false alarms and missed reports. The intelligence of the early warning improves the accuracy and response speed of the system.

[0106] Based on the same inventive concept, the embodiment of the present application also provides a method for implementing the above-mentioned system for implementing the comprehensive real-time assessment of sports risks in a venue. The implementation scheme for solving the problem provided by the method is similar to the implementation scheme recorded in the above-mentioned system, so the specific limitations in one or more embodiments of the method for implementing the comprehensive real-time assessment of sports risks in a venue provided below can be referred to the limitations of the system for implementing the comprehensive real-time assessment of sports risks in a venue above, and will not be repeated here.

[0107] In an exemplary embodiment, Figure 3 As shown, a comprehensive real-time assessment method for sports risks in a venue is provided, comprising the following steps S1 to S5:

[0108] Step S1: When an athlete is playing ball sports, real-time motion data of the athlete is collected; and initial motion information is calculated based on the real-time motion data; the real-time motion data includes image data and motion state data, and the motion state data includes the acceleration, angular velocity and posture change of the athlete; the initial motion information includes the position, speed, acceleration, motion mode of the athlete and the position, speed and acceleration of the ball.

[0109] Step S2: pre-process the initial motion information to obtain target motion information.

[0110] Step S3: construct a sports risk field based on the target motion information; the sports risk field includes a sports field potential field, an athlete kinetic energy field, a ball kinetic energy field and an athlete behavior and psychology field; construct a risk response influence function based on changes in the athlete's motion state in the sports risk field; and establish a sports risk assessment model based on the risk response influence function and the sports risk field.

[0111] Step S4: solving the motion risk assessment model to obtain a motion risk boundary value; the motion risk boundary value includes an upper risk boundary and a lower risk boundary; and calculating a risk reliability index according to the motion risk boundary value.

[0112] Step S5: When the risk reliability index exceeds the motion risk threshold, an alarm is issued.

[0113] Monitor the athlete's position, speed and acceleration in real time, calculate the potential and kinetic fields based on the athlete's field position and speed, comprehensively analyze the potential and kinetic fields, evaluate the athlete's overall energy state, and provide optimization suggestions and risk warnings to improve the athlete's performance and reduce the risk of injury. This application combines the athlete's position, speed and movement dynamics in the sports field for comprehensive analysis, providing athletes with real-time performance optimization and risk management solutions.

[0114] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A comprehensive real-time assessment system for sports risks in a venue, characterized in that: The comprehensive real-time assessment system for sports risks in the venue includes: A real-time monitoring module is used to collect real-time motion data of athletes when they are playing ball games; and calculate initial motion information based on the real-time motion data; the real-time motion data includes image data and motion state data, and the motion state data includes acceleration, angular velocity and posture change of the athletes; the initial motion information includes the position, speed, acceleration, motion mode of the athletes and the position, speed and acceleration of the ball; A data fusion module is used to pre-process the initial motion information to obtain target motion information; A data processing module is used to: construct a sports risk field based on the target sports information; the sports risk field is composed of the superposition of the sports field potential field, the athlete kinetic energy field, the ball kinetic energy field and the athlete behavior and psychology field; construct a risk response influence function based on the change of the athlete's sports state in the sports risk field; establish a sports risk assessment model based on the risk response influence function and the sports risk field; A risk assessment module is used to solve the motion risk assessment model to obtain a motion risk boundary value; the motion risk boundary value includes an upper risk boundary and a lower risk boundary; and a risk reliability index is calculated according to the motion risk boundary value; The optimization suggestion warning module is used to issue an alarm when the risk reliability index exceeds the motion risk threshold.

2. The comprehensive real-time assessment system for sports risks in a venue according to claim 1, characterized in that: The optimization suggestion warning module also includes a display and notification unit; the display and notification unit is used to visualize the athlete's real-time sports status data and risk reliability index.

3. The comprehensive real-time assessment system for sports risks in a venue according to claim 2, characterized in that: The display and notification unit is also used to send a warning notification to the user through visual, auditory and / or tactile means when the risk reliability index exceeds the motion risk threshold.

4. The comprehensive real-time assessment system for sports risks in a venue according to claim 1, characterized in that: The optimization suggestion warning module also includes a database and a history recording unit; the database and the history recording unit are used to: store the athlete's historical sports data and risk assessment records; the risk assessment records include the risk reliability index corresponding to the athlete's historical sports data.

5. The comprehensive real-time assessment system for sports risks in a venue according to claim 4, characterized in that: The optimization suggestion warning module is also used to: use an intelligent algorithm to obtain a trained risk prediction model based on historical sports data and risk assessment records; input the athlete's real-time sports data into the trained risk prediction model to predict a risk reliability index.

6. The comprehensive real-time assessment system for sports risks in a venue according to claim 5, characterized in that: The intelligent algorithm is a decision tree algorithm, a random forest algorithm or a convolutional neural network algorithm.

7. The comprehensive real-time assessment system for sports risks in a venue according to claim 1, characterized in that: A data processing module is used to construct the risk intensity of the potential energy field of the sports field, the risk intensity of the kinetic energy field of the athletes, the risk intensity of the kinetic energy field of the ball, and the risk intensity of the behavioral psychology field of the athletes based on the target motion information; The sports risk field is constructed by taking the weighted sum of the risk intensity of the potential energy field of the sports venue, the risk intensity of the kinetic energy field of the athlete, the risk intensity of the kinetic energy field of the ball and the risk intensity of the athlete's behavioral and psychological field.

8. The comprehensive real-time assessment system for sports risks in a venue according to claim 1, characterized in that: The comprehensive real-time assessment system for sports risks in the venue also includes a communication module; the real-time monitoring module, the data fusion module, the data processing module, the risk assessment module and the optimization suggestion warning module are communicatively connected through the communication module.

9. The comprehensive real-time assessment system for sports risks in a venue according to claim 1, characterized in that: The preprocessing is Kalman filtering processing.

10. A method for comprehensive real-time assessment of sports risks in a venue based on the comprehensive real-time assessment system for sports risks in a venue according to any one of claims 1 to 9, characterized in that: The method for comprehensive real-time assessment of sports risks in a venue includes: When an athlete plays a ball game, real-time motion data of the athlete is collected; and initial motion information is calculated based on the real-time motion data; the real-time motion data includes image data and motion state data, and the motion state data includes acceleration, angular velocity and posture change of the athlete; the initial motion information includes the position, speed, acceleration, motion mode of the athlete and the position, speed and acceleration of the ball; Preprocessing the initial motion information to obtain target motion information; Constructing a sports risk field based on the target sports information; the sports risk field includes a sports field potential field, an athlete kinetic energy field, a ball kinetic energy field, and an athlete behavior and psychology field; constructing a risk response influence function based on changes in the athlete's sports state in the sports risk field; establishing a sports risk assessment model based on the risk response influence function and the sports risk field; Solving the motion risk assessment model to obtain a motion risk boundary value; the motion risk boundary value includes an upper risk boundary and a lower risk boundary; calculating a risk reliability index according to the motion risk boundary value; When the risk reliability index exceeds the motion risk threshold, an alarm is issued.

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