Safety risk early warning method and system for physical training

Abstract

The invention discloses a safety risk early warning method and system for physical training, and relates to the technical field of physical training. Obtaining a quantum biological field abnormal coefficient, a muon bone mineral density variation coefficient, a motion posture coefficient and a training environment coefficient of each part of each trainee in real time; according to the coefficients, the safety risk value of each part of each trainee is accurately analyzed, and the safety risk evolution trend of each trainee is deeply analyzed; and finally, on the basis of the risk evolution trend, performing optimization and adjustment on protection measures of all parts of all trainees in a targeted manner. According to the method, through multi-dimensional data collection and analysis, dynamic monitoring and early warning of physical training safety risks are achieved, protection measures are optimized in time, the training risks are effectively reduced, reliable guarantee is provided for physical training safety, and the method is suitable for various physical training scenes.

Description

Technical Field

[0001] The present invention relates to the technical field of sports training, and in particular to a safety risk early warning method and system for sports training. Background Art

[0002] In the field of sports training, with the continuous improvement of competitive levels and the widespread development of national fitness, training intensity and professionalization are increasing. At the same time, safety risks during training are becoming increasingly prominent. Traditional sports training safety assurance relies mainly on the coach's experience and judgment and limited protective measures, such as wearing conventional protective gear and conducting simple warm-up instructions. However, these methods have obvious limitations and cannot accurately identify potential risks of individuals during training. They are difficult to meet the high standards of safety assurance required by modern sports training. Therefore, a safety risk warning method and system for sports training is needed.

[0003] Existing technology, such as the invention application patent with publication number CN116778390A, discloses a safety risk warning method and system for sports training, which includes: performing real-time limb movement analysis on all monitored objects based on sports training monitoring videos to obtain limb movement analysis results; predicting the limb movement trajectory of the monitored object based on the limb movement analysis results to obtain limb movement dynamic prediction results; evaluating the current safety risk coefficient of the monitored object based on the limb movement dynamic prediction results; judging whether it is necessary to issue a safety warning instruction based on the current safety risk coefficient to obtain a safety risk warning result; analyzing the limb movements of the monitored object based on the sports training monitoring object, and predicting the limb movement trajectory of the monitored object based on the limb movement analysis results, evaluating the safety risk coefficient based on the prediction result, and then judging whether to issue a safety warning, thereby realizing timely, accurate and effective safety risk warning during sports training.

[0004] In response to the above scheme, the inventors of this application have found that the above technology has at least the following technical problems: 1. The existing technology may rarely involve the monitoring of the abnormal coefficients of the quantum biofield of the trainees, and cannot timely detect abnormal conditions such as potential changes in the biomagnetic field inside the body, which may be early signals of physical fatigue, injury or disease. It is difficult to achieve real-time, dynamic and accurate monitoring of various parts. It is difficult to track the dynamic changes in bone density during training. At the same time, the existing technology may not conduct comprehensive and systematic monitoring and analysis of the effects of training environment coefficients such as temperature and humidity, light intensity and wind speed on the physical and athletic performance of trainees. Harsh environmental conditions may increase training risks, but without a quantitative environmental coefficient assessment, it is difficult to accurately judge the extent of the impact of the environment on training safety.

[0005] 2. Existing technologies may not have an assessment model that comprehensively considers and quantifies multiple key factors, such as the quantum biofield anomaly coefficient, muon bone density variation coefficient, exercise posture coefficient, and training environment coefficient. This makes it impossible to accurately calculate the corresponding safety risk values ​​for each body part of each trainee, and thus cannot comprehensively and accurately assess the safety risks during training. This makes it difficult to promptly identify the development of risks and take effective preventive measures. If the risk value is on an upward trend and is not detected in time, the optimal opportunity for intervention may be missed, leading to an increased risk of injury for the trainee.

[0006] 3. Existing technologies may not be able to optimize and adjust protective measures based on the specific safety risk evolution trends of different trainees' body parts. Smart protective gear parameters cannot automatically adjust stiffness and electromagnetic pulse patterns based on risk levels, and training intensity cannot be accurately adjusted based on factors such as bone density variation, making it difficult to achieve precise protection for trainees.

[0007] 4. Existing technologies may lack a comprehensive tiered warning mechanism, making it impossible to implement targeted early warning measures based on different risk levels. The collaborative use of multiple devices, such as smart protective gear, AR glasses, and coaching terminals, to provide clear and unambiguous early warnings in different ways can lead to trainers and coaches not paying enough attention to risks or responding in a timely manner, delaying the optimal time to address risks and increasing the probability of training safety accidents. Summary of the Invention

[0008] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a safety risk early warning method and system for sports training.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a safety risk warning method for sports training, including: Step 1, deployment of venues and personnel equipment: Deployment of venue equipment and personnel equipment at the target sports training venue, and then obtaining the quantum biofield anomaly coefficient, muon bone density variation coefficient, motion posture coefficient and training environment coefficient corresponding to each part of each trainee at the current moment.

[0010] Step 2. Prediction of personalized risks: Based on the quantum biofield anomaly index, muon bone density variation coefficient, movement posture coefficient and training environment coefficient corresponding to each part of each trainee, the safety risk value corresponding to each part of each trainee is analyzed, and the safety risk evolution trend corresponding to each part of each trainee is analyzed.

[0011] Step 3: Optimize protective measures: Based on the evolution trend of safety risks corresponding to each part of each trainee, optimize and adjust the protective measures corresponding to each part of each trainee.

[0012] The present invention secondly provides a safety risk warning system for sports training, including: a venue and personnel equipment deployment module: used to deploy venue equipment and personnel equipment at a target sports training venue, and then obtain the quantum biofield anomaly coefficient, muon bone density variation coefficient, movement posture coefficient and training environment coefficient corresponding to each part of each trainee at the current moment.

[0013] Personalized risk prediction module: used to analyze the safety risk value corresponding to each part of each trainee based on the quantum biofield anomaly index, muon bone density variation coefficient, movement posture coefficient and training environment coefficient corresponding to each part of each trainee, and analyze the safety risk evolution trend corresponding to each part of each trainee.

[0014] Protective measures optimization module: used to optimize and adjust the protective measures corresponding to each part of each training personnel according to the evolution trend of safety risks corresponding to each part of each training personnel.

[0015] The beneficial effects of the present invention are: 1. The embodiments of the present invention, through the deployment of personnel and equipment, the use of wearable diamond NV color center sensor arrays, portable muon scintillator detectors, quantum inertial measurement units and other equipment, can comprehensively collect the quantum biofield anomaly coefficient, muon bone density variation coefficient, motion posture coefficient and training environment coefficient corresponding to each part of each trainee. Through the normalization and analysis of these multi-dimensional data, the safety risk value of each part of each trainee can be accurately assessed, overcoming the limitation of traditional methods that only focus on a single indicator, and more comprehensively reflecting the potential risks in the training process. At the same time, collecting data such as biomagnetic field intensity and power spectral density and importing them into the quantum biofield anomaly coefficient analysis module can effectively detect abnormal changes in the biofield inside the body, providing more in-depth information for risk assessment.

[0016] 2. This embodiment of the present invention analyzes safety risk values ​​and safety risk evolution trends based on individual differences among trainees, combined with multi-dimensional coefficients. This personalized risk prediction more accurately reflects each trainee's risk profile at different training stages and in different body parts, providing a basis for developing targeted protective measures. For trainees with a high coefficient of variation in bone density, this coefficient of variation is analyzed and substituted into a calculation formula to determine a training intensity adjustment value, thereby adjusting training intensity and reducing injury risk. Furthermore, protective measures are optimized and adjusted based on safety risk evolution trends, achieving dynamic protection. Different smart protective gear parameter adjustment strategies and training intensity adjustment strategies are developed for low, medium, and high safety risk evolution trend levels. For low safety risk, the smart protective gear maintains a 10% increase in stiffness and sets the targeted electromagnetic pulse to the lowest intensity health-care mode; for medium safety risk, the smart protective gear's stiffness is increased by 30% and a medium-intensity stiffness enhancement mode is set; for high safety risk, the smart protective gear's stiffness is increased by 50% and a specific high-intensity health-care mode is set. This dynamic adjustment enables timely response to changing risks and provides more effective protection.

[0017] 3. The embodiments of the present invention, through real-time monitoring and analysis, provide early warning prompts based on the level of safety risk evolution trends. A mild early warning mechanism is activated when the risk is low, a more alarming early warning is triggered when the risk is medium, and an emergency early warning response is immediately initiated when the risk is high. When the safety risk is high, the smart protective gear will continue to vibrate strongly, issue a sharp alarm, flash red lights, and broadcast emergency warnings in a loop. The AR glasses will turn red and flash danger prompts, forcibly interrupting the training operation. At the same time, the coach and medical team terminals will synchronously trigger high-level alarms to facilitate rapid support. This real-time early warning and emergency response mechanism can promptly notify training personnel and related personnel when risks occur, take effective countermeasures, and minimize the occurrence of sports injuries.

[0018] 4. In an embodiment of the present invention, the risk warning threshold corresponding to each part of each trainee is dynamically adjusted. By collecting the physical signal of bone density and converting it into a bone density value, the ratio is calculated with the preset standard bone density value to obtain the bone density variation coefficient, and then the risk warning threshold is adjusted. This data-driven threshold adjustment can more accurately judge the risk level according to the actual situation of the trainee and the changes during the training process, and improve the accuracy and reliability of the warning. The method comprehensively considers multiple factors such as personnel, equipment, and environment to build a complete safety risk warning system. Through accurate risk assessment, personalized protective measures and real-time warning response, the safety of sports training can be effectively improved and the occurrence of sports injuries can be reduced. At the same time, the analysis and dynamic adjustment based on multi-dimensional data also improves the scientific nature of training and provides data support for the formulation and adjustment of training plans. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1 The present invention is a flowchart of the steps for implementing the method.

[0021] Figure 2 This is a schematic diagram of the system module connection of the present invention. DETAILED DESCRIPTION

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0023] The present invention is implemented as follows Figure 1 As shown, a safety risk warning method and system for sports training include: Step 1, deployment of venues and personnel equipment: deployment of venue equipment and personnel equipment at the target sports training venue, and then obtaining the quantum biofield anomaly coefficient, muon bone density variation coefficient, movement posture coefficient and training environment coefficient corresponding to each part of each trainee at the current moment.

[0024] In a specific embodiment, the deployment of venue equipment and personnel equipment at the target sports training venue is carried out, and the specific deployment process is as follows: Personnel equipment deployment: before each trainee trains at the target sports training venue, when they put on the equipment, the wearable diamond NV color center sensor array is first embedded in the lining of a special tight training suit in a modular form, covering the sports parts of the shoulders, elbows, waist, hips, knees and ankles. Each sensor node is connected by a flexible circuit to form a complete network. The portable muon scintillator detector is installed in a lightweight backpack, and the data transmission line is integrated through the backpack interface. The quantum inertial measurement unit is made into a small patch-type device and equipped with a low-power Bluetooth module for wireless data transmission. The metamaterial intelligent protective gear assists trainees in wearing the protective gear correctly. The control chip and drive device integrated inside the protective gear can respond to system instructions to perform local stiffness mutations and apply electromagnetic pulses.

[0025] Equipment deployment on the site: Metasurface antenna arrays are arranged on the walls and ceilings around the training site. The spacing between the antenna arrays is set to 3-5 meters. The antenna arrays are connected to the data processing center in the site through a wired network. The data processing center is set up in a corner of the site and is equipped with high-performance superconducting quantum processors, servers and data storage equipment. It is connected to the sensors on the trainees and the site antenna arrays through a wireless network. At the same time, a visual monitoring screen is set up to display risk assessment results, early warning information and statistical analysis of training data in real time. In addition, metasurface holographic projection equipment is deployed in the site, and each trainee is equipped with AR glasses.

[0026] In a specific embodiment, the quantum biofield abnormality coefficient, muon bone density variation coefficient, movement posture coefficient and training environment coefficient corresponding to each part of each trainee are obtained, and the specific acquisition process is as follows: A1. The biomagnetic field intensity, power spectrum density and autocorrelation function corresponding to each part of each trainee are collected, and normalized, and imported into the quantum biofield abnormality coefficient analysis module to output the quantum biofield abnormality coefficient corresponding to each part of each trainee.

[0027] It should be noted that biomagnetic field information is collected using a wearable diamond NV color center sensor array. Diamond NV color center sensors are extremely sensitive to magnetic fields. When biomagnetic fields are generated in various parts of the human body, the sensors can sense them and convert them into electrical signals. This allows the biomagnetic field intensity to be determined. Further, using a signal processing algorithm, the biomagnetic field signals are spectrally analyzed to obtain the power spectral density, which reflects the energy distribution of the signal at different frequencies. The autocorrelation function is calculated by calculating the correlation of the biomagnetic field signals at different time delays. Biomagnetic field signals fluctuate over time. For a biomagnetic field signal data sequence over a period of time, the signal sequence is multiplied by its corresponding point in the sequence after the time delay, and all products are summed and averaged to obtain an autocorrelation function.

[0028] It should also be noted that the analysis process of the quantum biofield abnormal coefficient corresponding to each part of each trainee is as follows: the biomagnetic field intensity, power spectrum density and autocorrelation function corresponding to each part of each trainee are recorded as and Substitute into the analysis formula:

[0029] The quantum biofield anomaly coefficient corresponding to each part of each trainee is obtained. Among them, π1, π2, and π3 represent the scaling factors corresponding to the biomagnetic field intensity, the power spectrum density, and the autocorrelation function, respectively, and e represents a natural constant.

[0030] A2. Collect tissue density values, muon energy loss values, and muon incident angles corresponding to various parts of each trainee, perform normalization processing, and import them into the muon bone density variation coefficient analysis module to output the muon bone density variation coefficient corresponding to various parts of each trainee.

[0031] It should be noted that the relevant data was collected using a portable muon scintillator detector, housed in a lightweight backpack. Muons are elementary particles that interact with human tissue as they pass through it, resulting in energy loss. Tissues of varying densities affect muon energy loss differently. By measuring the energy loss of muons as they pass through various parts of the body, tissue density can be inferred. Furthermore, an angle measurement device within the detector records the muon's angle of incidence, combining this information with the energy loss and angle of incidence.

[0032] It should also be noted that the coefficient of variation of the muon bone density corresponding to each part of each trainee is obtained by analyzing the above-mentioned analysis process of the quantum biofield anomaly coefficient corresponding to each part of each trainee.

[0033] A3. Acceleration, joint angles, and center of gravity offsets corresponding to various parts of each trainee are collected and normalized, and then imported into the motion posture coefficient analysis module to output the motion posture coefficients corresponding to various parts of each trainee.

[0034] It should be noted that a quantum inertial measurement unit is used to collect acceleration and joint angle information. The unit is made into a small patch-type device that can be attached to various parts of the trainee. At the same time, a pressure sensor array is installed on the ground of the training ground. The acceleration sensor in the quantum inertial measurement unit is based on the principles of quantum mechanics and can accurately measure the acceleration of an object. By integrating and processing the acceleration data, velocity and displacement information can be obtained, and then the change in joint angle can be obtained. For the collection of center of gravity offset, the pressure sensor array can measure the pressure distribution of the trainee at different positions, and the center of gravity offset can be obtained by analyzing the pressure data.

[0035] It should also be noted that the motion posture coefficients corresponding to each part of each trainee are obtained by analyzing the above-mentioned analysis process of the quantum biofield abnormality coefficients corresponding to each part of each trainee.

[0036] A4. Collect the temperature, humidity, light intensity, and wind speed corresponding to each part of each trainee, perform normalization processing, import them into the training environment coefficient analysis module, and output the training environment coefficient corresponding to each part of each trainee.

[0037] It should be noted that the smart wearable device is equipped with a small temperature and humidity sensor, a light sensor and a micro wind speed sensor to directly collect the temperature, humidity, light intensity and wind speed corresponding to each part of each trainee.

[0038] It should also be noted that the training environment coefficient corresponding to each part of each trainee is obtained by analyzing the above-mentioned analysis process of the quantum biofield anomaly coefficient corresponding to each part of each trainee.

[0039] Step 2. Prediction of personalized risks: Based on the quantum biofield anomaly index, muon bone density variation coefficient, movement posture coefficient and training environment coefficient corresponding to each part of each trainee, the safety risk value corresponding to each part of each trainee is analyzed, and the safety risk evolution trend corresponding to each part of each trainee is analyzed.

[0040] In a specific embodiment, the safety risk value corresponding to each part of each trainee is analyzed. The specific analysis process is as follows: the quantum biofield abnormality index, muon bone density variation coefficient, movement posture coefficient and training environment coefficient corresponding to each part of each trainee are recorded as and Where k represents the number corresponding to each trainee, k = 1, 2...n, n is a positive integer, g represents the number corresponding to each part, g = 1, 2...m, m is a positive integer, substitute into the calculation formula: The safety risk value corresponding to each part of each training personnel is obtained Among them, η1, η2, η3, and η4 respectively set the weight factors corresponding to the quantum biofield anomaly index of each department of the training personnel, the weight factors corresponding to the muon bone density variation coefficient, the weight factors corresponding to the movement posture coefficient, and the weight factors corresponding to the training environment coefficient.

[0041] It should be noted that η1, η2, η3, and η4 are all greater than 0 and less than 1.

[0042] It should also be noted that the quantum biofield abnormality index, muon bone density variation coefficient, movement posture coefficient, and training environment coefficient corresponding to each body part of each trainee were normalized. At the same time, a large amount of data on the quantum biofield abnormality index, muon bone density variation coefficient, movement posture coefficient, and training environment coefficient for each body part of each trainee was collected. Through PCA analysis, multiple correlated variables were converted into a small number of uncorrelated principal components. The weighting factors of each factor were then determined based on the variance contribution rate of the original variables contained in each principal component. For example, if the variance contribution rate of the quantum biofield abnormality index in a particular principal component is high, it indicates that it has a greater impact on that principal component and can be given a correspondingly higher weight.

[0043] In a specific embodiment, the safety risk evolution trend corresponding to each part of each trainee is analyzed, and the specific analysis process is as follows: the safety risk value corresponding to each part of each trainee at the current moment and the safety risk value corresponding to each part of each trainee at the nearest moment are calculated to obtain the safety risk value difference between each part of each trainee at the current moment and the part of the trainee at the nearest moment, and the safety risk value difference is compared with the safety risk value difference interval corresponding to each set safety risk evolution trend level. If the safety risk value difference is within the safety risk value difference interval corresponding to a set safety risk evolution trend level, the set safety risk evolution trend level is used as the safety risk evolution trend level corresponding to the part of the trainee. The safety risk evolution trend levels include low safety risk evolution trend level, medium safety risk evolution trend level and high safety risk evolution trend level, and according to the safety risk evolution trend level corresponding to each part of each trainee, an early warning prompt is given.

[0044] In a specific embodiment, the safety risk evolution trend level corresponding to each part of each trainee is used to provide an early warning. The specific early warning process is as follows: B1. If the safety risk evolution trend level corresponding to a part of a trainee is a low safety risk evolution trend level, a mild early warning mechanism will be activated. The trainee's smart protective gear will remind the trainee of the risk status with slight vibrations and slow flashing of the green LED light. The AR glasses will briefly display a green translucent prompt at the edge of the field of view to remind the trainee of the safety risk evolution trend level corresponding to the part of the trainee; the coach terminal will mark the risk part with a green flag icon on the monitoring screen, and push risk reminders on the mobile phone APP and management software.

[0045] B2. If the safety risk evolution trend level corresponding to a certain part of a trainee is a medium safety risk evolution trend level, a more alarming warning will be triggered. The trainee's smart protective gear will vibrate at a high frequency, flash yellow lights, and provide voice prompts. The AR glasses will highlight a yellow translucent prompt in the center of the field of view to indicate the safety risk evolution trend level corresponding to the trainee's part. A yellow flashing warning will appear on the coach's monitoring screen, and an emergency notification will pop up on the mobile phone and computer with an attached risk data report. The system will explain the risk causes and hazards in detail to the trainee and recommend reducing training intensity and suspending difficult movements.

[0046] B3. If the safety risk evolution trend level corresponding to a certain part of a trainee is a high safety risk evolution trend level, an emergency warning response will be immediately initiated. The trainee's smart protective gear will continue to vibrate strongly, issue a sharp alarm, flash red lights, and broadcast emergency warnings in a loop; the AR glasses will turn red and flash danger prompts, forcibly interrupting the training operation, and the coach and medical team terminals will simultaneously trigger a high-level alarm. The coach's monitoring screen will be covered by a red alarm. The medical team's equipment will obtain the trainee's detailed information and location for rapid support. The system will issue an order to the trainee to immediately stop all movement and wait for rescue on the spot.

[0047] Step 3: Optimize protective measures: Based on the evolution trend of safety risks corresponding to each part of each trainee, optimize and adjust the protective measures corresponding to each part of each trainee.

[0048] In a specific embodiment, the protective measures corresponding to each part of each trainee are optimized and adjusted, and the specific adjustment process is as follows: B1. If the safety risk evolution trend level corresponding to a part of a trainee is a low safety risk evolution trend level, the parameters of the smart protective gear corresponding to the part of the trainee are adjusted: the stiffness setting of the smart protective gear is increased by 10%, and the targeted electromagnetic pulse is set to the health care mode with the lowest intensity. The health care mode is a targeted electromagnetic pulse frequency of 5 Hz and an intensity of 5 mV / mm. At the same time, the training intensity adjustment value corresponding to the part of the trainee is analyzed, and adjustments are made according to the training intensity adjustment value corresponding to the part of the trainee.

[0049] B2. If the safety risk evolution trend level corresponding to a certain part of a trainee is a medium safety risk evolution trend level, the parameters of the smart protective gear corresponding to that part of the trainee will be adjusted; the stiffness setting of the smart protective gear will be increased by 30%, and the targeted electromagnetic pulse: set to a medium-intensity stiffness enhancement mode, with a targeted electromagnetic pulse frequency of 10Hz-15Hz and an intensity of 8mV / mm. At the same time, the training intensity adjustment value corresponding to that part of the trainee will be analyzed and adjusted according to the training intensity adjustment value corresponding to that part of the trainee.

[0050] B3. If the safety risk evolution trend level corresponding to a certain part of a trainee is a high safety risk evolution trend level, the parameters of the smart protective gear corresponding to that part of the trainee will be adjusted: the stiffness setting of the smart protective gear will be increased by 50%, and the targeted electromagnetic pulse will be set to the lowest intensity health care mode, with a targeted electromagnetic pulse frequency of 15Hz-20Hz and an intensity of 10mV / mm. At the same time, the training intensity adjustment value corresponding to that part of the trainee will be analyzed and adjusted according to the training intensity adjustment value corresponding to that part of the trainee.

[0051] In a specific embodiment, the training intensity adjustment value corresponding to the part of the trainee is analyzed in the following specific process: the muscle fatigue coefficient X corresponding to the part of the trainee is analyzed and substituted into the calculation formula: The training intensity adjustment value Ξ corresponding to the part of the trainee is obtained, wherein z and h are natural constants, and X′ is the set muscle fatigue coefficient threshold corresponding to the part of the trainee.

[0052] It is important to note that research reports on the relationship between bone density changes and training intensity from fields such as medicine and sports science have been consulted. Many professional studies may have analyzed bone density variation in different populations and under different training methods. The relevant data and threshold recommendations provided in these studies can be used as a reference to determine the threshold for the coefficient of variation of bone density by experts.

[0053] In a specific embodiment, the muscle fatigue coefficient corresponding to the part of the trainee is analyzed, and the specific analysis process is as follows: the diamond NV color center sensor array worn by the trainee is used to collect the muscle fatigue physical signal corresponding to the part of the trainee, and the muscle fatigue physical signal is converted into a muscle fatigue value through a mathematical model to obtain the muscle fatigue value corresponding to the part of the trainee, and the muscle fatigue value corresponding to the part of the trainee is ratio-calculated with the preset standard muscle fatigue value corresponding to the part to obtain the muscle fatigue coefficient X corresponding to the part of the trainee.

[0054] It's important to note that when muscles fatigue, the physiological state within the tissue changes, such as variations in ion concentration and accumulation of metabolic products. These changes can cause subtle variations in the local magnetic field. The electron spins of NV centers are extremely sensitive to external magnetic fields, and even slight variations in the magnetic field can cause tiny shifts in the energy levels of the NV center's electron spins. At this point, illuminating the NV center with a readout laser and detecting changes in the intensity and frequency of the excited NV fluorescence can provide the physical signature of muscle fatigue.

[0055] The present invention is implemented as follows Figure 2 As shown, a safety risk warning system for sports training includes: Step 1, deployment of venues and personnel equipment: deployment of venue equipment and personnel equipment at the target sports training venue, and then obtaining the quantum biofield anomaly coefficient, muon bone density variation coefficient, movement posture coefficient and training environment coefficient corresponding to each part of each trainee at the current moment.

[0056] Step 2. Prediction of personalized risks: Based on the quantum biofield anomaly index, muon bone density variation coefficient, movement posture coefficient and training environment coefficient corresponding to each part of each trainee, the safety risk value corresponding to each part of each trainee is analyzed, and the safety risk evolution trend corresponding to each part of each trainee is analyzed.

[0057] Step 3: Optimize protective measures: Based on the evolution trend of safety risks corresponding to each part of each trainee, optimize and adjust the protective measures corresponding to each part of each trainee.

[0058] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the scope of protection of the present invention.

Claims

1. A safety risk early warning method for sports training, characterized in that: include: Step 1: Deployment of venues, personnel and equipment: Deploy the venue equipment and personnel equipment at the target sports training venue, and then obtain the quantum biofield anomaly coefficient, muon bone density variation coefficient, movement posture coefficient and training environment coefficient corresponding to each part of each trainee at the current moment; Step 2: Prediction of personalized risks: Based on the quantum biofield abnormality index, muon bone density variation coefficient, movement posture coefficient, and training environment coefficient corresponding to each part of each trainee, the corresponding safety risk value of each part of each trainee is analyzed, and the corresponding safety risk evolution trend of each part of each trainee is analyzed; Step 3: Optimize protective measures: Based on the evolution trend of safety risks corresponding to each part of each trainee, optimize and adjust the protective measures corresponding to each part of each trainee.

2. A safety risk early warning method for sports training according to claim 1, characterized in that: The specific deployment process of the field equipment and personnel equipment at the target sports training field is as follows: Personnel and equipment deployment: Before each trainee trains at the target sports training venue, when they put on the equipment, the wearable diamond NV color center sensor array is first embedded in the lining of a special tight training suit in a modular form, covering the sports parts of the shoulders, elbows, waist, hips, knees and ankles. Each sensor node is connected by a flexible circuit to form a complete network. The portable muon scintillator detector is installed in a lightweight backpack, and the data transmission line is integrated through the backpack interface. The quantum inertial measurement unit is made into a small patch device and equipped with a low-power Bluetooth module for wireless data transmission. The metamaterial smart protective gear assists trainees in wearing the protective gear correctly. The control chip and drive device integrated in the protective gear can respond to system commands to perform local stiffness mutations and apply electromagnetic pulses. Equipment deployment on the site: Metasurface antenna arrays are arranged on the walls and ceilings around the training site. The spacing between the antenna arrays is set to 3-5 meters. The antenna arrays are connected to the data processing center in the site through a wired network. The data processing center is set up in a corner of the site and is equipped with high-performance superconducting quantum processors, servers and data storage equipment. It is connected to the sensors on the trainees and the site antenna arrays through a wireless network. At the same time, a visual monitoring screen is set up to display risk assessment results, early warning information and statistical analysis of training data in real time. In addition, metasurface holographic projection equipment is deployed in the site, and each trainee is equipped with AR glasses.

3. A safety risk early warning method for sports training according to claim 2, characterized in that: The specific acquisition process of obtaining the quantum biofield abnormality coefficient, muon bone density variation coefficient, movement posture coefficient and training environment coefficient corresponding to each part of each trainee is as follows: A1. Collect the biomagnetic field intensity, power spectrum density, and autocorrelation function corresponding to each part of each trainee, perform normalization processing, import them into the quantum biofield abnormality coefficient analysis module, and output the quantum biofield abnormality coefficient corresponding to each part of each trainee; A2. Collect tissue density values, muon energy loss values, and muon incident angles corresponding to various parts of each trainee, perform normalization processing, import them into the muon bone density variation coefficient analysis module, and output the muon bone density variation coefficient corresponding to various parts of each trainee; A3. Collect the acceleration, joint angle, and center of gravity offset corresponding to each part of each trainee, perform normalization processing, import them into the motion posture coefficient analysis module, and output the motion posture coefficient corresponding to each part of each trainee; A4. Collect the temperature, humidity, light intensity, and wind speed corresponding to each part of each trainee, perform normalization processing, import them into the training environment coefficient analysis module, and output the training environment coefficient corresponding to each part of each trainee.

4. A safety risk early warning method for sports training according to claim 3, characterized in that: The specific analysis process of analyzing the safety risk values ​​corresponding to each part of each trainee is as follows: The quantum biofield abnormality index, muon bone density variation coefficient, movement posture coefficient and training environment coefficient corresponding to each part of each trainee are recorded as and Where k represents the number corresponding to each trainee, k = 1, 2...n, n is a positive integer, g represents the number corresponding to each part, g = 1, 2...m, m is a positive integer, substitute into the calculation formula: The safety risk value corresponding to each part of each training personnel is obtained Among them, η1, η2, η3, and η4 respectively set the weight factors corresponding to the quantum biofield anomaly index of each department of the training personnel, the weight factors corresponding to the muon bone density variation coefficient, the weight factors corresponding to the movement posture coefficient, and the weight factors corresponding to the training environment coefficient.

5. A safety risk early warning method for sports training according to claim 4, characterized in that: The safety risk evolution trend corresponding to each part of each training personnel is analyzed. The specific analysis process is as follows: The difference between the safety risk value corresponding to each part of each trainee at the current moment and the safety risk value corresponding to each part of each trainee at the nearest moment is calculated to obtain the difference in safety risk value between each part of each trainee at the current moment and the part of the trainee at the nearest moment, and the safety risk value difference is compared with the safety risk value difference interval corresponding to each set safety risk evolution trend level. If the safety risk value difference is within the safety risk value difference interval corresponding to a set safety risk evolution trend level, the set safety risk evolution trend level will be used as the safety risk evolution trend level corresponding to the part of the trainee. The safety risk evolution trend levels include low safety risk evolution trend level, medium safety risk evolution trend level and high safety risk evolution trend level, and according to the safety risk evolution trend level corresponding to each part of each trainee, an early warning prompt is given.

6. A safety risk early warning method for sports training according to claim 5, characterized in that: The protective measures corresponding to each part of each training personnel are optimized and adjusted. The specific adjustment process is as follows: B1. If the safety risk evolution trend level for a certain part of a trainee is low, the parameters of the smart protective gear corresponding to that part of the trainee are adjusted: the stiffness of the smart protective gear is increased by 10%, and the targeted electromagnetic pulse is set to the lowest intensity health care mode, with a targeted electromagnetic pulse frequency of 5Hz and an intensity of 5mV / mm. At the same time, the training intensity adjustment value corresponding to that part of the trainee is analyzed and adjusted accordingly; B2. If the safety risk evolution trend level corresponding to a certain part of a trainee is a medium safety risk evolution trend level, the parameters of the smart protective gear corresponding to that part of the trainee are adjusted; the stiffness setting of the smart protective gear is increased by 30%, and the targeted electromagnetic pulse is set to a medium-intensity stiffness enhancement mode, with a targeted electromagnetic pulse frequency of 10Hz-15Hz and an intensity of 8mV / mm. At the same time, the training intensity adjustment value corresponding to that part of the trainee is analyzed and adjusted accordingly; B3. If the safety risk evolution trend level corresponding to a certain part of a trainee is a high safety risk evolution trend level, the parameters of the smart protective gear corresponding to that part of the trainee will be adjusted: the stiffness setting of the smart protective gear will be increased by 50%, and the targeted electromagnetic pulse will be set to the lowest intensity health care mode, with a targeted electromagnetic pulse frequency of 15Hz-20Hz and an intensity of 10mV / mm. At the same time, the training intensity adjustment value corresponding to that part of the trainee will be analyzed and adjusted according to the training intensity adjustment value corresponding to that part of the trainee.

7. A safety risk early warning method for sports training according to claim 6, characterized in that: The specific analysis process of the training intensity adjustment value corresponding to the part of the trainee is as follows: Analyze the muscle fatigue coefficient X corresponding to this part of the trainee and substitute it into the calculation formula: The training intensity adjustment value Ξ corresponding to the part of the trainee is obtained, wherein z and h are natural constants, and X′ is the set muscle fatigue coefficient threshold corresponding to the part of the trainee.

8. A safety risk early warning method for sports training according to claim 7, characterized in that: The specific analysis process of analyzing the muscle fatigue coefficient corresponding to the part of the trainee is as follows: Through the diamond NV color center sensor array worn by the trainee, the muscle fatigue physical signal corresponding to the part of the trainee is collected, and the signal is converted into a muscle fatigue value through a mathematical model, thereby obtaining the muscle fatigue value corresponding to the part of the trainee, and the muscle fatigue value corresponding to the part of the trainee is ratio-calculated with the preset standard muscle fatigue value corresponding to the part to obtain the muscle fatigue coefficient X corresponding to the part of the trainee.

9. A safety risk early warning method for sports training according to claim 5, characterized in that: According to the safety risk evolution trend level corresponding to each part of each training personnel, an early warning prompt is provided. The specific early warning process is as follows: B1. If the safety risk evolution trend level of a certain part of a trainee is low, a mild warning mechanism will be activated. The trainee's smart protective gear will vibrate slightly and slowly flash a green LED to remind them of the risk status. The AR glasses will briefly display a green translucent light at the edge of their field of vision to indicate the safety risk evolution trend level of that part of the trainee. The coach terminal will mark the risk areas with a green flag icon on the monitoring screen and push risk reminders to the mobile app and management software; B2. If the safety risk evolution trend level for a certain part of a trainee's body is medium, a more alarming warning will be triggered. The trainee's smart protective gear will vibrate at a high frequency, flash yellow lights, and provide voice prompts. The AR glasses will highlight a yellow translucent indicator in the center of the field of view to indicate the safety risk evolution trend level for that part of the trainee's body. The coach's monitoring screen will display a flashing yellow warning, and an emergency notification will pop up on the phone and computer with a risk data report. The system will explain the risk causes and hazards to the trainee in detail, and recommend reducing training intensity and suspending difficult movements. B3. If the safety risk evolution trend level corresponding to a certain part of a trainee is high, an emergency warning response will be immediately initiated. The trainee's smart protective gear will vibrate continuously and strongly, emit a sharp alarm, flash red lights, and broadcast emergency warnings in a loop. The entire screen of the AR glasses turns red and flashes a danger warning, forcibly interrupting the training operation. The coach and medical team terminals simultaneously trigger high-level alarms, and the coach's monitoring screen is covered by a red alarm. The medical team's equipment obtains the trainee's detailed information and location for rapid support. The system issues an order to the trainee to immediately stop all movement and wait for rescue on the spot.

10. A safety risk warning system for sports training that implements the safety risk warning method for sports training according to any one of claims 1 to 9, characterized in that: include: Site and personnel equipment deployment module: used to deploy site equipment and personnel equipment at the target sports training site, and then obtain the quantum biofield anomaly coefficient, muon bone density variation coefficient, movement posture coefficient and training environment coefficient corresponding to each part of each training personnel at the current moment; Personalized risk prediction module: used to analyze the safety risk value corresponding to each part of each trainee based on the quantum biofield abnormality index, muon bone density variation coefficient, movement posture coefficient and training environment coefficient corresponding to each part of each trainee, and analyze the safety risk evolution trend corresponding to each part of each trainee; Protective measures optimization module: used to optimize and adjust the protective measures corresponding to each part of each training personnel according to the evolution trend of safety risks corresponding to each part of each training personnel.

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