Simulated live wire treatment training method based on artificial intelligence mixed display

Abstract

The invention provides a simulated live wire treatment training method based on artificial intelligence mixed display, and relates to the technical field of emergency rescue training. The method comprises the following steps: acquiring a battlefield environment simulation scene, analyzing environment parameters required by various scenes, including air temperature, light intensity, wind speed, noise intensity and smoke concentration, adjusting control parameters of various environment simulation devices, and generating an environment synchronization coefficient. In the invention, by simulating and restoring the actual combat condition, enhancing the adaptability of the trainee, and utilizing the mixed reality technology, the injury condition is accurately mapped to the dummy model, so that the trainee can intuitively perceive the trauma level in the training, the action track of the trainee in the rescue process is accurately recorded in combination with action capture, and objective quantitative analysis on the rescue operation is realized; the system dynamically adjusts the state of wound tissues, helps trainees to perceive the real-time change of the treatment effect in the training process, and provides scientific training feedback for emergency rescue workers in combination with the score of the treatment skill of the trainee.

Description

Technical Field

[0001] The present invention relates to the technical field of emergency rescue training, and in particular to a simulated fire rescue training method based on artificial intelligence mixed display. Background Art

[0002] The field of emergency rescue training technology includes emergency rescue skills training and assessment methods for emergencies, battlefield environments, disaster rescue and other special situations. The core content is to build a realistic simulation training environment so that trainees can master multiple key skills such as trauma treatment, emergency treatment, and rapid response in a highly restored crisis scene. Relying on the simulation system, through scenario simulation, human models, motion capture, interactive feedback and other technical means, trainees can intuitively learn the rescue operation process and conduct targeted training and assessment. In application scenarios such as medical rescue, fire rescue, and battlefield medical treatment, the training aims to improve the immersion, interactivity and practicality of the training. Through virtual reality, augmented reality, mixed reality, combined with physical simulation equipment, rescue personnel can conduct targeted training in different complex environments to improve emergency response capabilities and treatment levels.

[0003] Among them, the simulated fire-line rescue training method based on artificial intelligence mixed display refers to a method of using mixed reality technology to conduct high-risk trauma rescue training in a virtual fire-line environment. By constructing a digital injury simulation system, trauma scenes are generated by data entry, and different injuries and environmental conditions are virtualized in combination with human models, so that trainees can identify and analyze different types of high-risk orthopedic trauma in a mixed reality environment. During the training process, target recognition and positioning technology is used to mark the simulated trauma sites, and motion capture technology is used to record the trainees' rescue operations and analyze the standardization of their movements. The training process includes hemostasis skill training, fracture fixation operations, self-rescue and mutual rescue drills in battlefield environments, etc., and the real feeling of rescue operations is simulated through force feedback devices. The training system combines artificial intelligence data analysis technology to compare and evaluate the trainees' rescue process to ensure that the training process meets standardized treatment standards and improves the trainees' emergency treatment capabilities in high-intensity situations.

[0004] Traditional on-the-spot first-aid training methods have limitations in terms of the accuracy of injury modeling, the data analysis of the training process, and the treatment feedback mechanism. They cannot fully reproduce the emergency rescue challenges in high-risk environments. The trauma model relies on fixed preset injury scenarios and lacks a dynamic adjustment mechanism, making it difficult for trainees to experience the real-time impact of treatment on injuries during the operation, which affects the accumulation of practical operation experience. During the training process, the evaluation of treatment actions mainly relies on observation and basic interaction feedback, and cannot achieve accurate joint angle tracking and contact pressure monitoring, making it difficult to quantify the accuracy and standardization of treatment actions, resulting in the lack of a scientific basis for evaluation results, leading to limitations in the emergency response and rescue accuracy of rescue personnel in real environments, and affecting the actual rescue efficiency and treatment quality. Summary of the Invention

[0005] To solve the technical problems existing in the prior art, an embodiment of the present invention provides a simulation on-the-spot first-aid training method based on artificial intelligence hybrid display. The technical solution is as follows:

[0006] A simulation on-the-spot first-aid training method based on artificial intelligence hybrid display, comprising the following steps:

[0007] S1: Obtain a simulated battlefield environment scene, analyze the environmental parameters required for various scenarios, including temperature, light intensity, wind speed, noise intensity, and smoke concentration, adjust the control parameters of various environmental simulation devices, and generate an environmental synchronization coefficient;

[0008] S2: Based on the environmental synchronization coefficient, according to the preset injury conditions, calculate the epidermal tear radius, predict the muscle stress distribution, calculate the blood vessel rupture risk value, and use mixed reality to map the injury to a dummy to obtain a hierarchical injury coefficient;

[0009] S3: Call the hierarchical injury coefficient, use an image acquisition device to identify the angles of multiple joints of the trainee, and combine with a pressure sensor to record the contact pressure distribution, and generate a treatment action trajectory set;

[0010] S4: Call the treatment action trajectory set, analyze the deviation between the trainee and the standard action and record the degree of posture deviation, calculate the impact of each treatment action on the injury in real time, adjust the trauma tissue mapping parameters, and generate an injury change value;

[0011] S5: Call the injury change value, calculate the treatment efficiency of the trainee by analyzing the evolution result of the dummy's injury, the standardization level of the trainee's actions, and the treatment duration, evaluate the skill level, and obtain a treatment score quantity.

[0012] As a further solution of the present invention, the environmental synchronization coefficient includes an air temperature adjustment parameter, a light intensity compensation value, and a noise simulation level. The hierarchical damage coefficient specifically includes an epidermal tear radius parameter, a muscle stress distribution value, and a blood vessel rupture risk value. The set of treatment action trajectories includes a wrist joint flexion and extension trajectory, an elbow joint rotation trajectory, and a contact pressure distribution matrix. The injury condition change value specifically includes a trauma expansion rate, a blood flow change coefficient, and a posture deviation degree. The treatment score includes a treatment efficiency index, an action standardization score, and an injury condition control score.

[0013] As a further solution of the present invention, the steps of obtaining a battlefield environment simulation scenario, analyzing the environmental parameters required for various scenarios, including air temperature, light intensity, wind speed, noise intensity, and smoke concentration, and adjusting the control parameters of various environmental simulation devices to generate an environmental synchronization coefficient are specifically as follows:

[0014] S101: Obtain a battlefield environment simulation scenario, analyze various environmental parameters required for the target scenario, including air temperature, light intensity, wind speed, noise intensity, and smoke concentration, and establish a scenario environmental parameter set;

[0015] S102: Call the scenario environmental parameter set, calculate the control parameters required for various environmental simulation devices according to the real-time temperature, light intensity, wind speed, noise intensity, and smoke concentration, and establish a device control parameter set;

[0016] S103: Call the device control parameter set, adjust the control parameters of various environmental simulation devices in real time, and establish an environmental synchronization coefficient.

[0017] As a further solution of the present invention, based on the environmental synchronization coefficient, according to the preset injury conditions, calculate the epidermal tear radius, predict the muscle stress distribution, calculate the blood vessel rupture risk value, and use mixed reality to map the injury conditions onto a dummy to obtain the steps of the hierarchical damage coefficient are specifically as follows:

[0018] S201: Call the environmental synchronization coefficient, according to the preset injury conditions, obtain the elastic modulus of the skin layer and the impact momentum data, calculate the deformation radius of the skin tissue under the impact, analyze the stress distribution of the skin tear point, calculate the ratio of the impact momentum action time to the skin layer rupture threshold, calculate the epidermal tear expansion range, and establish epidermal injury parameters;

[0019] S202: Call the epidermal injury parameters, obtain the muscle fiber orientation and stress distribution coefficient of the muscle layer, calculate the stress concentration area of the muscle fiber force point, analyze the compressive deformation of the muscle tissue, calculate the maximum stress bearing ratio of the muscle tissue, and combine the force distribution and the shock wave diffusion rate to calculate the expansion trend of the muscle layer injury area, and establish a muscle injury prediction value;

[0020] S203: Invoke the muscle injury prediction value, obtain blood pressure data of the vascular layer and trauma depth parameters, calculate the compression state of the blood vessel wall, analyze the risk value of blood vessel rupture, calculate the blood flow diffusion path, and map the injury condition onto a dummy using mixed reality to establish a hierarchical injury coefficient.

[0021] As a further solution of the present invention, the steps of invoking the hierarchical injury coefficient, using an image acquisition device to identify the angles of multiple joints of the trainee, and combining with a pressure sensor to record the contact pressure distribution to generate a set of rescue action trajectories are specifically as follows:

[0022] S301: Invoke the hierarchical injury coefficient, use an image acquisition device to collect real-time action image data of the trainee, extract the real-time angle information of the wrist joint, elbow joint, and shoulder joint, and establish the trainee joint angle data;

[0023] S302: Invoke the trainee joint angle data, use time series analysis to calculate the angle change rate of the wrist joint, elbow joint, and shoulder joint, analyze the stability of each joint during the rescue process, calculate the time continuity and trajectory smoothness of the trainee's actions, and establish the trainee motion trajectory data;

[0024] S303: Invoke the trainee motion trajectory data, use multiple pressure sensors on the surface of the dummy to obtain the pressure distribution data of the contact area, and combine with the action trajectory data to establish a set of rescue action trajectories.

[0025] As a further solution of the present invention, the specific formula for analyzing the stability of each joint during the rescue process is:

[0026]

[0027] Calculate the joint motion stability parameter;

[0028] where, S k represents the joint motion stability parameter, B represents the total number of time steps, θ b represents the joint angle value at the b-th time step, θ b+1 represents the joint angle value at the next time step, Δb represents the time interval, represents the joint angle mean, b is the index of the time step, and b + 1 is the index of the next time step.

[0029] As a further solution of the present invention, the steps of invoking the set of rescue action trajectories, analyzing the deviation between the trainee and the standard action and recording the degree of posture deviation, calculating the impact of each rescue action on the injury condition in real time, adjusting the trauma tissue mapping parameters, and generating the injury condition change value are specifically as follows:

[0030] S401: Call the set of rescue action trajectories, calculate the deviation value between the joint rotation trajectory and the standard rescue posture, extract the posture deviation direction and amplitude, calculate the change trend of the student's body center of gravity position, analyze the stability of the support points, calculate the stability index of the rescue posture in combination with the rescue strength, and establish the posture deviation degree value;

[0031] S402: Call the posture deviation degree value, calculate the influence amount of the rescue contact force on the blood vessel layer rupture risk value, analyze the correlation between the hemostatic pressing force and the blood flow diffusion rate, evaluate the impact of each action of the student on the injury condition, and establish the injury impact parameter;

[0032] S403: Call the injury impact parameter, calculate the trauma recovery trend according to the real-time impact of each action on the injury condition, update the mapping parameter of the traumatized tissue, and establish the injury condition change value.

[0033] As a further solution of the present invention, the specific formula for calculating the trauma recovery trend is:

[0034]

[0035] Calculate the trauma recovery trend parameter;

[0036] Among them, R T represents the trauma recovery trend parameter, w a represents the tissue regeneration coefficient at the a-th time step, D a represents the tissue repair progress value at the a-th time step, F a represents the injury aggravation factor at the a-th time step, P a represents the injury diffusion degree at the a-th time step, T a represents the healing time increment at the a-th time step, S a represents the tissue integrity index at the a-th time step, B a represents the tissue injury reference value at the a-th time step, A represents the total number of time steps, and a is the index of the time step.

[0037] As a further solution of the present invention, the steps of calling the injury condition change value, calculating the rescue efficiency of the student, and evaluating the skill level to obtain the rescue score amount by analyzing the evolution result of the dummy injury condition, the standardization level of the student's actions, and the rescue duration are specifically as follows:

[0038] S501: Call the injury condition change value, analyze the evolution trend of the dummy injury condition by calculating the change amount of the blood flow diffusion rate and the change trend of the trauma area, and establish the injury condition evolution parameter;

[0039] S502: Invoke the injury evolution parameters, call the trajectory data of the trainee's rescue actions, compare the actual actions during the rescue process with the standard actions, quantify the deviation amplitude of the trainee's rescue posture, and establish an action standardization score.

[0040] S503: Invoke the action standardization score, combine it with the duration of the rescue task, calculate the efficiency index of the rescue operation, quantify the trainee's skill level, and establish a rescue score quantity.

[0041] As a further solution of the present invention, the specific formula for quantifying the deviation amplitude of the trainee's rescue posture is:

[0042]

[0043] Calculate the comprehensive index of posture deviation;

[0044] Where W Δ represents the numerical result for the overall difference in the trainee's rescue posture, SA z represents the actual posture position of the trainee at the z-th sampling point, SS z represents the standard posture position at the z-th sampling point, Z represents the total number of sampling points, ZA z represents the trainee's action speed value at the z-th sampling point, ZS z represents the standard action speed value at the z-th sampling point, VA z represents the trainee's action acceleration value at the z-th sampling point, VS z represents the standard action acceleration value at the z-th sampling point, and z represents the index number of the sampling point.

[0045] The beneficial effects brought by the technical solutions provided in the embodiments of the present invention at least include:

[0046] By simulating and restoring the actual combat conditions, enhancing the adaptability of the trainees, using mixed reality technology to accurately map the injuries to the dummy model, enabling the trainees to intuitively perceive the trauma levels during training, combining motion capture to accurately record the action trajectories of the trainees during the rescue process, realizing objective quantitative analysis of the rescue operations, dynamically adjusting the state of the trauma tissues, helping the trainees to perceive the real-time changes in the rescue effects during the training process, and combining the scoring of the trainees' rescue skills to provide scientific training feedback for emergency rescue personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0048] Figure 1 Schematic diagram of the workflow of the present invention;

[0049] Figure 2 Refined flowchart of S1 of the present invention;

[0050] Figure 3 Refined flowchart of S2 of the present invention;

[0051] Figure 4 Refined flowchart of S3 of the present invention;

[0052] Figure 5 Refined flowchart of S4 of the present invention;

[0053] Figure 6 Refined flowchart of S5 of the present invention. Detailed implementation manners

[0054] The technical solutions in the present invention will be described below with reference to the accompanying drawings.

[0055] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either of the two can be selected.

[0056] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the accompanying drawings and specific embodiments.

[0057] Please refer to Figure 1 , the present invention provides a technical solution, a simulation live-fire treatment training method based on artificial intelligence hybrid display, including the following steps:

[0058] S1: Obtain a battlefield environment simulation scene, analyze the environmental parameters required for various scenes, including temperature, light intensity, wind speed, noise intensity, smoke concentration, adjust the control parameters of various environmental simulation devices, and generate an environmental synchronization coefficient;

[0059] S2: Based on the environmental synchronization coefficient, according to the preset injury conditions, calculate the epidermal tear radius, predict the muscle stress distribution, calculate the blood vessel rupture risk value, and use mixed reality to map the injury to the dummy to obtain a hierarchical injury coefficient;

[0060] S3: Call the hierarchical injury coefficient, use an image acquisition device to identify the angles of multiple joints of the trainee, and combine with a pressure sensor to record the contact pressure distribution, and generate a treatment action trajectory set;

[0061] S4: Invoke the rescue action trajectory set, analyze the deviation between the trainee and the standard action, record the degree of posture deviation, calculate in real time the impact of each rescue action on the injury condition, adjust the trauma tissue mapping parameters, and generate an injury condition change value;

[0062] S5: Invoke the injury condition change value, calculate the rescue efficiency of the trainee by analyzing the evolution result of the dummy's injury condition, the standardization level of the trainee's actions, and the rescue duration, evaluate the skill level, and obtain a rescue score.

[0063] The environment synchronization coefficient includes an air temperature adjustment parameter, a light intensity compensation value, and a noise simulation level. The hierarchical injury coefficient specifically includes an epidermal tear radius parameter, a muscle stress distribution value, and a blood vessel rupture risk value. The rescue action trajectory set includes a wrist joint flexion / extension trajectory, an elbow joint rotation trajectory, and a contact pressure distribution matrix. The injury condition change value specifically includes a trauma expansion rate, a blood flow change coefficient, and a degree of posture deviation. The rescue score includes a rescue efficiency index, an action standardization score, and an injury condition control score.

[0064] Please refer to Figure 2 , obtain a battlefield environment simulation scenario, analyze the environmental parameters required for various scenarios, including air temperature, light intensity, wind speed, noise intensity, and smoke concentration, and adjust the control parameters of various environmental simulation devices. The steps to generate the environment synchronization coefficient are specifically as follows:

[0065] S101: Obtain a battlefield environment simulation scenario, analyze various environmental parameters required for the target scenario, including air temperature, light intensity, wind speed, noise intensity, and smoke concentration, and establish a scenario environmental parameter set;

[0066] When obtaining a battlefield environment simulation scenario, it is first necessary to determine the physical environmental elements involved in the target scenario, including parameters such as air temperature, light intensity, wind speed, noise intensity, and smoke concentration. Under different battlefield conditions, the initial values of each parameter can be obtained through sensor collection or historical data analysis. The temperature data is obtained by a temperature sensor, and its measured value T env usually ranges between -40°C and 50°C. In order to remove abnormal fluctuations and obtain a stable temperature trend, a moving average can be used to calculate the temperature mean T avg , and the calculation formula is as follows:

[0067]

[0068] where, T avg is the calculated temperature mean (°C), T env,t is the temperature value (°C) measured at the t-th time, and N T is the total number of temperature measurements.

[0069] For example, in five consecutive measurements, temperature values of 18°C, 19°C, 20°C, 18°C, and 17°C are obtained. Then, calculate the average temperature:

[0070]

[0071] The light intensity is measured by a photosensitive sensor, and its unit is usually expressed in lux. In the daytime environment, the light intensity may reach 10,000 lux, while at night it may be as low as 10 lux. To screen out valid data, the median L of the light intensity can be calculated med to eliminate the influence of extreme data. The wind speed measurement is collected by a wind speed sensor, with the unit of m / s. After the data is obtained, filtering processing is required to eliminate sudden abnormal values. The noise measurement is obtained through a sound level meter, with the unit of dB. In the battlefield environment, the noise is usually between 50 dB and 120 dB. Finally, the smoke concentration is measured by a photoelectric induction sensor or a laser scattering sensor, with the unit of mg / m³. By calculating the standard deviation σ of the measurement data C to evaluate the data stability, and its calculation formula is as follows:

[0072]

[0073] where σ C is the standard deviation of the smoke concentration data (mg / m³), C smoke,c is the smoke concentration value of the c-th measurement (mg / m³), C mean is the average value of the smoke concentration (mg / m³), and Nc is the total number of smoke measurements.

[0074] For example, five measured smoke concentration values are 1.2, 1.4, 1.3, 1.1, and 1.5, and the average value is

[0075]

[0076] Then calculate the standard deviation:

[0077]

[0078] After the data calculation is completed, all parameters are stored in the scene environment parameter set.

[0079] S102: Call the scene environment parameter set, and calculate the control parameters required for various environmental simulation devices according to the real-time temperature, light intensity, wind speed, noise intensity, and smoke concentration, and establish a device control parameter set;

[0080] After calling the scene environment parameter set, calculate the control parameters of the environmental simulation device according to the real-time monitoring data. The control parameter T of the temperature simulation device set is determined by the target temperature T target and the current temperature T realIt is calculated that the calculation formula is as follows:

[0081] T set = T target - T real ;

[0082] Among them, T set is the temperature adjustment value (°C), T target is the target temperature (°C), and T real is the current temperature (°C).

[0083] For example, if the target ambient temperature is set to 35°C and the current temperature is 28°C, then the calculated temperature adjustment value is

[0084] T set = 35 - 28 = 7°C;

[0085] The light intensity control parameter L set is calculated from the target light L target and the current light L real The wind speed control parameter V set is calculated from the target wind speed V target and the current wind speed V real The noise control parameter dB set is calculated from the target noise dB target and the current noise dB real The smoke concentration control parameter C set is calculated from the target concentration C target and the current concentration C real For example, if the target noise is 100 dB and the current noise is 65 dB, then the calculated noise control parameter is as follows:

[0086] dB set = 100 - 65 = 35 dB;

[0087] All calculated control parameters are stored in the device control parameter set.

[0088] S103: Call the device control parameter set to adjust the control parameters of multiple environmental simulation devices in real time and establish an environment synchronization coefficient;

[0089] After calling the device control parameter set, it is necessary to adjust the environmental simulation device in real time. The temperature simulation device uses a heating or cooling device, and the heating or cooling method is determined according to the positive or negative value of T set and calculate the heating power P heat , and the calculation formula is as follows:

[0090]

[0091] Among them, P heat is the heating power (W), Cp is the specific heat capacity of air (J / kg·K), taking 1005 J / kg·K, M air is the mass of air (kg), assumed to be 2 kg, ΔT is the temperature change (K), assumed to be 7 K, T time is the heating time (s), assumed to be 60 s.

[0092] Calculate the heating power:

[0093]

[0094] The light intensity simulation device realizes the change of light intensity by adjusting the LED output power. The wind speed simulation device adjusts the wind speed by adjusting the fan speed. The noise simulation device adjusts the noise level by controlling the speaker volume. The adjustment of smoke concentration requires controlling the spray rate of the smoke generating device. For example, if the target smoke concentration is 5 mg / m 3 and the current concentration is 1.5 mg / m 3 and the room volume is 100 m 3 and the diffusion time is 300 s, then calculate the smoke spray rate:

[0095]

[0096] Among them, R smoke is the spray rate (mg / s), V room is the scene volume (m3), C set is the required supplementary smoke concentration (mg / m3), T spread is the set diffusion time (s).

[0097] Calculate the spray rate:

[0098]

[0099] All devices are adjusted in real time according to the calculated parameters, and finally an environmental synchronization coefficient is formed to make different environmental factors change synchronously, realizing the dynamic control of environmental simulation.

[0100] Please refer to Figure 3 , based on the environmental synchronization coefficient, according to the preset injury conditions, calculate the epidermal tear radius, predict the muscle stress distribution, calculate the risk value of blood vessel rupture, and map the injury to the dummy using mixed reality. The steps to obtain the hierarchical injury coefficient are specifically as follows:

[0101] S201: Call the environmental synchronization coefficient, obtain the elastic modulus and impact momentum data of the skin layer according to the preset injury conditions, calculate the deformation radius of the skin tissue under the impact, analyze the stress distribution of the skin tear point, calculate the ratio of the impact momentum action time to the skin layer rupture threshold, calculate the epidermal tear expansion range, and establish epidermal injury parameters;

[0102] After calling the environmental synchronization coefficient, according to the preset injury conditions, the elastic modulus and impact momentum data of the skin layer need to be obtained first. The elastic modulus of the skin layer reflects the deformation ability of the skin under the action of external force. Its determination method is usually to measure the stress-strain curve through a tensile test, and then calculate the slope of its linear area. The impact momentum is calculated by the impact force and contact time. Different impact conditions, such as high-speed fragments, blunt impact, etc., will affect the size of the impact momentum. Next, calculate the deformation radius of the skin tissue under the impact. This parameter determines the distribution of the impact energy on the skin surface. Subsequently, analyze the stress distribution of the skin tear point to determine which areas are under the greatest stress. The principal stress analysis method is usually used to calculate the force at the tear point, and calculate the ratio of the impact momentum action time to the skin layer rupture threshold. This ratio determines whether the skin will rupture. If the ratio is greater than 1, it means that the impact action time exceeds the skin's tolerance. Finally, by calculating the epidermal tear extension range and combining the stress-strain relationship of the skin tissue, the tear propagation trend is determined. The following is the formula for calculating the skin tear extension range:

[0103]

[0104] Among them, A s is the extended area of ​​epidermal tear (m2), R d is the deformation radius of the skin tissue (m). For example, if a certain impact causes the skin deformation radius R d If the tear area is 0.02m, the tear area is calculated as follows:

[0105] A s =π×(0.02) 2 =0.001256m 2 ;

[0106] After the calculation is completed, the tear area and related data are stored in the epidermal damage parameter set.

[0107] S202: calling epidermal injury parameters, obtaining the direction of muscle fibers and stress distribution coefficients in the muscle layer, calculating the stress concentration area of ​​the muscle fiber stress points, analyzing the compression deformation of the muscle tissue, calculating the maximum stress bearing ratio of the muscle tissue, combining the force distribution and the shock wave diffusion rate, calculating the expansion trend of the muscle layer injury area, and establishing a muscle injury prediction value;

[0108] After invoking the epidermal injury parameters, it is necessary to further analyze the injury condition of the muscle layer. First, obtain the data of the muscle fiber orientation in the muscle layer. This data is used to determine the directionality of the muscle force. Different arrangements of muscle fibers will affect their ability to withstand external forces. Next, obtain the stress distribution coefficient, which represents the degree of uniform force in different regions of the muscle layer, and calculate the stress concentration area at the force points of the muscle fibers. Usually, the stress concentration area is often located at the intersection of muscle fibers or the center position where the impact force is received. Analyze the compression deformation of the muscle tissue and evaluate the degree of compression of the external force on the muscle tissue. If the compression deformation of the muscle tissue exceeds its physiological limit, it may lead to fiber breakage or necrosis. Further calculate the maximum stress-bearing ratio of the muscle tissue. This ratio is used to measure whether the muscle layer has exceeded its bearing limit. Finally, combine the force distribution and the shock wave diffusion rate to calculate the expansion trend of the damaged area in the muscle layer. The following is the formula for calculating the maximum stress-bearing ratio of the muscle tissue:

[0109]

[0110] Where, R s is the maximum stress-bearing ratio (dimensionless), σ v is the VonMises stress (Pa), which is used to evaluate the comprehensive force condition of the muscle layer, and σ max is the ultimate bearing stress of the muscle tissue (Pa). For example, if the VonMises stress of a certain muscle tissue is 5000 Pa and the ultimate bearing stress of the muscle is 6000 Pa, then calculate its bearing ratio as follows:

[0111]

[0112] Store the calculated maximum stress-bearing ratio and the expansion trend of the damaged area into the muscle injury prediction value.

[0113] S203: Invoke the muscle injury prediction value, obtain the blood pressure data of the blood vessel layer and the trauma depth parameter, calculate the compression state of the blood vessel wall, analyze the risk value of blood vessel rupture, calculate the blood flow diffusion path, and use mixed reality to map the injury condition onto the dummy to establish a hierarchical injury coefficient;

[0114] After invoking the muscle injury prediction value, further analyze the injury condition of the vascular layer. First, obtain the blood pressure data of the vascular layer, which is used to determine the pressure level inside the blood vessel. Combine the trauma depth parameter to analyze the force on the blood vessel in the injured area, calculate the compression state of the blood vessel wall. Usually, the circumferential stress formula is used to estimate the force on the blood vessel wall under blood pressure and analyze the risk value of blood vessel rupture. If the stress of the blood vessel wall exceeds its rupture threshold, there is a high risk of rupture. Then, calculate the blood flow diffusion path, which is used to determine the diffusion of blood to surrounding tissues after blood vessel rupture. The influencing factors include blood pressure magnitude, trauma shape, and permeability of surrounding tissues. Finally, use mixed reality technology to map the injury condition onto a dummy to provide a visual injury assessment. The following is the formula for calculating the circumferential stress of the blood vessel wall:

[0115]

[0116] Among them, σ c is the circumferential stress of the blood vessel wall (Pa), P v is the blood pressure inside the blood vessel (Pa), r v is the blood vessel radius (m), t v is the blood vessel wall thickness (m). For example, if the blood pressure P v inside a certain blood vessel is 12000 Pa, the blood vessel radius r v is 0.002 m, and the blood vessel wall thickness t v is 0.0005 m, then calculate the compression state of the blood vessel wall as follows:

[0117]

[0118] Store the calculated data such as the compression state of the blood vessel wall and the blood flow diffusion path into the hierarchical injury coefficient set.

[0119] Please refer to Figure 4 , the steps of invoking the hierarchical injury coefficient, using an image acquisition device to identify the angles of multiple joints of the trainee, combining with a pressure sensor to record the contact pressure distribution, and generating the rescue action trajectory set are specifically as follows:

[0120] S301: Invoke the hierarchical injury coefficient, use an image acquisition device to collect the action image data of the trainee in real time, extract the real-time angle information of the wrist joint, elbow joint, and shoulder joint, and establish the trainee joint angle data;

[0121] After invoking the hierarchical damage coefficient, use the image acquisition device to collect the action image data of the trainee in real time. First, install a high-frame-rate camera or a depth perception camera to obtain the overall movement of the trainee during the rescue operation. The image data captured by these devices needs to go through background filtering and human key point detection to remove irrelevant information and only extract the skeletal joint points of the trainee. Then, use skeletal tracking calculation to extract the real-time angle information of the wrist joint, elbow joint, and shoulder joint. The joint angle is usually calculated from the included angle of the vectors connecting the joint points. The calculation formula is as follows:

[0122]

[0123] Where, θ j is the j-th joint angle (°), and are the vectors of the two bone segments on both sides of the joint respectively, and are the magnitudes of the corresponding vectors. For example, if the upper arm vector of the shoulder is (3, 4) and the forearm vector is (5, 12), then the angle is calculated as follows:

[0124]

[0125] The calculated joint angle information is stored in the trainee joint angle dataset.

[0126] S302: Invoke the trainee joint angle data, use time series analysis to calculate the angle change rate of the wrist joint, elbow joint, and shoulder joint, analyze the stability of each joint during the rescue process, calculate the time continuity and trajectory smoothness of the trainee's actions, and establish the trainee movement trajectory data;

[0127] The specific formula for analyzing the stability of each joint during the rescue process is:

[0128]

[0129] Calculate the joint movement stability parameter;

[0130] Where, S k represents the joint movement stability parameter, B represents the total number of time steps, θ b represents the joint angle value at the b-th time step, θ b+1 represents the joint angle value at the next time step, Δb represents the time interval, represents the mean joint angle, b is the index of the time step, and b + 1 is the index of the next time step.

[0131] Formula:

[0132]

[0133] Detailed Explanation of the Formula and Derivation Process of Formula Calculation:

[0134] The formula is used to calculate the joint movement stability parameter, and the result is used to evaluate the angular change rate and movement smoothness of the joint during the trainee's rescue process, and to determine the action coherence and trajectory smoothness of the trainee.

[0135] Parameter Meaning and Set Values:

[0136] S k Represents the joint movement stability parameter, measuring the stability of joint movement;

[0137] B represents the total number of time steps, assumed to be 10;

[0138] θ b Represents the joint angle value at the b-th time step, in degrees, assumed to be 45, 47, 50, 48, 46, 44, 43, 42, 41, 40;

[0139] θ b+1 Represents the joint angle value at the (b + 1)-th time step, in degrees, assumed to be 47, 50, 48, 46, 44, 43, 42, 41, 40, 39;

[0140] Δb represents the time interval, in seconds, assumed to be 0.2;

[0141] Represents the average joint angle, in degrees, assumed to be 44.6.

[0142] Substitute the parameters into the formula for calculation:

[0143]

[0144] S k = 8 + 3.04 = 11.04;

[0145] The numerical result S k = 11.04 represents the joint movement stability of the trainee. A larger value indicates a faster change in joint angle and possible jitter or instability in the action, while a smaller value indicates a smoother action of the trainee. This calculation result can be used for the subsequent establishment of the trainee's movement trajectory data to further analyze the time continuity and trajectory smoothness of the rescue process.

[0146] S303: Call the trainee's movement trajectory data, use multiple pressure sensors on the surface of the dummy to obtain the pressure distribution data of the contact area, and combine the action trajectory data to establish a treatment action trajectory set;

[0147] After calling the trainee's movement trajectory data, the pressure distribution data of the contact area is obtained by using multiple pressure sensors on the surface of the dummy. First, high-precision pressure sensors are installed at key parts of the dummy to monitor the pressure applied during the rescue process in real time, and the pressure values at each contact point are extracted. Then, combined with the trainee's movement trajectory data, the force conditions of different parts during the rescue process are analyzed. For example, during cardiopulmonary resuscitation, the pressure value in the chest area needs to meet a specific range to ensure the effectiveness of the compression and avoid injury. The average pressure is calculated to measure the stability of the rescue process. The average contact pressure is defined as follows:

[0148]

[0149] where P avg,m is the average contact pressure of the m-th group of sensors (N / m 2 ), P q is the pressure value recorded by the q-th sensor (N / m2), N m is the total number of sensors in this group. For example, when the pressure values recorded by five sensors are 1000, 1200, 1100, 1050, and 1150 N / m 2 respectively, the average contact pressure is calculated as follows:

[0150]

[0151] Finally, by combining the pressure distribution data and the movement trajectory data, the force consistency during the trainee's rescue process is analyzed, and all the data is stored in the treatment action trajectory set.

[0152] Please refer to Figure 5 and call the treatment action trajectory set to analyze the deviation between the trainee and the standard action and record the degree of posture deviation, and calculate the impact of each treatment action on the injury condition in real time. The steps to adjust the trauma tissue mapping parameters and generate the injury condition change value are as follows:

[0153] S401: Call the treatment action trajectory set, calculate the deviation value between the joint rotation trajectory and the standard treatment posture, extract the posture deviation direction and deviation amplitude, calculate the change trend of the trainee's body center of gravity position, analyze the stability of the support point, and combine the rescue force to calculate the stability index of the rescue posture, and establish the value of the degree of posture deviation;

[0154] After calling the rescue action trajectory set, calculate the deviation value between the joint rotation trajectory and the standard rescue posture. First, obtain the joint movement trajectory of the standard rescue posture and use it as a reference benchmark. Extract the movement trajectory points of the trainee's wrist joint, elbow joint, and shoulder joint from the trajectory set, calculate the joint position coordinates at each time point, and calculate the deviation value between the trainee's joint rotation trajectory and the standard trajectory based on the Euclidean distance. Then, extract the posture deviation direction and deviation amplitude, determine the displacement direction of each joint relative to the standard posture during movement, and calculate the linear deviation amount of each joint from the standard trajectory. Subsequently, calculate the change trend of the trainee's body center of gravity position, obtain the contact force data of the trainee's support point, and combine the force distribution in the trainee's foot support area to calculate the offset amount of the center of gravity relative to the support point during movement to measure the stability of the trainee's posture. The formula for calculating the change trend of the center of gravity is as follows:

[0155]

[0156] Among them, ΔG is the change trend of the trainee's center of gravity (m / s), G t is the position of the trainee's center of gravity at time t (m), T g is the total calculation time (s). For example, if the changes in the center of gravity position of the trainee are 0.5 m, 0.6 m, 0.55 m, and 0.65 m in 3 s, then the change trend of the center of gravity is calculated as follows:

[0157]

[0158] Next, analyze the stability of the support point. According to the force distribution of the trainee's support point, calculate its force uniformity, judge whether the trainee maintains a stable support posture during the rescue process, and combine the rescue force to calculate the stability index of the rescue posture. The rescue force is calculated from the palm contact pressure, and all data is stored in the posture deviation degree value set.

[0159] S402: Call the posture deviation degree value, calculate the influence amount of the rescue contact force on the risk value of blood vessel layer rupture, analyze the correlation between the hemostatic pressing force and the blood flow diffusion rate, evaluate the impact of each action of the trainee on the injury condition, and establish an injury impact parameter;

[0160] After calling the posture deviation degree value, calculate the influence amount of the rescue contact force on the risk value of blood vessel layer rupture. Based on the compression state of the blood vessel wall, obtain the external rescue pressure P c and the internal blood pressure P v of the blood vessel, calculate the stress change amount of the blood vessel wall. The formula for the circumferential stress of the blood vessel wall is as follows:

[0161]

[0162] Among them, σ c is the circumferential stress of the blood vessel wall (Pa), Pv is the intravascular blood pressure (mmHg), r v is the blood vessel radius (mm), t v is the blood vessel wall thickness (mm). The set values refer to the average data of small and medium arteries in the human body: P v = 100 mmHg (13.3 kPa), r v = 1.2 mm, t v = 0.3 mm, then

[0163]

[0164] If the calculated σ c exceeds the critical stress for blood vessel rupture, the risk of blood vessel rupture is relatively high. The current calculated value indicates that the blood vessel compression is still within the safe range.

[0165] Analyze the correlation between the hemostatic pressing force P c and the blood flow diffusion rate. The blood flow diffusion rate D f measures the diffusion ability of blood in tissues, and the calculation method is as follows:

[0166]

[0167] where D f is the blood flow diffusion rate (mm 2 / s), η is the blood dynamic viscosity (set value is 3.5 mPa·s), P v is the intravascular blood pressure (13.3 kPa), P c is the rescue pressing force (set value is 12.5 kPa), and ρ is the blood density (set value is 1060 kg / m3).

[0168] Substitute the values for calculation:

[0169]

[0170] The result shows that under the current pressing force, the blood still has a very low diffusion rate, indicating that the blood flow is not completely blocked but is significantly slowed down. The pressing force still needs to be adjusted to achieve the best hemostatic effect. If P c is increased by 1.0 kPa, then D f may drop close to 0, indicating an effective hemostatic state.

[0171] S403: Invoke the injury impact parameters, calculate the trauma recovery trend according to the real-time impact of each action on the injury, update the mapping parameters of the traumatized tissue, and establish the injury change value;

[0172] The specific formula for calculating the trauma recovery trend is:

[0173]

[0174] Calculate the trauma recovery trend parameter;

[0175] Among them, R T represents the trauma recovery trend parameter, w a represents the tissue regeneration coefficient at the a-th time step, D a represents the tissue repair progress value at the a-th time step, F a represents the injury aggravation factor at the a-th time step, P a represents the injury diffusion degree at the a-th time step, T a represents the healing time increment at the a-th time step, S a represents the tissue integrity index at the a-th time step, B a represents the tissue injury reference value at the a-th time step, A represents the total number of time steps, and a is the index of the time step.

[0176] Formula:

[0177]

[0178] Detailed explanation of the formula and the derivation process of the formula calculation:

[0179] The formula is used to calculate the trauma recovery trend parameter, and the obtained result is used to evaluate the recovery of the trauma tissue and update the mapping parameters of the trauma tissue to reflect the injury changes in real time.

[0180] Parameter meaning and setting value:

[0181] w a represents the tissue regeneration coefficient at the a-th time step, reflecting the regeneration ability of tissue cells, assumed to be 1.05;

[0182] D a represents the tissue repair progress value at the a-th time step, assumed to be 80;

[0183] F a represents the injury aggravation factor at the a-th time step, measuring the adverse effect of external stimuli on wound recovery, assumed to be 0.3;

[0184] P a represents the injury diffusion degree at the a-th time step, assumed to be 12;

[0185] T a represents the healing time increment at the a-th time step, assumed to be 12;

[0186] S a represents the tissue integrity index at the a-th time step, assumed to be 85;

[0187] B aRepresents the baseline value of tissue damage at the a-th time step, assumed to be 90;

[0188] A represents the total number of time steps, assumed to be 5.

[0189] Substitute the parameters into the formula for calculation:

[0190]

[0191] R T = 6.7 + 2.236 = 8.936;

[0192] The numerical results indicate that the trend of trauma recovery is relatively good. The larger the value, the faster the wound heals, and the smaller the value, the slower the trauma recovery. This calculation result can be used for further calculation of the injury condition change value and updating of the trauma tissue mapping parameters.

[0193] Please refer to Figure 6 , call the injury condition change value, and calculate the treatment efficiency of the trainee, evaluate the skill level, and obtain the treatment score by analyzing the evolution result of the dummy's injury condition, the standardization level of the trainee's actions, and the treatment duration. The specific steps are as follows:

[0194] S501: Call the injury condition change value, analyze the evolution trend of the dummy's injury condition by calculating the change amount of the blood flow diffusion rate and the change trend of the trauma area, and establish the injury condition evolution parameters;

[0195] After calling the injury condition change value, calculate the change amount of the blood flow diffusion rate and the change trend of the trauma area. First, extract the blood flow diffusion data of the dummy's blood vessel layer, record the blood flow diffusion rate at different time points, calculate the change amount of the blood flow rate within consecutive time intervals, and judge whether the blood flow diffusion is accelerating or decelerating. Then, measure the change trend of the trauma area, record the dynamic change of the wound boundary, and calculate the area reduction rate of the trauma area within the specified time period. Subsequently, analyze the evolution trend of the dummy's injury condition, compare the relationship between the change of the blood flow diffusion rate and the change of the trauma area, and evaluate whether the injury condition is in the stage of deterioration or recovery. The calculation of the change amount of the blood flow diffusion rate is as follows:

[0196]

[0197] Among them, ΔV bf is the change amount of the blood flow diffusion rate (m / s 2 ), V bf,t and V bf,t+1 are the blood flow diffusion rates (m / s) at time t and t + 1 respectively, and ΔT is the time interval (s). For example, if in a certain measurement, the blood flow diffusion rate drops from 2.5 m / s to 2.2 m / s within 2 seconds, then the calculation of the change amount of the blood flow diffusion rate is as follows:

[0198]

[0199] If the change amount is negative, it indicates that the blood flow diffusion rate decreases and the trend of wound repair increases. All calculated data are stored in the injury evolution parameter set.

[0200] S502: Call the injury evolution parameters and the data of the trainee's treatment action trajectory. By comparing the actual actions during the treatment process with the standard actions, quantify the deviation amplitude of the trainee's rescue posture and establish an action standardization score;

[0201] The specific formula for quantifying the deviation amplitude of the trainee's rescue posture is:

[0202]

[0203] Calculate the comprehensive index of posture deviation;

[0204] Among them, W Δ represents the numerical result for the overall difference in the trainee's rescue posture, SA z represents the actual posture position of the trainee at the z-th sampling point, SS z represents the standard posture position at the z-th sampling point, Z represents the total number of sampling points, ZA z represents the trainee's action speed value at the z-th sampling point, ZS z represents the standard action speed value at the z-th sampling point, VA z represents the trainee's action acceleration value at the z-th sampling point, VS z represents the standard action acceleration value at the z-th sampling point, and z represents the index number of the sampling point.

[0205] Formula:

[0206]

[0207] Detailed explanation of the formula and the derivation process of the formula calculation:

[0208] Calculate the deviation degree of the rescue posture, and the result is used to quantify the posture accuracy during the trainee's treatment process;

[0209] Parameter meaning and set value:

[0210] SA z represents the actual posture position of the trainee at the z-th sampling point, in millimeters. Assume S is 800, 810, 815, 805, 820;

[0211] SS z represents the standard posture position at the z-th sampling point, in millimeters. Assume it is 805, 805, 810, 810, 815;

[0212] ZA zRepresents the action speed value of the trainee at the z-th sampling point, with the unit of millimeters per second, assumed to be 150, 140, 145, 155, 160;

[0213] ZS z Represents the standard action speed value at the z-th sampling point, with the unit of millimeters per second, assumed to be 145, 145, 140, 150, 155;

[0214] VA z Represents the action acceleration value of the trainee at the z-th sampling point, with the unit of millimeters per square second, assumed to be 5, 6, 4, 7, 5;

[0215] VS z Represents the standard action acceleration value at the z-th sampling point, with the unit of millimeters per square second, assumed to be 6, 5, 5, 6, 6;

[0216] Z is the total number of sampling points, assumed to be 5.

[0217] Substitute the parameters into the formula for calculation:

[0218]

[0219]

[0220] W Δ = 5 + 3 - 5.5 = 2.5;

[0221] The numerical results show that there are certain deviations between the trainee's rescue actions and the standard actions. A smaller deviation value indicates that the trainee's actions are more standardized, while a larger one means that the trainee's actions need further correction. This calculation result can be used for the calculation of action standardization scores and further optimize the accuracy of rescue actions.

[0222] S503: Call the action standardization score, combine with the duration of the treatment task, calculate the efficiency index of the treatment operation, quantify the trainee's skill level, and establish a treatment score quantity;

[0223] After calling the action standardization score, combine with the duration of the treatment task to calculate the efficiency index of the treatment operation. First, obtain the total duration of the trainee's execution of the treatment operation, record the time consumption of each key treatment step, extract the overall treatment task completion time, then calculate the efficiency of the trainee in completing each step during the treatment process, evaluate the fluency and stability of the rescue actions, and then quantify the trainee's skill level. By calculating the ratio of the standardization score of the rescue action to the treatment task completion time, determine the comprehensive index of the trainee's rescue efficiency. The calculation of the rescue efficiency index is as follows:

[0224]

[0225] Among them, E resis the efficiency index (score / second) of the treatment operation, S act is the standardized score (out of 100) of the rescue action, T res is the total duration (s) of the treatment task. For example, if a trainee's rescue score is 93.67 and the total task duration is 120 seconds, the treatment efficiency index is calculated as follows:

[0226]

[0227] Finally, in combination with the efficiency index, the treatment score of the trainee is established and stored in the dataset.

[0228] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions according to the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that includes one or more collections of available media. The available media can be magnetic media (such as floppy disks, hard disks, magnetic tapes), optical media (such as DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0229] It should be understood that the term "and / or" in this article is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context before and after.

[0230] In the present invention, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or a similar expression means any combination of these items, including any combination of single item(s) or plural item(s). For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0231] It should be understood that in various embodiments of the present invention, the sequence numbers of the above - mentioned processes do not imply the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.

[0232] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods for each specific application to implement the described functions, but such implementation should not be considered to exceed the scope of the present invention.

[0233] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the devices, apparatuses, and units described above can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated herein.

[0234] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling, direct coupling, or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

[0235] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0236] In addition, in each embodiment of the present invention, each functional unit may be integrated into a processing unit, may exist physically alone for each unit, or two or more units may be integrated into one unit.

[0237] If the above-mentioned function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0238] As described above, the above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A simulation live-fire treatment training method based on artificial intelligence hybrid display, characterized in that, The method includes: S1: Obtain a battlefield environment simulation scenario, analyze the environmental parameters required for various scenarios, including air temperature, light intensity, wind speed, noise intensity, and smoke concentration, adjust the control parameters of various environmental simulation devices, and generate an environment synchronization coefficient; S2: Based on the environment synchronization coefficient, according to the preset injury conditions, calculate the epidermal tear radius, predict the muscle stress distribution, calculate the blood vessel rupture risk value, and use mixed reality to map the injury to a dummy to obtain a hierarchical injury coefficient; S3: Invoke the hierarchical injury coefficient, use an image acquisition device to identify the angles of multiple joints of the trainee, and combine with pressure sensors to record the contact pressure distribution to generate a set of treatment action trajectories; S4: Invoke the set of treatment action trajectories, analyze the deviation between the trainee and the standard action and record the degree of posture deviation, calculate the impact of each treatment action on the injury in real time, adjust the trauma tissue mapping parameters, and generate an injury change value; S5: Invoke the injury change value, calculate the treatment efficiency of the trainee, evaluate the skill level, and obtain a treatment score by analyzing the evolution result of the dummy's injury, the standardization level of the trainee's actions, and the treatment duration.

2. The simulation live-fire treatment training method based on artificial intelligence hybrid display according to claim 1, characterized in that, The environment synchronization coefficient includes an air temperature adjustment parameter, a light intensity compensation value, and a noise simulation level. The hierarchical injury coefficient is specifically an epidermal tear radius parameter, a muscle stress distribution value, and a blood vessel rupture risk value. The set of treatment action trajectories includes a wrist joint flexion / extension trajectory, an elbow joint rotation trajectory, and a contact pressure distribution matrix. The injury change value is specifically a trauma expansion rate, a blood flow change coefficient, and a posture deviation degree. The treatment score includes a treatment efficiency index, an action standardization score, and an injury control score.

3. The simulation live-fire treatment training method based on artificial intelligence hybrid display according to claim 1, characterized in that, The steps of obtaining a battlefield environment simulation scenario, analyzing the environmental parameters required for various scenarios, including air temperature, light intensity, wind speed, noise intensity, and smoke concentration, adjusting the control parameters of various environmental simulation devices, and generating an environment synchronization coefficient are specifically as follows: S101: Obtain a battlefield environment simulation scenario, analyze the various environmental parameters required for the target scenario, including air temperature, light intensity, wind speed, noise intensity, and smoke concentration, and establish a scenario environmental parameter set; S102: Invoke the scenario environmental parameter set, calculate the control parameters required for various environmental simulation devices according to the real-time temperature, light intensity, wind speed, noise intensity, and smoke concentration, and establish a device control parameter set; S103: Invoke the device control parameter set, adjust the control parameters of various environmental simulation devices in real time, and establish an environment synchronization coefficient.

4. The simulation live-fire treatment training method based on artificial intelligence hybrid display according to claim 1, characterized in that, The steps of calculating the epidermal tear radius, predicting the muscle stress distribution, calculating the blood vessel rupture risk value based on the environment synchronization coefficient according to the preset injury conditions, and using mixed reality to map the injury to a dummy to obtain a hierarchical injury coefficient are specifically as follows: S201: Invoke the environment synchronization coefficient, obtain the elastic modulus of the skin layer and the impact momentum data according to the preset injury conditions, calculate the deformation radius of the skin tissue under the impact, analyze the stress distribution of the skin tear point, calculate the ratio of the impact momentum action time to the skin layer rupture threshold, calculate the epidermal tear expansion range, and establish epidermal injury parameters; S202: Invoke the epidermal injury parameters, obtain the muscle fiber orientation and stress distribution coefficient of the muscle layer, calculate the stress concentration area of the muscle fiber force points, analyze the compressive deformation of the muscle tissue, calculate the maximum stress bearing ratio of the muscle tissue, combine the force distribution and shock wave diffusion rate, calculate the expansion trend of the damaged area of the muscle layer, and establish a muscle injury prediction value; S203: Invoke the muscle injury prediction value, obtain the blood pressure data and trauma depth parameters of the blood vessel layer, calculate the compressive state of the blood vessel wall, analyze the risk value of blood vessel rupture, calculate the blood flow diffusion path, and use mixed reality to map the injury condition onto the dummy to establish a hierarchical injury coefficient.

5. The simulation live-fire treatment training method based on artificial intelligence hybrid display according to claim 1, characterized in that, The steps of invoking the hierarchical injury coefficient, using an image acquisition device to identify the angles of multiple joints of the trainee, and combining with a pressure sensor to record the contact pressure distribution to generate a set of treatment action trajectories are specifically as follows: S301: Invoke the hierarchical injury coefficient, use an image acquisition device to collect the action image data of the trainee in real time, extract the real-time angle information of the wrist joint, elbow joint, and shoulder joint, and establish the trainee joint angle data; S302: Invoke the trainee joint angle data, use time series analysis to calculate the angle change rate of the wrist joint, elbow joint, and shoulder joint, analyze the stability of each joint during the rescue process, calculate the time continuity and trajectory smoothness of the trainee's actions, and establish the trainee movement trajectory data; S303: Invoke the trainee movement trajectory data, use multiple pressure sensors on the surface of the dummy to obtain the pressure distribution data of the contact area, and combine with the action trajectory data to establish a set of treatment action trajectories.

6. The simulation live-fire treatment training method based on artificial intelligence hybrid display according to claim 5, wherein, The specific formula for analyzing the stability of each joint during the rescue process is: Calculate the joint movement stability parameter; Among them, S k represents the joint movement stability parameter, B represents the total number of time steps, θ b represents the joint angle value at the b-th time step, θ b+1 represents the joint angle value at the next time step, Δb represents the time interval, represents the mean joint angle, b is the index of the time step, and b + 1 is the index of the next time step.

7. The simulation first-aid training method based on artificial intelligence hybrid display according to claim 1, characterized in that, The steps of invoking the set of treatment action trajectories, analyzing the deviation between the trainee and the standard action and recording the degree of posture deviation, calculating the impact of each treatment action on the injury condition in real time, and adjusting the mapping parameters of the damaged tissue to generate an injury condition change value are specifically as follows: S401: Invoke the set of treatment action trajectories, calculate the deviation value between the joint rotation trajectory and the standard treatment posture, extract the posture deviation direction and deviation amplitude, calculate the change trend of the trainee's body center of gravity position, analyze the stability of the support point, and combine the rescue force to calculate the stability index of the rescue posture to establish a degree of posture deviation value; S402: Invoke the degree of posture deviation value, calculate the impact amount of the rescue contact force on the blood vessel layer rupture risk value, analyze the correlation between the hemostatic pressing force and the blood flow diffusion rate, evaluate the impact of each action of the trainee on the injury condition, and establish an injury condition impact parameter; S403: Invoke the injury condition impact parameter, calculate the trauma recovery trend according to the real-time impact of each action on the injury condition, update the mapping parameters of the damaged tissue, and establish an injury condition change value.

8. The simulation live-fire treatment training method based on artificial intelligence hybrid display according to claim 7, characterized in that, The specific formula for calculating the trauma recovery trend is: Calculate the trauma recovery trend parameter; Among them, R T represents the wound recovery trend parameter, w a represents the tissue regeneration coefficient at the a-th time step, D a represents the tissue repair progress value at the a-th time step, F a represents the injury aggravation factor at the a-th time step, P a represents the injury diffusion degree at the a-th time step, T a represents the healing time increment at the a-th time step, S a represents the tissue integrity index at the a-th time step, B a represents the tissue injury baseline value at the a-th time step, A represents the total number of time steps, and a is the index of the time step.

9. The simulation first-aid training method based on artificial intelligence hybrid display according to claim 1, characterized in that The steps of invoking the injury condition change value, calculating the rescue efficiency of the trainee, evaluating the skill level, and obtaining the rescue score by analyzing the evolution result of the dummy's injury condition, the standardization level of the trainee's actions, and the rescue duration are specifically as follows: S501: Invoke the injury condition change value, analyze the evolution trend of the dummy's injury condition by calculating the change amount of the blood flow diffusion rate and the change trend of the trauma area, and establish injury evolution parameters; S502: Invoke the injury evolution parameters, and call the trainee's treatment action trajectory data. By comparing the actual actions and standard actions during the treatment process, quantify the deviation range of the trainee's rescue posture, and establish an action standardization score; S503: Invoke the action standardization score, combine it with the duration of the treatment task, calculate the efficiency index of the treatment operation, quantify the trainee's skill level, and establish a treatment score.

10. The simulation live-fire treatment training method based on artificial intelligence hybrid display according to claim 9, characterized in that, The specific formula for quantifying the deviation range of the trainee's rescue posture is: Calculate the comprehensive index of posture deviation; Among them, W Δ represents the numerical result for the overall difference in the rescue postures of the trainees, SA z represents the actual posture position of the trainee at the z-th sampling point, SS z represents the standard posture position at the z-th sampling point, Z represents the total number of sampling points, ZA z represents the movement speed value of the trainee at the z-th sampling point, ZS z represents the standard movement speed value at the z-th sampling point, VA z represents the movement acceleration value of the trainee at the z-th sampling point, VS z represents the standard movement acceleration value at the z-th sampling point, z represents the index number of the sampling point.

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