Life condition simulation system based on treatment feedback and treatment evaluation method

Abstract

The invention discloses an injury condition simulation system based on treatment feedback and a treatment evaluation method, and relates to the technical field of injury condition treatment simulation, and the system comprises a simulation definition module which generates initial vital sign parameters and natural evolution increment under a non-intervention condition according to an injury condition type defined in a treatment scene, the physical injury condition simulation dummy comprises a physical injury condition simulation dummy, a treatment detection module for analyzing and quantifying treatment actions of practicers and generating corresponding intervention signals, a central control module for calculating and outputting vital sign parameters and body surface performance parameters based on pathophysiology laws and an intervention mapping model, and a state feedback module for driving each execution unit of the physical injury condition simulation dummy. The system can analyze and score execution parameters of each treatment action of practicers, generate action quality indexes and intervention signals, provide quantifiable and comparable operation feedback for students, and map treatment action effects to dynamic update of injury states, so that vital signs and body surface manifestation change along with operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of injury treatment simulation, in particular to an injury simulation system based on treatment feedback and a treatment evaluation method. BACKGROUND

[0002] In the existing injury simulation technology, for the training of practical teaching personnel, it is usually relied on a single simulation dummy with a preset injury or a simulation device with fixed vital signs to simulate the injury, and the dynamic feedback capability of the training personnel's treatment action is lacking. In simple terms, the student can only observe the preset vital sign changes, and cannot obtain feedback information such as whether the treatment action is correct, effective, and whether it will affect the evolution of the injury.

[0003] The existing simulation system can usually only display the vital signs of the simulated patient, such as heart rate, blood pressure, and respiratory rate, according to a fixed injury evolution curve, and cannot respond to the actual operation of the student in real time. For example, in chest compression, artificial respiration, hemostasis, or drug injection operations, the existing patient simulation system cannot evaluate the execution quality of the action, nor can it feedback the evaluation results of the related action to the dynamic changes of the vital signs, so that the student lacks such a "operation-simulation feedback-correction" closed loop in the training process. Therefore, in this case, it is difficult for the personnel to determine the causal relationship between the correctness of the action and the actual treatment effect, and it is impossible to correct the operation action or improve the treatment strategy through feedback, so that the training effect is limited. SUMMARY

[0004] I) Technical problem to be solved The present application provides an injury simulation system based on treatment feedback and a treatment evaluation method, which can realize the dynamic closed loop of the treatment action of the training personnel and the simulation evolution of the injury, so that the treatment operation of the training personnel produces real-time feedback to the vital signs and body surface performance of the simulation device.

[0005] II) Technical solution To achieve the above object, the present application provides the following technical solution: an injury simulation system based on treatment feedback for controlling an injury simulation dummy, comprising: A simulation definition module sets the initial physiological parameter set of a physiological model according to the defined injury type in the treatment scene, and calculates the natural evolution increment under the condition of no intervention; A treatment detection module, according to the medical treatment process corresponding to the set injury type, at each treatment process stage, real-time collects each treatment action performed by the training personnel according to a preset time window, analyzes and quantifies each treatment action, and generates an intervention signal containing action type, action parameter and time stamp; a central control module, configured to, when the simulation simulation of the injury simulation dummy is started, take the physiological parameter set updated at the end of the previous time window as an iterative input at the beginning of each current time window, and obtain the natural evolution increment corresponding to the current time window without intervention from the simulation definition module; if an intervention signal is detected in the time window, calculate the correction effect of the intervention signal on the natural evolution increment based on the pathophysiological rule and the intervention mapping model, correct the updated physiological parameter set of the time window to generate, and output at the end of the time window as the iterative input of the next time window; map and output the corresponding vital sign parameters and body surface performance parameters based on the updated physiological parameter set; a state feedback module configured to receive the vital sign parameters and body surface performance parameters output by the central control module, map the body surface performance parameters into driving instructions, and issue the driving instructions to each execution unit of the entity injury simulation dummy, and convert the vital sign parameters into standardized data streams for real-time visual display and process data storage.

[0006] Further, the initial physiological parameter set set by the simulation definition module includes setting the blood volume, blood pressure and heart rate in the human circulatory system, setting the respiratory frequency in the human respiratory system, setting the pupil reaction in the human nervous system, and setting the initial body temperature and the amount of bleeding.

[0007] Further, the simulation definition module maps the initial physiological parameter set into a numerical form, and uses a natural evolution model without intervention to calculate the natural evolution increment of the initial physiological parameter set according to the defined injury type and pathophysiological rule, wherein the natural evolution increment is a time sequence of changes of the physiological parameter set corresponding to each type of physiological model.

[0008] Further, in the rescue detection module, according to the current injury type and the corresponding standard medical rescue process, the standard rescue action to be performed by the training personnel at each rescue process stage is determined; wherein the rescue detection module comprises a sensing and collecting unit corresponding to each type of standard rescue action, configured to acquire the action parameters of the rescue action performed by the training personnel, wherein the action parameters include the timing, intensity, position, frequency and duration of the applied rescue action.

[0009] Further, the rescue detection module compares the action parameters of the rescue action collected by the rescue detection module with the preset standard value for each rescue action at each process stage, calculates the execution quality score, and quantifies the effectiveness and deviation of each rescue action of the training personnel; After the quantitative analysis is completed, the rescue detection module converts each rescue action and its score into the intervention signal, which is used to identify the timing and effect size of the action in the rescue process; the rescue detection module generates the intervention signal at the time point when each rescue action occurs and transmits it to the central control module through the communication interface.

[0010] Further, when the injury simulation dummy starts, the central control module iteratively calculates according to the set time window during the simulation evolution, and the running logic of each current time window is specifically: Receives the physiological parameter set generated at the end of the previous time window as the initial iteration input of the current window, and receives the intervention signal generated by the rescue detection module in the current time window, including the action type, action parameters and score of each rescue action; Using the constructed intervention mapping model, the action parameters and scores of each rescue action are mapped to the intervention incremental changes of the corresponding physiological parameters; In the current time window, the intervention incremental changes are weighted and superimposed with the natural evolution increment under the condition of no intervention to update the physiological parameter set of the current time window; The physiological parameter set generated at the end of the current window is used as the iteration starting input of the next time window.

[0011] Further, the central control module introduces a discrete state mechanism, which judges whether any physiological parameter in the updated physiological parameter set reaches the set threshold after generating the updated physiological parameter set of the current window, and if so, triggers the corresponding discrete event.

[0012] Further, in the state feedback module, the updated physiological parameter set output from the central control module is received, including vital sign parameters and body surface manifestation parameters; wherein the vital sign parameters include numerical sequences of blood pressure, heart rate, respiratory rate, blood oxygen and body temperature; the body surface manifestation parameters include features that can be directly reflected on the entity injury simulation dummy; The state feedback module converts the body surface manifestation parameters into control instructions of the execution unit through a mapping function, and each control instruction contains a target value, a change rate and an execution time; wherein the mapping function defines the action corresponding to each body surface manifestation parameter and its display effect; The state feedback module converts the vital sign parameters into standardized data streams, which are output for real-time display and recording; wherein the standardization process includes unit unification, data sampling rate synchronization, waveform generation and time stamp labeling.

[0013] A rescue evaluation method suitable for rescue evaluation of an injury simulation dummy, comprising the following steps: According to the injury type defined in the rescue scene of the injury simulation dummy, the initial physiological parameter set of the physiological model is set, and the natural evolution increment of the initial physiological parameter set of the injury simulation dummy under the condition of no intervention is generated; According to the medical treatment process corresponding to the defined injury type, at each treatment process stage, each treatment action performed by the training personnel is collected in real time according to a preset time window, the action parameters of each treatment action are analyzed and quantified, and a corresponding quantitative score is generated. A rescue evaluation report for the injury simulation dummy is generated, including each action score, process stage score, overall treatment process score, and action sequence and timing correctness analysis.

[0014] III) Beneficial effects: Compared with the prior art, the present application has the following beneficial effects: The system of the present application sets the initial physiological parameters through the simulation definition module, and generates the vital sign sequence changing with time in combination with the natural evolution under the condition of no intervention, which truly reflects the natural evolution process of different injury types under the condition of no rescue, and provides a reliable simulation basis for training. The rescue detection module in the system collects the rescue actions of the training personnel in real time and quantitatively generates the intervention signal, and then the central control module acts the intervention signal on the physiological parameter evolution, realizes the dynamic adjustment of the injury state, and makes the training process present a closed-loop interaction of "rescue, injury feedback, and re-rescue", thereby enhancing the pertinence and authenticity of the practical teaching.

[0015] The physiological parameter set updated by the central control module in the system is mapped to generate the body surface performance parameters that can be used for monitoring display and physical injury simulation dummy, realizes the visual and perceptual injury demonstration, and enables the trainer to intuitively observe the relationship between the rescue effect and the injury change. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 A principle block diagram of a wound simulation system based on rescue feedback provided by the embodiment of the present application; Figure 2 In the wound simulation system based on rescue feedback provided by the embodiment of the present application, an execution flow diagram of each module is shown; Figure 3 A schematic diagram of a physical wound simulation dummy controlled by the wound simulation system based on rescue feedback provided by the embodiment of the present application; Figure 4 In the wound simulation system based on rescue feedback provided by the embodiment of the present application, a connection diagram of the state feedback module to each execution unit is shown; Figure 5 A flow diagram of a rescue evaluation method provided by the embodiment of the present application; In the figure: 100. Simulation definition module; 200. Treatment and detection module; 300. Central control module; 400. Status feedback module. Detailed Implementation

[0017] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0018] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0019] Furthermore, the terms "first" and "second" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0020] It should be noted that, where there is no conflict, the features in the embodiments of the present invention can be combined with each other.

[0021] In conventional practical training for instructors, the inventors have discovered that training often relies on simulated mannequins with fixed injury scenarios for demonstrations. These mannequins typically only display vital signs such as heart rate, blood pressure, and respiratory rate according to a fixed injury progression curve, and cannot provide real-time responses to the evolving injury based on the trainee's actual actions. This leads to the inability to assess the quality of the actions performed or to adjust the simulated vital signs in real time to reflect the treatment effect. Consequently, the trainee's treatment actions and the changes in the simulated injury state lack a closed-loop connection. Even if the actions are correct, the immediate impact on indicators such as blood pressure, heart rate, respiration, and injury progression cannot be visually observed. In other words, trainees struggle to grasp the correlation between the correctness of their actions and the actual effectiveness.

[0022] To solve the above problems, combined with Figures 1 to 5As shown, this embodiment of the invention provides an injury simulation system and treatment assessment method based on treatment feedback. The system can realize a dynamic closed loop between the treatment actions of trainees and the evolution of the simulated injury, so that the trainees' operations can generate real-time feedback on vital signs and physical manifestations. At the same time, it can quantify and score each treatment action to achieve a visual assessment of the operation effect.

[0023] In addition, the system supports personalized training in multiple injury scenarios (multiple types of injuries), provides continuous and logically consistent injury simulation, and enhances realism through the multi-dimensional dynamic performance of the injury simulation system. The corresponding treatment assessment of the training data can be used for action analysis, teaching improvement and process optimization, thereby improving the effectiveness and scientific nature of simulation teaching.

[0024] Specifically, please refer to the following: Figure 1 and Figure 2 The simulation definition module 100 is the foundation of the entire injury simulation system. It is mainly used to set the injury type according to the rescue scenario, initialize the human physiological state, and generate the injury evolution sequence under no-intervention conditions, providing basic data for feedback, evaluation and central control of subsequent rescue actions.

[0025] More specifically, the simulation definition module 100 first sets the initial physiological model parameters according to the defined injury type, such as massive hemorrhage, shock, respiratory failure or multiple injuries. These initial physiological model parameters include setting the blood volume, blood pressure and heart rate in the human circulatory system, setting the respiratory rate in the human respiratory system, setting the pupillary response in the human nervous system, and setting the initial body temperature and amount of bleeding.

[0026] Assuming a severe traumatic injury with massive hemorrhage, the initial set of physiological parameters would be as follows: blood volume could be set to be below 70% of normal; blood pressure to drop to 90 / 60 mmHg; heart rate to rise to 110 bpm; respiratory rate to increase to 24 breaths / min; pupils to be dilated; blood loss to be set at 1500 mL; and body temperature to be slightly below normal. These parameters, quantified, form the initial set of physiological parameters, which serves as input for subsequent evolutionary calculations.

[0027] Now for reference Figure 3 The figure shows an injury simulation robot controlled by the injury simulation system in an embodiment of the present invention. The injury simulation robot is designed with reference to the general characteristic levels of young men. Various physiological model parameters initially set for the robot are mapped to a unified numerical form, forming a set of physiological parameters, as shown in Table 1 below.

[0028] Table 1 Parameters of various physiological models After structuring the above set of physiological parameters, it can be used for mathematical modeling and numerical evolution calculations, ensuring that the simulation process is programmable and repeatable.

[0029] The purpose of establishing the natural evolution model in the simulation definition module 100 is to generate a sequence of injury changes over time under no-intervention conditions, reflecting the natural progression of the injury in the absence of treatment. In some embodiments of the present invention, it is constructed based on pathophysiological laws, such as decreased blood volume leading to decreased blood pressure and increased heart rate, continued bleeding leading to further reduction in blood volume, and worsening shock.

[0030] More specifically, the initial set of physiological parameters is input into the kinetic model. The kinetic equations can be derived from ordinary differential equations, discrete-time window iterations, or discrete sequences based on empirical parameters. No specific limitations are specified here.

[0031] Then, a function for the change of each physiological parameter set over time is defined. In some embodiments of the present invention, the change functions corresponding to each physiological parameter set are as follows.

[0032] A function relating blood volume changes, used to simulate bleeding or blood loss: ; in, For time Blood volume at any given time, in units as above; In the time window Internal blood loss This represents the blood volume for the next time window. Specifically, it indicates that blood volume decreases as the amount of bleeding decreases, directly affecting blood pressure and heart rate.

[0033] Regarding the blood pressure change function, blood pressure is related to blood volume and heart rate: ; in, For time Blood pressure at all times The current heart rate. This is a function for calculating blood pressure, which can be based on empirical formulas or pathophysiological models. For example, blood pressure decreases during low blood volume, and an increase in heart rate has a slight compensatory effect on systolic blood pressure. This formula can be understood as follows: blood pressure decreases as blood volume decreases, and changes in heart rate can have a certain regulatory effect on systolic blood pressure.

[0034] Regarding the heart rate change function, it is used for physiological compensatory responses: ; in, For time Heart rate at all times To set the initial heart rate, The compensation coefficient represents the strength of the effect of decreased blood volume on increased heart rate. This formula shows that a decrease in blood volume triggers a compensatory response of increased heart rate. The specific explanation and limitations of its variation formula are not provided here. It is important to note that all the above physiological parameter sets need to be calculated iteratively over time windows to form a sequence of each parameter over time.

[0035] Regarding the evolutionary calculations of time series data, in the embodiments of the invention, the values ​​of each set of physiological parameters in the current time window are typically calculated in steps of seconds or minutes, taking into account the coupling effects between various systems. For example, a decrease in blood volume affects blood pressure, and a decrease in blood pressure leads to an increase in heart rate; increased respiration affects blood oxygen saturation. After each step of the calculation is completed, the set of physiological parameters is updated to provide a basis for the next step of evolution or intervention.

[0036] The natural evolution increments it generates are time-varying sequences of physiological parameter sets corresponding to various physiological models, which can be represented as two-dimensional matrices or time series data.

[0037] Now, continuing with the injury simulation robot mentioned above, for the simulation definition module 100, the injury type "severe hemorrhage with shock" needs to be selected first. Then, the physiological parameter set is initialized, quantifying blood pressure, heart rate, blood volume, respiratory rate, pupil diameter, bleeding volume, and body temperature. Next, the physiological parameter set is input into the natural evolution model, which iteratively calculates the increase in bleeding volume, decrease in blood pressure, increase in heart rate, and changes in respiratory rate at second-step intervals, outputting time-series data to drive surface manifestations such as bleeding pump velocity, chest rise and fall, and pupil adjustment.

[0038] The desired effect can be abstractly understood as follows: trainees can observe that if no rescue is provided, the injured person's blood pressure will continue to drop, heart rate will increase, and bleeding will increase, forming a realistic evolution scenario and providing a reference for rescue actions.

[0039] In summary, it can be understood that the simulation definition module 100 defines an initial set of physiological parameters, constructs the set, calculates natural evolution using pathophysiological laws, and finally outputs time-series data and surface manifestations. It realizes the mapping from injury type to numerical physiological state and the simulation of injury evolution over time, providing fundamental data for the closed-loop evolution of subsequent treatment interventions, action assessments, and central control. Simultaneously, it can demonstrate the intuitive effects of injury development during training.

[0040] Regarding the treatment and detection module 200 in this system, its main function can be understood as follows: First, based on the injury type of the simulated patient set in the simulation definition module 100 and the corresponding standard medical treatment procedure, it determines the treatment actions that the trainee needs to perform. It then collects, analyzes, and quantifies each action of the trainee in real time, generating corresponding intervention signals. Finally, it transmits the intervention signals to the subsequent central control module 300 to drive the dynamic evolution of the injury physiological parameter set, achieving a closed-loop treatment feedback. Simply put, the treatment and detection module 200 transforms the trainee's operational behavior into quantifiable intervention data to guide the dynamic evolution of the injury simulation.

[0041] Specifically, the treatment and detection module 200 first queries the corresponding standard medical treatment process based on the current injury type, such as massive bleeding, shock, respiratory failure, fracture, etc., and breaks down each process into multiple treatment stages, each stage containing several standard treatment actions.

[0042] Taking the aforementioned case of massive bleeding accompanied by shock as an example, the standard treatment procedure is shown in Table 2 below.

[0043] Table 2 Standard Treatment Procedures Regarding the 200-level action collection module for each stage of treatment, each type of standard action corresponds to a sensor collection unit, which is used to acquire the action parameters of the trainees in real time.

[0044] More specifically, in some embodiments of the present invention, the following are included: Regarding the acquisition of timing data, namely the start and end times of actions, a timestamp synchronization device can be used to record the precise time when the action occurs. Regarding the acquisition of force: the pressure or force applied to the wound or operation site can be measured using a force sensor to measure the pressing or pulling force; Regarding position acquisition: The spatial coordinates of the action on the body or simulation device can be determined using displacement sensors; Regarding frequency acquisition: The rate of repetitive actions, such as the frequency of pressing, can be measured using an accelerometer / gyroscope to detect the speed and frequency of the action; Regarding the duration of the action: the total duration of the action.

[0045] During the execution of an action, the detection module reads sensor data and compares it with a standard action template. The comparison indicators include, but are not limited to, whether the applied force, positional accuracy, action frequency, and duration meet the standards.

[0046] Each action is then scored based on its actual execution quality, with scores ranging from 0 to 100 indicating the degree of completion. In other embodiments of the invention, a weighted average method can be used, weighting each action parameter (force, position, frequency, etc.) according to its importance to obtain a total action score. For example, the scoring formula for the pressure hemostasis action is:

[0047] ; in, To ensure the degree of matching between the actual applied force and the standard applied force, For action position matching degree, Matching degree for action duration; , and The corresponding weighting coefficient is determined based on the type of injury and is not specifically limited here.

[0048] Finally, corresponding intervention signals are generated. The intervention signals generated for each action include, but are not limited to: action type (identifying which type of rescue action), action parameters (quantitative score, intensity, location, frequency, duration), and timestamp (the precise time when the action occurred).

[0049] Whenever a rescue action occurs, an intervention signal is generated immediately to ensure that the central control module 300 can obtain the latest action information in real time and send it to the central control module 300 through the communication interface (wired or wireless) to achieve dynamic feedback.

[0050] Using the aforementioned simulated robot for bleeding and shock as an example, trainees perform a "pressure hemostasis" action on the simulated injured person. The system identifies the current injury as "massive bleeding" and enters the pressure hemostasis phase. During the first treatment phase, corresponding sensors collect action parameters: applied force 12N, correct pressure position, and pressure duration 12 seconds. The system analyzes the parameters, calculates the matching degree of each item and the total score, generates an intervention signal, and immediately sends the intervention signal to the central control module 300. The central control module 300 adjusts the physiological parameter set based on the score. After the trainee completes the action, they can observe changes in the injured person's blood pressure, bleeding volume, heart rate, and other vital signs on the simulation device, forming a closed-loop feedback.

[0051] Combination Figure 2As shown, the central control module 300 is the core technical module of the injury simulation system in this embodiment of the invention. Its core function is to dynamically update physiological parameters based on the natural evolution without intervention and intervention signals from rescue actions, thereby simulating the evolution of a real injury. Regarding the natural evolution increment, as summarized above, it can be understood as the amount by which the patient's physiological state changes naturally over time under conditions without any intervention. It originates from the initial set of physiological parameters and the pathophysiological model set by the injury simulation module. This includes examples such as the "rate of change of heart rate increasing with increased blood loss," the "rate of change of blood pressure decreasing with decreased blood volume," and the "rate of change of respiratory rate increasing with hypoxia," etc.

[0052] From a mathematical perspective, each physiological parameter has a continuously changing function, and the increment can be calculated using numerical integration within a time window.

[0053] Regarding intervention signals and intervention increments, based on the relevant description of the treatment detection module 200 above, the intervention signals originate from the treatment detection module 200 and include action type (such as chest compressions, hemostasis, artificial respiration, drug injection), action parameters (force, frequency, duration, location, etc.) and scores (action execution quality, such as whether the force meets the standard and whether the rhythm is correct).

[0054] In the intervention mapping model constructed by the central control module 300, the intervention signal is mapped to changes in physiological parameters, i.e., the intervention increment. In some embodiments of the present invention, this intervention mapping model is constructed using parameterized functions (linear or nonlinear). For example, the greater the force of chest compression, the higher the recovery increment of heart rate and blood pressure, but excessive force may have the opposite effect.

[0055] Regarding the construction logic of the intervention mapping model, it is important to note that it requires analyzing the physiological mechanism of each type of rescue action, such as hemostasis reducing bleeding and chest compressions increasing blood pressure and blood oxygenation. Then, a mathematical mapping function is established; specifically, a parameterized function is created for each type of action. This can use linear or nonlinear formulas, and can be fitted and corrected using experimental or simulated training data to ensure the model provides physiologically reasonable feedback under different action qualities. The input consists of the action parameters and scores for the corresponding rescue action, and the output is the change in the corresponding physiological parameters. Furthermore, in some embodiments, normalization and weighting of the action parameters are considered to ensure that the intervention increment is within a reasonable physiological range.

[0056] In simple terms, the central control module 300 maps the trainee's rescue actions to the injury status through intervention, thereby achieving dynamic feedback on the injury.

[0057] Specifically, the initial set of physiological parameters received by the central control module 300 is generated by the simulation definition module 100, including physiological parameters such as blood pressure, heart rate, respiratory rate, blood volume, body temperature, and pupillary response; the physiological parameter set evolved at the previous moment is the set of physiological parameters recorded in the previous time window; and the intervention signal is generated by the treatment detection module 200, including action type, action parameters (force, position, frequency, duration), timestamp, etc.

[0058] The mapping of intervention signals can be understood as each treatment action corresponding to a specific physiological effect. As shown above, the intervention mapping model can use parameterized functions to convert intervention inputs (action parameters, scores) into incremental changes in intervention.

[0059] For example, the immediate effect on blood volume is calculated as an increment of a function relating the force applied to the rescue action, the duration of the action, and the overall score. Similarly, state variables such as blood pressure, heart rate, and respiratory rate can all be incremented using a similar function. It is important to note that the central control module 300 immediately calculates the immediate increment of the action on the set of physiological parameters at the moment the intervention signal is received. Multiple actions can accumulate and affect the set of physiological parameters, forming a comprehensive intervention effect.

[0060] Regarding the integration of the natural evolution increment in the central control module 300, let's first review the explanation of the natural evolution increment. It is defined by the simulation definition module 100, which describes the evolution sequence of the injury state over time under no-intervention conditions, such as a decrease in blood pressure due to bleeding. The central control module 300 combines the intervention effect with the aforementioned natural evolution increment. This combination process can employ continuous-time integration or discrete-time accumulation to ensure the smooth evolution of the physiological parameter set over time.

[0061] Based on the above, taking chest compressions as an intervention signal for rescue actions as an example, the compression depth detected within the current time window will be... Pressing frequency and pressure point deviation The cyclic perfusion index is used as input to the intervention mapping model for calculation. ,Right now As for the incremental change in the intervention, the function used to construct the intervention mapping model can be: ; in, The calculation measures the effect of compression depth on blood pressure. A value that is too small indicates that the compression is too shallow and ineffective, while a value that is too large indicates that the compression is too deep and approaches saturation. The calculation measures the effect of compression frequency on blood pressure; too low or too high a frequency will reduce efficiency. The calculation focuses on the negative impact of location deviation; the greater the location deviation, the worse the effect.

[0062] The above examples show the intervention increment of chest compressions. This can be superimposed on the natural deterioration trend to generate the blood pressure value that the injury simulation dummy will simulate at the start of the next time window.

[0063] In some embodiments of the present invention, taking the discrete transition mechanism as an example, the system adopts a long discrete time window. That is to say, the simulation evolution is not calculated in real time and is iteratively updated according to the set time window, such as 10 seconds or 0.5 minutes. The physiological parameter set is updated in each time window.

[0064] Specifically, within each predefined time window, the physiological parameter set generated at the end of the previous window is received as the initial iteration input for this window. Intervention signals within this window are detected and collected. An intervention mapping model is used to generate incremental changes in intervention, and finally, the intervention increments are weighted and superimposed with the natural evolution increments. It is important to note that the weight settings can be adjusted according to the training scenario; for example, when the intervention effect is emphasized, the weight coefficient corresponding to the intervention signal score can be increased.

[0065] The physiological parameter set at the end of the current time window is obtained by weighted superposition of the intervention increment and the natural evolution increment. The parameter set at the end of the current time window is used as the starting input for the next time window. In this way, the state of each time window can evolve continuously, reflecting the combination of intervention and natural evolution.

[0066] Furthermore, in some embodiments of this invention, it is considered to check whether key physiological parameters exceed set thresholds after each time window. For example, if the heart rate is >180 beats / minute, an acute blood loss state is triggered; if the blood pressure is <80 / 50 mmHg, a shock state is triggered. Going further, if a discrete state event is triggered, the natural evolution increment calculation rules for the next window are adjusted. For example, in a shock state, the rate of blood pressure decline is accelerated, and the delay in the heart rate response to intervention increases, thus simulating the phased abrupt changes in severe injury.

[0067] After each time window's detection iteration, updated vital signs and surface manifestation parameters are output. Vital signs specifically include blood pressure, heart rate, blood volume, respiratory rate, and body temperature, used to dynamically reflect the evolution of the injury. Surface manifestation parameters are visualized actions that can be mapped to the simulation device, such as blood flow, skin color, and pupil size. This output data drives the status feedback module 400 to achieve dynamic representation of the physical or virtual injured person.

[0068] Using the scenario of massive hemorrhage accompanied by shock as described above, Table 3 below shows the evolution of physiological parameter sets at different time points during the training process, as well as the comparison of intervention actions, intervention effects, and natural evolution.

[0069] Table 3 Processing Flowchart In summary, it can be understood that the central control module 300 maps the intervention signal into a state variable change through a parameterized function, then superimposes it with the natural evolution, separates it from the set of physiological parameters of long-term evolution, and finally outputs a state that can directly drive the injury simulation dummy to achieve closed-loop feedback.

[0070] Finally, there is the status feedback module 400. In the status feedback module 400, the latest set of physiological parameters, including vital signs and surface manifestation parameters, is first received from the central control module 300. As explained above, vital signs include numerical sequences of blood pressure, heart rate, respiratory rate, blood oxygen saturation, and body temperature. Surface manifestation parameters include features directly reflected on the physical injury simulation dummy, such as skin color, location and amount of bleeding, pupil diameter, and chest rise and fall.

[0071] The status feedback module 400 converts the body surface parameters into control instructions for the execution unit through a mapping function. This mapping function primarily defines the action or display effect corresponding to each parameter, as shown below. Figure 4 As shown, in some embodiments of the present invention, the execution unit for control conversion is: calculating the pulse rate based on heart rate and blood pressure and driving the pulse simulator; calculating the output flow rate of the bleeding pump based on the amount and location of bleeding and driving the fluid simulation device; driving the chest wall fluctuation device based on the respiratory rate and amplitude; and driving the pupil adjustment mechanism based on the pupil diameter.

[0072] It is important to note that each control command includes the target value, rate of change, and execution time to ensure that the physical device's actions are synchronized with the physiological state.

[0073] Simultaneously, the status feedback module 400 converts vital sign parameters into a standardized data stream, which can be output for real-time display and recording according to the data interface specifications of monitoring equipment or recording systems. This standardization process includes unit unification, data sampling rate synchronization, waveform generation, and timestamp annotation, ensuring that trainees or teaching systems can directly read, display, or store physiological data.

[0074] In summary, it can be understood that the entire status feedback process maintains a closed-loop operation, that is, the evolution output of the central control module 300 is reflected in the physical device in real time through this module, while the vital signs parameter output is used for visualization and monitoring, forming a continuous and observable feedback on the evolution of the injury and the effect of intervention.

[0075] Regarding the treatment assessment method implemented in the practice of this invention, please refer to the following: Figure 5In conjunction with the aforementioned injury simulation system, the first step is S10: Based on the injury types defined in the injury simulation dummy treatment scenario, an initial set of physiological parameters for the physiological model is set, and the natural evolution increment of the initial physiological parameter set of the injury simulation dummy under no-intervention conditions is generated. This step also involves setting the initial set of physiological parameters for the physiological model based on the injury types defined in the treatment scenario, including blood volume, blood pressure, heart rate, respiratory rate, pupillary response, body temperature, and amount of bleeding, and generating initial vital sign parameters. In some embodiments of this invention, the natural evolution increment under no-intervention conditions can be optionally generated for reference, but it should be noted that this is not a necessary step because central control and state feedback are not considered.

[0076] Next, in S20: Based on the defined medical treatment procedures corresponding to the injury type, at each stage of the treatment process, the trainees' actions are collected in real time within a preset time window. The action parameters of each action are analyzed and quantified, generating a corresponding quantitative score. This step determines the key actions that trainees must perform at each stage of the treatment process based on the standard medical treatment procedures corresponding to the injury type. Data collection can be achieved using the sensors mentioned above.

[0077] The collected raw motion data is analyzed, the key parameter values ​​of each motion are calculated, the motion parameters are compared with preset standard values, and a quantitative score is generated, including indicators such as motion intensity, duration, timing correctness and operation accuracy.

[0078] The category, parameters, and quality score of each action are then integrated into a corresponding signal for unified recording and analysis. However, in pure action evaluation, this step can be directly understood as action score result data.

[0079] Finally, step S30 generates a treatment assessment report for the injury simulation dummy, including scores for each action, process stage scores, overall treatment process scores, and an analysis of the correctness of the action sequence and timing. This step summarizes the scores for each action to form individual action scores, process stage scores, and overall treatment process scores. Furthermore, the initial injury state or natural evolution increments can be referenced to analyze the potential role of actions in injury improvement or control, forming an action effectiveness analysis.

[0080] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. The scope of patent protection of the present invention shall be determined by the claims. Similarly, any equivalent structural changes made based on the description and drawings of the present invention shall also be included within the scope of protection of the present invention.

Claims

1. A injury simulation system based on treatment feedback, used to control an injury simulation dummy, characterized in that, include: The simulation definition module sets the initial set of physiological parameters for the physiological model based on the injury type defined in the treatment scenario, and calculates its natural evolution increment under no-intervention conditions. The treatment and detection module, according to the set medical treatment process corresponding to the injury type, collects each treatment action performed by the trainee in real time at each stage of the treatment process according to a preset time window, analyzes and quantifies each treatment action, and generates an intervention signal containing action type, action parameters and timestamp. The central control module is used to take the set of physiological parameters updated at the end of the previous time window as the iterative input at the beginning of each current time window when the injury simulation dummy starts the simulation, and to obtain the natural evolution increment without intervention corresponding to the current time window from the simulation definition module. If an intervention signal is detected within the time window, the correction effect of the intervention signal on the natural evolution increment is calculated based on pathophysiological laws and the intervention mapping model. The updated set of physiological parameters for the time window is then generated and output at the end of the time window as the iterative input for the next time window. Based on the updated set of physiological parameters, the corresponding vital signs parameters and body surface manifestation parameters are mapped and output. The status feedback module receives vital sign parameters and body surface performance parameters output by the central control module, maps the body surface performance parameters into driving instructions, sends them to each execution unit of the physical injury simulation dummy, and converts the vital sign parameters into a standardized data stream for real-time visualization and process data storage.

2. The injury simulation system based on treatment feedback according to claim 1, characterized in that, The initial set of physiological parameters set by the simulation definition module includes setting blood volume, blood pressure and heart rate in the human circulatory system, setting respiratory rate in the human respiratory system, setting pupil response in the human nervous system, and setting initial body temperature and bleeding volume.

3. The injury simulation system based on treatment feedback according to claim 2, characterized in that, The simulation definition module maps the initial physiological parameter set into numerical form. Using a non-interventional natural evolution model, it performs time series evolution calculations on the initial physiological parameter set according to the defined injury type and pathophysiological laws to obtain the natural evolution increment. The natural evolution increment is the change sequence of the physiological parameter set corresponding to various physiological models over time.

4. The injury simulation system based on treatment feedback according to claim 1, characterized in that, In the treatment detection module, based on the current injury type and its corresponding standard medical treatment procedure, the standard treatment actions that trainees need to perform at each stage of the treatment procedure are determined. The treatment detection module includes a corresponding sensor acquisition unit configured for each type of standard treatment action, used to acquire the action parameters of the treatment actions performed by the trainees. The action parameters include the timing, force, location, frequency and duration of the applied treatment actions.

5. The injury simulation system based on treatment feedback according to claim 4, characterized in that, The rescue and detection module collects the action parameters of the rescue actions. For each rescue action in each process stage, the rescue and detection module calculates its action parameters and compares them with preset standard values ​​to calculate the execution quality score and quantify the effectiveness and deviation of each rescue action of the trainers. After the analysis and quantification are completed, the treatment detection module converts each treatment action and its scoring result into the intervention signal, which is used to identify the timing and magnitude of the action in the treatment process; the treatment detection module generates the intervention signal at the time point of each treatment action and transmits it to the central control module through the communication interface.

6. The injury simulation system based on treatment feedback according to claim 1, characterized in that, When the injury simulation dummy is activated, the central control module performs iterative calculations according to a set time window during the simulation evolution process. The specific operating logic for each current time window is as follows: The system receives the set of physiological parameters generated at the end of the previous time window as the initial iteration input for the current window, and receives the intervention signal generated by the treatment and detection module within the current time window, including the action type, action parameters, and score of each treatment action. Using the constructed intervention mapping model, the action parameters and scores of each treatment action are mapped to the intervention increment changes of the corresponding physiological parameters; Within the current time window, the incremental changes from the intervention are weighted and superimposed with the incremental changes from natural evolution under no-intervention conditions to update the physiological parameter set for the current time window; The set of physiological parameters generated at the end of the current window is used as the starting input for the iteration of the next time window.

7. The injury simulation system based on treatment feedback according to claim 6, characterized in that, The central control module introduces a discrete state mechanism. After generating the updated set of physiological parameters for the current window, it determines whether any physiological parameter in the updated set has reached a set threshold. If it has, the corresponding discrete event is triggered.

8. The injury simulation system based on treatment feedback according to claim 3, characterized in that, The status feedback module receives an updated set of physiological parameters output from the central control module, including vital sign parameters and body surface performance parameters; wherein, the vital sign parameters include a numerical sequence of blood pressure, heart rate, respiratory rate, blood oxygen, and body temperature; and the body surface performance parameters include features that can be directly reflected on the physical injury simulation dummy. The status feedback module converts the body surface performance parameters into control instructions for the execution unit through a mapping function. Each control instruction includes a target value, a rate of change, and an execution time. The mapping function defines the action and its display effect corresponding to each body surface performance parameter. The status feedback module converts vital sign parameters into a standardized data stream, which is then output for real-time display and recording. The standardization process includes unit unification, data sampling rate synchronization, waveform generation, and timestamp annotation.

9. A method for assessing treatment, using a treatment feedback-based injury simulation system as described in any one of claims 1 to 8, characterized in that, Includes the following steps: Based on the injury types defined in the injury simulation dummy treatment scenario, an initial set of physiological parameters for the physiological model is set, and the natural evolution increment of the initial set of physiological parameters of the injury simulation dummy under no-intervention conditions is generated. According to the medical treatment process corresponding to the defined injury type, each treatment action performed by the trainee is collected in real time at each stage of the treatment process according to a preset time window. The action parameters of each treatment action are analyzed and quantified to generate a corresponding quantitative score. Generate a treatment assessment report for the injury simulation dummy, including scores for each action, process stage scores, overall treatment process scores, and analysis of the correctness of action sequence and timing.

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