Wattle wound field real-time hemostasis method based on biological information sensing technology

Abstract

The invention relates to a battle wound field real-time hemostasis method based on a biological information sensing technology, and the technical scheme comprises the steps: introducing a time sequence biological signal for modeling, and carrying out the dynamic prediction of a battle wound state; on the basis of a biosensor data flow, a multi-dimensional time sequence state space is constructed, and the multi-dimensional time sequence state space comprises a nonlinear change trend of a bleeding rate, an evolution mode of tissue physiological parameters and a dynamic evolution process of a blood coagulation process; external intervention requirements are minimized while a hemostasis strategy is dynamically adjusted through active learning; defining multi-objective optimization modeling of key objectives of hemostasis efficiency, tissue damage minimization and pressure adaptability; estimating distribution prediction of success probabilities of different hemostasis strategies; the intelligent response mechanism is composed of a nanoscale blood coagulation factor carrier and a phase change regulation and control network. An environment triggering response model is constructed, and the blood coagulation factor carrier selectively releases blood coagulation factors according to biological signals including the pH value, the temperature and the bleeding rate.

Description

Technical Field

[0001] The invention relates to a real-time hemostasis method at a war wound site, and in particular to a real-time hemostasis method at a war wound site based on a bio-information sensing technology. Background Art

[0002] Although the current real-time hemostasis method for battlefield trauma based on bioinformatics sensing technology has made breakthrough progress in accuracy, automation, adaptive regulation, etc., there are still many shortcomings and limitations. These problems are mainly concentrated in sensor performance, algorithm reliability, real-time response capability, biocompatibility, battlefield adaptability, and equipment energy consumption. First, in terms of sensor performance, although the current micro-biosensors can monitor key physiological information such as bleeding rate, platelet activity, and hemodynamic parameters in real time, due to the complex battlefield environment, the stability and accuracy of the sensor still face certain challenges. For example, optical sensors may be disturbed under extreme weather conditions (such as strong sunlight, rain and fog environment), resulting in reduced data acquisition accuracy, while flexible pressure sensors may produce errors under intense exercise or external mechanical shock, affecting the pressure calculation of the wound area. In addition, the signal attenuation problem of ultrasonic sensors in high-noise environments has not been completely solved, especially in extreme cases such as battlefield explosions and high-speed ballistic impacts, where the interference of ultrasonic signals will affect the precise positioning of bleeding points.

[0003] Secondly, in terms of algorithm reliability, the current dynamic pressure optimization model mainly relies on biomechanical finite element analysis and time series modeling, but due to the high individualization of human physiological characteristics, the algorithm's generalization ability between different individuals is still limited. For example, parameters such as vascular compliance, blood flow shear force, and tissue stiffness vary greatly between different individuals. Fixed algorithm models may not be able to accurately adapt to all combat trauma patients, resulting in inaccurate hemostatic pressure adjustment. In addition, although machine learning algorithms can optimize hemostatic strategies through historical data training, in battlefield environments, due to the high real-time requirements for data acquisition and the lack of sufficient individual physiological characteristics database, the algorithm may not be able to make the best decision quickly when facing emergencies, and there is a certain delay. In addition, the real-time response capability of the hemostatic system still has bottlenecks. Although the sensor can collect physiological signals within milliseconds, the calculation and feedback loop of the entire system still requires a certain amount of time for data processing and decision-making. Especially when dynamically adjusting the pressure, the mechanical response time of the micro-actuator is long, which may cause a lag effect in the pressure adjustment of the wound area, which is crucial for rapid hemostasis. Especially in the case of high-flow arterial rupture, even a delay of a few seconds may cause a large amount of blood loss and affect the survival rate of the wounded. Another key issue is biocompatibility. Although chitosan nanogels, reversible hemostatic materials, and intelligent coagulation factor release systems currently used for hemostasis have shown good hemostatic effects in laboratory tests, they may still cause local inflammation, immune rejection, or affect tissue repair during long-term use. For example, some nanoscale hemostatic materials may undergo physical or chemical degradation under high temperature conditions, resulting in the inability to stably control the release rate of coagulation factors, which in turn affects the hemostatic effect.

[0004] In addition, since the battlefield environment is full of bacteria and viruses, hemostatic materials may provide a breeding ground for bacteria while sealing the wound, increasing the risk of infection, and the current intelligent hemostatic system does not yet have full antibacterial and anti-infection functions. On the other hand, battlefield adaptability is also a major challenge for current technology. Although the system can adapt to a variety of trauma types, its hemostatic effect may still be affected when facing high-explosive shock waves, extreme low temperature environments (such as plateau battlefields) or high temperature and high humidity environments (such as tropical jungle combat). For example, in extremely cold conditions, the electrochemical performance of biosensors may decrease, resulting in inaccurate data readings, and in high humidity environments, flexible electronic components may become damp, affecting the durability of the equipment. Summary of the invention

[0005] The purpose of the present invention is to provide a real-time hemostasis method for combat trauma sites based on bio-information sensing technology, thereby solving some of the drawbacks and deficiencies pointed out in the background technology.

[0006] The present invention solves the above-mentioned technical problems by adopting the following technical solution, which includes the following steps:

[0007] S1. Dynamic modeling of wound biosignals:

[0008] S1.1. Introduce time series biological signal modeling to dynamically predict the state of combat trauma; based on the biosensor data stream, construct a multi-dimensional time series state space, including: nonlinear change trend of bleeding rate, evolution pattern of tissue physiological parameters, and dynamic evolution process of coagulation process;

[0009] S2. Intelligent hemostasis decision system:

[0010] S2.1. Minimize the need for external intervention while dynamically adjusting hemostasis strategies through active learning;

[0011] S2.2, define the key objectives of hemostasis efficiency, tissue damage minimization, and pressure adaptation for multi-objective optimization modeling;

[0012] S2.3. Estimation of the distribution of success probabilities of different hemostatic strategies;

[0013] S3. Intelligent response mechanism of adaptive coagulation materials:

[0014] The intelligent response mechanism is composed of nanoscale coagulation factor carriers and phase change regulation networks:

[0015] S3.1. Construct an environmental trigger response model, whereby the coagulation factor carrier selectively releases coagulation factors according to biological signals including pH value, temperature, and bleeding rate;

[0016] S3.2. The coagulation factor carrier adopts a flexible adjustment structure to adjust the hemostatic effect in time according to the changes in wound data;

[0017] S3.3, using a reversible control model to construct a coagulation factor carrier with potential thrombotic risk to intelligently reduce the coagulation strength and ensure blood flow balance;

[0018] S4. Computation-driven dynamic pressure optimization model:

[0019] S4.1. Biomechanical pressure distribution modeling: Calculate the pressure distribution points around the wound based on sensor feedback;

[0020] S4.2, pressure adaptive regulation: combining the function of intelligent coagulation materials to calculate the required effective pressure;

[0021] S4.3. Feedback closed-loop optimization: By collecting tissue response data, pressure distribution is adjusted in real time to achieve hemostasis.

[0022] Furthermore, the wound biological signal dynamic modeling method includes:

[0023] Through fluid mechanics simulation combined with time series analysis, a nonlinear bleeding rate change model is established. Based on the fusion of optical sensing and ultrasonic sensing, the wound opening size and arteriovenous damage are calculated in real time. At the same time, combined with pulse wave analysis, blood flow trends are predicted, and recurrent neural networks are used to detect the risk of secondary bleeding. The nonlinear evolution process of bleeding rate is expressed as follows:

[0024]

[0025] in:

[0026] Q(t) represents the bleeding rate at time t; α 1 represents the initial bleeding rate, which depends on the degree of vascular damage; β 1 α is the bleeding attenuation coefficient caused by vascular contraction and coagulation process; 2 Reflects the periodic effect of the pulse wave on the bleeding rate; γ 1 The blood flow pulsation frequency driven by the pulse; δ 1 A small disturbance term representing the influence of the environment and external forces.

[0027] Furthermore, the wound biological signal dynamic modeling method includes:

[0028] The local pH changes were detected by micro pH sensors, and a tissue metabolic state prediction model was established by combining infrared thermal imaging and local temperature sensing. The tissue hypoxia, microcirculatory failure and potential necrosis risks were analyzed through data-driven modeling. The physiological state evolution model is expressed as follows:

[0029] S(t)=λ 1 tanh(λ 2 pH(t)λ 3 T(t))+δ 2

[0030] in:

[0031] S(t) is the tissue metabolic activity index, which is used to evaluate tissue viability; pH(t) represents the local pH of the tissue at time t, ranging from 6.8 to 7.4; T(t) is the local tissue temperature in degrees Celsius, which is used to evaluate the microcirculatory state; λ 1 is the metabolic activity sensitivity factor, which determines the rate of change of the overall physiological state; 2 The weight of pH in tissue metabolism is determined by fitting biosensor data; λ 3 The role of temperature factors in tissue survival; 2 Represents the error correction term caused by external factors.

[0032] Furthermore, the wound biological signal dynamic modeling method includes:

[0033] Based on the dynamic analysis of coagulation factors + time series modeling, a dynamic feedback control mechanism of the coagulation process is established. Biosensor + machine learning technology is used to track platelet activation, fibrin polymerization and coagulation factor release, and the future state of the coagulation process is predicted through a recursive neural network. The coagulation process evolution model can be expressed as follows:

[0034]

[0035] in:

[0036] C(t) is the coagulation efficiency index at time t; F(t) represents the concentration of coagulation factors, which is detected in real time by the biosensor; θ 1 is the logarithmic growth factor of the coagulation process, describing the activation rate of the initial coagulation factor; θ 2 Reflects the degree of dependence of platelet response on coagulation factors; θ 3 is the rate at which the coagulation factor decays over time; θ 4 Controls the exponential decay rate of the coagulation process; δ 3 Represents interference caused by abnormal bleeding or inflammatory response.

[0037] Furthermore, the calculation-driven dynamic pressure optimization model construction method:

[0038] Based on the optimization of pressure distribution by biomechanical finite element modeling, combined with a flexible pressure sensor array, the pressure distribution in the wound area is calculated in real time; the physiological state of different tissues, including local vascular compliance, hemodynamic parameters, and tissue stiffness, is sensed, and the applied pressure is adjusted dynamically; a hierarchical pressure optimization model is built for different types of vascular injuries including arterial, venous, and capillary bleeding; and the pressure distribution function of the wound area is defined as follows:

[0039]

[0040] in:

[0041] P(x,y,t) represents the local pressure at the wound area coordinate (x,y) at time t; α 1 is the initial base pressure applied, which depends on the wound type and biomechanical properties; β 1 Control the spread of pressure around the wound; α 2 Reflects the periodic effect of pulse fluctuation on local pressure; γ 1 Represents the pulse frequency, which determines the pulsation characteristics of pressure regulation; δ 1 Represents the environmental noise term, which is used to correct the error caused by external interference.

[0042] Furthermore, the calculation-driven dynamic pressure optimization model construction method:

[0043] The intelligent coagulation material including chitosan nanogel adaptively adjusts the release rate of coagulation factors according to the bleeding situation, thereby affecting the required hemostatic pressure; the pressure is adjusted to the optimal range by real-time monitoring of platelet activity, fibrin formation rate, and local blood oxygen concentration biological parameters; the adaptive adjustment of pressure is described by the following formula:

[0044] P c (t) = λ 1 tanh(λ 2 C(t)λ 3 O(t))+δ 2

[0045] in:

[0046] P c (t) is the local regulated pressure at time t; C(t) represents the activity level of coagulation factors; O(t) is the local blood oxygen concentration; λ 1 Set the pressure adjustment range; λ 2 The weight that affects the adjustment of coagulation factor activity to pressure; 3 Control the influence of blood oxygen concentration on pressure regulation; 2 To fine-tune the correction term, individual physiological differences are taken into account.

[0047] Furthermore, the calculation-driven dynamic pressure optimization model construction method:

[0048] Combined with pulse wave sensor + blood oxygen detection, the hemostasis strategy is optimized through real-time biological signal feedback;

[0049] Specific adjustment strategies include:

[0050] If an abnormal increase in the pulse signal is detected in the wound area, the local pressure is increased;

[0051] If the blood oxygen level drops sharply, reduce the applied pressure;

[0052] If blood component monitoring shows that the coagulation process has stabilized, the pressure is gradually reduced to allow hemostasis to enter the wound repair stage;

[0053] Pressure regulation based on biological signal feedback can be described by the following model:

[0054]

[0055] in:

[0056] P d (t) represents the dynamic adjustment amplitude of local pressure; B(t) represents the pulse signal strength, which is used to evaluate the changes in blood flow; θ 1 Control the impact of pulse wave on pressure adjustment; θ2 Set the weight of the pulse signal in pressure regulation; θ 3 Set the decay rate of pressure adjustment; θ 4 The time factor that affects the adjustment rate; δ 3 Represents the external noise correction term.

[0057] The present invention is a real-time hemostasis method for battlefield trauma based on bioinformation sensing technology. Through intelligent biosensors, computationally driven pressure optimization models, dynamic analysis of coagulation factors and adaptive hemostasis strategies, it realizes an efficient, accurate and intelligent hemostasis solution in battlefield environments. Its beneficial effects include the following aspects:

[0058] Through optical, ultrasonic and micro-biosensors, the bleeding rate, blood vessel damage, coagulation factor concentration and tissue metabolic state of the wound are monitored in real time. Time series modeling and recurrent neural network (RNN) are combined to predict the risk of secondary bleeding and dynamically adjust the hemostasis strategy.

[0059] The computationally driven dynamic pressure optimization model is combined with a flexible pressure sensor to evaluate the pressure distribution in the wound area in real time, and dynamically adjust the applied pressure based on biomechanical finite element analysis. Through nano-chitosan coagulation materials and reversible hemostasis control strategies, the system can intelligently adjust the release rate of coagulation factors based on real-time monitoring of platelet activity, fibrin formation rate and local blood oxygen concentration, which can not only accelerate hemostasis, but also reduce coagulation strength when the risk of thrombosis increases, ensuring blood flow balance.

[0060] The hemostasis strategy is optimized through real-time biological signal feedback. For example, if the pulse wave in the wound area is abnormally enhanced, the system automatically increases the local pressure; if the blood oxygen drops sharply, the system reduces the applied pressure to prevent tissue necrosis; if the coagulation process is stable, the system gradually reduces the pressure to allow hemostasis to enter the repair stage. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 The present invention is a flow chart of the real-time hemostasis method at the battlefield trauma site based on the bio-information sensing technology.

[0062] Figure 2 This is a flow chart of the wound biological signal dynamic modeling method of the present invention.

[0063] Figure 3 This is a flow chart of the method for constructing a calculation-driven dynamic pressure optimization model of the present invention. DETAILED DESCRIPTION

[0064] The specific implementation modes of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0065] The real-time hemostasis method for combat trauma based on bio-information sensing technology uses the S1 step, i.e., the introduction of time series biological signal modeling, to dynamically predict the combat trauma state. Based on the biosensor data stream, a multi-dimensional time series state space is constructed to ensure the accuracy, dynamic adaptability, and personalized optimization of the hemostasis strategy. First, the nonlinear change trend of the bleeding rate is due to the fact that after the blood vessels of the wound are damaged, the blood flow is not stable, but is affected by the coupling of multiple factors such as blood pressure, vasoconstriction reaction, blood viscosity, and coagulation factors, resulting in a nonlinear change in the bleeding rate.

[0066] This method uses multimodal biosensing technology, including optical blood flow detection, ultrasonic sensing, bioelectric signal monitoring, etc., to construct a nonlinear function of the bleeding rate over time, and combines recursive neural network (RNN) or long short-term memory network (LSTM) to predict the future bleeding rate to achieve early intervention. Secondly, the evolution pattern of tissue physiological parameters refers to the dynamic changes of local traumatic tissue, including pH value, blood oxygen concentration, tissue temperature, electrolyte concentration, etc. These parameters directly affect the coagulation efficiency and tissue repair process. Through flexible pH sensors, infrared temperature detection and electrochemical sensors, the changes in tissue environment are collected in real time, and a multivariate time series model is established. The change trend of tissue state is calculated using a data-driven method to predict whether there is a risk of secondary bleeding, tissue necrosis or inflammation, ensuring that the hemostasis plan meets the metabolic needs of the tissue. Finally, the dynamic evolution of the coagulation process involves complex biochemical reactions such as platelet activation, coagulation factor release, and fibrin network formation. This process is not only affected by blood fluid dynamics, but also closely related to the wound microenvironment.

[0067] The S2 step aims to minimize the need for external intervention while dynamically adjusting the hemostasis strategy through active learning, making the hemostasis process more intelligent and adaptive, and reducing the dependence on medical staff. First, the method obtains wound status data through multimodal biosensors and uses active learning algorithms to enable the system to continuously optimize its own hemostasis decisions when facing new trauma situations, reducing the need for manual adjustments.

[0068] The core of active learning is to continuously update the model so that it can effectively learn the optimal hemostasis strategy under limited annotated data. Secondly, in order to ensure the optimality of the hemostasis scheme, this method defines multiple key objectives, including hemostasis efficiency, tissue damage minimization, pressure adaptability, etc., to construct a multi-objective optimization model. In terms of hemostasis efficiency, the system needs to ensure that blood flow loss is reduced in the shortest time, while taking into account the rate of platelet aggregation, coagulation factor release and fibrin formation; in terms of tissue damage minimization, the system needs to ensure that the hemostasis pressure does not cause local ischemic necrosis, and consider the feedback of hemodynamics and tissue physiological state to optimize the pressurization mode; in terms of pressure adaptability, the system needs to dynamically adjust the pressure of the hemostasis device so that it is sufficient to stop bleeding without causing additional damage to the surrounding healthy tissues. Finally, this method uses Bayesian optimization or probabilistic graphical models to predict the distribution of the success probability of different hemostasis strategies, comprehensively analyzes historical data, current physiological state and environmental factors, calculates the success rate distribution of each hemostasis scheme, and selects the optimal strategy for implementation.

[0069] Step S3 uses an intelligent response mechanism to make the hemostasis process highly adaptive and accurate, reduce the risk of excessive coagulation, improve hemostasis efficiency, and take into account tissue repair and blood flow balance. The intelligent response mechanism consists of nanoscale coagulation factor carriers and phase change regulation networks to ensure that the coagulation process can dynamically respond to changes in the physiological environment at the site of combat trauma, provide accurate hemostasis, and avoid secondary damage caused by blood flow obstruction. First, based on the environmental trigger response model, the system uses biosensors to monitor key parameters such as the pH value, temperature, and bleeding rate of the wound in real time, and uses intelligent algorithms to determine the current hemostasis needs, and the nanoscale coagulation factor carriers are selectively released accordingly. In the case of severe bleeding, such as a decrease in pH (tissue acidification) and a decrease in local temperature (blood loss leading to insufficient perfusion), the coagulation factor carrier will accelerate the release rate of the coagulation factor to ensure that the hemostatic material can quickly act on the wound surface. In the case of mild bleeding or slowing bleeding, the carrier reduces the release of coagulation factors to avoid local excessive coagulation leading to thrombosis.

[0070] Secondly, the coagulation factor carrier adopts a flexible adjustment structure, which enables it to adjust the hemostatic effect in time according to the changes in wound data. Unlike the traditional fixed-dose released hemostatic materials, the carrier of the present invention can sense the dynamic changes of the wound and control the coagulation factor release rate through the opening and closing of the nanopore structure. For example, when the bleeding intensifies, the microchannel openness of the carrier increases, increasing the diffusion rate of the coagulation factor, and when the bleeding slows down, the carrier gradually closes the microchannel to prevent excessive accumulation of coagulation factors. Finally, in order to prevent secondary damage, this method constructs a reversible control model so that the coagulation factor carrier has the ability to intelligently adjust the coagulation strength. When a potential thrombosis risk is detected, the system can actively reduce the activity of local coagulation factors to ensure blood flow balance. This mechanism monitors the blood flow velocity and local blood viscosity to ensure that even in a high coagulation state, sufficient blood flow can be maintained to prevent microvascular blockage after wound healing.

[0071] Step S4 makes the hemostasis process more accurate and efficient through biomechanical pressure distribution modeling, pressure adaptive regulation and feedback closed-loop optimization, and can dynamically adapt to wound changes, ensuring hemostasis while reducing tissue damage. First, in terms of biomechanical pressure distribution modeling, this method uses a high-resolution flexible pressure sensor array to collect biomechanical data around the wound in real time, including local tissue pressure distribution, vascular elasticity, tissue density, and the transmission characteristics of external pressure. Based on these data, a finite element biomechanical model of the bleeding area is systematically constructed to calculate the optimal pressure distribution point to ensure the best hemostasis effect at the lowest effective pressure, while avoiding tissue necrosis due to excessive pressure. The pressure calculation not only takes into account the direct compression of the bleeding point, but also involves the supporting role of the surrounding tissues, optimizing the pressure distribution so that the pressure can be evenly distributed and effectively guide the blood flow to stop.

[0072] Secondly, in terms of pressure adaptive regulation, this method combines the role of intelligent coagulation materials to calculate the required effective pressure to ensure hemostasis while maintaining normal blood supply to the tissue. Intelligent coagulation materials, such as nanocoagulation colloids or microfluidic hemostatic gels, can adjust their coagulation rate and adhesion according to data such as blood composition, coagulation factor activity, and blood flow velocity monitored by biosensors, thereby reducing dependence on high pressure. When the local coagulation process is detected to be accelerated, the system will appropriately reduce external pressure to avoid long-term ischemia of local tissues; when the bleeding is not completely controlled, the system will dynamically increase local pressure to reduce blood flow velocity and accelerate the role of coagulation materials, thereby improving hemostasis efficiency. Finally, in terms of feedback closed-loop optimization, this method uses a biosensor network to collect tissue response data in real time, including local blood oxygen saturation, microcirculation status, blood flow shear force, and tissue perfusion, and inputs these data into the intelligent algorithm for calculation and prediction to adjust the pressure distribution in real time, so that the hemostasis scheme is adaptive. For example, if it is detected that the local tissue oxygen saturation drops too quickly, it indicates that the pressure may be too high, and the system will automatically reduce the pressure to prevent tissue necrosis; if it is detected that the blood flow rate is still high, it means that hemostasis has not been completed, and the system will further increase the pressure while optimizing the release rate of coagulation materials. This closed-loop optimization mechanism ensures that the hemostasis system can dynamically adjust according to physiological feedback to avoid excessive pressure or insufficient hemostasis caused by fixed pressure settings.

[0073] Embodiment 1:

[0074] During a special operations mission, team member A was ambushed by the enemy while performing a reconnaissance mission. The femoral artery in his left thigh was severely torn by shrapnel, causing heavy bleeding. The tactical medical team quickly launched first aid and intervened using a real-time hemostasis method based on bioinformatics sensing technology to ensure that team member A stopped bleeding within a limited time and avoided hemorrhagic shock.

[0075] Dynamic modeling of wound biosignals and evaluation of bleeding rate

[0076] The battlefield medical staff immediately activated the intelligent hemostasis system, which acquired physiological data of the wound site through optical sensors (such as near-infrared spectroscopy) and ultrasonic sensors (such as Doppler blood flow monitoring) and calculated that the opening area of ​​the ruptured artery was 1.2 cm. 2 At the same time, the pulse wave analysis shows that the pulse frequency is 1.1Hz (66BPM). The initial blood flow rate is calculated to be Q0 = 150mL / min. At this time, the system substitutes the nonlinear bleeding rate evolution equation to calculate the predicted bleeding rate in the next 10 seconds to evaluate the urgency of hemostasis measures.

[0077] Calculation parameter settings

[0078] α 1=150mL / min, indicating the initial bleeding rate of combat injuries, arterial rupture is generally between 120-180mL / min;

[0079] β 1 =0.08s -1 , represents the adaptive contraction ability of arterial blood vessels, and this value is usually between 0.05-0.1s -1 The larger the value, the faster the coagulation;

[0080] α 2 =30mL / min, indicating the periodic effect of the pulse wave on the bleeding rate, generally 15-40mL / min;

[0081] γ 1 =2π×1.1rad / s, indicating the frequency of pulse blood flow fluctuation, corresponding to 66BPM;

[0082] δ 1 =5mL / min. Considering the disturbances that may be caused by vibration and movement in the battlefield environment, 3-10mL / min is usually taken.

[0083] Substituting the parameters into the bleeding rate equation:

[0084] Q(t)=150e -0.08t +30sin(2π×1.1t)+5

[0085] Calculation results and analysis:

[0086] At t = 5 seconds, calculate:

[0087] Q(5)=150e -0.08×5 +30sin(2π×1.1×5)+5=95.6+24.6+5=125.2mL / min

[0088] This indicates that the blood flow rate has decreased within 5 seconds, but it is still far above the safe value of arterial hemostasis of 50 mL / min. Therefore, the hemostasis strategy needs to be further adjusted.

[0089] At t = 10 seconds, calculate:

[0090] Q(10)=150e -0.08×10 +30sin(2π×1.1×10)+5=61.2+(-29.4)+5=36.8mL / min

[0091] At this point, the bleeding rate has dropped below 40 mL / min, indicating that the initial hemostatic measures are effective, but pressure still needs to be maintained to consolidate the coagulation effect.

[0092] In the first stage (0-5 seconds), the intelligent hemostasis system uses high-pressure compression hemostasis mode, applies 12kPa of pressure to the ruptured artery, and sprays nano-coagulation materials (including chitosan thrombin complex) to promote the coagulation process. In the second stage (5-10 seconds), the pressure is automatically adjusted to 8kPa to prevent excessive compression from causing local tissue ischemia. After 10 seconds, the hemostasis device enters the adaptive control mode, dynamically maintaining a stable pressurization of 5kPa according to the coagulation state and tissue blood oxygen level detected by the sensor, ensuring that blood fluidity is maintained within a reasonable range and avoiding thrombosis.

[0093] The intelligent hemostasis system continuously monitors the biological signals of the injured person A. At 15 seconds, the pulse wave monitoring found that the local pulse wave intensity in the bleeding area increased by 20%, indicating that there may be a risk of secondary bleeding. The system automatically increases the hemostasis pressure to 9kPa and releases coagulation factors again. At the same time, it adjusts the shape of the pressurized area to make the pressure more concentrated on the ruptured arterial segment to prevent new bleeding events caused by damage to new blood vessels. After 25 seconds of real-time regulation by the intelligent hemostasis system, the bleeding rate of the injured person A steadily dropped to 12mL / min, which is close to the safety threshold. Through this method, the tactical medical team successfully controlled the arterial bleeding in a short period of time, ensuring that the wounded survived within the "golden time" and received subsequent medical treatment.

[0094] The next stage of the task of this embodiment is to evaluate the viability of tissues, predict the risk of tissue necrosis, and dynamically adjust the treatment plan to ensure that the wounded can wait safely for evacuation to the field hospital. At this time, the intelligent hemostasis system begins to enter the stage of tissue physiological state monitoring and metabolic evaluation. It uses micro pH sensors, infrared thermal imaging and local temperature sensors to establish a tissue metabolic state prediction model to analyze the hypoxia, microcirculatory failure and potential necrosis risk in the wound area, and provide intelligent intervention solutions. In the integrated module of the intelligent hemostasis system, the micro pH sensor begins to detect the local pH of the wound to evaluate the metabolic environment of the tissue. The sensor measured the pH value of the wound of team member A as pH0 = 7.2, indicating that the tissue is still in the normal metabolic range (between 6.8-7.4), but due to trauma and local bleeding, there is a possibility of further acidification. At the same time, the infrared thermal imaging system begins to scan the temperature of the wound area. The sensor measures the temperature around the wound as T0 = 33.5 ° C, which is 4 ° C lower than the normal skin temperature (36.5-37 ° C), indicating that the local blood perfusion is insufficient and may cause tissue hypoxia. At this point, the system starts the tissue metabolic state prediction model and substitutes the physiological data to calculate the tissue viability index St.

[0095] Calculation parameter settings:

[0096] λ 1 =1.5, metabolic activity sensitivity factor, ranging from 1.2 to 1.8, with larger values ​​indicating that the tissue is more sensitive to metabolic changes; 2=0.8, pH influence factor on metabolism, ranging from 0.6 to 1.0, the higher the value, the greater the impact of pH fluctuations on tissue metabolism; 3 =0.5, the influence weight of temperature factor, the value range is 0.4-0.7, the larger the value, the higher the ability of temperature change to regulate tissue metabolism; δ 2 =0.05, representing a small correction term for environmental interference, ranging from 0.02-0.08.

[0097] Calculate the metabolic activity index of S0 tissue:

[0098] S(0)=1.5tanh(0.8×7.20.5×33.5)+0.05

[0099] S(0)=1.5tanh(5.7616.75)+0.05

[0100] S(0)=1.5tanh(-10.99)+0.05

[0101] Since tanh-10.99≈-1, then

[0102] S(0)=1.5×(-1)+0.05=-1.45

[0103] The calculation results show that S0 = -1.45, which is close to the danger zone of tissue metabolic failure (the normal tissue metabolic activity index ranges from -1.0 to 1.0). Below -1.0 indicates that the local tissue may be in a state of severe hypoxia and the treatment strategy needs to be adjusted immediately. Based on this result, the intelligent hemostasis system adjusts the local pressure to 5kPa to restore microcirculation, and starts the release of thermosensitive coagulation hydrogel to reduce the impact of local temperature drop. At the same time, the system detects that the pH value of team member A is gradually decreasing, and it is expected to drop to 6.9 after t = 5min, further increasing the risk of tissue metabolic disorders. Therefore, the intelligent system actively releases a small amount of sodium bicarbonate buffer solution to maintain local acid-base balance and monitor the real-time changes in pH.

[0104] Calculation of tissue state at t = 5 min:

[0105] Assume that after adjustment by the intelligent system, the pH value rises back to pH5 = 7.0 and the local temperature rises to T5 = 35.0°C. The system calculates S5 again:

[0106] S(5)=1.5tanh(0.8×7.00.5×35.0)+0.05

[0107] S(5)=1.5tanh(5.617.5)+0.05

[0108] S(5)=1.5tanh(-11.9)+0.05

[0109] Since tanh-11.9≈-1, then:

[0110] S(5)=1.5×(-1)+0.05=-1.45

[0111] The calculation results showed that despite the increase in local temperature, the tissue metabolic activity index had not returned to a safe range, indicating that the local tissue was still in a metabolic depression and required further intervention. Based on the feedback, the system further reduced the hemostatic pressure to 4kPa and simultaneously activated the oxygen-releasing microcapsules to increase the local oxygenation level and reduce the risk of tissue necrosis.

[0112] After 10 minutes of continuous dynamic monitoring and intervention by the intelligent hemostasis system, the local temperature of team member A recovered to 36.2°C, the pH value stabilized at 7.1, and the tissue metabolic activity index rose to S10 = -0.6, entering the safe range (> -1.0). Finally, the system maintained the hemostasis pressure at 3.5 kPa, restoring the microcirculation to the optimal state, and automatically entered the maintenance mode, waiting for medical evacuation.

[0113] In this embodiment, the tactical medical team still needs to further ensure the smooth completion of the coagulation process to prevent secondary bleeding and microthrombosis. At this time, the intelligent system enters the dynamic feedback control stage of the coagulation process. Based on biosensing + machine learning technology, it tracks platelet activation, fibrin polymerization and coagulation factor release in real time, and uses recursive neural networks (RNN) to predict the future state of the coagulation process to dynamically optimize the release of hemostatic materials and local pressure regulation. The system calculates the coagulation efficiency index Ct at the wound site through time series modeling, and adjusts the hemostatic intervention measures in real time to ensure the best hemostatic effect.

[0114] The coagulation monitoring module of the intelligent hemostasis system begins to activate, and obtains the coagulation factor concentration, platelet aggregation rate and fibrin formation of the local blood through micro-biosensors. The initial test data are as follows:

[0115] Coagulation factor concentration: F(0) = 4.5 U / mL (normal range 3-7 U / mL), indicating that the initial coagulation process has been initiated but has not yet stabilized; platelet aggregation rate: 120106 / μL (normal range 100-400106 / μL), still within the safe range, but requires further observation; fibrin formation rate: 0.85 mg / dL / min (normal is 0.5-1.2 mg / dL / min), indicating that coagulation is ongoing, but the risk of excessive coagulation needs to be monitored.

[0116] Calculation parameter settings:

[0117] θ 1=2.0, logarithmic growth factor of coagulation process, ranging from 1.5 to 2.5, with higher values ​​indicating faster platelet activation rate;

[0118] θ 2 =0.7, the degree of platelet response to coagulation factors, ranging from 0.5 to 0.9, with higher values ​​indicating that platelet aggregation is more dependent on coagulation factors;

[0119] θ 3 =1.2, the decay rate of coagulation factors over time, ranging from 1.0-1.5, affects the consumption rate of coagulation factors;

[0120] θ 4 =0.09s -1 , exponential decay rate, range 0.05-0.12s -1 , affecting the duration of the coagulation process;

[0121] δ 3 =0.3, interference from abnormal bleeding or inflammatory response, range 0.1-0.5, larger values ​​represent higher external interference, such as wound infection or hemostasis failure.

[0122] Calculate the C0 initial coagulation efficiency index:

[0123] C(0)=2.0ln(1+0.7×4.5)1.2e -0.09×0 +0.3

[0124] C(0)=2.0ln(1+3.15)1.2e 0 +0.3

[0125] C(0)=2.0ln(4.15)1.2+0.3

[0126] C(0)=2.0×1.421.2+0.3=2.841.2+0.3=1.94

[0127] The initial coagulation efficiency index C0 = 1.94, indicating that the coagulation process is ongoing, but has not yet reached the ideal hemostasis state (generally, Ct>2.5 is required to consider that hemostasis is basically completed). The system optimizes the hemostasis strategy based on this calculation result:

[0128] Release additional coagulation factors: The system releases an appropriate amount of 0.5U / mL of artificial coagulation factors to increase the local Ft to 5.0U / mL and accelerate platelet aggregation. Adjust external hemostasis pressure: The intelligent hemostasis device reduces the local pressure to 4.5kPa to ensure that platelets will not be destroyed due to excessive pressure and affect the coagulation process. Monitor the consumption rate of coagulation factors: Continuously track the fibrin formation rate to prevent the coagulation process from being interrupted due to insufficient coagulation factors.

[0129] Calculation of coagulation state at t = 10 seconds

[0130] After 10 seconds of adjustment, the new sensor data:

[0131] F10 = 5.0 U / mL, which means that the coagulation factor has increased and entered a stable range;

[0132] The platelet aggregation rate increased to 135106 / μL, indicating enhanced coagulation activity;

[0133] The fibrin formation rate reached 1.05 mg / dL / min, entering the optimal coagulation state.

[0134] Calculate C10:

[0135] C(10)=2.0ln(1+0.7×5.0)1.2e -0.09×10 +0.3

[0136] C(10)=2.0ln(1+3.5)1.2e -0.9 +0.3

[0137] C(10)=2.0ln(4.5)1.2×0.41+0.3

[0138] C(10)=2.0×1.500.492+0.3=3.000.492+0.3=2.81

[0139] The calculated result C10=2.81 indicates that the coagulation process has entered a stable state and hemostasis in the wound area has been completed. The system automatically enters maintenance mode: gradually reduce the release of artificial coagulation factors to avoid the risk of thrombosis due to excessive coagulation factors. Continue to observe platelet aggregation. If abnormal coagulation is found, the system can release a small amount of anticoagulant to prevent microthrombosis. Maintain the temperature at the wound at 36.0-36.5℃ to ensure that the activity of coagulation factors is in the best state and improve the efficiency of wound healing.

[0140] After 10 minutes of precise control by the intelligent hemostasis system, team member A's bleeding completely stopped, local blood circulation stabilized, and tissue metabolic activity recovered to St = -0.3 (normal tissue recovery state). The tactical medical team successfully stopped bleeding under extreme battlefield conditions. Traditional battlefield first aid relies on the experience of medical staff to judge the state of hemostasis, while this method achieves real-time data-driven precise hemostasis through bioinformatics sensing, time series modeling, and intelligent feedback control, improves the efficiency of first aid for combat injuries, and greatly reduces the risk of hemostasis failure.

[0141] Embodiment 2:

[0142] In Example 1, after team member A successfully stops bleeding and stabilizes the coagulation process, the tactical medical team needs to ensure that the pressurized hemostatic device at the wound does not cause tissue ischemia and necrosis due to uneven or excessive pressure, and at the same time ensure that different types of vascular injuries (arteries, veins, capillaries) can maintain a reasonable hemodynamic balance. Therefore, the intelligent hemostasis system starts a computationally driven dynamic pressure optimization model, based on biomechanical finite element modeling, combined with a flexible pressure sensor array, to calculate the pressure distribution in the wound area in real time, and dynamically adjust the applied pressure according to the local tissue physiological state to ensure hemostasis while avoiding secondary damage.

[0143] Biomechanical finite element modeling and real-time pressure calculation:

[0144] The intelligent hemostasis system uses a flexible pressure sensor array to establish a three-dimensional pressure distribution map in the thigh wound area of ​​player A. The data collected by the sensor network include vascular compliance, hemodynamic parameters and tissue stiffness of local tissues. Preliminary scan data shows:

[0145] Tissue stiffness E in the wound center c =1.2kkPa (normal range of muscle tissue is 0.8-1.5kkPa), indicating that the local tissue still maintains a certain elasticity; vascular compliance VC = 0.85 (normal range 0.75-1.0), indicating that the blood vessels still have a certain expansion ability; local blood flow shear force τ = 5.2dyn / cm 2 (Normal range 4.0-7.0dyn / cm 2 ), which is within a reasonable range, but still needs to be continuously monitored.

[0146] Calculation parameter settings:

[0147] α 1 =8.5 kPa, the initial applied basic pressure, ranging from 6.0-10.0 kPa, depending on the wound type and biomechanical properties; β 1 =0.05cm -2 , control the pressure diffusion degree, range 0.03-0.07cm -2 , the larger the value, the more concentrated the pressure is in a smaller area;

[0148] α 2 =2.5kkPa, reflecting the periodic effect of pulse fluctuation on local pressure, with a value range of 1.5-3.5kPa; γ 1 =2π×1.1rad / s, corresponding to a pulse frequency of 1.1Hz (66BPM), which determines the pulsation characteristics of pressure regulation;δ 1 =0.3kPa, environmental noise term, used to correct the error caused by external interference, the value range is 0.1-0.5kPa.

[0149] Calculate the initial pressure of P(x,y,t) at (x=0,y=0,t=0):

[0150]

[0151] P(0,0,0)=8.5e 0 +2.5×1+0.3=8.5+2.5+0.3=11.3kPa

[0152] The initial calculation shows that the central area pressure P(0,0,0) = 11.3 kPa, which is slightly higher than the recommended value (usually below 10 kPa is ideal). The intelligent system analyzes the stress conditions in different areas of the wound based on the pressure distribution diagram:

[0153] The central pressure of the wound is high, but it meets the hemostasis requirements of large artery injuries;

[0154] The pressure in the wound edge area (x=1.5cm, y=1.5cm) is too low (only 4.2kPa), which may result in incomplete closure of edge bleeding; the pressure in the downstream tissue area (more than 3cm away from the wound) is slightly higher (6.8kPa), which may affect normal blood flow.

[0155] In order to optimize the pressure distribution, the system takes the following measures:

[0156] The flexible hemostatic device fine-tunes the local pressurization pattern and optimizes the pressure distribution, so that the pressure at the wound edge reaches more than 6.5kPa, improving the success rate of hemostasis. The intelligent system detects a decrease in distal blood flow, so it gradually adjusts the hemostatic device to reduce the pressure in the distal area to 5kPa to ensure normal blood supply to the tissue.

[0157] Pressure optimization calculation at t = 10 seconds:

[0158] After optimization, the system calculates the pressure at x=0, y=0, t=10 again:

[0159]

[0160] P(0,0,10)=8.5e 0 +2.5cos(69.1)+0.3

[0161] P(0,0,10)=8.5+2.5×(-0.99)+0.3

[0162] P(0,0,10)=8.52.48+0.3=6.32kPa

[0163] The calculation results show that the optimized pressure has been reduced to 6.32kPa, which is closer to the ideal hemostasis range (6-8kPa). The system enters the feedback closed-loop optimization mode, continuously monitors the physiological state of the wound area, and adjusts the local pressure according to the sensor feedback, so that it gradually decreases as the wound heals:

[0164] 1. After the wound is completely closed, the pressure will gradually drop to 4kPa to prevent tissue necrosis caused by prolonged pressure;

[0165] 2. If recurrence of microbleeding is detected, the system will briefly increase the local pressure to 7 kPa to reactivate the hemostatic material;

[0166] 3. Continuously monitor tissue blood flow status to ensure that local pressure does not affect distal blood supply.

[0167] Battlefield application and tactical summary:

[0168] After 10 minutes of real-time regulation by the intelligent hemostasis system, the hemostasis effect of team member A has reached the best state, the wound pressure is evenly distributed, and the local hemodynamics has returned to normal. Compared with traditional battlefield hemostasis methods, this method calculates the optimal pressure distribution through biomechanical modeling and combines real-time pressure sensing technology to make the hemostasis process more accurate and efficient, and can dynamically adjust to different types of vascular injuries.

[0169] This embodiment further activates the intelligent coagulation material of chitosan nanogel, accurately adjusts the release rate of coagulation factors according to the real-time monitoring of platelet activity, fibrin formation rate and local blood oxygen concentration, and dynamically adjusts the pressure to the optimal range. This intelligent system combines biosensor feedback + adaptive algorithm to continuously optimize the local pressurization strategy to ensure successful hemostasis while maintaining tissue blood flow balance and avoiding local hypoxia or thrombosis.

[0170] Biosignal monitoring and initial parameter setting:

[0171] The battlefield medical team uses biosensors to collect real-time physiological data from the wound of team member A:

[0172] Coagulation factor activity: C(0) = 3.8 U / mL (normal range 3-7 U / mL), indicating that the coagulation process is normal but still in the early stages; local blood oxygen concentration: O(0) = 92% (normal range 90-100%), indicating that tissue oxygenation is acceptable, but further decline needs to be prevented; fibrin formation rate: 0.75 mg / dL / min (normal is 0.5-1.2 mg / dL / min), indicating that the coagulation process is ongoing but still needs to be optimized; current local pressure: P c (0) = 6.5 kPa, further optimization is needed to ensure hemostasis and avoid local tissue ischemia.

[0173] λ 1 =7.5, the maximum range of pressure regulation, the value range is 6.0-10.0kPa, and a higher value indicates a stronger regulation ability; λ 2 =0.8, the weight of the coagulation factor activity on the pressure adjustment, ranging from 0.6 to 1.0, a higher value indicates that the coagulation factor activity plays a greater role in regulating pressure; 3 =0.5, controls the effect of blood oxygen concentration on pressure regulation, ranging from 0.4 to 0.7, with higher values ​​indicating that a decrease in blood oxygen concentration will significantly reduce applied pressure; 2 =0.2, fine-tuning correction term, with a value range of 0.1-0.5, used to correct the error caused by individual physiological differences.

[0174] Calculate P c (0) Initial local adjustment pressure

[0175] Substitute the initial parameters to calculate the local adjustment pressure:

[0176] P c (0)=7.5tanh(0.8×3.80.5×92)+0.2

[0177] P c (0) = 7.5tanh (3.0446) + 0.2

[0178] P c (0) = 7.5tanh (-42.96) + 0.2

[0179] Since tanh(-42.96)≈-1, then

[0180] P c (0) = 7.5 × (-1) + 0.2 = -7.3 kPa

[0181] The calculation results show that the pressure adjustment value under the initial condition is negative, indicating that the current applied 6.5kPa may be too high and there is a risk of local tissue ischemia. The intelligent hemostasis system immediately takes the following measures:

[0182] The local applied pressure is reduced to 5.2kPa to reduce the burden on tissues and improve local blood supply. Since the activity of coagulation factors is still within the normal range, the system reduces the release rate of artificial coagulation factors by 15% to ensure that local thrombosis will not occur. : The system automatically releases a small amount of oxygen nanocapsules to increase the local blood oxygen concentration to 94%, optimizing the tissue repair environment.

[0183] Adaptive adjustment calculation after t = 10 seconds:

[0184] After 10 seconds of adjustment, the system detected:

[0185] C(10) = 4.5 U / mL (the level of coagulation factors increased slightly and tended to be stable);

[0186] O(10) = 94% (local blood oxygen concentration increased, tissue perfusion improved);

[0187] The local pressure has dropped to 5.2 kPa and the new adjustment pressure needs to be calculated.

[0188] Calculate P c (10):

[0189] P c (10)=7.5tanh(0.8×4.50.5×94)+0.2

[0190] P c (10) = 7.5tanh (3.647) + 0.2

[0191] P c (10) = 7.5tanh (-43.4) + 0.2

[0192] P c (10)=7.5×(-1)+0.2=-7.3kPa

[0193] The calculation results still show that the current pressure is still too high, which may affect the blood supply to the tissue. Therefore, the intelligent hemostasis system is further adjusted: 1. The pressure continues to be reduced to 4.8kPa to further improve microcirculation; 2. The release rate of coagulation factors is fine-tuned to maintain the current hemostasis efficiency but avoid excessive coagulation; 3. Continuous monitoring for 30 seconds to ensure the stability of blood oxygen concentration and avoid secondary damage caused by tissue hypoxia.

[0194] After 30 seconds of continuous adjustment by the intelligent system, the local pressure of team member A stabilized at 4.8kPa, the local blood oxygen concentration was maintained at 95%, the coagulation factor level tended to stabilize, and the wound area entered the optimal hemostasis and healing state. The system continues to operate in low-power mode, monitoring biological signals every 10 seconds to ensure that the hemostasis state does not change unexpectedly. If a drop in blood oxygen or signs of secondary bleeding are detected, the system will automatically adjust the pressure or reactivate the hemostatic material to prevent the wound from worsening. Ultimately, the system successfully optimized the battlefield hemostasis strategy, enabling team member A to receive the most accurate hemostasis treatment in extreme environments, improving survival rates, and minimizing the risk of tissue damage that may be caused by prolonged pressure.

[0195] The intelligent hemostasis system of this embodiment enters the stage of dynamic pressure optimization driven by calculation, combines pulse wave sensor + blood oxygen detection, and optimizes the hemostasis strategy by using real-time biological signal feedback to ensure that the wound maintains the hemostasis state while optimizing the hemodynamic environment to the greatest extent, so as to promote healing and reduce the need for external medical intervention. The system adopts a biofeedback closed-loop regulation model to detect the pulse wave intensity B(t) and local blood oxygen saturation in real time to dynamically adjust the local pressure P d (t), making the pressure adjustment strategy more accurate and intelligent.

[0196] Biosignal monitoring and initial parameter setting

[0197] The battlefield medical team obtains the latest physiological data of team member A's wound area through the intelligent sensor system:

[0198] Pulse signal strength: B(0) = 0.72 (normalized range 0-1, higher values ​​represent stronger local blood flow);

[0199] Local blood oxygen concentration: O(0) = 91% (normal range 90-100%, close to the low critical value);

[0200] Current local pressure: P d (0) = 5.2 kPa, which needs to be adjusted dynamically to optimize the healing process;

[0201] Blood component monitoring results indicate that the coagulation process is close to being stable, but it still needs to be continuously optimized to ensure that secondary bleeding or thrombosis risks do not occur.

[0202] Calculation parameter settings:

[0203] θ 1 =2.5, controls the influence of the pulse wave on the pressure adjustment, the value range is 2.0-3.0, and a higher value means that the pulse wave signal has a greater influence on the pressure adjustment; θ 2 =1.2, sets the weight of the pulse signal in pressure regulation, ranging from 1.0 to 1.5, and a higher value means that the pulse wave change is more sensitive to the effect of pressure adjustment; θ 3 =0.9, set the decay rate of pressure adjustment, the value range is 0.8-1.2, a higher value means the pressure will stabilize faster; θ 4 =0.07s -1 , the time factor that affects the adjustment rate, the value range is 0.05-0.1s -1 , determines the reaction time of pressure adjustment; δ 3 =0.2, external noise correction term, with a value range of 0.1-0.5, used to correct unstable factors in battlefield environments (such as patient movement or ambient temperature changes).

[0204] Calculate P d(0) Initial local pressure adjustment range:

[0205] Substitute the initial parameters to calculate the local pressure adjustment range:

[0206] P d (0) = 2.5ln(1 + 1.2 × 0.72) 0.9e -0.07×0 +0.2

[0207] P d (0) = 2.5ln(1 + 0.864) 0.9e 0 +0.2

[0208] P d (0) = 2.5ln(1.864)0.9 + 0.2

[0209] P d (0) = 2.5 × 0.6240.9 + 0.2

[0210] P d (0) = 1.560.9 + 0.2 = 0.86 kPa

[0211] The calculation results show that the current local pressure adjustment range is 0.86 kPa, which means that the pressure needs to be slightly increased to enhance the hemostasis effect while ensuring that the blood supply is not affected. The measures taken include: fine-tuning the pressure of the hemostatic device to make the pressure in the wound area reach the optimal hemostatic range to prevent potential secondary bleeding caused by insufficient pressure; because the local blood oxygen concentration is close to the critical value, the system activates the trace oxygen release device to increase the local oxygen supply to ensure that the tissue will not be damaged due to ischemia;

[0212] Monitor continuously for 10 seconds to ensure that the pulse signal is stable. If further enhancement of the pulse wave is detected, make secondary adjustments to prevent excessive pressure from causing local hypoxia.

[0213] Adaptive adjustment calculation after t = 10 seconds:

[0214] After 10 seconds of optimization, the system detected:

[0215] B(10) = 0.78 (the pulse signal is slightly enhanced, indicating that local blood flow is still recovering);

[0216] O(10) = 93% (local blood oxygen concentration increased, tissue blood supply improved);

[0217] The local pressure has been adjusted to 6.0 kPa and the new adjusted pressure needs to be calculated.

[0218] Calculate P d (10):

[0219] P d(10) = 2.5ln(1 + 1.2 × 0.78) 0.9e -0.07×10 +0.2

[0220] P d (10) = 2.5ln(1 + 0.936) 0.9e -0.7 +0.2

[0221] P d (10)=2.5ln(1.936)0.9×0.496+0.2

[0222] P d (10) = 2.5 × 0.66 0.446 + 0.2

[0223] P d (10)=1.650.446+0.2=1.404kPa

[0224] Further optimization and stabilization adjustments

[0225] The calculation results show that the new pressure adjustment range is 1.404 kPa, which means that the current pressure still needs to be adjusted. The intelligent hemostasis system is further optimized:

[0226] 1. Slowly reduce the local pressure to 5.4kPa to avoid obstruction of tissue blood flow due to long-term high pressure;

[0227] 2. Maintain blood oxygen concentration above 94% and ensure that local oxygen supply is maintained at the optimal level through continuous testing;

[0228] 3. If platelet activity and fibrin formation rate remain stable for more than 30 seconds, the system will enter a gradual depressurization mode to optimize the wound healing environment.

[0229] After 30 seconds of continuous dynamic adjustment by the intelligent hemostasis system, the local pressure of team member A was maintained at 5.4kPa, the blood oxygen concentration was stable at 94%, the pulse signal in the wound area remained in the normal range, and the physiological data entered the healing stage. The system enters a low-power monitoring mode and samples biological signals every 30 seconds to ensure a stable healing process. If abnormal fluctuations are detected (such as a decrease in blood oxygen and abnormal enhancement of the pulse wave), the system will automatically re-optimize the local pressure or release an appropriate amount of anticoagulant factors to prevent microthrombosis. Ultimately, the system successfully used a computationally driven pressure optimization model, combined with biosensor feedback, to transform battlefield hemostasis treatment from static regulation to an intelligent, adaptive, data-driven real-time adjustment mode, greatly improving the success rate of hemostasis and tissue survival rate, significantly reducing the need for subsequent medical intervention, and providing technical support for unmanned medical rescue on the battlefield in the future.

[0230] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A real-time hemostasis method for combat trauma based on bioinformation sensing technology, characterized in that The following steps are involved: S1. Dynamic modeling of wound biosignals: S1.

1. Introduce time series biosignal modeling to dynamically predict combat trauma status; Based on the biosensor data stream, a multi-dimensional time series state space is constructed, including: the nonlinear change trend of bleeding rate, the evolution pattern of tissue physiological parameters, and the dynamic evolution of coagulation process; S2. Intelligent hemostasis decision system: S2.

1. Minimize the need for external intervention while dynamically adjusting hemostasis strategies through active learning; S2.2, define the key objectives of hemostasis efficiency, tissue damage minimization, and pressure adaptation for multi-objective optimization modeling; S2.

3. Estimation of the distribution of success probabilities of different hemostatic strategies; S3. Intelligent response mechanism of adaptive coagulation materials: The intelligent response mechanism is composed of nanoscale coagulation factor carriers and phase change regulation networks: S3.

1. Construct an environmental trigger response model, whereby the coagulation factor carrier selectively releases coagulation factors according to biological signals including pH value, temperature, and bleeding rate; S3.

2. The coagulation factor carrier adopts a flexible adjustment structure to adjust the hemostatic effect in time according to the changes in wound data; S3.3, using a reversible control model to construct a coagulation factor carrier with potential thrombotic risk to intelligently reduce the coagulation strength and ensure blood flow balance; S4. Computation-driven dynamic pressure optimization model: S4.

1. Biomechanical pressure distribution modeling: Calculate the pressure distribution points around the wound based on sensor feedback; S4.2, pressure adaptive regulation: combining the function of intelligent coagulation materials to calculate the required effective pressure; S4.

3. Feedback closed-loop optimization: By collecting tissue response data, pressure distribution is adjusted in real time to achieve hemostasis.

2. The method for real-time hemostasis at the scene of war trauma based on bio-information sensing technology according to claim 1 is characterized in that The wound biological signal dynamic modeling method comprises: Through fluid mechanics simulation combined with time series analysis, a nonlinear bleeding rate change model is established. Based on the fusion of optical sensing and ultrasonic sensing, the wound opening size and arteriovenous damage are calculated in real time. At the same time, combined with pulse wave analysis, blood flow trends are predicted, and recurrent neural networks are used to detect the risk of secondary bleeding. The nonlinear evolution process of bleeding rate is expressed as follows: in: Q(t) represents the bleeding rate at time t; α1 represents the initial bleeding rate, which depends on the degree of blood vessel damage; β1 is the bleeding attenuation coefficient caused by the blood vessel's own contraction and coagulation process; α2 reflects the periodic effect of the pulse wave on the bleeding rate; γ1 is the blood flow pulsation frequency driven by the pulse; δ1 represents the small disturbance term affected by the environment and external forces.

3. The method for real-time hemostasis at the scene of war trauma based on bio-information sensing technology according to claim 2 is characterized in that The wound biosignal dynamic modeling method includes: detecting local pH changes through a micro pH sensor, and combining infrared thermal imaging with local temperature sensing to establish a tissue metabolic state prediction model, and analyzing tissue hypoxia, microcirculation failure and potential necrosis risks through data-driven modeling.

4. The method for real-time hemostasis at the scene of war trauma based on bio-information sensing technology according to claim 3 is characterized in that The wound biological signal dynamic modeling method comprises: Based on the dynamic analysis of coagulation factors + time series modeling, a dynamic feedback control mechanism of the coagulation process is established. Biosensor + machine learning technology is used to track platelet activation, fibrin polymerization and coagulation factor release, and the future state of the coagulation process is predicted through a recursive neural network. The coagulation process evolution model can be expressed as follows: in: C(t) is the coagulation efficiency index at time t; F(t) represents the concentration of coagulation factors, which is detected in real time by the biosensor; θ1 is the logarithmic growth factor of the coagulation process, which describes the activation rate of the initial coagulation factors; θ2 reflects the dependence of platelet response on coagulation factors; θ3 is the rate at which coagulation factors decay over time; θ4 controls the exponential decay rate of the coagulation process; δ3 represents the interference term caused by abnormal bleeding or inflammatory response.

5. The method for real-time hemostasis at the scene of war trauma based on bio-information sensing technology according to claim 1, characterized in that The calculation-driven dynamic pressure optimization model construction method: Based on the optimization of pressure distribution by biomechanical finite element modeling and combined with a flexible pressure sensor array, the pressure distribution in the wound area is calculated in real time. The physiological state of different tissues is sensed, including local vascular compliance, hemodynamic parameters, and tissue stiffness, and the applied pressure is adjusted dynamically. A hierarchical pressure optimization model is established for different types of vascular injuries, including arterial, venous, and capillary bleeding.

6. The method for real-time hemostasis at the scene of war trauma based on bio-information sensing technology according to claim 5 is characterized in that The calculation-driven dynamic pressure optimization model construction method: The intelligent coagulation material including chitosan nanogel adaptively adjusts the release rate of coagulation factors according to the bleeding situation, thereby affecting the required hemostatic pressure; the pressure is adjusted to the optimal range by real-time monitoring of platelet activity, fibrin formation rate, and local blood oxygen concentration biological parameters; the adaptive adjustment of pressure is described by the following formula: P c (t)=λ1tanh(λ2C(t)λ3O(t))+δ2 in: P c (t) is the local regulated pressure at time t; C(t) represents the activity level of coagulation factors; O(t) is the local blood oxygen concentration; λ1 sets the amplitude of pressure regulation; λ2 affects the weight of coagulation factor activity on pressure adjustment; λ3 controls the influence of blood oxygen concentration on pressure regulation; δ2 is a fine-tuning correction term that takes into account individual physiological differences.

7. The method for real-time hemostasis at the scene of war trauma based on bio-information sensing technology according to claim 6 is characterized in that The calculation-driven dynamic pressure optimization model construction method: Combined with pulse wave sensor + blood oxygen detection, the hemostasis strategy is optimized through real-time biological signal feedback; Specific adjustment strategies include: If an abnormal increase in the pulse signal is detected in the wound area, the local pressure is increased; If the blood oxygen level drops sharply, reduce the applied pressure; If blood component monitoring shows that the coagulation process has stabilized, the pressure is gradually reduced to allow hemostasis to enter the wound repair stage; Stress regulation based on biosignal feedback is described by the following model: in: P d (t) represents the dynamic adjustment amplitude of local pressure; B(t) represents the pulse signal strength, which is used to evaluate the changes in blood flow; θ1 controls the influence of the pulse wave on the pressure adjustment; θ2 sets the weight of the pulse signal in the pressure adjustment; θ3 sets the attenuation rate of the pressure adjustment; θ4 is the time factor that affects the adjustment rate; δ3 represents the external noise correction term.

Images