Electric hemostatic instrument control method and system

Abstract

The invention discloses an electric hemostatic instrument control method and system, and belongs to the technical field of biomedical engineering. According to the method, a multi-level intelligent control framework is formed by constructing an equipment compression state model, a dynamic process model, an environment state model, a basic limb occlusion pressure model and a target compression force model. The method comprises the following steps: firstly, evaluating an equipment matching state based on cuff width, equipment response speed and personnel limb conicity, optimizing process control by combining initial compression force, pressurization speed and compression duration, and judging system reliability according to environment temperature and vibration amplitude; the coefficients, the real-time pulse pressure and the oxyhemoglobin saturation are fused, and the basic limb occlusion pressure is calculated through a two-way adjusting mechanism; and finally, in combination with the dynamic process coefficient and the basic pulse pressure, target compression force is output through smooth amplitude limiting processing. The multi-parameter self-adaptive precise regulation and control of the compression force are realized, and the rapid hemostasis and the tissue safety protection are effectively considered.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of biomedical engineering, and particularly relates to a control method and system of an electric hemostasis instrument. BACKGROUND

[0002] As a key medical device for controlling limb bleeding during surgery or after trauma, the core goal of the electric hemostasis instrument is to achieve rapid and effective hemostasis while minimizing complications such as nerve damage and tissue ischemia caused by improper compression. Therefore, developing a control method and system that can intelligently and accurately adjust the compression force is of great significance to improve the success rate of treatment and patient safety.

[0003] Currently, traditional electric hemostasis instruments mostly use feedback control strategies based on preset fixed pressure or a single physiological parameter (such as cuff pressure). Some improved technologies attempt to introduce more advanced physiological indicators, such as monitoring the distal oxygen saturation (SpO2) or using an algorithm based on the "loss of pulse point" (LOP) to dynamically adjust the pressure. These methods have improved the safety of use to some extent, but their control logic is still relatively single.

[0004] However, the existing technology still has obvious defects. First, it fails to comprehensively consider the systematic effects of device hardware characteristics (such as cuff width, response speed), patient individual differences (such as limb taper), and external environmental disturbances (such as temperature, vibration) on the actual compression effect. Second, the control model relies on one-way pressure reduction to prevent ischemia, lacking a two-way intelligent adjustment mechanism that actively and moderately controls the compression force based on physiological feedback under the premise of ensuring safety. This results in insufficient precision, adaptability, and reliability of existing devices in complex application scenarios. SUMMARY

[0005] In view of the deficiencies of the prior art, the application provides an electric hemostasis instrument control method and system, which solves the above problems.

[0006] To achieve the above purpose, the application is implemented by the following technical solutions: an electric hemostasis instrument control method, comprising the following steps:

[0007] Based on the cuff width, device response speed, and personnel limb taper, a device compression state model is constructed to output a device compression state coefficient;

[0008] Based on the initial compression force of the cuff, the pressure increasing speed, and the compression time, a dynamic process model is constructed to output a dynamic process coefficient;

[0009] Based on the environmental temperature and the vibration amplitude (which is the vibration amplitude of the electric hemostasis instrument), an environmental state model is constructed to output an environmental state coefficient;

[0010] The basic limb occlusion pressure model is constructed based on the real-time pulse pressure (pulse pressure = systolic pressure - diastolic pressure) and blood oxygen saturation under the environment state coefficient and the device compression state coefficient, and outputs the basic limb occlusion pressure.

[0011] The target compression force model is constructed based on the basic limb occlusion pressure and the basic pulse pressure under the dynamic process coefficient, and outputs the target compression force.

[0012] Based on the above technical solutions, the application further provides the following optional technical solutions:

[0013] Further technical solutions: the step of constructing the target compression force model based on the basic limb occlusion pressure and the basic pulse pressure under the dynamic process coefficient and outputting the target compression force is:

[0014] The difference between the basic pulse pressure and the reference pulse pressure is processed by ratio to obtain the basic pulse pressure deviation index;

[0015] The target compression force model is constructed based on the basic limb occlusion pressure and the basic pulse pressure deviation index under the dynamic process coefficient, and the target compression force is obtained, and the target compression force model is represented as:

[0016]

[0017] Among them, The target compression force is represented as, The system maximum safety pressure is represented as, The dynamic process coefficient is represented as, The basic limb occlusion pressure is represented as, The basic pulse pressure deviation index is represented as, The basic pulse pressure correction weight is represented as.

[0018] Further technical solutions: the step of constructing the basic limb occlusion pressure model based on the real-time pulse pressure (pulse pressure = systolic pressure - diastolic pressure) and blood oxygen saturation under the environment state coefficient and the device compression state coefficient and outputting the basic limb occlusion pressure is:

[0019] The bidirectional basic adjustment model is constructed based on the real-time pulse pressure and the real-time blood oxygen saturation, and the bidirectional adjustment coefficient is obtained, and the bidirectional adjustment model is represented as:

[0020]

[0021] Among them, The bidirectional adjustment coefficient is represented as, The real-time pulse pressure is represented as, The target pulse pressure is represented as, The allowed deviation target pulse pressure value is represented as, The target blood oxygen saturation is represented as, Indicates real-time blood oxygen saturation. This indicates that deviations from the target blood oxygen saturation value are permissible. Indicates the pulse pressure regulation weight. Indicates the weighting of blood oxygen saturation regulation, the ;

[0022] A basic limb occlusion pressure model is constructed based on the environmental state coefficient, equipment compression state coefficient, and bidirectional adjustment coefficient to obtain the basic limb occlusion pressure. The basic limb occlusion pressure model is expressed as follows:

[0023]

[0024] in, This indicates the baseline limb occlusion pressure. Indicates a two-way adjustment factor. This indicates the preset limb occlusion pressure baseline value. Represents the environmental state coefficient. This indicates the equipment compression state coefficient.

[0025] Further technical solution: The steps for constructing an environmental state model and outputting environmental state coefficients based on ambient temperature and vibration amplitude (the vibration amplitude of the electric hemostat) are as follows:

[0026] The vibration index is obtained by comparing the vibration amplitude with the vibration reference amplitude.

[0027] The temperature deviation index is obtained by comparing the absolute difference between the ambient temperature and the optimal operating temperature with the allowable deviation from the optimal temperature.

[0028] An environmental state model is constructed based on the temperature deviation index and the vibration index, and environmental state coefficients are obtained. The environmental state model is expressed as follows:

[0029]

[0030] in, Represents the environmental state coefficient. This indicates the temperature deviation index. Indicates the vibration index. Represents the weight coefficient and The Furthermore, the higher the value, the more ideal the environmental conditions.

[0031] Further technical solution: The steps for constructing a dynamic process model and outputting dynamic process coefficients based on the initial compression force, compression speed, and compression duration of the cuff are as follows:

[0032] The absolute difference between the initial compression force of the cuff and the ideal initial compression force is compared with the allowable deviation from the ideal initial compression force to obtain the compression force deviation index.

[0033] The absolute difference between the pressurization rate and the ideal pressurization rate is compared with the allowable deviation from the ideal pressurization rate to obtain the pressurization rate deviation index.

[0034] The absolute difference between the compression duration and the ideal compression duration is compared with the allowable deviation from the ideal compression duration to obtain the compression duration deviation index.

[0035] A dynamic process model is constructed based on the pressure deviation index, the pressurization speed deviation index, and the pressure duration deviation index, and dynamic process coefficients are obtained. The dynamic process model is expressed as follows:

[0036]

[0037] in, Represents the coefficients of the dynamic process. This indicates that the pressure deviates from the index. This indicates that the rate of pressurization deviates from the exponential value. This indicates that the duration of compression deviates from the index. Represents the weight coefficient and The Furthermore, the larger the value, the more ideal the dynamic process.

[0038] Further technical solution: The steps for constructing a device compression state model and outputting the device compression state coefficient based on cuff width, device response speed, and personnel limb cone are as follows:

[0039] The differences between the cuff width, equipment response speed, and personnel limb taper and their corresponding ideal values ​​are compared with the corresponding ideal values ​​to obtain the cuff width deviation index, equipment response speed deviation index, and personnel limb taper deviation index.

[0040] A model of equipment compression state is constructed based on the cuff width deviation index, equipment response speed deviation index, and personnel limb conicity deviation index. Equipment compression state coefficients are obtained, and the equipment compression state is represented by this model as follows:

[0041]

[0042] in, Indicates the equipment compression state coefficient. This indicates that the cuff width deviates from the index. This indicates that the device's response speed deviates from the index. Indicates the deviation index of a person's limb cone. This indicates that the cuff width deviates from the sensitivity coefficient. This indicates that the device's response speed deviates from the sensitivity coefficient. The sensitivity coefficient representing the deviation of a person's limb from the cone shape. And the greater the value is, the more ideal the device compression state is.

[0043] The application provides an electrodynamic hemostasis apparatus control system.

[0044] Compared with the prior art, the application has the following beneficial effects:

[0045] 1. The application establishes a multi-factor fusion decision mechanism by comprehensively considering device state, dynamic process, environmental interference and patient physiological feedback, thereby significantly improving the precision and system robustness of compression force control.

[0046] 2. The application optimizes the compression strategy by introducing a dynamic process model, thereby avoiding tissue damage or hemostasis delay caused by improper process parameters, and improving hemostasis efficiency.

[0047] 3. The application enhances the adaptability of the device in complex application scenarios by introducing an environmental state model, thereby ensuring the reliability of sensor readings and control execution.

[0048] 4. The application can intelligently balance hemostasis effect and tissue protection demand based on a bidirectional regulation model of real-time pulse pressure and blood oxygen saturation, thereby realizing a leap from one-way safety protection to bidirectional intelligent optimization, and effectively reducing the risk of complications. BRIEF DESCRIPTION OF DRAWINGS

[0049] Figure 1 The application is a flowchart. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical scheme and advantages of the application clearer, the application is further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the application, and are not used to limit the application.

[0051] The specific implementation of the application is described in detail below in combination with specific examples.

[0052] Please refer to Figure 1 The application provides an electrodynamic hemostasis apparatus control method, which comprises the following steps:

[0053] A device compression state model is constructed based on cuff width, device response speed and personnel limb taper, to output a device compression state coefficient;

[0054] A dynamic process model is constructed based on cuff initial compression force, compression speed and compression duration, to output a dynamic process coefficient;

[0055] An environmental state model is constructed based on the ambient temperature and the vibration amplitude (vibration amplitude of the electric hemostasis device) to output an environmental state coefficient;

[0056] A basic limb occlusion pressure model is constructed based on the environmental state coefficient and the real-time pulse pressure (pulse pressure = systolic pressure - diastolic pressure) and blood oxygen saturation under the device compression state coefficient to output the basic limb occlusion pressure;

[0057] A target compression force model is constructed based on the basic limb occlusion pressure and the basic pulse pressure under the dynamic process coefficient to output the target compression force.

[0058] Through the above technical solutions, the application solves the compression force control deviation problem caused by poor hardware adaptability, unstable compression process and environmental interference. Through the synergistic effect of multi-dimensional parameter models, the risk of insufficient compression or tissue damage caused by ignoring individual differences in traditional methods is avoided under the premise of ensuring hemostasis effect. At the same time, the bidirectional adjustment mechanism can dynamically adjust the pressure threshold according to real-time physiological data, which not only prevents ischemia caused by excessive compression, but also quickly increases the pressure to a safe range when the amount of bleeding suddenly increases, significantly improving the control reliability and adaptability in complex application scenarios.

[0059] Preferably, the step of constructing a device compression state model based on the cuff width, device response speed and personnel limb taper to output a device compression state coefficient is:

[0060] The difference between the cuff width, device response speed and personnel limb taper and the corresponding ideal value is processed by ratio to obtain a cuff width deviation index, a device response speed deviation index and a personnel limb taper deviation index;

[0061] A device compression state model is constructed based on the cuff width deviation index, the device response speed deviation index and the personnel limb taper deviation index to obtain the device compression state coefficient, and the device compression state model is represented as:

[0062]

[0063] wherein, represents the device compression state coefficient, represents the cuff width deviation index, represents the device response speed deviation index, represents the personnel limb taper deviation index, represents the cuff width deviation sensitivity coefficient, represents the device response speed deviation sensitivity coefficient, represents the personnel limb taper deviation sensitivity coefficient, and The greater the value, the more ideal the device compression state.

[0064] The sleeve width deviation index refers to the relative deviation degree of the actual sleeve width from the ideal sleeve width, which can be specifically realized by calculating the difference between the actual sleeve width and the ideal sleeve width, and the proportion of the ideal sleeve width, and is used to reflect the influence of the sleeve size and the matching degree of the patient's limb on the compression effect. The device response speed deviation index refers to the relative deviation degree of the actual device response speed from the ideal response speed, which can be specifically realized by calculating the difference between the actual response speed and the ideal response speed, and the proportion of the ideal response speed, and is used to quantify the influence of the dynamic performance of the device on the pressure regulation accuracy. The personnel limb taper deviation index refers to the relative deviation degree of the actual limb taper from the ideal taper, which can be specifically realized by calculating the difference between the actual taper and the ideal taper, and the proportion of the ideal taper, and is used to represent the unevenness of the compression distribution caused by the anatomical structure difference of the patient. The device compression state coefficient is a quantitative index for comprehensively evaluating the adaptability of the device hardware characteristics and the individual characteristics of the patient, which can be specifically realized by mapping each deviation index to a continuous value of 0 to 1 through an exponential function model, and is used to dynamically optimize the subsequent pressure control parameters.

[0065] Specifically, the construction process of the device compression state model is as follows: first, by comparing the actual sleeve width, the device response speed and the personnel limb taper with the corresponding ideal values respectively, the deviation index of each parameter is calculated. For example, when the actual sleeve width is less than the ideal value, the sleeve width deviation index is positive, and vice versa, which reflects the potential influence of the sleeve size deviation on the compression contact area. Secondly, the three deviation indexes are input into the exponential function model, the influence of significant deviation is amplified through square operation, and the weight of each parameter is adjusted combined with the sensitivity coefficient (each sensitivity coefficient can be set by expert experience value or determined by analytic hierarchy process). For example, if the device response speed is more critical to the overall compression state, the sensitivity can be improved by increasing the value of Finally, the device compression state coefficient output by the model will be used as a key parameter to participate in the calculation of the basic limb occlusion pressure, so as to dynamically compensate for the interference caused by the device hardware difference and the individual characteristics of the patient in the subsequent pressure regulation process.

[0066] Compared with the prior art, the traditional method usually only controls the pressure based on a single fixed parameter or a static model, without considering the comprehensive influence of dynamic factors such as sleeve width, device response speed and limb taper. For example, the prior art may only adjust the pressure according to the preset sleeve size, while ignoring the uneven local pressure distribution caused by device response delay or limb taper change. The present scheme quantifies the differences between device hardware characteristics and individual characteristics of patients into adjustable coefficients by constructing a multi-parameter dynamic model, so that the compression force control can adapt to different device configurations and anatomical structures in real time, thereby overcoming the control deviation caused by the insufficient parameter adaptability of the traditional method.

[0067] By the technical solution, the application can effectively solve the problem of inaccurate compression control caused by device hardware differences and changes in patient limb characteristics. For example, when a narrower cuff or a device with slower response speed is used, the model can trigger a pressure compensation mechanism by reducing the device compression state coefficient, avoiding ineffective hemostasis caused by insufficient contact area or delayed response. At the same time, for patients with different limb tapers, the model can automatically adjust the compression force distribution strategy to reduce the risk of local tissue damage caused by anatomical structure differences, thereby improving the safety and reliability of the hemostasis process.

[0068] Preferably, the step of constructing a dynamic process model based on the initial compression force of the cuff, the compression speed, and the compression duration to output a dynamic process coefficient is:

[0069] The absolute difference between the initial compression force of the cuff and the ideal initial compression force is processed by ratio with the allowed deviation from the ideal initial compression force value to obtain a compression force deviation index;

[0070] The absolute difference between the compression speed and the ideal compression speed is processed by ratio with the allowed deviation from the ideal compression speed value to obtain a compression speed deviation index;

[0071] The absolute difference between the compression duration and the ideal compression duration is processed by ratio with the allowed deviation from the ideal compression duration value to obtain a compression duration deviation index;

[0072] A dynamic process model is constructed based on the compression force deviation index, the compression speed deviation index, and the compression duration deviation index to obtain a dynamic process coefficient, and the dynamic process model is represented as:

[0073]

[0074] wherein, represents the dynamic process coefficient, represents the compression force deviation index, represents the compression speed deviation index, represents the compression duration deviation index, represents the weight coefficient and , the and the greater the value, the more ideal the dynamic process.

[0075] Preferably, the step of constructing an environment state model based on the ambient temperature and the vibration amplitude (the vibration amplitude of the electric hemostat) to output an environment state coefficient is:

[0076] The vibration amplitude is processed by ratio with the vibration reference amplitude to obtain a vibration index;

[0077] The absolute difference between the ambient temperature and the optimal working temperature is processed by ratio with the allowed deviation from the optimal temperature value to obtain a temperature deviation index;

[0078] An environment state model is constructed based on the temperature deviation index and the vibration index, and an environment state coefficient is obtained, and the environment state model is expressed as:

[0079]

[0080] wherein, represents the environment state coefficient, represents the temperature deviation index, represents the vibration index, represents the weight coefficient, and , the and the greater the value, the more ideal the environment state.

[0081] The vibration index refers to the ratio of the actual vibration amplitude to the vibration reference amplitude allowed by the equipment design, and can be realized by dividing the vibration amplitude measured by the vibration sensor in real time by the preset reference value, for quantifying the interference degree of mechanical vibration on the stability of the equipment. The temperature deviation index refers to the ratio of the absolute difference between the environment temperature and the optimal working temperature of the equipment to the allowed deviation range, and can be realized by dividing the absolute value of the difference between the environment temperature collected by the temperature sensor and the preset optimal temperature by the allowed deviation threshold, for representing the influence of temperature fluctuation on the sensor accuracy and material deformation. The exponential function in the environment state model is used for nonlinear fusion of temperature and vibration factors, and the temperature deviation index can be processed by a square term exponential decay function to accelerate the elimination of the negative influence when the temperature is close to the ideal temperature, and the vibration index can be processed by a linear exponential decay function to maintain the linear correspondence relationship of the interference degree, and the distribution of the weight coefficient can be adjusted according to the actual application scene, for example, the weight coefficient of the temperature term can be increased in a high-temperature environment to preferentially suppress temperature interference.

[0082] Specifically, during the operation of the equipment, the vibration amplitude is collected by the sensor in real time and compared with the preset vibration reference amplitude, if the actual vibration amplitude exceeds the reference value, the vibration index will increase, indicating that the mechanical vibration interference is intensified. The difference between the environment temperature and the optimal working temperature is calculated by absolute value, and then is processed by ratio with the allowed deviation value, when the temperature deviation exceeds the allowed range, the temperature deviation index rises significantly. The temperature deviation index adopts a square term exponential decay function, so that when the temperature is close to the optimal value, the negative influence of the temperature on the environment state coefficient decreases rapidly; the vibration index adopts a linear exponential decay function, to ensure that the vibration interference and the coefficient change are directly corresponding. By adjusting the weight coefficients and , for example, when there is high-frequency equipment vibration in the operating room, the can be set to 0.7 to focus on suppressing vibration interference, and in a low-temperature storage environment, the Set to 0.8 to strengthen temperature compensation. The final output of the environmental state coefficient Reflects the pros and cons of environmental conditions in real time, when Approaches 1, indicating that environmental interference has been effectively suppressed, thereby providing dynamic compensation parameters for subsequent limb occlusion pressure calculation.

[0083] Compared with the prior art, the traditional method usually only monitors a single environmental factor or uses a linear superposition method to process multiple environmental disturbances, such as triggering an alarm only according to a temperature threshold without quantifying it as a continuous coefficient, or simply adding the temperature and vibration deviation values as an environmental evaluation index. The present scheme fuses the two types of environmental factors through a nonlinear function, rapidly reduces the influence weight when the temperature approaches the ideal value, while retaining the linear response characteristics of vibration interference, making the environmental state evaluation more in line with the actual physical laws. In addition, in the prior art, environmental factors are often used to trigger device protection mechanisms rather than dynamic pressure compensation control. The present scheme directly embeds the environmental state coefficient into the limb occlusion pressure calculation model, achieving active suppression of environmental interference.

[0084] Through the above technical solutions, the present application can effectively eliminate the sensor measurement errors caused by temperature fluctuations and the influence of material deformation on pressure application, while suppressing the problem of unstable device working state caused by mechanical vibration. For example, in the ambulance transfer scene, when the vehicle bumps cause the vibration amplitude to increase sharply, the increase in the vibration index will reduce the environmental state coefficient , thereby automatically increasing the pressure compensation amount when calculating the basic limb occlusion pressure to maintain effective hemostasis; in a low-temperature operating room, the increase in the temperature deviation index will trigger the model to reduce , and then adjust the occlusion pressure calculation to avoid insufficient compression force caused by changes in material rigidity. In this way, the device can accurately evaluate and maintain the ideal compression state under different environmental conditions, avoiding the risk of hemostasis failure or excessive compression caused by environmental interference.

[0085] Preferably, the step of constructing a basic limb occlusion pressure model based on the environmental state coefficient and the real-time pulse pressure (pulse pressure = systolic pressure - diastolic pressure) and the blood oxygen saturation under the device compression state coefficient to output the basic limb occlusion pressure is:

[0086] A two-way basic adjustment model is constructed based on the real-time pulse pressure and the real-time blood oxygen saturation to obtain a two-way adjustment coefficient, and the two-way adjustment model is represented as:

[0087]

[0088] Wherein, represents the two-way adjustment coefficient, represents the real-time pulse pressure, represents the target pulse pressure, represents the allowed deviation from the target pulse pressure value, represents a target blood oxygen saturation, represents a real-time blood oxygen saturation, represents a target blood oxygen saturation value, represents a pulse pressure adjustment weight, represents a blood oxygen saturation adjustment weight, and the ;

[0089] The basic limb occlusion pressure model is constructed based on the environmental state coefficient, the device compression state coefficient, and the bidirectional adjustment coefficient, and the basic limb occlusion pressure is obtained, and the basic limb occlusion pressure model is represented as:

[0090]

[0091] wherein, represents a basic limb occlusion pressure, represents a bidirectional adjustment factor, represents a preset limb occlusion pressure reference value, represents an environmental state coefficient, represents a device compression state coefficient.

[0092] The bidirectional adjustment coefficient is a correction factor dynamically adjusted by the deviation proportion of the real-time pulse pressure from the target value and the deviation proportion of the real-time blood oxygen saturation from the target value, and can be realized by instrument real-time acquisition of physiological parameters and calculation combined with a preset threshold, for balancing the conflict between hemostasis demand and tissue oxygen supply. The environmental state coefficient is a comprehensive index reflecting the device working environment temperature and vibration interference, and can be realized by temperature sensor and accelerometer measurement data and exponential function fusion processing, for suppressing the interference of external environment on pressure control. The device compression state coefficient is a comprehensive parameter representing the sleeve fitting degree and device performance, and can be calculated by measuring the matching degree of sleeve width and limb taper and the deviation degree of device response speed, for evaluating the influence of hardware state on compression effect. The preset limb occlusion pressure reference value is an initial pressure reference value set according to clinical experience, and can be set by using the standard pressure range of different limb parts, as the initial basis for pressure regulation.

[0093] Specifically, when the real-time pulse pressure is higher than the target value, the positive adjustment term enhances the correction of the reference value through the weight coefficient (which can be set by expert experience value or determined by analytic hierarchy process), to promote the basic occlusion pressure to improve effective hemostasis; when the real-time blood oxygen saturation is lower than the target value, the negative adjustment term enhances the correction of the reference value through the weight coefficient The baseline value is reduced to avoid excessive compression leading to tissue ischemia. The value range of the bidirectional adjustment coefficient is constrained between 0 and 2 to prevent dramatic pressure fluctuations caused by abnormal single physiological parameters. The environmental state coefficient and the device compression state coefficient act on the denominator of the baseline value through an exponential function. When the environmental temperature deviates from the optimal working temperature or the device response speed decreases, the denominator increases, and the basic occlusion pressure is dynamically reduced to avoid the risk of compression caused by hardware or environmental abnormalities. For example, when a strong vibration disturbance occurs in the operating room, the vibration index rises, causing the environmental state coefficient to decrease. At this time, the denominator increases to automatically reduce the basic occlusion pressure, avoiding pressure loss of control caused by device vibration.

[0094] Compared with the prior art, the traditional method only relies on single pressure feedback or one-way adjustment mechanism, such as one-way reduction of pressure only according to blood oxygen saturation, which cannot achieve dynamic balance between hemostasis and oxygen supply. The present scheme responds to the bidirectional changes of pulse pressure and blood oxygen saturation through the bidirectional adjustment coefficient, and combines multi-dimensional parameters of environmental and device states to form a closed-loop intelligent adjustment mechanism. The prior art does not consider the matching degree of device response speed and limb taper in the calculation of basic occlusion pressure, while the present scheme quantifies the influence of hardware adaptability on pressure control through the device compression state coefficient, for example, when the cuff width and limb circumference do not match, the coefficient decreases to trigger the denominator to increase, thereby automatically correcting the baseline value.

[0095] Through the above technical scheme, the present application can dynamically balance the hemostasis strength and tissue oxygen demand according to the real-time physiological parameters in the operation, automatically adjust the basic occlusion pressure when environmental interference or device state abnormalities occur, and avoid excessive or insufficient pressure caused by one-way adjustment. For example, when the patient's blood pressure fluctuates, the bidirectional adjustment mechanism can maintain the basic occlusion pressure within a safe range, and when the temperature in the operating room changes suddenly causing sensor drift, the environmental state coefficient can timely suppress the influence of abnormal data on pressure calculation, thereby improving the calculation accuracy and clinical adaptability of the basic limb occlusion pressure.

[0096] Preferably, the step of constructing a target compression force model based on the basic limb occlusion pressure under the dynamic process coefficient and the basic pulse pressure to output a target compression force is:

[0097] The difference between the basic pulse pressure and the reference pulse pressure is processed by ratio to obtain a basic pulse pressure deviation index;

[0098] A target compression force model is constructed based on the basic limb occlusion pressure under the dynamic process coefficient and the basic pulse pressure deviation index to obtain a target compression force, and the target compression force model is represented as:

[0099]

[0100] wherein, represents a target compression force, represents a system maximum safe pressure, represents a dynamic process coefficient, represents a base limb occlusion pressure, represents a base pulse pressure deviation index, represents a base pulse pressure correction weight.

[0101] The base pulse pressure deviation index refers to the ratio of the difference between the base pulse pressure and the reference pulse pressure to the allowed deviation from the reference pulse pressure value. It can be calculated by comparing the real-time measured pulse pressure of the patient with the preset reference value, and is used to quantify the deviation degree of the current physiological state from the ideal value. The dynamic process coefficient is a comprehensive parameter reflecting the deviation degree of the initial compression force, the pressure increasing speed, and the compression duration during the operation of the device. It can be calculated by weighting the compression force deviation index, the pressure increasing speed deviation index, and the compression duration deviation index, and is used to dynamically correct the generation logic of the target compression force. The hyperbolic tangent function in the target compression force model is a mathematical function with nonlinear saturation characteristics, which can be implemented by using a standard hyperbolic tangent function, and is used to map the input parameters to a preset pressure range to ensure that the output pressure does not exceed the system safety threshold. The base pulse pressure correction weight is a parameter for adjusting the influence of the pulse pressure deviation index on the target compression force. It can be set as a fixed value according to different clinical scenarios or dynamically adjusted by an adaptive algorithm, and is used to control the priority of physiological feedback in pressure regulation.

[0102] Specifically, the generation process of the target compression force first converts the difference between the real-time physiological state of the patient and the preset reference value into a standardized index by calculating the base pulse pressure deviation index. Then, the base limb occlusion pressure is dynamically corrected in combination with the dynamic process coefficient, which can reflect the comprehensive deviation of the initial compression force, the pressure increasing speed, and the compression duration during the operation of the device. The corrected base limb occlusion pressure and the pulse pressure deviation index are input into the target compression force model, and the nonlinear characteristics of the hyperbolic tangent function are used to automatically constrain the calculation results within the system maximum safe pressure range. In this process, the dynamic process coefficient acts as an adjustment factor for the denominator, and automatically reduces the coefficient value when the device operating parameters deviate from the ideal state, thereby suppressing the risk of pressure over-regulation caused by fluctuations in hardware performance. The setting of the base pulse pressure correction weight allows the clinical operator to adjust the influence of physiological feedback on pressure regulation according to actual needs, for example, increasing the weight to enhance the sensitivity of physiological parameters when the risk of bleeding is high.

[0103] Compared with the prior art, the conventional method sets the compression force only based on a single static parameter or a fixed threshold, and cannot adapt to fluctuations in the device operating state and individual differences of patients. The prior art lacks a quantitative evaluation mechanism for dynamic process parameters, and does not couple the physiological deviation index with the device state parameter for calculation. The scheme realizes bidirectional feedback control of the device operating state and the physiological state of the patient by establishing a joint regulation mechanism of the dynamic process coefficient and the physiological deviation index. At the same time, the non-linear function is used to automatically constrain the pressure output range, overcoming the technical defect that the conventional linear regulation method is easy to exceed the safety threshold.

[0104] Through the above technical scheme, the application can dynamically adjust the target compression force according to the real-time monitored physiological parameters and device operating state during the hemostasis process after the operation or trauma. In the case of deviation of the initial compression force of the device or unstable compression speed, the dynamic process coefficient is used to automatically reduce the pressure regulation sensitivity, avoiding the risk of overpressure caused by fluctuations in the performance of the device. When the pulse pressure deviation deviates from the preset reference range, the pulse pressure deviation index is used to timely correct the target compression force, maintaining effective hemostasis while reducing tissue ischemic injury. The scheme is especially suitable for application scenarios where the limb taper difference is significant or the environmental vibration interference is large. By comprehensively considering the device dynamic parameters and physiological feedback indicators, the accuracy and safety of the compression force control are improved.

[0105] An electric hemostasis instrument control system adopts the electric hemostasis instrument control method.

[0106] It should be noted that, in this text, relational terms such as first and second are used merely to distinguish one entity or action from another, and do not necessarily require or imply that there is any such actual relationship or order between these entities or actions. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device.

[0107] Although the embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. An electrohemostat control method, characterized by, The method comprises the following steps: a device compression state model is constructed based on the cuff width, the device response speed and the personnel limb taper to output a device compression state coefficient; a dynamic process model is constructed based on the cuff initial compression force, the pressurization speed and the compression time length to output a dynamic process coefficient; an environment state model is constructed based on the environment temperature and the vibration amplitude to output an environment state coefficient; a basic limb occlusion pressure model is constructed based on the environment state coefficient and the real-time pulse pressure and the blood oxygen saturation under the device compression state coefficient to output a basic limb occlusion pressure; a target compression force model is constructed based on the basic limb occlusion pressure and the basic pulse pressure under the dynamic process coefficient to output a target compression force.

2. The control method of the electrohemostat according to claim 1, wherein, The step of constructing the target compression force model based on the basic limb occlusion pressure and the basic pulse pressure under the dynamic process coefficient to output the target compression force is: a basic pulse pressure deviation index is obtained by ratio processing the difference between the basic pulse pressure and the reference pulse pressure and the allowed deviation reference pulse pressure value; a target compression force model is constructed based on the basic limb occlusion pressure and the basic pulse pressure deviation index under the dynamic process coefficient to obtain the target compression force, and the target compression force model is expressed as: wherein, represents a target compression force, represents a system maximum safe pressure, represents a dynamic process coefficient, represents a base limb occlusion pressure, represents a base pulse pressure deviation index, represents a base pulse pressure correction weight.

3. The control method of the electrohemostat according to claim 2, wherein The step of constructing the basic limb occlusion pressure model based on the real-time pulse pressure and the blood oxygen saturation under the environment state coefficient and the device compression state coefficient to output the basic limb occlusion pressure is: a two-way basic adjustment model is constructed based on the real-time pulse pressure and the real-time blood oxygen saturation to obtain a two-way adjustment coefficient, and the two-way adjustment model is expressed as: wherein, represents a bidirectional regulation coefficient, represents a real-time pulse pressure, represents a target pulse pressure, represents a deviation allowed from the target pulse pressure value, represents a target blood oxygen saturation, represents a real-time blood oxygen saturation, represents a deviation allowed from the target blood oxygen saturation value, represents a pulse pressure regulation weight, represents a blood oxygen saturation regulation weight, and the ; a basic limb occlusion pressure model is constructed based on the environment state coefficient, the device compression state coefficient and the two-way adjustment coefficient to obtain the basic limb occlusion pressure, and the basic limb occlusion pressure model is expressed as: in, This indicates the baseline limb occlusion pressure. Indicates a two-way adjustment factor. This indicates the preset limb occlusion pressure baseline value. Represents the environmental state coefficient. This indicates the equipment compression state coefficient.

4. The control method of the electrohemostat according to claim 3, wherein The step of constructing the environment state model based on the environment temperature and the vibration amplitude to output the environment state coefficient is: a vibration index is obtained by ratio processing the vibration amplitude and the vibration reference amplitude; a temperature deviation index is obtained by ratio processing the absolute difference between the environment temperature and the optimal working temperature and the allowed deviation optimal temperature value; an environment state model is constructed based on the temperature deviation index and the vibration index to obtain the environment state coefficient, and the environment state model is expressed as: wherein, represents an environmental condition coefficient, represents a temperature deviation index, represents a vibration index, represents a weight coefficient and , said and the greater the value the more ideal the environmental condition.

5. The control method of the electrohemostat according to claim 3, wherein The step of constructing the dynamic process model based on the cuff initial compression force, the pressurization speed and the compression time length to output the dynamic process coefficient is: a compression force deviation index is obtained by ratio processing the absolute difference between the cuff initial compression force and the ideal initial compression force and the allowed deviation ideal initial compression force value; a pressurization speed deviation index is obtained by ratio processing the absolute difference between the pressurization speed and the ideal pressurization speed and the allowed deviation ideal pressurization speed value; a compression time length deviation index is obtained by ratio processing the absolute difference between the compression time length and the ideal compression time length and the allowed deviation ideal compression time length value; a dynamic process model is constructed based on the compression force deviation index, the pressurization speed deviation index and the compression time length deviation index to obtain the dynamic process coefficient, and the dynamic process model is expressed as: wherein, represents a dynamic process coefficient, represents a compression force deviation index, represents a compression speed deviation index, represents a compression time deviation index, represents a weight coefficient and , the and the greater the value the more ideal the dynamic process.

6. The control method of the electrohemostat according to claim 3, wherein, The step of constructing the device compression state model based on the cuff width, the device response speed and the personnel limb taper to output the device compression state coefficient is: The difference between the cuff width, the device response speed and the personnel limb taper and the corresponding ideal value is processed by ratio to obtain a cuff width deviation index, a device response speed deviation index and a personnel limb taper deviation index; A device compression state model is constructed based on the cuff width deviation index, the device response speed deviation index and the personnel limb taper deviation index to obtain a device compression state coefficient, and the device compression state model is expressed as: wherein, represents a device compression state coefficient, represents a cuff width deviation index, represents a device response speed deviation index, represents a person limb taper deviation index, represents a cuff width deviation sensitivity coefficient, represents a device response speed deviation sensitivity coefficient, represents a person limb taper deviation sensitivity coefficient, and the greater the value, the more ideal the device compression state.

7. An electrohemostat control system characterized by, The electric hemostat control method of any one of claims 1-6 is adopted.

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