Defibrillator high-precision energy discharge control method based on CPR pressing guidance

Abstract

The invention discloses a defibrillator high-precision energy discharge control method based on CPR pressing guidance, which is used in the field of defibrillator discharge control, and comprises the following steps: monitoring physiological parameters of a patient and a capacitance state of a defibrillator in real time through a sensor; analyzing the state of the patient and the performance of the capacitor; the discharging energy, the charging voltage and the charging time are adjusted according to the analysis result of the microprocessor; in the discharging process of the defibrillator, a redundancy design strategy is implemented, and an optimal capacitor combination is selected through a microprocessor; a visual display interface is provided, and the patient state and the equipment operation condition are monitored in real time. According to the invention, the discharge energy can be adjusted according to the specific condition of a patient, the treatment is ensured to be effective and safe, the charging process is optimized by calculating the optimal voltage of required energy discharge, the efficiency and the response speed are improved, the charging time controlled by the microprocessor is shortened, the cardiac resuscitation is ensured to be carried out in time, and meanwhile, sufficient energy is fully stored.

Description

Technical Field

[0001] The present invention relates to the field of defibrillator discharge control, and in particular to a high-precision energy discharge control method for a defibrillator based on CPR compression guidance. Background Art

[0002] A defibrillator is a medical device used to treat cardiac arrest. It mainly treats ventricular fibrillation or pulseless ventricular tachycardia by applying precisely controlled electrical shocks to the heart. Its working principle is to first charge the energy storage capacitor to the target voltage, and then quickly release the electrical energy through a specific control circuit, so that the current flows through the patient's heart instantly. The key to a defibrillator is to precisely control the discharge energy to ensure that the energy is sufficient to restart the heart without causing excessive damage to the myocardium.

[0003] With the advancement of technology, modern defibrillators can automatically adjust the discharge energy according to the patient's specific situation, making treatment more personalized and safer. Defibrillators are divided into automatic external defibrillators and manual defibrillators, which are widely used in public places and medical institutions, and play a vital role in improving the survival rate of patients with cardiac arrest.

[0004] Traditional equipment may have limitations in real-time monitoring and data analysis, resulting in the inability to accurately and timely identify the patient's physiological state and device status. The lack of efficient pattern recognition and abnormality detection functions makes it impossible to quickly make correct diagnoses and treatments in emergency situations. Old medical equipment usually lacks personalized treatment capabilities and cannot dynamically adjust treatment plans based on the patient's specific physiological parameters. This rigidity of treatment may lead to poor treatment effects and increase patient discomfort and treatment risks.

[0005] Traditional defibrillators may have deficiencies in capacitor status monitoring and adaptive adjustment, which may affect the performance and response speed of the device at critical moments. The charging strategy and life management of the capacitor are insufficient, which limits the effective use time and reliability of the device. In addition, the performance parameters and combination optimization of the capacitor may not be fully considered, resulting in suboptimal performance of the defibrillator.

[0006] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0007] In order to overcome the above problems, the present invention aims to propose a high-precision energy discharge control method for a defibrillator based on CPR compression guidance, with the aim of solving the problems that traditional equipment may have limitations in real-time monitoring and data analysis, resulting in the inability to accurately and timely identify the patient's physiological state and equipment status, lack of efficient pattern recognition and abnormality detection functions, making it impossible to quickly make a correct diagnosis and treatment in an emergency situation, and old medical equipment usually lacks personalized treatment capabilities and cannot dynamically adjust the treatment plan according to the patient's specific physiological parameters. The rigidity of this treatment may lead to poor treatment effects and increase patient discomfort and treatment risks.

[0008] To this end, the specific technical solution adopted by the present invention is as follows: A high-precision energy discharge control method for a defibrillator based on CPR compression guidance, the high-precision energy discharge control method for a defibrillator comprising the following steps: S1, monitor the patient's physiological parameters and the capacitance status of the defibrillator in real time through sensors; S2, inputting the real-time monitored physiological parameters and capacitor status data into a microprocessor to analyze the patient's status and the performance of the capacitor; S3, adjusting the discharge energy, charging voltage and charging time according to the analysis results of the microprocessor; S4. During the discharge process of the defibrillator, a redundant design strategy is implemented and the best capacitor combination is selected by the microprocessor; S5. Provide a visual display interface to monitor patient status and equipment operating status in real time.

[0009] Optionally, inputting the real-time monitored physiological parameters and capacitance status data into a microprocessor, and analyzing the patient's status and the performance of the capacitor includes the following steps: S21, inputting the real-time monitoring data of the patient's physiological parameters and the capacitance state data of the defibrillator into the microprocessor, wherein the physiological parameters include the patient's heart rhythm state, vital sign parameters and physiological reactions; S22, using an electrocardiogram analysis algorithm to perform real-time analysis on the patient's heart rhythm state; S23, formulating the discharge energy according to the discharge energy decision algorithm based on the results of the electrocardiogram analysis and the patient's physical parameters, the physical parameters including the patient's age and weight; S24, monitoring the capacitor state of the defibrillator, and using an adaptive adjustment algorithm to dynamically adjust the charging voltage and charging time according to the current state of the capacitor; S25. Use CPR guidance algorithm to provide real-time CPR operation guidance based on the patient's physiological response.

[0010] Optionally, using an electrocardiogram analysis algorithm to perform real-time analysis on the patient's heart rhythm state includes the following steps: S221, collecting the patient's vital sign parameters and electrocardiogram signals and performing preprocessing; S222, extracting electrophysiological features from the preprocessed electrocardiogram signal; S223, calculating the heart rate according to the interval of the QRS complex wave, and analyzing the regularity of the heart rate in combination with the electrophysiological characteristics; S224, analyzing the rhythm state of the electrocardiogram to identify abnormal heart rhythms such as arrhythmia, tachycardia or bradycardia; S225. Determine whether the patient's heart rhythm state is normal based on the analysis results of the regularity of the heart rate and the rhythm state of the electrocardiogram.

[0011] Optionally, calculating the heart rate according to the interval of the QRS complex wave and analyzing the regularity of the heart rate in combination with the electrophysiological characteristics comprises the following steps: S2231, obtaining an electrocardiogram signal; S2232, identifying the QRS complex wave in the electrocardiogram signal using a peak detection algorithm to obtain the position of the continuous QRS wave; S2233, calculating the heart rate value by measuring the time interval between consecutive QRS waves; S2234, performing a preliminary analysis of the heart rate pattern based on the heart rate calculation result, and observing whether there is any irregularity in the time interval; S2235. Extract other electrophysiological features from electrocardiogram signals; S2236, combining the preliminary analysis of heart rate regularity with other electrophysiological characteristics, and performing a comprehensive analysis to obtain the final regularity of the heart rate; The calculation formula of heart rate value is: .

[0012] Optionally, monitoring the capacitor state of the defibrillator and using an adaptive adjustment algorithm to dynamically adjust the charging voltage and charging time according to the current state of the capacitor includes the following steps: S241, collecting capacitance status data of the defibrillator in real time; S242, inputting the capacitance state data into an adaptive adjustment algorithm; S243, analyzing the current state of the capacitor using an adaptive adjustment algorithm; S244, adjusting the charging voltage and charging time of the defibrillator according to the analysis result of the current state of the capacitor; S245. Feedback the adjusted charging voltage and charging time to the defibrillator, and perform charging operations.

[0013] Optionally, analyzing the current state of the capacitor using the adaptive adjustment algorithm includes the following steps: S2431, calculating the remaining energy of the capacitor according to the capacitor state data; S2432, using the prediction model to predict the energy consumption of the capacitor in the next ten minutes; S2433, calculating whether the capacitor needs to be charged and the charging time according to the remaining energy and the predicted energy consumption; S2434. Calculate the optimal charging voltage and charging time using an adaptive adjustment algorithm based on the current state of the capacitor and the estimated energy demand.

[0014] Optionally, using the prediction model to predict the energy consumption of the capacitor in the next ten minutes includes the following steps: S24321. Divide the time series data into windows of preset fixed size, each window containing capacitor state data within the next ten minutes, and normalize the capacitor state data; S24322, construct an input layer to accept data in the shape of a window of a preset fixed size; S24323. Add a convolution layer to extract features of the time series data through multiple convolution kernels in the convolution layer; S24324, add ReLU activation function after convolutional layer to introduce nonlinearity; S24325, add a pooling layer to reduce the data dimension, then integrate the features of the time series data through a fully connected layer, and finally build an output layer for prediction; S24326. Select a loss function according to the nature of the prediction task and adjust the weight of the prediction model; S24327. Train the prediction model on the training set and evaluate the performance of the prediction model on the test set; S24328. Use the trained prediction model to predict the energy consumption in the next ten minutes based on the latest capacitor status data.

[0015] Optionally, according to the current state of the capacitor and the estimated energy demand, calculating the optimal charging voltage and charging time using an adaptive adjustment algorithm includes the following steps: S24341, obtaining the current state of the capacitor; S24342. Obtain the predicted energy demand within the next ten minutes; S24343, preset threshold and scale factor; S24344, calculating the optimal charging voltage and charging time based on the current voltage, energy demand, preset threshold and proportional factor, and ensuring that the charging voltage and charging time do not exceed the maximum allowable value of the capacitor; S24345. Set the charging parameters of the capacitor according to the calculation results and complete the formulation of the charging plan.

[0016] Optionally, using a CPR guidance algorithm to provide real-time CPR operation guidance based on the patient's physiological response includes the following steps: S251. Collect and analyze patients’ physiological response data; S252, judging the physiological state of the patient according to the physiological parameters of the patient; S253, predict the most needed CPR steps based on physiological status; S254, calculating optimal CPR parameters according to the predicted CPR steps; S255, generating and displaying real-time CPR guidance based on optimal CPR parameters; S256. Monitor the effectiveness of CPR and adjust CPR instructions based on changes.

[0017] Optionally, during the discharge process of the defibrillator, implementing a redundant design strategy and selecting the best capacitor combination by a microprocessor includes the following steps: S41. Evaluate the performance parameters of the capacitor during the discharge process of the defibrillator; S42, generating all capacitor combinations using a combination algorithm according to electrical requirements and physical limitations of the device; S43, calculating the expected discharge energy for each capacitor combination, and analyzing the total energy when the capacitors are connected in parallel or in series; S44, estimating the discharge time of each capacitor combination, and analyzing the differences in charge and discharge characteristics between the capacitors; S45. Compare the performance indicators of different capacitor combinations using multi-objective optimization; S46. Select the best capacitor combination based on the performance indicators and the preset requirements of the defibrillator; S47, adjusting the internal circuit of the defibrillator according to the selected capacitor combination configuration; S48. Test the actual discharge performance of the selected capacitor combination under the control of the microprocessor.

[0018] Compared with the prior art, this application has the following beneficial effects: 1. The present invention uses sensing technology to monitor multiple physiological parameters of patients and the capacitance status of defibrillators in real time to achieve comprehensive data analysis, which not only improves the accuracy of the data, but also can quickly identify critical situations through pattern recognition and anomaly detection; using artificial intelligence and machine learning technology, according to the patient's electrocardiogram and physiological parameters, dynamically adjust the treatment plan, including the discharge energy and frequency of defibrillation, and reduce the patient's discomfort and risks, while improving the treatment effect.

[0019] 2. The state of the defibrillator capacitor in the present invention is monitored and adjusted in real time by an advanced adaptive algorithm to ensure the best performance of the device at critical moments, including predicting the capacitor life based on the frequency of use and environmental conditions and adjusting the charging strategy based on these predictions, thereby maximizing the effective use time of the device. Advanced algorithms are used to dynamically adjust CPR guidance based on the patient's real-time physiological response to improve the success rate of resuscitation, adapt to changes in the patient's body shape and condition, and enhance the pertinence and effectiveness of CPR operations. In-depth cardiac function assessment is performed by analyzing subtle features in the electrocardiogram, which helps to make a timely diagnosis and predict possible heart problems in patients, providing a basis for preventive measures. Combined with advanced predictive models and adaptive adjustment technology, the energy use and charging strategy of the capacitor are optimized to extend the capacitor life, while ensuring that the defibrillator is available at any time when needed. The use of deep learning and big data analysis technology not only improves the accuracy of prediction and analysis, but also can dig out valuable insights from massive data to provide support for clinical decision-making, making emergency measures more accurate and effective.

[0020] 3. The present invention can adjust the discharge energy according to the specific conditions of the patient to ensure that the treatment is both effective and safe. By calculating the optimal voltage for the required energy discharge, the microprocessor optimizes the charging process, improves efficiency and response speed, and shortens the charging time controlled by the microprocessor to ensure timely cardiac resuscitation while fully storing sufficient energy.

[0021] 4. The microprocessor of the present invention can select the best capacitor configuration by evaluating the performance parameters of the capacitor and analyzing different capacitor combinations, thereby improving the performance and reliability of the defibrillator. A combination algorithm is used to generate capacitor combinations, and the best combination is selected through multi-objective optimization, thereby enhancing the adaptability of the defibrillator in emergency medical situations. The capacitor combination configuration adjusted by the microprocessor can ensure the best performance during the discharge process and improve the treatment effect.

[0022] 5. The present invention monitors the patient status and equipment operating status in real time through a visual interface, so that medical staff can respond more quickly and accurately. The visual display of real-time data helps to improve the efficiency and accuracy of medical response, especially in emergency situations. The visual interface makes complex data easy to understand and operate, thereby improving the operational convenience and efficiency of the medical team. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above characteristics, features and advantages of the present invention and their implementation methods and methods will become more clearly understood in conjunction with the following description of the embodiments, which are described in detail in conjunction with the accompanying drawings. Herein, a schematic diagram is shown: Figure 1 The invention discloses a high-precision energy discharge control method for a defibrillator based on CPR compression guidance according to an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] According to an embodiment of the present invention, a high-precision energy discharge control method for a defibrillator based on CPR compression guidance is provided.

[0026] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to the high-precision energy discharge control method of a defibrillator based on CPR compression guidance according to an embodiment of the present invention, the high-precision energy discharge control method of a defibrillator comprises the following steps: S1. Monitor the patient's physiological parameters and the capacitance status of the defibrillator in real time through sensors.

[0027] It needs to be explained that a variety of sensors need to be installed on the patient. The sensors can monitor key physiological parameters such as electrocardiogram (ECG), blood pressure, and oxygen saturation. The sensors are usually small and easy for patients to wear, and can transmit data in real time. In addition, the sensors continuously collect the patient's physiological data and transmit it to the monitoring end in real time via wireless or wired means. Physiological data include key indicators such as heart rate, blood pressure, and oxygenation level, which are crucial for assessing the patient's immediate health status. Capacitor status monitoring of the defibrillator is also key. The defibrillator needs to be kept charged so that it can be used quickly in the event of arrhythmia. The monitoring end will analyze the collected data and determine whether there are any abnormalities through specific algorithms and thresholds. If abnormalities in the patient's physiological parameters or problems with the defibrillator status are detected, an alarm will be immediately issued so that medical staff can respond quickly.

[0028] S2. Input the real-time monitored physiological parameters and capacitor status data into the microprocessor to analyze the patient's status and the performance of the capacitor.

[0029] Preferably, the real-time monitored physiological parameters and capacitance status data are input into a microprocessor, and the analysis of the patient's status and the performance of the capacitor includes the following steps: S21, inputting the real-time monitoring data of the patient's physiological parameters and the capacitance state data of the defibrillator into the microprocessor, wherein the physiological parameters include the patient's heart rhythm state, vital sign parameters and physiological reactions; S22, using an electrocardiogram analysis algorithm to perform real-time analysis on the patient's heart rhythm state; S23, formulating the discharge energy according to the discharge energy decision algorithm based on the results of the electrocardiogram analysis and the patient's physical parameters, the physical parameters including the patient's age and weight; S24, monitoring the capacitor state of the defibrillator, and using an adaptive adjustment algorithm to dynamically adjust the charging voltage and charging time according to the current state of the capacitor; S25. Use CPR guidance algorithm to provide real-time CPR operation guidance based on the patient's physiological response.

[0030] It needs to be explained that the physiological parameters collected by the sensor and the capacitor status data of the defibrillator are input into the microprocessor, and the physiological parameters include heart rhythm status, vital signs parameters (such as blood pressure, oxygen saturation) and physiological reactions. The microprocessor uses an electrocardiogram analysis algorithm to perform real-time analysis of the patient's heart rhythm status, including identifying heart rate, detecting arrhythmia, etc., to evaluate the patient's immediate heart condition. Based on the electrocardiogram analysis results and the patient's vital signs parameters (such as age and weight), the microprocessor uses a discharge energy decision algorithm to determine the appropriate defibrillation discharge energy. The microprocessor monitors the state of the defibrillator capacitor and uses an adaptive adjustment algorithm to dynamically adjust the charging voltage and time according to the current state of the capacitor, ensuring that the defibrillator is always in the best working state. The microprocessor also uses a CPR guidance algorithm to provide real-time cardiopulmonary resuscitation (CPR) operation guidance based on the patient's physiological reactions, helping on-site medical staff or emergency personnel to perform emergency operations more effectively.

[0031] Preferably, using an electrocardiogram analysis algorithm to perform real-time analysis of a patient's heart rhythm state comprises the following steps: S221, collecting the patient's vital sign parameters and electrocardiogram signals and performing preprocessing; S222, extracting electrophysiological features from the preprocessed electrocardiogram signal; S223, calculating the heart rate according to the interval of the QRS complex wave, and analyzing the regularity of the heart rate in combination with the electrophysiological characteristics; S224, analyzing the rhythm state of the electrocardiogram to identify abnormal heart rhythms such as arrhythmia, tachycardia or bradycardia; S225. Determine whether the patient's heart rhythm state is normal based on the analysis results of the regularity of the heart rate and the rhythm state of the electrocardiogram.

[0032] Among them, electrophysiological characteristics include: P wave characteristics, QRS complex wave characteristics, T wave characteristics, PR interval and QT interval, ST segment and heart rate variability; P wave characteristics: P wave represents atrial contraction. Analysis of the shape, duration, amplitude and interval of the P wave can help evaluate atrial function and atrial rhythm abnormalities. QRS complex wave characteristics: QRS complex wave represents the rapid depolarization of the ventricle and is the most significant waveform in the electrocardiogram. Analysis of the width, shape and amplitude of the QRS complex wave can be used to evaluate ventricular function and the location of the ventricular pacemaker. T wave characteristics: T wave represents ventricular repolarization. Analysis of T wave shape, size, and direction helps to evaluate the process of ventricular repolarization and possible myocardial ischemia. PR interval and QT interval: PR interval represents the time from atrial excitation to ventricular excitation, and its length can reflect the atrioventricular node function. QT interval represents the time of the entire ventricular depolarization and repolarization. Abnormality in its length may indicate abnormality of ventricular repolarization. ST segment: The ST segment represents the period from the completion of ventricular depolarization to the beginning of ventricular repolarization. Changes in the ST segment (such as elevation or depression) may indicate myocardial ischemia or myocardial infarction. Heart rate variability (HRV): Heart rate variability refers to the change in the interval between two consecutive heart beats. The analysis of HRV can reflect the regulatory effect of the autonomic nervous system on the heart and is related to the risk of various cardiovascular diseases.

[0033] Preferably, calculating the heart rate according to the interval of the QRS complex wave and analyzing the regularity of the heart rate in combination with the electrophysiological characteristics comprises the following steps: S2231, obtaining an electrocardiogram signal; S2232, identifying the QRS complex wave in the electrocardiogram signal using a peak detection algorithm to obtain the position of the continuous QRS wave; S2233, calculating the heart rate value by measuring the time interval between consecutive QRS waves; S2234, performing a preliminary analysis of the heart rate pattern based on the heart rate calculation result, and observing whether there is any irregularity in the time interval; S2235. Extract other electrophysiological features from electrocardiogram signals; S2236, combining the preliminary analysis of heart rate regularity with other electrophysiological characteristics, and performing a comprehensive analysis to obtain the final regularity of the heart rate; The calculation formula of heart rate value is: .

[0034] Preferably, monitoring the capacitor state of the defibrillator and dynamically adjusting the charging voltage and charging time according to the current state of the capacitor using an adaptive adjustment algorithm comprises the following steps: S241, collecting capacitance status data of the defibrillator in real time; S242, inputting the capacitance state data into an adaptive adjustment algorithm; S243, analyzing the current state of the capacitor using an adaptive adjustment algorithm; S244, adjusting the charging voltage and charging time of the defibrillator according to the analysis result of the current state of the capacitor; S245. Feedback the adjusted charging voltage and charging time to the defibrillator, and perform charging operations.

[0035] In addition, it should be noted that the capacitor status data includes capacitor charge level, charge rate, capacitor health status, temperature data, discharge times and history, and self-discharge rate; Capacitor charge level: This is a key measure of the energy stored in a capacitor. Usually expressed as a voltage value (volts), the charge level of the capacitor determines the energy available to the defibrillator when it is discharged.

[0036] Charge rate: This indicates how quickly the capacitor charges, i.e. how long it takes for the capacitor to go from low to full, which is very important to ensure that the defibrillator can be used quickly in an emergency.

[0037] Capacitor health: This reflects the overall health and effectiveness of the capacitor, including its degree of age or wear. The capacitor's health directly affects its performance and reliability.

[0038] Temperature data: The temperature of the capacitor during the charging and discharging process is also an important indicator. Excessive temperature may indicate that the capacitor is at risk of overload or damage.

[0039] Discharge times and history: Recording the discharge times and history of a capacitor can help assess its long-term performance and reliability. Frequent discharges may affect the life and efficiency of the capacitor.

[0040] Self-discharge rate: Capacitors can slowly lose energy even when not in use, a process called self-discharge. Monitoring the self-discharge rate can help predict the usability of a capacitor after a period of non-use.

[0041] Preferably, analyzing the current state of the capacitor using an adaptive adjustment algorithm comprises the following steps: S2431, calculating the remaining energy of the capacitor according to the capacitor state data; S2432, using the prediction model to predict the energy consumption of the capacitor in the next ten minutes; S2433, calculating whether the capacitor needs to be charged and the charging time according to the remaining energy and the predicted energy consumption; S2434. Calculate the optimal charging voltage and charging time using an adaptive adjustment algorithm based on the current state of the capacitor and the estimated energy demand.

[0042] Preferably, using the prediction model to predict the energy consumption of the capacitor in the next ten minutes includes the following steps: S24321. Divide the time series data into windows of preset fixed size, each window containing capacitor state data within the next ten minutes, and normalize the capacitor state data; S24322, construct an input layer to accept data in the shape of a window of a preset fixed size; S24323. Add a convolution layer to extract features of the time series data through multiple convolution kernels in the convolution layer; S24324, add ReLU activation function after convolutional layer to introduce nonlinearity; S24325, add a pooling layer to reduce the data dimension, then integrate the features of the time series data through a fully connected layer, and finally build an output layer for prediction; S24326. Select a loss function according to the nature of the prediction task and adjust the weight of the prediction model; S24327. Train the prediction model on the training set and evaluate the performance of the prediction model on the test set; S24328. Use the trained prediction model to predict the energy consumption in the next ten minutes based on the latest capacitor status data.

[0043] Preferably, according to the current state of the capacitor and the estimated energy demand, calculating the optimal charging voltage and charging time using an adaptive adjustment algorithm includes the following steps: S24341, obtaining the current state of the capacitor; S24342. Obtain the predicted energy demand within the next ten minutes; S24343, preset threshold and scale factor; S24344, calculating the optimal charging voltage and charging time based on the current voltage, energy demand, preset threshold and proportional factor, and ensuring that the charging voltage and charging time do not exceed the maximum allowable value of the capacitor; S24345. Set the charging parameters of the capacitor according to the calculation results and complete the formulation of the charging plan.

[0044] It should be explained that the above steps involve energy management of capacitors. By analyzing the state data of capacitors (such as voltage, current, etc.), the remaining energy of capacitors can be estimated, which is the basis of power management. Next, the prediction model is used to estimate the energy consumption in the future (for example, within ten minutes), which is crucial for optimizing the use of power and planning charging cycles. Combining the remaining energy and predicted energy consumption, decisions on whether charging is needed and the charging time are made, which helps maintain the health of capacitors and improve their efficiency.

[0045] The construction of the prediction model includes data preprocessing (such as window division and normalization), building the model structure (including input layer, convolution layer, activation function, pooling layer and fully connected layer), selecting a suitable loss function, training the model on the training set, and finally evaluating the model performance on the test set. It utilizes the powerful ability of deep learning in time series data analysis to accurately predict the energy consumption of capacitors, thereby providing a reliable basis for subsequent energy management.

[0046] Finally, the actual charging strategy is formulated. In this process, the optimal charging voltage and time are calculated by setting thresholds and proportional factors, combining the current state of the capacitor and the predicted energy demand. This approach not only takes into account the current energy demand, but also the safety and efficiency of the capacitor. In addition, it is crucial to ensure that the calculated charging parameters do not exceed the maximum allowable value of the capacitor to avoid capacitor damage and increase the service life. Based on these calculation results, the charging parameters of the capacitor are set and the charging plan is completed.

[0047] Preferably, using a CPR guidance algorithm to provide real-time CPR operation guidance based on the patient's physiological response includes the following steps: S251. Collect and analyze patients’ physiological response data; S252, judging the physiological state of the patient according to the physiological parameters of the patient; S253, predict the most needed CPR steps based on physiological status; S254, calculating optimal CPR parameters according to the predicted CPR steps; S255, generating and displaying real-time CPR guidance based on optimal CPR parameters; S256. Monitor the effectiveness of CPR and adjust CPR instructions based on changes.

[0048] It needs to be explained that CPR operations are optimized by real-time analysis and application of the patient's physiological data. The process makes full use of data-driven decision support in every link, from collecting key physiological parameters (such as heart rate, blood pressure, etc.), judging the patient's current physiological state, to predicting and implementing the most suitable CPR steps. In addition, the optimal CPR parameters are calculated according to the patient's specific situation, and CPR guidance, such as compression depth and rate, is generated and adjusted in real time. The core of this method lies in personalization and adaptability. It not only improves the accuracy and effectiveness of CPR operations, but also increases the possibility of successful treatment by continuously monitoring the patient's response and adjusting the CPR guidance accordingly.

[0049] S3. Adjust the discharge energy, charging voltage and charging time according to the analysis results of the microprocessor.

[0050] It is important to explain that the microprocessor receives and analyzes data from sensors on the patient, mainly the electrocardiogram (ECG). Based on this data, it can determine the state of the heart, such as whether ventricular fibrillation or ventricular asystole occurs. The microprocessor determines the most suitable defibrillation shock energy based on the analysis results. In some cases, higher or lower energies may be more effective. For example, a larger adult may require higher energy than a child or a thin adult. The device needs to adjust the charging voltage according to the required discharge energy. The microprocessor calculates the optimal voltage required to achieve the required energy discharge and adjusts the device accordingly. The charging time refers to the time required for the defibrillator to be ready to discharge. This time should be as short as possible to ensure timely cardiac resuscitation while ensuring that enough energy is fully stored.

[0051] S4. During the discharge process of the defibrillator, a redundant design strategy is implemented and the best capacitor combination is selected by the microprocessor.

[0052] Preferably, during the discharge process of the defibrillator, implementing a redundant design strategy and selecting the best capacitor combination by a microprocessor comprises the following steps: S41. Evaluate the performance parameters of the capacitor during the discharge process of the defibrillator; S42, generating all capacitor combinations using a combination algorithm according to electrical requirements and physical limitations of the device; S43, calculating the expected discharge energy for each capacitor combination, and analyzing the total energy when the capacitors are connected in parallel or in series; S44, estimating the discharge time of each capacitor combination, and analyzing the differences in charge and discharge characteristics between the capacitors; S45. Compare the performance indicators of different capacitor combinations using multi-objective optimization; S46. Select the best capacitor combination based on the performance indicators and the preset requirements of the defibrillator; S47, adjusting the internal circuit of the defibrillator according to the selected capacitor combination configuration; S48. Test the actual discharge performance of the selected capacitor combination under the control of the microprocessor.

[0053] It should be explained that, first, the microprocessor adjusts the discharge energy, charging voltage and charging time according to the patient's physiological data to ensure that the treatment is both effective and safe. Then, the patient's status and equipment operation status are monitored in real time through an intuitive visual interface to improve the efficiency and accuracy of medical response. In addition, it explains how to optimize the capacitor combination through redundant design strategies and microprocessor control, including evaluating capacitor performance, generating capacitor combinations, calculating and analyzing discharge energy and time, and performing multi-objective optimization to select the best capacitor combination, which not only improves the performance of the defibrillator, but also increases its reliability and safety in emergency medical situations.

[0054] S5. Provide a visual display interface to monitor patient status and equipment operating status in real time.

[0055] It should be explained that in order for medical staff or rescuers to obtain information quickly and accurately, it is crucial to design an intuitive and easy-to-understand interface. This interface usually displays key physiological data (such as heart rate, blood pressure, oxygen saturation), ECG waveforms and any warnings or instructions; by connecting to sensors on the patient (such as ECG electrodes, blood pressure cuffs, pulse oxygen saturation sensors, etc.), the device can collect the patient's physiological data in real time, and the physiological data is transmitted to the display interface in real time, allowing medical staff to observe the patient's status changes in real time. In addition to monitoring the patient's status, this interface will also display the device's own operating status, including battery power, device readiness, any functional abnormalities or maintenance requirements. This provides important information to ensure that the device is in optimal operating condition. In special cases, the interface may also provide step-by-step instructions or warning information, such as instructing users on how to correctly place electrodes or issuing warnings when the device detects an operating error.

[0056] In summary, with the help of the above technical solutions of the present invention, the present invention monitors multiple physiological parameters of the patient and the capacitance state of the defibrillator in real time through sensing technology to achieve comprehensive data analysis, which not only improves the accuracy of the data, but also can quickly identify critical situations through pattern recognition and anomaly detection; using artificial intelligence and machine learning technology, dynamically adjust the treatment plan according to the patient's electrocardiogram and physiological parameters, including the discharge energy and frequency of defibrillation, and reduce the patient's discomfort and risks, while improving the treatment effect; the state of the defibrillator capacitor is monitored and adjusted in real time through advanced adaptive algorithms to ensure the best performance of the device at critical moments, including the capacitance based on the frequency of use and environmental conditions. The capacitor life is predicted and the charging strategy is adjusted according to these predictions, thereby maximizing the effective use time of the device. Advanced algorithms are used to dynamically adjust CPR guidance according to the patient's real-time physiological response to improve the success rate of resuscitation, adapt to changes in the patient's body shape and condition, and enhance the pertinence and effectiveness of CPR operations. In-depth cardiac function assessment is performed by analyzing subtle features in the electrocardiogram, which helps to make timely diagnosis and predict possible heart problems in patients, providing a basis for preventive measures. Combined with advanced prediction models and adaptive adjustment technology, the energy use and charging strategy of capacitors are optimized, the capacitor life is extended, and at the same time, the defibrillator is ensured to be available when needed. Deep learning and big data analysis technology not only improve the accuracy of prediction and analysis, but also can mine valuable insights from massive data to provide support for clinical decision-making, making emergency measures more accurate and effective; the present invention can adjust the discharge energy according to the specific conditions of the patient (such as body shape, heart state), ensure that the treatment is both effective and safe, and optimize the charging process by calculating the optimal voltage for the required energy discharge, improve efficiency and response speed, and shorten the charging time controlled by the microprocessor to ensure timely cardiac resuscitation while fully storing enough energy; the microprocessor of the present invention can select the best capacitor configuration by evaluating the performance parameters of the capacitor and analyzing different capacitor combinations, improve the performance and reliability of the defibrillator, use a combination algorithm to generate a capacitor combination, and select the best combination through multi-objective optimization, thereby enhancing the adaptability of the defibrillator in emergency medical situations, and the capacitor combination configuration adjusted by the microprocessor can ensure the best performance during the discharge process and improve the treatment effect; the present invention monitors the patient status and equipment operation status in real time through a visual interface, so that medical staff can respond more quickly and accurately, and the visual display of real-time data helps to improve the efficiency and accuracy of medical response, especially in emergency situations, the visual interface makes complex data easy to understand and operate, and improves the convenience and efficiency of the medical team.

[0057] Although the present invention has been disclosed as above with preferred embodiments, the embodiments are merely examples for the convenience of description and are not intended to limit the present invention. Those skilled in the art may make several changes and modifications without departing from the spirit and scope of the present invention. The scope of protection claimed by the present invention shall be based on the claims.

Claims

1. A high-precision energy discharge control method for a defibrillator based on CPR compression guidance, characterized in that: The high-precision energy discharge control method of the defibrillator comprises the following steps: S1, monitor the patient's physiological parameters and the capacitance status of the defibrillator in real time through sensors; S2, inputting the real-time monitored physiological parameters and capacitor status data into a microprocessor to analyze the patient's status and the performance of the capacitor; S3, adjusting the discharge energy, charging voltage and charging time according to the analysis results of the microprocessor; S4. During the discharge process of the defibrillator, a redundant design strategy is implemented and the best capacitor combination is selected by the microprocessor; S5. Provide a visual display interface to monitor patient status and equipment operating status in real time.

2. The high-precision energy discharge control method for a defibrillator based on CPR compression guidance according to claim 1, characterized in that: The step of inputting the real-time monitored physiological parameters and capacitance status data into a microprocessor and analyzing the patient's status and the performance of the capacitance comprises the following steps: S21, inputting the real-time monitoring data of the patient's physiological parameters and the capacitance state data of the defibrillator into the microprocessor, wherein the physiological parameters include the patient's heart rhythm state, vital sign parameters and physiological reactions; S22, using an electrocardiogram analysis algorithm to perform real-time analysis on the patient's heart rhythm status; S23, formulating the discharge energy according to the discharge energy decision algorithm based on the results of the electrocardiogram analysis and the patient's physical parameters, wherein the physical parameters include the patient's age and weight; S24, monitoring the capacitor state of the defibrillator, and using an adaptive adjustment algorithm to dynamically adjust the charging voltage and charging time according to the current state of the capacitor; S25. Use CPR guidance algorithm to provide real-time CPR operation guidance based on the patient's physiological response.

3. The high-precision energy discharge control method for a defibrillator based on CPR compression guidance according to claim 2, characterized in that: The real-time analysis of the patient's cardiac rhythm state using an electrocardiogram analysis algorithm comprises the following steps: S221, collecting the patient's vital sign parameters and electrocardiogram signals and performing preprocessing; S222, extracting electrophysiological features from the preprocessed electrocardiogram signal; S223, calculating the heart rate according to the interval of the QRS complex wave, and analyzing the regularity of the heart rate in combination with the electrophysiological characteristics; S224, analyzing the rhythm state of the electrocardiogram to identify abnormal heart rhythms such as arrhythmia, tachycardia or bradycardia; S225. Determine whether the patient's heart rhythm state is normal based on the analysis results of the regularity of the heart rate and the rhythm state of the electrocardiogram.

4. The high-precision energy discharge control method for a defibrillator based on CPR compression guidance according to claim 3, characterized in that: The step of calculating the heart rate according to the interval of the QRS complex wave and analyzing the regularity of the heart rate in combination with the electrophysiological characteristics comprises the following steps: S2231, obtaining an electrocardiogram signal; S2232, identifying the QRS complex wave in the electrocardiogram signal using a peak detection algorithm to obtain the position of the continuous QRS wave; S2233, calculating the heart rate value by measuring the time interval between consecutive QRS waves; S2234, performing a preliminary analysis of the heart rate pattern based on the heart rate calculation result, and observing whether there is any irregularity in the time interval; S2235. Extract other electrophysiological features from electrocardiogram signals; S2236, combining the preliminary analysis of heart rate regularity with other electrophysiological characteristics, and performing a comprehensive analysis to obtain the final regularity of the heart rate; The calculation formula of the heart rate value is: 。 5. The high-precision energy discharge control method for a defibrillator based on CPR compression guidance according to claim 4, characterized in that: The method of monitoring the capacitor state of the defibrillator and dynamically adjusting the charging voltage and charging time according to the current state of the capacitor using an adaptive adjustment algorithm comprises the following steps: S241, collecting capacitance status data of the defibrillator in real time; S242, inputting the capacitance state data into an adaptive adjustment algorithm; S243, analyzing the current state of the capacitor using an adaptive adjustment algorithm; S244, adjusting the charging voltage and charging time of the defibrillator according to the analysis result of the current state of the capacitor; S245. Feedback the adjusted charging voltage and charging time to the defibrillator, and perform charging operations.

6. The high-precision energy discharge control method for a defibrillator based on CPR compression guidance according to claim 5, characterized in that: The method of analyzing the current state of the capacitor using an adaptive adjustment algorithm comprises the following steps: S2431, calculating the remaining energy of the capacitor according to the capacitor state data; S2432, using the prediction model to predict the energy consumption of the capacitor in the next ten minutes; S2433, calculating whether the capacitor needs to be charged and the charging time according to the remaining energy and the predicted energy consumption; S2434. Calculate the optimal charging voltage and charging time using an adaptive adjustment algorithm based on the current state of the capacitor and the estimated energy demand.

7. The high-precision energy discharge control method for a defibrillator based on CPR compression guidance according to claim 6, characterized in that: The method of using the prediction model to predict the energy consumption of the capacitor in the next ten minutes includes the following steps: S24321. Divide the time series data into windows of preset fixed size, each window containing capacitor state data within the next ten minutes, and normalize the capacitor state data; S24322, construct an input layer to accept data in the shape of a window of a preset fixed size; S24323. Add a convolution layer to extract features of the time series data through multiple convolution kernels in the convolution layer; S24324, add ReLU activation function after convolutional layer to introduce nonlinearity; S24325, add a pooling layer to reduce the data dimension, then integrate the features of the time series data through a fully connected layer, and finally build an output layer for prediction; S24326. Select a loss function according to the nature of the prediction task and adjust the weight of the prediction model; S24327. Train the prediction model on the training set and evaluate the performance of the prediction model on the test set; S24328. Use the trained prediction model to predict the energy consumption in the next ten minutes based on the latest capacitor status data.

8. The high-precision energy discharge control method for a defibrillator based on CPR compression guidance according to claim 7, characterized in that: The method of calculating the optimal charging voltage and charging time by using an adaptive adjustment algorithm according to the current state of the capacitor and the estimated energy demand includes the following steps: S24341, obtaining the current state of the capacitor; S24342. Obtain the predicted energy demand within the next ten minutes; S24343, preset threshold and scale factor; S24344, calculating the optimal charging voltage and charging time based on the current voltage, energy demand, preset threshold and proportional factor, and ensuring that the charging voltage and charging time do not exceed the maximum allowable value of the capacitor; S24345. Set the charging parameters of the capacitor according to the calculation results and complete the formulation of the charging plan.

9. The high-precision energy discharge control method for a defibrillator based on CPR compression guidance according to claim 8, characterized in that: The method of using a CPR guidance algorithm to provide real-time CPR operation guidance based on the patient's physiological response includes the following steps: S251. Collect and analyze patients’ physiological response data; S252, judging the physiological state of the patient according to the physiological parameters of the patient; S253, predict the most needed CPR steps based on physiological status; S254, calculating optimal CPR parameters according to the predicted CPR steps; S255, generating and displaying real-time CPR guidance based on optimal CPR parameters; S256. Monitor the effectiveness of CPR and adjust CPR instructions based on changes.

10. The high-precision energy discharge control method for a defibrillator based on CPR compression guidance according to claim 1, characterized in that: The method of implementing a redundant design strategy and selecting an optimal capacitor combination by a microprocessor during the discharge process of the defibrillator includes the following steps: S41. Evaluate the performance parameters of the capacitor during the discharge process of the defibrillator; S42, generating all capacitor combinations using a combination algorithm according to electrical requirements and physical limitations of the device; S43, calculating the expected discharge energy for each capacitor combination, and analyzing the total energy when the capacitors are connected in parallel or in series; S44, estimating the discharge time of each capacitor combination, and analyzing the differences in charge and discharge characteristics between the capacitors; S45. Compare the performance indicators of different capacitor combinations using multi-objective optimization; S46. Select the best capacitor combination based on the performance indicators and the preset requirements of the defibrillator; S47, adjusting the internal circuit of the defibrillator according to the selected capacitor combination configuration; S48. Test the actual discharge performance of the selected capacitor combination under the control of the microprocessor.

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