Wireless portable vital sign measuring method and system

Abstract

The invention relates to the technical field of medical monitoring, in particular to a wireless portable vital sign measuring method and system.The wireless portable vital sign measuring method comprises the following steps that wireless portable equipment is worn on a monitored person, data are collected through a heart rate sensor and a blood pressure monitor, signal amplification is conducted on heart rate data, filtering is conducted on the signals to remove noise, and the signals are sent to the monitored person; and obtaining a vital sign data record. According to the method, the complexity and irregularity of data fluctuation are monitored through entropy calculation, an abnormal fluctuation mode can be found as soon as possible, real-time health risk assessment is crucial to quick response to emergency situations, the cardiovascular health condition is assessed by comparing an entropy abnormal index with a health risk threshold value, and the health risk assessment accuracy is improved. A cardiovascular health abnormal signal is transmitted to a medical service provider through a wireless network, meanwhile, the safety and efficiency of transmission are ensured by adopting a data encryption and compression technology, and the operability and real-time performance of remote medical treatment are greatly optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of medical monitoring, and particularly to a wireless portable vital sign measurement method and system. Background Art

[0002] The technical field of medical monitoring encompasses various devices and methods for monitoring patients' vital signs and health conditions, enabling medical professionals to collect and analyze patients' physiological information in real time, such as heart rate, blood pressure, respiratory rate, and body temperature. With the development of technology, portable and wireless devices have become increasingly popular, supporting telemedicine services, providing continuous health monitoring for patients, and reducing the need for hospital visits. The devices are designed to be small and lightweight, allowing users to easily use them at home or on the move, significantly improving the quality of life of patients.

[0003] Among them, the wireless portable vital sign measurement method refers to remotely monitoring and recording an individual's vital sign data through wireless devices. The uses of such technologies include real-time health monitoring, disease prevention, emergency response, and long-term health management. By using wireless transmission technology, data can be transmitted to medical providers or cloud servers in real time, enabling the medical team to promptly understand the patient's health condition and respond quickly. The method has shown unique importance and effectiveness in multiple fields such as home care, elderly health monitoring, chronic disease management, sports health, and special working environment conditions.

[0004] Existing medical monitoring technologies have limitations in continuous and real-time monitoring, especially in aspects such as home care, chronic disease management, and special working environment conditions. Traditional monitoring methods rely on intermittent data collection, making it difficult to capture sudden events or rapidly changing vital signs, resulting in delayed diagnosis and response. Existing devices mostly rely on physical connections, restricting the patient's freedom of movement and comfort. At the same time, the data processing capabilities of the devices do not support complex data analysis, such as volatility analysis or entropy value calculation, limiting the in-depth understanding of the patient's condition and early warning capabilities. The deficiencies lead to the deterioration of the health condition or the failure to receive necessary medical intervention in a timely manner. Summary of the Invention

[0005] To address the limitations in continuous and real-time monitoring in the prior art, especially in aspects of home care, chronic disease management, and special operating environmental conditions. Traditional monitoring methods rely on intermittent data collection, making it difficult to capture sudden events or rapidly changing vital signs, resulting in delayed diagnosis and response. Most existing devices rely on physical connections, restricting the freedom of movement and comfort of patients. At the same time, the data processing capabilities of the devices do not support complex data analysis, such as volatility analysis or entropy value calculation, limiting the in-depth understanding of the patient's condition and early warning capabilities. The deficiencies lead to technical problems such as deterioration of health conditions or failure to receive necessary medical interventions in a timely manner. Embodiments of the present invention provide a wireless portable vital sign measurement method and system. The technical solutions are as follows: On the one hand, a wireless portable vital sign measurement method is provided, and the method includes: S1: Wear a wireless portable device on the person being monitored, collect data through a heart rate sensor and a blood pressure monitor, amplify the heart rate signal, filter the signal to remove noise, and obtain a vital sign data record; S2: According to the vital sign data record, analyze the fluctuation amplitude of the heart rate signal and the blood pressure signal, perform frequency domain decomposition on the heart rate signal, identify the change characteristics of the blood pressure signal, and obtain a vital sign volatility analysis result; S3: Through the vital sign volatility analysis result, perform entropy value calculation, calculate the entropy value of the data within a time window, and identify abnormal fluctuation patterns by comparing the entropy value changes of consecutive time windows, obtaining an entropy value anomaly index; S4: Use the entropy value anomaly index to compare with a set health risk threshold, identify the cardiovascular health risk level, and evaluate the health status by calculating the frequency of heart rate and blood pressure anomaly indexes, obtaining a cardiovascular health anomaly signal; S5: Based on the cardiovascular health anomaly signal, transmit it to a medical service provider through a wireless network, set data encryption for verification of transmission security, and perform data compression to optimize the transmission speed, obtaining a health monitoring log.

[0006] As a further solution of the present invention, the vital sign data record includes each measured value of the heart rate, each measured value of the blood pressure, the measurement time point, and the wireless portable device identifier. The vital sign volatility analysis result includes the correlation analysis between the heart rate and the blood pressure, volatility assessment, and potential health risk information. The entropy value anomaly index includes the entropy value within a differential time window, the abnormal entropy value window identifier, and the entropy value change rate. The cardiovascular health anomaly signal includes the number of heart rate anomalies, the number of blood pressure anomalies, and the health risk level classified according to the anomaly frequency. The health monitoring log includes encryption key information, compression ratio information, data transmission timestamp, and data reception verification status.

[0007] As a further aspect of the present invention, a wireless portable device is worn on the person to be monitored, and data is collected through a heart rate sensor and a blood pressure monitor. The steps of amplifying the signal of the heart rate data, filtering the signal to remove noise, and obtaining the vital sign data record are as follows: S101: Wear a wireless portable device on the person to be monitored, collect data through a heart rate sensor and a blood pressure monitor, detect the heartbeat pulse signal using the heart rate sensor, adjust the sensitivity of the heart rate sensor to capture heart rate changes, and synchronously detect blood pressure fluctuations through the blood pressure monitor to obtain amplified heart rate data; S102: Filter the signal to remove noise through the amplified heart rate data, set the frequency of the filter to isolate the noise outside the physiological signal frequency band, use the time window averaging technique to suppress random noise, and check the stability of the signal to obtain the filtered heart rate data; S103: Based on the filtered heart rate data, perform data recording and sorting, set time stamps and classification labels, and automatically store the collected data separately according to heart rate and blood pressure indicators to obtain the vital sign data record.

[0008] As a further aspect of the present invention, according to the vital sign data record, analyze the fluctuation amplitudes of the heart rate signal and the blood pressure signal, perform frequency domain decomposition on the heart rate signal, identify the change characteristics of the blood pressure signal, and the steps of obtaining the vital sign fluctuation analysis result are as follows: S201: Perform frequency domain decomposition on the heart rate signal through the vital sign data record, use the fast Fourier transform algorithm to separate multiple frequency components in the heart rate signal, identify the key frequency components and amplitudes of the heart rate signal, and obtain the heart rate frequency characteristic analysis result; S202: Use the heart rate frequency characteristic analysis result to perform a comparative analysis of the fluctuation amplitudes of the heart rate signal and the blood pressure signal, identify the fluctuation relationship between the heart rate signal and the blood pressure signal, and obtain the heart rate and blood pressure correlation analysis result; S203: Use the heart rate and blood pressure correlation analysis result to monitor and analyze the fluctuation patterns in the blood pressure signal, identify the key change patterns and trends of blood pressure fluctuations, and obtain the vital sign fluctuation analysis result.

[0009] As a further aspect of the present invention, through the vital sign fluctuation analysis result, perform entropy value calculation, calculate the entropy value of the data within the time window, and identify abnormal fluctuation patterns by comparing the entropy value changes of consecutive time windows, and the steps of obtaining the entropy value anomaly index are as follows: S301: Based on the vital sign fluctuation analysis result, perform entropy value calculation, calculate the entropy values of the heart rate and blood pressure data within the time window, evaluate the volatility of the data, and obtain the entropy value data within the time window; S302: Based on the entropy value data within the said time window, conduct entropy value comparison for consecutive time windows, identify the changes in entropy values, evaluate the entropy value fluctuation pattern and the degree of abnormality. Through the identification of abnormal patterns, monitor the potential health risks and physiological changes of vital signs, and obtain the analysis result of entropy value changes; S303: Through the said analysis result of entropy value changes, calibrate the abnormal fluctuation range and frequency, analyze the time windows deviating from the normal range, identify the potential physiological abnormalities and health problems of vital signs, and obtain the entropy value abnormal index.

[0010] As a further solution of the present invention, the formula for calculating the entropy values of heart rate and blood pressure data within the said time window is as follows: ; wherein, is the overall entropy value of heart rate data within the time window, represents the probability distribution value of the th data point within the time window, represents the total number of data points within the time window, represents the average value of the data within the time window, represents the window size, is the adjustment coefficient.

[0011] As a further solution of the present invention, the steps of using the said entropy value abnormal index to compare with the set health risk threshold, identify the cardiovascular health risk level, and evaluate the health status by calculating the frequency of heart rate and blood pressure abnormal indexes to obtain the cardiovascular health abnormal signal are specifically as follows: S401: Based on the said entropy value abnormal index, compare with the set health risk threshold, adjust the threshold to match different health conditions and age groups. If the entropy value exceeds the threshold, there are potential health risks in vital signs, and obtain the cardiovascular risk assessment data; S402: According to the said cardiovascular risk assessment data, identify the degree of cardiovascular health risk, sort and classify according to the severity of the entropy value abnormal index, and obtain the cardiovascular health risk level; S403: Adopt the said cardiovascular health risk level, evaluate the health status by measuring the frequency of heart rate and blood pressure abnormal indexes, calculate the cardiovascular health risk, and obtain the cardiovascular health abnormal signal.

[0012] As a further solution of the present invention, the formula for calculating the said cardiovascular health risk is as follows: ; wherein, is the predicted probability value of cardiovascular health abnormality, represents the intercept in the regression model, is the natural constant, is the frequency of abnormal heart rate, is the frequency of abnormal systolic blood pressure, is the frequency of abnormal diastolic blood pressure, 、 and are weight coefficients.

[0013] As a further solution of the present invention, based on the cardiovascular health abnormal signal, it is transmitted to a medical service provider through a wireless network, data encryption verification is set to ensure transmission security, and data compression is performed to optimize the transmission speed. The steps to obtain the health monitoring log are specifically as follows: S501: Utilize the cardiovascular health abnormal signal to be transmitted to a medical service provider through a wireless network, set network connection parameters including transmission frequency and data packet size, and check the privacy and integrity of the data during transmission to obtain encrypted transmission data; S502: Use the encrypted transmission data to perform data compression to optimize the transmission speed, adjust the data compression ratio to balance data integrity and transmission efficiency, and obtain compressed and optimized data; S503: Through the compressed and optimized data, perform data decompression and verification, record the information of each data transmission and processing, and real-time track and manage the health data of vital signs to obtain the health monitoring log.

[0014] On the other hand, an electric vehicle status monitoring system is provided. The electric vehicle status monitoring system is used to execute the above-mentioned electric vehicle status monitoring method. The system includes: The data recording module wears a wireless portable device on the person to be monitored, and collects heart rate and blood pressure data through a heart rate sensor and a blood pressure monitor to obtain the original vital sign data; The signal processing module amplifies the original vital sign data and removes noise through a high-pass filter to obtain the processed vital sign signal; The data analysis module analyzes the fluctuation patterns of the heart rate and blood pressure signals based on the processed vital sign signal, identifies the correlation between the heart rate signal and the blood pressure signal, and evaluates the volatility of the vital signs to obtain the volatility analysis result; The abnormal detection module performs entropy value analysis on the volatility analysis result, calculates the entropy values of multiple time windows and monitors the change trend of the entropy values to identify abnormal fluctuation patterns and obtain the entropy value abnormal index; The risk identification module compares the entropy value abnormal index with the set cardiovascular health risk threshold to identify the cardiovascular health risk level and obtain the cardiovascular health risk assessment result; The data transmission module encrypts and compresses the cardiovascular health risk assessment result and transmits it to a medical service provider through a wireless network to obtain the health monitoring log.

[0015] The beneficial effects brought by the technical solution provided by the embodiments of the present invention at least include: Collect heart rate and blood pressure data through a wireless portable device worn on the person being monitored, and perform signal amplification and filtering to remove noise, ensuring the accuracy and reliability of the obtained data, enhancing the fine monitoring of vital signs, and being able to more effectively identify health risks. By calculating the entropy value, monitor the complexity and irregularity of the fluctuations in the monitoring data, which helps to detect abnormal fluctuation patterns early. This real-time health risk assessment is crucial for a rapid response to emergencies. By comparing the entropy value anomaly index with the health risk threshold, not only can the cardiovascular health status be evaluated, but also the health assessment can be further refined through frequency analysis. Transmit the cardiovascular health anomaly signal to the medical service provider through a wireless network, and at the same time use data encryption and compression technologies to ensure the security and efficiency of the transmission, greatly optimizing the operability and real-time performance of telemedicine. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a schematic diagram of the working process of the present invention; Figure 2 It is a detailed flowchart of S1 of the present invention; Figure 3 It is a detailed flowchart of S2 of the present invention; Figure 4 It is a detailed flowchart of S3 of the present invention; Figure 5 It is a detailed flowchart of S4 of the present invention; Figure 6 It is a detailed flowchart of S5 of the present invention; Figure 7 It is a system flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0017] The following describes the technical solutions in the present invention in conjunction with the accompanying drawings.

[0018] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly, the use of the word "example" is intended to present concepts in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two can be selected.

[0019] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail in conjunction with the accompanying drawings and specific embodiments.

[0020] Please refer to Figure 1, embodiments of the present invention provide a wireless portable vital sign measurement method, and the processing flow of this method may include the following steps: S1: Wear a wireless portable device on the person to be monitored, collect data through a heart rate sensor and a blood pressure monitor, amplify the heart rate data signal, filter the signal to remove noise, and obtain vital sign data records; S2: According to the vital sign data records, analyze the fluctuation amplitudes of the heart rate signal and the blood pressure signal, perform frequency domain decomposition on the heart rate signal, identify the change characteristics of the blood pressure signal, and obtain the vital sign fluctuation analysis results; S3: Through the vital sign fluctuation analysis results, perform entropy value calculation, calculate the entropy value of the data within the time window, and identify abnormal fluctuation patterns by comparing the entropy value changes of consecutive time windows to obtain the entropy value anomaly index; S4: Use the entropy value anomaly index to compare with the set health risk threshold, identify the cardiovascular health risk level, and evaluate the health status by calculating the frequencies of the heart rate and blood pressure anomaly indexes to obtain the cardiovascular health abnormal signal; S5: Based on the cardiovascular health abnormal signal, transmit it to the medical service provider through a wireless network, set data encryption verification for transmission security, and perform data compression to optimize the transmission speed to obtain the health monitoring log.

[0021] The vital sign data records include each measured value of the heart rate, each measured value of the blood pressure, the measurement time point, and the wireless portable device identifier. The vital sign fluctuation analysis results include the correlation analysis between the heart rate and the blood pressure, the fluctuation assessment, and potential health risk information. The entropy value anomaly index includes the entropy value within the differential time window, the abnormal entropy value window identifier, and the entropy value change rate. The cardiovascular health abnormal signal includes the number of occurrences of heart rate abnormalities, the number of occurrences of blood pressure abnormalities, and the health risk level classified according to the abnormal frequency. The health monitoring log includes the encryption key information, the compression rate information, the data transmission timestamp, and the data reception verification status.

[0022] Please refer to Figure 2 , the steps of wearing a wireless portable device on the person to be monitored, collecting data through a heart rate sensor and a blood pressure monitor, amplifying the heart rate data signal, filtering the signal to remove noise, and obtaining vital sign data records are specifically as follows: S101: Wear a wireless portable device on the person to be monitored, collect data through a heart rate sensor and a blood pressure monitor, use the heart rate sensor to detect the heartbeat pulse signal, adjust the sensitivity of the heart rate sensor to capture heart rate changes, and synchronously detect blood pressure fluctuations through the blood pressure monitor to obtain amplified heart rate data; During the process of wearing a wireless portable device on the person being monitored, the settings of the heart rate sensor and blood pressure monitor are crucial. The sensor needs to accurately capture the heartbeat pulse signal, and the monitor synchronously records the blood pressure changes. The calibration of the device involves fine-tuning the sensitivity of the sensor to ensure that it can reflect the slightest changes in heart rate in real time. The adjustment of sensitivity is based on the real-time feedback of heart rate data, which is continuously optimized through the test feedback of the device. A highly sensitive sensor can quickly and accurately reflect the slightest changes in the heart rate interval under different physiological states. It is also crucial for the blood pressure monitor to synchronously detect blood pressure fluctuations, as the changes in blood pressure are closely related to the pressure and health status of the heart. By appropriately configuring and debugging the device, the accuracy and reliability of the monitoring data can be ensured, which is of great significance for long-term health monitoring and disease prevention. By real-time monitoring of heart rate and blood pressure, a more comprehensive analysis of the physiological state of the person being monitored can be carried out. The data is obtained through the synchronous operation of the heart rate sensor and blood pressure monitor to acquire amplified heart rate data.

[0023] S102: Through the amplified heart rate data, perform signal filtering to remove noise, set the frequency of the filter to isolate the noise outside the physiological signal frequency band, use the time window averaging technique to suppress random noise, check the stability of the signal, and obtain the filtered heart rate data; Perform signal filtering to remove noise through the amplified heart rate data, according to the formula: ; Calculate the filtered heart rate data. In the formula, represents the filtered heart rate data, represents the original heart rate data, represents the weight coefficient of the filter ; represents time, represents the number of samples used in the filter; The collected amplified heart rate data contains certain noise and needs to isolate unnecessary noise by setting a filter. Set the original heart rate data as [90, 92, 95, 94, 91, 90, 93, 95] bpm, and use a simple moving average filter for 8 samples. Each weight coefficient ; The calculation process is as follows: ; The result shows that after being processed by the filter, a relatively smooth heart rate data is obtained, which can more accurately reflect the average heart rate level of the person being monitored.

[0024] S103: Based on the filtered heart rate data, perform data recording and sorting, set time stamps and classification labels, and automatically store the collected data separately according to the heart rate and blood pressure indicators. The execution process of obtaining the vital sign data record is as follows; Optimize data storage by setting time stamps and classification labels. The heart rate and blood pressure data are stored separately. To ensure the accuracy and availability of the data, the time stamp ensures the accurate recording time of the data points, and the classification label facilitates subsequent data analysis and processing. The storage of each data point requires verifying the integrity and accuracy of the data, which involves data formatting and error checking to ensure that each recorded data is reliable. Through such a process, the efficiency of data processing and data quality can be effectively improved. It can be used for subsequent health condition analysis and medical judgment. The process of automatically storing the collected data separately according to the heart rate and blood pressure indicators requires delicate design to ensure data security and privacy, and at the same time, it is necessary to optimize the data query and access speed so that the data can be quickly obtained when needed to obtain the vital sign data record.

[0025] Please refer to Figure 3 , according to the vital sign data record, analyze the fluctuation amplitude of the heart rate signal and blood pressure signal, perform frequency domain decomposition on the heart rate signal, and identify the change characteristics of the blood pressure signal. The steps to obtain the vital sign fluctuation analysis result are specifically as follows: S201: Through the vital sign data record, perform frequency domain decomposition on the heart rate signal, use the fast Fourier transform algorithm to separate multiple frequency components in the heart rate signal, and identify the key frequency components and amplitudes of the heart rate signal. The execution process of obtaining the heart rate frequency characteristic analysis result is as follows; When performing frequency domain decomposition on the heart rate signal, use the fast Fourier transform algorithm to perform spectral analysis on the heart rate data. This process involves converting time series data into frequency components, which can clarify the amplitudes of different frequencies and their contribution degrees in the overall signal, and further identify the key frequency components. Based on the frequency domain analysis of the heart rate signal, by calculating and comparing the amplitudes of the main frequency components in detail, it is determined that some frequency components are dominant, which is crucial for analyzing heart function and predicting potential heart problems. For example, the fundamental frequency of the heart rate reflects the average speed of the heartbeat, while the high-frequency components reveal the irregularity or pathological characteristics of the heart rate. By analyzing the frequency components, it helps to better understand the patient's heart condition. This process not only improves the understanding of heart rate data but also enhances the ability to judge cardiac arrhythmias, providing a scientific basis for subsequent monitoring to obtain the heart rate frequency characteristic analysis result.

[0026] S202: Adopt the heart rate frequency characteristic analysis result to perform a comparative analysis of the fluctuation amplitudes of the heart rate signal and blood pressure signal, and identify the fluctuation relationship between the heart rate signal and blood pressure signal. The execution process of obtaining the heart rate and blood pressure correlation analysis result is as follows; In the comparative analysis of the fluctuation amplitudes of the heart rate signal and the blood pressure signal, the two signals are first preliminarily processed by an algorithm, including filtering and denoising, to ensure the accuracy of the analysis. The amplitude fluctuations of the two signals are compared by statistical methods to identify the correlation between the heart rate and the blood pressure. This analysis helps doctors understand the performance of the cardiovascular system under different physiological and pathological conditions, as well as the degree of influence of heart rate changes on blood pressure. For example, an increase in heart rate is related to an increase in blood pressure, and this relationship can be explained by the theory of pressure and volume responses of the cardiovascular system. Through the comprehensive analysis of physiological signals, the cardiovascular health status of patients can be more accurately evaluated, and potential health risks can be predicted. It not only provides the fluctuation patterns of heart rate and blood pressure, but also reveals how they interact, providing important information for disease diagnosis and health monitoring, and obtaining the analysis results of the correlation between heart rate and blood pressure.

[0027] S203: The execution process of using the analysis result of the correlation between heart rate and blood pressure to monitor and analyze the fluctuation pattern in the blood pressure signal, identify the key change patterns and trends of blood pressure fluctuations, and obtain the analysis result of vital sign fluctuations is as follows; Analyze the fluctuation pattern in the blood pressure signal according to the formula: ; Calculate the blood pressure volatility. In the formula, represents the blood pressure value of a single measurement, represents the average blood pressure value, represents the number of measurements; Consider a simple data set that includes 10 blood pressure measurement values: 120, 125, 130, 128, 132, 130, 134, 136, 138, 135; Calculate the average of the values to obtain ; The squares of the differences between each blood pressure value and the average value are 100.64, 34.56, 0.64, 7.84, 1.44, 0.64, 10.24, 27.04, 51.84, 17.64; Sum the squared values to obtain ; Calculate the volatility index according to the formula: ; This result shows that the blood pressure value fluctuates within a certain range, indicating that during this period, the blood pressure stability of the monitored person is poor and further medical monitoring or intervention is required.

[0028] Please refer to Figure 4, through the analysis results of vital sign fluctuations, perform entropy value calculation, calculate the entropy value of the data within the time window, and identify abnormal fluctuation patterns by comparing the entropy value changes of consecutive time windows. The specific steps for obtaining the entropy value anomaly index are as follows: S301: Based on the analysis results of vital sign fluctuations, perform entropy value calculation, calculate the entropy values of heart rate and blood pressure data within the time window, evaluate the volatility of the data, and the execution process for obtaining the entropy value data within the time window is as follows; The formula for calculating the entropy values of heart rate and blood pressure data within the time window is as follows: ; Among them, is the overall entropy value of the heart rate data within the time window, represents the probability distribution value of the th data point within the time window, represents the total number of data points within the time window, represents the average value of the data within the time window, represents the window size, is the adjustment coefficient; Parameter meaning and setting value: represents the probability distribution value of the th data point within the time window, which is calculated through the occurrence frequency of the heart rate data within this window; represents the total number of data points within this time window. For example, there are 600 heart rate readings within a 10 - minute window; represents the average value of the data within the time window, which is obtained by calculating the average of the heart rate readings; represents the window size. For example, the window size is selected as 10 minutes; is the adjustment coefficient, set to 0.5. This value is used for weighted calculation of skewness to improve the detection ability of data mutations. This value is selected based on experimental data of entropy value sensitivity adjustment; Substitute the parameters into the formula for calculation: Set that there are 600 heart rate data points within a 10 - minute window (600 seconds), the average value of the data points is 70 beats per minute, and the probability distribution Assume that the probabilities of each point are equal, that is , , , , the data point is distributed between 65 and 75 beats per minute, ; Substitute the parameters into the formula: ; Calculate the first part of entropy (probability distribution entropy): Probability of each data point ; Calculate each Logarithmic value ; Sum over all data points: ; The result of the first part is ; Calculate the second part of entropy (skewness weighted entropy): Set to represent the deviation of each heart rate data from the average value of 70. Assume that the heart rate data is evenly distributed between 65 and 75. The cubic mean of the deviation can be approximately estimated as: ; Calculate the square root of the sum of the cubes of the deviations and divide by the window size: ; Apply the adjustment coefficient ; ; Combine the results of the two parts to calculate the total entropy value: ; Result Indicates that within this time window, the entropy value of the heart rate data is relatively high, indicating that the heart rate has significant fluctuations during this period. This high entropy value of fluctuations can be used for further analysis of heart rate instability or potential health problems.

[0029] S302: According to the entropy value data within the time window, conduct a comparison of entropy values for consecutive time windows, identify changes in entropy values, evaluate the entropy value fluctuation pattern and degree of abnormality. Through the identification of abnormal patterns, monitor potential health risks and physiological changes in vital signs. The execution process for obtaining the entropy value change analysis result is as follows; During the process of comparing entropy values for consecutive time windows, it is necessary to calculate the entropy value of each time window through an algorithm. This calculation involves statistical analysis of vital sign data such as heart rate and blood pressure. Entropy value, as a measure of data randomness, is calculated based on the distribution of vital sign data. By comparing the entropy values within consecutive time windows, the change trend and pattern of entropy values can be identified, and the pattern and degree of abnormality of entropy value fluctuations can be evaluated to monitor the patient's health status in real-time. A sudden increase in the entropy value indicates a sharp change in vital signs, which is related to physiological abnormalities or health problems. Through this analysis, potential health risks can be identified at an early stage, providing data support for timely medical intervention. It is an important tool for monitoring and evaluating the patient's health status to obtain the entropy value change analysis result.

[0030] S303: The execution process of obtaining the entropy value anomaly index is as follows: Analyze the results of entropy value change, calibrate the abnormal fluctuation range and frequency, analyze the time window deviating from the normal range, and identify potential physiological abnormalities and health problems in vital signs; During the process of calibrating the abnormal fluctuation range and frequency, analyze the time window deviating from the normal range, which requires in-depth analysis of the long-term collected vital sign data. By setting reasonable thresholds to define what range of entropy value fluctuations is normal and what degree of change is abnormal, the setting of the thresholds is based on statistical data and clinical experience, which can help medical experts quickly identify potential physiological abnormalities and health problems. The calibrated abnormal fluctuation range and frequency provide a clear reference standard for medical monitoring, enabling the medical team to more accurately monitor and analyze the patient's health status, timely detect and intervene in health risks, which is an important tool for continuously monitoring and evaluating the patient's vital signs, making health management more systematic and data-driven, and providing a safer and more effective medical guarantee for patients, thus obtaining the entropy value anomaly index.

[0031] Please refer to Figure 5 , and use the entropy value anomaly index to compare with the set health risk threshold to identify the cardiovascular health risk level. The specific steps of obtaining the cardiovascular health abnormal signal by evaluating the health status through calculating the frequency of heart rate and blood pressure abnormal indicators are as follows: S401: Based on the entropy value anomaly index, compare it with the set health risk threshold, and adjust the threshold to match different health conditions and age groups. If the entropy value exceeds the threshold, there is a potential health risk in the vital signs. The execution process of obtaining the cardiovascular risk assessment data is as follows: It is necessary to adjust the threshold for different health conditions and age groups because the physiological indicators of different age groups are different and need to be precisely matched to ensure the accuracy of the assessment. By statistically analyzing the entropy value data of past patients, set an initial threshold and then dynamically adjust it according to the actual situation. The adjustment process takes into account the patient's medical history and living habits, as well as various physiological parameters related to the entropy value. If the entropy value exceeds the threshold, it indicates that there is a potential health risk in the vital signs, allowing medical providers to quickly identify individuals who need further examination and take preventive measures to obtain the cardiovascular risk assessment data.

[0032] S402: According to the cardiovascular risk assessment data, identify the degree of cardiovascular health risk, sort and classify according to the severity of the entropy value anomaly index. The execution process of obtaining the cardiovascular health risk level is as follows: The process of identifying the degree of cardiovascular health risk involves ranking and grading the severity of entropy value abnormal indicators. In this process, healthcare providers compare the entropy value data with established risk rating criteria, which are set based on historical health data and regularly updated to reflect the latest medical research results. In this way, doctors can quickly identify the level of cardiovascular health risk faced by patients according to the high or low entropy value, provide corresponding medical advice or preventive measures for patients. This classification makes medical intervention more targeted, increases the probability of preventing risks, is extremely important for medical decision-making and patient management, provides a more personalized and precise health management plan for patients, and obtains the cardiovascular health risk level.

[0033] S403: The execution process of adopting the cardiovascular health risk level to evaluate the health status by measuring the frequency of abnormal indicators of heart rate and blood pressure, calculating the cardiovascular health risk, and obtaining the cardiovascular health abnormal signal is as follows; The formula for calculating the cardiovascular health risk is as follows: ; Among them, is the predicted probability value of cardiovascular health abnormality, represents the intercept in the regression model, is the natural constant, is the frequency of heart rate abnormality, is the frequency of systolic blood pressure abnormality, is the frequency of diastolic blood pressure abnormality, , and are the weight coefficients.

[0034] Parameter meanings and setting values: is the model intercept, set to 1.5, reflecting the baseline risk, and this value is based on the average risk level in historical data; is the weight of the frequency of heart rate abnormality, set to 0.05, and this weight is obtained from the statistical analysis of the impact of heart rate abnormality on cardiovascular events; is the weight of the frequency of systolic blood pressure abnormality, set to 0.04, and this value comes from the epidemiological study on the correlation between systolic blood pressure abnormality and cardiovascular disease; is the weight of the frequency of diastolic blood pressure abnormality, set to 0.03, based on the medical research data on the relationship between diastolic blood pressure and cardiovascular disease; is the frequency of heart rate abnormality, set to record 5 heart rate abnormalities in one detection; is the frequency of systolic blood pressure abnormality, set to 3 times; Let the diastolic blood pressure abnormality frequency be set to 2 times; Substitute the parameters into the formula for calculation: ; The result of 0.255 indicates that based on the input heart rate and blood pressure abnormality frequencies, the predicted probability of cardiovascular health abnormality is 25.5%. This result indicates that the patient is at a relatively low cardiovascular risk level. Based on this probability, the medical team can further decide whether in-depth intervention is needed.

[0035] Please refer to Figure 6 , the steps of obtaining the health monitoring log by transmitting the cardiovascular health abnormality signal to the medical service provider via a wireless network, setting data encryption to verify transmission security, and performing data compression to optimize the transmission speed are as follows: S501: Use the cardiovascular health abnormality signal to transmit it to the medical service provider via a wireless network. Set the network connection parameters, including the transmission frequency and packet size, and check the privacy and integrity of the data during transmission. The execution process of obtaining the encrypted transmission data is as follows; Set the network connection parameters and check the data privacy according to the formula: ; Calculate the data transmission security. In the formula, is the security index value of data transmission, represents the failure rate of the network, represents the transmission time, represents the packet size, represents the total transmission capacity, is the natural constant; Set that in a given network environment, the average hourly failure rate is 0.02, that is, the probability of interruption per hour is 2%, the transmission time is 1 hour, the packet size is 200 KB, and the total transmission capacity is 1000 KB; Calculate the ratio of the packet size to the total transmission capacity to get ; Substitute the value into the formula to calculate the data transmission security index: ; This result indicates that considering the network failure rate and packet size, the security of the data during transmission is 81.87%. It shows that under the current network settings, the security of data transmission is acceptable, but there is still room for improvement. By increasing encryption technology or improving network parameters, such as reducing the failure rate or optimizing packet management, the security and efficiency of data transmission can be further improved.

[0036] S502: The execution process of obtaining compressed and optimized data by using encrypted data transmission, optimizing the transmission speed through data compression, and adjusting the data compression ratio to balance data integrity and transmission efficiency is as follows; In the process of optimizing the transmission speed through data compression, it is necessary to set a reasonable data compression ratio, which is to balance data integrity and transmission efficiency. The choice of compression ratio depends on the type of the original data and the required transmission speed. Adjusting the compression ratio through algorithms can significantly improve the efficiency of network transmission while keeping the data content intact without loss. For example, using common compression technologies such as ZIP or RLE can reduce the data volume without sacrificing data quality, speed up the transmission speed. After obtaining the compressed and optimized data, important cardiovascular health data can be transmitted to medical service providers in a shorter time, which is especially crucial for the rapid response to acute events, and obtain the compressed and optimized data.

[0037] S503: The execution process of obtaining a health monitoring log by decompressing and verifying the compressed and optimized data, recording information on each data transmission and processing, and real-time tracking and managing the health data of vital signs is as follows; In the process of decompressing and verifying the data, it is very important to ensure that each data transmission and processing is correctly recorded and managed. The data decompression and verification program not only ensures data integrity but also provides support for subsequent data analysis. Recording information on each data transmission and processing helps medical service providers track and manage patients' health data, providing detailed historical data and an analysis basis for patients' health management. Real-time tracking and managing the health data of vital signs ensure that the medical team can immediately obtain the latest health status of patients and effectively carry out medical interventions and health advice, and obtain the health monitoring log.

[0038] On the other hand, an electric vehicle status monitoring system is provided. The electric vehicle status monitoring system is used to execute the above-mentioned electric vehicle status monitoring method. The system includes: The data recording module wears a wireless portable device on the person to be monitored, and collects heart rate and blood pressure data through a heart rate sensor and a blood pressure monitor to obtain the original vital sign data; The signal processing module amplifies the original vital sign data and removes noise through a high-pass filter to obtain the processed vital sign signal; The data analysis module analyzes the fluctuation patterns of the heart rate and blood pressure signals based on the processed vital sign signal, identifies the correlation between the heart rate signal and the blood pressure signal, and evaluates the volatility of the vital signs to obtain the volatility analysis result; The anomaly detection module performs entropy value analysis on the volatility analysis result, calculates the entropy values of multiple time windows and monitors the change trend of the entropy values to identify abnormal fluctuation patterns and obtain the entropy value anomaly index; The risk identification module compares the entropy value anomaly index with the set cardiovascular health risk threshold to identify the cardiovascular health risk level and obtain the cardiovascular health risk assessment result; The data transmission module encrypts and compresses the cardiovascular health risk assessment result and transmits it to the medical service provider through a wireless network to obtain a health monitoring log.

[0039] As described above, it is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims

1. A wireless portable vital signs measurement method, characterized in that: The following steps are involved: The monitored person wears a wireless portable device, collects data through a heart rate sensor and a blood pressure monitor, amplifies the heart rate data, filters the signal to remove noise, and obtains a record of vital sign data; According to the vital sign data records, analyzing the fluctuation amplitudes of the heart rate signal and the blood pressure signal, performing frequency domain decomposition on the heart rate signal, identifying the change characteristics of the blood pressure signal, and obtaining the vital sign volatility analysis results; Based on the vital sign volatility analysis results, entropy calculation is performed to calculate the entropy value of the data in the time window, and by comparing the entropy value changes in consecutive time windows, abnormal fluctuation patterns are identified to obtain an entropy value abnormality index; The entropy value abnormality index is compared with the set health risk threshold to identify the cardiovascular health risk level, and the health status is evaluated by calculating the frequency of heart rate and blood pressure abnormality indicators to obtain a cardiovascular health abnormality signal; Based on the abnormal cardiovascular health signal, it is transmitted to the medical service provider via a wireless network, data encryption is set to verify the transmission security, and data compression is performed to optimize the transmission speed to obtain a health monitoring log.

2. The wireless portable vital signs measurement method according to claim 1, characterized in that: The vital signs data record includes each measurement value of the heart rate, each measurement value of the blood pressure, the measurement time point, and the wireless portable device identifier. The vital signs volatility analysis results include the correlation analysis of heart rate and blood pressure, volatility assessment, and potential health risk information. The entropy value abnormality indicator includes the entropy value within the differentiated time window, the abnormal entropy value window identifier, and the entropy value change rate. The cardiovascular health abnormality signal includes the number of abnormal heart rate occurrences, the number of abnormal blood pressure occurrences, and the health risk level classified according to the abnormal frequency. The health monitoring log includes encryption key information, compression rate information, data transmission timestamp, and data reception verification status.

3. The wireless portable vital sign measurement method according to claim 1, characterized in that: The steps of wearing a wireless portable device on the monitored person, collecting data through a heart rate sensor and a blood pressure monitor, amplifying the heart rate data, filtering the signal to remove noise, and obtaining vital sign data records are as follows: The monitored person wears a wireless portable device, collects data through a heart rate sensor and a blood pressure monitor, uses the heart rate sensor to detect heartbeat pulse signals, adjusts the sensitivity of the heart rate sensor to capture heart rate changes, and uses the blood pressure monitor to synchronously detect blood pressure fluctuations to obtain heart rate amplification data; Through the amplified heart rate data, signal filtering is performed to remove noise, the frequency of the filter is set, the noise outside the frequency band of the physiological signal is isolated, the random noise is suppressed by using the time window averaging technology, the stability of the signal is checked, and the filtered heart rate data is obtained; Based on the filtered heart rate data, data is recorded and sorted, timestamps and classification labels are set, and the collected data is automatically stored separately according to heart rate and blood pressure indicators to obtain vital sign data records.

4. The wireless portable vital sign measurement method according to claim 1, characterized in that: According to the vital sign data record, the steps of analyzing the fluctuation amplitude of the heart rate signal and the blood pressure signal, performing frequency domain decomposition on the heart rate signal, identifying the change characteristics of the blood pressure signal, and obtaining the vital sign volatility analysis result are specifically as follows: Perform frequency domain decomposition of the heart rate signal through the vital sign data record, use the fast Fourier transform algorithm to separate multiple frequency components in the heart rate signal, identify the key frequency components and amplitude of the heart rate signal, and obtain the heart rate frequency characteristic analysis result; Using the heart rate frequency characteristic analysis result, a comparative analysis of the fluctuation amplitudes of the heart rate signal and the blood pressure signal is performed to identify the fluctuation relationship between the heart rate signal and the blood pressure signal, and obtain a heart rate and blood pressure correlation analysis result; The heart rate and blood pressure correlation analysis results are used to monitor and analyze fluctuation patterns in blood pressure signals, identify key change patterns and trends in blood pressure fluctuations, and obtain vital sign fluctuation analysis results.

5. The wireless portable vital sign measurement method according to claim 1, characterized in that: The steps of performing entropy calculation based on the vital sign volatility analysis result, calculating the entropy value of the data in the time window, identifying abnormal fluctuation patterns by comparing the entropy value changes in consecutive time windows, and obtaining the entropy value abnormality index are as follows: Based on the vital sign volatility analysis result, entropy value calculation is performed to calculate the entropy value of the heart rate and blood pressure data within the time window, the volatility of the data is evaluated, and the entropy value data within the time window is obtained; According to the entropy data in the time window, entropy values ​​of continuous time windows are compared to identify changes in entropy values, evaluate entropy fluctuation patterns and abnormalities, monitor potential health risks and physiological changes of vital signs through identification of abnormal patterns, and obtain entropy change analysis results; Through the entropy value change analysis results, the abnormal fluctuation range and frequency are calibrated, the time window that deviates from the normal range is analyzed, the potential physiological abnormalities and health problems of vital signs are identified, and the entropy value abnormality index is obtained.

6. The wireless portable vital sign measurement method according to claim 5, characterized in that: The formula for calculating the entropy value of heart rate and blood pressure data within the time window is as follows: ； in, is the overall entropy value of the heart rate data in the time window, Represents the time window The probability distribution value of the data point, Represents the total number of data points in the time window, Represents the average value of the data in the time window. Represents the window size, is the adjustment factor.

7. The wireless portable vital sign measurement method according to claim 1, characterized in that: The entropy abnormality index is compared with the set health risk threshold to identify the cardiovascular health risk level, and the health status is evaluated by calculating the frequency of heart rate and blood pressure abnormality indicators. The specific steps of obtaining the cardiovascular health abnormality signal are as follows: Based on the entropy abnormality index, the index is compared with the set health risk threshold, and the threshold is adjusted to match the differentiated health conditions and age groups. If the entropy value exceeds the threshold, the vital signs have potential health risks, and cardiovascular risk assessment data is obtained; According to the cardiovascular risk assessment data, the cardiovascular health risk level is identified, and the entropy value abnormality indicators are sorted and graded according to their severity to obtain the cardiovascular health risk level; The cardiovascular health risk level is adopted to evaluate the health status, calculate the cardiovascular health risk, and obtain the cardiovascular health abnormality signal by measuring the frequency of abnormal heart rate and blood pressure indicators.

8. The wireless portable vital sign measurement method according to claim 7, characterized in that: The formula for calculating the cardiovascular health risk is as follows: ； in, is the predicted probability value of abnormal cardiovascular health, represents the intercept in the regression model, is a natural constant, Abnormal heart rate frequency, The frequency of abnormal systolic blood pressure, The frequency of abnormal diastolic blood pressure, , and is the weight coefficient.

9. The wireless portable vital sign measurement method according to claim 1, characterized in that: Based on the abnormal cardiovascular health signal, the health monitoring log is transmitted to the medical service provider through a wireless network, data encryption is set to verify the transmission security, and data compression is performed to optimize the transmission speed. The specific steps of obtaining the health monitoring log are: Utilizing the abnormal cardiovascular health signal, transmitting it to a healthcare provider via a wireless network, setting network connection parameters, including transmission frequency and data packet size, and checking the privacy and integrity of the data during transmission to obtain encrypted transmission data; The encrypted transmission data is adopted to perform data compression to optimize the transmission speed, and the data compression ratio is adjusted to balance the data integrity and transmission efficiency to obtain compressed optimized data; By compressing and optimizing the data, data decompression and verification are performed, information on each data transmission and processing is recorded, and health data of vital signs are tracked and managed in real time to obtain a health monitoring log.

10. A wireless portable vital signs measurement system, characterized in that: According to any one of claims 1 to 9, the wireless portable vital sign measurement method comprises: The data recording module collects heart rate and blood pressure data through a heart rate sensor and a blood pressure monitor by wearing a wireless portable device on the monitored person to obtain raw vital sign data; The signal processing module amplifies the original vital sign data, removes noise through a high-pass filter, and obtains a processed vital sign signal; The data analysis module analyzes the fluctuation patterns of the heart rate and blood pressure signals according to the processed vital sign signals, identifies the correlation between the heart rate signal and the blood pressure signal, evaluates the volatility of the vital signs, and obtains the volatility analysis results; The anomaly detection module performs entropy analysis on the volatility analysis results, calculates the entropy values ​​of multiple time windows and monitors the changing trend of the entropy values, identifies abnormal fluctuation patterns, and obtains entropy value anomaly indicators; The risk identification module compares the entropy value abnormality index with the set cardiovascular health risk threshold, identifies the cardiovascular health risk level, and obtains a cardiovascular health risk assessment result; The data transmission module encrypts and compresses the cardiovascular health risk assessment result, and transmits it to the medical service provider via a wireless network to obtain a health monitoring log.

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