A diabetes patient health monitoring method and system based on the Internet of Things

Abstract

The application discloses a kind of diabetes patient health monitoring method and system based on Internet of Things, it is related to health monitoring technical field, the monitoring method steps include: according to Internet of Things communication technology, integrated foot intelligent device is wirelessly connected with local data management platform, the temperature state of the characteristic position of many places in the sole of diabetic patient and the oxygen content state of foot tissue are monitored, the blood microcirculation operation level of diabetic patient foot is analyzed, to trigger foot artery monitoring early warning;Diabetic patients are tested for body position change, real-time monitoring of foot artery pressure fluctuation, analyze the foot artery function response delay level of diabetic patients in the process of body position change;According to the blood microcirculation operation level of diabetic patient foot and the foot artery function response delay level, the potential risk degree that diabetic patient is caused local blood flow obstruction due to foot blood vessel basement membrane thickening is comprehensively analyzed, and the health guidance report of corresponding grade is generated.

Description

Technical Field

[0001] This invention relates to the field of health monitoring technology, specifically to a method and system for health monitoring of diabetic patients based on the Internet of Things. Background Technology

[0002] With the continuous development of digital healthcare, the application of IoT technology in the medical and health field has become an important direction for the construction of smart healthcare systems. Through sensor networks, edge computing, and wireless communication technologies, IoT enables real-time perception, remote transmission, and intelligent analysis of patients' physiological states, driving the transformation of medical services from a traditional hospital-centric approach to patient-centric continuous health management. Against this backdrop, chronic disease management has become a core sub-field in IoT-based medical applications, particularly for diabetic patients with complex disease courses and high complication rates, where the need for long-term, continuous physiological data monitoring is particularly urgent. Among diabetic complications, diabetic foot, as a serious peripheral vascular disease, has a high incidence, strong insidiousness, and rapid progression, becoming one of the main causes of amputation and disability. Therefore, developing wearable, real-time, and intelligent monitoring methods and systems for monitoring blood circulation and assessing arterial function in diabetic foot has become a key research direction in intelligent diabetes management.

[0003] Currently available devices for monitoring foot health in diabetic patients generally suffer from limited functionality and narrow monitoring indicators, often only focusing on foot temperature detection or blood glucose data upload, lacking comprehensive analytical capabilities regarding foot microcirculation and arterial response lag. Furthermore, traditional hospital-based testing methods, such as ultrasound blood flow assessment or arterial pressure measurement, rely on specialized equipment and personnel, making continuous, real-time, and home-based health management difficult. These shortcomings limit the early identification and warning capabilities for diabetic foot complications, often leading patients to seek medical attention only after significant vascular lesions or tissue necrosis have occurred, thus delaying optimal treatment. Therefore, developing a wearable, multimodal sensing system for monitoring diabetic patients that automatically assesses and generates health reports is a crucial area for technological breakthroughs.

[0004] The root cause of the above situation lies in the fact that existing monitoring methods fail to deeply integrate and analyze multi-source physiological data and lack modeling of the response mechanism to dynamic changes in local blood circulation in the foot. Diabetic patients often experience early microcirculatory disturbances such as insufficient oxygen supply and uneven temperature distribution in the foot due to pathological changes caused by thickening of the vascular basement membrane leading to obstruction of local blood flow and decreased capillary permeability. However, traditional methods are unable to capture and quantify these physiological changes with high sensitivity and real-time, making it difficult to assess the risk level when there are no obvious clinical symptoms. Once a delayed response occurs, it can lead to foot ulcers in mild cases, and infection, gangrene, or even amputation in severe cases, seriously affecting the patient's quality of life and increasing the medical burden. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for health monitoring of diabetic patients based on the Internet of Things, which solves the problems mentioned in the background.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for health monitoring of diabetic patients based on the Internet of Things, comprising the following steps:

[0007] S1. By wearing an integrated smart foot device on the foot of diabetic patients, combined with Internet of Things communication technology, the integrated smart foot device is wirelessly connected to the local data management platform. After digital conversion and clock synchronization of the collected raw signals, time alignment and storage are performed.

[0008] S2. By wearing an integrated smart foot device, the temperature status and oxygen content of foot tissues at multiple characteristic locations on the soles of diabetic patients are monitored, and the microcirculation level of blood in the feet of diabetic patients is analyzed to trigger foot artery monitoring and early warning.

[0009] S3. After receiving the foot artery monitoring warning, conduct a postural change test on diabetic patients, monitor foot artery pressure fluctuations in real time, and analyze the level of foot artery functional response lag in diabetic patients during postural changes.

[0010] S4. Based on the level of microcirculation in the feet and the sluggishness of foot arterial function in diabetic patients, a comprehensive analysis is conducted to determine the potential risk of local blood flow obstruction caused by thickening of the foot vascular basement membrane, and a corresponding level of health guidance report is generated.

[0011] Preferably, step S1 specifically includes:

[0012] S11. By wearing an integrated smart foot device on the foot of diabetic patients, and combining it with Internet of Things communication technology, the integrated smart foot device is wirelessly connected to the local data management platform. After the raw signals collected by the integrated smart foot device are digitally converted, the converted digital signals are time-aligned and stored in the local data management platform in combination with the global clock synchronization mechanism.

[0013] The integrated smart foot device includes a pressure sensor, a thermistor-type temperature sensor, a near-infrared optical sensing unit, an NB-IoT module, and a SoC chip. The SoC chip is used to perform digital conversion, timestamp calibration, and preliminary data preprocessing on the collected raw signals.

[0014] Preferably, step S2 specifically includes:

[0015] S21. Monitor the temperature status of multiple characteristic locations on the soles of diabetic patients, and analyze the degree of difference in sole temperature distribution among diabetic patients, specifically including:

[0016] S211. By using the thermistor-type temperature sensor in the integrated foot smart device, the temperature status of multiple characteristic locations on the soles of diabetic patients is monitored, and the temperature values ​​of the soles of diabetic patients are obtained.

[0017] Among them, several characteristic locations on the sole of the foot include the forefoot, midfoot, and heel;

[0018] S212. Based on the plantar temperature values ​​of diabetic patients at various locations, and combined with a statistical averaging algorithm, determine the average plantar temperature value of diabetic patients.

[0019] S213. Correlate the average plantar temperature of diabetic patients with plantar temperature values ​​at various locations, and after dimensionless processing, analyze the degree of difference in plantar temperature distribution among diabetic patients to determine the plantar temperature difference coefficient, specifically: In the formula, This represents the coefficient of variation in foot temperature. This indicates the average temperature of the sole of the foot. This represents the foot temperature value at the i-th location.

[0020] Preferably, step S2 further includes:

[0021] S22. By using the near-infrared optical sensing unit in the integrated foot smart device, the oxygen content of the foot tissue of diabetic patients is monitored, and the first-band reflection intensity and the second-band reflection intensity of diabetic patients are obtained, wherein the wavelength of the first band is 850nm and the wavelength of the second band is 750nm.

[0022] S23. Correlate the first-band reflection intensity and the second-band reflection intensity of diabetic patients to analyze the oxygen supply capacity of the foot microcirculation and determine the foot oxygen supply capacity coefficient, specifically: In the formula, This represents the oxygen supply capacity coefficient of the foot. Indicates the reflection intensity of the first band. This indicates the reflection intensity of the second band.

[0023] Preferably, step S2 further includes:

[0024] S24. Based on the correlation between the obtained plantar temperature difference coefficient and the foot oxygen supply capacity coefficient, and after dimensionless processing, the microcirculation level of the feet in diabetic patients was analyzed to determine the microcirculation index, specifically: In the formula, Indicates the microcirculation operation index. This represents the oxygen supply capacity coefficient of the foot. Indicates the coefficient of difference in foot temperature;

[0025] S25. By comparing the microcirculation health reference value and the microcirculation operation index of the foot blood microcirculation operation level of diabetic patients in a healthy state, if the microcirculation operation index does not exceed the microcirculation health reference value, it indicates that the blood microcirculation operation of the foot of diabetic patients is abnormal at this time, and foot artery monitoring warning is triggered. Conversely, if the index exceeds the reference value, it indicates that the blood microcirculation operation of the foot of diabetic patients is normal, and no additional foot artery monitoring warning is triggered.

[0026] Preferably, step S3 specifically includes:

[0027] S31. After receiving the foot artery monitoring warning, with the diabetic patient in a supine position, the pressure sensor in the integrated foot smart device is attached to the foot artery position of the diabetic patient. During the set sampling period, the foot artery pressure fluctuation is monitored in real time at a fixed sampling frequency to obtain the foot artery pressure value of the diabetic patient at each monitoring time point during the sampling period.

[0028] S32. Perform numerical model analysis on the foot artery pressure values ​​of diabetic patients at each monitoring time point during the sampling period. Plot the foot artery pressure value on the vertical axis and time on the horizontal axis to construct a waveform diagram of the dorsalis pedis artery pressure of diabetic patients during the sampling period.

[0029] S33. Segment the dorsalis pedis artery pressure waveform during the sampling period to obtain waveforms of several foot artery pressure cycles. After feature recognition, extract the peak pressure, waveform rise duration, waveform fall duration, and interval duration between adjacent peaks in each foot artery pressure cycle. Combined with a statistical mean-calculation algorithm, calculate the initial peak pressure, initial waveform rise duration, initial waveform fall duration, and initial interval duration between adjacent peaks for the initial foot artery pressure cycle of diabetic patients during the sampling period, and construct the initial postural arterial pressure data information of diabetic patients.

[0030] The segmentation method involves marking each vertex in the dorsalis pedis artery pressure waveform as a peak point, using the time span between two adjacent peak points as the segmentation period, and using the waveform segment between two adjacent peak points as the waveform of the foot artery pressure period.

[0031] Preferably, step S3 further includes:

[0032] S34. During the preset body position change period, conduct body position change tests on diabetic patients and monitor the fluctuation of foot arterial pressure in real time during the body position change process at a fixed sampling frequency. Combined with the initial body position arterial pressure data construction process, obtain the body position arterial pressure data information. The body position arterial pressure data information includes the peak pressure after the change of the foot arterial pressure cycle, the rise time of the waveform after the change, the fall time of the waveform after the change, and the interval between adjacent peaks after the change.

[0033] Among them, the body position change process refers to the process by which a diabetic patient changes from a supine position to a sitting position;

[0034] S35. Calculate the difference between the initial peak pressure in the initial body position arterial pressure data and the changed peak pressure in the changed body position arterial pressure data to determine the peak pressure difference during the body position change process for diabetic patients. Calculate the ratio of the peak pressure difference to the duration of the body position change period to analyze the rate of change of pressure amplitude caused by the body position change process for diabetic patients and determine the arterial pressure change coefficient during the body position change process for diabetic patients.

[0035] Preferably, step S3 further includes:

[0036] S36. Correlate the initial arterial pressure data in the supine position with the arterial pressure data during positional changes, and combine this with the arterial pressure change coefficient of diabetic patients during positional changes. After dimensionless processing, analyze the lag level of foot arterial function response during positional changes in diabetic patients, and determine the foot arterial function lag index, specifically: In the formula, Indicates the foot artery lag index, This represents the initial peak pressure in a diabetic patient while lying supine. This indicates a characteristic factor of arterial waveform hysteresis in diabetic patients in a supine position. This indicates the duration of the initial adjacent peak interval in diabetic patients while in a supine position. This represents the coefficient of arterial pressure change in diabetic patients during postural changes. This indicates the peak waveform delay time difference during postural changes in diabetic patients;

[0037] Specifically, the arterial waveform hysteresis characteristic factor of diabetic patients in the supine position is calculated by the ratio of the initial waveform rise time to the initial waveform fall time, and the peak waveform delay time difference of diabetic patients during the position change process is calculated by the time difference between the initial peak pressure time and the peak pressure time after the change.

[0038] Preferably, step S4 specifically includes:

[0039] S41. Based on the triggering of foot artery monitoring and early warning, the determined microcirculation operation index is correlated with the foot artery function lag index. After dimensionless processing, the potential risk of local blood flow obstruction caused by thickening of the foot vascular basement membrane in diabetic patients is analyzed to determine the comprehensive risk index of vascular obstruction, specifically: In the formula, This indicates a comprehensive risk index for vascular obstruction. Indicates the foot artery lag index, Indicates the microcirculation operation index. Indicates the reference value for microcirculation health;

[0040] S42. Compare and analyze the comprehensive risk index of vascular obstruction with the preset risk threshold to determine whether the current diabetic patient is at risk of local blood flow obstruction, and generate a corresponding level of health guidance report, specifically including:

[0041] If the comprehensive risk index of vascular occlusion exceeds the risk threshold, it indicates that the current diabetic patient is at risk of local blood flow obstruction, and a Level 1 health guidance report is issued, which includes: recommending that the patient go to a medical institution as soon as possible for further hemodynamic examination;

[0042] If the comprehensive risk index of vascular occlusion does not exceed the risk threshold, it indicates that the current diabetic patient is not at risk of local blood flow obstruction, and a level-two health guidance report is issued, which includes: recommending regular physical examinations, while continuing to maintain the current lifestyle management, including a reasonable diet, regular exercise, control of sugar and lipid intake, and avoiding smoking and prolonged sitting.

[0043] A health monitoring system for diabetic patients based on the Internet of Things includes a deployment module, a microcirculation monitoring module, a foot artery monitoring module, and a health report generation module;

[0044] The deployment module is used to wirelessly connect the integrated smart foot device worn by diabetic patients to the local data management platform by combining IoT communication technology. After digital conversion and clock synchronization of the collected raw signals, the module performs time alignment and storage.

[0045] The microcirculation monitoring module is used to monitor the temperature and oxygen content of foot tissues at multiple characteristic locations on the soles of diabetic patients through an integrated smart foot device, analyze the microcirculation level of blood in the feet of diabetic patients, and trigger foot artery monitoring and early warning.

[0046] The foot artery monitoring module is used to perform postural change tests on diabetic patients after receiving a foot artery monitoring alert, monitor foot artery pressure fluctuations in real time, and analyze the level of foot artery functional response lag in diabetic patients during postural changes.

[0047] The health report generation module is used to comprehensively analyze the potential risk of local blood flow obstruction caused by thickening of the vascular basement membrane in the feet of diabetic patients based on the level of microcirculation in the feet and the level of sluggish response of the foot arteries, and generate a corresponding level of health guidance report.

[0048] This invention provides a method and system for health monitoring of diabetic patients based on the Internet of Things, which has the following beneficial effects:

[0049] (1) This invention proposes a health monitoring method and system for diabetic patients based on the Internet of Things. It has wearable, multimodal perception, intelligent early warning and automatic assessment capabilities, which effectively overcomes the problems of single monitoring dimension, lack of early warning mechanism, response delay and difficulty in home implementation in the existing technology. By constructing an integrated intelligent device with the foot as the core monitoring site, and integrating multi-source physiological information such as foot temperature distribution characteristics, tissue oxygen supply capacity and arterial pressure fluctuation data, it realizes comprehensive perception and dynamic modeling of the blood microcirculation status and foot arterial function response lag in diabetic foot. Furthermore, by constructing a multi-level assessment index of microcirculation operation index, foot arterial function lag index and vascular obstruction comprehensive risk index, it can scientifically quantify the potential risk level of local blood flow obstruction in patients, generate graded health guidance reports, and significantly improve the early identification rate and intervention timeliness of complications such as diabetic foot. Overall, it has the advantages of strong scalability, adaptability to home scenarios and no need to rely on professional hospital equipment, which promotes the management of chronic diseases of diabetes towards real-time, continuous and proactive, and has broad application prospects and important clinical significance.

[0050] (2) In terms of monitoring the microcirculation status of the foot, it breaks through the limitation of existing technologies that can only collect foot temperature or blood glucose data. By introducing a thermistor temperature sensor and a near-infrared optical sensing unit, it realizes the synchronous monitoring of temperature differences and tissue oxygenation status in multiple areas of the sole. On this basis, the statistical mean algorithm and dimensionless difference analysis method are used to construct the sole temperature difference coefficient and oxygen supply capacity coefficient, and further integrate them to form a microcirculation operation index. This integrated index system can not only reflect the early signs of local microcirculation disorders, but also trigger a precise foot artery monitoring and early warning mechanism by comparing with the health reference threshold. Compared with the disadvantage of traditional technologies that are difficult to perceive the dynamic changes of microcirculation, it can sensitively capture the potential abnormalities of early uneven temperature and insufficient oxygen supply in the sole, and realize the opportunity for early identification and real-time intervention of microcirculation abnormalities. In addition, the design of wireless transmission to the local processing platform through the NB-IoT module effectively ensures the high timeliness and stability of the data, provides technical support for continuous health management in home scenarios, and improves the universality and accessibility of chronic disease monitoring.

[0051] (3) To address the lack of modeling of foot arterial function response mechanism in traditional technologies, an innovative strategy combining postural change testing and arterial pressure waveform periodic analysis is proposed to achieve dynamic quantitative modeling of the lag level of foot arterial function response in diabetic patients. Through real-time acquisition and waveform processing of foot arterial pressure values ​​in supine and sitting-up states, initial postural arterial pressure data and postural arterial pressure data are constructed, and key physiological parameters such as peak pressure, waveform rise and fall duration, and interval between adjacent peaks are extracted. Furthermore, arterial waveform lag characteristic factors and peak waveform delay time difference are introduced to construct a foot arterial function lag index, which scientifically reflects the degree of lag in arterial response in blood flow regulation. Finally, the microcirculation operation index and the foot arterial function lag index are fused to generate a comprehensive vascular obstruction risk index, which is compared with the set risk threshold to achieve graded risk warning of local blood flow obstruction in diabetic foot. By fusing static and dynamic, multi-source and time series data, not only is the shortcoming of existing monitoring methods in quantifying arterial response delay difficult, but also a full-link risk assessment system based on physiological mechanisms is established, improving the ability to identify early pathological changes in diabetic foot. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of a method for monitoring the health of diabetic patients based on the Internet of Things according to the present invention.

[0053] Figure 2 This is a block diagram of a health monitoring system for diabetic patients based on the Internet of Things (IoT) according to the present invention.

[0054] Figure 3 This is a logic diagram of a method for monitoring the health of diabetic patients based on the Internet of Things, as proposed in this invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Example 1

[0057] Please see Figure 1 and Figure 3 This invention provides a method for health monitoring of diabetic patients based on the Internet of Things, including the following steps:

[0058] S1. By wearing an integrated smart foot device on the foot of diabetic patients, combined with Internet of Things communication technology, the integrated smart foot device is wirelessly connected to the local data management platform. After digital conversion and clock synchronization of the collected raw signals, time alignment and storage are performed.

[0059] S2. By wearing an integrated smart foot device, the temperature status and oxygen content of foot tissues at multiple characteristic locations on the soles of diabetic patients are monitored, and the microcirculation level of blood in the feet of diabetic patients is analyzed to trigger foot artery monitoring and early warning.

[0060] S3. After receiving the foot artery monitoring warning, conduct a postural change test on diabetic patients, monitor foot artery pressure fluctuations in real time, and analyze the level of foot artery functional response lag in diabetic patients during postural changes.

[0061] S4. Based on the level of microcirculation in the feet and the sluggishness of foot arterial function in diabetic patients, a comprehensive analysis is conducted to determine the potential risk of local blood flow obstruction caused by thickening of the foot vascular basement membrane, and a corresponding level of health guidance report is generated.

[0062] In this embodiment, an IoT-based health monitoring method for diabetic patients addresses the shortcomings of existing technologies, such as one-sided monitoring indicators, difficulty in achieving continuous home monitoring, and lack of dynamic modeling of microcirculation and arterial responses. It proposes a complete, highly integrated, and wearable intelligent monitoring process. By wirelessly connecting an integrated smart foot device to a local data management platform and combining it with multimodal sensors to collect physiological information on foot temperature distribution and tissue oxygenation, it can not only achieve real-time dynamic assessment of the foot's microcirculation level but also automatically trigger subsequent foot arterial function testing in abnormal conditions. Specifically, through postural change-induced testing and pressure fluctuation modeling analysis, the degree of lag in foot arterial function response is effectively quantified, and then combined with microcirculation indicators to comprehensively assess the potential risk of foot vascular lesions. This method effectively overcomes the dependence of traditional medical equipment on professional locations and personnel, enabling early identification and risk warning of diabetic foot complications in non-clinical environments. It improves the continuity, practicality, and forward-looking nature of detection, significantly meeting the long-term health management needs of diabetic patients in home settings.

[0063] Example 2

[0064] Please refer to Figure 1 and Figure 3 Specifically: S1 includes the following steps:

[0065] S11. By wearing an integrated smart foot device on the foot of diabetic patients, and combining it with Internet of Things communication technology, the integrated smart foot device is wirelessly connected to the local data management platform. After the raw signals collected by the integrated smart foot device are digitally converted, the converted digital signals are time-aligned and stored in the local data management platform in combination with the global clock synchronization mechanism.

[0066] The integrated smart foot device includes a pressure sensor, a thermistor-type temperature sensor, a near-infrared optical sensing unit, an NB-IoT module, and a SoC chip. The SoC chip is used to perform digital conversion, timestamp calibration, and preliminary data preprocessing on the collected raw signals.

[0067] Specifically, the steps in S2 include:

[0068] S21. Monitor the temperature status of multiple characteristic locations on the soles of diabetic patients, and analyze the degree of difference in sole temperature distribution among diabetic patients, specifically including:

[0069] S211. By using the thermistor-type temperature sensor in the integrated foot smart device, the temperature status of multiple characteristic locations on the soles of diabetic patients is monitored, and the temperature values ​​of the soles of diabetic patients are obtained.

[0070] Among them, several characteristic locations on the sole of the foot include the forefoot, midfoot, and heel;

[0071] S212. Based on the plantar temperature values ​​of diabetic patients at various locations, and combined with a statistical averaging algorithm, determine the average plantar temperature value of diabetic patients.

[0072] S213. Correlate the average plantar temperature of diabetic patients with plantar temperature values ​​at various locations, and after dimensionless processing, analyze the degree of difference in plantar temperature distribution among diabetic patients to determine the plantar temperature difference coefficient, specifically: In the formula, This represents the coefficient of variation in foot temperature. This indicates the average temperature of the sole of the foot. This represents the foot temperature value at the i-th location.

[0073] It should be noted that this formula originates from a standardized analysis method for temperature field distribution differences, aiming to quantify the degree of local deviation of various characteristic parts of the sole of a diabetic patient relative to the overall average temperature of the sole. The formula calculates the maximum absolute difference between the temperature at each monitoring point on the sole and the average temperature of the sole, normalizes it to the proportion of the average temperature, and derives the sole temperature difference coefficient. The larger the sole temperature difference coefficient, the more uneven the sole temperature distribution. The derivation process embodies the idea of ​​dimensionless processing, avoiding the influence of individual differences in absolute temperature levels on the analysis results. In the specific application of this invention, the sole temperature difference coefficient serves as an important priori indicator for assessing abnormal foot microcirculation, forming the basis for calculating the microcirculation operation index together with the foot oxygen supply capacity coefficient. When there is abnormal temperature distribution on the sole, it indicates uneven blood flow. Therefore, by introducing this formula, high-sensitivity capture of changes in the sole temperature field of diabetic patients is achieved, improving the ability to detect foot microcirculation disorders early, providing key decision-making basis for subsequent arterial function testing and risk warning, and demonstrating significant clinical value and practical effectiveness.

[0074] Specifically, the S2 steps also include:

[0075] S22. By using the near-infrared optical sensing unit in the integrated foot smart device, the oxygen content of the foot tissue of diabetic patients is monitored, and the first-band reflection intensity and the second-band reflection intensity of diabetic patients are obtained, wherein the wavelength of the first band is 850nm and the wavelength of the second band is 750nm.

[0076] The first and second band reflection intensities mentioned in S22 refer to the reflected light intensity values ​​after absorption and scattering of near-infrared light of different wavelengths within the subcutaneous tissue of a diabetic patient's foot by irradiating it with a near-infrared optical sensing unit. These two bands were selected because they have different penetration depths and response characteristics to blood oxygen saturation in biological tissues. The 850nm band is more sensitive to the absorption of deoxyhemoglobin, while the 750nm band is more significantly sensitive to the absorption of oxyhemoglobin. By simultaneously collecting and comparing the reflected light intensities of these two specific wavelengths, the changing trend of blood oxygen content in the tissue can be deduced, thereby assessing the local tissue composition. The oxygen supply capacity and microcirculation status of tissues are analyzed. The reflection intensity of these two bands is obtained by processing the raw light signal acquired by the photodetector in the sensing unit after the near-infrared emission light source irradiates the skin tissue. This method is non-invasive, real-time, and highly sensitive, providing a crucial technological foundation for non-invasive dynamic monitoring of tissue oxygenation. By introducing the reflection intensity of these two bands, the oxygen supply status of foot tissues in diabetic patients can be accurately obtained, providing key data support for the subsequent construction of oxygen supply capacity coefficients and microcirculation operation indices, and significantly improving the ability to identify early blood flow abnormalities.

[0077] S23. Correlate the first-band reflection intensity and the second-band reflection intensity of diabetic patients to analyze the oxygen supply capacity of the foot microcirculation and determine the foot oxygen supply capacity coefficient, specifically: In the formula, This represents the oxygen supply capacity coefficient of the foot. Indicates the reflection intensity of the first band. This indicates the reflection intensity of the second band, where, This represents the relative difference between the reflection intensity of the first band and the reflection intensity of the second band.

[0078] Specifically, the S2 steps also include:

[0079] S24. Based on the correlation between the obtained plantar temperature difference coefficient and the foot oxygen supply capacity coefficient, and after dimensionless processing, the microcirculation level of the feet in diabetic patients was analyzed to determine the microcirculation index, specifically: In the formula, Indicates the microcirculation operation index. This represents the oxygen supply capacity coefficient of the foot. Indicates the coefficient of difference in foot temperature;

[0080] It should be noted that this formula originates from the concept of multi-indicator fusion assessment in medical engineering, aiming to comprehensively evaluate the overall operational level of foot microcirculation in diabetic patients. The formula uses the oxygen supply capacity coefficient of foot tissues as a positive indicator, reflecting the adequacy of tissue perfusion and blood oxygen supply. At the same time, it introduces the plantar temperature difference coefficient as a negative interference factor to measure the risk of microcirculatory abnormalities caused by temperature field imbalance. This derivation process embodies the idea of ​​synergistic modeling of two key variables: oxygen adequacy and temperature balance, and has good physiological interpretability and mathematical stability. In this invention, the microcirculation operation index not only serves as a quantitative basis for the foot microcirculation status but also as one of the core criteria for triggering foot artery monitoring and early warning. When the microcirculation operation index is low, it indicates insufficient oxygen supply and significant abnormal temperature distribution, suggesting a high risk of microcirculatory impairment, thereby initiating a higher-level hemodynamic monitoring process. Therefore, this formula enhances the ability to perceive vascular lesions at the microscopic level, achieving a key leap from single-point signal acquisition to fusion decision assessment, providing solid indicator support and technical pathways for the early monitoring and intervention of diabetic complications.

[0081] S25. By comparing the microcirculation health reference value and the microcirculation operation index of the foot blood microcirculation operation level of diabetic patients in a healthy state, if the microcirculation operation index does not exceed the microcirculation health reference value, it indicates that the blood microcirculation operation of the foot of diabetic patients is abnormal at this time, and foot artery monitoring warning is triggered. Conversely, if the index exceeds the reference value, it indicates that the blood microcirculation operation of the foot of diabetic patients is normal, and no additional foot artery monitoring warning is triggered.

[0082] In this embodiment, through the specific implementation of S1 and S2, a foot health monitoring solution for diabetic patients with high sensitivity, real-time performance, and multimodal fusion capability is constructed, effectively overcoming the core pain points of existing technologies such as one-sided monitoring indicators, superficial data interpretation, and difficulty in achieving continuous home monitoring. First, raw signals are collected through an integrated intelligent foot device. The SoC chip performs digital conversion, timestamp calibration, and preliminary preprocessing on the collected raw analog signals. Then, using an NB-IoT communication module, the data is stored on a local data management platform, providing a stable and synchronous data foundation for subsequent multi-source data fusion analysis. Its innovation lies in simultaneously introducing two different types of sensing methods: one is using a thermistor-type temperature sensor to collect the temperature status of multiple characteristic areas of the sole (forefoot, midfoot, and heel), and analyzing the dispersion of temperature distribution through mean and difference coefficients; the other is acquiring 850°C data through near-infrared optical sensing technology. By analyzing the reflection intensity data of the 750nm and 100nm wavelengths, the oxygen-carrying capacity of the tissue is calculated, thereby quantifying the oxygen supply status of the foot. Furthermore, the foot temperature difference coefficient is correlated with the oxygen supply capacity coefficient, and after dimensionless processing, a comprehensive microcirculation operation index is generated. This fusion-based index system can accurately depict the insidious manifestations of early microcirculation disorders. By dynamically comparing the microcirculation operation index with health reference values, foot artery monitoring and early warning can be triggered in the early stages when patients have no obvious clinical symptoms, improving the timeliness and proactivity of risk identification. Compared with the limitations of existing technologies that only focus on blood glucose or single temperature signals, this solution has powerful multi-source fusion and intelligent decision-making capabilities, realizing the quantitative modeling and continuous tracking of the microcirculation status of diabetic feet. It truly meets the clinical needs of patients for home, daily, and long-term monitoring, effectively avoiding the risk of missed diagnoses and delayed treatment of complications due to slow or incomplete detection methods.

[0083] Example 3

[0084] Please refer to Figure 1 and Figure 3 Specifically: The specific steps of S3 include:

[0085] S31. After receiving the foot artery monitoring warning, with the diabetic patient in a supine position, the pressure sensor in the integrated foot smart device is attached to the foot artery position of the diabetic patient. During the set sampling period, the foot artery pressure fluctuation is monitored in real time at a fixed sampling frequency to obtain the foot artery pressure value of the diabetic patient at each monitoring time point during the sampling period.

[0086] S32. Perform numerical model analysis on the foot artery pressure values ​​of diabetic patients at each monitoring time point during the sampling period. Plot the foot artery pressure value on the vertical axis and time on the horizontal axis to construct a waveform diagram of the dorsalis pedis artery pressure of diabetic patients during the sampling period.

[0087] S33. Segment the dorsalis pedis artery pressure waveform during the sampling period to obtain waveforms of several foot artery pressure cycles. After feature recognition, extract the peak pressure, waveform rise duration, waveform fall duration, and interval duration between adjacent peaks in each foot artery pressure cycle. Combined with a statistical mean-calculation algorithm, calculate the initial peak pressure, initial waveform rise duration, initial waveform fall duration, and initial interval duration between adjacent peaks for the initial foot artery pressure cycle of diabetic patients during the sampling period, and construct the initial postural arterial pressure data information of diabetic patients.

[0088] The segmentation method involves marking each vertex in the dorsalis pedis artery pressure waveform as a peak point, using the time span between two adjacent peak points as the segmentation period, and using the waveform segment between two adjacent peak points as the waveform of the foot artery pressure period.

[0089] Specifically, the S3 steps also include:

[0090] S34. During the preset body position change period, conduct body position change tests on diabetic patients and monitor the fluctuation of foot arterial pressure in real time during the body position change process at a fixed sampling frequency. Combined with the initial body position arterial pressure data construction process, obtain the body position arterial pressure data information. The body position arterial pressure data information includes the peak pressure after the change of the foot arterial pressure cycle, the rise time of the waveform after the change, the fall time of the waveform after the change, and the interval between adjacent peaks after the change.

[0091] Among them, the body position change process refers to the process by which a diabetic patient changes from a supine position to a sitting position;

[0092] S35. Calculate the difference between the initial peak pressure in the initial body position arterial pressure data and the changed peak pressure in the changed body position arterial pressure data to determine the peak pressure difference during the body position change process for diabetic patients. Calculate the ratio of the peak pressure difference to the duration of the body position change period to analyze the rate of change of pressure amplitude caused by the body position change process for diabetic patients and determine the arterial pressure change coefficient during the body position change process for diabetic patients.

[0093] Specifically, the S3 steps also include:

[0094] S36. Correlate the initial arterial pressure data in the supine position with the arterial pressure data during positional changes, and combine this with the arterial pressure change coefficient of diabetic patients during positional changes. After dimensionless processing, analyze the lag level of foot arterial function response during positional changes in diabetic patients, and determine the foot arterial function lag index, specifically: In the formula, Indicates the foot artery lag index, This represents the initial peak pressure in a diabetic patient while lying supine. This indicates a characteristic factor of arterial waveform hysteresis in diabetic patients in a supine position. This indicates the duration of the initial adjacent peak interval in diabetic patients while in a supine position. This represents the coefficient of arterial pressure change in diabetic patients during postural changes. This indicates the peak waveform delay time difference during postural changes in diabetic patients;

[0095] Specifically, the arterial waveform hysteresis characteristic factor of diabetic patients in the supine position is calculated by the ratio of the initial waveform rise time to the initial waveform fall time, and the peak waveform delay time difference of diabetic patients during the position change process is calculated by the time difference between the initial peak pressure time and the peak pressure time after the change.

[0096] It should be noted that this formula is an innovative composite index designed to quantitatively assess the hysteresis response of the foot arteries to postural changes in diabetic foot vascular physiology, based on in-depth analysis of the vascular physiological characteristics of diabetic foot. The formula comprehensively considers multiple key physiological parameters, integrating arterial waveform dynamics, postural stress response indicators, and time-dependent factors reflecting the degree of blood flow regulation delay. The aim is to establish a mathematical model that accurately reflects the state of arterial function decline. The derivation process begins with the molecular part, starting with the original waveform of the foot artery in a supine position, extracting the initial peak pressure as the maximum output capacity of the foot artery in a static position. Simultaneously, through analysis... The ratio of the rise time to the fall time of the foot artery waveform yields the arterial waveform hysteresis characteristic factor. This factor reflects the asymmetry between vasoconstriction and vasodilation and is an important parameter for assessing vascular elasticity degradation. Combined with the time interval between two adjacent waveform peaks, the regularity and rhythm of arterial pulsation are characterized. Regarding the denominator, during the postural change test, arterial pressure waveform data were collected in real-time under different body positions. The initial interval between adjacent peaks and the time delay between the appearance of two waveform peaks were extracted for diabetic patients in a supine position. The former represents the initial response duration, while the latter directly reflects the degree of delayed response of arterial function. These two factors are then used to establish a ratio, and a [missing information - likely a mathematical formula or formula] is constructed. As a quantitative factor of the degree of delay, the complete index expression is represented by xdm, vrr, tcs, and Δttc as dynamic adjustment terms. The foot artery function lag index not only has high physical and physiological significance, but also enhances the adaptability and comparability of the index through dimensionless normalization. In this invention, the formula is integrated into S36 and used to dynamically model and quantitatively analyze the response function of the patient's foot artery after triggering foot artery monitoring and early warning, thus serving as one of the core parameters for constructing a comprehensive vascular occlusion risk index. By introducing the foot artery function lag index, this invention enables early detection and trend prediction of diabetic foot artery function decline in a home environment without the need for professional operation. This significantly improves the scientificity, accuracy, and real-time performance of early intervention for vascular lesions, effectively filling the technical gap in functional dynamic assessment in existing technologies, and demonstrating extremely high practical value and clinical translation prospects.

[0097] In this embodiment, S3 performs real-time modeling and quantitative analysis of foot artery functional response lag, overcoming the limitations of traditional technologies that rely on specialized hospital equipment, demanding testing conditions, and difficulty in continuous dynamic monitoring. This enables accurate capture and assessment of the dynamic characteristics of foot arterial blood flow in daily life scenarios for diabetic patients, improving the early identification of foot vascular lesions. First, after receiving a microcirculation abnormality warning, the patient is guided to perform a positional change test (from supine to sitting up), and combined with a built-in pressure sensor, arterial pressure fluctuation data is collected at high frequency, effectively simulating the blood flow regulation load change process, thereby obtaining foot arterial pressure response data with physiological stress characteristics. Based on this, a complete pressure waveform periodic analysis method is proposed. By identifying peak values ​​and segmenting the periodicity of the foot arterial pressure waveform, peak pressure, waveform rise duration, waveform fall duration, and the interval between adjacent peak values ​​are extracted, and statistically constructed arterial pressure data information under the initial and changed positions is generated. This processing method not only visualizes the patient's arterial response curve, but also... This study also provides quantitative data support for establishing response change rate and response lag in subsequent studies. Furthermore, by constructing the arterial pressure change coefficient and the foot arterial function lag index, it is the first time that the response time lag of blood vessels to postural induction changes has been quantified into a comparable mathematical indicator. This includes core variables such as peak delay time difference and waveform lag characteristic factors, which reflect the early manifestations of vascular function decline and can effectively identify potential pathological trends such as decreased vascular elasticity and slow flow regulation. Compared with traditional methods that only assess blood flow through Doppler ultrasound and other means, this process provides a dynamic arterial function testing pathway that can be completed at home without professional operation, enabling early quantitative identification of the key but often overlooked vascular pathological feature of foot arterial lag. At the same time, this step forms a causal logical closed loop with the preceding microcirculation analysis, ensuring that the occurrence of early warning is based on evidence and that the assessment indicators are scientific and accurate. This fundamentally improves the overall effectiveness of diabetic foot vascular risk monitoring methods and makes up for the significant deficiencies of existing technologies in dynamic response modeling and home self-testing.

[0098] Example 4

[0099] Please refer to Figure 1 and Figure 3 Specifically: The specific steps of S4 include:

[0100] S41. Based on the triggering of foot artery monitoring and early warning, the determined microcirculation operation index is correlated with the foot artery function lag index. After dimensionless processing, the potential risk of local blood flow obstruction caused by thickening of the foot vascular basement membrane in diabetic patients is analyzed to determine the comprehensive risk index of vascular obstruction, specifically: In the formula, This indicates a comprehensive risk index for vascular obstruction. Indicates the foot artery lag index, Indicates the microcirculation operation index. Indicates the reference value for microcirculation health;

[0101] It should be noted that this formula aims to integrate two key indicators—arterial function and microcirculation status—to comprehensively assess the risk of local blood flow obstruction caused by thickening of the basement membrane of blood vessels in the foot in diabetic patients. The formula employs a two-factor risk assessment mechanism in its construction. It measures the degree of deviation of the actual state of microcirculation from the healthy reference value, reflecting the degree of abnormality in local oxygen supply and temperature distribution. The two factors are squared and then squared to form a composite index with non-linear weighting characteristics, which can effectively avoid the risk underestimation or misjudgment caused by simple linear superposition. In the practical application of this invention, the comprehensive risk index of vascular obstruction calculated by this formula is used as the core basis for the final generation of health guidance reports, realizing a closed-loop management path from multi-dimensional physiological data perception to risk level quantification. It is particularly suitable for the early identification of the potential stage before the onset of diabetic foot, and can achieve prospective early warning of vascular abnormalities without obvious clinical symptoms. It significantly makes up for the lag and one-sidedness of traditional technologies that rely on a single indicator or manual judgment, and improves the intelligence and scientific level of diabetes chronic disease management.

[0102] S42. Compare and analyze the comprehensive risk index of vascular obstruction with the preset risk threshold to determine whether the current diabetic patient is at risk of local blood flow obstruction, and generate a corresponding level of health guidance report, specifically including:

[0103] If the comprehensive risk index of vascular occlusion exceeds the risk threshold, it indicates that the current diabetic patient is at risk of local blood flow obstruction, and a Level 1 health guidance report is issued, which includes: recommending that the patient go to a medical institution as soon as possible for further hemodynamic examination;

[0104] If the comprehensive risk index of vascular occlusion does not exceed the risk threshold, it indicates that the current diabetic patient is not at risk of local blood flow obstruction, and a level-two health guidance report is issued, which includes: recommending regular physical examinations, while continuing to maintain the current lifestyle management, including a reasonable diet, regular exercise, control of sugar and lipid intake, and avoiding smoking and prolonged sitting.

[0105] In this embodiment, S4 constructs a comprehensive risk index for vascular obstruction, deeply integrating the monitoring results of two core dimensions: foot microcirculation status and arterial functional response lag. This establishes a multi-source data comprehensive assessment model for diabetic foot vascular lesions, significantly improving the accuracy of identifying and classifying the risk of local blood flow obstruction in patients. Specifically, the microcirculation operation index and the foot arterial function lag index are uniformly incorporated into the risk analysis framework after dimensionless correlation processing. This not only achieves continuous pathological modeling from "abnormal local temperature and oxygen supply" to "abnormal arterial blood flow regulation capacity," but also scientifically constructs a comprehensive risk index for vascular obstruction by introducing "microcirculation health reference values" as a baseline, accurately reflecting the potential blood flow obstruction trend caused by microvascular lesions such as basement membrane thickening. This integrated assessment method overcomes the shortcomings of existing technologies where various indicators are scattered and it is difficult to uniformly quantify risk levels, making the risk assessment more comprehensive. Risk analysis has evolved from "single-point perception" to "overall trend recognition." Furthermore, by comparing the index with preset risk thresholds, the system automatically generates and intelligently grades health guidance reports, categorizing them into Level 1 and Level 2 reports based on risk level, providing personalized intervention suggestions of "immediate medical examination" and "regular physical examination," respectively. This design not only enhances clinical applicability but also provides patients with interpretable, actionable, and forward-looking health guidance, promoting a shift in chronic disease management from passive response to proactive prevention. Especially in the stage before patients exhibit obvious symptoms, the system effectively prevents further deterioration of the disease through self-assessment and the issuance of guidance reports. This complete mechanism, from multi-parameter fusion modeling to closed-loop output of intervention suggestions, represents a breakthrough from the traditional technology model that relies on doctors' subjective judgment and delayed responses, meeting the urgent need for self-management and remote assessment of diabetic foot risk within a smart healthcare system.

[0106] Example 5

[0107] Please refer to Figure 1 and Figure 2 Specifically: A health monitoring system for diabetic patients based on the Internet of Things, including a deployment module, a microcirculation monitoring module, a foot artery monitoring module, and a health report generation module;

[0108] The deployment module is used to wirelessly connect the integrated smart foot device worn by diabetic patients to the local data management platform by combining IoT communication technology. After digital conversion and clock synchronization of the collected raw signals, the module performs time alignment and storage.

[0109] The microcirculation monitoring module is used to monitor the temperature and oxygen content of foot tissues at multiple characteristic locations on the soles of diabetic patients through an integrated smart foot device, analyze the microcirculation level of blood in the feet of diabetic patients, and trigger foot artery monitoring and early warning.

[0110] The foot artery monitoring module is used to perform postural change tests on diabetic patients after receiving a foot artery monitoring alert, monitor foot artery pressure fluctuations in real time, and analyze the level of foot artery functional response lag in diabetic patients during postural changes.

[0111] The health report generation module is used to comprehensively analyze the potential risk of local blood flow obstruction caused by thickening of the vascular basement membrane in the feet of diabetic patients based on the level of microcirculation in the feet and the level of sluggish response of the foot arteries, and generate a corresponding level of health guidance report.

[0112] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for health monitoring of diabetic patients based on the Internet of Things, characterized in that: Includes the following steps, S1. By wearing an integrated smart foot device on the foot of diabetic patients, combined with Internet of Things communication technology, the integrated smart foot device is wirelessly connected to the local data management platform. After digital conversion and clock synchronization of the collected raw signals, time alignment and storage are performed. S2. By wearing an integrated smart foot device, the temperature status and oxygen content of foot tissues at multiple characteristic locations on the soles of diabetic patients are monitored, and the microcirculation level of blood in the feet of diabetic patients is analyzed to trigger foot artery monitoring and early warning. S3. After receiving the foot artery monitoring warning, conduct a postural change test on diabetic patients, monitor foot artery pressure fluctuations in real time, and analyze the level of foot artery functional response lag in diabetic patients during postural changes. S4. Based on the level of microcirculation in the feet and the sluggishness of foot arterial function in diabetic patients, a comprehensive analysis is conducted to determine the potential risk of local blood flow obstruction caused by thickening of the foot vascular basement membrane, and a corresponding level of health guidance report is generated.

2. The method for monitoring the health of diabetic patients based on the Internet of Things according to claim 1, characterized in that: The specific steps in S1 include: S11. By wearing an integrated smart foot device on the foot of diabetic patients, and combining it with Internet of Things communication technology, the integrated smart foot device is wirelessly connected to the local data management platform. After the raw signals collected by the integrated smart foot device are digitally converted, the converted digital signals are time-aligned and stored in the local data management platform in combination with the global clock synchronization mechanism. The integrated smart foot device includes a pressure sensor, a thermistor-type temperature sensor, a near-infrared optical sensing unit, an NB-IoT module, and a SoC chip. The SoC chip is used to perform digital conversion, timestamp calibration, and preliminary data preprocessing on the collected raw signals.

3. The method for monitoring the health of diabetic patients based on the Internet of Things according to claim 2, characterized in that: The specific steps in S2 include: S21. Monitor the temperature status of multiple characteristic locations on the soles of diabetic patients, and analyze the degree of difference in sole temperature distribution among diabetic patients, specifically including: S211. By using the thermistor-type temperature sensor in the integrated foot smart device, the temperature status of multiple characteristic locations on the soles of diabetic patients is monitored, and the temperature values ​​of the soles of diabetic patients are obtained. Among them, several characteristic locations on the sole of the foot include the forefoot, midfoot, and heel; S212. Based on the plantar temperature values ​​of diabetic patients at various locations, and combined with a statistical averaging algorithm, determine the average plantar temperature value of diabetic patients. S213. Correlate the average plantar temperature of diabetic patients with plantar temperature values ​​at various locations, and after dimensionless processing, analyze the degree of difference in plantar temperature distribution among diabetic patients to determine the plantar temperature difference coefficient, specifically: In the formula, This represents the coefficient of variation in foot temperature. This indicates the average temperature of the sole of the foot. This represents the foot temperature value at the i-th location.

4. The method for monitoring the health of diabetic patients based on the Internet of Things according to claim 3, characterized in that: The specific steps in S2 also include: S22. By using the near-infrared optical sensing unit in the integrated foot smart device, the oxygen content of the foot tissue of diabetic patients is monitored, and the first-band reflection intensity and the second-band reflection intensity of diabetic patients are obtained, wherein the wavelength of the first band is 850nm and the wavelength of the second band is 750nm. S23. Correlate the first-band reflection intensity and the second-band reflection intensity of diabetic patients to analyze the oxygen supply capacity of the foot microcirculation and determine the foot oxygen supply capacity coefficient, specifically: In the formula, This represents the oxygen supply capacity coefficient of the foot. Indicates the reflection intensity of the first band. This indicates the reflection intensity of the second band.

5. A method for monitoring the health of diabetic patients based on the Internet of Things according to claim 4, characterized in that: The specific steps in S2 also include: S24. Based on the correlation between the obtained plantar temperature difference coefficient and the foot oxygen supply capacity coefficient, and after dimensionless processing, the microcirculation level of the feet in diabetic patients was analyzed to determine the microcirculation index, specifically: In the formula, Indicates the microcirculation operation index. This represents the oxygen supply capacity coefficient of the foot. Indicates the coefficient of difference in foot temperature; S25. By comparing the microcirculation health reference value and the microcirculation operation index of the foot blood microcirculation operation level of diabetic patients in a healthy state, if the microcirculation operation index does not exceed the microcirculation health reference value, it indicates that the blood microcirculation operation of the foot of diabetic patients is abnormal at this time, and foot artery monitoring warning is triggered. Conversely, if the index exceeds the reference value, it indicates that the blood microcirculation operation of the foot of diabetic patients is normal, and no additional foot artery monitoring warning is triggered.

6. The method for monitoring the health of diabetic patients based on the Internet of Things according to claim 5, characterized in that: The specific steps of S3 include: S31. After receiving the foot artery monitoring warning, with the diabetic patient in a supine position, the pressure sensor in the integrated foot smart device is attached to the foot artery position of the diabetic patient. During the set sampling period, the foot artery pressure fluctuation is monitored in real time at a fixed sampling frequency to obtain the foot artery pressure value of the diabetic patient at each monitoring time point during the sampling period. S32. Perform numerical model analysis on the foot artery pressure values ​​of diabetic patients at each monitoring time point during the sampling period. Plot the foot artery pressure value on the vertical axis and time on the horizontal axis to construct a waveform diagram of the dorsalis pedis artery pressure of diabetic patients during the sampling period. S33. Segment the dorsalis pedis artery pressure waveform during the sampling period to obtain waveforms of several foot artery pressure cycles. After feature recognition, extract the peak pressure, waveform rise duration, waveform fall duration, and interval duration between adjacent peaks in each foot artery pressure cycle. Combined with a statistical mean-calculation algorithm, calculate the initial peak pressure, initial waveform rise duration, initial waveform fall duration, and initial interval duration between adjacent peaks for the initial foot artery pressure cycle of diabetic patients during the sampling period, and construct the initial postural arterial pressure data information of diabetic patients. The segmentation method involves marking each vertex in the dorsalis pedis artery pressure waveform as a peak point, using the time span between two adjacent peak points as the segmentation period, and using the waveform segment between two adjacent peak points as the waveform of the foot artery pressure period.

7. A method for monitoring the health of diabetic patients based on the Internet of Things according to claim 6, characterized in that: The specific steps in S3 also include: S34. During the preset body position change period, conduct body position change tests on diabetic patients and monitor the fluctuation of foot arterial pressure in real time during the body position change process at a fixed sampling frequency. Combined with the initial body position arterial pressure data construction process, obtain the body position arterial pressure data information. The body position arterial pressure data information includes the peak pressure after the change of the foot arterial pressure cycle, the rise time of the waveform after the change, the fall time of the waveform after the change, and the interval between adjacent peaks after the change. Among them, the body position change process refers to the process by which a diabetic patient changes from a supine position to a sitting position; S35. Calculate the difference between the initial peak pressure in the initial body position arterial pressure data and the changed peak pressure in the changed body position arterial pressure data to determine the peak pressure difference during the body position change process for diabetic patients. Calculate the ratio of the peak pressure difference to the duration of the body position change period to analyze the rate of change of pressure amplitude caused by the body position change process for diabetic patients and determine the arterial pressure change coefficient during the body position change process for diabetic patients.

8. A method for monitoring the health of diabetic patients based on the Internet of Things according to claim 7, characterized in that: The specific steps in S3 also include: S36. Correlate the initial arterial pressure data in the supine position with the arterial pressure data during positional changes, and combine this with the arterial pressure change coefficient of diabetic patients during positional changes. After dimensionless processing, analyze the lag level of foot arterial function response during positional changes in diabetic patients, and determine the foot arterial function lag index, specifically: In the formula, Indicates the foot artery lag index, This represents the initial peak pressure in a diabetic patient while lying supine. This indicates a characteristic factor of arterial waveform hysteresis in diabetic patients in a supine position. This indicates the duration of the initial adjacent peak interval in diabetic patients while in a supine position. This represents the coefficient of arterial pressure change in diabetic patients during postural changes. This indicates the peak waveform delay time difference during postural changes in diabetic patients; Specifically, the arterial waveform hysteresis characteristic factor of diabetic patients in the supine position is calculated by the ratio of the initial waveform rise time to the initial waveform fall time, and the peak waveform delay time difference of diabetic patients during the position change process is calculated by the time difference between the initial peak pressure time and the peak pressure time after the change.

9. A method for monitoring the health of diabetic patients based on the Internet of Things according to claim 8, characterized in that: The specific steps of S4 include: S41. Based on the triggering of foot artery monitoring and early warning, the determined microcirculation operation index is correlated with the foot artery function lag index. After dimensionless processing, the potential risk of local blood flow obstruction caused by thickening of the foot vascular basement membrane in diabetic patients is analyzed to determine the comprehensive risk index of vascular obstruction, specifically: In the formula, This indicates a comprehensive risk index for vascular obstruction. Indicates the foot artery lag index, Indicates the microcirculation operation index. Indicates the reference value for microcirculation health; S42. Compare and analyze the comprehensive risk index of vascular obstruction with the preset risk threshold to determine whether the current diabetic patient is at risk of local blood flow obstruction, and generate a corresponding level of health guidance report, specifically including: If the comprehensive risk index of vascular occlusion exceeds the risk threshold, it indicates that the current diabetic patient is at risk of local blood flow obstruction, and a Level 1 health guidance report is issued, which includes: recommending that the patient go to a medical institution as soon as possible for further hemodynamic examination; If the comprehensive risk index of vascular occlusion does not exceed the risk threshold, it indicates that the current diabetic patient is not at risk of local blood flow obstruction, and a level-two health guidance report is issued, which includes: recommending regular physical examinations, while continuing to maintain the current lifestyle management, including a reasonable diet, regular exercise, control of sugar and lipid intake, and avoiding smoking and prolonged sitting.

10. A health monitoring system for diabetic patients based on the Internet of Things (IoT), used to implement the health monitoring method for diabetic patients based on the IoT as described in any one of claims 1 to 9, characterized in that: It includes a deployment module, a microcirculation monitoring module, a foot artery monitoring module, and a health report generation module; The deployment module is used to wirelessly connect the integrated smart foot device worn by diabetic patients to the local data management platform by combining IoT communication technology. After digital conversion and clock synchronization of the collected raw signals, the module performs time alignment and storage. The microcirculation monitoring module is used to monitor the temperature and oxygen content of foot tissues at multiple characteristic locations on the soles of diabetic patients through an integrated smart foot device, analyze the microcirculation level of blood in the feet of diabetic patients, and trigger foot artery monitoring and early warning. The foot artery monitoring module is used to perform postural change tests on diabetic patients after receiving a foot artery monitoring alert, monitor foot artery pressure fluctuations in real time, and analyze the level of foot artery functional response lag in diabetic patients during postural changes. The health report generation module is used to comprehensively analyze the potential risk of local blood flow obstruction caused by thickening of the vascular basement membrane in the feet of diabetic patients based on the level of microcirculation in the feet and the level of sluggish response of the foot arteries, and generate a corresponding level of health guidance report.

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