A thermal radiation range infrared physiotherapy temperature control system

Abstract

This invention provides a method and system for controlling the temperature of infrared physiotherapy within a thermal radiation range, relating to the field of infrared physiotherapy temperature control technology. The method includes the following steps: acquiring temperature data from multiple heating zones of a belt-type physiotherapy device, as well as micro-vibration data from the device's built-in gyroscope; inferring the local contact pressure of each heating zone based on the temperature data; identifying the user's subtle body adjustment patterns; constructing a real-time heat distribution map; predicting local hotspot trends by combining the local contact pressure and subtle body adjustment patterns; and controlling the temperature of each heating zone by adjusting the heating power of the multiple heating zones based on the heat distribution deviation identified in the heat distribution map and the predicted local hotspot trends. This invention aims to solve the problem of localized heat accumulation forming "hotspots" when a user makes subtle body adjustments using a belt-type herbal pack infrared physiotherapy device, ensuring comfort, effectiveness, and safety during the physiotherapy process.

Description

Technical Field

[0001] This invention relates to the field of infrared physiotherapy temperature control technology, and more specifically, to an infrared physiotherapy temperature control system for thermal radiation range. Background Technology

[0002] In modern family health management, the waist-belt-style infrared herbal pack therapy device is widely used as a convenient auxiliary treatment to relieve lower back discomfort. The core design concept of this device combines traditional Chinese medicine hot compress with infrared radiation technology. It heats the herbal pack using a built-in electric heating element, generating infrared rays to achieve the effects of warming the meridians, dispelling cold, and promoting blood circulation. To optimize user experience and improve safety, these devices generally incorporate intelligent temperature control mechanisms. Specifically, a miniature gyroscope sensor is integrated inside the waist belt. This sensor can monitor and identify the user's macroscopic body position in real time, distinguishing whether the user is standing, sitting, or lying down. After receiving the positional data from the gyroscope, the control system will adjust the temperature according to the preset position. The system automatically adjusts the output power of the infrared heating element based on temperature response, thereby changing the temperature of the treatment area. This dynamic adjustment is based on the significant differences in the tightness of the fit and local pressure distribution between the treatment belt and the user's waist under different body positions. For example, when the user is lying down, the body's gravity increases the contact area and pressure between the belt and the back skin, making it easier and faster for heat to be transferred to the deeper layers of the skin. Maintaining a high heating temperature in this situation carries the risk of localized overheating or even low-temperature burns. Conversely, when the user is standing or active, the fit between the belt and the body may be relatively loose, reducing heat transfer efficiency. To ensure sufficient therapeutic effect, the heating temperature needs to be appropriately increased. Therefore, sensing the macroscopic body position using a gyroscope and adjusting the overall heating temperature accordingly is the mainstream technological approach for achieving intelligent and user-friendly control in this type of treatment device.

[0003] However, in real-world daily use, users don't always maintain a completely static posture. For example, a user might be sitting in an office for extended periods, wearing a physiotherapy device for lumbar support. The gyroscope accurately identifies the user's "sitting" posture and instructs the control system to adjust the heating temperature to the preset "sitting mode" setting. At this time, the heating surface of the physiotherapy device operates at a uniform temperature. However, during prolonged sitting, users often unconsciously make a series of subtle body adjustments to maintain comfort or complete tasks. For instance, a user might lean forward to reach for documents on their desk, causing a slight slippage or separation between the lumbar region and the physiotherapy device, resulting in a momentary decrease in pressure in certain areas. Immediately afterward, the user might lean back in their chair to relieve lumbar fatigue, at which point the contact pressure across the entire lumbar region increases, potentially creating new pressure points on both sides of the lumbar spine. More importantly, when the user makes slight left-right twists or shifts their center of gravity, the local contact pressure between the physiotherapy device and the skin on both sides of the lumbar region exhibits alternating and uneven increases and decreases. For example, when tilting to the left, the pressure in the left waist area increases instantaneously, while the pressure on the right side decreases; and vice versa. Although these movements occur frequently, the resulting angular changes are usually very small, far below the threshold required for the gyroscope to recognize "posture changes." For gyroscopes whose primary task is to detect large changes in posture (such as from standing to sitting), these minute body twists or shifts in center of gravity are likely to be filtered out by the system as normal body swaying or sensor noise, failing to trigger the control system to reassess the macroscopic posture and adjust the temperature.

[0004] It is these subtle, often overlooked bodily movements that cause dynamic and localized changes in the contact between the therapy device and the user's lower back skin. In areas where localized pressure increases momentarily, heat is transferred and accumulates more quickly, forming temporary "hot spots," causing the user to experience a noticeable burning sensation or discomfort on the localized skin. This discomfort is not continuous but rather appears and disappears with the user's subtle adjustments to their posture, exhibiting an intermittent and migratory characteristic. For example, when the user leans to the left, the left side of the lower back will feel burning, while when they return to a normal sitting posture or lean to the right, the burning sensation on the left side will lessen, but a new hot spot may appear on the right side. This dynamic shifting of the hot spot location and fluctuations in intensity make it difficult for the user to completely resolve the problem through simple posture adjustments.

[0005] Because the control system relies solely on the macroscopic information of a single "sitting posture" provided by the gyroscope, it cannot perceive the localized, instantaneous pressure changes caused by the user's subtle movements. Therefore, it continuously applies uniform temperature control to the entire infrared radiation zone, failing to provide timely and precise intervention for these dynamically generated "hot spots." Users experiencing recurring localized discomfort may frequently attempt to adjust their posture, but these adjustments are subtle enough not to trigger the gyroscope's macroscopic posture recognition, leaving the problem unresolved. In this situation, the physiotherapy device's intelligent control strategy is inadequate in dealing with the subtle dynamic changes in the user's daily activities, failing to provide a truly personalized and safe therapeutic experience. Over time, this not only affects the comfort and compliance of the therapy, reducing user trust in the device, but may even pose a potential risk of low-temperature burns in some sensitive skin areas. This cumulative effect is particularly pronounced with longer therapy sessions, as the skin's tolerance gradually decreases under repeated localized overheating stimulation. In addition, this uneven heating may also affect the uniform release and penetration of the active ingredients in the herbal pack, thereby reducing the overall therapeutic effect.

[0006] Therefore, how to go beyond macroscopic body position perception and achieve differentiated and precise dynamic control of the temperature of different areas on the heating surface of the physiotherapy device, so as to eliminate the problem of local heat accumulation and "hot spots" caused by the user's subtle movements, is an important challenge currently facing the technology. Summary of the Invention

[0007] The purpose of this invention is to provide a thermal radiation range infrared physiotherapy temperature control system, which aims to solve the problem of local heat accumulation and the formation of "hot spots" when the user makes subtle body adjustments in the waist belt-type Chinese medicine pack infrared physiotherapy device, and can ensure the comfort, effectiveness and safety of the physiotherapy process.

[0008] In a first aspect, the present invention provides a method for controlling the temperature of infrared physiotherapy in the thermal radiation range, comprising the following steps: acquiring temperature data of multiple heating zones of a belt-type physiotherapy device, and micro-vibration data of a gyroscope built into the physiotherapy device;

[0009] Based on temperature data, the local contact pressure of each heating zone is inferred; micro-vibration data is processed to identify subtle body adjustment patterns of the user; a real-time heat distribution map is constructed based on local contact pressure and temperature data; local hot spot trends are predicted by combining local contact pressure and subtle body adjustment patterns; and the temperature of each heating zone is controlled by adjusting the heating power of multiple heating zones based on the heat distribution deviation identified by the heat distribution map and the predicted local hot spot trends.

[0010] The infrared physiotherapy temperature control method provided by this invention cleverly integrates multi-source information, including the temperature response of the heating element (for inferring local contact pressure) and micro-vibration data from the built-in gyroscope (for identifying subtle body adjustment patterns and predicting hotspot trends), to construct a real-time heat distribution map. Based on this, the system can proactively identify heat distribution deviations or predict potential hotspots and initiate a multi-dimensional dynamic adjustment mechanism, including instantaneous power adjustment, proactive preventative intervention, and self-learning optimization of adjustment strategies. This concept enables the physiotherapy device to shift from passively responding to localized overheating to actively preventing and continuously maintaining a uniform heat distribution throughout the entire heating area, thus providing a seamless, comfortable, and safe physiotherapy experience even when the user is engaged in subtle activities.

[0011] In a second aspect, the present invention provides a thermal radiation range infrared physiotherapy temperature control system, comprising: an acquisition module for acquiring temperature data of multiple heating zones of a belt-type physiotherapy device, and micro-vibration data of a gyroscope built into the physiotherapy device;

[0012] The system comprises the following modules: an inference module to infer the local contact pressure of each heating zone based on temperature data; an identification module to process micro-vibration data and identify subtle body adjustment patterns of the user; a construction module to build a real-time heat distribution map based on local contact pressure and temperature data; a prediction module to predict local hot spot trends by combining local contact pressure and subtle body adjustment patterns; and a control module to control the temperature of each heating zone by adjusting the heating power of multiple heating zones based on the heat distribution deviation identified in the heat distribution map and the predicted local hot spot trends.

[0013] As can be seen from the above, the infrared physiotherapy temperature control method for thermal radiation range provided by the present invention can significantly improve the comfort and safety of the waist belt-type Chinese medicine pack infrared physiotherapy device. By sensing the local pressure changes caused by the user's subtle body adjustments in real time and making forward-looking and precise temperature adjustments, it can effectively avoid the accumulation of local heat to form "hot spots", thereby ensuring the uniformity of heat distribution throughout the physiotherapy process and improving the continuous effectiveness of the physiotherapy.

[0014] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0015] Figure 1 This is a flowchart of a method for controlling the temperature of infrared physiotherapy in the thermal radiation range, provided in an embodiment of the present invention.

[0016] Figure 2This is a schematic diagram of a thermal radiation range infrared physiotherapy temperature control system provided in an embodiment of the present invention.

[0017] Label Explanation:

[0018] 100. Acquisition Module; 200. Inference Module; 300. Recognition Module; 400. Construction Module; 500. Prediction Module; 600. Control Module. Detailed Implementation

[0019] 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. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] Reference Appendix Figure 1 The present invention provides a method for controlling the temperature of infrared physiotherapy in the thermal radiation range, comprising the following steps: acquiring temperature data of multiple heating areas of a belt-type physiotherapy device, and micro-vibration data of a gyroscope built into the physiotherapy device;

[0022] Based on temperature data, the local contact pressure of each heating zone is inferred; micro-vibration data is processed to identify subtle body adjustment patterns of the user; a real-time heat distribution map is constructed based on local contact pressure and temperature data; local hot spot trends are predicted by combining local contact pressure and subtle body adjustment patterns; and the temperature of each heating zone is controlled by adjusting the heating power of multiple heating zones based on the heat distribution deviation identified by the heat distribution map and the predicted local hot spot trends.

[0023] Traditional waist-mounted infrared therapy devices using traditional Chinese medicine packs rely primarily on monitoring the user's macroscopic body position for temperature control, and adjust the temperature according to preset body positions. The heating power is adjusted according to temperature correlation. However, this method ignores the dynamic changes in local contact pressure and heat distribution caused by subtle body adjustments while the user maintains a fixed macroscopic body position. These subtle body movements, such as slight twisting or shifting of the center of gravity, although insufficient to trigger macroscopic body position recognition, can significantly affect the tightness of the fit between the physiotherapy device and the skin, resulting in local heat accumulation, forming "hot spots," causing user discomfort and even the potential risk of low-temperature burns.

[0024] To address this issue, this application proposes a method for controlling the temperature of infrared physiotherapy within a thermal radiation zone. This method acquires temperature data from multiple heating zones of a belt-type physiotherapy device, along with micro-vibration data from the device's built-in gyroscope, and infers the local contact pressure of each heating zone based on the temperature data. The method further processes the micro-vibration data to identify subtle body adjustment patterns of the user. Based on the local contact pressure and temperature data, a real-time heat distribution map is constructed, and the trend of local hot spots is predicted by combining the local contact pressure and subtle body adjustment patterns. Finally, based on the heat distribution deviation identified by the heat distribution map and the predicted local hot spot trend, the heating power of multiple heating zones is adjusted to achieve precise temperature control of each heating zone. This application aims to solve the problem of local hot spots caused by the inability to sense subtle body movements of the user in existing technologies, thereby improving the comfort and safety of physiotherapy.

[0025] The term "belt-type physiotherapy device" as used in this application generally refers to a physiotherapy device that can be worn on the waist and has built-in heating elements and sensors for providing thermal radiation physiotherapy. "Heating zone" refers to an independently temperature-controlled area within the physiotherapy device, each area equipped with a temperature sensor. "Temperature data" refers to real-time temperature readings obtained from the temperature sensors in these heating zones. "Gyroscope" is a device used to measure or maintain orientation; here it is used to sense the minute movements of the physiotherapy device, i.e., "micro-vibration data." "Local contact pressure" refers to the pressure exerted on various localized areas of the contact surface between the physiotherapy device and the user's body; its magnitude affects the efficiency of heat transfer. "Fine body adjustment mode" refers to the minute, unconscious movements that occur in the user's body while maintaining a relatively stable macroscopic posture, such as slight shifts in center of gravity or muscle contractions. "Heat distribution map" is a real-time representation of the heat distribution on the contact surface between the physiotherapy device and the body, constructed based on temperature data and local contact pressure data. "Local hotspot trend" refers to the tendency for heat to accumulate in specific localized areas, potentially leading to excessively high temperatures. "Heating power" refers to the output power of the heating element, and the temperature of the heating area can be controlled by adjusting its value.

[0026] First, it is necessary to acquire temperature data from multiple heating zones of the belt-type physiotherapy device, as well as micro-vibration data from the device's built-in gyroscope. Temperature data can be acquired by deploying independent temperature sensors within each heating zone, such as thermistors, thermocouples, or infrared temperature sensors. These sensors monitor the temperature of their respective areas in real time and convert analog signals into digital signals, which are then transmitted to the main control unit via a data acquisition module. Micro-vibration data acquisition relies on the device's built-in gyroscope. The gyroscope continuously monitors the angular velocity and attitude changes of the physiotherapy device in three-dimensional space, capturing even minute body swaying or posture adjustments and generating corresponding micro-vibration data. For example, the gyroscope can continuously acquire angular velocity data along the X, Y, and Z axes at a high sampling rate (e.g., 100Hz or higher), reflecting the instantaneous motion state of the physiotherapy device.

[0027] Secondly, the local contact pressure of each heating zone is inferred based on the temperature data. This inference can be achieved in several ways. One method is to use a pre-established empirical model relating temperature and pressure. For example, in a laboratory setting, temperature tests can be conducted on heating zones under different local contact pressures, and their steady-state temperature or rate of temperature rise can be recorded to create a lookup table or regression model. In practical applications, when temperature data for a particular heating zone is obtained, the corresponding local contact pressure can be inferred from this model. Another method is to use the rate of temperature change in the heating zone to infer the local contact pressure. When the local contact pressure increases, heat transfer efficiency improves, leading to a faster rate of temperature rise or a higher steady-state temperature at the same heating power; conversely, a decrease in pressure slows the rate of temperature rise or lowers the steady-state temperature. Therefore, by monitoring the temperature change trend over time, the magnitude of the local contact pressure can be indirectly inferred.

[0028] Next, the micro-vibration data is processed to identify the user's subtle body adjustment patterns. This processing can include steps such as noise filtering, signal enhancement, and feature extraction. For example, digital filters (such as bandpass filters) can be used to remove environmental noise and sensor drift, preserving frequency components relevant to human activity. Subsequently, by analyzing the amplitude, frequency, and duration of the filtered micro-vibration data, different subtle body adjustment patterns can be identified. For instance, sustained low-amplitude vibrations may indicate a slight twisting of the body, while momentary high-amplitude vibrations may indicate a brief shift in the user's center of gravity. These patterns can be trained and identified using machine learning algorithms. For example, a large amount of micro-vibration data under different subtle body adjustment movements can be collected, labeled, and then used to train a support vector machine (SVM) or neural network model to automatically identify these patterns.

[0029] Then, a real-time heat distribution map is constructed based on local contact pressure and temperature data. The construction of the heat distribution map spatially maps the local contact pressure and temperature data of each heating zone. For example, the heating area of ​​the physiotherapy device can be divided into a two-dimensional grid, with each grid cell corresponding to a heating zone. For each grid cell, its current temperature data and inferred local contact pressure data are used as attribute values. Through interpolation algorithms (such as bilinear interpolation or Kriging interpolation), these discrete data points can be smoothed, thereby generating a continuous heat distribution map. This map can be visualized using color coding; for example, warm colors represent areas with higher temperatures or pressures, and cool colors represent areas with lower temperatures or pressures, thus intuitively showing the heat distribution of the entire physiotherapy area.

[0030] Subsequently, by combining localized contact pressure and subtle body adjustment patterns, localized hotspot trends are predicted. The prediction of localized hotspot trends is based on a comprehensive analysis of current localized contact pressure and the user's subtle body adjustment patterns. For example, if the localized contact pressure in a heating area remains consistently high, and at the same time, subtle body adjustments that could further increase the pressure in that area are identified (such as leaning forward), then a higher risk of localized hotspot formation in that area can be predicted. The prediction model can be a rule-based expert system or a prediction model trained on historical data. For example, a large amount of data on localized hotspots occurring under different localized contact pressures and subtle body adjustment patterns can be collected, and this data can be used to train a time-series prediction model (such as a recurrent neural network, RNN) to predict the probability and intensity of localized hotspots occurring in the future.

[0031] Finally, based on the heat distribution deviation identified by the heat distribution map and the predicted local hotspot trends, the temperature of each heating zone is controlled by adjusting the heating power of multiple heating zones. The control strategy can be a closed-loop control system. First, by analyzing the real-time heat distribution map, the deviation between the current heat distribution and the ideal distribution is identified; for example, the temperature of a certain area is significantly higher than that of other areas or the preset target temperature. Simultaneously, combined with the predicted local hotspot trends, warnings are issued for areas where hotspots may occur. Then, based on this information, the main control unit calculates the required heating power adjustment for each heating zone. For example, for areas with excessively high temperatures or predicted hotspot trends, their heating power can be reduced; for areas with low temperatures or insufficient heat transfer, their heating power can be appropriately increased. These adjustments are sent to the power controller of each heating zone via digital signals, thereby achieving precise and dynamic temperature control of each heating zone.

[0032] The infrared physiotherapy temperature control method proposed in this application significantly improves the intelligence and safety of the physiotherapy device by introducing the recognition of the user's subtle body adjustment patterns and the inference of local contact pressure. Traditional methods mainly rely on macroscopic body position recognition, which cannot capture the impact of the user's subtle movements on local heat distribution while maintaining the same macroscopic body position. For example, when sitting for a long time, the user may unconsciously lean forward or twist slightly from side to side. Although these movements are small, they are enough to change the tightness of the physiotherapy device in contact with the body, causing excessive heat accumulation in certain areas and forming "hot spots". Existing systems, lacking the ability to perceive these subtle changes, often cannot adjust the heating power in time, which may cause the user to feel local burning discomfort, or even pose a risk of low-temperature burns.

[0033] In contrast, the method of this application first acquires temperature data from multiple heating zones of the belt-type physiotherapy device and micro-vibration data from its built-in gyroscope. By analyzing the temperature data, the local contact pressure of each heating zone can be inferred, providing crucial information for understanding heat transfer efficiency. More importantly, this application performs fine processing on the micro-vibration data to identify the user's subtle body adjustment patterns. This innovation enables the system to perceive minute movements that are overlooked by traditional macro-position recognition.

[0034] Building upon this foundation, this application constructs a real-time heat distribution map based on local contact pressure and temperature data, intuitively displaying the heat distribution across the entire treatment area. Furthermore, by combining local contact pressure with identified subtle body adjustment patterns, the system can predict local hotspot trends, enabling early warning of potential hotspot areas. This predictive capability allows the system to shift from passive response to proactive intervention, taking measures before hotspots form.

[0035] Ultimately, this application, based on heat distribution deviations identified by a heat distribution map and predicted local hotspot trends, finely adjusts the heating power of multiple heating zones, thereby achieving precise temperature control of each heating zone. This multi-zone, dynamic, and predictive temperature control mechanism effectively avoids the formation of local hotspots, significantly improving the comfort and safety of physiotherapy. For example, when the system predicts that a certain area may form a hotspot due to subtle adjustments by the user, it can reduce the heating power of that area in advance, thus eliminating potential risks before the user feels discomfort. This refined and intelligent temperature control not only solves the problem of local overheating in traditional methods but also ensures the uniform release and penetration of effective ingredients in the herbal pack, thereby comprehensively improving the physiotherapy effect. Therefore, based on existing technologies, this application provides a more advanced, safe, and comfortable infrared physiotherapy temperature control solution for thermal radiation zones by introducing the perception and prediction of subtle body adjustments and local contact pressure.

[0036] In some embodiments, the step of predicting local hotspot trends by combining local contact pressure and subtle body adjustment patterns includes:

[0037] By preprocessing the micro-vibration data, low-frequency interference signals in the micro-vibration data are eliminated. The preprocessing steps specifically include: using a high-pass filtering method to preprocess the micro-vibration data; obtaining local contact pressure change data by monitoring changes in local contact pressure; and distinguishing between human micro-motion signals and environmental vibration interference in the micro-vibration data by correlating and verifying the preprocessed micro-vibration data and the local contact pressure change data.

[0038] Based on the correlation verification results, the subtle body adjustment pattern was confirmed;

[0039] By combining localized contact pressure with confirmed subtle bodily adjustment patterns, local hotspot trends can be predicted.

[0040] Specifically, preprocessing micro-vibration data aims to remove noise components unrelated to human micro-movements. Low-frequency interference signals typically originate from slow drift in the environment or from the equipment itself. Preprocessing can effectively filter out these interferences, making subsequent identification of human micro-movement signals clearer.

[0041] Simultaneously, by monitoring changes in local contact pressure, data on these changes can be obtained. Local contact pressure refers to the pressure distribution at the interface between the user's body and the physiotherapy device, and its changes are often closely related to subtle adjustments made by the body. For example, when a user slightly adjusts their sitting or lying posture, the contact point between the body and the physiotherapy device will undergo a slight displacement, resulting in a change in local contact pressure.

[0042] The purpose of correlating and verifying the preprocessed micro-vibration data with local contact pressure change data is to improve the accuracy of identifying human micro-motion signals through cross-verification of these two different types of data. Micro-vibration data reflects the dynamic changes of the body, while local contact pressure change data reflects the static or quasi-static changes in the contact state between the body and the physiotherapy device. When the two show a correlation over time, it can be more reliably determined that it is a human micro-motion signal rather than environmental vibration interference. For example, if the micro-vibration data fluctuates at a certain point in time, and the local contact pressure data also shows a corresponding change, then the fluctuation can be considered to be caused by human micro-motion.

[0043] Therefore, based on the correlation verification results, subtle body adjustment patterns can be confirmed. Once the micro-motion signals of the human body are accurately distinguished and confirmed, they can be used as valid inputs for subtle body adjustment patterns.

[0044] Ultimately, by combining localized contact pressure with confirmed subtle bodily adjustment patterns, localized hotspot trends can be predicted more accurately.

[0045] This application's solution effectively addresses the difficulty in distinguishing between human body micro-motion signals and environmental vibration interference in traditional methods by introducing micro-vibration data preprocessing, local contact pressure change monitoring, and a correlation verification mechanism between the two. Specifically, micro-vibration data is preprocessed to eliminate low-frequency interference signals, thereby improving data quality. Simultaneously, local contact pressure change data, as an independent physical quantity directly related to body posture adjustment, is used to assist in verifying the micro-vibration data. When the preprocessed micro-vibration data and local contact pressure change data show temporal consistency or correlation, the system can more reliably determine that these signals originate from the user's subtle body adjustments rather than environmental noise. This multi-source data fusion and cross-validation mechanism makes the identification of subtle body adjustment patterns more accurate and robust. It is precisely because the identification accuracy of subtle body adjustment patterns is significantly improved that the accuracy of subsequently predicting local hotspot trends by combining local contact pressure and confirmed subtle body adjustment patterns is guaranteed.

[0046] Through the above technical solution, this application can significantly improve the accuracy and reliability of recognizing subtle body adjustment patterns of users. By preprocessing micro-vibration data and verifying its correlation with local contact pressure change data, the system can effectively distinguish between human micro-motion signals and environmental vibration interference, avoiding invalid or erroneous local hot spot trend predictions caused by misjudgment. Therefore, the predicted local hot spot trends will be closer to the user's actual physical condition and needs, resulting in more precise and timely adjustment of heating power in multiple heating areas, effectively preventing local overheating or insufficient heat, and significantly improving the comfort, safety, and therapeutic effect of infrared physiotherapy in the thermal radiation zone.

[0047] In some preferred embodiments, a specific example is given below. Assume a user is wearing a waist-length physiotherapy device. During the treatment, the user may unconsciously make slight adjustments to their body due to discomfort from maintaining the same posture for an extended period, such as slightly twisting their waist or shifting their center of gravity. At this time, the physiotherapy device's built-in gyroscope will collect micro-vibration data, and simultaneously, pressure sensors in multiple heating zones will monitor subtle changes in local contact pressure.

[0048] First, the collected micro-vibration data is preprocessed, for example, by using a high-pass filter to remove low-frequency noise from the environment, ensuring that the data mainly retains high-frequency human body micro-motion signals. Simultaneously, the system continuously monitors the local contact pressure in each heating zone and records its changes.

[0049] Subsequently, the preprocessed micro-vibration data and local contact pressure change data are correlated and verified. For example, if the system detects significant fluctuations in the micro-vibration data within a certain time period, and within the same time period, the local contact pressure of the heating area corresponding to the fluctuating area also changes synchronously and regularly (e.g., the pressure in a certain area decreases while the pressure in an adjacent area increases), then the system will determine that these signals are caused by the user's subtle physical adjustments, rather than random vibrations from the external environment.

[0050] Based on this correlation verification, the system can accurately confirm that the user is making subtle body adjustments and identify the specific adjustment pattern. For example, is the user leaning to the left or leaning back? Ultimately, by combining these confirmed subtle body adjustment patterns with real-time local contact pressure data, the system can more accurately predict which areas are likely to experience localized hot spots or insufficient heat. For example, if the user leans to the left, the pressure on the left side increases, and micro-vibration data confirms this adjustment. The system will predict that the left side may accumulate heat more easily due to closer contact, thus adjusting the power of the left heating area in advance to maintain a comfortable temperature.

[0051] In some embodiments, the step of distinguishing between human micro-motion signals and environmental vibration interference in micro-vibration data by correlating and verifying preprocessed micro-vibration data and local contact pressure change data includes: after performing calibration, distinguishing between human micro-motion signals and environmental vibration interference in micro-vibration data by correlating and verifying preprocessed micro-vibration data and local contact pressure change data.

[0052] Specifically, through the following steps A1 A2 Calibration: A1. Guide the user to perform preset subtle body adjustment movements, and simultaneously collect micro-vibration data and local contact pressure change data during the preset subtle body adjustment movements; A2. Based on the collected micro-vibration data and local contact pressure change data, establish user-specific correlation parameters between the micro-vibration data and the local contact pressure change data;

[0053] After calibration, the steps to distinguish between human micro-motion signals and environmental vibration interference in the micro-vibration data by correlating and verifying the preprocessed micro-vibration data and local contact pressure change data include: based on user-specific correlation parameters, correlating and verifying the preprocessed micro-vibration data and local contact pressure change data to distinguish between human micro-motion signals and environmental vibration interference in the preprocessed micro-vibration data.

[0054] In step A1, "guiding the user to perform preset subtle body adjustment movements" refers to instructing the user to perform a series of specific, slight body posture adjustments, such as slight waist twisting, leaning forward, or leaning back, through the physiotherapy device's display interface, voice prompts, or accompanying applications. These preset movements are designed to simulate the subtle body adjustments that users may make during daily use of the physiotherapy device, in order to obtain relevant data under controlled conditions. "Synchronously collecting micro-vibration data and local contact pressure change data during the preset subtle body adjustment movements" means that while the user performs the above preset movements, the physiotherapy device's built-in gyroscope continuously records micro-vibration data and simultaneously monitors the changes in local contact pressure in multiple heating areas. Synchronous acquisition ensures the temporal correspondence between micro-vibration data and local contact pressure change data, providing a basis for subsequent correlation analysis.

[0055] In step A2, "establishing user-specific correlation parameters between micro-vibration data and local contact pressure change data" refers to constructing a model or parameter set that reflects the intrinsic relationship between a specific user's body micro-movements and local contact pressure changes through data analysis and modeling after collecting synchronous data during the preset action period. These parameters can be statistical correlation coefficients, regression model coefficients, or classifiers or predictive models trained through machine learning algorithms. Because these parameters are established based on the actual action data of a specific user, they are called "user-specific correlation parameters," which can more accurately capture the user's unique body micro-movement characteristics.

[0056] In practical applications, "based on user-specific correlation parameters, the pre-processed micro-vibration data and local contact pressure changes are correlated and verified to distinguish between human micro-motion signals and environmental vibration interference in the pre-processed micro-vibration data" refers to the system acquiring pre-processed micro-vibration data and local contact pressure change data in real time during normal use of the physiotherapy device. Subsequently, using user-specific correlation parameters established during the calibration phase, correlation analysis is performed on these real-time data. For example, when a specific pattern appears in the micro-vibration data, and this pattern shows a high correlation with the local contact pressure change data through user-specific correlation parameters, it can be determined with high confidence that the micro-vibration is caused by human micro-motion; conversely, if the correlation between the micro-vibration data and the local contact pressure change data does not conform to the pattern defined by the user-specific correlation parameters, it is more likely to be environmental vibration interference.

[0057] The solution presented in this application is able to more accurately distinguish between human body micro-motion signals and environmental vibration interference because it introduces a user-specific calibration mechanism. By guiding the user to perform preset subtle body adjustment movements, the system can acquire micro-vibration data and local contact pressure change data generated by the user's own body micro-movements under controlled and explicit conditions. Because this data directly originates from the user's own physiological responses and physical contact, the subsequently established user-specific correlation parameters can accurately reflect the user's unique body micro-motion characteristics. Based on this, when the system performs correlation verification in actual use, it no longer relies on general correlation rules that may not be applicable to all individuals, but instead relies on these highly personalized user-specific correlation parameters. This personalized correlation verification mechanism enables the system to more effectively identify micro-vibration patterns related to the user's body adjustments, while filtering out environmental vibration interference unrelated to the user's physical state, thereby significantly improving the accuracy and robustness of human body micro-motion signal recognition.

[0058] Through the above technical solution, this application can establish a personalized correlation model between micro-vibration data and local contact pressure change data for a specific user. Compared with the method of using general calibration parameters, this user-specific correlation parameter can more accurately capture the unique physiological and physical responses of different users when making subtle body adjustments, thereby significantly improving the accuracy of distinguishing between human micro-motion signals and environmental vibration interference. As a result, the system can more reliably identify the user's subtle body adjustment patterns, providing more accurate input for subsequent local hotspot trend prediction, and ultimately achieving more refined and personalized infrared physiotherapy temperature control within the thermal radiation range, effectively avoiding problems such as local overheating or poor physiotherapy effects caused by misjudgment.

[0059] In some preferred embodiments, a specific example is given below. Assume a user is using the waist belt-type physiotherapy device for the first time. Upon initial use, the system prompts the user to enter calibration mode. In calibration mode, the system guides the user to perform a series of preset, subtle body adjustment movements, such as prompting the user to "slightly twist your waist to the left, hold for 3 seconds, then return to the starting position," or "slightly bend forward, hold for 3 seconds, then return to the starting position," etc. During these movements, the physiotherapy device's built-in gyroscope synchronously collects micro-vibration data, and pressure sensors in multiple heating areas synchronously collect data on changes in local contact pressure.

[0060] For example, when a user slightly twists their waist to the left, the local contact pressure in the left heating area may increase, while the local contact pressure in the right heating area may decrease. Simultaneously, the gyroscope records specific rotational and tilting micro-vibration patterns. The system analyzes this synchronously acquired micro-vibration data and local contact pressure change data, for example, by calculating the cross-correlation coefficient between them or training a support vector machine (SVM) model, to establish a user-specific correlation pattern between micro-vibrations and pressure changes. This correlation pattern is the user-specific correlation parameter.

[0061] After calibration and the establishment of user-specific correlation parameters, when a user detects micro-vibrations during daily physiotherapy, the system immediately combines real-time local contact pressure change data with the previously established user-specific correlation parameters for judgment. For example, if the detected micro-vibration pattern highly matches the "twisting to the left" pattern defined in the user-specific correlation parameters, and the trend of increasing pressure on the left and decreasing pressure on the right is also consistent with the parameters, then the system will identify it with high confidence as a micro-movement signal indicating that the user is adjusting their body. Conversely, if the correlation between the micro-vibration pattern and pressure change does not conform to the user-specific correlation parameters—for example, micro-vibration occurs but the local contact pressure does not change significantly—the system will judge it as environmental vibration interference, thus avoiding misjudgment. In this way, the system can provide more accurate recognition of human micro-movements based on individual differences among users, thereby optimizing hotspot prediction and temperature control effects.

[0062] In some embodiments, after establishing user-specific association parameters, the method further includes: continuously monitoring the average local contact pressure of multiple heating zones;

[0063] When the average local contact pressure deviates by a preset amount relative to the average local contact pressure during calibration, it is determined that the wearing conditions have changed;

[0064] Based on changes in wearing conditions, the user-specific associated parameters are updated by adjusting the sensitivity or threshold of the user-specific associated parameters.

[0065] Specifically, continuous monitoring of the average local contact pressure of multiple heating zones refers to the system periodically collecting and calculating the average local contact pressure of all heating zones during normal operation of the physiotherapy device. This average value can serve as an indicator of the overall tightness of wear or contact status. The local contact pressure can be measured in real time by pressure sensors integrated within the heating zones, and the control unit performs data aggregation and averaging calculations.

[0066] A change in wearing conditions is determined when the average local contact pressure deviates by a preset amount from the average local contact pressure during calibration. This "preset amount" is a configurable threshold that defines what level of pressure change is considered a substantial change in wearing conditions. For example, this amount can be set based on empirical data, user testing, or machine learning models to balance the risks of false positives and false negatives. Once the difference between the detected average local contact pressure and the average local contact pressure during calibration exceeds this preset amount, the system determines that the wearing conditions have changed.

[0067] Based on changes in wearing conditions, the user-specific correlation parameters are updated by adjusting their sensitivity or threshold. These parameters, established during the calibration phase, distinguish between subtle human body movements and environmental vibration interference. When wearing conditions change, these parameters may no longer be applicable. Therefore, this application dynamically adjusts the sensitivity or threshold of these parameters to adapt to new wearing conditions. For example, if the fit becomes looser, the threshold for identifying subtle vibration signals may need to be lowered to capture weaker human body movements; conversely, if the fit becomes tighter, the threshold may need to be raised to avoid misinterpreting environmental noise as subtle human body movements. This adjustment can be linear, non-linear, or based on a preset lookup table.

[0068] This application's solution effectively addresses the problem of user-specific correlation parameters failing due to changes in wearing conditions during the use of a physiotherapy device by introducing a continuous monitoring and dynamic adjustment mechanism for wearing conditions. Specifically, by continuously monitoring the average local contact pressure of multiple heating zones, the system can monitor the overall contact state between the physiotherapy device and the user's body in real time. When this contact state changes significantly—that is, when the average local contact pressure deviates by a preset amount relative to the pressure during calibration—the system can promptly determine that the wearing conditions have changed. Since changes in wearing conditions directly affect the transmission characteristics of micro-vibration signals and the response mode of local contact pressure, by identifying these changes, the system can proactively adjust the user-specific correlation parameters used to distinguish between human micro-vibration signals and environmental vibration interference. This adjustment can increase or decrease the sensitivity of the parameters, or modify their judgment threshold, so that under new wearing conditions, the system can still accurately correlate and verify the pre-processed micro-vibration data with the local contact pressure change data, ensuring accurate identification of human micro-vibration signals and maintaining the accuracy of local hotspot prediction.

[0069] Through the above technical solution, this application overcomes the limitation of existing technologies where the accuracy of user-specific associated parameters decreases when wearing conditions change. Specifically, by continuously monitoring the average local contact pressure and judging changes in wearing conditions based on its deviation, the system can respond promptly to changes in wearing status caused by external environment or user behavior. Therefore, the sensitivity or threshold of user-specific associated parameters can be dynamically adjusted and updated, ensuring that the recognition of human micro-motion signals remains highly accurate throughout the entire physiotherapy process. This dynamic adaptability significantly improves the robustness and reliability of the infrared physiotherapy temperature control method in the thermal radiation range, avoiding problems such as inaccurate prediction of local hot spots and temperature control failure due to changes in wearing conditions, thus providing users with a more stable, safer, and more personalized physiotherapy experience.

[0070] In some embodiments, the step of updating user-specific association parameters by adjusting the sensitivity or threshold of user-specific association parameters based on changes in wearing conditions includes:

[0071] Continuously monitor the local contact pressure of each of the multiple heating zones;

[0072] Identify areas in multiple heating zones where local contact pressure deviates by a preset amplitude; based on the areas where local contact pressure deviates and the degree of deviation, update the user-specific associated parameters by adjusting the sensitivity or threshold of the corresponding area's user-specific associated parameters.

[0073] Specifically, continuous monitoring of the local contact pressure of multiple heating zones means that the system no longer focuses solely on the overall or average local contact pressure of the belt-type physiotherapy device, but rather performs real-time, refined monitoring of the local contact pressure of each individual heating zone. Each heating zone is typically equipped with an independent pressure sensor or pressure sensor array to obtain accurate pressure data for that zone. Identifying areas where the local contact pressure deviates from a preset amplitude across multiple heating zones means that, based on continuous monitoring, the system compares the current local contact pressure of each heating zone with a preset reference pressure (e.g., pressure during calibration or average pressure under normal wearing conditions). When the difference between the local contact pressure of one or more heating zones and the reference pressure exceeds a preset amplitude threshold, that zone is identified as a deviation area. This preset amplitude threshold can be set according to the actual application scenario and the required system sensitivity. Updating user-specific related parameters by adjusting the sensitivity or threshold of the corresponding area based on the area and degree of local contact pressure deviation means that once a deviation area and its degree are identified, the system will no longer uniformly adjust the user-specific related parameters for all areas, but will instead selectively adjust the user-specific related parameters for the corresponding area. For example, if the pressure in a certain area increases significantly, it may mean that the area is in closer contact with the body. In this case, the sensitivity of the micro-vibration data recognition in that area can be appropriately reduced to avoid misinterpreting minor vibrations caused by normal close contact as body adjustments. Conversely, if the pressure decreases, the sensitivity may need to be increased. The degree of deviation can be used to quantify the amplitude and direction of the adjustment; for example, the greater the deviation, the greater the amplitude of the adjustment. The sensitivity or threshold of user-specific correlation parameters is a key parameter for distinguishing human micro-motion signals from environmental vibration interference, and its adjustment directly affects the recognition accuracy of subtle body adjustment patterns.

[0074] This application's solution refines the monitoring of local contact pressure from an overall average level to individual heating zones, and adjusts user-specific correlation parameters based on the deviation and degree of local contact pressure in each zone, thus solving the problem of insufficient recognition accuracy that may result from relying solely on average pressure. When the wearing conditions of a specific heating zone change, such as an increase or decrease in pressure due to local adjustments in body posture, the system can accurately identify this local change. By locally adjusting the sensitivity or threshold of the user-specific correlation parameters for that specific zone, the system can ensure that the distinction between human body micro-motion signals and environmental vibration interference remains accurate and effective within that zone. For example, if the pressure in a certain zone increases, indicating a tighter contact, the gyroscope's micro-vibration signal may be more easily affected by body micro-motions. By reducing the sensitivity of the correlation parameters for that zone, oversensitivity can be avoided, reducing misjudgments. Conversely, if the pressure decreases, indicating a looser contact, the sensitivity may need to be increased to capture even weaker body micro-motions. This refined adjustment mechanism allows the system to more accurately adapt to changes in local wearing conditions, thereby improving the accuracy of recognizing subtle body adjustment patterns and, consequently, more accurately predicting local hotspot trends.

[0075] Through the above technical solution, this application achieves further optimization of the infrared physiotherapy temperature control method within the thermal radiation range. Compared to solutions that adjust parameters solely based on average local contact pressure, this application significantly improves the system's adaptability to changes in local wearing conditions by independently monitoring and analyzing the local contact pressure of each heating zone and adjusting user-specific parameters regionally according to local deviations. This refined adjustment mechanism avoids recognition errors caused by local variations being masked by the overall average value, ensuring the accuracy of identifying subtle body adjustment patterns and predicting local hotspot trends under different body parts and wearing conditions. Consequently, the temperature control of the physiotherapy device can more accurately respond to the user's actual needs, effectively avoiding local overheating or insufficient therapeutic effect, and improving the comfort and effectiveness of the physiotherapy.

[0076] In some embodiments, the step of updating user-specific correlation parameters by adjusting the sensitivity or threshold of user-specific correlation parameters for the corresponding area based on the region where the local contact pressure deviation occurs and the degree of deviation includes: when the degree of deviation of the region where the local contact pressure deviation occurs is within a preset critical range, performing the following step B1. B4:

[0077] B1. Obtain the historical change sequence of local contact pressure deviation; B2. Analyze the dynamic trend and rate of change of local contact pressure deviation based on the historical change sequence; B3. Based on the dynamic trend and rate of change, select one or more adjustment strategies from the preset set of adjustment strategies for combination, or adjust the execution intensity of the selected strategy.

[0078] B4. Based on the area and degree of deviation of local contact pressure, and according to the selected adjustment strategy or the intensity of the adjustment strategy, update the user-specific related parameters by adjusting the sensitivity or threshold of the corresponding area.

[0079] Specifically, when the deviation in the area where local contact pressure deviates reaches a preset critical range, it means that the pressure change in that area has reached a level requiring special attention, and the system needs a more refined adjustment strategy. This preset critical range can be set based on empirical data, user feedback, or experimental results, aiming to identify potential risk areas that may cause discomfort or affect the therapeutic effect.

[0080] In step B1, obtaining the historical change sequence of local contact pressure deviation refers to the system continuously recording and storing a series of data showing the change of local contact pressure deviation values ​​in a specific heating area over time. This historical sequence provides a data foundation for subsequent dynamic analysis. For example, local contact pressure deviation data can be collected every preset time interval (e.g., 1 second, 5 seconds), and data points from the most recent period (e.g., 30 seconds, 60 seconds) can be stored.

[0081] In step B2, analyzing the dynamic trend and rate of change of local contact pressure deviation based on historical change sequences involves performing time series analysis on the acquired historical data. The dynamic trend can refer to whether the pressure is continuously increasing, continuously decreasing, fluctuating periodically, or trending towards stability; the rate of change refers to how quickly the pressure changes. For example, these trends and rates can be identified by calculating the slope or second derivative of the historical sequence, or by applying algorithms such as moving averages or exponential smoothing. The aim is to gain a deeper understanding of the intrinsic laws governing pressure changes, rather than merely focusing on instantaneous values.

[0082] In step B3, based on the dynamic trend and rate of change, one or more adjustment strategies are selected from a preset set of adjustment strategies for combination, or the execution intensity of the selected strategy is adjusted. The preset set of adjustment strategies can include various parameter adjustment schemes for different pressure change patterns. For example, for rapidly increasing pressure, a "fast-response high-sensitivity adjustment strategy" might be selected; for slowly fluctuating pressure, a "smooth, gradual adjustment strategy" might be selected. Combining one or more strategies allows for more flexible adjustments. Adjusting the execution intensity of the selected strategy, for example, for a "fast-response high-sensitivity adjustment strategy," allows for further adjustment of the sensitivity enhancement based on the rate of change. The aim is to make parameter adjustment more intelligent and adaptive.

[0083] In step B4, based on the area and degree of deviation in local contact pressure, and according to the selected adjustment strategy or the intensity of its execution, the user-specific correlation parameters for the corresponding area are updated by adjusting the sensitivity or threshold. This means that the final parameter update is no longer a simple linear mapping, but a comprehensive adjustment combining regional characteristics, the degree of deviation, dynamic analysis results, and intelligent strategy selection. For example, if the analysis results show that the pressure in a certain area is increasing rapidly, and a "fast response high-sensitivity adjustment strategy" is selected, the sensitivity of the user-specific correlation parameters for that area may be significantly increased to identify subtle body adjustment patterns earlier.

[0084] This application's solution, by introducing the acquisition of historical change sequences of local contact pressure deviations and the analysis of dynamic trends and rates of change, enables the system to more comprehensively understand changes in user wearing conditions. It is precisely because the system can select or combine strategies from a preset set of adjustment strategies based on this dynamic information and adjust their execution intensity that the updates of specific user-related parameters are no longer passive responses based on a single instantaneous state, but rather proactive and refined adjustments based on the evolution pattern of pressure. Therefore, the system can more accurately distinguish between micro-motion signals from the human body and environmental vibration interference, thereby more reliably confirming subtle body adjustment patterns and providing a more solid foundation for subsequent local hotspot prediction.

[0085] Through the aforementioned technical solution, when the local contact pressure deviation is within a preset critical range, the system can conduct in-depth analysis of the dynamic trend and rate of change of pressure based on historical data, thereby selecting or combining the most suitable adjustment strategy for the current situation. This significantly improves the accuracy and adaptability of user-specific related parameter updates, avoiding misjudgments or delayed adjustments that may result from simple threshold judgments. Therefore, the infrared physiotherapy temperature control method within the thermal radiation range can more accurately identify the user's subtle body adjustment patterns and more effectively predict local hotspot trends, thus achieving more stable, comfortable, and safer temperature control, significantly enhancing the user experience.

[0086] Reference Appendix Figure 2 The present invention provides a thermal radiation range infrared physiotherapy temperature control system, including: an acquisition module 100, used to acquire temperature data of multiple heating areas of a waist belt physiotherapy device, and micro-vibration data of a built-in gyroscope of the physiotherapy device;

[0087] The inference module 200 is used to infer the local contact pressure of each heating zone based on temperature data; the identification module 300 is used to process micro-vibration data and identify the user's subtle body adjustment patterns; the construction module 400 is used to construct a real-time heat distribution map based on local contact pressure and temperature data; the prediction module 500 is used to predict local hot spot trends by combining local contact pressure and subtle body adjustment patterns; and the control module 600 is used to control the temperature of each heating zone by adjusting the heating power of multiple heating zones based on the heat distribution deviation identified by the heat distribution map and the predicted local hot spot trends.

[0088] In this document, relational terms such as first and second are used only to distinguish one entity or operation from another entity or operation, without necessarily requiring or implying any such actual relationship or order between these entities or operations.

[0089] The above description is merely an embodiment of the present invention and is not intended to limit the scope of protection of the present invention. For those skilled in the art, the present invention can have various modifications and variations. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A thermal radiation range infrared physiotherapy temperature control system, characterized in that, include: The acquisition module is used to acquire temperature data of multiple heating zones of the waist belt physiotherapy device, as well as micro-vibration data of the built-in gyroscope of the physiotherapy device; The inference module is used to infer the local contact pressure of each heating zone based on the temperature data; The recognition module is used to process micro-vibration data and identify the user's subtle body adjustment patterns; the subtle body adjustment patterns refer to the small, unconscious movements of the body that occur when the user's macroscopic body position remains unchanged. A module for building real-time heat distribution maps based on local contact pressure and temperature data; A prediction module is used to predict local hotspot trends by combining local contact pressure and subtle body adjustment patterns; the local hotspot trend refers to the tendency for heat to accumulate in a specific local area and potentially lead to excessively high temperatures; this includes the following steps: Perform calibration using the following steps A1-A2: A1. Guide users to perform preset subtle body adjustment movements, and simultaneously collect micro-vibration data and local contact pressure change data during the preset subtle body adjustment movements; A2. Based on the collected micro-vibration data and local contact pressure change data, establish user-specific correlation parameters between the micro-vibration data and the local contact pressure change data; After calibration, correlation analysis is performed using the user-specific correlation parameters established during the calibration phase to obtain correlation verification results: Based on user-specific correlation parameters, the correlation verification is performed between micro-vibration data and local contact pressure changes to distinguish between human micro-motion signals and environmental vibration interference in the micro-vibration data. The control module is used to control the temperature of each heating zone by adjusting the heating power of multiple heating zones based on the heat distribution deviation identified by the heat distribution map and the predicted local hot spot trend.

2. The infrared physiotherapy temperature control system for thermal radiation zone according to claim 1, characterized in that, The steps to predict local hotspot trends, combining localized contact pressure and subtle bodily adjustment patterns, include: Data on changes in local contact pressure are obtained by monitoring these changes. By correlating and verifying micro-vibration data with local contact pressure change data, we can distinguish between human micro-motion signals and environmental vibration interference in the micro-vibration data. Based on the correlation verification results, the subtle body adjustment pattern was confirmed; By combining localized contact pressure with confirmed subtle bodily adjustment patterns, local hotspot trends can be predicted.

3. The infrared physiotherapy temperature control system for thermal radiation zone according to claim 2, characterized in that, The steps for distinguishing human micro-motion signals from environmental vibration interference in micro-vibration data by correlating and verifying micro-vibration data with local contact pressure change data include: By preprocessing the micro-vibration data, low-frequency interference signals in the micro-vibration data are eliminated, and preprocessed micro-vibration data is obtained.

4. The infrared physiotherapy temperature control system for thermal radiation zone according to claim 3, characterized in that, The steps for preprocessing micro-vibration data include: High-pass filtering was used to preprocess the micro-vibration data.

5. The infrared physiotherapy temperature control system for thermal radiation zone according to claim 1, characterized in that, After establishing user-specific association parameters, the following is also included: Continuously monitor the average local contact pressure in multiple heating zones; When the average local contact pressure deviates by a preset amount relative to the average local contact pressure during calibration, it is determined that the wearing conditions have changed; Based on changes in wearing conditions, the user-specific associated parameters are updated by adjusting the sensitivity or threshold of the user-specific associated parameters.

6. The infrared physiotherapy temperature control system for thermal radiation zone according to claim 5, characterized in that, The steps for updating user-specific associated parameters by adjusting the sensitivity or threshold of these parameters based on changes in wearing conditions include: Continuously monitor the local contact pressure of each of the multiple heating zones; Identify areas in multiple heating zones where the local contact pressure deviates by a preset amplitude; Based on the area where the local contact pressure deviates and the degree of deviation, the user-specific associated parameters are updated by adjusting the sensitivity or threshold of the corresponding area.

7. The infrared physiotherapy temperature control system for thermal radiation zone according to claim 6, characterized in that, Based on the area and degree of deviation in local contact pressure, the steps for updating user-specific correlation parameters by adjusting the sensitivity or threshold of the corresponding area include: When the deviation in the area where the local contact pressure deviates is within a preset critical range, perform the following steps B1-B4: B1. Obtain the historical variation sequence of local contact pressure deviation; B2. Based on the historical change sequence, analyze the dynamic trend and rate of change of local contact pressure deviation; B3. Based on dynamic trends and rates of change, select one or more adjustment strategies from a preset set of adjustment strategies for combination, or adjust the execution intensity of the selected strategy; B4. Based on the area and degree of deviation of local contact pressure, and according to the selected adjustment strategy or the intensity of the adjustment strategy, update the user-specific related parameters by adjusting the sensitivity or threshold of the corresponding area.

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