Wearable intelligent clothing management system convenient for temperature adjustment

Abstract

The invention discloses a wearable intelligent clothing management system convenient for temperature adjustment. According to the invention, a physiologically-driven active temperature control mode is realized through the biological rhythm prediction engine, and the comfort level and health management level of a human body are remarkably improved. The system constructs a unique body temperature regulation curve model based on continuously monitored personal biological rhythm data, and can pre-judge body temperature demand changes of a user during scene switching from a static scene to a moving scene, from an indoor scene to an outdoor scene and the like in advance. Mild heating is automatically started in the morning metabolic rate rising stage, and vasoconstriction caused by sudden body temperature drop after getting up is prevented; or when afternoon cortisol secretion is reduced, local refrigeration is enhanced in advance, and body temperature imbalance caused by drowsiness is avoided. In combination with an emotional state recognition function, the system can dynamically adjust a collaborative strategy of the phase-change material and the air duct system aiming at abnormal perspiration or cold vibration caused by psychological fluctuations such as pressure and excitement, and body temperature optimization in physical and mental dimensions is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of clothing, and in particular relates to a wearable intelligent clothing management system that is convenient for regulating temperature. Background Art

[0002] Temperature-controlled clothing is a type of intelligent clothing. Its built-in temperature regulation system adjusts the internal temperature according to ambient temperature and user needs, maintaining comfort. Its core technologies include intelligent temperature control units, temperature sensors, and micro-heating / cooling elements. Suitable for use in extreme cold or high-temperature environments, temperature-controlled clothing is suitable for outdoor workers, athletes, and those in specialized industries. Energy-efficient, portable, and comfortable, these garments can effectively improve work efficiency and protect health. With technological advancements, temperature-controlled clothing continues to innovate in materials, design, and performance, becoming a new favorite in modern life and specialized industries.

[0003] However, existing temperature-controlled clothing technologies generally suffer from response lag and energy consumption imbalance. Traditional systems rely on a single temperature sensor to trigger passive adjustment and are unable to predict the complex heat stress caused by fluctuations in human metabolism and sudden changes in the environment. Fixed-power heating and cooling components are difficult to adapt to individual physiological differences, resulting in excessive energy consumption or local temperature control failure. Summary of the Invention

[0004] The purpose of the present invention is to provide a wearable intelligent clothing management system that is easy to adjust temperature in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: a wearable intelligent clothing management system that is convenient for regulating temperature, the system comprising: a multi-dimensional sensing module, an intelligent temperature control execution module, a biorhythm prediction engine, an energy management module, a human-computer interaction module and a cloud management platform;

[0006] The data output end of the multi-dimensional sensing module is directly connected to the real-time data interface of the biorhythm prediction engine via low-power Bluetooth, continuously transmitting the original data of human body temperature distribution, environmental parameters and biometric characteristics;

[0007] The prediction instruction output terminal of the biorhythm prediction engine is connected to the control logic unit of the intelligent temperature control execution module through a high-speed serial bus to dynamically issue a temperature adjustment strategy;

[0008] The power output end of the energy management module is connected to the power supply interface of the sensor module, the drive circuit of the temperature control execution module and the core processor of the human-computer interaction module through a flexible circuit;

[0009] The user command output terminal of the human-computer interaction module sends personalized setting data to the parameter configuration port of the biorhythm prediction engine through a wireless communication protocol, and at the same time, its data relay port establishes a two-way encrypted transmission channel with the data acquisition module of the cloud management platform;

[0010] The model update port of the cloud management platform maintains periodic synchronization with the algorithm library of the biorhythm prediction engine through a machine learning framework, and its device management port pushes the optimized instruction set to the firmware system of the intelligent temperature control execution module.

[0011] In a preferred embodiment, the biorhythm prediction engine is internally provided with: a biorhythm modeling module, an emotion-body temperature association module and an advance adjustment algorithm module.

[0012] In a preferred embodiment, the multi-dimensional sensing module consists of a distributed flexible temperature sensor array, an environmental sensing unit, and a biometric monitoring system. The flexible temperature sensor is woven from nano-scale conductive material and evenly distributed at 36 key locations on the inner layer of the garment, capturing the temperature field distribution on the human body surface in real time. The environmental sensing unit has a built-in cluster of micro-meteorological sensors that can simultaneously detect ambient temperature, humidity, wind speed, and ultraviolet intensity. Combined with a nine-axis motion posture sensor and a GPS positioning module, it can accurately calculate the spatial relationship between the human body's motion state and the external environment. The biometric monitoring system integrates a medical-grade heart rate monitoring belt and a skin galvanic response sensor. It continuously collects the user's heart rate variability and sweat electrolyte concentration through contact electrodes, constructs a physiological stress level assessment model, and provides multi-dimensional data support for temperature regulation.

[0013] In a preferred embodiment, the intelligent temperature control execution module utilizes a dynamic array of phase change materials, a micro-turbine air duct system, and a dual-mode temperature control component. The phase change material unit, composed of millions of micron-sized capsules, is encapsulated within the garment's interlayer. This intelligent phase transition between solid and liquid states is achieved through preset temperature thresholds, bidirectionally regulating the efficiency of heat absorption and release. An array of micro-turbine fans, located at the shoulders, back, and waist of the garment, incorporates ultra-thin vortex generators and shape memory alloy air ducts, dynamically adjusting the airflow path and intensity based on algorithmic instructions.

[0014] In a preferred embodiment, the physiological rhythm modeling module includes:

[0015] Data collection unit: collects user's body temperature, heart rate, activity level and other physiological data.

[0016] Time series analyzer: processes and stores continuously monitored data for subsequent analysis;

[0017] The physiological rhythm model formula is:

[0018] T(t)=T base+A·sin(2πf(t-φ)).

[0019] In a preferred embodiment, the emotion-body temperature association module is internally provided with:

[0020] Emotional state monitoring unit: Analyzes EDA and HRV data to identify emotional states;

[0021] Emotion-temperature mapper: establishes the relationship between emotional state and body temperature changes;

[0022] The formula for the effect of emotions on body temperature is:

[0023] ΔT emotion =k EDA EDA(t)+k HRV HRV(t);

[0024] Where: ΔTemotion is the change in body temperature caused by emotion. EDA(t) is the electrodermal activity at time t. HRV(t) is the heart rate variability at time t. EDA and k HRV is the weight coefficient of the impact of emotional state on body temperature.

[0025] In a preferred embodiment, the advance adjustment algorithm predicts future body temperature requirements based on an LSTM neural network and initiates pretreatment of the phase change material in advance based on the prediction results;

[0026] The calculation formula of the LSTM network prediction model is:

[0027] T predicted (t+Δt)=LSTM(T(t),HR(t),EDA(t),...);

[0028] in:

[0029] T predicted (t+Δt) is the predicted body temperature at time t+Δt. LSTM is a long short-term memory network. T(t), HR(t), EDA(t), … are the physiological and environmental data at the current moment.

[0030] In a preferred embodiment, the energy management module is constructed using a flexible photovoltaic fabric, a human kinetic energy recovery device, and an intelligent energy distribution system. The outer layer of the garment utilizes a composite process of perovskite solar panels and textile fibers. Piezoelectric fibers are woven into the joints to convert mechanical energy generated by activities like walking and running into electrical energy. This energy is then recovered using a micro-flywheel energy storage device. The energy center utilizes a hybrid energy storage architecture of supercapacitors and solid-state lithium batteries.

[0031] In a preferred embodiment, the human-computer interaction module enables two-way information exchange through a tactile feedback system and a smart terminal application. The garment's embedded tactile array, comprised of 256 micro linear motors, uses Morse coding to convert temperature conditions into vibration signals of varying frequencies. A companion mobile application generates a three-dimensional human body thermal map, displaying the temperature distribution of each body part in real time using a red-blue gradient.

[0032] In a preferred embodiment, the cloud-based management platform builds a comprehensive management system based on digital twin technology and swarm intelligence algorithms. The platform creates a virtual body temperature model for each user, analyzes historical body temperature data and environmental parameters through machine learning, and dynamically optimizes the personal temperature control strategy library.

[0033] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0034] 1. In the present invention, a physiologically driven active temperature control mode is realized through a biorhythm prediction engine, which significantly improves human comfort and health management level. Based on the continuously monitored personal biorhythm data, the system constructs a unique body temperature regulation curve model, which can predict in advance the changes in the user's body temperature demand when switching scenes from static to exercise, indoors to outdoors, etc. Mild heating is automatically started in the morning when the metabolic rate rises to prevent vasoconstriction caused by a sudden drop in body temperature after getting up; or local cooling is enhanced in advance when cortisol secretion decreases in the afternoon to avoid body temperature imbalance caused by sleepiness. Combined with the emotional state recognition function, the system can dynamically adjust the collaborative strategy of the phase change material and the air duct system for abnormal sweating or shivering caused by psychological fluctuations such as stress and excitement, to achieve body temperature optimization in both physical and mental dimensions, which is particularly suitable for daily health monitoring of high-intensity workers and patients with chronic diseases.

[0035] 2. In the present invention, a sustainable intelligent temperature control ecosystem is created through the dual predictive capabilities of biorhythms and the environment. The biorhythm prediction engine not only reduces the ineffective energy consumption of traditional temperature control systems, but its advanced adjustment mechanism can also extend the service life of phase change materials, allowing clothing to maintain stable temperature control capabilities for more than 72 hours in extreme environments. After the cloud platform integrates group biorhythm data, it can optimize the energy allocation strategy for team scenarios, formulate gradient temperature control plans based on individual differences of athletes in marathon training, or provide customized body temperature support based on the patient's postoperative recovery curve in the hospital ICU. This physiological adaptability gives the system both preventive medical value and engineering efficiency. It can not only reduce the risk of acute diseases such as heat stroke through early warning of abnormal body temperature, but also build an all-weather human thermal protection barrier for workers in special industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a block diagram of the overall system of the present invention;

[0037] Figure 2 This is a block diagram of the biorhythm prediction engine system in the present invention. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0039] Example:

[0040] Reference Figure 1-2 ,

[0041] A wearable intelligent clothing management system that is easy to adjust temperature. The system includes: a multi-dimensional sensing module, an intelligent temperature control execution module, a biorhythm prediction engine, an energy management module, a human-computer interaction module and a cloud management platform;

[0042] The data output of the multi-dimensional sensing module is directly connected to the real-time data interface of the biorhythm prediction engine via low-power Bluetooth, continuously transmitting human body temperature distribution, environmental parameters and raw biometric data;

[0043] The prediction instruction output of the biorhythm prediction engine is connected to the control logic unit of the intelligent temperature control execution module through a high-speed serial bus to dynamically issue temperature adjustment strategies;

[0044] The power output end of the energy management module is connected to the power supply interface of the sensor module, the drive circuit of the temperature control execution module and the core processor of the human-computer interaction module through a flexible circuit;

[0045] The user command output terminal of the human-computer interaction module sends personalized setting data to the parameter configuration port of the biorhythm prediction engine through a wireless communication protocol. At the same time, its data relay port establishes a two-way encrypted transmission channel with the data acquisition module of the cloud management platform.

[0046] The model update port of the cloud management platform maintains periodic synchronization with the algorithm library of the biorhythm prediction engine through a machine learning framework, while its device management port pushes optimized instruction sets to the firmware system of the intelligent temperature control execution module.

[0047] The internal settings of the biorhythm prediction engine include: physiological rhythm modeling module, emotion-body temperature association module and advance adjustment algorithm module.

[0048] The multi-dimensional sensing module consists of a distributed flexible temperature sensor array, an environmental sensing unit, and a biometric monitoring system. The flexible temperature sensors are woven from nano-scale conductive material and evenly distributed at 36 key locations on the inner layer of the garment, capturing the temperature distribution on the human body surface in real time. The environmental sensing unit houses a cluster of micro-meteorological sensors capable of simultaneously detecting ambient temperature, humidity, wind speed, and UV intensity. Combined with a nine-axis motion posture sensor and a GPS positioning module, it accurately calculates the spatial relationship between the human body's motion state and the external environment. The biometric monitoring system integrates a medical-grade heart rate monitor and galvanic skin response sensor. Contact electrodes continuously collect the user's heart rate variability and sweat electrolyte concentration, constructing a physiological stress level assessment model and providing multi-dimensional data support for temperature regulation.

[0049] The intelligent temperature control execution module utilizes a dynamic array of phase change materials, a micro-turbine duct system, and a dual-mode temperature control component to work together. The phase change material unit is composed of millions of micron-sized capsules, encapsulated in the garment's interlayer. It achieves intelligent phase transitions between solid and liquid states through preset temperature thresholds, bidirectionally regulating the efficiency of heat absorption and release. An array of micro-turbine fans, distributed across the garment's shoulders, back, and waist, is equipped with ultra-thin vortex generators and shape memory alloy air ducts, dynamically adjusting the air supply path and airflow intensity based on algorithmic instructions. The dual-mode temperature control component integrates a graphene flexible heating film with a semiconductor thermoelectric cooler to achieve rapid temperature increases and decreases in extreme environments ranging from -20°C to 50°C. Combined with the energy storage properties of the phase change material, the maximum temperature differential adjustment rate can reach 3°C per minute.

[0050] The circadian rhythm modeling module includes:

[0051] Data collection unit: collects user's body temperature, heart rate, activity level and other physiological data.

[0052] Time series analyzer: processes and stores continuously monitored data for subsequent analysis;

[0053] The physiological rhythm model formula is:

[0054] T(t)=T base +A·sin(2πf(t-φ))

[0055] The internal settings of the emotion-body temperature association module are:

[0056] Emotional state monitoring unit: Analyzes EDA and HRV data to identify emotional states;

[0057] Emotion-temperature mapper: establishes the relationship between emotional state and body temperature changes;

[0058] The formula for the effect of emotions on body temperature is:

[0059] ΔT emotion=k EDA EDA(t)+k HRV HRV(t);

[0060] Where: ΔTemotion is the change in body temperature caused by emotion. EDA(t) is the electrodermal activity at time t. HRV(t) is the heart rate variability at time t. EDA and k HRV is the weight coefficient of the impact of emotional state on body temperature.

[0061] The advanced adjustment algorithm predicts future body temperature needs based on the LSTM neural network and starts the pretreatment of the phase change material in advance based on the prediction results;

[0062] The calculation formula of the LSTM network prediction model is:

[0063] T predicted (t+Δt)=LSTM(T(t),HR(t),EDA(t),...);

[0064] in:

[0065] T predicted (t+Δt) is the predicted body temperature at time t+Δt. LSTM is a long short-term memory network. T(t), HR(t), EDA(t), … are the physiological and environmental data at the current moment.

[0066] The energy management module is constructed based on flexible photovoltaic fabrics, a human kinetic energy recovery device, and an intelligent energy distribution system. The outer layer of the garment utilizes a composite process of perovskite solar panels and textile fibers, achieving a 28% photoelectric conversion efficiency under natural lighting conditions. The daily power generation can support system operation for 8 hours. Piezoelectric fibers are woven into the joint motion areas, converting mechanical energy generated by exercises like walking and running into electrical energy, and kinetic energy recovery is achieved in conjunction with a micro-flywheel energy storage device. The energy hub utilizes a hybrid energy storage architecture of supercapacitors and solid-state lithium batteries. A dynamic power allocation algorithm prioritizes the use of clean energy converted from solar energy and kinetic energy. In low-temperature environments, the battery preheat protection mechanism is automatically activated to ensure stable power supply to the system at -30 degrees Celsius.

[0067] The human-computer interaction module enables two-way information exchange through a tactile feedback system and smart terminal applications. The garment is embedded with a tactile array consisting of 256 micro linear motors, which uses Morse coding technology to convert temperature conditions into vibration signals of varying frequencies. For example, short, sustained vibrations signal a localized overheating warning, while long vibration pulses indicate insufficient energy. The chest area is integrated with pressure-sensitive fabric that supports gestures such as sliding and pressing, allowing users to adjust the overall temperature setting with a swipe of their finger. A companion mobile app generates a three-dimensional human body heat map, displaying the temperature distribution of each body part in real time using a red-blue gradient. The built-in AI health advisor can combine abnormal body temperature data to provide hydration recommendations or exercise intensity reminders, and automatically trigger emergency contact calls in special circumstances.

[0068] The cloud-based management platform builds a full-dimensional management system based on digital twin technology and swarm intelligence algorithms. The platform creates a virtual body temperature model for each user, analyzes the correlation between historical body temperature data and environmental parameters through machine learning, and dynamically optimizes the personal temperature control strategy library. The meteorological data interface provides real-time access to satellite cloud maps and regional weather forecasts, predicting the impact of extreme weather on body temperature 12 hours in advance. The group management module supports the coordinated operation of multiple devices. In medical monitoring scenarios, it can simultaneously monitor abnormal body temperature fluctuations in patient groups and intelligently allocate cooling resources based on team exercise intensity in sports training scenarios. The epidemic monitoring mode uses geo-fencing technology to track the density of abnormal body temperatures in a region and, combined with thermal imaging data, establishes an epidemic transmission early warning model to provide decision support for public health management.

[0069] From the above, it can be seen that: in the present invention, a physiologically driven active temperature control mode is realized through the biorhythm prediction engine, which significantly improves the comfort and health management level of the human body. Based on the continuously monitored personal biorhythm data, the system constructs a unique body temperature regulation curve model, which can predict in advance the changes in the user's body temperature demand when switching from static to exercise, indoors to outdoors and other scenes. Automatically start mild heating in the morning when the metabolic rate rises to prevent vasoconstriction caused by a sudden drop in body temperature after getting up; or enhance local cooling in advance when cortisol secretion decreases in the afternoon to avoid body temperature imbalance caused by sleepiness. Combined with the emotional state recognition function, the system can dynamically adjust the collaborative strategy of the phase change material and the air duct system for abnormal sweating or shivering caused by psychological fluctuations such as stress and excitement, to achieve body temperature optimization in both physical and mental dimensions, which is particularly suitable for daily health monitoring of high-intensity workers and patients with chronic diseases.

[0070] In the present invention, a sustainable intelligent temperature control ecosystem is created through the dual predictive capabilities of biorhythms and the environment. The biorhythm prediction engine not only reduces the ineffective energy consumption of traditional temperature control systems, but its advanced adjustment mechanism can also extend the service life of phase change materials, allowing clothing to maintain stable temperature control capabilities for more than 72 hours in extreme environments. After the cloud platform integrates group biorhythm data, it can optimize the energy allocation strategy for team scenarios, formulate gradient temperature control plans based on individual differences of athletes in marathon training, or provide customized body temperature support based on the patient's postoperative recovery curve in the hospital ICU. This physiological adaptability gives the system both preventive medical value and engineering efficiency. It can not only reduce the risk of acute diseases such as heat stroke through early warning of abnormal body temperature, but also build an all-weather human thermal protection barrier for workers in special industries.

[0071] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprises" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further limitations, an element defined by the sentence "comprises a ..." does not exclude the presence of other identical elements in the process, method, article or device that includes the element.

[0072] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A wearable intelligent clothing management system that facilitates temperature regulation, characterized by: The system includes: a multi-dimensional sensing module, an intelligent temperature control execution module, a biorhythm prediction engine, an energy management module, a human-computer interaction module and a cloud management platform; The data output end of the multi-dimensional sensing module is directly connected to the real-time data interface of the biorhythm prediction engine via low-power Bluetooth, continuously transmitting the original data of human body temperature distribution, environmental parameters and biometric characteristics; The prediction instruction output terminal of the biorhythm prediction engine is connected to the control logic unit of the intelligent temperature control execution module through a high-speed serial bus to dynamically issue a temperature adjustment strategy; The power output end of the energy management module is connected to the power supply interface of the sensor module, the drive circuit of the temperature control execution module and the core processor of the human-computer interaction module through a flexible circuit; The user command output terminal of the human-computer interaction module sends personalized setting data to the parameter configuration port of the biorhythm prediction engine through a wireless communication protocol, and at the same time, its data relay port establishes a two-way encrypted transmission channel with the data acquisition module of the cloud management platform; The model update port of the cloud management platform maintains periodic synchronization with the algorithm library of the biorhythm prediction engine through a machine learning framework, and its device management port pushes the optimized instruction set to the firmware system of the intelligent temperature control execution module.

2. A wearable intelligent clothing management system for convenient temperature regulation according to claim 1, characterized in that: The internal configuration of the biorhythm prediction engine includes: a biorhythm modeling module, an emotion-body temperature association module and an advance adjustment algorithm module.

3. The wearable temperature-adjustable intelligent clothing management system according to claim 1, characterized in that: The multi-dimensional sensing module consists of a distributed flexible temperature sensor array, an environmental sensing unit, and a biometric monitoring system. The flexible temperature sensors are woven from nano-scale conductive materials and evenly distributed at 36 key locations on the inner layer of the garment, capturing the temperature distribution of the human body surface in real time. The environmental sensing unit has a built-in micro-meteorological sensor cluster that can simultaneously detect ambient temperature, humidity, wind speed, and UV intensity. Combined with a nine-axis motion posture sensor and a GPS positioning module, it accurately calculates the spatial relationship between the human body's motion state and the external environment. The biometric monitoring system integrates a medical-grade heart rate monitor and a galvanic skin response sensor. It continuously collects the user's heart rate variability and sweat electrolyte concentration through contact electrodes, builds a physiological stress level assessment model, and provides multi-dimensional data support for temperature regulation.

4. The wearable temperature-adjustable intelligent clothing management system according to claim 1, characterized in that: The intelligent temperature control execution module adopts a dynamic array of phase change materials, a micro-turbine air duct system and a dual-mode temperature control component to work together; the phase change material unit is composed of millions of micron-sized capsules, encapsulated in the clothing interlayer, and realizes intelligent phase change between solid and liquid through a preset temperature threshold, and bidirectionally adjusts the efficiency of heat energy absorption and release; the micro-turbine fan array is distributed on the shoulders, back and waist of the clothing, equipped with ultra-thin vortex generators and shape memory alloy air ducts, and dynamically adjusts the air supply path and airflow intensity according to algorithm instructions.

5. The wearable temperature-adjustable intelligent clothing management system according to claim 1, characterized in that: The physiological rhythm modeling module includes: Data collection unit: collects user's body temperature, heart rate, and activity level physiological data; Time series analyzer: processes and stores continuously monitored data for subsequent analysis; The physiological rhythm model formula is: T(t)=T base +A·sin(2πf(t-φ))。 6. The wearable temperature-adjustable intelligent clothing management system according to claim 1, characterized in that: The internal configuration of the emotion-body temperature association module includes: Emotional state monitoring unit: Analyzes EDA and HRV data to identify emotional states; Emotion-temperature mapper: establishes the relationship between emotional state and body temperature changes; The formula for the effect of emotions on body temperature is: ΔT emotion =k EDA ·EDA(t)+k HRV ·HRV(t); Where: ΔTemotion is the change in body temperature caused by emotion; EDA(t) is the electrodermal activity at time t; HRV(t) is the heart rate variability at time t; k EDA and k HRV is the weight coefficient of the impact of emotional state on body temperature.

7. The wearable temperature-adjustable intelligent clothing management system according to claim 1, characterized in that: The advanced adjustment algorithm predicts future body temperature needs based on the LSTM neural network and starts the pretreatment of the phase change material in advance according to the prediction results; The calculation formula of the LSTM network prediction model is: T predicted (t+Δt)=LSTM(T(t),HR(t),EDA(t),...); in: T predicted (t+Δt) is the predicted body temperature at time t+Δt; LSTM is the long short-term memory network; T(t), HR(t), EDA(t) are the physiological and environmental data at the current moment.

8. The wearable temperature-adjustable intelligent clothing management system according to claim 1, characterized in that: The energy management module is constructed based on flexible photovoltaic power generation fabrics, a human kinetic energy recovery device and an intelligent energy distribution system; the outer layer of the clothing adopts a composite process of perovskite solar panels and textile fibers, and piezoelectric fibers are woven into the joint activity area to convert the mechanical energy generated by walking and running into electrical energy, and cooperate with a micro flywheel energy storage device to realize kinetic energy recovery; the energy center adopts a hybrid energy storage architecture of supercapacitors and solid-state lithium batteries.

9. The wearable temperature-adjustable intelligent clothing management system according to claim 1, characterized in that: The human-computer interaction module achieves two-way information interaction through a tactile feedback system and smart terminal applications; the clothing is embedded with a tactile array consisting of 256 micro linear motors, which uses Morse coding technology to convert temperature conditions into vibration signals of different frequencies; the accompanying mobile application generates a three-dimensional human body thermal map, displaying the temperature distribution of each part in real time with red and blue gradient colors.

10. The wearable intelligent clothing management system for convenient temperature regulation according to claim 1, characterized in that: The cloud management platform builds a full-dimensional management system based on digital twin technology and swarm intelligence algorithms; the platform creates a virtual body temperature model for each user, analyzes the correlation between historical body temperature data and environmental parameters through machine learning, and dynamically optimizes the personal temperature control strategy library.

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