Human body comfortable temperature calculation method applied to non-uniform heating system of cold protective clothing

Abstract

The invention relates to a human body comfortable temperature calculation method applied to a non-uniform heating system of cold protective clothing, and the method comprises the following steps: constructing a human body geometric model, and dividing a plurality of target regions; performing grid division on the human body geometric model to obtain a human body grid model; for any grid unit of the human body grid model, calculating by using a biological heat transfer equation to obtain human body skin temperatures of the grid unit in different cold environments; and according to the human skin temperature, using an improved Fanger thermal comfort model to solve and obtain comfort temperatures of the different target areas. According to the invention, the wearing comfort of the cold protective clothing can be improved, and the heating efficiency and sustainability are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent clothing, and particularly to a method for calculating the comfortable temperature of the human body and a non-uniform heating cold-proof clothing system. Background Art

[0002] Electric heating clothing is a general term for a type of clothing that uses electric energy to drive electric heating elements inside the clothing. It generally consists of devices such as a power source, heating elements, automatic temperature control, and safety protection, which are connected to each other through wires. The heating elements are arranged at thermally sensitive parts of the human body such as the front chest, front abdomen, lower back, back, and joints.

[0003] Current heating clothing has some limitations. First, it often ignores the thermal physiological responses of the human body and its interaction with the body-clothing-environment system. There are differences in the physiological structures and functions of different parts of the human body, which leads to different temperature requirements for each part. For example, the trunk and head of the human body are more sensitive to temperature changes, while the limbs are relatively less sensitive. Second, it usually ignores the energy consumption of the heating device. Increasing the available area of the heating elements to enhance cold resistance will pose higher requirements for the power storage capacity and consume precious electric energy. In addition, the temperature regulation of heating clothing is usually ignored during complex activities, resulting in discomfort and overheating.

[0004] Therefore, there is a lack of a non-uniform heating cold-proof clothing that can provide appropriate heat for different parts of the body according to the above physiological differences, achieving a more personalized and comfortable wearing experience. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for calculating the comfortable temperature of the human body and a non-uniform heating cold-proof clothing system, which can provide appropriate heat for different parts of the body.

[0006] The technical solution adopted by the present invention to solve its technical problems is: to provide a method for calculating the comfortable temperature of the human body, including the following steps:

[0007] Construct a human body geometric model and divide it into several target regions;

[0008] Perform mesh division on the human body geometric model to obtain a human body mesh model;

[0009] For any grid unit of the human body mesh model, calculate the human skin temperature of this grid unit under different cold environments by using the bioheat transfer equation;

[0010] According to the human skin temperature, solve for the comfortable temperature of the different target regions by using an improved Fanger thermal comfort model.

[0011] Further, the calculation of the human skin temperature of the grid cell under different cold environments using the bioheat transfer equation is achieved by performing a temperature field calculation based on hydrodynamics.

[0012] Further, the bioheat transfer equation is

[0013]

[0014] where ρ is the density, c is the specific heat capacity, t is the time, k is the thermal conductivity, T b is the blood temperature, T t is the tissue temperature, ω is the blood perfusion rate, q met is the heat generated by tissue metabolism per unit volume.

[0015] Further, the temperature field calculation based on hydrodynamics is achieved by a CFD simulation tool, including the following steps:

[0016] Initialize the temperature field;

[0017] Set the boundary temperature of the boundary nodes;

[0018] Call the discretized bioheat transfer equation to perform iterative solution on the temperature field, and judge whether the iterative termination condition is satisfied after each iteration. If it is satisfied, take the current result as the final result.

[0019] Further, the improved Fanger thermal comfort model is

[0020] PMV i =[0.303exp(-0.036M i )+0.0275]·{M i -W i -0.305[5.733-0.007(M i -W i )-P a )-0.0014M i (34-t a )-3.96×10 -8 f cl [t cl +273) 4 -(t r +273) 4 -f cl h c (t cl -t a )}

[0021] where, for different parts i of the human body, PMV i is the thermal comfort value of the corresponding part, Mi is the metabolic heat production of the corresponding part, in W i is the work done by the corresponding part, in P a is the air pressure, in t a is the air temperature, in f cl is the clothing thermal resistance, in t cl is the skin temperature, in t r is the environmental radiation temperature, in h c is the convective heat transfer coefficient.

[0022] The present invention also provides a non-uniform heating cold-proof clothing system for the human body, including:

[0023] A temperature monitoring module, including a plurality of temperature monitoring units, which are placed in different heating areas of the inner layer of the cold-proof clothing and are used to collect the real-time temperature of the corresponding heating areas;

[0024] A non-uniform heating module, including a control module and a plurality of heating units placed in different said heating areas, and the control module is used to respectively control the heating units in the corresponding heating areas to adjust the temperature to the corresponding comfortable temperature according to the real-time temperature of each said heating area;

[0025] The comfortable temperature is determined by any of the calculation methods described above.

[0026] Further, the step of respectively controlling the heating units in the corresponding heating areas to adjust the temperature to the corresponding comfortable temperature according to the real-time temperature of each said heating area is realized by PID control, and the PID control includes the following steps:

[0027] Determine the PID parameters;

[0028] For any said heating area, based on the real-time temperature and the comfortable temperature, adjust the duty ratio of the PWM signal, and use the adjusted PWM signal to control the heating unit for temperature adjustment;

[0029] Repeat the above step until the set condition is met.

[0030] Further, it further includes a regulated power supply module, and the regulated power supply module is used to provide the system power supply after multi-stage voltage conversion and filtering.

[0031] Further, the control module is arranged on a PCB board, and the PCB board includes two signal layers, two ground layers and two power layers. The two signal layers are respectively located on the top layer and the bottom layer of the PCB board and are used to respectively arrange high-frequency signal transmission lines and power output components, and use thermal vias technology to reduce the temperature of key components during operation.

[0032] Beneficial effects

[0033] Due to the adoption of the above technical solution, compared with the prior art, the present invention has the following advantages and positive effects: (1) By constructing a human body grid model and calculating the human skin temperature of each grid cell in the model using the bioheat transfer equation, the present invention can then use the improved Fanger thermal comfort model to solve for the comfort temperature of different regions of the human body, enabling precise temperature adjustment according to the thermal requirements of different parts of the human body;

[0034] obtain the human skin temperature of the grid cell under different cold environments, and then use the improved Fanger thermal comfort model to solve for the comfort temperature of different regions of the human body, and can achieve precise temperature adjustment according to the thermal requirements of different parts of the human body;

[0035] (2) The present invention adopts a non-uniform heating strategy, avoiding unnecessary energy waste. At the same time, by precisely controlling the power output of each heating

[0036] unit, while meeting thermal comfort, it can reduce battery consumption and extend the usage time of the device;

[0037] (3) By combining the PID control algorithm and PWM technology, the present invention can quickly respond to changes in environmental temperature and human thermal requirements. The heating unit can reach the preset temperature within 3 seconds, which is more than 30% faster than traditional systems;

[0038] (4) Through the three-stage regulated power supply module and the carefully designed PCB layout, the present invention effectively improves the stability and anti-interference ability of the system;

[0039] (5) The present invention is adapted to various cold-proof outer garments in a detachable manner, and at the same time can readjust the heating requirements of different parts of the human body according to the thermal insulation performance of different outer garments. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a schematic diagram of the division of the human body model section in the first embodiment of the present invention;

[0041] Figure 2 is a schematic diagram of the human body grid model in the first embodiment of the present invention;

[0042] Figure 3 is a flowchart of the skin temperature field calculation in the first embodiment of the present invention;

[0043] Figure 4 is a flowchart of the second embodiment of the present invention;

[0044] Figure 5 is a flowchart of the temperature control in the second embodiment of the present invention;

[0045] Figure 6 is a flowchart of the microsecond-level delay in the second embodiment of the present invention;

[0046] Figure 7It is the control circuit diagram of the heating unit in the second embodiment of the present invention;

[0047] Figure 8 It is the control circuit diagram of the temperature detection module in the second embodiment of the present invention;

[0048] Figure 9 It is the control circuit diagram of the A / D conversion module in the second embodiment of the present invention;

[0049] Figure 10 It is the principle circuit diagram of the voltage stabilizing power supply module in the second embodiment of the present invention;

[0050] Figure 11 It is the control circuit diagram of the system power supply module in the second embodiment of the present invention. Specific Embodiment

[0051] The following further elaborates the present invention in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

[0052] The first embodiment of the present invention relates to a method for calculating the comfortable temperature of different parts for heating. To avoid the risk of cold stress that may be encountered by the human body during physiological temperature measurement experiments in a cold environment, a three-dimensional full-scale human body grid model is established using numerical simulation technology, and the predicted skin temperature values of the human body under different cold environments are obtained through a numerical solution platform. Subsequently, the human thermal comfort model is improved, and the skin temperature values calculated through the three-dimensional human body model are introduced into the modified human comfort model, and finally, the comfortable temperature values of different parts of the human body are obtained.

[0053] I. Calculation process of skin temperature of different parts in a cold environment.

[0054] 1. Establish a three-dimensional human body grid model

[0055] As Figure 1 shown, a three-dimensional human body geometric model is obtained through direct modeling in the SCDM platform, and the main parts of the human body are divided into sections.

[0056] As Figure 2 shown, based on the geometric model, mesh division is carried out, and the entire human body mesh division process is completed in ANSYS ICEM. Subsequently, an initial mesh of a mixture of quadrilaterals and hexagons is constructed to ensure that the mesh size and resolution meet the analysis requirements. Further, the mesh refinement tool is used to optimize the mesh, capture the geometric details and anatomical features of the human body model, and improve the mesh quality through local refinement.

[0057] 2. Calculate the human skin temperature values under different cold rings

[0058] After the human body mesh model is completed, the bioheat transfer equation will be applied to each element. The mathematical model expression of this equation is shown in Equation 2-1. It can calculate the change of temperature at any position inside the human body over time.

[0059]

[0060] Among them, ρ is the density, kg / m3; c is the specific heat capacity, J / (kg·℃); t is the time, s; k is the thermal conductivity, W / m·℃; T b is the blood temperature, ℃; T t is the tissue temperature, ℃; ω is the blood perfusion rate, mL / min / 100g; q met is the heat generated by tissue metabolism per unit volume, W / m 3 .

[0061] 3. Complete the calculation of the skin temperature field in Fluent

[0062] As Figure 3 shown, using the interpreted UDF function, the discretized control equation is rewritten through C programming. The program first enters the main function to control the execution flow of the entire program. In the main function, multiple functional functions are called in sequence. The initialize temperature field function is responsible for initializing the temperatures of all nodes in the three-dimensional grid to zero, and the apply boundary condition function sets the temperatures of the boundary nodes to the given boundary temperatures. Subsequently, the program enters a time step loop, and each iteration executes the update temperature field function to calculate the new temperature field according to the discretized heat conduction equation. After the update temperature field function is completed, the program calls the copy temperature field function to apply the new temperature field to the old temperature field. Then, the check time step number and termination condition function is used to check the current time step number and whether the termination condition is met. If the termination condition is not reached, the program continues to calculate the next time step, otherwise the program ends. Finally, the output result and end program function is responsible for outputting the final result and ending the execution of the program.

[0063] II. Heating temperature calculation process

[0064] 1. Make theoretical corrections to the Fanger model

[0065] The Fanger thermal comfort model is an effective tool for analyzing human thermal comfort. One of its calculation cores is the PMV (Predicted Mean Vote) index of thermal comfort. However, in current research, this model mainly focuses on indoor spaces and discusses the comfort of the whole human body as a single entity, without calculating regional differences. Therefore, it is necessary to simulate the space between the cold-proof clothing and the human body as an indoor space, construct corresponding PMV formulas for different regions of the human body, and substitute the heat transfer parameters and air flow parameters of each region into the calculation. The calculation of PMV values for different regions is affected by factors such as local clothing, activity level, and local metabolic rate. The revised calculation formula after section division can be expressed as:

[0066] PMV i =[0.303exp(-0.036M i )+0.0275]·{M i -W i -0.305[5.733-0.007(M i -W i )-P a )-0.0014M i (34-t a )-3.96×10 -8 f cl [t cl +273) 4 -(t r +273) 4 -f cl h c (t cl -t a )}

[0067] Among them, for different parts i of the human body, PMV i is the thermal comfort value of the corresponding part, M i is the metabolic heat production of the corresponding part, W; W i is the work done by the corresponding part, W; P a is the air pressure, Pa; t a is the air temperature, °C; f cl is the clothing thermal resistance, m 2 ·K / W; t cl is the skin temperature, °C; t r is the environmental radiation temperature, °C; h c is the convective heat transfer coefficient, W / m 2 ·K.

[0068] 2. Model solution

[0069] The Fanger model is implemented using the Matlab programming language, and the parameters of various parts of the human body are input to calculate the PMV value. When performing the calculation, the following conditions are set: the environmental humidity is fixed at 50% and does not change with time; the radiant temperature is the same as the environmental temperature; the wind speed is 0 m / s; the thermal resistance value of the human body clothing is 1 clo, and this setting is consistent with the actual environment. Considering that in the environment of 0-15 °C, too high a thermal resistance value will cause the difference between the human body's thermal comfort temperature and skin temperature to be not obvious, affecting the analysis of different heating temperature segments; the metabolic index is uniformly set to 0.5 met, representing the metabolic level of the human body in a static standing state.

[0070] According to the modified model, the PMV values of various parts and the corresponding comfort temperatures under different environmental conditions can be calculated.

[0071] The second embodiment of the present invention relates to a non-uniform heating cold-proof clothing system based on the calculation of a three-dimensional human body thermal comfort model. Before designing the system, it is necessary to calculate the heating comfort temperature values of various parts of the human body first. To avoid the cold stress risk that may be encountered by the human body in participating in physiological temperature measurement experiments in a cold environment, this embodiment uses numerical simulation technology to establish a three-dimensional full-scale human body grid model, and obtains the predicted skin temperature values of the human body under different cold environments through a numerical solution platform. Then, the human body thermal comfort model is improved, and the skin temperature values calculated by the three-dimensional human body model are introduced into the modified human body comfort model, and finally the comfort temperature values of different parts of the human body are obtained as the control basis for the temperature of the subsequent heating system.

[0072] This embodiment uses an intelligent temperature control framework, which is constructed based on the advanced PID (Proportional-Integral-Derivative) control algorithm. Using the STM32F407VET6 microcontroller, this framework can receive data from the temperature monitoring module in real time and dynamically adjust the power output of the heating element. Through fine adjustment, the framework can quickly respond to small changes in the temperature of the inner layer of the cold-proof clothing, ensuring that the wearer can maintain a suitable body temperature under different environmental conditions. In addition, the intelligent temperature control framework also has self-learning ability, and can automatically adjust the control parameters according to the user's usage habits and preferences to achieve a more personalized temperature control effect.

[0073] In order to further improve the accuracy and response speed of temperature control, this embodiment combines the PWM (Pulse Width Modulation) technology with the PID control strategy. The PWM technology precisely controls the power supply time of the heating element by adjusting the pulse width of the electrical signal, thereby realizing fine adjustment of the heating power. This control method not only improves the heating efficiency, but also reduces the energy consumption caused by temperature fluctuations, ensuring that the heating unit can quickly and accurately reach and stabilize at the preset temperature.

[0074] This system includes the following hardware modules:

[0075] (1) Non-uniform heating module: Based on ergonomic principles, the system divides the human body into multiple heating zones, and each zone is equipped with an independent heating unit. As Figure 7 shown, these heating units independently adjust the working temperature through an intelligent temperature control framework according to the heat demand of the corresponding parts of the human body and environmental conditions to achieve non-uniform heating.

[0076] (2) Temperature monitoring module: As Figure 8 shown, the temperature monitoring module constructed by using an LM358 dual operational amplifier can accurately measure the real-time temperature inside the cold-proof clothing. The module consists of multiple monitoring units, and each unit is responsible for collecting temperature data of a specific area and providing accurate temperature feedback to the intelligent temperature control framework through signal processing.

[0077] (3) A / D conversion module: As Figure 9 shown, the AD7190BRUZ chip is selected as the core of the A / D conversion module. This chip supports 24-bit high-precision conversion and can convert the analog temperature signal into a digital signal for the microcontroller to process. The addition of this module greatly improves the temperature measurement accuracy and stability of the system.

[0078] (4) Voltage regulation and power supply module: As Figure 10 and Figure 11 shown, in order to ensure the stable operation of each module of the system, a three-stage voltage regulation and power supply module is designed. This module provides a stable power supply for the system through multiple-stage voltage conversion and filtering,

[0079] while protecting the system from the influence of power fluctuations and electromagnetic interference.

[0080] The PCB design of this system adopts a six-layer board layout, including two signal layers, two ground layers, and two power supply layers. The top layer and the bottom layer are respectively used to arrange high-frequency signal transmission lines and power output components. The middle ground layer provides a stable reference plane for the signal layer, effectively reducing parasitic capacitance and electromagnetic interference. In addition, for key components such as the microprocessing unit and the PWM controller, a thermal via technology is specially designed to achieve effective thermal management. The power integrity is ensured through decoupling capacitors and power grid design, ensuring a stable power supply for sensitive circuits.

[0081] The system is adapted to the clothing in the following ways:

[0082] The system is combined with a single-layer fabric to form a prototype. The prototype is installed in the innermost layer of the cold-proof clothing in a detachable form, and finally an experimental clothing is formed. The specific composition method mainly includes: First, prepare a white embryo fabric inner lining according to the size of the inner lining of a cold-proof clothing (specification: 175 / 100), and ensure that the embryo fabric inner lining can match the cold-proof clothing. For the convenience of experimental dressing and undressing, the door access and the parts below the back neckline to the hem of the inner lining are bonded in the form of Velcro. Then, the system, heating wires and sensors are arranged on the white embryo fabric inner lining. Finally, the inner lining is combined with the cold-proof clothing. In order to fully study whether the heating characteristics of the system can exert appropriate cold-proof ability, the outer cold-proof clothing should be selected in a relatively light and thin basic style. The cold-proof clothing jacket selected in this experiment is a style with a stand-up collar without a hood, the filling material is duck down (the filling amount is about 65g), the fabric is 100% nylon, and the lining is 100% polyester fiber.

[0083] The following further describes this embodiment in combination with a specific non-uniform heating system and its control method.

[0084] I. Calculation process of skin temperature at different parts in a cold environment.

[0085] 1. Establish a three-dimensional human body mesh model

[0086] In the SCDM platform, a three-dimensional human body geometric model is obtained through direct modeling, and the main parts of the human body are divided into sections.

[0087] Based on the geometric model, mesh division is carried out. The entire human body mesh division process is completed in ANSYS ICEM. Then, an initial mesh mixed with quadrilaterals or hexagons is constructed to ensure that the mesh size and resolution meet the analysis requirements. Further, the mesh refinement tool is used to optimize the mesh, capture the geometric details and anatomical features of the human body model, and improve the mesh quality through local refinement.

[0088] 2. Calculate the human skin temperature values under different cold environments

[0089] After the human body mesh model is completed, the bioheat transfer equation will be applied to each unit. The mathematical model expression form of this equation is shown in Formula 2-1. The temperature change of any position inside the human body over time can be calculated.

[0090]

[0091] Among them, ρ is the density, kg / m3; c is the specific heat capacity, J / (kg·°C); t is the time, s; k is the thermal conductivity, W / m·°C; T b is the blood temperature, °C; T t is the tissue temperature, °C; ω is the blood perfusion rate, mL / min / 100g; q met is the heat generated by tissue metabolism per unit volume, W / m3 。

[0092] 3. Complete the calculation of the skin temperature field in Fluent

[0093] Adopt the interpreted UDF function, and rewrite the discretized governing equations through C programming. The program first enters the main function to control the execution flow of the entire program. In the main function, multiple functional functions are called in sequence. The initialize temperature field function is responsible for initializing the temperatures of all nodes in the three-dimensional grid to zero, and the apply boundary conditions function sets the temperatures of the boundary nodes to the given boundary temperatures. Subsequently, the program enters a time step loop, and the update temperature field function is executed in each iteration to calculate the new temperature field according to the discretized heat conduction equation. After the update temperature field function is completed, the program calls the copy temperature field function to apply the new temperature field to the old temperature field. Then, the check time step number and termination condition function is used to check the current time step number and whether the termination condition is met. If the termination condition is not reached, the program continues to calculate the next time step; otherwise, the program ends. Finally, the output result and end program function is responsible for outputting the final result and ending the execution of the program.

[0094] II. Heating temperature calculation process

[0095] 3. Make theoretical corrections to the Fanger model

[0096] The Fanger thermal comfort model is an effective tool for analyzing human thermal comfort. One of the calculation cores is the predicted mean vote (PMV) index of thermal comfort. However, in current research, this model mainly discusses the comfort situation of the whole human body and does not perform regional difference calculations. Calculating the PMV values of different parts of the human body requires substituting the heat transfer parameters and air flow parameters of each region into the calculation according to the PMV calculation formula. The calculation of PMV values in different regions is affected by factors such as local clothing, activity level, and local metabolic rate. The calculation formula after the revised section division can be expressed as:

[0097] PMV i =[0.303exp(-0.036M i )+0.0275]·{M i -W i -0.305[5.733-0.007(M i -W i )-P a )-0.0014M i (34-t a )-3.96×10 -8 f cl [t cl +273) 4 -(tr +273) 4 -f cl h c (t cl -t a )}

[0098] Among them, for different parts i of the human body, PMV i is the thermal comfort value of the corresponding part, M i is the metabolic heat production of the corresponding part, W; W i is the work done by the corresponding part, W; P a is the air pressure, Pa; t a is the air temperature, °C; f cl is the clothing thermal resistance, m 2 ·K / W; t cl is the skin temperature, °C; t r is the environmental radiation temperature, °C; h c is the convective heat transfer coefficient, W / m 2 ·K.

[0099] 4. Model Solving

[0100] The Fanger model is implemented using the Matlab programming language, and the parameters of each part of the human body are input to calculate the PMV value. When calculating, the following conditions are set: the environmental humidity is fixed at 50%, not changing with time; the radiation temperature is the same as the environmental temperature; the wind speed is 0 m / s; the clothing thermal resistance value of the human body is 1 clo, and this setting is consistent with the actual environment. Considering that in the environment of 0 - 15 °C, too high a thermal resistance value will cause the difference between the human thermal comfort temperature and the skin temperature to be not obvious, affecting the analysis of different heating temperature segments; the metabolic index is uniformly set to 0.5 met, representing the metabolic level of the human body in a standing still state.

[0101] According to the modified model, the PMV values of each part and the corresponding skin temperature under different environmental conditions can be calculated.

[0102] III. Detailed Description of Hardware Raw Materials and Components

[0103] 1. The heating unit is composed of high-performance conductive heating wires, which are designed into different shapes and sizes according to the heat requirements of different parts of the human body. The heating wires are made of metal materials with high temperature resistance and high resistance to ensure long life and high efficiency.

[0104] 2. The temperature sensors use high-precision NTC thermistors, which are distributed at key parts of the clothing, such as the chest, abdomen, back and limbs, to monitor and feedback the temperature in real time.

[0105] 3. Microcontroller (STM32F407VET6): As the control center of the system, it is responsible for receiving sensor data, executing the PID algorithm, and outputting control signals to the PWM control unit.

[0106] 4. LM358 Dual Operational Amplifier: Forms the core of the temperature monitoring module, used for signal amplification and processing to ensure the accuracy and stability of sensor signals.

[0107] 5. AD7190BRU A / D Converter: Responsible for converting analog signals into digital signals. Its 24-bit high-precision conversion ability ensures the accurate reading of temperature data.

[0108] 6. Voltage Regulator Power Supply Module: Adopts the U9-RE033AIDBZR chip and MCP6001UT voltage follower to achieve multi-stage voltage regulation and ensure stable power supply for all components of the system.

[0109] 7. PCB Layout: Adopts a six-layer board design to optimize signal transmission and power distribution, improving the anti-interference ability and stability of the system.

[0110] IV. Detailed Description of Software Control Modes

[0111] 1) Overall Control Flow

[0112] In an embedded system, the PID controller is a widely used feedback control algorithm that continuously adjusts the control output to make the output value of the system approach the expected value. In this embodiment, this controller is used to control the temperature and humidity of a series of monitoring points. The specific process is as Figure 4 shown. First, define some constants in the code, such as the I2C address of the SHT30 sensor, PID parameters, and control period. Then, the structure PID is defined, including the input and output of the controller, PID parameters, and other related variables. Under different measurement modes, the SHT30_CMD enumeration lists different commands. In addition, the code also includes an enumeration for describing the monitored objects, such as age, body area, and monitoring area. Finally, define the control parameters through a structure named "monitor_point_ctl_params_t", which includes information such as monitoring points and PID controllers. Defining the key components of PID control in the embedded system through the above code provides a basis for achieving precise temperature and humidity control.

[0113] 2) Temperature Control

[0114] First is the initialization method of the PID controller. Before the program starts to execute, it is necessary to initialize the parameters of the PID controller first, including the target temperature, PWM cycle, output limit, and proportional, integral, and derivative coefficients. Among them, the comfortable temperature value is based on the above calculation results; the PWM cycle can affect the response speed and stability of the controller; set the maximum and minimum limits of the controller output. This limit ensures that the controller output is within a reasonable range, preventing the system from over-regulating or failing to achieve the desired control effect. Determine the proportional, integral, and derivative coefficients of the controller to affect the response characteristics of the controller. These parameters determine the degree of response of the controller to the deviation, accumulated deviation, and rate of change of the deviation, thus affecting the stability, response speed, and anti-interference ability of the controller.

[0115] Secondly is the monitoring of the inner layer temperature of the cold-proof clothing. In the software implementation of the non-uniform heating system, the inner layer temperature of the cold-proof clothing is a crucial link. The accurate inner layer temperature of the cold-proof clothing is a response parameter of the system and is also crucial for maintaining the stability and safety of the entire heating process. An NTC sensor is used as the acquisition tool for the inner layer temperature of the cold-proof clothing. The data values collected by multiple sensors are exchanged through the I2C communication protocol. At the software level, by designing a data acquisition program that matches the hardware, this program can regularly query the sensor data at a set time interval and store the obtained temperature values in the system memory for subsequent processing. In order to ensure the accuracy and reliability of the monitoring data, a data filtering algorithm is introduced in the software design. It can identify and eliminate abnormal data caused by sensor noise or transient changes, thus obtaining smoother and more reliable temperature measurement data. In addition, through the temperature compensation mechanism, the system can dynamically adjust the heating strategy according to the current inner layer temperature conditions of the cold-proof clothing to achieve the temperature control effect. In the software implementation of the inner layer temperature of the cold-proof clothing, the real-time performance of the system is also concerned. By optimizing the program code and adopting the interrupt-driven data acquisition method, the system can quickly respond to the changes in the inner layer temperature of the cold-proof clothing and timely adjust the output of the heating power to ensure that the wearer is in a thermally comfortable state.

[0116] Finally is the main loop of the temperature control task, as Figure 5As shown, the main loop is based on an accurate PID control algorithm. By continuously monitoring and adjusting the process, it ensures that the temperature output of the heating system closely matches the preset target. After the system is initialized, the key parameters of the PID controller are first set, and these parameters are determined based on the system design requirements and previous research. Subsequently, the data acquisition stage begins, where the NTC sensor measures the temperature of the inner layer of the cold-proof clothing in real time, providing input for the control algorithm. The execution of the main loop follows the following sequence: First, the temperature data of the inner layer of the cold-proof clothing read from multiple NTC sensors is input into the PID controller; Second, the controller calculates the PWM duty cycle based on the deviation, and this duty cycle is directly related to the heating power of the heating element; Then, the generated PWM signal is used to adjust the working temperature of the heating unit to meet the human thermal comfort requirements; At the same time, the collected temperature data is filtered to eliminate noise, ensuring the stability of the control process; In addition, the system dynamically adjusts the PID parameters through a feedback mechanism to adapt to the continuous change of the temperature of the inner layer of the cold-proof clothing; Finally, the main loop is repeated at a set time interval to ensure the real-time response ability of the system. Through the above loop control strategy, the system can quickly respond to the fluctuations in the temperature of the inner layer of the cold-proof clothing, ensuring the thermal comfort of the wearer. In addition, the precise PID control and the adjustment of the PWM signal enable the system to achieve effective energy utilization while meeting the thermal comfort requirements.

[0117] 3) Microsecond feedback response

[0118] During the research of this system, a delay function operation program was written to ensure that the hardware device responds promptly to the time series and to ensure the normal communication between the system and the hardware. At the same time, this operation can further ensure that the heating unit completes the execution of the action instruction within a short time (<2 seconds). The microsecond-level delay function can ensure that tasks are executed within the specified time. In addition, this system is a power consumption-sensitive system, and the heating efficiency of each part varies. Through the microsecond-level delay operation, the working mode of the core processor can be further optimized, thereby realizing power management and power consumption optimization. If the delay function is implemented improperly or unstably, it may cause problems such as timing errors and communication abnormalities in the system, thus affecting the reliability and stability of the system. The program is based on the SysTick timer and realizes the microsecond-level delay function through a series of functions. First, the Get_Micros() function is used to obtain the current microsecond-level timestamp. By calculating the number of clock cycles per microsecond and the count value of the SysTick timer, combined with the system clock frequency, the accurate capture of the microsecond-level time is achieved. Subsequently, the Delay_Micros() function realizes the microsecond-level delay function by recording the start timestamp and checking the difference between the current timestamp and the start timestamp in a loop to reach the specified delay time. The configure_timer() function is used to configure the SysTick timer, including operations such as setting the clock source and clearing the counter, to ensure the normal operation of the timer. Finally, the HAL_Delay_us() function realizes the microsecond-level delay by checking the SysTick count value in a loop. The execution flow of each function is as Figure 6 shown.

[0119] 4) Experimental results

[0120] As shown in Table 1 below, this system was tested by checking whether the heating units in different parts can reach the preset temperature values under different environmental conditions. Specifically, under the conditions where the ambient temperature is set to -10°C and -5°C respectively, the ambient humidity is maintained at 50±5%, and the ambient wind speed is maintained at 0 m / s.

[0121] Table 1 Target temperature values of heating units in each part

[0122]

[0123]

Claims

1. A method for calculating human body comfort temperature, characterized in that: The following steps are involved: Construct a human body geometric model and divide it into several target areas; Meshing the human body geometric model to obtain a human body mesh model; For any grid unit of the human body grid model, the human skin temperature of the grid unit under different cold environments is calculated using the biological heat transfer equation; According to the human skin temperature, the improved Fanger thermal comfort model is used to solve and obtain the comfortable temperatures of the different target areas.

2. The method according to claim 1, characterized in that The method of using the biological heat transfer equation to calculate the human skin temperature of the grid unit under different cold environments is achieved by performing temperature field calculation based on fluid dynamics.

3. The method according to claim 2, characterized in that The bioheat transfer equation is Where ρ is density, c is specific heat, t is time, k is thermal conductivity, T b is the blood temperature, T t is tissue temperature, ω is blood perfusion rate, q met The heat generated by metabolism per unit volume of tissue.

4. The method according to claim 3, characterized in that The temperature field calculation based on fluid dynamics is realized by a CFD simulation tool, comprising the following steps: Initialize the temperature field; Set the boundary temperature of the boundary nodes; The discretized biological heat transfer equation is called to iteratively solve the temperature field, and after each iteration, it is determined whether an iteration termination condition is met. If so, the current result is used as the final result.

5. The method according to claim 1, characterized in that The improved Fanger thermal comfort model is: PMV i =[0.303exp(-0.036M i )+0.0275]·{M i -W i -0.305[5.733-0.007(M i -W i )-P a )-0.0014M i (34-t a )-3.96×10 -8 f cl [t cl +273) 4 -(t r +273) 4 ]-f cl h c (t cl -t a )} Among them, for different parts of the human body, PMV i is the thermal comfort value of the corresponding part, M i is the metabolic heat production of the corresponding part, W i is the work done by the corresponding part, P a is the air pressure, t a is the air temperature, f cl is the thermal resistance of clothing, t cl is the skin temperature, t r is the ambient radiation temperature, h c is the convective heat transfer coefficient.

6. A non-uniform heating and cold-proof clothing system for human body, characterized in that: include: The temperature monitoring module includes a plurality of temperature monitoring units, which are placed in different heating areas of the inner layer of the cold-proof clothing to collect the real-time temperature of the corresponding heating area; The non-uniform heating module comprises a control module and a plurality of heating units placed in different heating areas, wherein the control module is used to control the heating units in the corresponding heating areas to adjust the temperature to a corresponding comfortable temperature according to the real-time temperature of each heating area; The comfort temperature is determined by a calculation method according to any one of claims 1-5.

7. The system according to claim 6, characterized in that According to the real-time temperature of each heating area, the heating units in the corresponding heating areas are controlled to adjust the temperature to the corresponding comfortable temperature, which is achieved by PID control. The PID control includes the following steps: Determine PID parameters; For any of the heating areas, adjusting the duty cycle of the PWM signal based on the real-time temperature and the comfortable temperature, and controlling the heating unit to adjust the temperature using the adjusted PWM signal; Repeat the previous step until the set conditions are met.

8. The system according to claim 6, characterized in that It also includes a voltage-stabilized power supply module, which is used to provide system power after multi-stage voltage conversion and filtering.

9. The system according to claim 6, characterized in that The control module is arranged on a PCB board, which includes two signal layers, two ground layers and two power supply layers. The two signal layers are respectively located on the top layer and the bottom layer of the PCB board, and are used to arrange high-frequency signal transmission lines and power output components respectively, and thermal via technology is used to reduce the temperature of key components during operation.

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