Seat cushion heating integrated intelligent control heating system

Abstract

The invention relates to the technical field of heating systems, and particularly discloses a seat cushion heating integrated intelligent control heating system which comprises a user sensing module, a temperature control module, an energy consumption management module, a safety protection module, a communication interaction module and a self-checking diagnosis module. The user sensing module collects comprehensive data through a multi-source sensor group and generates a user habit feature set, and the temperature control module dynamically adjusts the working mode of a heating area according to the data and real-time environment parameters, so that personalized heating is realized; for example, according to sitting postures and physiological characteristics of different users, appropriate temperatures are provided for different temperature zones, the comfort level of the users is improved, a safety protection module compares an energy consumption evaluation index with a preset safety threshold value, a multi-stage protection strategy is implemented, and when the energy consumption evaluation index is too high, measures such as power reduction, partition power failure and forced heat dissipation are taken, so that the safety of the users is improved. The temperature of the seat cushion is effectively prevented from being too high, and safety accidents such as user scalding and fire disasters are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of heating systems, and particularly to a heating system for seat heating integrated with intelligent control. Background Art

[0002] In the technical field of heating systems, especially seat heating systems, there are many deficiencies in the existing technologies.

[0003] 1. Poor user experience: Traditional seat heating systems cannot accurately perceive the actual needs of users. They cannot comprehensively collect data on the pressure distribution on the seat surface, are difficult to analyze the impact of the user's sitting posture on the heating effect, and cannot perform personalized heating adjustment in combination with the user's physiological characteristic data. Under different environmental temperature and humidity conditions, they cannot dynamically adjust the heating mode, resulting in possible local overheating or overcooling during the user's use, and the comfort level is relatively low.

[0004] 2. Prominent energy consumption problem: Existing technologies lack an effective energy consumption management mechanism. They cannot construct a reasonable energy consumption evaluation model based on the temperature adjustment coefficient and battery state parameters, and it is difficult to achieve precise control of energy consumption. In actual use, there may be a phenomenon of overheating, causing energy waste. For devices relying on battery power supply (such as electric vehicles, etc.), it will affect their cruising range.

[0005] 3. Relatively large potential safety hazards: In terms of safety protection, traditional seat heating systems lack a perfect protection strategy. They do not implement multi-level protection by comparing the energy consumption evaluation index and the preset safety threshold. Once an abnormal situation occurs, such as the local temperature of the seat being too high, effective measures may not be taken in time, and there are safety risks such as scalding users or causing fires.

[0006] 4. Difficult system maintenance: Traditional systems do not have a self-check and diagnosis function, and cannot detect the impedance of heating elements and calibrate sensors. This makes it difficult to detect and repair system failures in a timely manner, increasing the maintenance cost and time cost, and reducing the reliability and stability of the system. Summary of the Invention

[0007] The purpose of the present invention is to provide a heating system for seat heating integrated with intelligent control to solve the problems raised in the above background art.

[0008] To achieve the above purpose, the present invention provides the following technical solution: A heating system for seat heating integrated with intelligent control, comprising: a user perception module, a temperature control module, an energy consumption management module, a safety protection module, a communication interaction module, and a self-check and diagnosis module;

[0009] The user perception module uses a multi-source sensor group to collect seat surface pressure distribution data, ambient temperature and humidity data, and user physiological characteristic data. After data fusion processing, it finally generates a set of user habit characteristics;

[0010] The temperature control module dynamically adjusts the working mode of the heating area according to the set of user habit characteristics and real-time environmental parameters, and then outputs a temperature adjustment coefficient T wxs ;

[0011] The energy consumption management module constructs an energy consumption evaluation model based on the temperature adjustment coefficient Twxs and battery state parameters, and generates an energy consumption evaluation index Nhzs;

[0012] The safety protection module implements a multi-level protection strategy by comparing N hzs with a preset safety threshold;

[0013] The communication interaction module realizes two-way data synchronization between the vehicle-mounted ECU and the mobile terminal;

[0014] The self-check diagnosis module performs impedance detection of heating elements and sensor calibration work.

[0015] Preferably, the user perception module includes: a pressure distribution unit, an environment perception unit, and a biometric recognition unit; the pressure distribution unit uses a 16×24 piezoelectric sensor array to detect the contact area S contact and the pressure gradient ΔP at a sampling rate of 50 Hz. This array can accurately capture the pressure changes at different positions on the seat surface, providing basic data for subsequent analysis of the user's sitting posture and pressure distribution; the environment perception unit integrates a DS18B20 temperature sensor and an HIH6130 humidity sensor to synchronously collect the temperature and humidity data inside and outside the vehicle. The DS18B20 temperature sensor can accurately measure the temperature, and the HIH6130 humidity sensor can accurately obtain the humidity information, providing comprehensive environmental data for the system; the biometric recognition unit is equipped with an MLX90614 infrared array sensor to detect the body surface temperature distribution, and captures the sitting posture characteristic parameters through an FDC2214 capacitance sensor. The infrared array sensor can non-contactedly sense the human body surface temperature, and the capacitance sensor can effectively detect the human sitting posture, thereby obtaining the user's physiological characteristics.

[0016] Preferably, the method for generating the set of user habit characteristics is as follows: (1) Perform Haar wavelet denoising processing on the pressure distribution data to extract the pressure center coordinates where xi and yi are the coordinates of each point in the sensor array, and pi is the pressure value at the corresponding point; (2) Calculate the thermal conductivity where ΔTenv is the environmental temperature difference, Tskin represents the human body surface temperature, and Tseat represents the seat temperature; (3) Construct an LSTM neural network model, and train it with historical usage data [P(x,y), Rdc, tuse] to obtain the user-preferred heating intensity level R dj ∈ 1 - 5.

[0017] Preferably, the temperature control module includes: a dynamic partitioning unit, a fuzzy PID unit, and a thermal equilibrium unit; the dynamic partitioning unit divides the seat into 3 - 6 independent temperature zones according to the pressure distribution entropy value Hp = -∑ipi·log2(pi), where pi is the probability of each state in the pressure distribution, and the entropy value Hp reflects the degree of disorder of the pressure distribution; the fuzzy PID unit establishes a membership function μ(Twxs) = Low, Medium, High and outputs the PWM duty cycle where e is the error, that is, the difference between the target temperature and the actual temperature, Kp is the proportionality coefficient, which determines the immediate response degree to the error; Ki is the integral coefficient, which is used to eliminate the steady-state error of the system; Kd is the differential coefficient, which can predict the change trend of the error and adjust the control amount in advance; the thermal equilibrium unit uses the finite element method to simulate the temperature field distribution and dynamically adjusts the heat diffusion compensation amount Qcomp = λ·(Tneighbor - Tlocal) between adjacent temperature zones, where λ is the thermal conductivity, Tneighbor is the temperature of the adjacent temperature zone, and Tlocal is the temperature of the current temperature zone.

[0018] Preferably, the safety protection module executes the following strategies:

[0019] When Nhzs > K1: Activate the three-level protection:

[0020] Power reduction stage: Limit Twxs to 0.7×Twxs current , by reducing the temperature adjustment coefficient, reducing the heating power, so as to reduce energy consumption and system load;

[0021] Zone power-off: Cut off the power supply to the high entropy value area where H p > 2.5bit, that is, the area where the pressure distribution is relatively chaotic and abnormal conditions may exist, stop heating in this area to avoid potential risks;

[0022] Forced heat dissipation: Start the Peltier semiconductor refrigeration chip to maintain T seat ≤ 45°C to prevent the seat temperature from being too high and ensure user safety.

[0023] Preferably, when K2 < Nhzs ≤ K1: Start the adaptive PI control, and its control parameters are adjusted as follows:

[0024]

[0025] According to the proportional relationship between the energy consumption evaluation index Nhzs and the threshold K1, the system dynamically adjusts the proportional coefficient Kp and the integral coefficient Ki of the PI controller.

[0026] Preferably, when Nhzs ≤ K2: Enable the reinforcement learning agent to optimize the heating strategy

[0027] R = ω1·T comfort -ω2·P heat

[0028] Where ω1 and ω2 are weight coefficients, Tcomfort represents user comfort, and Pheat is the heating power consumption.

[0029] Preferably, the communication interaction module realizes: transmitting the heating state data to the mobile APP through BLE5.0, generating a visual heat map, using the low-power Bluetooth technology of BLE5.0 to transmit the heating state data of the seat cushion to the mobile APP, and the APP converts these data into a visual heat map.

[0030] The heating system with integrated intelligent control of seat cushion heating proposed by the present invention has the following beneficial effects:

[0031] 1. Improve user experience: The user perception module collects comprehensive data through a multi-source sensor group and generates a set of user habit characteristics. The temperature control module dynamically adjusts the working mode of the heating area based on this data and real-time environmental parameters to achieve personalized heating; for example, according to the sitting postures and physiological characteristics of different users, provide appropriate temperatures for different temperature zones, improving user comfort.

[0032] 2. Optimize energy consumption management: The energy consumption management module constructs an energy consumption evaluation model based on the temperature adjustment coefficient and battery state parameters, generates an energy consumption evaluation index. According to this index, the system can reasonably adjust the heating strategy, avoid energy waste, reduce energy consumption while ensuring the heating effect, improve energy utilization efficiency, and is especially beneficial for extending the battery life of equipment such as electric vehicles.

[0033] 3. Enhance safety protection: The safety protection module implements a multi-level protection strategy by comparing the energy consumption evaluation index with a preset safety threshold. When the energy consumption evaluation index is too high, measures such as reducing power, zoning power-off, and forced heat dissipation are taken to effectively prevent the seat cushion from overheating, avoid safety accidents such as scalding users and fires, and ensure the safety of users during use.

[0034] 4. Facilitate system maintenance: The self-check diagnosis module performs impedance detection of heating elements and sensor calibration work, can timely detect problems in the system and repair them, which not only reduces maintenance costs and time costs, but also improves the reliability and stability of the system, and extends the service life of the seat cushion heating system. Description of the Drawings

[0035] Figure 1 This is the principle block diagram of a heating system with integrated intelligent control for seat cushion heating according to the present invention;

[0036] Figure 2 This is the principle block diagram of the user perception module of the present invention;

[0037] Figure 3 This is the principle block diagram of the temperature control module of the present invention. Specific embodiments

[0038] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] Please refer to Figures 1 - 3 , the present invention provides a technical solution: a heating system with integrated intelligent control for seat cushion heating, including: a user perception module 100, a temperature control module 200, an energy consumption management module 300, a safety protection module 400, a communication interaction module 500, and a self-check diagnosis module 600;

[0040] The user perception module 100 uses a multi-source sensor group to collect seat surface pressure distribution data, ambient temperature and humidity data, and user physiological characteristic data. After data fusion processing, a user habit characteristic set is finally generated; the temperature control module 200 dynamically adjusts the working mode of the heating area according to the user habit characteristic set and real-time environmental parameters, and then outputs a temperature adjustment coefficient T wxs ; the energy consumption management module 300 constructs an energy consumption evaluation model based on the temperature adjustment coefficient Twxs and battery state parameters, and generates an energy consumption evaluation index Nhzs; the safety protection module 400 implements a multi-level protection strategy by comparing N hzs with a preset safety threshold; the communication interaction module 500 realizes two-way data synchronization between the vehicle-mounted ECU and the mobile terminal; the self-check diagnosis module 600 performs impedance detection of the heating element and sensor calibration work.

[0041] More specifically, the user perception module 100 includes: a pressure distribution unit 110, an environmental perception unit 120, and a biometric recognition unit 130;

[0042] The pressure distribution unit 110 uses a 16×24 piezoelectric sensor array to detect the contact area S at a sampling rate of 50 Hz contactAnd a pressure gradient ΔP, this array can accurately capture the pressure changes at different positions on the seat surface, providing basic data for subsequent analysis of the user's sitting posture and pressure distribution; the environmental perception unit 120 integrates a DS18B20 temperature sensor and an HIH6130 humidity sensor to synchronously collect the temperature and humidity data inside and outside the vehicle. The DS18B20 temperature sensor can accurately measure the temperature, and the HIH6130 humidity sensor can accurately obtain the humidity information, providing comprehensive environmental data for the system; the biometric recognition unit 130 is equipped with an MLX90614 infrared array sensor to detect the body surface temperature distribution, and captures the sitting posture characteristic parameters through an FDC2214 capacitance sensor. The infrared array sensor can non-contactedly sense the body surface temperature of the human body, and the capacitance sensor can effectively detect the sitting posture of the human body, thereby obtaining the physiological characteristics of the user;

[0043] The data collected by the pressure distribution unit 110, the environmental perception unit 120, and the biometric recognition unit 130 complement each other, providing rich and comprehensive raw data for the data fusion processing of the entire heating system. The user habit feature set generated after the multi-source data fusion lays a solid foundation for the accurate decision-making and intelligent control of subsequent modules such as the temperature control module 200 and the energy consumption management module 300, ensuring the efficient implementation of the overall function of the heating system.

[0044] More specifically, the method for generating the user habit feature set is as follows:

[0045] (1) Perform Haar wavelet denoising processing on the pressure distribution data and extract the pressure center coordinates xi and yi are the coordinates of each point in the sensor array, and pi is the pressure value of the corresponding point. The pressure center coordinates calculated by this formula can reflect the main force-bearing position of the user on the seat; (2) Calculate the heat transfer coefficient where ΔTenv is the environmental temperature difference, Tskin represents the human body surface temperature, and Tseat represents the seat temperature. This formula is used to measure the ease of heat transfer from the human body to the seat. The larger the heat transfer coefficient, the easier the heat transfer; (3) Construct an LSTM neural network model, and input historical usage data [P(x,y), Rdc, tuse] to train and obtain the user-preferred heating intensity level R dj ∈ 1-5, the LSTM neural network is good at processing time series data. By inputting historical data such as pressure center coordinates, heat transfer coefficients, and usage time, the model is trained to predict the user-preferred heating intensity level, providing a basis for personalized temperature control;

[0046] By performing Haar wavelet denoising processing on the pressure distribution data to extract the pressure center coordinates, calculating the thermal conductivity to measure the ease of heat transfer, and then using the LSTM neural network model, which is good at processing time series data, to input the above data and the usage time for training, the user-preferred heating intensity level is obtained, providing a key basis for the heating system to achieve precise personalized temperature control and significantly improving the user experience.

[0047] More specifically, the temperature control module 200 includes: a dynamic zoning unit 210, a fuzzy PID unit 220, and a thermal equilibrium unit 230; the dynamic zoning unit 210 divides the seat cushion into 3-6 independent temperature zones according to the pressure distribution entropy value Hp = -∑ipi·log2(pi), where pi is the probability of each state in the pressure distribution, and the entropy value Hp reflects the degree of chaos of the pressure distribution. By calculating the entropy value through this formula and dividing the seat cushion into different temperature zones according to the size of the entropy value, independent control of the temperature in different regions can be achieved to meet the different temperature requirements of different parts of the user. The fuzzy PID unit 220 establishes a membership function μ(Twxs) = Low, Medium, High and outputs the PWM duty cycle. Among them, e is the error, that is, the difference between the target temperature and the actual temperature, Kp is the proportionality coefficient, which determines the immediate response degree to the error; Ki is the integral coefficient, which is used to eliminate the steady-state error of the system; Kd is the differential coefficient, which can predict the change trend of the error and adjust the control amount in advance. By mapping the temperature adjustment coefficient Twxs to different states (low, medium, high) in the membership function through fuzzy logic, the parameters of the PID controller are adjusted, and a suitable PWM duty cycle is output to accurately control the heating power. The thermal equilibrium unit 230 uses the finite element method to simulate the temperature field distribution and dynamically adjusts the heat diffusion compensation amount Qcomp = λ·(Tneighbor - Tlocal) between adjacent temperature zones, where λ is the thermal conductivity, Tneighbor is the temperature of the adjacent temperature zone, and Tlocal is the temperature of the current temperature zone. This formula is used to calculate the heat diffusion compensation amount required due to the temperature difference between adjacent temperature zones. By simulating the temperature field with the finite element method, the heat transfer situation between different temperature zones can be analyzed more accurately, and the heat diffusion compensation amount can be dynamically adjusted to ensure that the temperature of each area of the seat cushion is uniform.

[0048] The dynamic zoning unit 210 of the temperature control module 200 divides 3-6 independent temperature zones according to the pressure distribution entropy value to achieve independent control of the temperature of different parts to meet the differentiated needs of users; the fuzzy PID unit 220 establishes a membership function and uses fuzzy logic to adjust the parameters of the PID controller to output a suitable PWM duty cycle to accurately control the heating power; the thermal equilibrium unit 230 uses the finite element method to simulate the temperature field, calculates and dynamically adjusts the heat diffusion compensation amount between adjacent temperature zones to ensure that the temperature of each area of the seat cushion is uniform, and multiple units cooperate to provide users with a precise and comfortable heating experience.

[0049] More specifically, the security protection module 400 executes the following policies:

[0050] When Nhzs > K1: Activate the three-level protection:

[0051] Power reduction stage: Limit Twxs to 0.7 × Twxs current , by reducing the temperature adjustment coefficient, reducing the heating power, so as to reduce energy consumption and system load;

[0052] Partition power-off: Turn off the power supply to the high-entropy value area where H p > 2.5bit, that is, the area where the pressure distribution is relatively chaotic and abnormal conditions may exist, stop heating in this area to avoid potential risks;

[0053] Forced heat dissipation: Start the Peltier semiconductor refrigeration chip to maintain T seat ≤ 45°C to prevent the seat cushion temperature from being too high and ensure user safety.

[0054] When K2 < Nhzs ≤ K1: Start the adaptive PI control, and its control parameters are adjusted as follows:

[0055]

[0056] According to the proportional relationship between the energy consumption evaluation index Nhzs and the threshold K1, the system dynamically adjusts the proportional coefficient Kp and the integral coefficient Ki of the PI controller. This adjustment mechanism aims to achieve adaptive control of the heating power and ensure effective optimization of energy consumption while ensuring safety.

[0057] When Nhzs ≤ K2: Enable the reinforcement learning agent to optimize the heating strategy

[0058] R = ω1·T comfort -ω2·P heat

[0059] Where ω1 and ω2 are weight coefficients, Tcomfort represents the user comfort level (which can be measured by factors such as the proximity of the temperature to the user's preferred temperature), and Pheat is the heating power consumption. Through the reinforcement learning algorithm, with the goal of improving user comfort and reducing heating power consumption, the heating strategy is continuously optimized.

[0060] More specifically, the communication and interaction module (500) realizes: Transmit the heating status data to the mobile APP through BLE5.0, generate a visual heat map, and use the low-power Bluetooth technology of BLE5.0 to transmit the heating status data of the seat cushion (such as the temperature of each area, etc.) to the mobile APP. The APP converts these data into a visual heat map to intuitively display the heating situation of the seat cushion and facilitate the user to understand.

[0061] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A seat cushion heating integrated intelligent control heating system, characterized in that: Including: A user perception module (100), a temperature control module (200), an energy consumption management module (300), a safety protection module (400), a communication interaction module (500), and a self-check and diagnosis module (600); The user perception module (100) uses a multi-source sensor group to collect seat surface pressure distribution data, ambient temperature and humidity data, and user physiological characteristic data. After data fusion processing, a user habit characteristic set is finally generated; The temperature control module (200) dynamically adjusts the working mode of the heating area according to the user's habit feature set and real-time environmental parameters, and then outputs a temperature adjustment coefficient T wxs ; The energy consumption management module (300) constructs an energy consumption evaluation model based on the temperature adjustment coefficient Twsx and battery state parameters, and generates an energy consumption evaluation index Nhzs; The safety protection module (400) is compared with N hzs and preset security thresholds to implement multi-level protection strategies; The communication interaction module (500) realizes two-way data synchronization between the in-vehicle ECU and the mobile terminal; The self-check and diagnosis module (600) performs heating element impedance detection and sensor calibration work.

2. A seat cushion heating integrated intelligent control heating system according to claim 1, characterized in that: The user perception module (100) includes: The pressure distribution unit (110) uses a 16×24 piezoelectric sensor array to detect the contact area S at a sampling rate of 50 Hz. contact As well as the pressure gradient ΔP, the array can accurately capture the pressure changes at different locations on the seat surface, providing basic data for subsequent analysis of the user's sitting posture and pressure distribution; An environment perception unit (120), integrating a DS18B20 temperature sensor and a HIH6130 humidity sensor, synchronously collecting temperature and humidity data inside and outside the vehicle. The DS18B20 temperature sensor can accurately measure temperature, and the HIH6130 humidity sensor can accurately obtain humidity information, providing comprehensive environment data for the system; A biometric recognition unit (130), equipped with an MLX90614 infrared array sensor to detect the body surface temperature distribution, and capturing sitting posture characteristic parameters through an FDC2214 capacitance sensor. The infrared array sensor can non-contactedly sense the human body surface temperature, and the capacitance sensor can effectively detect the human sitting posture, thereby obtaining the physiological characteristics of the user.

3. A seat cushion heating integrated intelligent control heating system according to claim 2, characterized in that: The method for generating the user habit characteristic set is as follows: (1) Perform Haar wavelet denoising on the pressure distribution data to extract the pressure center coordinates xi and yi are the coordinates of each point in the sensor array, and pi is the pressure value at the corresponding point; (2) Calculation of thermal conductivity Where ΔTenv is the ambient temperature difference, Tskin represents the human body surface temperature, and Tseat represents the seat cushion temperature; (3) Construct an LSTM neural network model and input historical usage data [P(x, y), Rdc, tuse] to train the user's preferred heating intensity level R dj ∈1-5.

4. A seat cushion heating integrated intelligent control heating system according to claim 3, characterized in that: The temperature control module (200) includes: A dynamic partition unit (210), dividing the seat into 3-6 independent temperature zones according to the pressure distribution entropy value Hp = -∑ipi·log2(pi), where pi is the probability of each state in the pressure distribution, and the entropy value Hp reflects the degree of disorder of the pressure distribution; Fuzzy PID unit (220), establishes membership function μ(Twxs)=Low, Medium, High, outputs PWM duty cycle Among them, e is the error, that is, the difference between the target temperature and the actual temperature; Kp is the proportional coefficient, which determines the degree of immediate response to the error; Ki is the integral coefficient, which is used to eliminate the steady-state error of the system; Kd is the differential coefficient, which can predict the error change trend and adjust the control amount in advance; A heat balance unit (230), using the finite element method to simulate the temperature field distribution, and dynamically adjusting the heat diffusion compensation amount between adjacent temperature zones Qcomp = λ·(Tneighbor - Tlocal), where λ is the heat conductivity, Tneighbor is the temperature of the adjacent temperature zone, and Tlocal is the temperature of the current temperature zone.

5. A seat cushion heating integrated intelligent control heating system according to claim 4, characterized in that: The execution strategy of the safety protection module (400) is as follows: When Nhzs > K1: Activate the three-level protection: Power reduction stage: limit Twxs to 0.7×Twxs current , by reducing the temperature regulation coefficient and reducing the heating power, so as to reduce energy consumption and system load; Partition power off: Close H p Power is supplied to the high entropy area with a value of >2.5bit, i.e., the area with a chaotic pressure distribution and possible abnormal conditions. Heating of this area is stopped to avoid potential risks. Forced cooling: Start the Peltier semiconductor cooling sheet to maintain T seat ≤45℃, prevents the seat cushion from overheating and ensures user safety.

6. A seat cushion heating integrated intelligent control heating system according to claim 5, characterized in that: When K2 < Nhzs ≤ K1: Start the adaptive PI control, and its control parameters are adjusted as follows: According to the proportional relationship between the energy consumption evaluation index Nhzs and the threshold K1, the system dynamically adjusts the proportional coefficient Kp and the integral coefficient Ki of the PI controller.

7. A seat cushion heating integrated intelligent control heating system according to claim 6, characterized in that: When Nhzs ≤ K2: Enable the reinforcement learning agent to optimize the heating strategy R=ω1·T comfort -ω2·Ph eat Where ω1 and ω2 are weight coefficients, Tcomfort represents user comfort, and Pheat is the heating power consumption.

8. A seat cushion heating integrated intelligent control heating system according to claim 7, characterized in that: The communication interaction module (500) realizes: transmitting the heating status data to the mobile terminal APP via BLE5.0 to generate a visual heat map, and using the BLE5.0 low-power Bluetooth technology to transmit the heating status data of the seat cushion to the mobile terminal APP, and the APP converts the data into a visual heat map.

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