Control method of intelligent temperature control clothes

Abstract

The invention discloses a control method of intelligent temperature control clothes. The control method comprises the specific steps that S101, hardware self-inspection, parameter loading, network connection and state initialization are firstly carried out; s102, sensing an external environment and acquiring data; s103, carrying out preprocessing and feature extraction work on the collected data; s104, performing intelligent decision making and strategy generation work; s105, adjusting and controlling the temperature according to the generated temperature control strategy; s106, the user operates and controls the system in a man-machine interaction mode; s107, regulating and controlling the input energy; and S108, storing and recording the working data of the system. The invention relates to a control method of an intelligent temperature control garment. The control method has the advantages of being accurate, intelligent, convenient, energy-saving, safe and reliable.

Description

Technical Field

[0001] The present invention belongs to the technical field of clothing, and specifically relates to a control method for an intelligent temperature control clothing. Background Technique

[0002] An intelligent temperature control clothing realizes precise regulation of the microenvironment temperature of the human body through the coordinated operation of multiple modules such as environmental perception, intelligent decision-making, and execution control. It is an innovative product with the capabilities of precisely and intelligently sensing the environment and physiological states, supporting human-computer interaction and dynamic energy consumption management, and integrating multiple safety protection mechanisms.

[0003] In the prior art, traditional temperature control clothing generally has three deficiencies: First, the control accuracy is insufficient. It mostly relies on a single sensor or a fixed threshold strategy and cannot dynamically adjust by fusing human motion states, physiological parameters, and environmental data in real time, easily resulting in temperature overshoot or adjustment lag. Second, there is an imbalance between convenience and energy conservation. It lacks an intelligent energy consumption management mechanism, the manual adjustment mode is cumbersome to operate, and continuous heating / cooling leads to short battery life. Third, there is a lack of safety protection. Most only have basic overheat protection and do not form a hardware-level short-circuit detection, biocompatible material design, and fault diagnosis system, presenting potential risks of low-temperature frostbite or circuit safety. Therefore, a control method for an intelligent temperature control clothing needs to be designed. Summary of the Invention

[0004] The purpose of the present invention is to provide a control method for an intelligent temperature control clothing to solve the above problems, that is, to solve the problems mentioned in the above background technique.

[0005] To solve the above problems, the present invention provides a technical solution:

[0006] A control method for an intelligent temperature control clothing, the specific steps include:

[0007] S101. First, perform hardware self-check, parameter loading, network connection, and status initialization:

[0008] During hardware detection, after the energy management module powers the entire system, under the coordination of the intelligent decision-making module, the main control chip starts the hardware self-check of each module. Various sensors in the environmental perception module check whether they can collect data normally; the heating, cooling, and ventilation units in the execution control module check whether the circuits are unobstructed; the data storage module checks whether the storage device is readable and writable; the communication protocol module checks whether the communication interface is normal; the safety protection module checks whether the temperature over-limit protection and short-circuit detection functions are ready. During parameter loading, the intelligent decision-making module reads the user-preset temperature control modes, such as "sports mode" and "sleep mode", from the data storage module, and at the same time loads the previously stored historical data to provide a reference for subsequent decisions. Under the instruction of the intelligent decision-making module, the communication protocol module attempts to connect to the local Bluetooth device or the cloud server. If the connection is successful, a data transmission channel will be established for subsequent data interaction and remote control. The intelligent decision-making module sends an instruction to the execution control module to reset the heating, cooling, and ventilation units to the standby state. At the same time, the indicator light in the human-computer interaction module displays the system ready state to inform the user that the system has completed initialization.

[0009] S102. Sense the external environment and collect data:

[0010] Various sensors in the environmental perception module start to collect data in real time. The body surface temperature sensor is attached to the human skin to accurately obtain the body surface temperature; the environmental temperature, humidity, air pressure, and wind speed sensors comprehensively monitor the external environment; the motion state sensor captures the human motion state and posture changes in real time through a three-axis accelerometer and a gyroscope; the physiological parameter module collects the physiological data of the human body, and the collected data will be transmitted to the intelligent decision-making module through the internal communication unit specified by the communication protocol module.

[0011] S103. Preprocess the collected data and extract features:

[0012] After receiving the raw data from the environmental perception module, the intelligent decision-making module first performs data preprocessing. The data preprocessing unit applies median filtering to the temperature data to remove possible noise and outliers; for the motion data, complementary filtering is used to fuse the attitude angles to improve the data accuracy. Then, the exercise intensity index, the temperature difference between the inside and outside of the clothing, and the humidity gradient characteristic parameters are calculated, and the multi-dimensional data is normalized for subsequent decision-making analysis. Part of the processed data is used for the current decision, and the other part will be stored in the data storage module.

[0013] S104. Make intelligent decisions and generate strategies:

[0014] The intelligent decision-making module performs pattern matching and reasoning based on the preprocessed data in combination with the fuzzy control rules in the temperature control rule library. For example, it determines the user's exercise status and physiological status based on the exercise intensity index and heart rate data, and determines whether to activate the heating, cooling, or ventilation function in combination with the temperature difference inside and outside the clothing and the humidity gradient. At the same time, it uses a BP neural network to predict the body temperature change in the next period of time, and adopts a particle swarm algorithm to dynamically adjust the heating / cooling power ratio to achieve efficient energy utilization and precise temperature control;

[0015] S105. Implement the adjustment and control of temperature according to the generated temperature control strategy:

[0016] The intelligent decision-making module sends a control signal to the execution control module according to the generated temperature control strategy. The heating sub-module adjusts the heating power according to the duty cycle of the PWM signal; the cooling sub-module realizes cooling by adjusting the current of the thermoelectric cooler and the rotation speed of the fan; the ventilation control unit controls the opening and closing degree of the air valve according to the instruction to achieve ventilation adjustment. The feedback adjustment loop in the execution control module will monitor the working status of the actuator in real time and feedback this information to the intelligent decision-making module for real-time adjustment;

[0017] S106. The user operates and controls the system through the human-computer interaction method:

[0018] The local interaction unit in the human-computer interaction module displays the current temperature, power, and working mode information in real time. The user can operate through the touch screen or buttons, such as setting the temperature threshold and switching the working mode. The operation instruction will be transmitted to the intelligent decision-making module through the communication protocol module. The intelligent decision-making module adjusts the temperature control strategy according to the user's instruction; the communication protocol module transmits the data in the intelligent decision-making module to the mobile APP through Bluetooth or the cloud server. The user can remotely monitor and control the working status of the clothing on the mobile APP, such as setting geographical fence temperature control and viewing the historical temperature change curve. At the same time, the mobile APP can also send the user's feedback information back to the intelligent decision-making module for strategy optimization;

[0019] S107. Regulate and control the input energy:

[0020] The energy management module monitors the battery power and the power consumption of each module in real time. When it detects that the user is in a stationary state and continues for a period of time, it reduces the sampling frequency of the sensors in the environmental perception module to reduce the power consumption of data acquisition; at the same time, it makes the communication protocol module enter the low-power mode to reduce the energy consumption of wireless communication. When the power is lower than a certain threshold, it automatically shuts down the non-essential modules and adjusts the working power of the execution control module to give priority to ensuring the operation of the core functions;

[0021] S108. Store and record the system working data:

[0022] The data storage module classifies and stores the collected data, decision-making information, and execution results according to preset rules. It stores the complete status data once an hour, and triggers immediate storage for abnormal events, with data caching for a period of time before and after. At the same time, the communication protocol module regularly uploads the locally stored data to the cloud server for big data analysis and algorithm optimization. The server generates an update package based on the analysis results and downloads it to the clothing through the communication protocol module. The intelligent decision-making module updates the temperature control rule library and algorithm model according to the update package to achieve self-optimization and upgrade of the system.

[0023] Preferably, the energy management module is connected to the data storage module, the data storage module is connected to the environment perception module, the energy management module is connected to the environment perception module, the environment perception module is connected to the intelligent decision-making module, the intelligent decision-making module is connected to the data storage module, the intelligent decision-making module is connected to the execution control module, the execution control module is connected to the feedback loop module, the energy management module is connected to the human-computer interaction module, the human-computer interaction module is connected to the safety protection module, the safety protection module is connected to the feedback control module, the feedback control module is connected to the energy management module, the intelligent decision-making module is connected to the safety protection module, the intelligent decision-making module is connected to the communication protocol module, and the communication protocol module is connected to the external device module.

[0024] Preferably, the energy management module includes a power supply unit module, an energy monitoring module, a power consumption adjustment module, and an emergency control module. The power supply unit module is connected to the energy monitoring module, the energy monitoring module is connected to the power consumption adjustment module, and the power consumption adjustment module is connected to the emergency control module;

[0025] The power supply unit module uses a rechargeable lithium battery and is equipped with a wireless charging function. The energy monitoring module monitors the battery voltage in real time through an ADC. The power consumption adjustment module dynamically adjusts the sensor sampling frequency. The emergency control module is used to automatically switch to the energy-saving mode when the battery power is less than 10%.

[0026] Preferably, the data storage module includes a local storage module, a cloud synchronization module, a data classification module, and a data clearing module. The local storage module is connected to the cloud synchronization module, the cloud synchronization module is connected to the data classification module, and the data classification module is connected to the data clearing module;

[0027] The local storage module uses EEPROM to store user personalized settings. The cloud synchronization module uploads temperature data to the server through the NB-IoT / LTE-M module. The data classification module classifies and stores environmental parameters, control instructions, and energy consumption data according to timestamps. The data clearing module sets an automatic overwrite policy to cyclically overwrite old data after 30 days of storage.

[0028] Preferably, the environmental perception module includes a sensor module, a motion monitoring module, and a physiological monitoring module. The sensor module is connected to the motion monitoring module, and the motion monitoring module is connected to the physiological monitoring module.

[0029] The sensor module includes a temperature sensor group and a humidity sensor. The motion monitoring module integrates a three-axis accelerometer and a gyroscope to identify the motion intensity. The physiological monitoring module can be optionally equipped with a heart rate sensor and a galvanic skin response sensor for collecting physiological data.

[0030] Preferably, the intelligent decision-making module includes a preprocessing module, a temperature control rule module, an optimization algorithm module, and a decision output module. The preprocessing module is connected to the temperature control rule module, the temperature control rule module is connected to the optimization algorithm module, and the optimization algorithm module is connected to the decision output module.

[0031] The preprocessing module is used to filter and denoise sensor data. The temperature control rule module has built-in fuzzy control rules. When the body surface temperature > 37.5°C during exercise, cooling is started. The optimization algorithm module supports self-learning of the BP neural network. The decision output module generates a PWM control signal or a level signal for controlling the output of the decision.

[0032] Preferably, the execution control module includes a heating module, a cooling module, a ventilation control module, and a feedback regulation module. The heating module is connected to the cooling module, the cooling module is connected to the ventilation control module, and the ventilation control module is connected to the feedback regulation module.

[0033] The heating module includes a flexible electrothermal film and its drive circuit for heating the clothing. The cooling module uses a thermoelectric cooler with a miniature cooling fan. The ventilation control module uses an air valve to control the ventilation effect. The feedback regulation module includes an execution status sensor to monitor the power consumption of the heating film through a current sensor.

[0034] Preferably, the human-computer interaction module includes a local interaction module, a remote control module, a status display module, and an input verification module. The local interaction module is connected to the remote control module, the remote control module is connected to the status display module, and the status display module is connected to the input verification module.

[0035] The local interaction module includes a touch screen and physical buttons for manual operation control of the system. The remote control module can be connected to and control external devices through Bluetooth connection. The status display module includes an LED indicator group to distinguish the heating / cooling / standby states. The input verification module is used to set temperature threshold protection.

[0036] Preferably, the communication protocol module includes an internal communication module, an external communication module, a protocol conversion module, and a data encryption module. The internal communication module is connected to the external communication module, the external communication module is connected to the protocol conversion module, and the protocol conversion module is connected to the data encryption module.

[0037] The internal communication module transmits data based on the sensor data transmission protocol of the I2C / SPI bus. The external communication module supports Bluetooth Mesh networking and can achieve collaborative temperature control for multiple garments or multiple positions on a garment. The protocol conversion module is compatible with the conversion between Modbus RTU and JSON data formats. The data encryption module encrypts remotely transmitted data using an encryption algorithm.

[0038] Preferably, the safety protection module includes a temperature protection module, a short-circuit detection module, a probe protection module, and a fault diagnosis module. The temperature protection module is connected to the short-circuit detection module, the short-circuit detection module is connected to the probe protection module, and the probe protection module is connected to the fault diagnosis module.

[0039] The temperature protection module is used to automatically cut off the power supply of the actuator when the detected temperature > 42°C or < 10°C. The short-circuit detection module sets a fuse and an overcurrent detection chip in the heating / cooling circuit to detect short-circuit conditions during system operation. The probe protection module is used to protect each sensor execution probe with a medical-grade silicone material. The fault diagnosis module is used to indicate abnormal states through the LED blinking frequency code for timely detection of abnormalities.

[0040] The beneficial effects of the present invention are as follows: The present invention relates to a control method for an intelligent temperature-controlled garment, which has the characteristics of being precise, intelligent, convenient, energy-saving, safe, and reliable. In specific use, compared with the traditional control method for intelligent temperature-controlled garments, the control method of the present intelligent temperature-controlled garment has the following

[0041] beneficial effects:

[0042] First, the environmental perception module can accurately and real - time obtain human physiological parameters and external environmental information, providing a rich and accurate data basis for subsequent intelligent decision - making. The intelligent decision - making module integrates a variety of advanced algorithms. It can not only quickly generate reasonable temperature control strategies based on current data but also has self - learning and optimization capabilities, and can continuously adjust according to users' usage habits and environmental changes. Each sub - unit of the execution control module has a clear division of labor, can efficiently achieve the temperature regulation function, and the feedback regulation loop ensures the accuracy and stability of control. The data storage module realizes the effective management of historical data and cloud synchronization, providing strong support for human - machine interaction and strategy update. The human - machine interaction module enables users to conveniently interact with the system, and the remote control function further improves the convenience of use. The energy management module effectively extends the battery life by dynamically adjusting power consumption and emergency mode settings. The safety protection module provides reliable guarantee for the stable operation of the system and the safety of users, avoiding potential dangers caused by abnormal situations;

[0043] Secondly, from the rigorous self - inspection and parameter loading during system initialization, to the multi - dimensional information acquisition in data collection, then through the optimization of data pre - processing and the accurate judgment of intelligent decision - making, and finally to the efficient regulation of execution control, the whole process is clear - logical, scientific and reasonable. The feedback regulation mechanism enables the system to adjust strategies in real - time according to the actual execution situation, ensuring the accuracy and stability of temperature control. The human - machine interaction link allows users to deeply participate in the operation of the system, meeting personalized needs. Energy management runs through the whole process, minimizing energy consumption while ensuring system performance. The data storage and update steps enable the system to continuously learn and evolve, adapting to the changes of different users and environments. Overall, this control method significantly improves the performance, comfort and intelligence level of intelligent temperature - controlled clothing, providing users with a high - quality usage experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] For ease of explanation, the present invention is described in detail by the following specific embodiments and accompanying drawings.

[0045] Figure 1 The working flow chart of the present invention;

[0046] Figure 2 The system schematic diagram of the present invention;

[0047] Figure 3 For the present invention Figure 2 The schematic diagram of the energy management module of the present invention;

[0048] Figure 4 For the present invention Figure 2 The schematic diagram of the data storage module of the present invention;

[0049] Figure 5 For the present invention Figure 2Schematic diagram of the environmental perception module;

[0050] Figure 6 For the present invention Figure 2 Schematic diagram of the intelligent decision-making module;

[0051] Figure 7 For the present invention Figure 2 Schematic diagram of the execution control module;

[0052] Figure 8 For the present invention Figure 2 Schematic diagram of the human-computer interaction module;

[0053] Figure 9 For the present invention Figure 2 Schematic diagram of the communication protocol module;

[0054] Figure 10 For the present invention Figure 2 Schematic diagram of the security protection module;

[0055] In the figure: 1. Energy management module; 2. Data storage module; 3. Environmental perception module; 4. Intelligent decision-making module; 5. Execution control module; 6. Feedback loop module; 7. Human-computer interaction module; 8. Feedback control module; 9. Communication protocol module; 10. External device module; 15. Security protection module; 11. Power supply unit module; 12. Energy monitoring module; 13. Power consumption adjustment module; 14. Emergency control module; 21. Local storage module; 22. Cloud synchronization module; 23. Data classification module; 24. Data clearing module; 31. Sensor module; 32. Motion monitoring module; 33. Physiological monitoring module; 41. Preprocessing module; 42. Temperature control rule module; 43. Optimization algorithm module; 44. Decision output module; 51. Heating module; 52. Refrigeration module; 53. Ventilation control module; 54. Feedback regulation module; 71. Local interaction module; 72. Remote control module; 73. Status display module; 74. Input verification module; 91. Internal communication module; 92. External communication module; 93. Protocol conversion module; 94. Data encryption module; 151. Temperature protection module; 152. Short-circuit detection module; 153. Probe protection module; 154. Fault diagnosis module. Detailed implementation manners

[0056] As Figures 1-10 shown, the following technical solutions are adopted in this detailed implementation manner:

[0057] Example:

[0058] A control method for an intelligent temperature control clothing, the specific steps include:

[0059] S101. First, perform hardware self-check, parameter loading, network connection, and status initialization:

[0060] When the hardware is detected, after the energy management module 1 powers the entire system, under the coordination of the main control chip by the intelligent decision-making module 4, the hardware self-check of each module begins. Various sensors in the environment perception module 3 check whether they can collect data normally; the heating, cooling, and ventilation units in the execution control module 5 check whether the circuits are unobstructed; the data storage module 2 checks whether the storage device is readable and writable; the communication protocol module 9 checks whether the communication interface is normal; the safety protection module 15 checks whether the temperature overrun protection and short-circuit detection functions are ready; when parameters are loaded, the intelligent decision-making module 4 reads the temperature control modes preset by the user, such as "sports mode" and "sleep mode", from the data storage module 2, and at the same time loads the previously stored historical data to provide reference for subsequent decisions; the communication protocol module 9 attempts to connect to the local Bluetooth device or the cloud server under the instruction of the intelligent decision-making module 4. If the connection is successful, a data transmission channel will be established for subsequent data interaction and remote control; the intelligent decision-making module 4 sends an instruction to the execution control module 5 to reset the heating, cooling, and ventilation units to the standby state. At the same time, the indicator light in the human-computer interaction module 7 displays the system ready state to inform the user that the system has completed initialization;

[0061] S102. Sense the external environment and collect data:

[0062] Various sensors in the environment perception module 3 start to collect data in real time. The body surface temperature sensor is attached to the human skin to accurately obtain the body surface temperature; the environmental temperature, humidity, air pressure, and wind speed sensors comprehensively monitor the external environment; the motion state sensor captures the motion state and posture changes of the human body in real time through a three-axis accelerometer and a gyroscope; the physiological parameter module collects the physiological data of the human body, and the collected data will be transmitted to the intelligent decision-making module 4 through the internal communication unit specified by the communication protocol module 9;

[0063] S103. Preprocess the collected data and extract features:

[0064] After the intelligent decision-making module 4 receives the raw data transmitted from the environment perception module 3, it first performs data preprocessing. The data preprocessing unit applies median filtering to the temperature data to remove possible noise and outliers; for the motion data, complementary filtering is used to fuse the attitude angles to improve the accuracy of the data. Then, the exercise intensity index, the temperature difference between inside and outside the clothing, and the humidity gradient characteristic parameters are calculated, and the multi-dimensional data is normalized for subsequent decision-making analysis. Part of the processed data is used for the current decision, and the other part will be stored in the data storage module 2;

[0065] S104. Perform intelligent decision-making and generate strategies:

[0066] The intelligent decision-making module 4 performs pattern matching and reasoning based on the preprocessed data in combination with the fuzzy control rules in the temperature control rule library. For example, it judges the user's exercise state and physiological state according to the exercise intensity index and heart rate data, determines whether to activate the heating, cooling, or ventilation function in combination with the temperature difference inside and outside the clothing and the humidity gradient. At the same time, it uses a BP neural network to predict the body temperature change in the next period of time, and adopts a particle swarm algorithm to dynamically adjust the heating / cooling power ratio to achieve efficient utilization of energy and precise control of temperature;

[0067] S105. Implement the adjustment and control of temperature according to the generated temperature control strategy:

[0068] The intelligent decision-making module 4 sends a control signal to the execution control module 5 according to the generated temperature control strategy. The heating sub-module adjusts the heating power according to the duty cycle of the PWM signal; the cooling sub-module realizes cooling by adjusting the current of the thermoelectric cooler and the rotation speed of the fan; the ventilation control unit controls the opening and closing degree of the air valve according to the instruction to realize ventilation adjustment. The feedback adjustment loop in the execution control module 5 will monitor the working state of the actuator in real time and feedback this information to the intelligent decision-making module 4 for real-time adjustment;

[0069] S106. The user operates and controls the system through a human-computer interaction method:

[0070] The local interaction unit in the human-computer interaction module 7 displays the current temperature, power, and working mode information in real time. The user can operate through the touch screen or buttons, such as setting the temperature threshold and switching the working mode. The operation instruction will be transmitted to the intelligent decision-making module 4 through the communication protocol module 9. The intelligent decision-making module 4 adjusts the temperature control strategy according to the user instruction; the communication protocol module 9 transmits the data in the intelligent decision-making module 4 to the mobile APP through Bluetooth or the cloud server. The user can remotely monitor and control the working state of the clothing on the mobile APP, such as setting geographical fence temperature control and viewing the historical temperature change curve. At the same time, the mobile APP can also send the user's feedback information back to the intelligent decision-making module 4 for strategy optimization;

[0071] S107. Regulate and control the input energy:

[0072] The energy management module 1 monitors the battery power and the power consumption of each module in real time. When it detects that the user is in a stationary state and for a certain period of time, it reduces the sampling frequency of the sensors in the environmental perception module 3 to reduce the power consumption of data acquisition; at the same time, it makes the communication protocol module 9 enter the low-power mode to reduce the energy consumption of wireless communication. When the power is lower than a certain threshold, it automatically turns off the unnecessary modules and adjusts the working power of the execution control module 5 to give priority to ensuring the operation of the core functions;

[0073] S108. Store and record the system working data:

[0074] The data storage module 2 classifies and stores the collected data, decision-making information, and execution results according to preset rules. It stores the complete status data once an hour, triggers immediate storage for abnormal events, and has data caches for a period of time before and after. Meanwhile, the communication protocol module 9 regularly uploads the locally stored data to the cloud server for big data analysis and algorithm optimization. The server generates an update package based on the analysis results and downloads it to the clothing through the communication protocol module 9. The intelligent decision-making module 4 updates the temperature control rule library and algorithm model according to the update package to achieve self-optimization and upgrade of the system.

[0075] Among them, the energy management module 1 is connected to the data storage module 2, the data storage module 2 is connected to the environment perception module 3, the energy management module 1 is connected to the environment perception module 3, the environment perception module 3 is connected to the intelligent decision-making module 4, the intelligent decision-making module 4 is connected to the data storage module 2, the intelligent decision-making module 4 is connected to the execution control module 5, the execution control module 5 is connected to the feedback loop module 6, the energy management module 1 is connected to the human-computer interaction module 7, the human-computer interaction module 7 is connected to the safety protection module 15, the safety protection module 15 is connected to the feedback control module 8, the feedback control module 8 is connected to the energy management module 1, the intelligent decision-making module 4 is connected to the safety protection module 15, the intelligent decision-making module 4 is connected to the communication protocol module 9, and the communication protocol module 9 is connected to the external device module 10.

[0076] Among them, the energy management module 1 includes a power supply unit module 11, an energy monitoring module 12, a power consumption adjustment module 13, and an emergency control module 14. The power supply unit module 11 is connected to the energy monitoring module 12, the energy monitoring module 12 is connected to the power consumption adjustment module 13, and the power consumption adjustment module 13 is connected to the emergency control module 14;

[0077] The power supply unit module 11 uses a rechargeable lithium battery and is equipped with a wireless charging function. The energy monitoring module 12 monitors the battery voltage in real time through an ADC. The power consumption adjustment module 13 dynamically adjusts the sensor sampling frequency. The emergency control module 14 is used to automatically switch to the energy-saving mode when the battery power is less than 10%.

[0078] Among them, the data storage module 2 includes a local storage module 21, a cloud synchronization module 22, a data classification module 23, and a data clearing module 24. The local storage module 21 is connected to the cloud synchronization module 22, the cloud synchronization module 22 is connected to the data classification module 23, and the data classification module 23 is connected to the data clearing module 24;

[0079] The local storage module 21 uses EEPROM to store user personalized settings. The cloud synchronization module 22 uploads temperature data to the server through the NB-IoT / LTE-M module. The data classification module 23 classifies and stores environmental parameters, control instructions, and energy consumption data according to timestamps. The data clearing module 24 sets an automatic overwrite policy to cyclically overwrite old data after 30 days of storage being full.

[0080] Among them, the environmental perception module 3 includes a sensor module 31, a motion monitoring module 32, and a physiological monitoring module 33. The sensor module 31 is connected to the motion monitoring module 32, and the motion monitoring module 32 is connected to the physiological monitoring module 33;

[0081] The sensor module 31 includes a temperature sensor group and a humidity sensor. The motion monitoring module 32 integrates a three-axis accelerometer and a gyroscope to identify the motion intensity. The physiological monitoring module 33 can be optionally equipped with a heart rate sensor and a galvanic skin sensor for collecting physiological data.

[0082] Among them, the intelligent decision-making module 4 includes a preprocessing module 41, a temperature control rule module 42, an optimization algorithm module 43, and a decision output module 44. The preprocessing module 41 is connected to the temperature control rule module 42, the temperature control rule module 42 is connected to the optimization algorithm module 43, and the optimization algorithm module 43 is connected to the decision output module 44;

[0083] The preprocessing module 41 is used to filter and denoise sensor data. The temperature control rule module 42 has built-in fuzzy control rules. When the body surface temperature > 37.5 °C during exercise, cooling is started. The optimization algorithm module 43 supports self-learning of the BP neural network. The decision output module 44 generates a PWM control signal or a level signal for controlling the output of the decision.

[0084] Among them, the execution control module 5 includes a heating module 51, a cooling module 52, a ventilation control module 53, and a feedback adjustment module 54. The heating module 51 is connected to the cooling module 52, the cooling module 52 is connected to the ventilation control module 53, and the ventilation control module 53 is connected to the feedback adjustment module 54;

[0085] The heating module 51 includes a flexible electrothermal film and its drive circuit for heating the clothing. The cooling module 52 uses a thermoelectric cooler with a miniature cooling fan. The ventilation control module 53 uses an air valve to control the ventilation effect. The feedback adjustment module 54 includes an execution status sensor to monitor the power consumption of the heating film through a current sensor.

[0086] Among them, the human-computer interaction module 7 includes a local interaction module 71, a remote control module 72, a status display module 73, and an input verification module 74. The local interaction module 71 is connected to the remote control module 72, the remote control module 72 is connected to the status display module 73, and the status display module 73 is connected to the input verification module 74;

[0087] The local interaction module 71 includes a touch screen and physical buttons for manual operation control of the system. The remote control module 72 can be connected to an external device for control by using a Bluetooth connection. The status display module 73 includes an LED indicator group to distinguish the heating / cooling / standby states. The input verification module 74 is used to set temperature threshold protection.

[0088] Among them, the communication protocol module 9 includes an internal communication module 91, an external communication module 92, a protocol conversion module 93, and a data encryption module 94. The internal communication module 91 is connected to the external communication module 92, the external communication module 92 is connected to the protocol conversion module 93, and the protocol conversion module 93 is connected to the data encryption module 94;

[0089] The internal communication module 91 transmits data based on the sensor data transmission protocol of the I2C / SPI bus. The external communication module 92 supports Bluetooth Mesh networking and can achieve collaborative temperature control of multiple garments or multiple positions of a garment. The protocol conversion module 93 is compatible with the conversion between Modbus RTU and JSON data formats. The data encryption module 94 encrypts remote transmission data by using an encryption algorithm.

[0090] Among them, the safety protection module 15 includes a temperature protection module 151, a short-circuit detection module 152, a probe protection module 153, and a fault diagnosis module 154. The temperature protection module 151 is connected to the short-circuit detection module 152, the short-circuit detection module 152 is connected to the probe protection module 153, and the probe protection module 153 is connected to the fault diagnosis module 154;

[0091] The temperature protection module 151 is used to automatically cut off the power supply of the actuator when the detected temperature > 42°C or < 10°C. The short-circuit detection module 152 sets a fuse and an over-current detection chip in the heating / cooling circuit to detect the short-circuit condition of the system operation. The probe protection module 153 is used to protect the probes of each sensor actuator by using medical-grade silicone material. The fault diagnosis module 154 is used to indicate the abnormal state through the LED blinking frequency code for timely detection of abnormalities.

[0092] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A control method for an intelligent temperature-controlled garment, characterized in that: The specific steps include: S101. First, perform hardware self-check, parameter loading, network connection, and status initialization: During hardware detection, after the energy management module (1) powers the entire system, under the coordination of the main control chip in the intelligent decision-making module (4), hardware self-check of each module starts. Various sensors in the environment perception module (3) check whether they can collect data normally; the heating, cooling, and ventilation units in the execution control module (5) check whether the circuits are unobstructed; the data storage module (2) checks whether the storage device is readable and writable; the communication protocol module (9) checks whether the communication interface is normal; the safety protection module (15) checks whether the temperature over-limit protection and short-circuit detection functions are ready. During parameter loading, the intelligent decision-making module (4) reads the temperature control modes preset by the user, such as "exercise mode" and "sleep mode", from the data storage module (2), and at the same time loads the previously stored historical data to provide reference for subsequent decisions. Under the instruction of the intelligent decision-making module (4), the communication protocol module (9) attempts to connect to local Bluetooth devices or cloud servers. If the connection is successful, a data transmission channel will be established for subsequent data interaction and remote control. The intelligent decision-making module (4) sends instructions to the execution control module (5) to reset the heating, cooling, and ventilation units to the standby state. At the same time, the indicator light in the human-computer interaction module (7) shows the system ready state to inform the user that the system has completed initialization; S102. Perceive the external environment and collect data: Various sensors in the environment perception module (3) start to collect data in real time. The body surface temperature sensor is attached to the human skin to accurately obtain the body surface temperature; the environmental temperature, humidity, air pressure, and wind speed sensors comprehensively monitor the external environment; the motion state sensor captures the motion state and posture changes of the human body in real time through a three-axis accelerometer and gyroscope; the physiological parameter module collects the physiological data of the human body, and the collected data will be transmitted to the intelligent decision-making module (4) through the internal communication unit specified by the communication protocol module (9); S103. Perform preprocessing and feature extraction on the collected data: After receiving the original data transmitted by the environment perception module (3), the intelligent decision-making module (4) first performs data preprocessing. The data preprocessing unit applies median filtering to the temperature data to remove possible noise and outliers; for the motion data, complementary filtering is used to fuse the attitude angles to improve the accuracy of the data. Then, the exercise intensity index, temperature difference between inside and outside the clothing, and humidity gradient characteristic parameters are calculated, and the multi-dimensional data is normalized for subsequent decision-making analysis. Part of the processed data is used for the current decision, and the other part will be stored in the data storage module (2); S104. Perform intelligent decision-making and strategy generation: The intelligent decision-making module (4) performs pattern matching and reasoning based on the preprocessed data in combination with the fuzzy control rules in the temperature control rule base. For example, it determines the user's exercise state and physiological state based on the exercise intensity index and heart rate data, and determines whether to activate the heating, cooling, or ventilation function in combination with the temperature difference inside and outside the clothing and the humidity gradient. At the same time, it uses a BP neural network to predict the body temperature change in the next period of time, and adopts a particle swarm algorithm to dynamically adjust the heating / cooling power ratio to achieve efficient energy utilization and precise temperature control; S105. Implement the adjustment and control of temperature according to the generated temperature control strategy: The intelligent decision-making module (4) sends a control signal to the execution control module (5) according to the generated temperature control strategy. The heating sub-module adjusts the heating power according to the duty cycle of the PWM signal; the cooling sub-module realizes cooling by adjusting the current of the thermoelectric cooler and the rotation speed of the fan; the ventilation control unit controls the opening and closing degree of the air valve according to the instruction to achieve ventilation adjustment. The feedback adjustment loop in the execution control module (5) will monitor the working state of the actuator in real time and feed this information back to the intelligent decision-making module (4) for real-time adjustment; S106. The user operates and controls the system through a human-computer interaction method: The local interaction unit in the human-computer interaction module (7) displays the current temperature, power, and working mode information in real time. The user can operate through the touch screen or buttons, such as setting the temperature threshold and switching the working mode. The operation instructions will be transmitted to the intelligent decision-making module (4) through the communication protocol module (9). The intelligent decision-making module (4) adjusts the temperature control strategy according to the user's instructions; the communication protocol module (9) transmits the data in the intelligent decision-making module (4) to the mobile APP through Bluetooth or the cloud server. The user can remotely monitor and control the working state of the clothing on the mobile APP, such as setting geographical fence temperature control and viewing the historical temperature change curve. At the same time, the mobile APP can also send the user's feedback information back to the intelligent decision-making module (4) for strategy optimization; S107. Regulate and control the input energy: The energy management module (1) monitors the battery power and the power consumption of each module in real time. When it detects that the user is in a stationary state for a period of time, it reduces the sampling frequency of the sensors in the environmental perception module (3) to reduce the power consumption of data acquisition; at the same time, it makes the communication protocol module (9) enter the low-power mode to reduce the energy consumption of wireless communication. When the power is lower than a certain threshold, it automatically shuts down unnecessary modules and adjusts the working power of the execution control module (5) to give priority to ensuring the operation of core functions; S108. Store and record the system working data: The data storage module (2) classifies and stores the collected data, decision-making information, and execution results according to preset rules, stores the complete status data once an hour, stores immediately when an abnormal event is triggered, and has data caches for a period of time before and after. Meanwhile, the communication protocol module (9) regularly uploads the locally stored data to the cloud server for big data analysis and algorithm optimization. The server generates an update package based on the analysis results and downloads it to the clothing through the communication protocol module (9). The intelligent decision-making module (4) updates the temperature control rule library and algorithm model according to the update package to achieve the self-optimization and upgrade of the system.

2. The control method of an intelligent temperature control clothing according to claim 1, characterized in that: The energy management module (1) is connected to the data storage module (2), the data storage module (2) is connected to the environment perception module (3), the energy management module (1) is connected to the environment perception module (3), the environment perception module (3) is connected to the intelligent decision-making module (4), the intelligent decision-making module (4) is connected to the data storage module (2), the intelligent decision-making module (4) is connected to the execution control module (5), the execution control module (5) is connected to the feedback loop module (6), the energy management module (1) is connected to the human-computer interaction module (7), the human-computer interaction module (7) is connected to the safety protection module (15), the safety protection module (15) is connected to the feedback control module (8), the feedback control module (8) is connected to the energy management module (1), the intelligent decision-making module (4) is connected to the safety protection module (15), the intelligent decision-making module (4) is connected to the communication protocol module (9), and the communication protocol module (9) is connected to the external device module (10).

3. The control method of an intelligent temperature control clothing according to claim 2, characterized in that: The energy management module (1) includes a power supply unit module (11), an energy monitoring module (12), a power consumption adjustment module (13), and an emergency control module (14). The power supply unit module (11) is connected to the energy monitoring module (12), the energy monitoring module (12) is connected to the power consumption adjustment module (13), and the power consumption adjustment module (13) is connected to the emergency control module (14). The power supply unit module (11) uses a rechargeable lithium battery and is equipped with a wireless charging function. The energy monitoring module (12) monitors the battery voltage in real time through an ADC. The power consumption adjustment module (13) dynamically adjusts the sensor sampling frequency. The emergency control module (14) is used to automatically switch to the energy-saving mode when the battery power is less than 10%.

4. The control method of an intelligent temperature control garment according to claim 2, characterized in that: The data storage module (2) includes a local storage module (21), a cloud synchronization module (22), a data classification module (23), and a data clearing module (24). The local storage module (21) is connected to the cloud synchronization module (22), the cloud synchronization module (22) is connected to the data classification module (23), and the data classification module (23) is connected to the data clearing module (24). The local storage module (21) uses EEPROM to store user personalized settings. The cloud synchronization module (22) uploads temperature data to the server through the NB-IoT / LTE-M module. The data classification module (23) classifies and stores environmental parameters, control instructions, and energy consumption data according to timestamps. The data clearing module (24) sets an automatic overwrite policy to cyclically overwrite old data after 30 days of storage.

5. The control method of an intelligent temperature control clothing according to claim 2, characterized in that: The environmental perception module (3) includes a sensor module (31), a motion monitoring module (32), and a physiological monitoring module (33). The sensor module (31) is connected to the motion monitoring module (32), and the motion monitoring module (32) is connected to the physiological monitoring module (33). The sensor module (31) includes a temperature sensor group and a humidity sensor. The motion monitoring module (32) integrates a three-axis accelerometer and a gyroscope to identify the motion intensity. The physiological monitoring module (33) can be optionally equipped with a heart rate sensor and a galvanic skin response sensor for collecting physiological data.

6. The control method of an intelligent temperature control garment according to claim 2, characterized in that: The intelligent decision-making module (4) includes a preprocessing module (41), a temperature control rule module (42), an optimization algorithm module (43), and a decision output module (44). The preprocessing module (41) is connected to the temperature control rule module (42), the temperature control rule module (42) is connected to the optimization algorithm module (43), and the optimization algorithm module (43) is connected to the decision output module (44). The preprocessing module (41) is used to filter and denoise sensor data. The temperature control rule module (42) has built-in fuzzy control rules. When the body surface temperature > 37.5°C during exercise, cooling is started. The optimization algorithm module (43) supports self-learning of BP neural networks. The decision output module (44) generates a PWM control signal or a level signal for controlling the output of the decision.

7. The control method of an intelligent temperature control garment according to claim 2, wherein: The execution control module (5) includes a heating module (51), a cooling module (52), a ventilation control module (53), and a feedback adjustment module (54). The heating module (51) is connected to the cooling module (52), the cooling module (52) is connected to the ventilation control module (53), and the ventilation control module (53) is connected to the feedback adjustment module (54). The heating module (51) includes a flexible electrothermal film and its drive circuit for heating the clothing. The cooling module (52) uses a thermoelectric cooler with a miniature cooling fan. The ventilation control module (53) uses an air valve to control the ventilation effect. The feedback adjustment module (54) includes an execution status sensor to monitor the power consumption of the heating film through a current sensor.

8. The control method of an intelligent temperature-controlled clothing according to claim 2, characterized in that: The human-computer interaction module (7) includes a local interaction module (71), a remote control module (72), a status display module (73), and an input verification module (74). The local interaction module (71) is connected to the remote control module (72), the remote control module (72) is connected to the status display module (73), and the status display module (73) is connected to the input verification module (74). The local interaction module (71) includes a touch screen and physical buttons for manual operation control of the system. The remote control module (72) can be connected to an external device for control by means of Bluetooth connection. The status display module (73) includes an LED indicator group for distinguishing the heating / cooling / standby status. The input verification module (74) is used for setting temperature threshold protection.

9. The control method of an intelligent temperature control garment according to claim 2, characterized in that: The communication protocol module (9) includes an internal communication module (91), an external communication module (92), a protocol conversion module (93), and a data encryption module (94). The internal communication module (91) is connected to the external communication module (92). The external communication module (92) is connected to the protocol conversion module (93). The protocol conversion module (93) is connected to the data encryption module (94). The internal communication module (91) performs data transmission based on the sensor data transmission protocol of the I2C / SPI bus. The external communication module (92) supports Bluetooth Mesh networking and can achieve collaborative temperature control of multiple garments or multiple positions of a garment. The protocol conversion module (93) is compatible with the conversion between Modbus RTU and JSON data formats. The data encryption module (94) encrypts remotely transmitted data using an encryption algorithm.

10. The control method of an intelligent temperature-controlled clothing according to claim 2, characterized in that: The safety protection module (15) includes a temperature protection module (151), a short-circuit detection module (152), a probe protection module (153), and a fault diagnosis module (154). The temperature protection module (151) is connected to the short-circuit detection module (152). The short-circuit detection module (152) is connected to the probe protection module (153). The probe protection module (153) is connected to the fault diagnosis module (154). The temperature protection module (151) is used to automatically cut off the power supply of the actuator when the detected temperature > 42°C or < 10°C. The short-circuit detection module (152) sets a fuse and an overcurrent detection chip in the heating / cooling circuit for detecting short-circuit conditions during system operation. The probe protection module (153) is used to protect the probes of each sensor actuator with medical-grade silicone material. The fault diagnosis module (154) is used to indicate abnormal states through the LED blinking frequency code for timely detection of abnormalities.

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