Ventilation system and method based on user state evaluation and intelligent temperature sensor

Abstract

The invention discloses a ventilation system and method based on user state evaluation and an intelligent temperature sensor, the intelligent temperature sensor is used for monitoring the indoor environment temperature in real time, and a user state evaluation device is used for monitoring the body state of a user in real time and evaluating the comfort level of the user. The intelligent temperature sensor and the user state evaluation device are connected with the input end of the central processor, the output end of the central processor is connected with the ventilation quantity adjusting device, and the central processor receives and analyzes user comfort information evaluated by the user state evaluation device and indoor environment temperature data monitored by the intelligent temperature sensor. And the required ventilation quantity is determined by combining preset comfort level parameters, and a ventilation adjusting instruction is generated, so that the indoor ventilation quantity is adjusted in real time. By monitoring the user state and the environment temperature in real time, the ventilation quantity can be automatically adjusted according to the actual demand of the user and the environment change, and the system has the advantages of being intelligent, energy-saving, environment-friendly, efficient and convenient, and is suitable for application and popularization.

Description

Technical Field

[0001] The present invention relates to the technical field of ventilation systems, and in particular to a ventilation system and method based on user status assessment and intelligent temperature sensors. Background Art

[0002] Existing ventilation systems typically operate according to preset temperature or humidity parameters and lack the ability to dynamically adjust. This results in poor ventilation and significant energy waste. Furthermore, to meet energy-saving requirements, modern buildings are becoming increasingly airtight, resulting in a reduction in natural ventilation and an increased reliance on mechanical ventilation systems. Furthermore, the frequent functional transitions within office buildings make it difficult for existing ventilation systems to quickly adapt to changes in occupancy density and heat loads. Chemical pollutants released by interior decoration materials, electronic equipment, and cleaning agents also place higher demands on the ventilation system's purification capabilities.

[0003] Traditional ventilation systems primarily rely on fixed environmental parameters (such as temperature, humidity, and CO2 concentration) for regulation. Their control logic is often based on preset thresholds or schedules. While these systems can maintain indoor air quality to a certain extent, they still have many shortcomings. First, traditional systems cannot dynamically adjust to users' real-time physiological states and behavioral needs, resulting in a mismatch between ventilation performance and actual user needs. For example, when users' activity levels increase or their body temperature rises, the system cannot respond promptly to increase ventilation volume. Furthermore, traditional systems often rely on a single environmental sensor (such as a temperature sensor), which cannot fully reflect the complexity of the indoor environment. For example, uneven indoor temperature distribution or the presence of localized heat sources can lead to poor ventilation performance. Furthermore, due to the lack of intelligent control strategies, traditional systems often operate at a fixed power level, resulting in energy waste. For example, in unoccupied areas or during low-demand periods, the system still runs at high power, failing to achieve demand-based adjustment. Furthermore, traditional systems often use simple rule-based control logic, which cannot adapt to complex environmental changes and user needs. With the rapid development of smart buildings and Internet of Things technologies, users have put forward higher requirements for the comfort, health and energy efficiency of the indoor environment. As an emerging solution, the smart ventilation system aims to overcome the shortcomings of traditional systems by introducing advanced environmental perception technology and intelligent control algorithms.

[0004] Current intelligent ventilation control methods do not fully meet people's usage needs. For example, in a ventilation control system for smart buildings, patent number CN205002301U, a window control device, an indoor adjustment device, a sensor device, a mobile client, and a central processor are used. When the indoor air quality is poor or people feel stuffy, the window control device can be used to open the window; when people are outside or open the window indoors and the weather is bad and rainy, the window control device can be used to close the window. The window control device is connected to the central processor and can accept the central processor's control to control the window. The sensor device is located indoors in the smart building and is connected to the central processor. The sensor device uses one or more of an air quality sensor, a temperature sensor, a humidity sensor, and a carbon dioxide concentration sensor. These sensor devices can transmit indoor environmental data to the central processor for analysis. Its indoor conditioning device and mobile client are separately connected to the central processor. The indoor conditioning device uses one or more of an air heater, air cooler, negative oxygen ion generator, air purifier, air humidifier, and exhaust device. The exhaust device is equipped with a filter and a switch valve at the air outlet. The indoor conditioning device can adjust according to the information fed back by the central processor to make the indoor environment suitable for living. Although this ventilation system can maintain natural wind indoors, it is not the currently commonly used circulating air. Moreover, the system fails to monitor the user's physical condition in real time, assess the user's comfort, and accurately respond to changes in ventilation volume based on the user's reaction.

[0005] For example, in patent number CN118746157A, an intelligent control method and related equipment for an energy-saving ventilation system obtains indoor environmental parameters through preset sensors and sorts the indoor environmental parameters to obtain sorting parameters; constructs a time series parameter curve based on the sorting parameters, and generates a time series parameter matrix based on the coordinates of points on the time series parameter curve; obtains weather forecast data and indoor occupant density change data, and uses a preset multivariate regression analysis algorithm to predict the indoor environment based on the time series parameter matrix, weather forecast data, and indoor occupant density change data to obtain a prediction result; if the indoor environmental quality level is not within a predetermined range, a corresponding intelligent ventilation control strategy is determined based on the sorting parameters and the indoor environmental quality level; and based on this intelligent ventilation control strategy, the ventilation system is speed-regulated and energy-efficient, addressing the low efficiency and slow response of traditional ventilation systems. However, the system cannot respond promptly to increased ventilation volume when user activity or body temperature rises. In unoccupied areas or during low-demand periods, the system still operates at high power, failing to achieve on-demand adjustment. The system also fails to monitor the user's physical condition in real time, assess user comfort, and accurately respond to changes in ventilation volume based on user reactions. Summary of the Invention

[0006] The purpose of the present invention is to provide a ventilation system and method based on user status assessment and intelligent temperature sensors, which can intelligently adjust the ventilation volume by real-time monitoring of user status and ambient temperature, improve ventilation effect and energy utilization, and solve the above-mentioned problems existing in the prior art.

[0007] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0008] The present invention provides a ventilation system based on user status evaluation and intelligent temperature sensor, comprising an intelligent temperature sensor, a user status evaluation device, a central processor and a ventilation volume adjustment device. The intelligent temperature sensor is arranged indoors in a building and is used to monitor the indoor ambient temperature in real time. The user status evaluation device is used to monitor the user's physical condition in real time and evaluate the user's comfort. The intelligent temperature sensor and the user status evaluation device are respectively connected to the input end of the central processor, and the output end of the central processor is connected to the ventilation volume adjustment device. The central processor receives and analyzes the user comfort information evaluated by the user status evaluation device and the indoor ambient temperature data monitored by the intelligent temperature sensor, determines the required ventilation volume in combination with preset comfort parameters, and generates a ventilation adjustment instruction to the ventilation volume adjustment device to adjust the indoor ventilation volume in real time.

[0009] Furthermore, the user status assessment device dynamically corrects the user comfort index UCI weight coefficient through joint analysis of heart rate variability and skin temperature gradient, and assesses the user's comfort in real time.

[0010] Furthermore, the intelligent temperature sensor adopts a distributed grid layout, and digital temperature sensors are installed in the indoor space according to 1.5m×1.5m grid nodes.

[0011] Furthermore, the ventilation volume adjustment device includes a variable frequency fan, an electric air valve and a fresh air unit, and the rotation speed of the variable frequency fan and the opening of the electric air valve are adjusted according to the ventilation adjustment instruction generated by the central processor.

[0012] Furthermore, the central processor is installed on the fresh air unit.

[0013] Furthermore, the central processor receives and integrates the data from the user status evaluation device and the intelligent temperature sensor, and uses multi-source data fusion technology to perform data processing and analysis.

[0014] The present invention also provides a ventilation method based on user status evaluation and intelligent temperature sensor, which uses the ventilation system based on user status evaluation and intelligent temperature sensor, including the following steps:

[0015] S1. Start the user status assessment device and intelligent temperature sensor. The central processor completes system self-test and initialization, verifies sensor network connectivity, and loads control algorithm parameters. It records the ambient temperature distribution and noise level in the no-load state to establish a background reference model, and collects baseline data to set user comfort parameters and ambient temperature thresholds.

[0016] S2. The user status assessment device collects user physiological parameters and behavioral status data in real time, updates the user comfort index (UCI) at preset intervals, and triggers an emergency response mechanism based on abnormal data. The intelligent temperature sensor uploads grid-wide temperature data at preset intervals, initiates high-frequency sampling in areas with local temperature mutations, and monitors the temperature of each indoor area in real time, transmitting the data to the central processor.

[0017] S3: The central processor pre-processes the received data, integrating user status and ambient temperature data using multi-source data fusion technology, and calculates the comprehensive environmental quality index (EQI). Combining the predicted temperature and user activity trends, it generates ventilation instructions. When the outdoor temperature is suitable, the windows are opened and the opening angle is adjusted. If natural ventilation cannot meet the demand, the fresh air unit is activated and the air volume is adjusted according to the dynamic base ventilation volume (DBV).

[0018] S4. Based on the evaluation results, fuzzy logic control or reinforcement learning algorithm is used to generate ventilation adjustment instructions;

[0019] S5. The ventilation volume adjustment device adjusts the speed of the variable frequency fan and / or the opening and closing degree of the electric air valve according to the ventilation adjustment instruction to achieve dynamic adjustment of the ventilation volume. The system monitors the adjustment effect in real time and optimizes the control strategy through the feedback mechanism.

[0020] Furthermore, the intelligent temperature sensor is a MEMS sensor, and its layout in the building covers a vertical ground height of 0.1m, a vertical sitting breathing zone height of 1.2m, and a vertical standing breathing zone height of 2.0m. The intelligent temperature sensor builds a wireless sensor network through low-power wide area network technology, supports multi-hop transmission and anti-interference mechanism to ensure real-time data.

[0021] Furthermore, in step S3, the central processor adopts the Kalman filter-Bayesian network joint algorithm to integrate the user comfort index UCI and the ambient temperature distribution T grid and external meteorological data to generate a comprehensive environmental quality index EQI.

[0022] Furthermore, if the comprehensive environmental quality index EQI < the preset comfort threshold EQI min , start the reinforcement learning algorithm, with the dual goals of minimizing energy consumption and maximizing comfort, and dynamically optimize the ventilation volume; if a local high temperature area T is detected grid >Tthreshold , triggering the gradient priority ventilation strategy, giving priority to increasing the fresh air volume in this area.

[0023] Compared with the prior art, the present invention has the following beneficial technical effects:

[0024] The ventilation system and method based on user status assessment and intelligent temperature sensor of the present invention can monitor user status and ambient temperature in real time by setting up intelligent temperature sensors, user status assessment devices, central processors and ventilation volume adjustment devices, and automatically adjust the ventilation volume according to the actual needs of users and environmental changes, so as to ensure indoor air circulation and suitable temperature, improve ventilation effect and energy utilization rate, and have the advantages of intelligence, energy saving, environmental protection, high efficiency and convenience; moreover, the ventilation control system is simple to operate, low in cost, can be controlled in real time, has a high degree of automation, and is suitable for promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0026] Figure 1 Schematic diagram of the structure of the ventilation system based on user status assessment and intelligent temperature sensor of the present invention;

[0027] Figure 2 This is a structural diagram of the ventilation system based on user status assessment and intelligent temperature sensor of the present invention when applied.

[0028] Explanation of the accompanying symbols: 1. Intelligent temperature sensor; 2. User status evaluation device; 3. Ventilation volume adjustment device; 4. Central processor; 5. Outdoor air inlet; 6. Fresh air unit; 7. Variable frequency fan; 8. Air supply outlet; 9. Air supply room. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0030] Example 1

[0031] like Figure 1As shown, the ventilation system based on user status evaluation and intelligent temperature sensor of this embodiment 1 includes an intelligent temperature sensor 1, a user status evaluation device 2, a central processor 4 and a ventilation volume adjustment device 3. The intelligent temperature sensor 1 is arranged indoors in the building for real-time monitoring of the indoor ambient temperature. The user status evaluation device 2 is used to monitor the user's physical condition in real time and evaluate the user's comfort. The intelligent temperature sensor 1 and the user status evaluation device 2 are respectively connected to the input end of the central processor 4, and the output end of the central processor 4 is connected to the ventilation volume adjustment device 3. The central processor 4 receives and analyzes the user comfort information evaluated by the user status evaluation device 2 and the indoor ambient temperature data monitored by the intelligent temperature sensor 1, determines the required ventilation volume in combination with the preset comfort parameters, and generates a ventilation adjustment instruction to the ventilation volume adjustment device 3 to adjust the indoor ventilation volume in real time.

[0032] Specifically, the user state evaluation device 2 dynamically corrects the user comfort index UCI weight coefficient through joint analysis of heart rate variability and skin temperature gradient, monitors the user's physical state in real time, and evaluates the user's comfort.

[0033] Preferably, the user status assessment device 2 integrates a multimodal sensor, including:

[0034] Wearable device: used to monitor heart rate, skin temperature, exercise intensity, skin electricity, etc., using the Fitbit InspireHR sports wristband to monitor the above data. The wristband measures 47.61×32.66×16.40mm, the main unit weighs 23.33 grams, the skin electricity measurement range is 0.01-100us, the measurement accuracy is 0.01us, the measurement site is the wrist and fingertips, the skin temperature measurement range is -25℃-+55℃, and the measurement accuracy is 0.01℃.

[0035] Infrared sensor: Infrared thermal imager (body surface temperature distribution), which is an online infrared thermal imaging sensor with an infrared resolution of 640×512, a temperature measurement range of 20℃-50℃, and a temperature measurement accuracy of ±0.3℃.

[0036] The data processing method is to construct a user comfort index (UCI, UserComfortIndex) based on a machine learning model. The calculation formula is:

[0037] UCI=α·T skin +β·HR var +γ·Act level

[0038] Among them, T skin is skin temperature, HR var Heart rate variability, Act levelis the user activity intensity, α, β, γ are weight coefficients, and are optimized through training of user historical data.

[0039] In addition, the intelligent temperature sensor 1 adopts a distributed grid layout, and high-precision digital temperature sensors are installed in the indoor space according to 1.5m×1.5m grid nodes. Preferably, the digital temperature sensor is a MEMS sensor, and the layout in the building covers a vertical height of 0.1m (ground), 1.2m (sitting breathing zone) and 2.0m (standing breathing zone). The intelligent temperature sensor 1 adopts a wireless sensor network based on low-power wide area network (LPWAN) technology, supports multi-hop transmission and anti-interference mechanism to ensure real-time data (delay ≤ 200ms).

[0040] The temperature prediction model uses LSTM (Long Short-Term Memory Network) to train historical temperature data and predict the temperature change trend in the next 5 minutes. The formula is:

[0041] T t+5 =f LSTM (T t-n:t ,W env ,Act level )

[0042] Among them, W env are environmental parameters (humidity, wind speed), Act level The intensity of user activity.

[0043] At this time, the central processor 4 is installed on the fresh air unit 6. The central processor 4 receives and integrates the data from the user status evaluation device 2 and the intelligent temperature sensor 1, uses multi-source data fusion technology (Bayesian network) to process and analyze the data, and generates ventilation adjustment instructions based on fuzzy logic control or reinforcement learning algorithm to achieve dynamic optimization of ventilation volume.

[0044] The central processor 4 adopts the Kalman filter-Bayesian network joint algorithm to integrate the user status data (UCI), the ambient temperature distribution (T grid ) and external meteorological data to generate a comprehensive environmental quality index (EQI, Environmental Quality Index).

[0045] If the comprehensive environmental quality index EQI <EQI min (preset comfort threshold), start the reinforcement learning (Q-Learning) algorithm, with the dual goals of minimizing energy consumption and maximizing comfort, dynamically optimize the ventilation volume; if a local high temperature area (T grid >T threshold ), triggering the gradient priority ventilation strategy, giving priority to increasing the fresh air volume in this area.

[0046] In this embodiment 1, the ventilation volume adjustment device 3 includes a variable frequency fan 7, an electric air valve and a fresh air unit 6, supports 0-100% stepless speed regulation, and adjusts the speed of the variable frequency fan 7 (PID control) and the opening of the electric air valve in real time by receiving instructions from the central processor 4. Specifically, the central processor 4 receives data from the user status evaluation device 2 and the intelligent temperature sensor 1, analyzes and processes the data, adjusts the speed of the variable frequency fan 7 and the opening of the electric air valve in real time, and generates ventilation adjustment instructions.

[0047] At this time, the ventilation volume regulating device 3 automatically adjusts the ventilation volume according to the instructions of the central processor 4 to ensure that the indoor air circulation and temperature are appropriate, and controls the indoor temperature within a comfortable temperature range by controlling indoor ventilation or starting air conditioning. The indoor ventilation methods include natural ventilation and mechanical ventilation using the fresh air unit 6.

[0048] Dynamic Baseline Ventilation (DBV) is introduced to dynamically adjust the basic ventilation rate according to the indoor occupant density. The formula is:

[0049] DBV=V base ·(1+k·N max / N user )

[0050] Among them, V base Is the basic ventilation rate, N user is the number of real-time users, N max is the maximum capacity of the space, and k is the adjustment coefficient.

[0051] In specific applications, the ventilation system based on user status evaluation and intelligent temperature sensor of this embodiment 1 is as follows: Figure 2 As shown, an intelligent temperature sensor 1 is provided in the air supply room 9 for real-time monitoring of the ambient temperature of the air supply room 9, a user status evaluation device 2 is used to monitor the user status in real time, and a central processor 4 is installed on the fresh air unit 6. The central processor 4 determines the required ventilation volume according to the indoor ambient temperature and the user status, and then sends the air into the air supply room 9 through the air supply port 8 through the outdoor air inlet 5 and the variable frequency fan 7.

[0052] The specific implementation steps of the ventilation system based on user status assessment and intelligent temperature sensor in this embodiment 1 include:

[0053] S1. First, start the user status assessment device 2 and the intelligent temperature sensor 1. The central processor 4 completes the system self-check and initialization. The central processor 4 also completes the sensor network connectivity verification and control algorithm parameter loading (UCI weight, EQ I threshold). The central processor 4 records the ambient temperature distribution and noise level in the no-load state, establishes a background reference model, and collects benchmark data to set user comfort parameters and ambient temperature thresholds.

[0054] S2, the user status assessment device 2 collects the user's physiological parameters and behavioral status data in real time, updates the UCI value every 10 seconds, and abnormal data (such as a sudden increase in heart rate) triggers an emergency response mechanism; the intelligent temperature sensor 1 uploads the full grid temperature data every 30 seconds, and starts high-frequency sampling (5 seconds / time) in the local mutation area (temperature difference ≥ 2°C). The intelligent temperature sensor 1 monitors the temperature of each area in the room in real time and transmits the data to the central processor 4.

[0055] S3, the central processor 4 pre-processes the received data, uses multi-source data fusion technology to integrate user status and ambient temperature data, and calculates EQ I, combined with the predicted temperature (T t+5 ) and user activity trends to generate ventilation instructions; when the outdoor temperature is suitable (T out ∈[T min ,T max ]), open the window and adjust the opening and closing angle. If natural ventilation cannot meet the needs, start the fresh air unit 6 and adjust the air volume according to DBV.

[0056] S4. Based on the evaluation results, fuzzy logic control or reinforcement learning algorithm is used to generate ventilation adjustment instructions, and the ventilation volume adjustment device 3 ensures that the indoor temperature is appropriate and the ventilation volume is sufficient.

[0057] S5. The ventilation volume regulating device 3 adjusts the speed of the variable frequency fan 7 or the opening and closing degree of the air valve according to the instruction to realize dynamic regulation of the ventilation volume. The system monitors the regulation effect in real time and optimizes the control strategy through the feedback mechanism.

[0058] The ventilation system based on user status assessment and intelligent temperature sensor of the first embodiment monitors the user status and ambient temperature in real time, intelligently adjusts the ventilation volume, and dynamically maps the user comfort through the UCI index, thereby solving the "one-size-fits-all" drawback of the traditional system, and improving the comfort level by ≥30%. At the same time, the ventilation volume is adjusted according to actual needs to reduce ineffective ventilation. Compared with the fixed air volume system, the measured energy saving rate can reach 20%-35%, avoiding energy waste and complying with the concept of green environmental protection. In addition, the ventilation control system is simple to operate and can perform real-time control. At the same time, it has a high degree of automation, and the user does not need to make manual adjustments. The system can automatically complete the ventilation adjustment. The cost is low and suitable for popularization and application.

[0059] Example 2

[0060] The ventilation method based on user status assessment and intelligent temperature sensor of this embodiment 2 adopts the ventilation system based on user status assessment and intelligent temperature sensor, and specifically includes the following steps:

[0061] S1. Start the user status assessment device 2 and the intelligent temperature sensor 1. The central processor 4 completes system self-test and initialization, sensor network connectivity verification, and control algorithm parameter loading. It records the ambient temperature distribution and noise level in the no-load state to establish a background reference model, and collects baseline data to set user comfort parameters and ambient temperature thresholds.

[0062] S2, the user status assessment device 2 collects user physiological parameters and behavioral status data in real time, updates the user comfort index UCI at preset intervals, and abnormal data triggers an emergency response mechanism; the intelligent temperature sensor 1 uploads the temperature data of the entire grid at preset intervals, starts high-frequency sampling in local temperature mutation areas, and monitors the temperature of each area in the room in real time and transmits the data to the central processor 4;

[0063] S3 and the central processor 4 pre-process the received data, integrating user status and ambient temperature data using multi-source data fusion technology, and calculating the comprehensive environmental quality index (EQI). Combining the predicted temperature and user activity trends, they generate ventilation instructions. When the outdoor temperature is suitable, the windows are opened and the opening angle is adjusted. If natural ventilation cannot meet the demand, the fresh air unit 6 is started and the air volume is adjusted according to the dynamic base ventilation volume (DBV).

[0064] S4. Based on the evaluation results, fuzzy logic control or reinforcement learning algorithm is used to generate ventilation adjustment instructions;

[0065] S5. The ventilation volume regulating device 3 adjusts the speed of the variable frequency fan 7 and / or the opening and closing degree of the electric air valve according to the ventilation regulation instruction to realize dynamic regulation of the ventilation volume. The system monitors the regulation effect in real time and optimizes the control strategy through the feedback mechanism.

[0066] Preferably, the intelligent temperature sensor 1 is a MEMS sensor, and its arrangement indoors in the building covers a vertical ground height of 0.1m, a vertical sitting breathing zone height of 1.2m, and a vertical standing breathing zone height of 2.0m. The intelligent temperature sensor 1 builds a wireless sensor network through low-power wide area network technology, supports multi-hop transmission and anti-interference mechanism, to ensure real-time data.

[0067] Among them, in step S3, the central processor 4 adopts the Kalman filter-Bayesian network joint algorithm to integrate the user comfort index UCI, the ambient temperature distribution T grid and external meteorological data to generate a comprehensive environmental quality index EQI.

[0068] In this embodiment 2, if the comprehensive environmental quality index EQI < the preset comfort threshold EQI min , start the reinforcement learning algorithm, with the dual goals of minimizing energy consumption and maximizing comfort, and dynamically optimize the ventilation volume; if a local high temperature area T is detected grid >T threshold , triggering the gradient priority ventilation strategy, giving priority to increasing the fresh air volume in this area.

[0069] The embodiments described above are merely descriptions of preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by persons skilled in the art should fall within the scope of protection defined by the claims of the present invention.

Claims

1. A ventilation system based on user status assessment and intelligent temperature sensor, characterized in that: It includes an intelligent temperature sensor, a user status evaluation device, a central processor and a ventilation volume adjustment device. The intelligent temperature sensor is set indoors in a building and is used to monitor the indoor ambient temperature in real time. The user status evaluation device is used to monitor the user's physical condition in real time and evaluate the user's comfort. The intelligent temperature sensor and the user status evaluation device are respectively connected to the input end of the central processor, and the output end of the central processor is connected to the ventilation volume adjustment device. The central processor receives and analyzes the user comfort information evaluated by the user status evaluation device and the indoor ambient temperature data monitored by the intelligent temperature sensor, determines the required ventilation volume in combination with the preset comfort parameters, and generates a ventilation adjustment instruction to the ventilation volume adjustment device to adjust the indoor ventilation volume in real time.

2. The ventilation system based on user status assessment and intelligent temperature sensor according to claim 1, characterized in that: The user status evaluation device dynamically corrects the user comfort index UC I weight coefficient through joint analysis of heart rate variability and skin temperature gradient, and evaluates the user's comfort in real time.

3. The ventilation system based on user status assessment and intelligent temperature sensor according to claim 1, characterized in that: The intelligent temperature sensor adopts a distributed grid layout, and digital temperature sensors are installed in the indoor space according to 1.5m×1.5m grid nodes.

4. The ventilation system based on user status assessment and intelligent temperature sensor according to claim 1, characterized in that: The ventilation volume adjustment device includes a variable frequency fan, an electric air valve and a fresh air unit. The rotation speed of the variable frequency fan and the opening of the electric air valve are adjusted according to the ventilation adjustment instruction generated by the central processor.

5. The ventilation system based on user status assessment and intelligent temperature sensor according to claim 4, characterized in that: The central processor is installed on the fresh air unit.

6. The ventilation system based on user status assessment and intelligent temperature sensor according to any one of claims 1 to 5, characterized in that: The central processor receives and integrates the data from the user status evaluation device and the intelligent temperature sensor, and uses multi-source data fusion technology to perform data processing and analysis.

7. A ventilation method based on user status assessment and intelligent temperature sensor, characterized in that: The ventilation system based on user status assessment and intelligent temperature sensor according to any one of claims 1 to 6 comprises the following steps: S1. Start the user status assessment device and intelligent temperature sensor. The central processor completes system self-test and initialization, verifies sensor network connectivity, and loads control algorithm parameters. It records the ambient temperature distribution and noise level in the no-load state to establish a background reference model, and collects baseline data to set user comfort parameters and ambient temperature thresholds. S2. The user status assessment device collects user physiological parameters and behavioral status data in real time, updates the user comfort index (UCI) at preset intervals, and triggers an emergency response mechanism based on abnormal data. The intelligent temperature sensor uploads grid-wide temperature data at preset intervals and initiates high-frequency sampling in areas with local temperature mutations. The intelligent temperature sensor monitors the temperature of each indoor area in real time and transmits the data to the central processor. S3: The central processor pre-processes the received data, integrating user status and ambient temperature data using multi-source data fusion technology, and calculates the comprehensive environmental quality index (EQI). Combining the predicted temperature and user activity trends, it generates ventilation instructions. When the outdoor temperature is suitable, the windows are opened and the opening angle is adjusted. If natural ventilation cannot meet the demand, the fresh air unit is activated and the air volume is adjusted according to the dynamic base ventilation volume (DBV). S4. Based on the evaluation results, fuzzy logic control or reinforcement learning algorithm is used to generate ventilation adjustment instructions; S5. The ventilation volume adjustment device adjusts the speed of the variable frequency fan and / or the opening and closing degree of the electric air valve according to the ventilation adjustment instruction to achieve dynamic adjustment of the ventilation volume. The system monitors the adjustment effect in real time and optimizes the control strategy through the feedback mechanism.

8. The ventilation method based on user status assessment and intelligent temperature sensor according to claim 7, characterized in that: The smart temperature sensor is a MEMS sensor, and its layout in the building covers a vertical ground height of 0.1m, a vertical sitting breathing zone height of 1.2m, and a vertical standing breathing zone height of 2.0m. The smart temperature sensor uses low-power wide area network technology to build a wireless sensor network, supports multi-hop transmission and anti-interference mechanism to ensure real-time data.

9. The ventilation method based on user status assessment and intelligent temperature sensor according to claim 7, characterized in that: In step S3, the central processor adopts the Kalman filter-Bayesian network joint algorithm to integrate the user comfort index UCI, the ambient temperature distribution T grid and external meteorological data to generate a comprehensive environmental quality index EQI.

10. The ventilation method based on user status assessment and intelligent temperature sensor according to claim 9, characterized in that: If the comprehensive environmental quality index EQI < the preset comfort threshold EQI min , start the reinforcement learning algorithm, with the dual goals of minimizing energy consumption and maximizing comfort, and dynamically optimize the ventilation volume; if a local high temperature area T is detected grid >T threshold , triggering the gradient priority ventilation strategy, giving priority to increasing the fresh air volume in this area.

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