A temperature and humidity independent control system

Abstract

The application discloses a temperature and humidity independent control system, and relates to the field of environment control.The system comprises an acquisition module, which acquires real-time physiological data and fuses the data into a comprehensive data package; a receiving module, which receives real-time physiological data of a corresponding user and local environmental parameters of a location where the user is based on the comprehensive data package, runs an internal biological thermodynamic algorithm, and generates personalized temperature setting values and personalized sensible humidity setting values; a fusion module, which inputs the personalized temperature setting values and the personalized sensible humidity setting values into a multi-objective optimization algorithm for dynamic fusion to generate globally set temperature and globally set sensible humidity by taking minimizing collective dissatisfaction as a target in a central controller; and a judgment module, which compares and judges the globally set sensible humidity and outdoor air humidity to generate a mode instruction.The application realizes fine and intelligent environment control of individual comfort demand and overall system energy efficiency in a multi-user scenario.

Description

Technical Field

[0001] This invention relates to the field of environmental control, and in particular to an independent temperature and humidity control system. Background Technology

[0002] In the field of environmental control, independent temperature and humidity control technology, by employing independent dehumidification equipment such as solution dehumidification and rotary dehumidification in conjunction with a high-temperature cold source, achieves decoupling of sensible heat load and latent heat load of the air. This effectively avoids the energy loss caused by excessive cooling and reheating in traditional condensation dehumidification methods, thus improving the overall energy efficiency of the system. Current technological development mainly focuses on optimizing the regeneration energy consumption of independent dehumidification units, improving material performance, and utilizing advanced control algorithms to achieve dynamic matching of temperature and humidity loads, driving the continuous development of this technology towards refinement and intelligence.

[0003] Existing independent temperature and humidity control systems still have limitations in creating personalized comfort environments. They typically aim to maintain uniform temperature and humidity settings within a space, lacking consideration for individual user differences. Due to physiological factors such as human thermal comfort perception, metabolic rate, and activity level, individual differences exist. Uniform settings cannot simultaneously meet the comfort needs of different users in the same space, which may lead some users to feel uncomfortable and manually adjust the settings, thereby affecting the stability of system control and energy efficiency optimization. How to integrate users' real-time physiological states into the control closed loop, enabling the system to proactively adapt to the differentiated needs of multiple users, is a key challenge in improving the intelligence level and user experience of independent temperature and humidity control technology. Summary of the Invention

[0004] In view of the aforementioned existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides an independent temperature and humidity control system that solves the problem that existing technologies cannot integrate the user's real-time physiological state into the control closed loop to dynamically meet the differentiated comfort needs of multiple users.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] This invention provides an independent temperature and humidity control system, which includes an acquisition module for acquiring real-time physiological data and fusing it into a comprehensive data package;

[0008] The receiving module, based on a comprehensive data packet, receives real-time physiological data and local environmental parameters of the corresponding user's location from the personal thermal and humidity comfort digital twin model, runs an internal biothermodynamic algorithm, and generates personalized temperature and humidity settings.

[0009] The fusion module, with the central controller aiming to minimize collective dissatisfaction, inputs the fully personalized temperature setting and the personalized perceived humidity setting into a multi-objective optimization algorithm for dynamic fusion, generating a global set temperature and a global set perceived humidity.

[0010] The judgment module compares the globally set perceived humidity with the outdoor air humidity to generate mode instructions;

[0011] The pre-processing module sends mode commands to the Janus membrane intelligent fresh air exchanger. By adjusting the physical field applied to the Janus membrane of the Janus membrane intelligent fresh air exchanger, it changes the selectivity of water vapor permeation direction and performs humidity pre-processing on the introduced fresh air to obtain pre-processed fresh air.

[0012] The update module mixes the pre-treated fresh air with the indoor return air to form mixed air. Based on the globally set temperature and globally set perceived humidity, the mixed air is adjusted through independent temperature and humidity processing channels. The adjusted air is then sent into the room to update the indoor environmental status.

[0013] As a preferred embodiment of the independent temperature and humidity control system of the present invention, the acquisition of real-time physiological data and its fusion into a comprehensive data package includes the following steps:

[0014] Collect dry-bulb temperature, relative humidity, and carbon dioxide concentration to generate a set of indoor environmental parameters;

[0015] Based on communication with user smart terminals using low-power Bluetooth beacons, the system determines user identities and three-dimensional spatial coordinates, and generates an online user list.

[0016] Heart rate variability, wrist skin temperature, and estimated metabolic equivalents are used to construct an individual physiological dataset;

[0017] The indoor environmental parameter set, online user list and personal physiological dataset are aligned and standardized with a unified timestamp and packaged into a comprehensive data package.

[0018] As a preferred embodiment of the independent temperature and humidity control system described in this invention, the personal thermal and humidity comfort digital twin model receives real-time physiological data of the corresponding user and local environmental parameters of the user's location based on a comprehensive data package, runs an internal biothermodynamic algorithm, and generates personalized temperature setpoints and personalized perceived humidity setpoints, including the following steps:

[0019] The central controller activates a pre-set personal thermal and humidity comfort digital twin model corresponding to the identity identifier in the online user list.

[0020] Based on the activated personal thermal and humidity comfort digital twin model, it receives the heart rate variability, wrist skin temperature and estimated metabolic equivalent from the corresponding user's personal physiological dataset, and receives the local air dry bulb temperature and relative humidity corresponding to the three-dimensional spatial coordinates of the corresponding user extracted from the indoor environmental parameter set.

[0021] Based on the reverse optimization training of user historical physiological data and environmental data, a multi-steady-state comfort field simulation is obtained. The received heart rate variability, wrist skin temperature, estimated metabolic equivalent, local dry-bulb temperature and relative humidity are used as field parameters. The steady-state attractor in the comfort field is found through iteration. The coordinates of the steady-state attractor are mapped to the personalized temperature setting value and the personalized perceived humidity setting value.

[0022] The central controller collects digital twin models of the individual thermal and humidity comfort of all online users, and forms a set of personalized settings by combining multiple personalized temperature settings and personalized humidity settings.

[0023] As a preferred embodiment of the independent temperature and humidity control system described in this invention, the central controller, with the objective of minimizing collective dissatisfaction, dynamically fuses the fully personalized temperature setpoint and the personalized perceived humidity setpoint into a multi-objective optimization algorithm to generate a global set temperature and a global set perceived humidity, including the following steps:

[0024] Each personalized temperature setting and personalized perceived humidity setting combination in the set of personalized settings is substituted into the biothermodynamic equations inside the corresponding personal thermal and humidity comfort digital twin model to solve for the user's predictive dissatisfaction value in each combination environment.

[0025] The central controller defines the optimization objective as minimizing the sum of the predictive dissatisfaction values ​​of all online users, using the global set temperature and the global set perceived humidity as decision variables, and setting the constraint that the predictive dissatisfaction of each user must not exceed the acceptable threshold.

[0026] The predicted dissatisfaction values ​​of all online users, along with the optimization objective and constraints, are input into a multi-objective optimization algorithm, and a dynamic fusion solution is performed.

[0027] Under the premise of satisfying the constraints, the multi-objective optimization algorithm iteratively finds the combination of decision variables that minimizes the sum of the predictive dissatisfaction values ​​of all online users, and outputs the global set temperature and global set perceived humidity that achieve the minimization objective.

[0028] As a preferred embodiment of the independent temperature and humidity control system of the present invention, the method for generating a mode command based on a comparison between the globally set perceived humidity and the outdoor air humidity includes the following steps:

[0029] Based on the global set perceived humidity, the central controller queries the built-in comfort-humidity mapping table to obtain the desired indoor air humidity target value;

[0030] The central controller reads the outdoor air dew point temperature data provided by the outdoor air sensor and calculates the outdoor air humidity based on the outdoor air dew point temperature and standard atmospheric pressure.

[0031] Compare the difference between the outdoor air humidity content and the indoor air humidity content target value, and read the preset seasonal mode identifier;

[0032] Based on the difference comparison results and the seasonal pattern identifier, the mode command is output according to the preset logic rules.

[0033] As a preferred embodiment of the independent temperature and humidity control system described in this invention, the following steps are included: sending mode commands to the Janus membrane intelligent fresh air exchanger, and changing the water vapor permeation direction selectivity by adjusting the physical field applied to the Janus membrane of the Janus membrane intelligent fresh air exchanger:

[0034] The mode command is transmitted to the local controller of the Janus membrane intelligent fresh air exchanger. The mode command is parsed, and the preset voltage-mode mapping relationship is queried according to the mode command to determine the DC voltage value and polarity that needs to be applied to the electrode pairs on both sides of the Janus membrane of the Janus membrane intelligent fresh air exchanger.

[0035] The local controller of the Janus membrane intelligent fresh air exchanger drives the high-voltage power supply to output DC voltage to the electrode pairs on both sides of the Janus membrane of the Janus membrane intelligent fresh air exchanger.

[0036] The DC electric field applied to the Janus membrane of the Janus membrane smart fresh air exchanger changes the orientation and distribution of the functional groups inside the Janus membrane, thereby regulating the selective difference of water vapor permeation from the indoor exhaust side to the outdoor fresh air side and from the outdoor fresh air side to the indoor exhaust side.

[0037] As a preferred embodiment of the independent temperature and humidity control system of the present invention, the following steps are included: Pre-treating the humidity of the introduced fresh air to obtain pre-treated fresh air:

[0038] Outdoor air, as fresh air to be treated, flows through one side of the Janus membrane of the Janus membrane smart fresh air exchanger, which has been subjected to a directional electric field. The membrane utilizes the altered permeability selectivity to preferentially allow water vapor molecules to permeate from the other side of the Janus membrane to the fresh air side under the action of the directional electric field.

[0039] Janus membranes reduce the absolute moisture content of the fresh air flowing through the surface of the Janus membrane by promoting the unidirectional permeation of water vapor molecules, thus achieving sensible heat exchange between the fresh air and the exhaust air.

[0040] The air, after undergoing sensible heat exchange and dehumidification, flows out from the fresh air outlet of the Janus membrane intelligent fresh air exchanger, becoming pre-treated fresh air that meets the humidity control target of the mode command.

[0041] As a preferred embodiment of the independent temperature and humidity control system of the present invention, the process of mixing pretreated fresh air with indoor return air to form mixed air includes the following steps:

[0042] Pretreated fresh air flowing from the fresh air outlet of the Janus membrane smart fresh air exchanger is sent into an inlet of the mixing chamber.

[0043] The indoor return air collected from the indoor return air vent is sent to another inlet of the mixing chamber, where the pre-treated fresh air and the indoor return air are mixed in the mixing chamber according to a preset volumetric flow rate ratio.

[0044] The mixed air is brought into a mixing chamber to achieve a uniform distribution of temperature and humidity, thus forming mixed air.

[0045] As a preferred embodiment of the independent temperature and humidity control system of the present invention, the mixed air is adjusted according to a globally set temperature and a globally set perceived humidity through mutually independent temperature processing channels and humidity processing channels, including the following steps:

[0046] The temperature processing channel receives the global set temperature and generates a frequency conversion control signal based on the difference between the global set temperature and the current dry-bulb temperature of the mixed air.

[0047] The variable frequency compressor in the temperature processing channel adjusts the refrigerant flow according to the variable frequency control signal to perform sensible heat treatment on the mixed air.

[0048] The humidity processing channel receives the globally set perceived humidity and generates a solution regeneration control signal based on the difference between the globally set perceived humidity and the current perceived humidity of the mixed air.

[0049] The solution dehumidification unit in the humidity treatment channel adjusts the solution concentration according to the solution regeneration control signal and performs latent heat treatment on the mixed air to ensure that the treated air meets the global set temperature and global set perceived humidity.

[0050] As a preferred embodiment of the independent temperature and humidity control system of the present invention, the process of introducing regulated air into the room to update the indoor environmental conditions includes the following steps:

[0051] After being regulated by the temperature and humidity processing channels to meet the global set temperature and global set perceived humidity, the air is delivered to the indoor space as the air supply airflow through the air supply duct.

[0052] The supply airflow exchanges heat and mass with the heat and moisture sources in the indoor space, changing the distribution of dry-bulb temperature and relative humidity at various points in the indoor space.

[0053] The updated dry-bulb temperature and relative humidity of the air at various points in the indoor space are collected in real time by the indoor multi-sensor node network, forming a new set of indoor environmental parameters.

[0054] A new set of indoor environmental parameters, along with a continuously updated list of online users and individual physiological datasets, constitute the comprehensive data package for the next control cycle. The beneficial effects of this invention are as follows: The acquisition module collects and integrates user physiological data and environmental parameters in real time to form a comprehensive data package; using the personal thermal and humidity comfort digital twin model in the receiving module, a biothermodynamic algorithm is run based on the user's real-time physiological data and local environmental parameters to generate personalized temperature and perceived humidity settings; the central controller in the fusion module, aiming to minimize collective dissatisfaction, dynamically fuses multiple personalized settings through a multi-objective optimization algorithm to generate globally unified temperature and perceived humidity settings; the judgment module generates a mode command based on a comparison of this global perceived humidity with outdoor air humidity; the preprocessing module sends the mode command to the Janus membrane intelligent fresh air exchanger, adjusting the water vapor permeation direction selectivity by regulating the physical field applied to the Janus membrane to preprocess the humidity of the fresh air; the update module mixes the preprocessed fresh air with indoor return air, and after adjusting the mixed air through independent temperature and humidity processing channels, it sends it into the room, thereby dynamically updating the indoor environmental state. This achieves refined and intelligent environmental control that balances individual comfort needs and overall system energy efficiency in multi-user scenarios. Attached Figure Description

[0055] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a schematic diagram of an independent temperature and humidity control system. Detailed Implementation

[0057] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0058] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0059] Secondly, the term "one embodiment" or "example" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the invention. The appearance of an embodiment in different places in this specification does not necessarily refer to the same embodiment, nor is it a single or selective embodiment that mutually excludes other embodiments.

[0060] Reference Figure 1 As one embodiment of the present invention, this embodiment provides an independent temperature and humidity control system, comprising the following steps:

[0061] The acquisition module acquires real-time physiological data and integrates it into a comprehensive data packet.

[0062] The system collects dry-bulb temperature, relative humidity, and carbon dioxide concentration to generate a set of indoor environmental parameters.

[0063] Furthermore, a multi-sensor node network deployed indoors periodically and synchronously collects readings of dry-bulb temperature, relative humidity, and carbon dioxide concentration at each monitoring point. These nodes transmit the collected readings to a central processing unit via a wireless communication protocol. The central processing unit verifies and performs preliminary filtering on the received data from each point, removing obvious outliers. Then, it organizes all valid readings within the same collection period according to a preset spatial coordinate index, forming a structured dataset, i.e., an indoor environmental parameter set, and establishes a real-time spatial distribution map reflecting the indoor thermal and humidity environment and air quality.

[0064] Specifically, by constructing a distributed, synchronously sampled set of indoor environmental parameters, control decisions can grasp the actual microclimate distribution within the room, rather than just the conditions at a single point. This allows subsequent personalized thermal and humidity comfort digital twin models to obtain the real environmental parameters of the user's specific location, rather than a general room average. This provides a precise spatial context for generating truly personalized settings, which is the physical basis for achieving precise personalized control. Simultaneous acquisition of multiple parameters also avoids misjudgments of thermal and humidity coupling states caused by data latency, improving the timeliness and accuracy of control.

[0065] The system communicates with user smart terminals using low-power Bluetooth beacons to determine user identities and three-dimensional spatial coordinates, and generates an online user list.

[0066] Furthermore, low-power Bluetooth beacon arrays are pre-deployed at key indoor locations, with each beacon continuously broadcasting a signal containing its unique identifier. Upon entering the coverage area, a user's smart terminal device scans and receives signals from multiple beacons. By measuring the received signal strength and combining it with the known 3D coordinates of the beacons, the smart terminal calculates its own real-time 3D spatial coordinates using trilateration or fingerprint positioning algorithms. Simultaneously, the smart terminal packages its encrypted, authenticated user identity along with the calculated real-time 3D spatial coordinates and sends it to the central processing unit via the wireless network. The central processing unit collects data reported by all online smart terminals, updates and maintains in real-time a list containing user identities and their corresponding 3D spatial coordinates—the online user list.

[0067] Specifically, the system dynamically binds user identities to precise locations in real time, forming a structured list. Using low-power Bluetooth beacons, it achieves centimeter- to meter-level positioning accuracy, enabling the system to associate the individual "Zhang San" with a specific spatial point one meter above the ground in the southeast corner of the room. The generated online user list is not a static sign-in sheet, but a dynamically updated space-identity mapping table. This not only gives the invocation of the personal thermal and humidity comfort digital twin model identity-based guidance, but also provides the model with the necessary spatial location input for its calculations, allowing for the real-time extraction of local data from the set of indoor environmental parameters corresponding to that location.

[0068] Heart rate variability, wrist skin temperature, and estimated metabolic equivalents are used to construct an individual physiological dataset.

[0069] Furthermore, the user's smart wearable device continuously monitors and records physiological signals. The built-in sensors and algorithms of the smart wearable device periodically collect heart rate data and obtain heart rate variability indicators through time-domain or frequency-domain analysis. A skin temperature sensor measures wrist skin temperature. Combining accelerometer data with preset user basal metabolic rate parameters, the processor within the smart wearable device runs a standard algorithm to estimate the user's real-time metabolic equivalent. The indicators, after initial local processing—heart rate variability, wrist skin temperature, and estimated metabolic equivalent—are transmitted by the smart wearable device to the central processing unit via an encrypted wireless data link. The central processing unit maintains a data buffer for each identity, timestamps the received physiological indicators, and updates them to the corresponding user's real-time data record, forming the user's personal physiological dataset.

[0070] Specifically, the input to the control system has been expanded from simple environmental physical parameters to real-time physiological parameters of the human body. Heart rate variability reflects the activity state and stress level of the autonomic nervous system, wrist skin temperature is a direct manifestation of core body temperature changes and peripheral vasoconstriction and vasodilation, and estimated metabolic equivalent characterizes the energy consumption level of physical activity. These three together constitute the key physiological dimensions reflecting an individual's thermoregulation state and heat load. Traditional control logic can only adjust based on the deviation between the set temperature and the actual temperature, completely ignoring the real-time feedback of the human being as the subject of the thermal environment. By constructing a personal physiological dataset, an objective physiological evidence chain directly characterizing the user's thermal comfort state is obtained. This makes the personal thermal and humidity comfort digital twin model no longer a guess based on a general model or subjective voting, but rather has physiological input specific to the user at this moment. For example, a decrease in heart rate variability may indicate that the user is in a relaxed or drowsy state and has a reduced need for coolness; an increase in metabolic equivalent directly indicates that the user needs a lower temperature setting to dissipate heat.

[0071] The indoor environmental parameter set, online user list and personal physiological dataset are aligned and standardized with a unified timestamp and packaged into a comprehensive data package.

[0072] Furthermore, the central processing unit maintains a high-precision time synchronization service. When the indoor environmental parameter set, the updated entries of the online user list, and the personal physiological dataset data frames from smart wearable devices arrive, the central processing unit marks each data piece with a precise timestamp based on a unified time reference. The data fusion process starts according to a fixed control cycle, for example, once every thirty seconds. Within each cycle, the fusion process extracts the environmental readings that best match the three-dimensional spatial coordinates of each user in the online user list from the indoor environmental parameter set, based on the unified timestamp, and extracts the latest physiological indicators within the same time window from the personal physiological dataset. These data from different sources are standardized, including unifying the units and normalizing the values ​​to the same range, and encapsulated into data objects with a unified structure. The data object contains a user's identity identifier, spatial coordinates, local environmental parameters, and personal physiological data at a specific moment. The data object set of all online users is then packaged into a complete integrated data packet.

[0073] Specifically, the key lies in spatiotemporal alignment and data fusion, constructing a multi-dimensional, cross-modal, and strictly spatiotemporally synchronized environment-person-physiology holographic snapshot. Individually, the indoor environmental parameter set, the online user list, and the personal physiological dataset are all independent and valuable information streams. However, without precise alignment, time and spatial differences will exist between them, causing Zhang San's physiological data at point A to be incorrectly paired with environmental data at a slightly earlier time at point B, resulting in misleading decisions. Through mandatory unified timestamps and spatial coordinate-based matching, it is ensured that at the moment of decision-making, all data points to the same user at the same time and location. Standardization eliminates the differences in the dimensions and magnitudes of different sensors. The resulting comprehensive data package is no longer a fragmented data set, but a decision fact unit with close internal connections and guaranteed spatiotemporal consistency, ensuring the accuracy and reliability of model predictions. It is a key transformation hub in the entire control chain from data collection to intelligent decision-making, and an indispensable data preparation link for achieving precise and personalized control.

[0074] The receiving module, based on a comprehensive data packet, receives real-time physiological data and local environmental parameters of the corresponding user's location from the personal thermal and humidity comfort digital twin model, runs an internal biothermodynamic algorithm, and generates personalized temperature and humidity settings.

[0075] The central controller activates a pre-set personal thermal and humidity comfort digital twin model corresponding to the identity identifier in the online user list.

[0076] Furthermore, after receiving the list of online users, the central controller iterates through each identity in the list. For each identity, the central controller searches and matches in a pre-set digital twin model library. The model library pre-stores personal thermal and humidity comfort digital twin model instances that are bound one-to-one with each authorized user identity. After a successful match, the central controller loads the corresponding personal thermal and humidity comfort digital twin model instance from storage into the running memory.

[0077] Specifically, through precise matching of identity identifiers, a unique virtual copy is activated for each individual. The personal thermal and humidity comfort digital twin model is not a general model, but a personalized product obtained by reverse optimization training based on the corresponding user's historical physiological and environmental data. It embeds the user's unique thermal physiological response pattern and comfort preferences. The activation process is essentially to transform the abstract user concept into a concrete, calculable, and predictive digital entity, shifting to a one-to-one precise service paradigm. Each activated model becomes a dedicated computing engine for generating personalized settings for the corresponding user.

[0078] Based on the activated personal thermal and humidity comfort digital twin model, it receives the heart rate variability, wrist skin temperature and estimated metabolic equivalent from the corresponding user's personal physiological dataset, and receives the local dry-bulb temperature and relative humidity corresponding to the three-dimensional spatial coordinates of the corresponding user extracted from the indoor environmental parameter set.

[0079] Furthermore, for each activated personal thermal comfort digital twin model, the central controller performs a data binding operation. Based on the model's corresponding identity, the central controller searches for and extracts the latest heart rate variability, wrist skin temperature, and estimated metabolic equivalent values ​​matching that identity from the personal physiological dataset within the comprehensive data package. Using the three-dimensional spatial coordinates corresponding to the same identity, the central controller queries the indoor environmental parameter set to find the monitoring point that best matches those coordinates and extracts the local dry-bulb temperature and relative humidity values ​​for that point. The central controller then passes these five extracted data points—heart rate variability, wrist skin temperature, estimated metabolic equivalent, local dry-bulb temperature, and relative humidity—as a complete set of input parameters to the corresponding personal thermal comfort digital twin model.

[0080] Specifically, by bridging the gap through three-dimensional spatial coordinates, the physical parameters of the user's actual microenvironment are precisely matched. This means that the local dry-bulb temperature and relative humidity received by the model are not the average of the entire room, nor the reading of a fixed sensor, but rather the real-time environment as perceived by the user's body. This coupling ensures that the model simulates a highly realistic scenario: a specific user, in their current specific physiological state, in a specific physical environment at a specific location. This results in highly realistic and individualized calculations, providing high-fidelity input conditions for generating settings that truly reflect the user's comfort needs in their current location and state. This is an indispensable data preparation step for achieving accurate and personalized predictions.

[0081] Based on the reverse optimization training of user historical physiological data and environmental data, a multi-steady-state comfort field simulation is obtained. The received heart rate variability, wrist skin temperature, estimated metabolic equivalent, local dry-bulb temperature and relative humidity are used as field parameters. The steady-state attractor in the comfort field is found through iteration. The coordinates of the steady-state attractor are mapped to the personalized temperature setting value and the personalized perceived humidity setting value.

[0082] Furthermore, upon receiving input parameters, the personal thermal and humidity comfort digital twin model initiates an internal multi-steady-state comfort field simulation. This simulation is a high-dimensional state-space mapping relationship pre-constructed through reverse optimization training using the user's historical physiological data and environmental data. The simulation process uses the input heart rate variability, wrist skin temperature, estimated metabolic equivalent, local dry-bulb temperature, and relative humidity as the determining parameters of the current field state. In this parameterized state space, iterative calculations are used to find the steady-state attractor of the comfort potential energy surface, i.e., the point of lowest potential energy or equilibrium region. The iterative algorithm simulates the evolution trajectory of the state point in the field until it converges to a stable state. The coordinates of the converged steady-state attractor in the state space are interpreted as a pair of specific numerical outputs, namely, personalized temperature setting and personalized perceived humidity setting, through a preset mapping rule.

[0083] Specifically, a dynamic system simulation paradigm based on multi-steady-state comfort fields and attractor search is adopted to replace traditional comfort assessments based on static regression equations or fixed thresholds. Multi-steady-state comfort field simulation understands human thermal comfort as a dynamic and complex system that may contain multiple comfort equilibrium points. Input parameters determine the shape of this field, and the iterative process of finding attractors simulates the dynamic process of the human thermal regulation system tending towards equilibrium, capturing the nonlinearity and state dependence of comfort. For example, even with the same physiological parameters, a user who has just entered an indoor space and a user who has been sitting still for a period of time may have different tendencies towards a stable comfort state. The model iteratively simulates this dynamic process to find the most likely stable comfort point achievable under the current overall state. This reflects the dynamic adaptive characteristics of human thermal sensation better than static formulas, resulting in personalized temperature and humidity settings that better match the user's dynamic and immediate needs. This represents a qualitative leap in computational logic from static mapping to dynamic simulation.

[0084] The central controller collects digital twin models of the individual thermal and humidity comfort of all online users, and forms a set of personalized settings by combining multiple personalized temperature settings and personalized humidity settings.

[0085] Furthermore, the central controller establishes a temporary data storage structure to aggregate the output results of the personal thermal and humidity comfort digital twin models from all online users. Whenever a personal thermal and humidity comfort digital twin model outputs its corresponding personalized temperature setting and personalized perceived humidity setting, the central controller associates these values ​​with the user's identity and stores them as a record in the aforementioned temporary data storage structure. The central controller continuously monitors the output of all activated models until it confirms that all online users' personal thermal and humidity comfort digital twin models have completed calculations and reported results. All the records accumulated in the temporary data storage structure represent the pairing combination of each user's identity and their corresponding set of personalized temperature and personalized perceived humidity settings.

[0086] Specifically, scattered individual needs are structurally aggregated into a demand pool for group decision-making. Each individual thermal and humidity comfort digital twin model operates independently without interference, ensuring the purity and independence of each individual's needs. The central controller does not interfere with the calculation process of each model, but only faithfully collects all outputs. The resulting set of personalized settings is not a single value, but a demand spectrum reflecting the differentiated comfort needs of all users in the current indoor environment. This set clearly shows the distribution of temperature and humidity preferences within the user group. For example, there may be different clusters that prefer low temperature and dryness, high temperature and humidity, and moderate temperature. This avoids the information loss caused by early averaging or selecting representative users, and preserves the complete expression of all individual needs. This makes it possible to find a global setting that can satisfy all users to the greatest extent, realizing the key data transformation link from satisfying individuals to coordinating the group.

[0087] The fusion module, with the central controller aiming to minimize collective dissatisfaction, inputs the fully personalized temperature setting and the personalized perceived humidity setting into a multi-objective optimization algorithm for dynamic fusion, generating a global set temperature and a global set perceived humidity.

[0088] Each personalized temperature setting and personalized perceived humidity setting combination in the set of personalized settings is substituted into the biothermodynamic equations within the corresponding personal thermal and humidity comfort digital twin model to solve for the user's predictive dissatisfaction value in each combination environment.

[0089] Furthermore, the central controller iterates through the set of personalized settings. For each entry in the set, the central controller extracts the identity identifier, personalized temperature setting, and personalized perceived humidity setting. Based on the identity identifier, the central controller again invokes the corresponding personal thermal and humidity comfort digital twin model. During this invocation, the central controller passes the extracted personalized temperature and humidity settings, along with the user's current real-time personal physiological dataset and local environmental parameters, as input parameters to the biothermodynamic equations within the personal thermal and humidity comfort digital twin model. The personal thermal and humidity comfort digital twin model executes these biothermodynamic equations, calculating the user's predicted thermal and humidity sensation under the environment represented by the personalized setting, based on the input physiological and environmental parameters. This sensation is then quantified and output as a specific predictive dissatisfaction value. After the iteration is complete, the central controller obtains the predictive dissatisfaction value corresponding to each combination of personalized settings.

[0090] Specifically, a cyclical mechanism of design verification and consequence pre-assessment was implemented. This not only generated personalized needs but also assessed the potential comfort impact on all other users if the environment was set to meet a user's personalized needs. The central controller then substituted each user's proposed ideal environment into the individual thermal and humidity comfort digital twin model of all users and conducted simulations. This allowed decision-makers to anticipate the group reactions that different options might bring. For example, user A's preference for low temperatures might cause user B to feel cold. The predictive dissatisfaction value provides a quantifiable measure for objectively comparing the impact of different combinations of personalized settings on the entire group.

[0091] The expression for predictive dissatisfaction value is: ;

[0092] in, For the first Predictive dissatisfaction value for each user For the first Personal biothermodynamic equations for each user For the first Personalized temperature settings for each user. For the first Real-time personal physiological datasets of individual users For the first Local environment parameters for individual users Index for users.

[0093] The central controller defines the optimization objective as minimizing the sum of the predictive dissatisfaction values ​​of all online users, using the globally set temperature and the globally set perceived humidity as decision variables, and setting the constraint that the predictive dissatisfaction of each user must not exceed an acceptable threshold.

[0094] Furthermore, the central controller establishes a mathematical optimization problem framework. Within this framework, the central controller explicitly specifies the objective function as the sum of the predicted dissatisfaction values ​​of all online users. The central controller defines the globally set temperature and the globally set perceived humidity as the decision variables for this optimization problem. The central controller sets constraints for the optimization problem, stipulating that for each online user, their corresponding predicted dissatisfaction value must be less than or equal to a predefined acceptable threshold. This acceptable threshold represents the threshold for ensuring basic user comfort and avoiding severe discomfort. The optimization objective and constraints together constitute a constrained optimization problem model.

[0095] Specifically, the complex multi-user comfort coordination problem is formalized into a constrained optimization mathematical model with a clear Pareto improvement orientation. Traditional methods may use majority voting, averaging, or choosing a compromise point, which often lack theoretical basis and may sacrifice the basic comfort of a few individuals. The defined objective of minimizing the sum of overall dissatisfaction is essentially seeking a solution that maximizes total social welfare. The added constraint that individual predictive dissatisfaction must not exceed a threshold sets a safety bottom line for the comfort rights of each user, ensuring that the final global solution will not come at the expense of the basic comfort of any user. It is as if, while searching for the overall optimal solution, an inviolable comfort zone is defined for each person.

[0096] The predicted dissatisfaction values ​​of all online users, along with the optimization objective and constraints, are input into a multi-objective optimization algorithm for dynamic fusion solution.

[0097] Furthermore, the set of predictive dissatisfaction values ​​of all online users, the defined objective function, and the set constraints are passed as complete input parameters to the built-in multi-objective optimization algorithm solver. Upon receiving these inputs, the multi-objective optimization algorithm solver begins the solution process. First, based on the domain of the decision variables, the solver initializes one or more candidate combinations of global set temperature and global set perceived humidity as initial solutions. Then, according to the optimization objective and constraints, the solver uses specific optimization strategies, such as gradient-based search, heuristic algorithms, or evolutionary algorithms, to evaluate, compare, iterate, and evolve these candidate solutions. This process continues under the control of the algorithm, aiming to dynamically explore the solution space and find combinations of decision variables that simultaneously satisfy all constraints and minimize the objective function value.

[0098] Specifically, the subjective challenge of integrating the needs of multiple users is transformed into an objective computational problem that can be solved automatically by an algorithm. Traditional coordination relies on the experience of managers or simple rules, while multi-objective optimization algorithms can perform global or near-global searches in the parameter space. They can handle complex nonlinear relationships between objectives and constraints, as well as coupling relationships between decision variables. For example, a small change in global temperature may have different degrees of impact on the predictive dissatisfaction values ​​of all users. By capturing and weighing these impacts, and considering that the user's physiological state and environmental data are different each time, the algorithm will recalculate an optimal solution that adapts to the specific situation at that time, rather than a fixed set value.

[0099] Under the premise of satisfying the constraints, the multi-objective optimization algorithm iteratively finds the combination of decision variables that minimizes the sum of the predictive dissatisfaction values ​​of all online users, and outputs the global set temperature and global set perceived humidity that achieve the minimization objective.

[0100] Furthermore, the multi-objective optimization algorithm continuously performs iterative calculations during the solution process. In each iteration, the algorithm generates new or modifies existing candidate combinations of global set temperature and global set perceived humidity, and calls the corresponding calculation model to evaluate the sum of the predicted dissatisfaction values ​​of all users under this combination. It checks whether the constraint that the predicted dissatisfaction of each user does not exceed an acceptable threshold is met. By comparing the objective function values ​​of different candidate solutions with the constraint satisfaction, the better solutions are retained and the worse solutions are eliminated. New candidate solutions are generated based on this. The iterative process is repeated until a preset termination condition is reached, such as reaching the maximum number of iterations, the objective function value converging to a stable region, or finding a feasible solution that satisfies all constraints. When the termination condition is reached, the algorithm selects an optimal solution from all evaluated candidate solutions. This optimal solution is the combination of global set temperature and global set perceived humidity that satisfies all constraints and whose corresponding sum of predicted dissatisfaction values ​​of all users is the smallest among all feasible solutions. This optimal solution is then used as the final output.

[0101] Specifically, the iterative optimization process ensures that the output results are obtained after systematic searching and rigorous comparison. Under given constraints, the results reduce the overall dissatisfaction of the group to the theoretical minimum, balance all conflicting individual preferences, and ensure that no one's comfort level falls below the acceptable minimum standard. Mathematical optimization guarantees the quality of understanding, making the control decision scientific and objective, and improving the fairness and overall satisfaction of the group's environmental control.

[0102] The judgment module compares the globally set perceived humidity with the outdoor air humidity to generate mode instructions.

[0103] Based on the global set perceived humidity, the central controller queries the built-in comfort-humidity mapping table to obtain the desired indoor air humidity target value.

[0104] Furthermore, after obtaining the globally set perceived humidity level, the central controller accesses a predefined comfort-humidity mapping table stored in non-volatile memory. This mapping table uses perceived humidity levels or values ​​as indexes, associating them with corresponding recommended indoor air humidity ranges or precise values. The central controller uses the globally set perceived humidity level as a lookup key to perform a matching search in the mapping table, reading and outputting the corresponding indoor air humidity value. This value represents the absolute indoor air humidity level that needs to be maintained to achieve the perceived comfort level agreed upon by the user group, i.e., the desired indoor air humidity target value. This realizes the conversion from subjective perceived comfort needs to objective physical control parameters.

[0105] Specifically, it achieves intelligent translation from subjective feeling instructions to objective physical targets. Perceived humidity is a subjective feeling index that integrates the influence of factors such as temperature, humidity, and wind speed. It cannot be directly used to control air conditioning or fresh air equipment. Traditional control systems usually directly set a fixed relative humidity or moisture content value, ignoring the nonlinearity and context-dependent nature of human perception. By querying a preset comfort-moisture content mapping table, the globally set perceived humidity obtained based on group physiological optimization is intelligently converted into a clear, measurable, and directly driving target value for indoor air moisture content of physical equipment. This mapping table is established by combining the research results of thermal comfort and environmental engineering data, and encapsulates the subjective feeling relationship of the human body to different moisture content levels within a specific temperature range.

[0106] The central controller reads outdoor air dew point temperature data provided by the outdoor air sensor and calculates the outdoor air humidity based on the outdoor air dew point temperature and standard atmospheric pressure.

[0107] Furthermore, the central controller reads the outdoor air dew point temperature in real time from outdoor air sensors deployed outdoors via a data communication interface. The central controller then calls its internal calculation program. This program first converts the read outdoor air dew point temperature into the corresponding saturated vapor pressure according to physical formulas. This saturated vapor pressure is the water vapor partial pressure of the current outdoor air. The program then substitutes this calculated water vapor partial pressure value with a preset standard atmospheric pressure value into the moisture content calculation formula. Combining the ratio of the dry air gas constant to the water vapor gas constant, the central controller finally obtains a moisture content value representing the absolute humidity level of the outdoor air.

[0108] Specifically, rigorous physical calculations are used to accurately quantify outdoor humidity threats or resources. Compared to directly measuring relative humidity, dew point temperature is a more stable absolute humidity indicator unaffected by temperature changes. Directly reading dew point temperature and performing physical conversion avoids interference caused by drastic changes in relative humidity due to outdoor temperature fluctuations. This makes the obtained outdoor air moisture content values ​​more reliable and better reflect the true moisture load potential of outdoor air. Calculations using standard formulas ensure the physical accuracy and repeatability of the results, laying a solid physical foundation for subsequent accurate comparisons with indoor targets. This reflects a shift from simple data collection to precise state perception based on physical principles, meaning that judgments about the outdoor environment are no longer based on a volatile relative humidity reading.

[0109] The expression for outdoor air humidity content is: ;

[0110] in, This refers to the outdoor air humidity. The gas constant for dry air. The gas constant of water vapor. The partial pressure of water vapor in outdoor air. Atmospheric pressure. The difference between the outdoor air humidity content and the target value of the indoor air humidity content is compared, and the preset seasonal mode identifier is read.

[0111] Furthermore, after simultaneously obtaining both the outdoor air humidity content and the target indoor air humidity content, the central controller performs an arithmetic subtraction operation to calculate the difference between the outdoor air humidity content and the target indoor air humidity content. This difference quantifies whether the outdoor air humidity level is high or low relative to the desired indoor humidity level, and the degree of high or low. The central controller reads a preset seasonal pattern identifier from the configuration storage area. This seasonal pattern identifier is typically set based on calendar, geographic location, or long-term meteorological data to characterize macroscopic time periods with different climatic characteristics, such as summer, winter, or transitional seasons. The result of the difference calculation is combined with the seasonal pattern identifier.

[0112] Specifically, a two-dimensional humidity situational awareness framework was constructed. Simple humidity comparisons (differences) only provide a static relationship between indoor and outdoor humidity levels. However, the same difference can have drastically different control implications in different seasonal contexts. For example, if the outdoor humidity is higher than the indoor target value, it means the outdoor area is a source of moisture in summer, requiring prevention of moisture intrusion; while in winter, it may mean the outdoor humidity is acceptable and no special treatment is needed. By introducing seasonal pattern identifiers, the central controller interprets the purely physical quantity difference within a macro-level seasonal strategy context. This allows decisions to be based not only on the current difference between indoor and outdoor humidity but also on the current seasonal context. This two-dimensional perception approach mimics the experience of operators who consider both current data and seasonal common sense when making decisions, improving the rationality and foresight of the humidity control strategy and avoiding suboptimal or even counterproductive control actions that might result from considering only instantaneous differences.

[0113] Based on the difference comparison results and the seasonal pattern identifier, the mode command is output according to the preset logic rules.

[0114] Furthermore, the central controller stores a set of predefined logical rules. These rules exist in the form of conditional statements, the condition part of which examines both the difference between the outdoor air humidity and the target value of the indoor air humidity, and the current seasonal mode identifier. The central controller takes the calculated difference result and the read seasonal mode identifier as input and matches them with the conditions in the rule base. For example, one rule might be: if the seasonal mode identifier is summer and the difference result is positive (more humid outdoors), then the output mode instruction is dehumidification priority mode; another rule might be: if the seasonal mode identifier is winter and the difference result is negative (drier outdoors), then the output mode instruction is humidification recovery mode. The central controller executes the first matching rule and performs the corresponding action, that is, generates and outputs a specific mode instruction to guide the operation of the Janus membrane intelligent fresh air exchanger.

[0115] Specifically, it implements contextualized strategy selection, upgrading simple comparison and judgment to rule-based intelligent decision-making. The pre-set logical rules essentially formalize the humidity control domain. Mode commands are no longer triggered by fixed thresholds, but are generated by a rule engine that comprehensively considers absolute humidity difference and seasonal context. This ensures the control strategy has high adaptability and strategic flexibility. For example, in transitional seasons, when the indoor and outdoor humidity difference is small, the rules may output an efficient total heat recovery mode to save energy; while under extreme humidity differences, regardless of the season, a powerful dehumidification or humidification mode may be triggered to ensure comfort. This rule-based decision-making approach allows the Janus membrane intelligent fresh air exchanger to dynamically and intelligently adapt to complex and changing indoor and outdoor environmental conditions, breaking through the limitations of traditional fresh air control based solely on temperature or simple humidity comparisons. It achieves predictive and strategic management of fresh air humidity load, maximizing the exchanger's energy-saving potential and comfort assurance performance.

[0116] The pre-processing module sends mode commands to the Janus membrane intelligent fresh air exchanger. By adjusting the physical field applied to the Janus membrane of the Janus membrane intelligent fresh air exchanger, it changes the selectivity of water vapor permeation direction and performs humidity pre-processing on the introduced fresh air to obtain pre-processed fresh air.

[0117] The mode command is transmitted to the local controller of the Janus membrane smart fresh air exchanger. The mode command is parsed, and the preset voltage-mode mapping relationship is queried according to the mode command to determine the DC voltage value and polarity that needs to be applied to the electrode pairs on both sides of the Janus membrane of the Janus membrane smart fresh air exchanger.

[0118] Furthermore, the central controller sends the generated mode command to the local controller of the Janus membrane intelligent fresh air exchanger via the communication bus. Upon receiving the mode command, the local controller of the Janus membrane intelligent fresh air exchanger immediately starts the parsing program, reads the command code in the mode command, and accesses its stored voltage-mode mapping table. This table, indexed by different mode command codes, maps a series of electrical parameters that need to be applied to the electrode pairs on both sides of the Janus membrane of the Janus membrane intelligent fresh air exchanger, including the specific value of the DC voltage and the corresponding polarity direction. By querying, the local controller of the Janus membrane intelligent fresh air exchanger finds the table entry that perfectly matches the current mode command code, and determines the target DC voltage value and polarity that the high-voltage power supply needs to output in the next stage.

[0119] Specifically, it achieves precise encoding and transmission of strategies to physical fields. The mode command is an abstract, logical control command, while the specific physical parameters voltage and polarity are needed to drive the Janus membrane to change its selectivity. The voltage-mode mapping table serves as a key translation dictionary, accurately mapping the high-level control strategy to the physical stimuli required for the underlying actuation. The mapping relationship is established in advance through extensive experimental calibration, ensuring the optimization of the Janus membrane performance under different operating modes. This guarantees the reliable execution of commands, enabling intelligent decisions to be translated into actions in the physical world without loss and with precision.

[0120] The local controller of the Janus membrane intelligent fresh air exchanger drives the high-voltage power supply to output DC voltage to the electrode pairs on both sides of the Janus membrane.

[0121] Furthermore, after determining the target DC voltage value and polarity, the local controller of the Janus membrane intelligent fresh air exchanger generates a corresponding pulse width modulation control signal or direct digital command. The control signal is sent to the high-voltage power supply module integrated inside the Janus membrane intelligent fresh air exchanger. After receiving the control command, the high-voltage power supply module starts its internal power conversion circuit to convert the input conventional low-voltage DC or AC power into a high-stability DC voltage with a specific value and specified polarity that meets the requirements. The high-voltage power supply module applies this generated DC voltage safely and reliably to the pre-prepared transparent electrode pairs on both sides of the Janus membrane of the Janus membrane intelligent fresh air exchanger through insulated wires, establishing a uniform and controllable DC electric field in the microstructure of the Janus membrane of the Janus membrane intelligent fresh air exchanger.

[0122] Specifically, the performance of the functional membrane is controlled by applying a pure physical field—a DC electric field. The output capability of the high-voltage power supply ensures that the electric field strength is sufficient to overcome the inherent orientation energy barrier of the functional groups inside the Janus membrane. The electronic control method features fast response speed, high control precision, no mechanical wear, low noise, and high reliability. By controlling the voltage and polarity, it is equivalent to using electrical signals to switch the microstructure of the Janus membrane.

[0123] The DC electric field applied to the Janus membrane of the Janus membrane smart fresh air exchanger changes the orientation and distribution of the functional groups inside the Janus membrane, thereby regulating the selective difference of water vapor permeation from the indoor exhaust side to the outdoor fresh air side and from the outdoor fresh air side to the indoor exhaust side.

[0124] Furthermore, when a DC voltage is applied to the electrode pairs on both sides of the Janus membrane, a DC electric field is generated inside the Janus membrane of the Janus membrane smart fresh air exchanger, penetrating the membrane body. This field exerts electrostatic force on the functional polymer chains constituting the Janus membrane or the polar molecular groups grafted into the membrane channels. Under the action of electrostatic force, the polar or polarizable functional groups undergo physical rotation, stretching, or displacement, changing their overall orientation and spatial distribution density within the membrane body. This change in orientation and distribution affects the affinity and selectivity of the diffusion path experienced by water vapor molecules as they pass through the porous structure of the Janus membrane. In particular, it asymmetrically modulates the energy barrier and rate of water vapor permeation from one side of the membrane (such as the indoor exhaust side) to the other side (such as the outdoor fresh air side), macroscopically creating a controllable difference in the selective direction of water vapor permeation.

[0125] Specifically, the Janus membrane utilizes the principle of electric field-induced dynamic microstructure reconstruction to actively regulate the membrane's transport properties. The Janus membrane is not a passive diffusion medium, but a smart material whose transport performance can be actively and reversibly regulated by an external electric field. As a non-contact, energy-concentrated driving method, the DC electric field can efficiently and quickly rearrange the functional groups within the membrane, thereby changing the membrane's preference for specific molecules (such as water vapor). Through selective active regulation, the Janus membrane can flexibly switch between multiple modes, such as promoting water vapor discharge from indoors to outdoors, promoting water vapor replenishment from outdoors to indoors, or bidirectional symmetrical exchange, thus achieving programmable management of the water vapor transport path.

[0126] Outdoor air, as fresh air to be treated, flows through one side of the Janus membrane of the Janus membrane smart fresh air exchanger, which has been subjected to a directional electric field. The membrane utilizes its altered permeability selectivity to preferentially allow water vapor molecules to permeate from the other side of the Janus membrane to the fresh air side under the action of the directional electric field.

[0127] Furthermore, under the action of the ventilation fan, outdoor air is introduced as fresh air to be treated, flowing over the fresh air side surface of the Janus membrane in the Janus membrane intelligent fresh air exchanger, where a specific directional DC electric field has been applied. At this time, the functional groups inside the Janus membrane have formed a specific asymmetric orientation under the action of the electric field. The indoor return air flows over the other side of the Janus membrane, namely the exhaust side. Since the permeability selectivity of the Janus membrane has been regulated, the membrane structure exhibits low resistance or high affinity for water vapor molecules permeating from the exhaust side to the fresh air side, while exhibiting high resistance for reverse permeation. Driven by the natural water vapor partial pressure difference between indoor exhaust air and outdoor fresh air, water vapor molecules tend to migrate from the relatively humid indoor exhaust side through the opened transfer channel to the relatively humid outdoor fresh air side. The Janus membrane utilizes the unidirectional transfer characteristics enhanced by the electric field to guide the flow direction of water vapor.

[0128] Specifically, under electric field modulation, the mass transfer process of the Janus membrane becomes highly directionally asymmetrical. Even when the humidity on the fresh air side may be higher than that on the exhaust air side (e.g., during transitional seasons), the modulated Janus membrane can still suppress or reduce the infiltration of water vapor from the fresh air side into the room due to its asymmetry. This is equivalent to adding a humidity valve with intelligent control over the opening and closing direction to the fresh air duct. The mechanical force of the fan provides the driving force for airflow, while the selectivity provided by the Janus membrane determines the permitted direction of water vapor migration across the membrane. The combination of the two decouples airflow and humidity transfer to a certain extent, allowing the system to actively choose whether to exhaust indoor moisture or introduce outdoor moisture. This is a revolutionary improvement over the traditional physical process of total heat exchange.

[0129] Janus membranes reduce the absolute moisture content of the fresh air flowing through their surface by promoting the unidirectional permeation of water vapor molecules, thus achieving sensible heat exchange between the fresh air and exhaust air.

[0130] Furthermore, since the Janus membrane preferentially allows water vapor to permeate from the indoor exhaust side to the outdoor fresh air side, this unidirectional permeation process is macroscopically manifested as follows: the water vapor content of the fresh air flowing through the fresh air side surface of the Janus membrane is continuously and partially replaced or diluted into water vapor from the indoor exhaust air, which may have different humidity levels. The net effect of mass exchange across the membrane is to reduce the absolute moisture content of the fresh air flow itself, making it drier. The Janus membrane material of the Janus membrane intelligent fresh air exchanger itself also has thermal conductivity. When fresh air and exhaust air flow through both sides of the Janus membrane at the same time, due to the temperature difference, heat will be conducted from the high-temperature side to the low-temperature side through the solid matrix of the Janus membrane, and sensible heat exchange is completed between the fresh air and exhaust air. The fresh air treated by the Janus membrane intelligent fresh air exchanger undergoes both humidity reduction (or regulation) and temperature regulation.

[0131] Specifically, the Janus membrane intelligent fresh air exchanger accomplishes two things simultaneously within a compact physical device: first, it uses selective membrane permeation controlled by an electric field to actively and intelligently regulate latent heat (humidity); second, it passively and efficiently recovers sensible heat (temperature) based on the thermal conductivity of the material. These two are achieved in parallel through the different physical properties (selective permeation and heat conduction) of the same Janus membrane, but the control logic is independent. Humidity regulation is controlled by the electric field, and heat recovery is naturally driven by the temperature difference. This allows the device to flexibly change the humidity treatment strategy (such as switching from dehumidification to humidification) without adding complex mechanical structures, while always maintaining efficient heat recovery. This improves the intelligence level and overall energy efficiency of fresh air pretreatment while saving space and energy consumption.

[0132] The air, after undergoing sensible heat exchange and dehumidification, flows out from the fresh air outlet of the Janus membrane intelligent fresh air exchanger, becoming pre-treated fresh air that meets the humidity control target of the mode command.

[0133] Furthermore, the air processed within the Janus membrane intelligent fresh air exchanger, after undergoing sensible heat exchange with the indoor exhaust air and dehumidification primarily driven by the unidirectional infiltration effect of the Janus membrane, gathers in the fresh air collection channel inside the Janus membrane intelligent fresh air exchanger. The treated air then flows out from the dedicated fresh air outlet on the Janus membrane intelligent fresh air exchanger. The physical state of the outflowing air, especially its absolute moisture content, has undergone the expected change due to the selective infiltration effect of the Janus membrane. Compared to untreated outdoor fresh air directly introduced, its moisture content level is closer to the indoor air moisture content target value desired by the central controller based on the global settings for perceived humidity and seasonal strategies. This air flowing out from the fresh air outlet of the Janus membrane intelligent fresh air exchanger is defined as pre-treated fresh air, which has initially met the humidity control target set by the mode command.

[0134] Specifically, the humidity parameters have been actively and intelligently adjusted to meet specific indoor comfort needs. This semi-finished product carries the intention of upstream intelligent decision-making. For example, in dehumidification priority mode, it is drier than outdoor air; in humidification recovery mode, it may be more humid than outdoor air. Targeted humidity pretreatment completed at the building boundary (fresh air inlet) pre-processes some of the moisture load that would otherwise be borne by the large indoor air conditioning dehumidification / humidification system, thereby reducing the capacity and energy consumption pressure of indoor terminal humidity treatment equipment, realizing source management of moisture load, and improving the response speed and energy efficiency of the entire independent temperature and humidity control system.

[0135] The update module mixes the pre-treated fresh air with the indoor return air to form mixed air. Based on the globally set temperature and globally set perceived humidity, the mixed air is adjusted through independent temperature and humidity processing channels. The adjusted air is then sent into the room to update the indoor environmental status.

[0136] Pretreated fresh air flowing from the fresh air outlet of the Janus membrane smart fresh air exchanger is sent into an inlet of the mixing chamber.

[0137] Furthermore, the pretreated fresh air, driven by a fan, is drawn from the fresh air outlet of the Janus membrane intelligent fresh air exchanger through connecting pipes. This airflow is directionally delivered to one of the inlets of an air mixing device. This inlet is designated to receive the pretreated fresh air. The flow state and pressure parameters of the pretreated fresh air entering the mixing chamber need to match those of the mixing chamber to ensure effective mixing with other airflows within the mixing chamber. The pretreated fresh air undergoes pressure balancing and flow reshaping at the mixing chamber inlet.

[0138] Specifically, the key starting point for achieving independent paths and controllable convergence of fresh and return air is the pre-treated fresh air, which represents a source of fresh air with specific humidity characteristics that has undergone boundary intelligent adjustment. By sending it into the mixing chamber through a dedicated inlet, the identity and state of this airflow are clearly defined and independent before entering the mixing process. This avoids premature mixing with return air in the long-distance duct, which would dilute its unique humidity control value. This creates conditions for achieving a preset ratio of fresh and return air mixing within the mixing chamber, allowing the humidity control information carried by the pre-treated fresh air to be clearly and quantitatively introduced into subsequent air handling processes. This ensures that the humidity control chain of the entire system remains logically clear and its effects are traceable.

[0139] The indoor return air collected from the indoor return air vent is sent to another inlet of the mixing chamber, where the pre-treated fresh air and the indoor return air are mixed in the mixing chamber according to a preset volumetric flow rate ratio.

[0140] Furthermore, indoor air is drawn into the return air duct through the return air inlet and flows into the mixing chamber driven by the return air fan. The mixing chamber has another independent inlet specifically for receiving indoor return air collected from the room. The pre-treated fresh air and indoor return air enter the mixing chamber from their respective inlets. Inside the mixing chamber, baffles or static mixers are arranged to promote full contact and mixing of the two airflows. The speed of the fresh air fan and the valve opening of the return air fan are coordinated and controlled by the central controller to ensure that the volumetric flow rate of the pre-treated fresh air and indoor return air is strictly maintained at a preset ratio, such as meeting the minimum fresh air volume requirement. The two airflows begin to exchange heat and mix in the mixing chamber through turbulent diffusion and convection.

[0141] Specifically, the mixing ratio of pre-treated fresh air and indoor return air is dynamically adjusted according to a preset logic. This ratio is a crucial degree of control. During periods of good air quality, the fresh air ratio is appropriately reduced to save energy; when the indoor population is dense or CO2 concentration is high, the fresh air ratio is increased. By controlling the ratio, the initial state of the mixed air can be finely adjusted to be closer to the global set value, thereby reducing the adjustment load and energy consumption of subsequent temperature and humidity processing channels. Mixing the intelligently pre-treated, better-conditioned fresh air with indoor return air on demand is a key strategy for optimizing the energy efficiency of the entire air handling process from the source.

[0142] The mixed air is brought into a mixing chamber to achieve a uniform distribution of temperature and humidity, thus forming mixed air.

[0143] Furthermore, the pretreated fresh air and indoor return air continuously exchange momentum, heat, and mass in the turbulent region of the mixing chamber. The internal structure of the mixing chamber, such as using multi-layered staggered perforated plates and installing static mixing blades, aims to maximize the turbulence and contact area of ​​the airflow, and shorten the time and distance required for uniform mixing. After sufficient turbulent mixing, the air particles from the pretreated fresh air and indoor return air reach a statistically uniform state in terms of temperature and humidity. At the outlet section of the mixing chamber, the temperature and humidity gradients of the air can be ignored. The air flowing out from this section can be regarded as a single airflow with uniform temperature and humidity parameters, i.e., mixed air. The formation of mixed air marks the successful integration of two different air sources, fresh and return air.

[0144] Specifically, if mixing is insufficient, the mixed air will exhibit uneven spatial distribution of temperature and humidity, with hot and cold spots, and wet and dry spots. This unevenness will cause the downstream temperature and humidity sensor readings to be unrepresentative, leading to misjudgments and oscillations in the controller. It will also significantly reduce the adjustment effectiveness of the temperature and humidity processing channels. By forcing the air in the mixing chamber to achieve a uniform distribution, the mixed air is ensured to be a clear target with a defined state and single parameter. This provides a stable and reliable input for the independent and precise adjustment of the temperature and humidity processing channels. This is equivalent to establishing a standardized, unified reference point in the complex and ever-changing air handling process, simplifying control complexity and improving the overall system's control accuracy and stability.

[0145] The temperature processing channel receives the global set temperature and generates a frequency conversion control signal based on the difference between the global set temperature and the current dry-bulb temperature of the mixed air.

[0146] Furthermore, in the air handling unit, the dedicated controller of the temperature handling channel continuously receives the global set temperature command from the central controller. Temperature sensors installed at the inlet of the temperature handling channel or the outlet of the mixing chamber measure the current dry-bulb temperature of the mixed air in real time. The controller of the temperature handling channel runs a proportional-integral-derivative (PID) control algorithm, using the global set temperature as the set point and the current dry-bulb temperature of the mixed air as the process variable, to obtain the deviation value between the two. The PID control algorithm performs comprehensive calculations based on the magnitude, rate of change, and accumulation of the deviation value, and generates a corresponding variable frequency control signal in real time. The variable frequency control signal is an analog voltage signal or a digital pulse signal, the frequency or duty cycle of which is proportional to the required compressor power adjustment, driving the variable frequency compressor to change its speed to eliminate the temperature deviation.

[0147] Specifically, through proportional-integral-derivative closed-loop feedback control, the temperature processing channel can dynamically and smoothly respond to changes in the mixed air temperature, achieving precise temperature control with no overshoot and no steady-state error. The nonlinear mapping relationship between the frequency converter control signal and the compressor power has been optimized, enabling the compressor to operate in its high-efficiency range most of the time. This ensures a balance between the speed, stability, and energy efficiency of temperature control. It is one of the key interfaces connecting central intelligent decision-making and underlying physical execution, and its control quality directly determines whether the final supply air temperature can accurately track the optimized comfort target for the entire group.

[0148] The variable frequency compressor in the temperature processing channel adjusts the refrigerant flow according to the variable frequency control signal to perform sensible heat treatment on the mixed air.

[0149] Furthermore, the variable frequency compressor is the heart of the refrigeration / heat pump circuit in the temperature processing channel. The variable frequency compressor receives a variable frequency control signal from the temperature processing channel controller. Its internal motor drive circuit analyzes this signal and adjusts the operating frequency of the compressor motor accordingly. The change in motor frequency directly causes a change in compressor speed, which in turn alters the refrigerant mass flow rate drawn from the evaporator, compressed, and discharged to the condenser. This change in refrigerant flow rate, through the coordinated regulation of the electronic expansion valve, changes the evaporation temperature and pressure of the refrigerant flowing through the evaporator coil. When the mixed air flows over the surface of the evaporator coil, it exchanges heat with the low-temperature refrigerant inside the coil. The sensible heat in the air is absorbed by the refrigerant, thus cooling (or heating, in heating mode) the mixed air. By adjusting the refrigerant flow rate, the amount of sensible heat removed (or added) from the mixed air is controlled, thereby processing the mixed air to the target temperature.

[0150] Specifically, the output of the temperature processing channel can perfectly match the real-time sensible heat load demand determined by the upstream optimization algorithm, avoiding temperature fluctuations and energy waste caused by excess or insufficient capacity. Variable frequency operation also allows the compressor to work in the high-efficiency zone of partial load most of the time, improving the energy efficiency ratio. By precisely adjusting the refrigerant as the energy carrier, it achieves fine control of the sensible heat state of the air, ensuring that the temperature of the air delivered into the room strictly conforms to the global set temperature. It is the final execution link to achieve precise temperature control.

[0151] The humidity processing channel receives the globally set perceived humidity and generates a solution regeneration control signal based on the difference between the globally set perceived humidity and the current perceived humidity of the mixed air.

[0152] Furthermore, the humidity processing channel has an independent controller. This controller receives a global setpoint humidity command from the central controller. Humidity sensors measure the temperature and relative humidity of the mixed air before processing, and convert these into a current perceived humidity value using a preset perceived humidity calculation model. The controller internally runs a control algorithm that compares the global setpoint humidity with the calculated current perceived humidity, obtaining the difference. This difference is input into the dedicated control logic of the humidity processing channel, which may include proportional-integral-derivative control or more complex algorithms. This generates a control signal to adjust the regeneration process of the solution dehumidification unit within the humidity processing channel. This signal, called the solution regeneration control signal, controls the power of the regeneration heater, the airflow of the regeneration fan, or the opening of the regeneration solution flow valve. The goal is to adjust the concentration of the dehumidifying solution by changing the solution regeneration intensity, thereby indirectly controlling the dehumidification capacity.

[0153] Specifically, a feedforward-feedback composite humidity control system was implemented with the goal of improving user comfort. The control target is not a fixed relative humidity or moisture content, but rather a temperature-coupled humidity level that better reflects human sensation. The controller maps deviations in perceived humidity to adjustments in the solution regeneration intensity. This is a control process involving nonlinear relationships. By generating a solution regeneration control signal, the performance of the dehumidifying solution is actively adjusted to match its moisture absorption capacity with the current moisture load. This avoids insufficient dehumidification leading to excessive humidity, or excessive dehumidification leading to energy waste and over-drying. By treating perceived humidity as the controlled variable, humidity control better serves the ultimate goal of user comfort, rather than simply pursuing the stability of physical parameters.

[0154] The solution dehumidification unit in the humidity treatment channel adjusts the solution concentration according to the solution regeneration control signal and performs latent heat treatment on the mixed air to ensure that the treated air meets the global set temperature and global set perceived humidity.

[0155] Furthermore, the solution dehumidification unit is the core of the humidity treatment channel, comprising a dehumidifier and a regenerator. Solution regeneration control signals are sent to the regenerator's actuators, such as adjusting the input power of the regeneration heater. Changes in the regeneration heater's power affect the heat used to regenerate the dilute solution, thereby altering the amount of water evaporated from the dilute solution and increasing the concentration of the solution returning to the dehumidifier. Higher concentration dehumidification solutions have lower water vapor partial pressure. When air (or mixed air) treated by the temperature treatment channel flows through the dehumidifier and comes into direct contact with the high-concentration solution, the water vapor partial pressure in the air is higher than that at the solution surface. Water vapor is absorbed by the solution, reducing the air's humidity and completing the latent heat treatment. Through closed-loop control of the regeneration intensity, the solution concentration is dynamically maintained at the required level, ensuring that the humidity of the treated air, after conversion, reaches the globally set humidity level. The air simultaneously meets both temperature and perceived humidity requirements.

[0156] Specifically, the system achieves the final independent convergence and attainment of temperature and humidity at the air handling terminal. The solution dehumidification unit, by adjusting the core physical property parameter of solution concentration, realizes independent and precise processing of the latent heat of the air. Compared to condensation dehumidification, solution dehumidification can deeply dehumidify at room temperature with minimal temperature change after treatment, avoiding the need for reheating and exhibiting good decoupling from the temperature processing channel. Dynamic adjustment of the solution concentration through regeneration control allows the humidity processing capacity to continuously and rapidly match changing moisture loads. Air processed sequentially or in parallel by the temperature and humidity processing channels achieves independent and simultaneous convergence of temperature and humidity parameters to the globally set temperature and globally set perceived humidity, marking the final realization of a complete control chain from optimizing overall demand to boundary pretreatment and then to independent terminal processing.

[0157] After being regulated by the temperature and humidity processing channels to meet the global set temperature and global set perceived humidity, the air is delivered to the indoor space as a supply airflow through the supply air duct.

[0158] Furthermore, the air flowing out from the ends of the temperature and humidity processing channels has had its physical state parameters precisely matched to the dry-bulb temperature and humidity requirements corresponding to the global set temperature and global set perceived humidity issued by the central controller through independent adjustment processes. This air, meeting the requirements, is uniformly collected into the main air supply duct. Under the action of the air supply fan, the air gains kinetic energy and flows along the air supply duct network. The air supply duct may include main ducts, branch ducts, diffusers, or nozzles, effectively distributing the air to various areas of the indoor space. The air is delivered into the indoor space from the air outlet at a certain speed and direction. This airflow entering the room is the supply airflow, which carries the precisely processed temperature and humidity status and begins to interact with the indoor environment.

[0159] Specifically, the supply airflow is the carrier that embodies all the intelligent decisions and physical processing results of the entire control chain. Its temperature and humidity are not fixed setpoints, but rather dynamic optimal solutions that maximize group comfort after complex optimization and independent adjustment. Through the supply air duct, this carefully prepared air product is distributed to the room in an organized manner, becoming an active intervention force that changes the indoor environmental state. This ensures that the results of all advanced control algorithms and precision processing upstream can be effectively and reliably applied to the target space. It is the key physical link connecting the air handling terminal and the controlled indoor environment, ensuring the parameter quality and distribution uniformity of the supply airflow.

[0160] The supply airflow exchanges heat and mass with the heat and moisture sources in the indoor space, changing the distribution of dry-bulb temperature and relative humidity at various points in the indoor space.

[0161] Furthermore, after the supply airflow enters the indoor space, due to the difference in temperature and humidity between it and the original indoor air, it will immediately mix with the indoor air through diffusion and convection. During the mixing process, the supply airflow undergoes complex heat and mass exchange with various heat and moisture sources in the room. Heat sources include body heat emitted by people in the room, heat generated by lighting and electronic equipment, and heat transferred in or out through the building envelope. Moisture sources include moisture released by people's breathing and skin, transpiration from plants, and moisture generated by cooking or washing activities. The supply airflow affects the dry-bulb temperature of the indoor air through sensible heat exchange. For example, cold air absorbs heat and lowers the room temperature, while hot air releases heat and raises the room temperature. The supply airflow affects the absolute moisture content of the indoor air through the diffusion and convection of water vapor, thereby changing the relative humidity.

[0162] Specifically, the supply airflow is not directly set as a static environmental parameter, but rather acts as a dynamic input, continuously interacting and neutralizing with inherent indoor heat and humidity disturbances (people and equipment). This is a dynamic balance process between an artificially created comfortable environment and naturally occurring heat and humidity loads. The supply airflow state generated by the innovative control strategy is a counteractor or supplement calculated after pre-considering the influence of these heat and humidity sources. Its goal is to ensure that the net result of the interaction between the supply airflow and indoor disturbance sources brings the environmental parameters at various points in the room close to the comfort range. Understanding this process emphasizes that the output of the control system (supply airflow) and the final goal (indoor comfortable environment) are not directly equivalent, but need to be indirectly achieved through complex indoor aerodynamics and heat and mass transfer processes. This highlights the dynamic and complex nature of environmental control.

[0163] The updated dry-bulb temperature and relative humidity of the air at various points in the indoor space are collected in real time by the indoor multi-sensor node network, forming a new set of indoor environmental parameters.

[0164] Furthermore, multiple sensor nodes distributed in different locations within the indoor space operate continuously. Each sensor node periodically measures the dry-bulb temperature and relative humidity of the air near its installation point. After analog-to-digital conversion and preliminary processing within the node, these measurements are sent to the central controller or a designated data aggregation point via a wireless or wired communication network. The central controller or data aggregation point collects data packets from all online sensor nodes according to a unified time sequence and binds the temperature and humidity readings in each data packet to their corresponding sensor three-dimensional spatial coordinates. The set of all bound coordinate-temperature-humidity data pairs is summarized in the latest sampling period to form a new set of indoor environmental parameters reflecting the spatial distribution of the indoor thermal and humidity environment at the current moment, replacing the old set from the previous period and becoming the latest snapshot of the environmental state.

[0165] Specifically, through multi-point distributed measurement, the system captures the real, non-uniform temperature and humidity field formed by the interaction between the air supply and the heat and moisture source in the indoor environment. It can detect areas that are locally too cold, too hot, humid, or dry, which is something that traditional single-point control cannot detect. The formation of a new set of indoor environmental parameters means that the control system obtains objective and comprehensive feedback data on the actual effects of its previous cycle of control actions.

[0166] A new set of indoor environmental parameters, along with a continuously updated list of online users and individual physiological datasets, constitute the comprehensive data package for the next control cycle.

[0167] Furthermore, while receiving a new set of indoor environmental parameters, the central controller also receives and updates the online user list and personal physiological dataset in parallel. The online user list is continuously refreshed by the interaction between Bluetooth positioning beacons and user smart terminals, while the personal physiological dataset is periodically updated by user smart wearable devices. The central controller starts a new control cycle according to a preset control cycle sequence, such as every tens of seconds or minutes. At the beginning of each control cycle, the central controller aligns and integrates the latest version of the new set of indoor environmental parameters, the continuously updated online user list, and the latest received personal physiological dataset according to a unified time reference. The aligned and integrated data is packaged into a structured new comprehensive data package containing comprehensive information on the environment, people, and physiology. The new comprehensive data package is then sent to the starting step of the control process to start the next new control cycle.

[0168] Specifically, the construction of the new integrated data package marks the end of the previous complete control loop from perception-decision-execution to environmental change-re-perception, and the beginning of the next closed loop. It resynchronizes and binds the changes in environmental state (a new set of indoor environmental parameters), the dynamics of personnel location and composition (a continuously updated online user list), and the real-time signals of human physiological responses (personal physiological datasets). This continuous binding and iteration makes the entire control system a truly dynamic and adaptive system. Based on the control effect of the previous cycle and the latest user status, it formulates the control strategy for the next cycle, thereby tracking the dynamic changes in indoor heat and humidity load, personnel flow, and fluctuations in individual physiological states. This ensures that control decisions are always based on the latest and most comprehensive information, achieving a fundamental leap from open-loop or fixed-strategy control to closed-loop, data-driven, adaptive intelligent control. It is the core mechanism for maintaining continuous environmental comfort.

[0169] In summary, this invention acquires and integrates user physiological data and environmental parameters in real time to form a comprehensive data package. Utilizing a personal thermal and humidity comfort digital twin model in the receiving module, a biothermodynamic algorithm is run based on the user's real-time physiological data and local environmental parameters to generate personalized temperature and perceived humidity settings. The central controller in the fusion module, aiming to minimize collective dissatisfaction, dynamically fuses multiple personalized settings using a multi-objective optimization algorithm to generate globally unified temperature and perceived humidity settings. The judgment module generates a mode command based on a comparison of this global perceived humidity with outdoor air humidity. The preprocessing module sends the mode command to the Janus membrane intelligent fresh air exchanger, adjusting the physical field applied to the Janus membrane to change its water vapor permeation direction selectivity, thus pre-processing the fresh air for humidity. The update module mixes the pre-processed fresh air with indoor return air and adjusts the mixed air through independent temperature and humidity processing channels before sending it into the room, thereby dynamically updating the indoor environmental state. This achieves refined and intelligent environmental control that balances individual comfort needs with overall system energy efficiency in multi-user scenarios.

[0170] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. An independent temperature and humidity control system, characterized in that: This includes an acquisition module that acquires real-time physiological data and fuses it into a comprehensive data packet, comprising the following steps: The system collects dry-bulb temperature, relative humidity, and carbon dioxide concentration to generate a set of indoor environmental parameters; it then communicates with the user's smart terminal using a low-power Bluetooth beacon to determine the user's identity and three-dimensional spatial coordinates. Generate a list of online users; Heart rate variability, wrist skin temperature, and estimated metabolic equivalents are used to construct an individual physiological dataset; The indoor environmental parameter set, online user list and personal physiological dataset are aligned and standardized with a unified timestamp and packaged into a comprehensive data package; The receiving module, based on a comprehensive data packet, receives the corresponding user's real-time physiological data and local environmental parameters of their location from the personal thermal and humidity comfort digital twin model. It then runs an internal biothermodynamic algorithm to generate personalized temperature and humidity settings, including the following steps: The central controller activates a pre-set personal thermal and humidity comfort digital twin model corresponding to the identity identifier in the online user list. Based on the activated personal thermal and humidity comfort digital twin model, it receives the heart rate variability, wrist skin temperature and estimated metabolic equivalent from the corresponding user's personal physiological dataset, and receives the local air dry bulb temperature and relative humidity corresponding to the three-dimensional spatial coordinates of the corresponding user extracted from the indoor environmental parameter set. Based on the reverse optimization training of user historical physiological data and environmental data, a multi-steady-state comfort field simulation is obtained. The received heart rate variability, wrist skin temperature, estimated metabolic equivalent, local dry-bulb temperature and relative humidity are used as field parameters. The steady-state attractor in the comfort field is found through iteration. The coordinates of the steady-state attractor are mapped to the personalized temperature setting value and the personalized perceived humidity setting value. The central controller collects digital twin models of the individual thermal and humidity comfort of all online users and forms a set of personalized settings by combining multiple personalized temperature settings and personalized perceived humidity settings. The fusion module, with the central controller aiming to minimize collective dissatisfaction, inputs the fully personalized temperature setting and the personalized perceived humidity setting into a multi-objective optimization algorithm for dynamic fusion, generating a global set temperature and a global set perceived humidity. The judgment module compares the globally set perceived humidity with the outdoor air humidity to generate mode instructions; The pre-processing module sends mode commands to the Janus membrane intelligent fresh air exchanger. By adjusting the physical field applied to the Janus membrane of the Janus membrane intelligent fresh air exchanger, it changes the selectivity of water vapor permeation direction and performs humidity pre-processing on the introduced fresh air to obtain pre-processed fresh air. The update module mixes the pre-treated fresh air with the indoor return air to form mixed air. Based on the globally set temperature and globally set perceived humidity, the mixed air is adjusted through independent temperature and humidity processing channels. The adjusted air is then sent into the room to update the indoor environmental status.

2. The independent temperature and humidity control system as described in claim 1, characterized in that: The central controller, aiming to minimize collective dissatisfaction, dynamically fuses the fully personalized temperature setpoint and the personalized perceived humidity setpoint into a multi-objective optimization algorithm to generate a global set temperature and a global set perceived humidity. This process includes the following steps: Each personalized temperature setting and personalized perceived humidity setting combination in the set of personalized settings is substituted into the biothermodynamic equations inside the corresponding personal thermal and humidity comfort digital twin model to solve for the user's predictive dissatisfaction value in each combination environment. The central controller defines the optimization objective as minimizing the sum of the predictive dissatisfaction values ​​of all online users, using the global set temperature and the global set perceived humidity as decision variables, and setting the constraint that the predictive dissatisfaction of each user must not exceed the acceptable threshold. The predicted dissatisfaction values ​​of all online users, along with the optimization objective and constraints, are input into a multi-objective optimization algorithm, and a dynamic fusion solution is performed. Under the premise of satisfying the constraints, the multi-objective optimization algorithm iteratively finds the combination of decision variables that minimizes the sum of the predictive dissatisfaction values ​​of all online users, and outputs the global set temperature and global set perceived humidity that achieve the minimization objective.

3. The independent temperature and humidity control system as described in claim 2, characterized in that: Based on a comparison between the globally set perceived humidity and the outdoor air humidity, a mode command is generated, including the following steps: Based on the global set perceived humidity, the central controller queries the built-in comfort-humidity mapping table to obtain the desired indoor air humidity target value; The central controller reads the outdoor air dew point temperature data provided by the outdoor air sensor and calculates the outdoor air humidity based on the outdoor air dew point temperature and standard atmospheric pressure. Compare the difference between the outdoor air humidity content and the indoor air humidity content target value, and read the preset seasonal mode identifier; Based on the difference comparison results and the seasonal pattern identifier, the mode command is output according to the preset logic rules.

4. The independent temperature and humidity control system as described in claim 3, characterized in that: The mode command is sent to the Janus membrane smart air exchanger. By adjusting the physical field applied to the Janus membrane of the Janus membrane smart air exchanger, the selectivity of water vapor permeation direction is changed, including the following steps: The mode command is transmitted to the local controller of the Janus membrane intelligent fresh air exchanger. The mode command is parsed, and the preset voltage-mode mapping relationship is queried according to the mode command to determine the DC voltage value and polarity that needs to be applied to the electrode pairs on both sides of the Janus membrane of the Janus membrane intelligent fresh air exchanger. The local controller of the Janus membrane intelligent fresh air exchanger drives the high-voltage power supply to output DC voltage to the electrode pairs on both sides of the Janus membrane of the Janus membrane intelligent fresh air exchanger. The DC electric field applied to the Janus membrane of the Janus membrane smart fresh air exchanger changes the orientation and distribution of the functional groups inside the Janus membrane, thereby regulating the selective difference of water vapor permeation from the indoor exhaust side to the outdoor fresh air side and from the outdoor fresh air side to the indoor exhaust side.

5. The independent temperature and humidity control system as described in claim 4, characterized in that: The process of pre-treating the humidity of the introduced fresh air to obtain pre-treated fresh air includes the following steps: Outdoor air, as fresh air to be treated, flows through one side of the Janus membrane of the Janus membrane smart fresh air exchanger, which has been subjected to a directional electric field. The membrane utilizes the altered permeability selectivity to preferentially allow water vapor molecules to permeate from the other side of the Janus membrane to the fresh air side under the action of the directional electric field. Janus membranes reduce the absolute moisture content of the fresh air flowing through the surface of the Janus membrane by promoting the unidirectional permeation of water vapor molecules, thus achieving sensible heat exchange between the fresh air and the exhaust air. The air, after undergoing sensible heat exchange and dehumidification, flows out from the fresh air outlet of the Janus membrane intelligent fresh air exchanger, becoming pre-treated fresh air that meets the humidity control target of the mode command.

6. The independent temperature and humidity control system as described in claim 5, characterized in that: The pretreated fresh air is mixed with the indoor return air to form mixed air, including the following steps: Pretreated fresh air flowing from the fresh air outlet of the Janus membrane smart fresh air exchanger is sent into an inlet of the mixing chamber. The indoor return air collected from the indoor return air vent is sent to another inlet of the mixing chamber, where the pre-treated fresh air and the indoor return air are mixed in the mixing chamber according to a preset volumetric flow rate ratio. The mixed air is brought into a mixing chamber to achieve a uniform distribution of temperature and humidity, thus forming mixed air.

7. The independent temperature and humidity control system as described in claim 6, characterized in that: Based on the globally set temperature and globally set perceived humidity, the mixed air is regulated through independent temperature and humidity processing channels, including the following steps: The temperature processing channel receives the global set temperature and generates a frequency conversion control signal based on the difference between the global set temperature and the current dry-bulb temperature of the mixed air. The variable frequency compressor in the temperature processing channel adjusts the refrigerant flow according to the variable frequency control signal to perform sensible heat treatment on the mixed air. The humidity processing channel receives the global set perceived humidity and generates a solution regeneration control signal based on the difference between the global set perceived humidity and the current perceived humidity of the mixed air. The solution dehumidification unit in the humidity treatment channel adjusts the solution concentration according to the solution regeneration control signal and performs latent heat treatment on the mixed air to ensure that the treated air meets the global set temperature and global set perceived humidity.

8. The independent temperature and humidity control system as described in claim 7, characterized in that: The conditioned air is then introduced into the room to refresh the indoor environment, including the following steps: After being regulated by the temperature and humidity processing channels to meet the global set temperature and global set perceived humidity, the air is delivered to the indoor space as the air supply airflow through the air supply duct. The supply airflow exchanges heat and mass with the heat and moisture sources in the indoor space, changing the distribution of dry-bulb temperature and relative humidity at various points in the indoor space. The updated dry-bulb temperature and relative humidity of the air at various points in the indoor space are collected in real time by the indoor multi-sensor node network, forming a new set of indoor environmental parameters. A new set of indoor environmental parameters, along with a continuously updated list of online users and individual physiological datasets, constitute the comprehensive data package for the next control cycle.

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