Indoor radiation refrigeration method and system based on nonlinear temperature control algorithm

Abstract

The invention discloses an indoor radiation refrigeration method and system based on a nonlinear temperature control algorithm, and belongs to the technical field of building energy saving and heating ventilation air conditioning. The temperature and relative humidity information of a current room and the image information of personnel are collected in real time through a sensor; the target temperature of each room is set through a multi-mode large language model non-inductive adjustment mode; calculating the dew point temperature of each room in real time based on the current room temperature and the relative humidity; and according to the room target temperature, the current room temperature and the room relative humidity parameter, a four-section nonlinear temperature control algorithm is executed, and four outlet water temperature control functions are adopted to dynamically adjust the outlet water temperature of radiation refrigeration. A multi-mode large language model is combined with a four-section nonlinear temperature control algorithm, the intelligent radiation refrigeration method and system with the'perception-decision-execution 'closed-loop capacity are constructed, the system can adapt to a complex environment to adjust the outlet water temperature of radiation refrigeration, and the proper indoor temperature is ensured.

Description

Technical Field

[0001] This application belongs to the field of building energy conservation and HVAC technology, specifically involving an indoor radiative cooling method and system based on a nonlinear temperature control algorithm. Background Technology

[0002] Traditional air conditioning, which cools air simply by driving air convection through a fan, is not only energy-intensive and has limited cooling effect, but also suffers from problems such as high noise, a strong draft, and dryness. Radiant floor heating systems have been favored since their introduction and are widely used in northern my country. However, radiant cooling systems have not been widely adopted or promoted in my country due to the tendency for condensation to form on the surface of the radiant panels and insufficient cooling capacity. Despite this, radiant cooling offers many advantages: high comfort without a draft, significant energy savings, compatibility with winter heating systems, and the ability to improve air quality.

[0003] In recent years, radiant cooling has become a hot topic in the field of building energy conservation. Currently, research on the application of radiant cooling technology mainly focuses on improving energy efficiency, optimizing control strategies, and preventing condensation. Using the cooling capacity of the soil through a ground source heat pump system significantly reduces system energy consumption. Electric regulating valves are installed in each pipe to control the flow rate. Humidity and temperature sensors within the room can calculate the precise dew point temperature, thereby intelligently controlling the outlet water temperature to prevent condensation and actively reducing room humidity, thus lowering the dew point temperature.

[0004] The invention patent CN110793135A, entitled "An Integrated Floor Heating and Air Conditioning Unit," discloses an integrated floor heating and air conditioning unit. This unit integrates a water module controller into a wired controller. The target indoor temperature is set via the indoor wired controller, and the outlet water temperature is intelligently controlled based on the temperature difference between the target and actual indoor temperatures. However, the outlet water temperature control algorithm used in this integrated unit is linear. Due to the lag in heat exchange between the water pipes and the floor, problems such as insufficient control accuracy and response delays may occur under complex operating conditions. Closing the valve to stop the heat exchange between the cold water and the room is not energy-efficient. Although the integrated wired controller allows setting the indoor temperature and intelligently controlling the outlet water temperature, the target temperature setting still relies on traditional button settings, using local dew point temperature rather than real-time calculation. Dew point temperature is affected by temperature and relative humidity, and these parameters in the actual house will deviate from local data. Daily activities such as cooking, mopping, and bathing can have varying degrees of impact.

[0005] The invention patent CN113639347A, entitled "A Floor Radiant Heating and Cooling System Based on a Soil Source Heat Pump," discloses a floor radiant heating and cooling system based on a soil source heat pump. In this system, all flow and air volume regulating valves in the pipes are electrically operated and have built-in temperature probes to display valve opening and temperature. Simultaneously, a stirrer and temperature sensor are built into the mixing tank to control the water temperature to be approximately 1.5°C higher than the local dew point temperature, enabling precise control of the outlet water temperature. However, the calculated dew point temperature based on the actual room temperature and humidity differs from the local dew point temperature. If the water temperature is controlled solely based on the local dew point temperature, condensation may occur in rooms with significant temperature and humidity differences, and the cooling efficiency may not reach optimal levels.

[0006] The invention patent CN116907035A, titled "Anti-Condensation System and Method," discloses an anti-condensation system and method. This system changes its anti-condensation mode based on the difference between the room temperature and the dew point temperature, and collects parameters such as the room's dew point temperature in real time using a data acquisition device. While this patent's method of controlling indoor relative humidity and temperature by manipulating a dehumidifier, water pump, and valves can effectively prevent condensation, it only uses a fixed range of values ​​as the basis for changing the anti-condensation mode, neglecting the possibility that adjusting the relative humidity and temperature values ​​might cause the room to become dry or result in insufficient cooling.

[0007] The invention patent CN117232084B, entitled "Control Method and Device for Radiant Air Conditioning System Based on Human-Computer Interaction," discloses a control method and device for a radiant air conditioning system based on human-computer interaction, which can improve the regulation efficiency and accuracy of a floor radiant and ventilated combined cooling system. While this system can allocate air volume and water flow according to the overall cooling load, it lacks fine-grained control for the specific needs of different rooms or areas. In multi-area application scenarios, this may lead to some areas being overcooled or overheated, affecting overall energy efficiency.

[0008] The invention patent CN106091255A, entitled "An Intelligent Temperature Control System for Home Air Conditioners," discloses an intelligent temperature control system for home air conditioners. This system acquires outdoor and indoor temperature values ​​through a data acquisition unit, combines this with a preset temperature value from a setting unit, and then, after analysis and comparison by a control unit, controls the air conditioner to select an appropriate fan speed level, thus achieving intelligent temperature control. However, in cooling mode, when the difference between the outdoor and indoor temperatures is within a certain range, the system only selects the fan speed based on the ratio of the indoor temperature to the preset temperature. This simple and fixed control strategy lacks dynamic adjustment and adaptive capabilities, making it difficult to meet the growing demand for intelligent control.

[0009] In summary, existing radiant cooling systems mainly suffer from the following technical problems:

[0010] First, the temperature setting of the refrigeration system relies on manual adjustment and lacks intelligent automatic adjustment function;

[0011] Second, the cooling process generally adopts a linear change strategy, which cannot be dynamically controlled in multiple stages and nonlinearities according to changes in the environment and load, resulting in low cooling efficiency and low energy utilization.

[0012] Third, anti-condensation control relies on a fixed dew point temperature threshold, ignoring real-time changes in room temperature and humidity, resulting in a rigid control strategy and insufficient intelligence.

[0013] Fourth, the multi-mode control system does not differentiate between the actual needs of different areas, and the regional adjustment granularity is coarse, which can easily cause uneven local heating and cooling.

[0014] Fifth, traditional intelligent temperature control systems have poor perception generalization and weak task reasoning ability. They rely on preset control strategies to calculate target temperature, which limits them to specific scenarios and tasks and lacks the ability to reason about different scenarios. Summary of the Invention

[0015] To address the problems in related technologies, this application provides an indoor radiative cooling method and system based on a nonlinear temperature control algorithm. By combining a multimodal large language model with a four-segment nonlinear temperature control algorithm, an intelligent radiative cooling system with a closed-loop capability of "perception-decision-execution" is constructed. This system has the ability to adaptively adjust to complex environmental changes and can achieve efficient, stable, and comfortable temperature control in scenarios with multiple users, multiple rooms, and variable loads. It solves the technical problems of traditional systems, such as reliance on manual settings, lag in adjustment response, and insufficient user experience.

[0016] The technical solution is as follows:

[0017] On the one hand, an indoor radiative cooling method based on a nonlinear temperature control algorithm is provided, including:

[0018] Step S1: The sensor collects the current room temperature T in real time. r Relative humidity (RH) r And personnel image information;

[0019] Step S2: Set the target temperature T for each room using the multimodal large language model's seamless adjustment mode. a ;

[0020] After inputting the current room temperature, relative humidity, and personnel image information into the multimodal large language model, the optimal target temperature T is output. a If the people in the room believe that the output target temperature T a If it is not suitable, manual intervention will be made to adjust it, and the adjustment behavior will be automatically fed back to the multimodal large language model to optimize the knowledge base;

[0021] Step S3: Based on the current room temperature T r and room relative humidity (RH) r Real-time calculation of dew point temperature T in each room d ;

[0022] Step S4: Based on the dew point temperature T d Target temperature T a Room temperature T r Room relative humidity (RH) r The parameters are used to execute a four-segment nonlinear temperature control algorithm to dynamically adjust the outlet water temperature T. w The four-stage nonlinear temperature control algorithm includes an anti-condensation cooling algorithm model activated in the first stage, a rapid cooling algorithm model activated in the second stage, a forward-looking temperature regulation algorithm model activated in the third stage, and a temperature approximation algorithm model activated in the fourth stage.

[0023] Furthermore, step S4 specifically includes:

[0024] If the target temperature T a Greater than room temperature T r Then determine the room's relative humidity (RH). r Is it greater than 55%?

[0025] If the room's relative humidity (RH) r If the temperature is ≤55%, then proceed directly to the second stage, where the radiant cooling system activates the floor coils and ceiling radiant cooling plates, and the rapid cooling algorithm model is adopted.

[0026] If the room's relative humidity (RH) r If the temperature exceeds 55%, the system enters the first stage, activating the anti-condensation cooling algorithm model. The radiant cooling system then activates the ceiling-mounted radiant cooling panels to control the outlet water temperature T. w Dehumidify and cool; when the room's relative humidity (RH) r When the percentage is ≤55%, proceed to the second stage, followed by the third and fourth stages in sequence.

[0027] In the third stage, when the room temperature T r With target temperature T a When the temperature difference is ≤5℃, the forward-looking temperature regulation algorithm model is activated, and the radiant cooling system turns on the floor coils and ceiling radiant cooling plates.

[0028] In the fourth stage, when the outlet water temperature T is in the third stage... w With floor temperature T F When the difference is ≤0.5℃, the temperature approximation algorithm model is activated, the radiant cooling system turns on the floor coils and ceiling radiant cooling plates, and the PID control algorithm model is used to adjust the outlet water temperature T. w .

[0029] Furthermore, if entering the first stage, the outlet water temperature T is adopted. w The control function is:

[0030] T w =T d -5℃

[0031] Among them, T w T represents the outlet water temperature. d This is the dew point temperature.

[0032] Furthermore, if the process proceeds to the second stage, the effluent temperature T is adjusted using the transfer function method. w Outlet water temperature T w The control function is:

[0033]

[0034] Among them, T w t represents the outlet water temperature, t represents the operating time of the radiant cooling system after startup, and t1 represents the end time of the first stage.

[0035] Furthermore, if entering the third stage, the outlet water temperature T will be used. w The control function is:

[0036]

[0037] Among them, T w t is the outlet water temperature, τ is a constant, and t3 is the time node value at the end of the third stage.

[0038] Furthermore, if entering the fourth stage, the outlet water temperature T will be used. w The control function is:

[0039] e(t) = (T) F -0.5)-T w

[0040]

[0041] Among them, K p For proportional gain, K i For integral gain, K d Let e(t) be the differential gain, and T be the temperature deviation. F For floor temperature, T w The outlet water temperature is t, and the operating time of the radiant cooling system after startup is t.

[0042] On the other hand, an indoor radiant cooling system based on a nonlinear temperature control algorithm is provided. Employing the aforementioned cooling method, the system includes:

[0043] Floor coils, ceiling radiant cooling panels, central controller, sub-controllers, main water distributor, branch water distributors, main water collector, branch water collectors, and heat pump units are all connected to the central controller via communication.

[0044] Furthermore, the sub-controller includes a temperature sensor, a humidity sensor, a video input module, a voice input module, and a touch screen input module.

[0045] Furthermore, each branch of the main water distributor is equipped with a mixing valve, which controls the final outlet water temperature T by controlling the heat pump unit and the mixing valve on each branch. w When multiple rooms are cooled simultaneously, the chilled water temperature output by the heat pump unit is equal to the outlet water temperature T in each room. w The lowest value.

[0046] The technical solution includes at least the following technical effects:

[0047] 1. This application introduces a multimodal large language model, which identifies image information of people in a room (such as the number of people and their age) through sensors and combines it with environmental parameters obtained by the sensors to perform intelligent reasoning and output the optimal target temperature T. a The introduction of a multimodal large language model enables the system to possess strong perceptual generalization and task reasoning capabilities, achieving highly personalized and scenario-adaptive temperature control strategies. Simultaneously, the system supports multiple input methods, including voice and sub-controller panels, to adjust the target temperature T. a While achieving a high level of intelligent control, it enhances the flexibility and convenience of user operation, which is superior to the single control method of traditional temperature control systems.

[0048] 2. This application specifies the outlet water temperature T. w In terms of control execution, a four-segment nonlinear temperature control algorithm is adopted, corresponding to four stages: anti-condensation cooling, rapid cooling, look-ahead temperature adjustment, and temperature approximation. Different control logics and mathematical models are designed for each stage to achieve dynamic and precise adjustment of the outlet water temperature. Compared with traditional single linear control or full-range PID regulation, this method has advantages such as fast response speed, low energy consumption, and strong control stability, significantly improving the system's adaptability and temperature control performance in complex scenarios such as multiple users, multiple rooms, and variable loads.

[0049] 3. The look-ahead temperature control algorithm used in this application is based on a room temperature T. r With target temperature T a When the difference is less than or equal to 5℃, the system activates the look-ahead temperature regulation algorithm model to actively reduce the cooling intensity, thereby lowering the outlet water temperature T. wThe temperature rises, thus buffering the overcooling caused by the system's thermal inertia in advance. This algorithm enables a smoother control process when the temperature approaches the set value, effectively improving temperature control accuracy and indoor comfort, while further reducing energy consumption and achieving good energy-saving effects.

[0050] 4. This application constructs an intelligent radiative cooling system with a closed-loop "perception-decision-execution" capability by combining a multimodal large language model with a four-segment nonlinear temperature control algorithm. This system possesses adaptive adjustment capabilities to complex environmental changes and can achieve efficient, stable, and comfortable temperature control in scenarios with multiple users, multiple rooms, and varying loads. It solves the technical problems of traditional systems, such as reliance on manual settings, delayed adjustment response, and insufficient user experience.

[0051] It should be understood that the above general description and the following detailed description are merely exemplary and do not limit this application. Attached Figure Description

[0052] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0053] Figure 1 A control flowchart of an indoor radiant cooling method based on a nonlinear temperature control algorithm is provided for a preferred embodiment of this application;

[0054] Figure 2 The outlet water temperature T in the rapid cooling stage provided by a preferred embodiment of this application w A schematic diagram illustrating the control effect;

[0055] Figure 3 The floor temperature T provided in a preferred embodiment of this application F Room temperature T r and outlet water temperature T w A schematic diagram of the curve showing the change over time;

[0056] Figure 4 The room temperature T provided in a preferred embodiment of this application r Dew point temperature T d and room relative humidity (RH) r A schematic diagram of the curve showing the change over time;

[0057] Figure 5 A schematic diagram of an indoor radiant cooling system based on a nonlinear temperature control algorithm, provided as a preferred embodiment of this application;

[0058] Figure 6 A framework diagram of an indoor radiant cooling system based on a nonlinear temperature control algorithm is provided for a preferred embodiment of this application;

[0059] Figure 7 This is a schematic diagram of the prompt word content of a multimodal large language model provided in a preferred embodiment of this application;

[0060] Figure 8 A flowchart of a dialogue system based on retrieval-enhanced generation (RAG) provided in a preferred embodiment of this application;

[0061] Figure 9 This is an architecture diagram of a knowledge base-optimized multimodal large language model provided in a preferred embodiment of this application;

[0062] Explanation of reference numerals in the attached figures:

[0063] 1. Heat pump unit; 2. Main water distributor; 3. Main water collector; 4. Central controller; 5. Floor coil; 6. Ceiling radiant cooling plate; 7. Sub-controller; 8. Branch water distributor; 9. Branch water collector; 10. Mixing valve; 11. Ambient temperature water source. Detailed Implementation

[0064] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0065] This application provides an indoor radiative cooling method and system based on a nonlinear temperature control algorithm. The system incorporates a multimodal large language model, using occupant image information to determine the number and age of people in the room, and combines this with environmental parameters acquired through sensors to infer and output the optimal target temperature T. a The system also supports manual setting of the target temperature via voice or input through the control panel; the system collects the current room temperature (T) in real time. r relative humidity (RH) of the room r Data, combined with the standard dew point calculation formula, is used to calculate the dew point temperature T in real time. d It is used to dynamically determine whether there is a risk of condensation; at the outlet water temperature T w In terms of control, the system adopts an established four-segment nonlinear temperature control algorithm to dynamically adjust the outlet water temperature T. w .

[0066] Example 1

[0067] like Figure 5As shown, a preferred embodiment of this application provides an indoor radiant cooling system based on a nonlinear temperature control algorithm. The radiant cooling system includes floor coils 5, ceiling radiant cooling plates 6, a central controller 4, a sub-controller 7, a main water distributor 2, branch water distributors 8, a main water collector 3, branch water collectors 9, and a heat pump unit 1. Each branch of the main water distributor 2 is equipped with a mixing valve 10. The heat pump unit 1, sub-controller 7, main water distributor 2, branch water distributors 8, and mixing valves 10 are all communicatively connected to the central controller 4, using the ModBus communication protocol to ensure flexible interaction between the devices. The final outlet water temperature T is controlled by controlling the heat pump unit 1, the ambient temperature water source 11, and the mixing valves 10 on each branch. w When multiple rooms are cooled simultaneously, the chilled water temperature output by heat pump unit 1 is equal to the outlet water temperature T in each room. w The lowest value.

[0068] The sub-controller 7 includes a temperature sensor, a relative humidity sensor, a video input module, a voice input module, and a touchscreen input module. Users can adjust the target temperature T in each room via the voice input module and the touchscreen input module of the sub-controller 7. a .

[0069] like Figure 5 As shown, the radiant cooling system supplies water to two rooms separately via the main water distributor 2. Branch water distributors at the rooms further distribute the water to the ceiling radiant cooling panels 6 and the floor coils 5, thus achieving the distribution of chilled water between the two terminals within the rooms. The temperatures of the two rooms can be adjusted independently. When both rooms are cooling simultaneously, the chilled water temperature output by the heat pump unit 1 is equal to the outlet water temperature T in both rooms. w The minimum value is set to ensure the needs of the rooms with the highest cooling load. Simultaneously, a mixing valve 10 is installed on each branch of the main water distributor 2 to mix and regulate the water supply according to the temperature requirements of the corresponding rooms, thereby ensuring that each room receives the outlet water temperature T that meets its set value. w This enables precise temperature control in each room.

[0070] The central controller 4 establishes communication connections with the heat pump unit 1, the sub-controller 7, the main water distributor 8, the branch water distributor 2, and the mixing valve 10. The multimodal large language model and the algorithm used for system control in this application are integrated into the central controller to achieve unified management and intelligent control of all components.

[0071] In one embodiment, such as Figure 6As shown, the radiant cooling system mainly includes a multimodal sensing module (visual, voice, temperature and humidity), a sub-controller 7, a central controller 4, piping components, and a multimodal large language model. The sub-controller 7 is responsible for receiving multimodal sensing signals and transmitting them to the central controller. The central controller 4 uses a four-segment nonlinear algorithm to analyze and process the input signals. In the multimodal large language model's non-intrusive adjustment mode, it sends image information to the multimodal large language model and receives the returned target temperature T. a This allows for the adjustment of the opening status of each valve and the opening angle of the mixing valve 10, thereby achieving intelligent control of the heat pump unit 1, water distributor, mixing valve 10, etc. in the pipeline components, and ultimately controlling the outlet water temperature T. w Precise adjustment and stable control.

[0072] Example 2

[0073] A preferred embodiment of this application provides an indoor radiant cooling method based on a nonlinear temperature control algorithm, the specific steps of which are as follows:

[0074] Step S1: Real-time acquisition of the current room temperature T using temperature sensors, relative humidity sensors, and vision sensors in each room. r Room relative humidity (RH) r And personnel image information.

[0075] Step S2: Set the target temperature T for each room a The mode used is as follows:

[0076] Multimodal large language model seamless adjustment mode: The multimodal large language model performs inference based on real-time environmental parameters and personnel image information, and automatically sets the optimal target temperature T. a That is, the local air temperature, current room temperature, relative humidity, number of people in the room, gender, age, and status are input into the multimodal large language model, and the optimal target temperature T is output. a If the people in the room believe that the output target temperature T a If it is not suitable, adjustments will be made, and the adjustment behavior will be automatically fed back to the multimodal large language model to optimize the knowledge base;

[0077] In the seamless adjustment mode of the multimodal large language model, the multimodal large language model can automatically adjust the target temperature T according to the dynamic changes in ambient temperature and the number of people in the room. a This enables intelligent optimization and control of indoor temperature, thereby improving the overall comfort level.

[0078] In step S1, the current room temperature T is collected. rAfter obtaining room image information, the multimodal large language model queries local real-time weather data via the internet. Based on this input information, the model performs inference, comprehensively considering environmental factors and human comfort, and finally determines an optimal target temperature T. a .

[0079] Step S3: During the operation of the radiant cooling system, continuously calculate the dew point temperature according to the standard dew point temperature formula, based on the current room temperature T. r and room relative humidity (RH) r Real-time calculation of dew point temperature T in each room d It is used to help determine air conditioning needs.

[0080] Standard dew point temperature calculation formula:

[0081]

[0082] Where T is the current temperature (unit: °C); RH is the relative humidity (unit: %); a = 17.27 and b = 237.7 are empirical constants.

[0083] The standard dew point temperature calculation formula is the Magnus-Tetens approximation formula, which is applicable to the range of temperature from 0℃ to 60℃, relative humidity from 0% to 100%, and dew point temperature from 0℃ to 50℃.

[0084] Step S4: Based on the dew point temperature T d Target temperature T a Room temperature T r Room relative humidity (RH) r The parameters are used to execute a four-segment nonlinear temperature control algorithm to dynamically adjust the outlet water temperature T. w This allows for precise control over the cooling effect.

[0085] The four-stage nonlinear temperature control algorithm includes a first-stage anti-condensation cooling algorithm model, a second-stage rapid cooling algorithm model, a third-stage look-ahead temperature regulation algorithm model, and a fourth-stage temperature approximation algorithm model. The final outlet water temperature T is controlled by regulating the heat pump unit and the mixing valve on each branch. w .

[0086] like Figure 1 As shown, the control flow in this embodiment is as follows:

[0087] After the radiative cooling system is started, it uses a multimodal large language model for imperceptible adjustment of the target temperature T. a Configure the settings to obtain the current room temperature T using sensors. r Relative humidity (RH) r Based on room image information, determine the target temperature T. aIs it greater than the room temperature T? r If the target temperature T a Less than room temperature T r If the target temperature T is not high, the heat pump unit will not be turned on. a Greater than room temperature T r Then determine the room's relative humidity (RH). r Is it greater than 55% if the room's relative humidity (RH) is... r If the relative humidity (RH) is less than or equal to 55%, it will directly enter the rapid cooling phase. r If the temperature exceeds 55%, the system enters the anti-condensation cooling phase. The rapid cooling phase is followed by the forward-looking temperature adjustment phase and the temperature approach phase.

[0088] like Figure 2 As shown in the figure, the outlet water temperature T is reduced during the rapid cooling phase. w The control situation when the temperature drops to 20℃. The arrow in the figure points to a magnified view of the initial stage (0 to 1 second), which more clearly reflects the control of the outlet water temperature T during the initial stage of rapid cooling. w The control effect.

[0089] According to Part 4, "Requirements for Indoor Environmental Parameters," of the current national standard "Indoor Air Quality Standard" (GB / T 18883-2022), the recommended range for indoor relative humidity is 40% to 80%. This application selects 55% as the preferred relative humidity threshold for the rapid cooling stage. While meeting this standard, it retains sufficient room for upward and downward fluctuation, which is conducive to achieving a lower dew point temperature and effectively preventing the indoor air from becoming too dry.

[0090] In this embodiment, the outlet water temperature T w The control strategy is as follows:

[0091] Phase 1, when the room relative humidity (RH) r When the concentration is >55%, the anti-condensation cooling algorithm model is activated, the radiant cooling system turns on the ceiling radiant cooling plates, and the outlet water temperature T is controlled. w While ensuring condensation dehumidification efficiency, energy saving and operational safety are also taken into account, and the outlet water temperature T is adopted. w The control function is:

[0092] T w =T d -5℃

[0093] Among them, T w T represents the outlet water temperature. d This is the dew point temperature.

[0094] The second stage, when the room relative humidity (RH) rWhen the humidity is ≤55%, the rapid cooling algorithm model is activated, and the radiant cooling system turns on the floor coils and ceiling radiant cooling plates. During this stage, the outlet water temperature T w The tracking control method using the transfer function exhibits a nonlinear, exponentially decaying dynamic response, with the final outlet water temperature T... w The control function is:

[0095]

[0096] Among them, T w t represents the outlet water temperature (unit: °C), t represents the operating time of the radiant cooling system after startup (unit: min), and t1 represents the end time of the first stage (unit: min).

[0097] A nonlinear double exponential decay control function is used to control the outlet water temperature T. w It can be adjusted to have good dynamic response characteristics, enabling the outlet water temperature to quickly approach the target value in a short time without violent fluctuations. Compared with linear control, it has stronger system adaptability and environmental adaptability, and is particularly suitable for scenarios with sudden load changes such as a sharp rise in indoor temperature.

[0098] In the third stage, when the room temperature T r With target temperature T a When the temperature difference is less than or equal to 5℃, the look-ahead temperature regulation algorithm model is activated, and the radiant cooling system turns on the floor coils and ceiling radiant cooling plates. Due to the lag in heat transfer within the system, the cooling intensity is appropriately reduced during this stage to keep the outlet water temperature T w The water temperature quickly rises to a level less than or equal to 0.5°C above the floor temperature, mitigating overrush caused by temperature control lag, and the outlet water temperature T... w The control function is:

[0099]

[0100] Among them, T w τ is the outlet water temperature, τ is a constant, and t3 is the time node value at the end of the third stage (unit: min).

[0101] This control function ensures the outlet water temperature T. w The temperature can quickly rise to a level less than or equal to 0.5°C above the floor temperature, which can actively buffer the overcooling caused by thermal inertia. This allows the temperature control system to reduce the cooling intensity before reaching the target temperature, effectively improving temperature control accuracy and indoor comfort.

[0102] In the fourth stage, when the outlet water temperature T is in the third stage... w With floor temperature T FWhen the difference is ≤0.5℃, the temperature approximation algorithm model is activated, the radiant cooling system turns on the floor coils and ceiling radiant cooling plates, and the PID control algorithm model is used to adjust the outlet water temperature T. w To maintain thermal balance and prevent backheating, the central controller adjusts the output water temperature T. w The temperature deviation e(t) is calculated by subtracting the fixed temperature difference from the floor temperature.

[0103] e(t) = (T) F -0.5)-T w

[0104] Among them, T F Floor temperature (unit: °C);

[0105] The controller output directly affects the regulation of the outlet water temperature T. w :

[0106]

[0107] Among them, K p For proportional gain, K i For integral gain, K d For differential gain, T w The outlet water temperature is t, and the operating time of the radiant cooling system after startup is t.

[0108] The PID controller, based on proportional, integral, and derivative actions, achieves high-precision, low-overshoot, and fast-convergence regulation of the outlet water temperature. It effectively suppresses system disturbances caused by floor warming or ambient temperature fluctuations, maintaining indoor thermal balance. Compared to traditional open-loop temperature maintenance or setpoint strategies, this control strategy significantly extends system stabilization time, reduces temperature fluctuations, and improves energy efficiency.

[0109] The radiant cooling control method allows for independent temperature adjustment in each room. When multiple rooms are cooled simultaneously, the chilled water temperature output by the heat pump unit equals the outlet water temperature T in each room. w The minimum value is set to ensure the needs of the rooms with the highest cooling load. Simultaneously, a mixing valve is installed on each branch of the main manifold to mix and regulate the water supply according to the temperature requirements of the corresponding rooms, thereby ensuring that each room receives the outlet water temperature T that meets its set parameters. w This enables precise temperature control in each room.

[0110] In one preferred solution, the multimodal large language model to be accessed can be Flamingo, GPT-4V, Qwen2.5 Omni, etc. These models all support multimodal input and can process various data formats such as text, images, and videos, and are suitable for multimodal perception and interaction scenarios.

[0111] Figure 9The diagram illustrates the architecture of a knowledge-based optimized multimodal large language model. In the central controller's algorithm, the model is used by calling its API. In the multimodal large language model's seamless adjustment mode, after system startup, image information acquired by the sub-controller's camera and local temperature information queried online are used as input. Utilizing the model's visual understanding capabilities, the image information is parsed to obtain information such as the number of people in the room, their gender, age, and state. Finally, by combining this information and performing inference, an optimal target temperature T is obtained. a Based on the preset prompt, the model will output the target temperature T. a .

[0112] like Figure 7 The diagram illustrates the cue words used in a multimodal large language model. These cue words serve as input instructions, guiding the multimodal model to perform specific tasks. By setting cue words, the model's task objectives can be clarified, the contextual structure of the input can be constructed, and the content, format, and style of the model's generated results can be controlled.

[0113] Figure 8 The flowchart shown is for a dialogue system based on Retrieval Enhanced Generation (RAG). The multimodal large language model has general language understanding and multimodal information processing capabilities, and can be used to parse image information in input images, such as identifying the number of people in a room, individual gender, age, and status.

[0114] RAG is a technology that combines external knowledge bases (such as documents, databases, and web pages) with a multimodal large language model, allowing the model to "look up information" before generating answers, thereby improving accuracy and reliability.

[0115] In this embodiment, two methods, prompt words and Retrieval-Enhanced Generation (RAG), are used to optimize the multimodal large language model, which can improve model performance to adapt to specific tasks and achieve a more efficient dialogue system. By introducing Retrieval-Enhanced Generation (RAG) technology, different types of information can be dynamically added to the knowledge base, thereby improving the accuracy and reliability of the language model's generated results. In this embodiment, it is preferable to input the personal information of resident personnel (such as temperature preferences, physical condition, living habits, etc.) into the knowledge base to achieve personalized responses for individuals, such as making intelligent control decisions based on the user's temperature preferences.

[0116] like Figure 3 As shown, in a specific application scenario, the outdoor temperature is 35℃ and the room temperature T r The temperature is 33℃, and the relative humidity (RH) in the room is... r The rate is 75%. When multiple rooms require different outlet water temperatures, the water temperature output by the heat pump unit is the outlet water temperature T. w The lowest room outlet water temperature Tw The main and branch controllers regulate the piping to different rooms via mixing valves. In rooms where the water temperature is not the lowest possible, the mixing valves are adjusted to allow ambient temperature water to flow in and regulate the water temperature. The radiant cooling systems in both rooms start simultaneously. Room images captured by the branch controller's camera and real-time air temperature data obtained from the network are input into a multimodal large language model. After analyzing and reasoning the input information, the multimodal large language model obtains the target temperature T for room 1. a The target temperature for room 2 is 24℃, and the target temperature for room 2 is 21℃, which is then sent to the central controller. The central controller then obtains the room temperature T through the temperature sensor and relative humidity sensor of the sub-controller. r and room relative humidity (RH) r The room temperature at this time is T r The temperature is 33℃, and the relative humidity (RH) in the room is... r The dew point temperature T is calculated using the standard dew point formula, which is 75%. d The temperature was 28.0℃, and the target temperature T was determined to be... a Less than room temperature T r Room relative humidity (RH) r When the relative humidity exceeds 55%, the system activates the anti-condensation cooling algorithm model, the heat pump unit starts, outputting 23°C chilled water, and the radiant cooling system activates the ceiling radiant cooling panels for dehumidification and cooling. When the relative humidity sensor on the controller detects that the relative humidity has dropped to 55%, the rapid cooling algorithm model is activated, and the radiant cooling system activates the floor coils and ceiling radiant cooling panels. At this time, the room temperature T... r The temperature is 30℃; when the room temperature T r With target temperature T a When the difference is less than or equal to 5℃, the forward-looking temperature regulation algorithm model is activated, and the radiant cooling system turns on the floor coils and ceiling radiant cooling plates; when the third stage of outlet water temperature T is reached... w After the temperature rises, the temperature approximation algorithm model is activated, and the valves leading to the ceiling radiant cooling plate pipes and floor coils are opened. The outlet water temperature T in room 1 is... w The temperature is controlled at 23.5℃, and the outlet water temperature T in room 2 is... w The temperature should be controlled at 20.5℃.

[0117] like Figure 4 As shown in the figure, the current room temperature T is displayed. r Dew point temperature T d and room relative humidity (RH) r The trend of relative humidity (RH) over time is clearly shown in the graph. r For dew point temperature T d Impact: With the increase of room relative humidity (RH) r Rapidly decreasing, dew point temperature T dThe temperature also dropped significantly, verifying the role of the anti-condensation cooling stage in this application and providing the basic conditions for the rapid cooling stage.

[0118] The structures, proportions, sizes, etc., shown in the accompanying drawings of this specification are only for the purpose of assisting those skilled in the art in understanding and reading the content disclosed in the specification, and are not intended to limit the implementation conditions of this application. Therefore, they have no substantial technical significance. Any modifications to the structure, changes in the proportions, or adjustments to the size should still fall within the scope of the technical content disclosed in this application, provided that they do not affect the effects and purposes that this application can produce.

[0119] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. An indoor radiant cooling method based on a nonlinear temperature control algorithm, characterized in that, include: Step S1: Real-time acquisition of current room temperature T r Relative humidity (RH) r And personnel image information; Step S2: Set the target temperature T for each room using the multimodal large language model's seamless adjustment mode. a The system inputs the current room temperature, relative humidity, and personnel image information into a multimodal large language model, and outputs the optimal target temperature T. a If the people in the room believe that the output target temperature T a If it is not suitable, adjustments will be made, and the adjustment behavior will be automatically fed back to the multimodal large language model to optimize the knowledge base; Step S3: Based on the current room temperature T r and room relative humidity (RH) r Real-time calculation of dew point temperature T in each room d ; Step S4: Based on the dew point temperature T d Target temperature T a Current room temperature T r Room relative humidity (RH) r The parameters are used to execute a four-segment nonlinear temperature control algorithm to dynamically adjust the outlet water temperature T. w The four-stage nonlinear temperature control algorithm includes an anti-condensation cooling algorithm model activated in the first stage, a rapid cooling algorithm model activated in the second stage, a forward-looking temperature regulation algorithm model activated in the third stage, and a temperature approximation algorithm model activated in the fourth stage.

2. The indoor radiant cooling method based on a nonlinear temperature control algorithm according to claim 1, characterized in that, Step S4 specifically includes: If the target temperature T a Greater than the current room temperature T r Then determine the room's relative humidity (RH). r Is it greater than 55%? If the room relative humidity RH r If the temperature is ≤55%, then proceed directly to the second stage, where the radiant cooling system activates the floor coils and ceiling radiant cooling plates, and the rapid cooling algorithm model is adopted. If the room relative humidity RH r If the temperature exceeds 55%, the system enters the first stage, activating the anti-condensation cooling algorithm model. The radiant cooling system then activates the ceiling-mounted radiant cooling panels to control the outlet water temperature T. w Dehumidify and cool; when the room's relative humidity (RH) r When the percentage is ≤55%, proceed to the second stage, followed by the third and fourth stages in sequence. In the third stage, when the room temperature T r With target temperature T a When the temperature difference is ≤5℃, the forward-looking temperature regulation algorithm model is activated, and the radiant cooling system turns on the floor coils and ceiling radiant cooling plates. In the fourth stage, when the outlet water temperature T is in the third stage... w With floor temperature T F When the difference is ≤0.5℃, the temperature approximation algorithm model is activated, the radiant cooling system turns on the floor coils and ceiling radiant cooling plates, and the PID control algorithm model is used to adjust the outlet water temperature T. w .

3. The indoor radiant cooling method based on a nonlinear temperature control algorithm according to claim 2, characterized in that, If the first stage is entered, the anti-condensation cooling algorithm model will be activated, using an outlet water temperature T. w The control function is: T w =T d -5℃ Among them, T w T represents the outlet water temperature. d This is the dew point temperature.

4. The indoor radiant cooling method based on a nonlinear temperature control algorithm according to claim 2, characterized in that, If the process enters the second stage, a rapid cooling algorithm model will be activated, and the outlet water temperature T will be adjusted using a transfer function method. w Outlet water temperature T w The control function is: Among them, T w t represents the outlet water temperature, t represents the operating time of the radiant cooling system after startup, and t1 represents the end time of the first stage.

5. The indoor radiant cooling method based on a nonlinear temperature control algorithm according to claim 2, characterized in that, If the process enters the third stage, the forward-looking temperature control algorithm model will be activated, using an outlet water temperature T. w The control function is: Among them, T w t is the outlet water temperature, τ is a constant, and t3 is the time node value at the end of the third stage.

6. The indoor radiant cooling method based on a nonlinear temperature control algorithm according to claim 2, characterized in that, If the process enters the fourth stage, the temperature approximation algorithm model will be activated, using an outlet water temperature T. w The control function is: e(t)=(T F -0.5)-T w Among them, K p For proportional gain, K i For integral gain, K d Let e(t) be the differential gain, and T be the temperature deviation. F For floor temperature, T w The outlet water temperature is t, and the operating time of the radiant cooling system after startup is t.

7. An indoor radiant cooling system based on a nonlinear temperature control algorithm, employing the indoor radiant cooling method based on a nonlinear temperature control algorithm as described in any one of claims 1-6, characterized in that, include: Floor coils, ceiling radiant cooling panels, central controller, sub-controllers, main water distributor, branch water distributors, main water collector, branch water collectors, and heat pump units. The heat pump units, sub-controllers, main water distributors, and branch water distributors are all connected to the central controller via communication.

8. The indoor radiant cooling system based on a nonlinear temperature control algorithm according to claim 7, characterized in that, The sub-controller includes a temperature sensor, a relative humidity sensor, a video input module, a voice input module, and a touch screen input module.

9. The indoor radiant cooling system based on a nonlinear temperature control algorithm according to claim 7, characterized in that, Each branch of the main water distributor is equipped with a mixing valve, which is connected to the central controller. By controlling the heat pump unit and the mixing valve on each branch, the final outlet water temperature T is controlled. w When multiple rooms are cooled simultaneously, the chilled water temperature output by the heat pump unit is equal to the outlet water temperature T in each room. w The lowest value.

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