Fabricated self-heating intelligent wall system and control method thereof

Abstract

The invention discloses a fabricated self-heating intelligent wall system and a control method thereof, and belongs to the technical field of intelligent walls, the fabricated self-heating intelligent wall system comprises a composite wall module, an intelligent temperature control system and a pipeline integration interface, the composite wall module is formed by conformally compounding a base layer structure layer, a multifunctional composite sandwich structure and a decorative surface layer, each composite wall module comprises a honeycomb aluminum core layer, a graphene heat conduction layer, a flexible heat conduction medium and a flexible ceramic heat conduction film, and each multifunctional composite sandwich structure comprises a spontaneous heating layer, a PCM phase change energy storage layer and a heat preservation sealing layer. Rapid and uniform heating is achieved through the collaborative heat conduction design of the graphene circuit and the honeycomb aluminum substrate, the water and electricity pipelines and the decoration surface layer are integrated in combination with the BIM prefabrication technology, the intelligent temperature control system predicts the heat load based on machine learning, the heating power of all areas is dynamically adjusted, the limitation of step-by-step implementation of traditional decoration and heating is broken through, and the construction efficiency is improved. And a novel wall-mounted instant-heating industrial construction mode is realized.

Description

Technical Field

[0001] This invention relates to the field of intelligent wall installation technology, and more specifically, to a prefabricated self-heating intelligent wall installation system and its control method. Background Technology

[0002] Prefabricated buildings are buildings that are assembled from various prefabricated components. Compared with existing construction processes, they have a shorter construction period and lower cost, and are therefore widely used.

[0003] Traditional building heating systems have the following drawbacks: 1. Radiators / underfloor heating need to be installed separately, which overlaps with the decoration process and results in a long construction period; 2. The electric heating film poses a risk of localized overheating; 3. Existing prefabricated walls only allow for the pre-embedding of pipelines, without integrating heating functions. Summary of the Invention

[0004] 1. Technical problems to be solved To address the problems existing in the prior art, the present invention aims to provide a prefabricated self-heating intelligent wall system and its control method. The present invention achieves rapid and uniform heating through the synergistic thermal conductivity design of graphene circuits and honeycomb aluminum substrates. It integrates water and electricity pipelines with decorative surface layers using BIM prefabrication technology. The intelligent temperature control system predicts heat load based on machine learning and dynamically adjusts the heating power of each area. The present invention breaks through the limitations of the traditional separate implementation of decoration and heating, and realizes a new industrialized construction model of wall-mounted instant heating. Attached Figure Description

[0005] Figure 1 This is an installation diagram of a prefabricated self-heating intelligent wall system according to the present invention; Figure 2 This is a modular diagram of a prefabricated self-heating intelligent wall system according to the present invention; Figure 3 This is a flowchart illustrating the steps of a prefabricated self-heating intelligent wall control method according to the present invention. Detailed Implementation

[0006] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention. Example

[0007] Please see Figure 1-2A prefabricated self-heating intelligent wall system includes: a composite wall module, an intelligent temperature control system, and a pipeline integration interface. The composite wall module is formed by a conformal composite of a base structure layer, a multifunctional composite sandwich structure, and a decorative surface layer. The composite wall module includes a honeycomb aluminum core layer, a graphene thermal conductive layer, a flexible thermal conductive medium, and a flexible ceramic thermal conductive film. The multifunctional composite sandwich structure includes a self-heating layer, a PCM phase change energy storage layer, and a thermal insulation and sealing layer. The decorative surface layer is a microcrystalline stone composite board, and the surface of the decorative surface layer is provided with 3D printed decorative textures. The intelligent temperature control system includes a data acquisition layer module, a control processing layer module, and an intelligent control module. The data acquisition layer module includes a distributed temperature sensing network infrared thermometer, an environmental sensor, and a heat flow monitoring unit. The control processing layer module includes an edge computing unit microcontroller, a human body temperature analyzer, a migration optimization unit, and a phase change state identification unit. The intelligent control module includes a distributed temperature control module, an AI algorithm, and an energy management module. The pipeline integration interface includes an assembly module, an electrical integration module, a circuit module, a water circuit module, and a data bus.

[0008] In a specific embodiment of the present invention, breakthrough optimization of building energy consumption is achieved through three-level innovation of materials, structure, and algorithm. The graphene honeycomb aluminum substrate, with its ultra-high thermal conductivity of 2053W / mK, allows heat to be rapidly and evenly diffused throughout the entire wall surface, improving the efficiency compared to traditional heating. The PCM phase change material layer absorbs / releases latent heat through solid-liquid phase change, effectively buffering diurnal temperature fluctuations and reducing air conditioning energy consumption. The LSTM transfer learning algorithm further enhances energy efficiency management, accelerates convergence by reusing pre-trained model weights, accurately identifies the PCM phase change state by combining it with the PWARX model, dynamically adjusts the graphene thermal pathway, and optimizes the energy storage / release cycle in conjunction with time-of-use electricity pricing strategies. In addition, graphene far-infrared radiation simulates the solar thermal effect, improving physical comfort and providing health benefits. Meanwhile, the distributed infrared thermometer monitors the user's body surface temperature in real time, achieving closed-loop regulation of human body and environment dual feedback.

[0009] Specifically, the honeycomb aluminum core layer adopts a bridge-shaped mechanical structure with a thickness of 20-50mm, achieving high strength, lightweight, and space saving. The graphene thermal conductive layer covers the surface of the honeycomb aluminum core layer, utilizing its thermal conductivity of 2053W / mK and far-infrared radiation characteristics in the 6-26μm band to achieve rapid heating and health care functions. The graphene is printed with conductive ink according to a patterned design. The flexible thermal conductive medium is embedded in the gaps of the honeycomb structure. The flexible thermal conductive medium is a boron nitride-doped silicone composite material with a thermal conductivity ≥5W / (m·K) and a breakdown voltage ≥10kV / mm, enhancing thermal uniformity and improving sound insulation and shock resistance. The flexible ceramic thermal conductive film is set on the outer surface of the graphene thermal conductive layer, and the thickness of the flexible ceramic thermal conductive film is 1-3mm. The patterned design of the graphene conductive circuit includes a main heating area and an auxiliary compensation area, with a resistance ratio of 3:1. The linewidths of the main heating area and the auxiliary compensation area are 2-3 mm and 0.5-1 mm, respectively.

[0010] Specifically, the self-heating layer integrates a graphene nano-heating plate that converts electrical energy into far-infrared heat energy, improving the electrothermal conversion efficiency. The self-heating layer has a thickness of 1.5mm. The PCM phase change energy storage layer is a paraffin derivative-based phase change material that absorbs / releases latent heat through solid-liquid phase change, reducing temperature fluctuations and lowering air conditioning energy consumption. The thermal insulation and sealing layer is made of high-density fiberglass wool or rock wool, and has a thickness of 12mm. The thermal insulation and sealing layer incorporates a honeycomb protrusion design to enhance the bonding strength with concrete and prevent cracking.

[0011] Specifically, the distributed temperature sensing network infrared thermometer is deployed in different locations indoors. The distributed temperature sensing network infrared thermometer is used to collect users' body surface temperature in a non-contact manner and supports the calculation of the average body temperature of multiple users. The environmental sensor integrates a DS18B20 high-precision digital temperature sensor to monitor the temperature and humidity of the wall surface and indoor and outdoor environment in real time. The heat flow monitoring unit is embedded with a graphene thermal conductive layer and a honeycomb aluminum core layer to track the heat transfer efficiency and the state of the phase change material PCM. The edge computing unit microcontroller is used to execute the basic PID control algorithm to dynamically adjust the heating / cooling power and avoid temperature overshoot. The edge computing unit microcontroller is an STM32 / STC89C52. The human body temperature analyzer is used to automatically trigger cooling or heating commands based on a preset body temperature threshold of 36.5℃. The migration optimization unit reuses the weights of the pre-trained model and fine-tunes the high-level network to adapt to the current scenario, reducing training costs. The phase transition state recognition unit combines the PWARX piecewise autoregressive model to determine the solid-liquid phase transition stage of the PCM in real time, where the accuracy of the phase transition stage is R²=0.99, and optimizes the energy storage and release strategy. The distributed temperature control module is an ARM chip installed in each wall panel. The distributed temperature control module is used to collect temperature data from multiple areas in real time. The AI ​​algorithm predicts indoor heat demand based on LSTM neural network and dynamically adjusts the heating power. The energy management module is used to support photovoltaic DC power supply and is compatible with 48V safe voltage system.

[0012] Specifically, the intelligent temperature control system also includes fuzzy control algorithm, neural network control algorithm, MPC predictive control algorithm and reinforcement learning multi-agent strategy; The fuzzy control algorithm is used to handle nonlinear and uncertain environments using fuzzy logic rules. Uncertain environments include, but are not limited to, sudden changes in humidity and disturbances caused by human activity. The neural network control algorithm is used to predict future temperature trends based on historical data and improve control accuracy in complex scenarios by training and learning the dynamic characteristics of the environment. The neural network control algorithm includes LSTM (Long Short-Term Memory) network, BiLSTM, and a hybrid CNN-LSTM model. The MPC (Multi-Process Control) predictive control algorithm combines the system dynamic model to perform multi-step prediction and optimize control commands in advance. The reinforcement learning multi-agent strategy is used to coordinate multiple air conditioning units to achieve global energy consumption optimization. The fuzzy control algorithm adapts to human comfort needs in home temperature control and dynamically adjusts the temperature curve. When the user's heart rate is abnormal, the temperature is dynamically adjusted using the cosine function formula: T=ACOS(wt+θ). The MPC predictive control algorithm is suitable for multi-variable coupled scenarios and reduces energy consumption fluctuations.

[0013] Specifically, the assembly module consists of an intrusive snap-fit ​​groove, a beveled surface, and a positioning block pre-installed inside the composite wall module. The assembly module supports rapid stacking and concrete pouring for fixation. The electrical integration module is a standardized junction box installed inside the composite wall module. The electrical integration module is used to connect the composite wall module and the intelligent temperature control system in series to achieve dry construction. The circuit module is a flexible copper foil crossboard for conformal design of PCB board and graphene circuit. The water circuit module is a PEX spiral pipe pre-embedded inside the composite wall module. The data bus is a CAN bus integrated inside the keel of the composite wall module to realize communication networking between wall panels. The pipeline integration interface adopts a magnetic quick-connect structure, which includes: Gold-plated spring contacts are installed at the power supply interface, and the current carrying capacity of the gold-plated spring contacts is ≥30A; A waterproof fiber optic connector is installed at the data interface; and Self-sealing stainless steel quick-connect fittings installed at water inlets.

[0014] Please see Figure 3 A method for controlling prefabricated self-heating intelligent wall panels, comprising: S1. Material prefabrication and modular production; S2. On-site assembly and construction; S3, Intelligent System Integration; S4. System debugging and optimization.

[0015] Specifically, step S1 includes: S101, Graphene honeycomb aluminum substrate prefabrication: adopts a bridge-shaped mechanical structure with a thickness ≤1cm, filled with a flexible thermally conductive medium, and a graphene thermally conductive layer is composited on the surface to form a lightweight, high thermal conductivity substrate. A graphene nano heating plate is pre-installed on the inner side of the substrate, with an electrothermal conversion efficiency >99%. The wires are reserved to the outer side of the board, and hexadecane PCM microcapsules are filled in the vertical cavity to buffer the temperature difference through solid-liquid phase change. S102, Multifunctional composite panel assembly: The heat insulation layer, PCM layer and heating layer are bonded in sequence to form an integrated composite panel for heating-heat storage-heat insulation. The panel edge is pre-set with snap-fit ​​grooves, junction boxes and H-shaped keel slots to support rapid assembly and circuit series connection. Step S2 includes: S201. Base treatment and keel installation: Fix C-shaped keels along the ground and top of the wall, with the spacing of expansion screws ≤ 400mm. Correct the verticality of the vertical keels and make their length 5mm shorter than the net distance. In the doorway area, install from the opening to both sides. If there is no doorway, proceed from the main wall into the room in sequence. S202, Wall panel module assembly: Embed the C-shaped keel at the top / bottom of the wall and fix it with countersunk self-tapping screws, with the self-tapping screws 15mm from the edge of the panel and the center distance ≤300mm. Fit the new panel into the exposed H-shaped keel of the front panel and push it until it fits tightly. Insert the second to last panel into the keel at a 30° angle, slide it back to its original position and fix it. Fill the gaps between the panels with paraffin-based PCM material, and use the phase change expansion to push the elastic sealing film to seal the gaps and reduce the cold bridge effect. S203 Electrical system connection: Parallel composite board with reserved wires, connected to the pre-embedded junction box, heating tubes are connected in series with plug-in sleeves, and the contact head is connected by spring clip crimping. Thermostat, gateway and wireless router are installed in the wall cavity.

[0016] Specifically, step S3 includes: S301, Sensor Network Deployment: Embed DS18B20 distributed temperature sensors in the graphene layer to monitor the temperature and humidity of the wall surface and the environment, and install infrared thermometers indoors to collect the user's body surface temperature with an accuracy of ±0.1℃. S302 Edge Control Hardware Configuration: STM32 microcontroller performs basic PID control to dynamically adjust heating power, SSR-40DA solid-state relay contactless control circuit, integrated audible and visual alarm module; S303 and LSTM transfer learning algorithm deployment: reuse pre-trained source domain model weights to accelerate convergence, fix the bottom general feature layer of LSTM, fine-tune the high-level network to adapt to the current scenario, use the MAE loss function to optimize the prediction of future temperature trends, use the PWARX model to identify the PCM phase transition state, automatically switch the thermal path, and enable the graphene thermal conductive layer to conduct heat in winter and insulate heat in summer.

[0017] Specifically, step S4 includes: S401 Temperature control closed-loop verification: Set the target temperature to 26℃ via mobile APP, test the algorithm response speed and PCM temperature regulation effect, simulate extreme temperature difference, and verify the adaptive thermal resistance adjustment mechanism such as metal sheet warping / second airbag expansion. S402, Energy Consumption Strategy Configuration: Bind time-of-use electricity price data, optimize PCM storage / release cycle, set power metering device, and prioritize the control of non-core heating loads; S403, Coordinated Finishing of Decoration: The exterior is bonded with Class A fireproof decorative panels, integrating heating and decoration functions. It is connected to a smart home ecosystem that supports RS485 / Wi-Fi / ZigBee protocols, enabling multi-terminal control. After powering on, the system performs a self-test, and infrared thermal imaging shows a surface temperature difference of 1.7℃. In an ambient temperature of -10℃, it can raise the temperature of a 5㎡ room to 22℃ within 20 minutes.

[0018] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and its improved concept, should be covered within the scope of protection of the present invention.

Claims

1. A prefabricated self-heating intelligent wall system, characterized in that, include: The composite wall module, intelligent temperature control system, and pipeline integration interface are provided. The composite wall module is formed by a conformal composite of a base structure layer, a multi-functional composite sandwich structure, and a decorative surface layer. The composite wall module includes a honeycomb aluminum core layer, a graphene thermal conductive layer, a flexible thermal conductive medium, and a flexible ceramic thermal conductive film. The multifunctional composite sandwich structure includes a self-heating layer, a PCM phase change energy storage layer, and a thermal insulation and sealing layer. The decorative surface layer is a microcrystalline stone composite board, and the surface of the decorative surface layer is provided with 3D printed decorative textures. The intelligent temperature control system includes a data acquisition layer module, a control processing layer module, and an intelligent control module. The data acquisition layer module includes a distributed temperature sensing network infrared thermometer, an environmental sensor, and a heat flow monitoring unit. The control processing layer module includes an edge computing unit microcontroller, a human body temperature analyzer, a migration optimization unit, and a phase change state identification unit. The intelligent control module includes a distributed temperature control module, an AI algorithm, and an energy management module. The pipeline integration interface includes an assembly module, an electrical integration module, a circuit module, a water circuit module, and a data bus.

2. The prefabricated self-heating intelligent wall system according to claim 1, characterized in that, The honeycomb aluminum core layer adopts a bridge-shaped mechanical structure with a thickness of 20-50mm. The graphene thermal conductive layer covers the surface of the honeycomb aluminum core layer, utilizing its thermal conductivity of 2053W / mK and far-infrared radiation characteristics in the 6-26μm band. The graphene is used to print conductive ink circuits according to a patterned design. The flexible thermal conductive medium is embedded in the gaps of the honeycomb structure. The flexible thermal conductive medium is a boron nitride-doped silicone composite material with a thermal conductivity ≥5W / (m·K) and a breakdown voltage ≥10kV / mm. The flexible ceramic thermal conductive film is disposed on the outer surface of the graphene thermal conductive layer, and the thickness of the flexible ceramic thermal conductive film is 1-3mm. The patterned design of the graphene conductive circuit includes a main heating area and an auxiliary compensation area, with a resistance ratio of 3:

1. The linewidths of the main heating area and the auxiliary compensation area are 2-3 mm and 0.5-1 mm, respectively.

3. The prefabricated self-heating intelligent wall system according to claim 2, characterized in that, The self-heating layer integrates a graphene nano heating plate that converts electrical energy into far-infrared heat energy. The thickness of the self-heating layer is 1.5mm. The PCM phase change energy storage layer is a phase change material filled with paraffin derivatives. The thermal insulation and sealing layer is high-density fiberglass wool or rock wool. The thickness of the thermal insulation and sealing layer is 12mm. The thermal insulation and sealing layer incorporates a honeycomb protrusion design.

4. The prefabricated self-heating intelligent wall system according to claim 3, characterized in that, The distributed temperature sensing network infrared thermometer is deployed in different locations indoors. It is used to collect users' body surface temperature in a non-contact manner and supports the calculation of the average body temperature of multiple users. The environmental sensor integrates a DS18B20 high-precision digital temperature sensor to monitor the temperature and humidity of the wall surface and indoor and outdoor environment in real time. The heat flow monitoring unit is embedded with a graphene thermal conductive layer and a honeycomb aluminum core layer to track heat transfer efficiency and the state of the phase change material PCM. The edge computing unit microcontroller is used to execute the basic PID control algorithm to dynamically adjust the heating / cooling power. The edge computing unit microcontroller is an STM32 / STC89C52. The human body temperature analyzer is used to automatically trigger cooling or heating commands based on a preset body temperature threshold of 36.5℃. The migration optimization unit reuses the pre-trained model weights to fine-tune the high-level network to adapt to the current scenario. The phase change state recognition unit combines the PWARX piecewise autoregressive model to determine the solid-liquid phase change stage of the PCM in real time, where the phase change stage accuracy is R²=0.99, and optimizes the energy storage and release strategy. The distributed temperature control module is an ARM chip installed in each wall panel. The distributed temperature control module is used to collect temperature data from multiple areas in real time. The AI ​​algorithm is based on LSTM neural network to predict indoor heat demand. The energy management module is used to support photovoltaic DC power supply and is compatible with 48V safe voltage system.

5. A prefabricated self-heating intelligent wall system according to claim 4, characterized in that, The intelligent temperature control system also includes fuzzy control algorithm, neural network control algorithm, MPC predictive control algorithm and reinforcement learning multi-agent strategy; The fuzzy control algorithm is used to handle nonlinear and uncertain environments using fuzzy logic rules. Uncertain environments include, but are not limited to, sudden changes in humidity and disturbances caused by human activity. The neural network control algorithm is used to predict future temperature trends based on historical data and improve control accuracy in complex scenarios by training and learning the dynamic characteristics of the environment. The neural network control algorithm includes LSTM (Long Short-Term Memory) network, BiLSTM, and a hybrid CNN-LSTM model. The MPC (Multi-Process Control) predictive control algorithm combines the system dynamic model to perform multi-step prediction and optimize control commands in advance. The reinforcement learning multi-agent strategy is used to coordinate multiple air conditioning units to achieve global energy consumption optimization. The fuzzy control algorithm adapts to human comfort needs in home temperature control and dynamically adjusts the temperature curve. When the user's heart rate is abnormal, the temperature is dynamically adjusted using the cosine function formula: T=ACOS(wt+θ). The MPC predictive control algorithm is suitable for multi-variable coupled scenarios and reduces energy consumption fluctuations.

6. A prefabricated self-heating intelligent wall system according to claim 5, characterized in that, The assembly module consists of an intrusive snap-fit ​​groove, beveled surface, and positioning block pre-installed inside the composite wall module. This assembly module supports rapid stacking and concrete pouring for fixation. The electrical integration module is a standardized junction box located inside the composite wall module. This electrical integration module connects the composite wall module and the intelligent temperature control system in series to achieve dry construction. The circuit module is a flexible copper foil cross-plate for conformal design of the PCB board and graphene circuit. The water circuit module is a PEX spiral pipe pre-embedded inside the composite wall module. The data bus is a CAN bus integrated inside the keel of the composite wall module to achieve inter-panel communication networking. The pipeline integration interface adopts a magnetic quick-connect structure, which includes... Gold-plated spring contacts are installed at the power supply interface, and the current carrying capacity of the gold-plated spring contacts is ≥30A; A waterproof fiber optic connector is installed at the data interface; and Self-sealing stainless steel quick-connect fittings installed at water inlets.

7. A control method for a prefabricated self-heating intelligent wall system, applied to the prefabricated self-heating intelligent wall system described in claims 1-6, characterized in that, Including the following steps: S1. Material prefabrication and modular production; S2. On-site assembly and construction; S3, Intelligent System Integration; S4. System debugging and optimization.

8. A method for controlling prefabricated self-heating intelligent wall panels, characterized in that, Step S1 includes: S101, Graphene honeycomb aluminum substrate prefabrication: adopts a bridge-shaped mechanical structure with a thickness ≤1cm, filled with a flexible thermally conductive medium, and a graphene thermally conductive layer is composited on the surface to form a lightweight, high thermal conductivity substrate. A graphene nano heating plate is pre-installed on the inner side of the substrate, with an electrothermal conversion efficiency >99%. The wires are reserved to the outer side of the board, and hexadecane PCM microcapsules are filled in the vertical cavity to buffer the temperature difference through solid-liquid phase change. S102, Multifunctional composite panel assembly: The heat insulation layer, PCM layer and heating layer are bonded in sequence to form an integrated composite panel for heating-heat storage-heat insulation. The panel edge is pre-set with snap-fit ​​grooves, junction boxes and H-shaped keel slots to support rapid assembly and circuit series connection. Step S2 includes: S201. Base treatment and keel installation: Fix C-shaped keels along the ground and top of the wall, with the spacing of expansion screws ≤ 400mm. Correct the verticality of the vertical keels and make their length 5mm shorter than the net distance. In the doorway area, install from the opening to both sides. If there is no doorway, proceed from the main wall into the room in sequence. S202, Wall panel module assembly: Embed the C-shaped keel at the top / bottom of the wall and fix it with countersunk self-tapping screws, with the self-tapping screws 15mm from the edge of the panel and the center distance ≤300mm. Fit the new panel into the exposed H-shaped keel of the front panel and push it until it fits tightly. Insert the second to last panel into the keel at a 30° angle, slide it back to its original position and fix it. Fill the gaps between the panels with paraffin-based PCM material, and use the phase change expansion to push the elastic sealing film to seal the gaps and reduce the cold bridge effect. S203 Electrical system connection: Parallel composite board with reserved wires, connected to the pre-embedded junction box, heating tubes are connected in series with plug-in sleeves, and the contact head is connected by spring clip crimping. Thermostat, gateway and wireless router are installed in the wall cavity.

9. A method for controlling prefabricated self-heating intelligent wall panels, characterized in that, Step S3 includes: S301, Sensor Network Deployment: Embed DS18B20 distributed temperature sensors in the graphene layer to monitor the temperature and humidity of the wall surface and the environment, and install infrared thermometers indoors to collect the user's body surface temperature with an accuracy of ±0.1℃. S302 Edge Control Hardware Configuration: STM32 microcontroller performs basic PID control to dynamically adjust heating power, SSR-40DA solid-state relay contactless control circuit, integrated audible and visual alarm module; S303 and LSTM transfer learning algorithm deployment: reuse pre-trained source domain model weights to accelerate convergence, fix the bottom general feature layer of LSTM, fine-tune the high-level network to adapt to the current scenario, use the MAE loss function to optimize the prediction of future temperature trends, use the PWARX model to identify the PCM phase transition state, automatically switch the thermal path, and enable the graphene thermal conductive layer to conduct heat in winter and insulate heat in summer.

10. A method for controlling prefabricated self-heating intelligent wall panels, characterized in that, Step S4 includes: S401 Temperature control closed-loop verification: Set the target temperature to 26℃ via mobile APP, test the algorithm response speed and PCM temperature regulation effect, simulate extreme temperature difference, and verify the adaptive thermal resistance adjustment mechanism such as metal sheet warping / second airbag expansion. S402, Energy Consumption Strategy Configuration: Bind time-of-use electricity price data, optimize PCM storage / release cycle, set power metering device, and prioritize the control of non-core heating loads; S403, Coordinated Finishing of Decoration: The exterior is bonded with Class A fireproof decorative panels, integrating heating and decoration functions. It is connected to a smart home ecosystem that supports RS485 / Wi-Fi / ZigBee protocols, enabling multi-terminal control. After powering on, the system performs a self-test, and infrared thermal imaging shows a surface temperature difference of 1.7℃. In an ambient temperature of -10℃, it can raise the temperature of a 5㎡ room to 22℃ within 20 minutes.

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