Airport terminal self-adaptive skin control system and method fusing wind-heat environment coupling simulation large model

Abstract

The invention provides an airport terminal self-adaptive skin control system and method fused with a wind-heat environment coupling simulation large model. The system comprises a general sensing module, an LLM-based building energy consumption and user comfort performance simulation agent module, a cloud database module integrating SQL and streaming data, and a self-adaptive skin control decision agent module. According to the system and the method, by constructing the local knowledge base, large model illusion can be further reduced, and the accuracy of a generated result is improved. Based on a knowledge retrieval strengthening method, the fine-tuned large model can effectively support terminal optimization fused with wind-heat environment coupling simulation in a design stage and an operation and maintenance stage, and a terminal adaptive skin control decision is made.

Description

Technical Field

[0001] The present invention belongs to the technical field of building adaptive skin control, and in particular relates to an adaptive skin control system and method for an airport terminal integrating a large-scale wind-heat environment coupling simulation model. Background Art

[0002] Terminals are tall, large structures with significant thermal stratification. Improper air conditioning control strategies can easily lead to significant vertical temperature stratification, impacting the passenger experience and causing additional energy consumption. Due to the complex structure, high passenger flow, and diverse functional spaces of these buildings, relying solely on simplified models to calculate temperature distributions can result in significant errors. CFD simulation results are required to correct energy consumption simulation parameters. This process involves the coordinated operation of multiple software programs. Relying on manual software operations by the design team would significantly reduce architectural design efficiency. Simulation accuracy is limited by local equipment performance, failing to leverage the advantages of cloud computing power. Furthermore, it cannot be effectively applied during operations and maintenance, failing to provide real-time thermal environment adjustment strategies tailored to specific usage scenarios. The terminal building envelope is a key vehicle for indoor-outdoor heat exchange. Developing a sound adaptive control strategy for it can effectively optimize the terminal's thermal environment, enhance user thermal comfort, and reduce HVAC energy consumption. The collaborative perception of data collected by multiple sensor types is a key basis for developing adaptive envelope control strategies. Existing methods are unable to effectively support the collection of user physiological signals in complex scenes with large traffic flows, nor can they effectively take into account multimodal knowledge such as real-time thermal environment data, user physiological data, and equipment energy consumption data.

[0003] Based on the demand information input by the user, the large model can retrieve multimodal information such as numbers, text, and images, and generate question-and-answer results through reasoning. Through methods such as prompt word engineering and model fine-tuning, the large model's support for solving vertical problems can be effectively improved, and cross-disciplinary knowledge reasoning such as CFD simulation, automatic construction of building energy models, and adaptive skin control can be achieved. By building a local knowledge base, the illusion of the large model can be further reduced and the accuracy of the generated results can be improved. Based on the knowledge retrieval enhancement method, the fine-tuned large model can effectively support the optimization of the terminal that integrates wind-heat environment coupling simulation during the design and operation and maintenance stages, and formulate decisions on the terminal's adaptive skin control. Summary of the Invention

[0004] The purpose of the present invention is to solve the problems in the prior art and propose an adaptive skin control system and method for an airport terminal that integrates a large-scale wind-heat environment coupling simulation model.

[0005] The present invention is achieved through the following technical solutions. The present invention proposes an adaptive skin control system for an airport terminal that integrates a large-scale wind-heat environment coupling simulation model. The system includes:

[0006] Synaesthesia module: This includes a 6G sensor node array and a thermal environment sensor array arranged in typical areas. The 6G sensor node array is arranged in accordance with the typical cross-section of the terminal building, covering the entire area. It can be integrated with lamps and displays to monitor the breathing and heart rate of users in the area. The thermal environment sensor array is arranged at different heights in the vertical direction of the typical area.

[0007] LLM-based building energy consumption and user comfort performance simulation intelligent agent module: It includes three sub-modules: CFD transient simulation sub-module, HVAC energy consumption calculation sub-module and personalized comfort calculation sub-module;

[0008] A cloud database module integrating SQL and streaming data: including a streaming data submodule and a structured data submodule; both the streaming data submodule and the structured data submodule are deployed on the cloud platform;

[0009] Adaptive epidermis control decision-making intelligent agent module: Based on a fine-tuned large model, it analyzes the matrix results fed back by the streaming data sub-module in real time, determines the indoor cooling and heating stratification positions, identifies typical energy consumption problems and user discomfort, formulates adaptive epidermis control strategies, and calculates the temperature and humidity set values ​​and air supply volume at each location in a typical area in real time, and outputs dynamic control instructions to the adaptive epidermis.

[0010] Furthermore, the CFD transient simulation submodule is based on the fine-tuned large model, combined with the grid division results of the terminal indoor space and the measurement results of the synaesthesia module, to retrieve the basic information description of the terminal building, design standards, relevant literature, industry reports and typical cases, define material properties, call the turbulence model and radiation model, automatically set the dynamic boundary conditions of the CFD simulation model, run the transient solver, and output the temperature, humidity and wind speed calculation results of each location in the typical area of ​​the terminal in the form of a pure digital matrix; the prompt words for fine-tuning the large model of the CFD transient simulation submodule include template files and case files; the template file includes a code format template for reading grid division results, a material definition code format template, a calculation model call code format template, a dynamic boundary setting code format template, a time step definition and a solver setting code format template; the case file is a set of code files that comply with the template file format requirements, have complete information and are executable.

[0011] Furthermore, the HVAC energy consumption calculation submodule automatically generates a building operation energy consumption simulation code file based on the fine-tuned large model, and combines the temperature, humidity, and wind speed calculation results to adjust the parameters in the indoor thermal environment calculation code in real time to calculate the real-time energy consumption matrix of each HVAC equipment in the typical area of ​​the terminal; the prompt words for fine-tuning the large model of the HVAC energy consumption calculation submodule include template files and case files; the template files include the CFD result code format template read in the building energy consumption simulation, the building geometry definition code format template, the material and structure definition code format template, the HVAC equipment definition code format template, and the simulation parameter code format template; the case file is a set of code files that comply with the template file format requirements, have complete information, and are executable.

[0012] Furthermore, the personalized comfort calculation submodule calculates the PMV and PDD indicators of users at various locations in the area in combination with the real-time temperature, humidity and wind speed calculation results; based on the fine-tuned large model, the PMV and PDD indicators are corrected in combination with the respiration, heart rate and body surface temperature of the users in the area to obtain a real-time personalized user comfort matrix for various locations in the typical area of ​​the terminal; the question-answer pairs used for fine-tuning the large model of the personalized comfort calculation submodule include the respiration, heart rate, body surface temperature indicators of multiple groups of users of different ages in the same typical area and the calculated PMV and PDD indicators, as well as the corresponding personalized comfort correction results.

[0013] Furthermore, the stream data submodule processes, analyzes, and dynamically judges the real-time data streams of the temperature matrix, humidity matrix, wind speed matrix, personalized user comfort matrix, and the HVAC equipment energy consumption matrix; the structured data submodule performs structured management on the analysis results of the stream data submodule to improve data query efficiency.

[0014] Furthermore, the system executes an environmental control cycle during operation; during the environmental control cycle, the thermal environment temperature, humidity, wind speed information of the typical area of ​​the terminal and the user's breathing, heart rate, and body surface temperature information collected by the synesthesia module are fed back into the building energy consumption and user comfort performance simulation intelligent body module based on LLM, replacing the existing parameters, re-executing the code generation and calculation solution, and iteratively generating the adaptive skin control decision; if the adaptive skin control decision intelligent body module does not identify the existence of energy consumption anomalies or user discomfort problems indoors, then based on this round of control decision information, the indoor HVAC equipment and the terminal adaptive skin nodes are regulated; if the adaptive skin control decision intelligent body module identifies the existence of energy consumption anomalies or user discomfort problems indoors, then based on the location of the problem and the monitoring results of the synesthesia module, the code generation and calculation solution process are repeated to iteratively make the adaptive skin control decision.

[0015] The present invention also proposes a control method for an adaptive skin control system of an airport terminal based on the large-scale simulation model integrating wind-heat environment coupling. The control method comprises the following steps:

[0016] S1. Perform noise reduction and word segmentation on user input information, understand the input information based on a large model, build a semantic model, vectorize and store it, and build a knowledge base for the terminal's adaptive surface and thermal environment regulation;

[0017] S2. Automatically construct the CFD simulation grid for the terminal's indoor thermal environment based on the terminal's typical regional building information model;

[0018] S3. Combine the cloud computing platform to run the terminal building's adaptive skin control system and generate a real-time control strategy for the adaptive skin;

[0019] S4. After the terminal building's adaptive skin performs adjustment, the difference between the measurement data of the synaesthesia module and the calculated value of the adaptive skin control decision-making intelligent agent module is compared. Based on the difference, a correction coefficient is calculated and fed back to the adaptive skin control decision-making intelligent agent module to calibrate the control strategy.

[0020] Furthermore, in step S4, after the system has run for a certain period of time, a question-answer pair is constructed based on the temperature and humidity set values ​​and air supply volume at each location in the typical area, the measurement data of the synesthesia module and the difference, and the adaptive epidermis control decision-making intelligent agent module is further fine-tuned; the difference is regularly and repeatedly monitored to calibrate the adaptive epidermis control decision-making intelligent agent module.

[0021] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of a control method of a terminal adaptive skin control system based on the large-scale simulation model of the integrated wind-heat environment coupling.

[0022] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implements the steps of a control method for an adaptive skin control system of a terminal building based on the large-scale simulation model of integrated wind-heat environment coupling.

[0023] Beneficial effects of the present invention:

[0024] This paper proposes an adaptive terminal skin control system and method that integrates a large-scale model with wind-heat environment coupling simulation. By building a local knowledge base, this system and method can further reduce the illusion of the large model and improve the accuracy of generated results. Based on a knowledge retrieval enhancement method, the fine-tuned large-scale model can effectively support terminal optimization integrated with wind-heat environment coupling simulation and formulate adaptive terminal skin control decisions during the design and operation phases. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0026] Figure 1 This is a flow chart of the terminal building adaptive skin control method integrating a large-scale wind-heat environment coupling simulation model described in the present invention. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0028] Specifically, combined Figure 1 The present invention proposes an adaptive skin control system for an airport terminal that integrates a large-scale wind-heat environment coupling simulation model. The system includes:

[0029] Synaesthesia module: includes 6G sensor node arrays and thermal environment sensor arrays arranged in typical areas such as the terminal check-in area, security area, and waiting hall. The 6G sensor node array is arranged in combination with the typical cross-section of the terminal to cover the entire area. It can be integrated with lamps and displays to monitor the breathing and heart rate of users in the area. Metal patches, conductive sheets and other reflective materials are arranged on the walls and hard furniture surfaces in the area to improve the signal reflection recovery rate. The thermal environment sensor array includes temperature sensors, humidity sensors, anemometers, and infrared thermal imagers. The thermal environment sensor array is arranged at different heights in the vertical direction of the typical area;

[0030] LLM-based building energy consumption and user comfort performance simulation intelligent agent module: It includes three sub-modules: CFD transient simulation sub-module, HVAC energy consumption calculation sub-module and personalized comfort calculation sub-module;

[0031] The CFD transient simulation submodule is based on a fine-tuned large model, combined with the grid division results of the terminal indoor space and the measurement results of the synaesthesia module, to retrieve the basic information description of the terminal building, design standards, relevant literature, industry reports and typical cases, define material properties, call turbulence models and radiation models, automatically set the dynamic boundary conditions of the CFD simulation model, run the transient solver, and output the temperature, humidity and wind speed calculation results of each location in the typical area of ​​the terminal in the form of a pure digital matrix; the prompt words for fine-tuning the large model of the CFD transient simulation submodule include template files and case files; the template files include a code format template for reading grid division results, a code format template for material definition, a code format template for calling a calculation model, a code format template for dynamic boundary setting, a code format template for time step definition and solver setting; the case files are a set of code files that comply with the template file format requirements, have complete information and are executable.

[0032] The HVAC energy consumption calculation submodule automatically generates a building operation energy consumption simulation code file based on the fine-tuned large model, and combines the temperature, humidity, and wind speed calculation results to adjust the parameters in the indoor thermal environment calculation code in real time to calculate the real-time energy consumption matrix of each HVAC equipment in the typical area of ​​the terminal. The prompt words for fine-tuning the large model of the HVAC energy consumption calculation submodule include template files and case files. The template files include CFD result code format templates read in the building energy consumption simulation, building geometry definition code format templates, material and structure definition code format templates, HVAC equipment definition code format templates, and simulation parameter code format templates. The case files are a set of code files that comply with the template file format requirements, have complete information, and are executable.

[0033] The personalized comfort calculation submodule calculates the PMV and PDD indicators of users at each location in the area based on the real-time temperature, humidity and wind speed calculation results; based on the fine-tuned large model, the PMV and PDD indicators are corrected in combination with the respiration, heart rate and body surface temperature of the users in the area to obtain a real-time personalized user comfort matrix for each location in the typical area of ​​the terminal; the question-answer pairs used for fine-tuning the large model of the personalized comfort calculation submodule include the respiration, heart rate, body surface temperature indicators of multiple groups of users of different ages in the same typical area and the calculated PMV and PDD indicators, as well as the corresponding personalized comfort correction results.

[0034] A cloud-based database module integrating SQL and streaming data includes a streaming data submodule and a structured data submodule, both of which are deployed on a cloud platform. The streaming data submodule processes, analyzes, and dynamically determines the real-time data streams of a temperature matrix, a humidity matrix, a wind speed matrix, a personalized user comfort matrix, and the HVAC equipment energy consumption matrix. The structured data submodule performs structured management on the analysis results of the streaming data submodule to improve data query efficiency.

[0035] Adaptive Surface Control Decision-Making Agent Module: Based on a fine-tuned large-scale model, it analyzes the matrix results fed back by the streaming data submodule in real time, determines the location of indoor heating and cooling stratification, identifies typical energy consumption issues and user discomfort such as "heat accumulation" and airflow short-circuiting, formulates adaptive surface control strategies, and calculates temperature and humidity setpoints and air volume for each location in typical areas in real time, outputting dynamic control instructions to HVAC equipment and adaptive surfaces (such as sunshade systems and smart glass). The image question-answer pairs used for fine-tuning the large-scale model include multiple sets of real-time temperature matrices, humidity matrices, wind speed matrices, personalized user comfort matrices, and HVAC equipment energy consumption matrices for typical areas of the terminal, as well as the corresponding indoor thermal environment problem judgment results and control decision information for HVAC equipment, sunshade systems, and smart glass.

[0036] When the system is run during the terminal design phase, since the synaesthesia module has no data output, the input data of the LLM-based building energy consumption and user comfort performance simulation intelligent agent module are the indoor thermal environment temperature, humidity, and wind speed set values ​​defined by the design team based on comprehensive design standards, relevant literature, industry reports, typical cases, and team experience.

[0037] The three submodules of the LLM-based building energy consumption and user comfort performance simulation agent module set a consistent simulation time step, determined by the computing power support capabilities of the cloud platform. The data recording frequency of the streaming data submodule is an integer multiple of this time step. The structured data submodule simultaneously records the temperature, humidity, and wind speed results of non-fixed points recorded by operators using handheld infrared thermal imagers and hot wire anemometers as supplementary data.

[0038] The system executes an environmental control cycle during operation; during the environmental control cycle, the thermal environment temperature, humidity, wind speed information of the typical area of ​​the terminal and the user's breathing, heart rate, and body surface temperature information collected by the synesthesia module are fed back into the building energy consumption and user comfort performance simulation intelligent body module based on LLM, replacing the existing parameters, re-executing the code generation and calculation solution, and iteratively generating the adaptive skin control decision; if the adaptive skin control decision intelligent body module does not identify the existence of energy consumption anomalies or user discomfort problems indoors, then based on this round of control decision information, it regulates the indoor air-conditioning units, fresh air units, supply and exhaust fan units, heat recovery devices and other HVAC equipment and shading systems, smart glass and other terminal adaptive skin nodes; if the adaptive skin control decision intelligent body module identifies the existence of energy consumption anomalies or user discomfort problems indoors, then based on the location of the problem and the monitoring results of the synesthesia module, it repeats the code generation and calculation solution process and iteratively generates the adaptive skin control decision.

[0039] The present invention also proposes a control method for an adaptive skin control system of an airport terminal based on the large-scale simulation model integrating wind-heat environment coupling. The control method comprises the following steps:

[0040] S1. Perform noise reduction and word segmentation on user input information, understand the input information based on a large model, build a semantic model, vectorize and store it, and build a knowledge base for the terminal's adaptive surface and thermal environment regulation;

[0041] S2. Automatically construct the CFD simulation grid for the terminal's indoor thermal environment based on the terminal's typical regional building information model;

[0042] S3. Combine the cloud computing platform to run the terminal building's adaptive skin control system and generate a real-time control strategy for the adaptive skin;

[0043] S4. After the terminal building's adaptive skin performs adjustment, the difference between the measurement data of the synaesthesia module and the calculated value of the adaptive skin control decision-making intelligent agent module is compared. Based on the difference, a correction coefficient is calculated and fed back to the adaptive skin control decision-making intelligent agent module to calibrate the control strategy.

[0044] In step S1, the user input information includes a textual description or attachment of basic terminal building information, terminal design standards, relevant literature and industry report attachments, a textual description or attachment of typical cases, site selection area policy attachments, a terminal design brief and design specification attachments, and terminal engineering drawings. If the amount of user input information is large, a knowledge graph of the terminal user input information is constructed based on the cross-modal alignment results of the user input information to improve information retrieval efficiency during system operation.

[0045] In step S2, the typical area building information model is a closed model, and there are no openings or undefined areas inside. Its doors, windows, air vents and return air vents are all restricted by clear boundaries. The model does not include details such as handrails and signboards that have little impact on the indoor thermal environment. In the case of insufficient computing resources, the wall shapes, furniture, etc. in the model are deleted to further reduce the complexity of the model and improve the efficiency of mesh division and simulation calculation. The divided grid increases the density in the vertical direction, and is locally encrypted for the air vents, return air vents, and heat source areas. The input prompt words for the large model include template files and case files. The template file includes unstructured mesh division rules and corresponding code format templates in CFD simulation. The case file is a set of code files that comply with the template file format requirements, have complete information and are executable.

[0046] In step S4, after the system has been running for a certain period of time, a question-answer pair is constructed based on the temperature and humidity set values ​​and air supply volume at each location in the typical area, the measurement data of the synesthesia module and the difference, and the adaptive epidermis control decision-making intelligent agent module is further fine-tuned; the difference is regularly and repeatedly monitored to calibrate the adaptive epidermis control decision-making intelligent agent module.

[0047] The present invention also proposes an electronic device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the steps of a control method of a terminal adaptive skin control system based on the large-scale simulation model of the integrated wind-heat environment coupling.

[0048] The present invention also proposes a computer-readable storage medium for storing computer instructions, which, when executed by a processor, implements the steps of a control method for an adaptive skin control system of a terminal building based on the large-scale simulation model of integrated wind-heat environment coupling.

[0049] The memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. The non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct RAM bus RAM (DR RAM). It should be noted that the memory of the methods described herein is intended to include, but is not limited to, these and any other suitable types of memory.

[0050] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware, or any combination thereof. When implemented using software, all or part of the embodiments may be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present application are generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated therein. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a high-density digital video disc (DVD)), or a semiconductor medium (eg, a solid state disc (SSD)).

[0051] During implementation, each step of the above method can be completed by an integrated logic circuit of the hardware in the processor or by instructions in the form of software. The steps of the method disclosed in conjunction with the embodiments of the present application can be directly embodied as being executed by a hardware processor, or can be executed by a combination of hardware and software modules in the processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in conjunction with its hardware. To avoid repetition, it will not be described in detail here.

[0052] It should be noted that the processor in the embodiments of the present application can be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method embodiment can be completed by an integrated logic circuit of the hardware in the processor or by instructions in the form of software. The above processor can be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The various methods, steps, and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor. The steps of the method disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium mature in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, or electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0053] The above is a detailed introduction to the terminal adaptive skin control system and method that integrates a large-scale wind-heat environment coupling simulation model proposed in the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. The terminal building's adaptive skin control system, which integrates a large-scale simulation model of wind and heat environment coupling, is characterized by: The system comprises: Synaesthesia module: This includes a 6G sensor node array and a thermal environment sensor array arranged in typical areas. The 6G sensor node array is arranged in accordance with the typical cross-section of the terminal building, covering the entire area. It can be integrated with lamps and displays to monitor the breathing and heart rate of users in the area. The thermal environment sensor array is arranged at different heights in the vertical direction of the typical area. LLM-based building energy consumption and user comfort performance simulation intelligent agent module: It includes three sub-modules: CFD transient simulation sub-module, HVAC energy consumption calculation sub-module and personalized comfort calculation sub-module; A cloud database module integrating SQL and streaming data: including a streaming data submodule and a structured data submodule; both the streaming data submodule and the structured data submodule are deployed on the cloud platform; Adaptive epidermis control decision-making intelligent agent module: Based on a fine-tuned large model, it analyzes the matrix results fed back by the streaming data sub-module in real time, determines the indoor cooling and heating stratification positions, identifies typical energy consumption problems and user discomfort, formulates adaptive epidermis control strategies, and calculates the temperature and humidity set values ​​and air supply volume at each location in a typical area in real time, and outputs dynamic control instructions to the adaptive epidermis.

2. The system according to claim 1, wherein: The CFD transient simulation submodule, based on a fine-tuned large model, combines the terminal building's indoor space gridding results and the synaesthesia module's measurement results to retrieve basic terminal building information descriptions, design standards, relevant literature, industry reports, and typical cases, define material properties, invoke turbulence and radiation models, automatically set dynamic boundary conditions for the CFD simulation model, run a transient solver, and output temperature, humidity, and wind speed calculation results for each location in a typical terminal area in the form of a pure digital matrix. The prompt words for fine-tuning the large model of the CFD transient simulation submodule include template files and case files; the template files include a code format template for reading meshing results, a code format template for material definition, a code format template for calling a calculation model, a code format template for dynamic boundary setting, and a code format template for time step definition and solver setting; The case file is a set of code files that comply with the template file format requirements, have complete information and are executable.

3. The system according to claim 1, wherein: The HVAC energy consumption calculation submodule automatically generates a building operation energy consumption simulation code file based on the fine-tuned large model, and combines the temperature, humidity, and wind speed calculation results to adjust the parameters in the indoor thermal environment calculation code in real time to calculate the real-time energy consumption matrix of each HVAC equipment in a typical area of ​​the terminal. The prompt words for fine-tuning the large model of the HVAC energy consumption calculation submodule include template files and case files. The template files include CFD result code format templates read in the building energy consumption simulation, building geometry definition code format templates, material and structure definition code format templates, HVAC equipment definition code format templates, and simulation parameter code format templates. The case file is a set of code files that comply with the template file format requirements, have complete information and are executable.

4. The system according to claim 1, wherein: The personalized comfort calculation submodule combines the real-time temperature, humidity, and wind speed calculation results to calculate the PMV and PDD indicators of users at each location in the area. Based on the fine-tuned large model, the PMV and PDD indicators are corrected in combination with the respiration, heart rate, and body surface temperature of users in the area to obtain a real-time personalized user comfort matrix for each location in a typical area of ​​the terminal. The question-and-answer pairs used for fine-tuning the large model of the personalized comfort calculation submodule include the respiration, heart rate, and surface temperature indicators of multiple groups of users of different ages in the same typical area, as well as the calculated PMV and PDD indicators, and the corresponding personalized comfort correction results.

5. The system according to claim 1, wherein: The stream data submodule processes, analyzes, and dynamically determines the real-time data streams of the temperature matrix, humidity matrix, wind speed matrix, personalized user comfort matrix, and the HVAC equipment energy consumption matrix; The structured data submodule performs structured management on the analysis results of the stream data submodule to improve data query efficiency.

6. The system according to claim 1, wherein: The system executes an environmental control cycle during operation; during the environmental control cycle, the thermal environment temperature, humidity, wind speed information of typical areas of the terminal and the user's breathing, heart rate, and body surface temperature information collected by the synesthesia module are fed back into the building energy consumption and user comfort performance simulation intelligent agent module based on the LLM, replacing the existing parameters, re-executing code generation and calculation solution, and iteratively generating adaptive skin control decisions; If the adaptive skin control decision-making intelligent agent module does not identify the existence of abnormal energy consumption or user discomfort problems indoors, then based on this round of control decision information, the indoor HVAC equipment and the terminal adaptive skin nodes are regulated; if the adaptive skin control decision-making intelligent agent module identifies the existence of abnormal energy consumption or user discomfort problems indoors, then based on the location of the problem and the monitoring results of the synaesthesia module, the code generation and calculation solution process are repeated to iterate the adaptive skin control decision.

7. A control method for an adaptive skin control system of an airport terminal building based on the large-scale simulation model integrating wind-heat environment coupling according to any one of claims 1 to 6, characterized in that: The control method comprises the following steps: S1. Perform noise reduction and word segmentation on user input information, understand the input information based on a large model, build a semantic model, vectorize and store it, and build a knowledge base for the terminal's adaptive surface and thermal environment regulation; S2. Automatically construct the CFD simulation grid for the terminal's indoor thermal environment based on the terminal's typical regional building information model; S3. Combine the cloud computing platform to run the terminal building's adaptive skin control system and generate a real-time control strategy for the adaptive skin; S4. After the terminal building's adaptive skin performs adjustment, the difference between the measurement data of the synaesthesia module and the calculated value of the adaptive skin control decision-making intelligent agent module is compared. Based on the difference, a correction coefficient is calculated and fed back to the adaptive skin control decision-making intelligent agent module to calibrate the control strategy.

8. The method according to claim 7, characterized in that In step S4, after the system has been running for a certain period of time, a question-answer pair is constructed based on the temperature and humidity set values ​​and air supply volume at each location in the typical area, the measurement data of the synesthesia module and the difference, and the adaptive epidermis control decision-making intelligent agent module is further fine-tuned; the difference is regularly and repeatedly monitored to calibrate the adaptive epidermis control decision-making intelligent agent module.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 7 to 8 are implemented.

10. A computer-readable storage medium for storing computer instructions, characterized in that: When the computer instructions are executed by a processor, the steps of the method according to any one of claims 7 to 8 are implemented.

Images