High-speed rail station building wind-water linkage control method and system based on energy efficiency adjustment

Abstract

The invention relates to the technical field of intelligent building energy conservation and environment control, in particular to a high-speed rail station building wind-water linkage control method and system based on energy efficiency adjustment, and the method comprises the steps: obtaining environment parameter data, and generating an initial control instruction according to the environment parameter data; a thermodynamic and hydrodynamic model is constructed through the mechanism modeling module, and a multi-objective optimization problem is generated in combination with an AI optimization algorithm; solving a Pareto optimal solution set according to a multi-objective optimization problem, and selecting an optimal control strategy meeting actual operation requirements; issuing the optimal control strategy to execution equipment, and monitoring the running state of the equipment and the change of environmental parameters in real time; and a control strategy is adjusted according to a monitoring result to form a closed-loop feedback mechanism. Through multi-dimensional data acquisition, AI optimization and closed-loop feedback, dynamic regulation and control of key parameters such as air temperature, humidity, airflow velocity and CO2 concentration in the high-speed rail station building are realized, so that balance between energy conservation and consumption reduction and environmental comfort is achieved, and remarkable technical advantages are achieved.

Description

Technical Field

[0001] This invention belongs to the field of intelligent building energy conservation and environmental control technology, specifically a method and system for the linkage control of wind and water in high-speed railway stations based on energy efficiency regulation. Background Technology

[0002] Existing energy consumption management systems for high-speed railway stations are mostly based on independent control modes for single devices, failing to fully consider the coupling relationship between air conditioning and ventilation systems, resulting in limited room for energy efficiency optimization. For example, under the dual-carbon target framework, intelligent energy consumption management for green high-speed railway stations needs to start with top-level planning, integrating systems, monitoring, and new energy applications. However, existing technologies lack integrated solutions in terms of implementation paths and closed-loop energy-saving linkages. Furthermore, traditional methods rarely combine mechanistic models with AI algorithms for global proactive optimization, making it difficult to calculate energy consumption comparisons under various operating routes in real time, thus limiting the achievement of extreme energy-saving targets.

[0003] Current wind and water linkage control strategies are mostly focused on local optimization, failing to form a comprehensive control system aimed at optimal energy efficiency. Although some research attempts to improve energy consumption management capabilities through digital twin technology, there is still room for improvement in the two-way feedback mechanism between refrigerant injection rate, injection volume, and energy consumption system. Furthermore, the early warning and alarm mechanisms and operation and maintenance models of existing technologies in railway passenger station application scenarios are not yet fully mature, and the ability for deep data integration needs further improvement. Therefore, existing technologies still need improvement and development in terms of energy efficiency regulation and wind-water linkage control. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and system for air conditioning and ventilation linkage control of high-speed railway stations based on energy efficiency regulation, in order to address the above-mentioned defects of the prior art. The aim is to solve the problems that the coupling relationship between air conditioning and ventilation systems has not been fully explored and the space for energy efficiency optimization is limited in the prior art. In particular, the traditional control strategy is difficult to achieve global active optimization and the two-way feedback mechanism between refrigerant injection rate and energy consumption system is imperfect.

[0005] The technical solution adopted by this invention to solve the technical problem is as follows: In a first aspect, the present invention provides a method for the coordinated control of ventilation and water systems in high-speed railway station buildings based on energy efficiency regulation, comprising: Acquire environmental parameter data and generate initial control commands based on the environmental parameter data; Thermodynamic and fluid dynamic models are constructed using a mechanism modeling module, and multi-objective optimization problems are generated by combining AI optimization algorithms. The Pareto optimal solution set is obtained based on the multi-objective optimization problem, and the optimal control strategy that meets the actual operation requirements is selected. The optimal control strategy is sent to the execution device, and the device's operating status and environmental parameter changes are monitored in real time. Adjust control strategies based on monitoring results to form a closed-loop feedback mechanism.

[0006] In some embodiments of this application, the step of acquiring environmental parameter data and generating initial control commands based on the environmental parameter data includes: Collect real-time data on indoor and outdoor temperature and humidity, CO2 concentration, wind speed, personnel density, and equipment operating status; The real-time data is cleaned and normalized to form a standardized dataset; Based on the standardized dataset, time series analysis or deep learning models are used to predict load changes over a future period. Initial control commands are generated based on the load changes to guide the initial adjustment of the air conditioning air volume, ventilation frequency, and temperature setpoint.

[0007] In some embodiments of this application, the step of constructing a thermodynamic and fluid dynamics model through a mechanism modeling module and generating a multi-objective optimization problem by combining an AI optimization algorithm includes: Construct a physical model of the air conditioning and ventilation system, including thermodynamic equations, fluid dynamics model, and coupling performance characteristics between equipment; Using historical operating data and real-time parameters, simulate various system operating routes; Based on reinforcement learning, genetic algorithms, or particle swarm optimization algorithms, generate multi-objective optimization problems with energy consumption per unit area, comfort index, and equipment wear rate as optimization objectives.

[0008] In some embodiments of this application, the step of solving the Pareto optimal solution set based on the multi-objective optimization problem and selecting the optimal control strategy that meets the actual operational requirements includes: A multi-objective optimization algorithm is used to solve for the Pareto optimal solution set, resulting in several feasible combinations of control strategies; Based on actual operational needs, the control strategy with the lowest overall energy consumption, highest comfort, and longest equipment lifespan is selected as the optimal control strategy.

[0009] In some embodiments of this application, the step of sending the optimal control strategy to the execution device and monitoring the device's operating status and environmental parameter changes in real time includes: The optimal control strategy is sent to the air conditioning and ventilation equipment via the communication interface; Real-time acquisition of equipment operating status data, including air volume, ventilation frequency, temperature setpoint, and refrigerant injection rate; Environmental parameters, including indoor temperature and humidity, CO2 concentration, and population density, are monitored through a sensor network.

[0010] In some embodiments of this application, the step of adjusting the control strategy based on monitoring results to form a closed-loop feedback mechanism includes: Compare the predicted results with the actual operating data and calculate the error value; Utilize online learning algorithms to update model parameters and optimize control strategies; The equipment operating parameters are readjusted based on the optimized control strategy to form an adaptive and updated closed-loop feedback mechanism.

[0011] Secondly, embodiments of the present invention also provide a high-speed railway station building feng shui linkage control system based on energy efficiency regulation, comprising: The data acquisition module is used to collect real-time data such as indoor and outdoor temperature and humidity, CO2 concentration, wind speed, personnel density, and equipment operating status. The load forecasting module is used to predict load changes over a future period of time based on historical data and real-time parameters, using time series analysis or deep learning models. The mechanism modeling module is used to build physical models of air conditioning and ventilation systems, including thermodynamic equations, fluid dynamics models, and coupling performance characteristics between equipment. The AI ​​optimization module is used to generate multi-objective optimization problems and solve Pareto optimal solutions using reinforcement learning, genetic algorithms, or particle swarm optimization algorithms. The linkage control module is used to dynamically adjust the air conditioning air volume, ventilation frequency and temperature setpoint according to the optimal control strategy. The feedback adjustment module is used to continuously optimize the control strategy based on the actual operating results, forming a closed-loop feedback mechanism.

[0012] Thirdly, embodiments of the present invention also provide a smart terminal, wherein the smart terminal includes a memory, a processor, and a high-speed railway station feng shui linkage control program based on energy efficiency regulation stored in the memory and executable on the processor. When the processor executes the high-speed railway station feng shui linkage control program based on energy efficiency regulation, it implements the steps of the high-speed railway station feng shui linkage control method based on energy efficiency regulation as described in any of the above embodiments.

[0013] Beneficial technical effects of the present invention: This invention achieves efficient and coordinated control of the feng shui system in high-speed railway stations by integrating environmental parameters, mechanism modeling, AI optimization, and closed-loop feedback. This effectively improves energy utilization efficiency, reduces operational energy consumption, and precisely regulates indoor environmental parameters to ensure passenger comfort. Furthermore, it enhances the system's adaptability, enabling dynamic optimization. In addition, it extends equipment lifespan, reduces maintenance costs, supports green energy-saving goals, and promotes the development of smart buildings. Overall, it improves the intelligence level and operational efficiency of high-speed railway stations. Attached Figure Description

[0014] Figure 1 This is a flowchart of the high-speed railway station building feng shui linkage control method based on energy efficiency regulation in an embodiment of the present invention; Figure 2 This is a functional block diagram of the high-speed railway station building feng shui linkage control system based on energy efficiency regulation, according to an embodiment of the present invention. Detailed Implementation

[0015] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0016] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0017] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0018] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the communication between the inner sides of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0019] This invention provides a method and system for the coordinated control of ventilation and water systems in high-speed railway stations based on energy efficiency regulation. The specific implementation method is described in conjunction with the appendix. Figure 1 and attached Figure 2 Please provide a detailed explanation.

[0020] See Figure 1 As shown, a method for feng shui linkage control of high-speed railway station buildings based on energy efficiency regulation includes: Acquire environmental parameter data and generate initial control commands based on the environmental parameter data; Thermodynamic and fluid dynamic models are constructed using a mechanism modeling module, and multi-objective optimization problems are generated by combining AI optimization algorithms. The Pareto optimal solution set is obtained based on the multi-objective optimization problem, and the optimal control strategy that meets the actual operation requirements is selected. The optimal control strategy is sent to the execution equipment, and the equipment operating status and environmental parameter changes are monitored in real time. Adjust control strategies based on monitoring results to form a closed-loop feedback mechanism.

[0021] In one specific embodiment of this application, acquiring environmental parameter data and generating initial control commands based on the environmental parameter data includes: Collect real-time data on indoor and outdoor temperature and humidity, CO2 concentration, wind speed, personnel density, and equipment operating status; Real-time data is cleaned and normalized to form a standardized dataset; Based on standardized datasets, time series analysis or deep learning models are used to predict load changes over a future period. Initial control commands are generated based on load changes to guide the initial adjustments of air conditioning air volume, ventilation frequency, and temperature setpoint.

[0022] In one specific embodiment of this application, a thermodynamic and fluid dynamics model is constructed through a mechanism modeling module, and a multi-objective optimization problem is generated by combining an AI optimization algorithm, including: Construct a physical model of the air conditioning and ventilation system, including thermodynamic equations, fluid dynamics model, and coupling performance characteristics between equipment; Using historical operating data and real-time parameters, simulate various system operating routes; Based on reinforcement learning, genetic algorithms, or particle swarm optimization algorithms, generate multi-objective optimization problems with energy consumption per unit area, comfort index, and equipment wear rate as optimization objectives.

[0023] In one specific embodiment of this application, solving for the Pareto optimal solution set based on a multi-objective optimization problem and selecting the optimal control strategy that meets actual operational requirements includes: A multi-objective optimization algorithm is used to solve for the Pareto optimal solution set, resulting in several feasible combinations of control strategies; Based on actual operational needs, the control strategy with the lowest overall energy consumption, highest comfort, and longest equipment lifespan is selected as the optimal control strategy.

[0024] In one specific embodiment of this application, the optimal control strategy is distributed to the execution device, and the device's operating status and environmental parameter changes are monitored in real time, including: The optimal control strategy is sent to the air conditioning and ventilation equipment via the communication interface; Real-time acquisition of equipment operating status data, including air volume, ventilation frequency, temperature setpoint, and refrigerant injection rate; Environmental parameters, including indoor temperature and humidity, CO2 concentration, and population density, are monitored through a sensor network.

[0025] In one specific embodiment of this application, the control strategy is adjusted based on the monitoring results to form a closed-loop feedback mechanism, including: Compare the predicted results with the actual operating data and calculate the error value; Utilize online learning algorithms to update model parameters and optimize control strategies; The equipment operating parameters are readjusted based on the optimized control strategy to form an adaptive and updated closed-loop feedback mechanism.

[0026] See Figure 2 As shown, the system's functional block diagram illustrates the logical relationships between the data acquisition module, load forecasting module, mechanism modeling module, AI optimization module, linkage control module, and feedback adjustment module.

[0027] In one specific embodiment of this application, environmental parameter data is first acquired through a data acquisition module. This data includes real-time information such as indoor and outdoor temperature and humidity, CO2 concentration, wind speed, personnel density, and equipment operating status. The data acquisition module achieves comprehensive monitoring of the above parameters through a network of various sensors deployed within the high-speed railway station. For example, temperature and humidity sensors are installed in key areas of the station, such as the waiting hall, ticket hall, and near entrances and exits, to capture changes in temperature and humidity; CO2 concentration sensors are deployed in densely populated areas to monitor air quality; and wind speed sensors are placed near ventilation openings and air conditioning vents to detect airflow speed. Furthermore, equipment operating status data is directly read through communication interfaces with air conditioning and ventilation equipment to ensure the real-time nature and accuracy of the data. The acquired data is cleaned and normalized to form a standardized dataset. This process is completed by the data processing unit within the data acquisition module. The cleaning operation includes removing outliers and filling in missing values, while the normalization process unifies data of different dimensions to the same numerical range for subsequent analysis.

[0028] In one specific embodiment of this application, the load forecasting module uses time series analysis or deep learning models based on a standardized dataset to predict load changes over a future period. The core of the load forecasting module lies in constructing a predictive model that reflects the dynamic changes in heat load within the station building. For example, when using a Long Short-Term Memory (LSTM) network as a deep learning model, the input data includes historical temperature and humidity trends, personnel flow patterns, and equipment operation records, with the output being the heating and cooling load demand for the next few hours. To improve forecast accuracy, the load forecasting module also incorporates external weather forecast data, such as obtaining future weather conditions through an API interface and using it as an auxiliary input variable. After the forecast results are generated, the load forecasting module transmits the data to the mechanism modeling module to guide subsequent optimization calculations.

[0029] In one specific embodiment of this application, the mechanism modeling module is responsible for constructing a physical model of the air conditioning and ventilation system, including thermodynamic equations, fluid dynamics models, and coupling performance characteristics between equipment. The thermodynamic equations describe the heat transfer process within the station building, such as heat exchange through wall conduction, air convection, and radiation. The fluid dynamics model simulates the airflow behavior within the station building, including pressure distribution, airflow velocity field, and temperature field changes within the air supply ducts. The coupling performance characteristics between equipment reflect the interaction between the air conditioning and ventilation equipment, such as the impact of refrigerant injection rate on cooling effect and the regulating effect of ventilation frequency on indoor air quality. The mechanism modeling module verifies the accuracy of the model by simulating various system operation routes and transmits the simulation results to the AI ​​optimization module.

[0030] In one specific embodiment of this application, the AI ​​optimization module uses reinforcement learning, genetic algorithms, or particle swarm optimization algorithms to generate multi-objective optimization problems and solve for Pareto optimal solutions. In actual implementation, the AI ​​optimization module first defines optimization objectives, including energy consumption per unit area, comfort index, and equipment wear rate.

[0031] In one specific embodiment of this application, the energy consumption per unit area is obtained by calculating the power consumption per square meter in the station building, the comfort index is obtained by comprehensively considering the influence of indoor temperature and humidity, CO2 concentration and airflow speed on human comfort, and the equipment wear rate is evaluated by statistically analyzing equipment operating time and load changes.

[0032] In one specific embodiment of this application, the AI ​​optimization module uses a genetic algorithm to generate an initial population and gradually approximates the optimal solution set through operations such as crossover and mutation. The final Pareto optimal solution set contains multiple feasible combinations of control strategies, which achieve different degrees of balance between energy consumption, comfort, and equipment lifespan. Based on actual operational requirements, the control strategy with the lowest overall energy consumption, highest comfort, and longest equipment lifespan is selected as the optimal control strategy and passed to the linkage control module.

[0033] In one specific embodiment of this application, the linkage control module dynamically adjusts the air conditioning air volume, ventilation frequency, and temperature setpoint according to the optimal control strategy. Specifically, the linkage control module sends control commands to the air conditioning and ventilation equipment via a communication interface, such as adjusting the operating frequency of the air conditioning compressor to change the cooling capacity, adjusting the speed of the air supply fan to control the air volume, or changing the opening degree of the fresh air valve to adjust the ventilation frequency. Simultaneously, the linkage control module collects real-time equipment operating status data, including air volume, ventilation frequency, temperature setpoint, and refrigerant injection rate, and monitors changes in environmental parameters, such as indoor temperature and humidity, CO2 concentration, and personnel density, through a sensor network. This data is transmitted to the feedback adjustment module for subsequent closed-loop feedback mechanisms.

[0034] In one specific embodiment of this application, the feedback adjustment module continuously optimizes the control strategy based on actual operating results, forming an adaptively updated closed-loop feedback mechanism. In the specific implementation process, the feedback adjustment module first compares the predicted results with the actual operating data and calculates the error value. For example, if there is a deviation between the predicted indoor temperature and the actual measured value, the absolute value of the deviation is calculated as an error index. Subsequently, the feedback adjustment module uses an online learning algorithm to update the model parameters and optimize the control strategy. For example, when using an incremental learning algorithm, each iteration adjusts the model weights based on new data samples, thereby gradually improving the prediction accuracy. The optimized control strategy readjusts the equipment operating parameters, such as further fine-tuning the airflow or temperature setpoint, to achieve more precise energy efficiency regulation. This process continuously cycles, forming a closed-loop feedback mechanism to ensure that the system is always in an optimal operating state.

[0035] In practical applications, the specific implementation of this invention can significantly improve the energy efficiency management level of high-speed railway stations. For example, during the high-temperature period in summer, the station has a high personnel density and high outdoor temperature. At this time, the system predicts the increase in cooling load demand through the load prediction module and generates the optimal control strategy through the mechanism modeling module and AI optimization module. The linkage control module increases the air conditioning cooling capacity and ventilation frequency according to the strategy, while the feedback adjustment module monitors the indoor temperature, humidity and CO2 concentration in real time to ensure that the environmental parameters are always kept within a comfortable range. At night or during periods of low personnel density, the system automatically reduces the operating intensity of air conditioning and ventilation equipment to reduce energy consumption. Through this dynamic adjustment method, this invention not only achieves global proactive optimization but also improves the two-way feedback mechanism between the refrigerant injection rate and the energy consumption system, solving the problem that traditional control strategies are difficult to achieve high-efficiency energy optimization.

[0036] The intelligent terminal of this invention includes a memory, a processor, and a high-speed railway station feng shui linkage control program based on energy efficiency regulation, stored in the memory and executable on the processor. When the processor executes the program, it implements the steps of the aforementioned method. A computer-readable storage medium stores the high-speed railway station feng shui linkage control program based on energy efficiency regulation; when executed by the processor, this program also implements the steps of the aforementioned method. In this way, the present invention can support the efficient operation of complex algorithms at the hardware level, providing a reliable technical guarantee for energy efficiency management of high-speed railway stations.

[0037] To enable those skilled in the art to fully understand and implement this invention, the specific implementation principle of this invention is further explained below in conjunction with a specific application scenario.

[0038] In the actual operation of high-speed railway stations, the system first acquires environmental parameter data through a data acquisition module. For example, during the high-temperature period in summer, temperature and humidity sensors are placed near waiting halls, ticket halls, and entrances / exits to monitor changes in temperature and humidity in real time; CO2 concentration sensors are installed in densely populated areas to capture changes in air quality; and wind speed sensors are placed near ventilation openings and air conditioning vents to detect airflow speed. Equipment operating status data is directly read through communication interfaces with air conditioning and ventilation equipment, ensuring the real-time nature and accuracy of the data. The acquired data is cleaned and normalized to form a standardized dataset. This process is completed by the data processing unit within the data acquisition module. The cleaning operation includes removing outliers and filling in missing values, while the normalization process unifies data of different dimensions into the same numerical range for subsequent analysis. Subsequently, the load forecasting module uses time series analysis or deep learning models based on the standardized dataset to predict load changes over a future period. For example, when using a Long Short-Term Memory (LSTM) network as a deep learning model, the input data includes historical temperature and humidity trends, personnel flow patterns, and equipment operation records, and the output is the heating and cooling load demand for the next few hours. To improve forecast accuracy, the load forecasting module also incorporates external weather forecast data, such as obtaining future weather conditions through an API interface and using it as an auxiliary input variable. After the forecast results are generated, the load forecasting module transmits the data to the mechanism modeling module to guide subsequent optimization calculations.

[0039] The mechanism modeling module is responsible for constructing the physical model of the air conditioning and ventilation system, including thermodynamic equations, fluid dynamics models, and coupling performance characteristics between equipment. For example, the thermodynamic equations describe the heat transfer process within the station building, including heat exchange through wall conduction, air convection, and radiation; the fluid dynamics model simulates the airflow behavior within the station building, including pressure distribution, airflow velocity field, and temperature field changes within the air supply ducts. The coupling performance characteristics between equipment reflect the interaction between the air conditioning and ventilation equipment, such as the impact of refrigerant injection rate on cooling effect and the regulating effect of ventilation frequency on indoor air quality. The mechanism modeling module verifies the accuracy of the model by simulating various system operation routes and transmits the simulation results to the AI ​​optimization module.

[0040] The AI ​​optimization module uses reinforcement learning, genetic algorithms, or particle swarm optimization algorithms to generate multi-objective optimization problems and solve for Pareto optimal solutions. In practical implementation, the AI ​​optimization module first defines optimization objectives, including energy consumption per unit area, comfort index, and equipment wear rate. Energy consumption per unit area is calculated by measuring electricity consumption per square meter within the station building. The comfort index comprehensively considers the impact of indoor temperature and humidity, CO2 concentration, and airflow speed on human comfort. Equipment wear rate is assessed by statistically analyzing equipment operating time and load changes. Subsequently, the AI ​​optimization module uses a genetic algorithm to generate an initial population and gradually approximates the optimal solution set through crossover and mutation operations. The final Pareto optimal solution set contains multiple feasible combinations of control strategies, achieving varying degrees of balance between energy consumption, comfort, and equipment lifespan. Based on actual operational requirements, the control strategy with the lowest overall energy consumption, highest comfort, and longest equipment lifespan is selected as the optimal control strategy and passed to the linkage control module.

[0041] The linkage control module dynamically adjusts the air conditioning supply volume, ventilation frequency, and temperature setpoint according to the optimal control strategy. For example, during the high-temperature period in summer, when the personnel density inside the station is high and the outdoor temperature is also high, the system predicts the increased cooling load demand through the load prediction module and generates the optimal control strategy through the mechanism modeling module and AI optimization module. The linkage control module increases the air conditioning cooling capacity and ventilation frequency according to this strategy, while the feedback adjustment module monitors the indoor temperature, humidity, and CO2 concentration in real time to ensure that environmental parameters are always kept within a comfortable range. At night or during periods of low personnel density, the system automatically reduces the operating intensity of the air conditioning and ventilation equipment to reduce energy consumption.

[0042] The feedback adjustment module continuously optimizes the control strategy based on actual operating results, forming an adaptive, closed-loop feedback mechanism. For example, if there is a deviation between the predicted indoor temperature and the actual measured value, the absolute value of the deviation is calculated as an error index. Subsequently, the feedback adjustment module uses online learning algorithms to update model parameters and optimize the control strategy. For example, when using an incremental learning algorithm, the model weights are adjusted based on new data samples in each iteration, thereby gradually improving prediction accuracy. The optimized control strategy readjusts the equipment operating parameters, such as further fine-tuning the air volume or temperature setpoint, to achieve more precise energy efficiency regulation. This process is continuously cyclical, forming a closed-loop feedback mechanism to ensure that the system is always in an optimal operating state.

[0043] Through the above steps, this invention can significantly improve the energy efficiency management level of high-speed railway station buildings in practical applications. For example, during the high-temperature period in summer, the system predicts the increase in cooling load demand through the load prediction module and generates the optimal control strategy through the mechanism modeling module and AI optimization module. The linkage control module increases the air conditioning cooling capacity and ventilation frequency according to this strategy, while the feedback adjustment module monitors the indoor temperature, humidity and CO2 concentration in real time to ensure that environmental parameters are always kept within a comfortable range. At night or during periods of low population density, the system automatically reduces the operating intensity of air conditioning and ventilation equipment to reduce energy consumption. Through this dynamic adjustment method, this invention not only achieves global proactive optimization but also improves the two-way feedback mechanism between the refrigerant injection rate and the energy consumption system, solving the problem that traditional control strategies are difficult to achieve in terms of high-efficiency energy optimization.

[0044] All content not described in detail in this specification is prior art known to those skilled in the art, and the model parameters of each electrical appliance are not specifically limited; conventional equipment can be used. Electrical control components not mentioned in this technical solution are not shown in the figures because they are prior art, and will not be described further here.

[0045] The above description is merely one embodiment of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made based on the present invention, as long as they do not lose the essence of the present invention, should be considered as falling within the protection scope of the present invention and subject to its restrictions.

[0046] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the system described above can be found in the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0047] It should be noted that the system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0048] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the invention.

[0049] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0050] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will all fall within the scope of protection of the present invention.

[0051] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. A high-speed rail station building wind-water linkage control method based on energy efficiency regulation, characterized in that, The system comprises: acquiring environmental parameter data and generating initial control instructions based on the environmental parameter data; constructing thermodynamic and fluid mechanics models through a mechanism modeling module and generating a multi-objective optimization problem using an AI optimization algorithm; solving a Pareto optimal solution set based on the multi-objective optimization problem and selecting an optimal control strategy that meets actual operation requirements; issuing the optimal control strategy to the execution device and monitoring the device operation state and environmental parameter changes in real time; adjusting the control strategy based on the monitoring results to form a closed-loop feedback mechanism.

2. The energy efficiency adjustment based high-speed rail station building wind-water linkage control method according to claim 1, characterized in that, The acquiring environmental parameter data and generating initial control instructions based on the environmental parameter data comprises: collecting real-time data of indoor and outdoor temperature and humidity, CO2 concentration, wind speed, personnel density, and device operation state; cleaning and normalizing the real-time data to form a standardized data set; predicting load changes in the future period of time based on the standardized data set using time series analysis or deep learning models; generating initial control instructions based on the load changes to guide the preliminary adjustment of air conditioning supply air volume, ventilation frequency, and temperature set value.

3. The energy efficiency adjustment based high-speed rail station building wind-water linkage control method of claim 1, wherein, The constructing thermodynamic and fluid mechanics models through a mechanism modeling module and generating a multi-objective optimization problem using an AI optimization algorithm comprises: constructing a physical model of the air conditioning and ventilation system, including thermodynamic equations, fluid mechanics models, and coupling performance characteristics between devices; simulating multiple system operation routes using historical operation data and real-time parameters; generating a multi-objective optimization problem with unit area energy consumption, comfort index, and device wear rate as optimization objectives based on reinforcement learning, genetic algorithm, or particle swarm optimization algorithm.

4. The energy efficiency adjustment based high-speed rail station building wind-water linkage control method according to claim 1, characterized in that, The solving a Pareto optimal solution set based on the multi-objective optimization problem and selecting an optimal control strategy that meets actual operation requirements comprises: solving a Pareto optimal solution set using a multi-objective optimization algorithm to obtain multiple feasible control strategy combinations; selecting a control strategy with the lowest comprehensive energy consumption, the highest comfort, and the longest device life as the optimal control strategy based on actual operation requirements.

5. The energy efficiency adjustment based high-speed rail station building wind-water linkage control method according to claim 1, characterized in that, The issuing the optimal control strategy to the execution device and monitoring the device operation state and environmental parameter changes in real time comprises: issuing the optimal control strategy to the air conditioning and ventilation devices through a communication interface; collecting device operation state data in real time, including supply air volume, ventilation frequency, temperature set value, and refrigerant filling rate; monitoring environmental parameter changes through a sensor network, including indoor temperature and humidity, CO2 concentration, and personnel density.

6. The energy efficiency regulation based air-water linkage control method for high-speed rail station building according to claim 1, characterized in that, The adjusting the control strategy based on the monitoring results to form a closed-loop feedback mechanism comprises: comparing the prediction results with actual operation data to calculate error values; updating model parameters using an online learning algorithm to optimize the control strategy; re-adjusting device operation parameters based on the optimized control strategy to form a self-adaptive and updated closed-loop feedback mechanism.

7. A high-speed rail station building wind-water linkage control system based on energy efficiency regulation, characterized in that, The system comprises: a data collection module for collecting real-time data of indoor and outdoor temperature and humidity, CO2 concentration, wind speed, personnel density, and device operation state; a load prediction module for predicting load changes in the future period of time based on historical data and real-time parameters using time series analysis or deep learning models; The mechanism modeling module is used for constructing a physical model of the air conditioning and ventilation system, including thermodynamic equations, fluid mechanics models and coupling performance characteristics between devices; The AI optimization module is used for generating a multi-objective optimization problem and solving a Pareto optimal solution set by using reinforcement learning, genetic algorithm or particle swarm optimization algorithm; The linkage control module is used for dynamically adjusting air supply quantity, ventilation frequency and temperature set value of the air conditioner according to the optimal control strategy; The feedback adjustment module is used for continuously optimizing the control strategy according to actual operation effect, forming a closed-loop feedback mechanism.

8. The high-speed rail station building air-water linkage control system based on energy efficiency regulation according to claim 7, characterized in that the data acquisition module is further used for comprehensively monitoring indoor and outdoor temperature and humidity, CO2 concentration, wind speed, personnel density and device operation state through a sensor network arranged in the high-speed rail station building.

9. The high-speed rail station building air-water linkage control system based on energy efficiency regulation according to claim 7, characterized in that the load prediction module is further used for combining external weather forecast data, obtaining future weather conditions through an API interface and taking the future weather conditions as auxiliary input variables, so as to improve load prediction accuracy. The intelligent terminal comprises a memory, a processor and a high-speed rail station building air-water linkage control program based on energy efficiency regulation stored in the memory and capable of running on the processor, and the processor implements the steps of the high-speed rail station building air-water linkage control method based on energy efficiency regulation when executing the high-speed rail station building air-water linkage control program based on energy efficiency regulation. ​ 10. A smart terminal, characterized by ​

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