Metro station environmental control temperature control method and system

Abstract

The invention discloses a subway station environment control and temperature control method, which comprises the following steps: introducing a dynamic thermal comfort model which takes a relative thermal index RWI as a core, comprehensively considers various influence factors such as human body metabolism rate, environment temperature, clothes thermal resistance and the like, and can accurately evaluate the thermal comfort feelings of passengers under different environment conditions. The system can intelligently adjust the temperature of the station hall and the platform layer by monitoring the outdoor temperature change in real time and combining the thermal comfort requirement of passengers in the riding process, so that the temperature is matched with the physiological and psychological feelings of the passengers. The dynamic adjusting mechanism not only can obviously improve the riding experience of passengers and reduce the dissatisfaction of the passengers caused by uncomfortable temperature, but also can effectively reduce the energy consumption and reduce the operation cost. In a word, through a scientific method and an advanced technology, individuation, intelligentization and greenization of subway station environment control are realized, and a more comfortable and healthier riding environment is provided for passengers.

Description

Technical Field

[0001] The present invention relates to the technical field of subway station environment control, and in particular to a subway station environment control and temperature control method and system. Background Art

[0002] Traditional subway station environmental control systems usually use fixed temperature and humidity settings, which cannot adapt to outdoor temperature fluctuations and temperature requirements of different areas inside the station. This design not only wastes energy, but also affects passenger comfort. The specific problems are as follows: 1. The traditional subway station environmental control system lacks flexibility. The system is usually unable to adjust according to the real-time passenger flow and changes in outdoor climate conditions, resulting in overcooling or overheating in certain periods or areas; 2. Due to the lack of effective regulation mechanisms, traditional systems may over-operate, such as providing a large amount of cooling when it is not needed, resulting in unnecessary energy waste; 3. The temperature and humidity in different areas may not meet the needs of all passengers due to fixed settings, resulting in uneven passenger experience. Finally, the fixed setting system may require more manual intervention for maintenance and adjustment, increasing operating costs; 4. When outdoor climate conditions change suddenly, traditional systems may take a long time to respond to these changes and cannot adjust the indoor environment in time. Fixed fresh air volume may lead to poor indoor air quality, especially during peak hours when there are many passengers and carbon dioxide concentration rises, affecting air quality.

[0003] According to statistics, the energy consumption of the ventilation and air-conditioning systems of subway stations accounts for more than 30% of the energy consumption of the entire subway system, and the fresh air load accounts for a considerable proportion of the energy consumption of the entire ventilation and air-conditioning system. Relevant research shows that due to the influence of piston wind and inlet and outlet infiltration, subway stations often have an excessive supply of fresh air, which increases the energy consumption of the air-conditioning system.

[0004] Therefore, how to reduce the energy consumption of subway station ventilation and air-conditioning systems while ensuring the thermal comfort of passengers has become an urgent problem to be solved in subway operation and design. Summary of the invention

[0005] The object of the present invention is to provide a subway station environment control and temperature control method and system to solve the above-mentioned problems.

[0006] The present invention is achieved through the following technical solutions: A subway station environment control and temperature control method, comprising: S1: Data collection; S1.1: Measure outdoor temperature parameters, humidity parameters and wind speed parameters at subway stations; S1.2: Through thermal comfort research, the human metabolic rate, clothing thermal resistance, clothing outer air boundary layer thermal resistance and average radiant heat per unit skin area are obtained.

[0007] S2: establishing a dynamic thermal comfort model, wherein the dynamic thermal comfort model includes a corresponding relationship between a relative thermal index RWI and an ASHRAE thermal sensation scale; The present invention proposes a dynamic thermal comfort model for a subway station, the determination principle of which is to formulate the changing temperatures of the subway station hall and platform layer according to the changes in outdoor temperature and taking into account the dynamic thermal comfort of passengers during the ride.

[0008] The relative thermal index (RWI) is selected as the dynamic thermal comfort model for passengers. The relative thermal index is a dimensionless number that takes into account parameters such as metabolic rate, time spent in the transition process, ambient temperature, and thermal resistance of clothing. If the RWI value is the same when the human body is in two different environments and activity states, it is considered that the thermal sensation of the human body in the two situations is similar.

[0009] The present invention has achieved revolutionary progress in the environmental control system of subway stations by introducing a dynamic thermal comfort model. The model takes the relative thermal index RWI as the core, comprehensively considers multiple influencing factors such as human metabolic rate, ambient temperature, and clothing thermal resistance, and can accurately evaluate the thermal comfort of passengers under different environmental conditions. By real-time monitoring of outdoor temperature changes and combining the thermal comfort needs of passengers during the ride, the system can intelligently adjust the temperature of the station hall and platform layer to match the physiological and psychological feelings of the passengers. This dynamic adjustment mechanism can not only significantly improve the passenger's riding experience and reduce passenger dissatisfaction caused by temperature discomfort, but also effectively reduce energy consumption and reduce operating costs. In addition, the intelligent design of the system also improves the system's adaptive ability and response speed, enabling it to quickly respond to rapid changes in indoor and outdoor environments and maintain the stability and comfort of the indoor environment. In short, the present invention realizes the personalization, intelligence and greening of subway station environmental control through scientific methods and advanced technologies, providing passengers with a more comfortable and healthy riding environment.

[0010] The relative thermal index RWI is selected as the passenger dynamic thermal comfort model, and its calculation formula is as follows: When Pa≤2269 Pa When Pa>2269 Pa Where: M--human metabolic rate, unit W / ㎡; τ--the time spent in the transition process, in seconds; t--temperature, unit ℃; I cw --Clothing thermal resistance, unit: clo; Ia -- Thermal resistance of the air boundary layer outside the clothing, unit: clo; R--Average radiant heat per unit skin area, unit: W / ㎡.

[0011] S3: Determine the environmental control target temperature; S3.1: Obtain a comfortable indoor RWI value, i.e., a target RWI value, through a thermal comfort correlation survey, wherein the thermal comfort correlation survey includes establishing an ASHRAE thermal sensation scale model to obtain an ASHRAE thermal sensation calculation value, and also includes obtaining a subjective average value of the ASHRAE thermal sensation scale by asking the thermal sensation of the station personnel, and performing a weighted average of the ASHRAE thermal sensation calculation value and the subjective average value of the ASHRAE thermal sensation scale to obtain a target ASHRAE thermal sensation scale value, and then determining a target RWI value corresponding to the target ASHRAE thermal sensation scale value based on the corresponding relationship between the relative thermal index RWI and the ASHRAE thermal sensation scale; S3.2: Knowing the target RWI value, reversely calculate the temperature t through the dynamic thermal comfort model to obtain the optimum temperature in the station; RWI is formulated by American experts based on the relevant thermal comfort data of subways in the United States and other Western countries. Due to the differences in race, region, subway environment, etc. between China and Western countries, and the long time since the relevant indicators were formulated, it is impossible to correctly evaluate the current status of summer thermal comfort in a certain subway. The target RWI value of the present invention is obtained by the following method: In the thermal comfort correlation survey, the ASHRAE thermal sensation scale model is first introduced, and the ASHRAE thermal sensation scale calculation value is obtained by the following formula: ASHRAE Thermal Sensation Scale Calculated Values Where M is the human metabolic rate (W / m²); w is the body's metabolic rate (W / m²); Pa is the water vapor pressure of the environment (Pa); f cl is the dress factor; t r is the mean radiant temperature (°C); h c is the convective heat transfer coefficient (W / (m²·°C)); t cl is the garment surface temperature (°C); t a is the station air temperature (°C); The above parameters are obtained through the data collection step; The ASHRAE thermal sensation scale is an integer range from -3 to +3, where -3 represents "very cold", -2 represents "cold", -1 represents "slightly cold", 0 represents "neutral", +1 represents "slightly warm", +2 represents "warm", and +3 represents "very warm", where "very cold", "cold", "slightly cold", "neutral", "slightly warm", "warm", and "very warm" are thermal sensations.

[0012] Based on the calculated value of the ASHRAE thermal sensation scale, the subjective average value of the ASHRAE thermal sensation scale is obtained by asking about thermal sensation, and the target ASHRAE thermal sensation scale value is obtained by weighted average of the calculated value of the ASHRAE thermal sensation and the subjective average value of the ASHRAE thermal sensation scale. Then, the target RWI value is obtained by establishing a relationship between the RWI value and the ASHRAE thermal sensation scale value. Finally, by comparing the ASHRAE thermal sensation scale, the corresponding target RWI value is obtained. Given the passenger's metabolic rate M, clothing thermal resistance Icw, clothing outer air boundary layer thermal resistance Ia, and average radiation heat gain per unit skin area R, the station's environmental control target temperature t can be obtained. This is used as one of the input parameters for indoor load calculation to guide the operation of the subway station environmental control system.

[0013] S4: Fresh air mode selection; The outdoor temperature parameters, humidity parameters and wind speed parameters collected by S1 are used as the basis for selecting the fresh air mode, where the fresh air modes include: minimum fresh air mode, maximum fresh air mode and non-mechanical fresh air mode; According to the comparison between the outdoor fresh air enthalpy value and the return air enthalpy value, and the monitoring results of the carbon dioxide concentration, when the outdoor fresh air enthalpy value is greater than or equal to the return air enthalpy value, monitor whether the carbon dioxide concentration exceeds the standard. If it exceeds the standard, the minimum fresh air mode is executed; if the carbon dioxide concentration does not exceed the standard, the non-mechanical fresh air operation is executed. If the outdoor fresh air enthalpy value is less than the return air enthalpy value, the fresh air mode is executed.

[0014] S5: Establish a load forecasting model; The loads in subway stations include indoor loads, fresh air loads and fan heating loads. The indoor loads include personnel loads, lighting loads, elevator loads, enclosure structure loads, advertising light box loads and AFC equipment loads. Fresh air loads include mechanical fresh air loads, screen door infiltration loads and entrance and exit infiltration loads. In the present application, the daily variation pattern of indoor load is relatively stable compared with that of fresh air load, and its specific value can be obtained by verifying the recorded data of the subway operating company and the on-site measured data; the fresh air load is affected by the outdoor meteorological parameters and has a large volatility; the screen door infiltration load is mainly generated by the air flow interaction between the platform and the tunnel, and the air in the tunnel interacts with the outdoors through the piston air shaft, and the entrance and exit infiltration is mainly the air flow interaction between the station hall and the outdoors. Therefore, the screen door infiltration load and the entrance and exit infiltration load also have an obvious strong correlation with the outdoor climate state and have a large volatility.

[0015] The present invention achieves refined management of energy consumption by carefully dividing the load of the subway station into three parts: indoor load, fresh air load and fan temperature rise load, and further subdividing the sub-loads of each part. The stability of the indoor load and the volatility of the fresh air load are fully utilized, and the accuracy of load prediction is improved by combining historical data and real-time monitoring data. In addition, the system can dynamically respond to changes in outdoor meteorological parameters and adjust the fresh air volume in real time to adapt to the external environment, thereby significantly improving energy efficiency. For the screen door infiltration load and the entrance and exit infiltration load, the present invention achieves effective control of these loads by accurately calculating the air flow interaction between the platform and the tunnel and between the station hall and the outdoors. This optimization strategy not only reduces energy consumption, but also improves passenger comfort, and reduces the risk of system failure through precise load management, and enhances the reliability and stability of the entire environmental control system. In short, the present invention realizes the personalization, intelligence and greening of subway station environmental control through scientific methods and advanced technologies, providing passengers with a more comfortable and healthy riding environment, while bringing both economic and environmental benefits to subway operators.

[0016] The formula for calculating the passenger load is as follows: The passenger load in a subway station is related to the passenger flow of the station and the time the passengers stay in the station. The passenger load of the station can be calculated by the following formula: Where: Q p ——Full heat load of passengers, kW; G c , G p ——The number of people in the station hall and platform, respectively; A1, A2——The number of people entering and leaving the station at each hour, respectively; a1, a2——the time passengers stay in the station hall and platform respectively, min; b1, b2 – the time passengers stay in the station hall and platform respectively after leaving the station, min.

[0017] The calculation formula of lighting load is as follows: The lighting load of subway stations mainly includes working lighting load and advertising lighting load. The calculation formula of lighting load of subway stations is as follows: Where: Q L ——Station lighting load (total power), W; m——Number of lamp types; n——the number of each type of lamp, units; W m ——The power of each lamp, W / unit; F——Total area of ​​the public area of ​​the subway station, m2; P L ——Lighting power per unit area in the station’s public areas, W / ㎡.

[0018] The advertising lighting load calculation formula is as follows: Where: Q A ——Station advertising lighting load (total power), W; n——Total number of advertising light boxes on the concourse floor, units; W A ——The power of the advertising light box, W / unit; F A ——The wall area of ​​the station hall level where advertising light boxes can be installed, m2; P A ——Advertising lighting power density per unit area at the station concourse level, W / ㎡.

[0019] The elevator load calculation formula is as follows: The down escalator load can be calculated according to the following formula: In the formula, Q ED ——Downward escalator load at the station, W; τ1——The ratio of the rated running time of the down escalator in this period to the length of the calculation period; P E1 ——Rated no-load power of down escalator, W; P E2 ——Low-speed no-load power of downward escalator, W.

[0020] The load of the upward escalator can be calculated according to the following formula: In the formula, Q EU ——Load of the upward escalator at the station, W; τ2——The ratio of the running time of the escalator at rated speed with load in this period to the length of the calculation period; P E3 ——Upward escalator load operating power, W; τ3——The ratio of the no-load running time of the escalator at rated speed in this period to the length of the calculation period; P E4 ——Rated no-load power of upward escalator, W; P E5 ——Low speed no-load power of upward escalator, W; P EF ——Full-load rated power of upward escalator, W; φ——average load rate during this period; n p ——The average number of passengers carried by a single upward escalator during this period, people; n E ——The maximum number of people who can stand on the escalator at the same time is people.

[0021] The frequency of use of vertical elevators is not high, and the load of vertical elevators can be calculated as 2kW / unit.

[0022] The AFC load calculation formula is as follows: It can be calculated based on the total power of the equipment as follows: In the formula, Q AFC ——Station AFC equipment load (total power), W; m——Number of AFC equipment types; n——the number of each type of equipment, units; W m ——The single-machine power of the equipment, W / unit.

[0023] The formula for calculating the load of the enclosure structure is as follows: The heat transfer load calculation formula of the shielding door is as follows: In the formula, Q psd ——Heat transfer of shielding door, W; K——comprehensive thermal conductivity on both sides of the shielding door, W / ㎡·K; F——area of ​​the shielding door, m2; Δt – temperature difference on both sides of the shielding door, °C.

[0024] The calculation formula of fresh air load is as follows: The fresh air load of the subway station with screen doors mainly includes mechanical fresh air load and unorganized infiltration load. Among them, the unorganized infiltration load includes tunnel infiltration load and entrance and exit infiltration load. Mechanical fresh air load and entrance and exit fresh air load are directly related to outdoor meteorological parameters; tunnel infiltration load is related to tunnel air state and station platform temperature state. The fresh air load can be calculated by the following formula: In the formula, Q ven t, Q inf , Q tunnel ——They are mechanical fresh air load in the public area of ​​the subway station with screen doors, infiltration load at the entrances and exits, and infiltration load at the screen doors, kW; V ven t、V inf 、V tunnel ——They are the mechanical fresh air volume in public areas, inlet and outlet air infiltration volume, and screen door air infiltration volume, m³ / h; ρ——air density, kg / m³; C p ——Specific heat capacity of air, kJ / (kg·K); Toutside, Tair, Ttunnel - outdoor air temperature, public area air temperature and tunnel air temperature respectively, ℃.

[0025] S6: Intelligent adjustment control; Intelligently adjust the chiller, water pump, fan frequency and water valve opening according to the load forecast results of S5 to maintain the indoor environment at the optimal temperature required by S3; S7: real-time feedback correction; Temperature and humidity sensors are set up in the station to monitor the indoor temperature and compare it with the optimal temperature in S3. If there is a deviation, it is fed back to S6 to execute the intelligent adjustment steps.

[0026] A subway station environmental control and temperature control system is used for data collection of S1, intelligent adjustment and control of S6 and real-time feedback correction of S7, characterized in that it includes an outdoor gas phase parameter collection module, an indoor temperature and humidity parameter collection module, and an intelligent control module, wherein the outdoor gas phase parameter collection module includes an outdoor temperature collection unit, an outdoor humidity collection unit, an outdoor fresh air enthalpy value collection unit, and an outdoor return air enthalpy value collection unit.

[0027] The intelligent control module controls the frequency of the chiller, the frequency of the water pump, the frequency of the fan and the opening of the water valve respectively.

[0028] The intelligent control module uses a deep learning algorithm to achieve precise real-time control of the chiller frequency, water pump frequency, fan frequency, and water valve opening. The deep learning algorithm specifically includes the following steps: S1: Data collection and preprocessing: divide the historical cooling load data, historical outdoor weather data, and historical heating load data in the station into training sets and test sets according to date, and standardize the data in the training sets respectively; S2: Establish an LSTM neural network model and train it. Then use the test set in S1 to verify the accuracy of the LSTM neural network prediction model. The LSTM neural network model outputs cooling load data by inputting target temperature and real-time heating load data. S3: Real-time control and optimization. According to the real-time predicted cooling load data, the frequency of the chiller, the frequency of the water pump, the frequency of the fan and the opening of the water valve are controlled in real time. According to the real-time outdoor weather data and real-time heat load data, the predicted output cooling load data is optimized.

[0029] Preferably, the mathematical expression of the loss function of the LSTM neural network model is: in: is the sample size; is the amount of cooling load; It is The sample The true value of the load; It is The sample The predicted value of the load; It is A set of weights is used to balance the importance of different loads.

[0030] Preferably, the cooling load data includes chiller load, water pump load, fan load and water load data, and the data is specifically cooling efficiency value.

[0031] The present invention realizes efficient control of the subway station environment by integrating an outdoor gas phase parameter acquisition module, an indoor temperature and humidity parameter acquisition module and an intelligent control module. The system uses a deep learning algorithm, especially an LSTM neural network model, to accurately control the operation of chillers, water pumps, fans and water valves in real time to adapt to real-time changing outdoor weather and heat load data. This data-driven intelligent control method not only improves energy efficiency, reduces operating costs, but also enhances passenger comfort. The system continuously optimizes the control strategy through a real-time feedback correction mechanism to ensure the stability and adaptability of the indoor environment. In addition, the automation and intelligence of the system reduce manual intervention, improve response speed and control efficiency. By reducing unnecessary energy consumption, the system also helps to achieve energy conservation and emission reduction, and contributes to environmental protection. In short, this system provides a reliable, energy-saving and comfortable environmental control solution for subway stations through its intelligent design.

[0032] Compared with the prior art, the present invention has the following advantages and beneficial effects: 1. The present invention has achieved revolutionary progress in the environmental control system of subway stations by introducing a dynamic thermal comfort model. The model takes the relative thermal index RWI as the core, comprehensively considers multiple influencing factors such as human metabolic rate, ambient temperature, and clothing thermal resistance, and can accurately evaluate the thermal comfort of passengers under different environmental conditions. By real-time monitoring of outdoor temperature changes and combining the thermal comfort needs of passengers during the ride, the system can intelligently adjust the temperature of the station hall and platform to match the physiological and psychological feelings of the passengers. This dynamic adjustment mechanism can not only significantly improve the passenger's riding experience and reduce passenger dissatisfaction caused by temperature discomfort, but also effectively reduce energy consumption and reduce operating costs. In addition, the intelligent design of the system also improves the system's adaptive ability and response speed, enabling it to quickly respond to rapid changes in indoor and outdoor environments and maintain the stability and comfort of the indoor environment. In short, the present invention has achieved the personalization, intelligence and greening of subway station environmental control through scientific methods and advanced technologies, providing passengers with a more comfortable and healthy riding environment; 2. The present invention achieves refined management of energy consumption by carefully dividing the load of the subway station into three parts: indoor load, fresh air load and fan temperature rise load, and further subdividing the sub-loads of each part. The stability of the indoor load and the volatility of the fresh air load are fully utilized, and the accuracy of load prediction is improved by combining historical data and real-time monitoring data. In addition, the system can dynamically respond to changes in outdoor meteorological parameters and adjust the fresh air volume in real time to adapt to the external environment, thereby significantly improving energy efficiency. For the screen door infiltration load and the entrance and exit infiltration load, the present invention achieves effective control of these loads by accurately calculating the air flow interaction between the platform and the tunnel and between the station hall and the outdoors. This optimization strategy not only reduces energy consumption, but also improves passenger comfort, and reduces the risk of system failure through precise load management, and enhances the reliability and stability of the entire environmental control system. In short, the present invention realizes the personalization, intelligence and greening of subway station environmental control through scientific methods and advanced technologies, providing passengers with a more comfortable and healthy riding environment, while bringing both economic and environmental benefits to subway operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings: Figure 1 is a flow chart of the method of the present invention; Figure 2 Schematic diagram of the relationship of the system of the present invention. DETAILED DESCRIPTION

[0034] In order to make the purpose, technical scheme and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments and drawings. The schematic implementation modes and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention. It should be noted that the present invention is already in the actual development and use stage. Example

[0035] like Figure 1 to Figure 2 As shown, this embodiment includes.

[0036] A subway station environment control and temperature control method, comprising: S1: Data collection; S1.1: Measure outdoor temperature parameters, humidity parameters and wind speed parameters at subway stations; S1.2: Through thermal comfort research, the human metabolic rate, clothing thermal resistance, clothing outer air boundary layer thermal resistance and average radiant heat per unit skin area are obtained.

[0037] S2: establishing a dynamic thermal comfort model, wherein the dynamic thermal comfort model includes a corresponding relationship between a relative thermal index RWI and an ASHRAE thermal sensation scale; The present invention proposes a dynamic thermal comfort model for a subway station, the determination principle of which is to formulate the changing temperatures of the subway station hall and platform layer according to the changes in outdoor temperature and taking into account the dynamic thermal comfort of passengers during the ride.

[0038] The relative thermal index (RWI) is selected as the dynamic thermal comfort model for passengers. The relative thermal index is a dimensionless number that takes into account parameters such as metabolic rate, time spent in the transition process, ambient temperature, and thermal resistance of clothing. If the RWI value is the same when the human body is in two different environments and activity states, it is considered that the thermal sensation of the human body in the two situations is similar.

[0039] The present invention has achieved revolutionary progress in the environmental control system of subway stations by introducing a dynamic thermal comfort model. The model takes the relative thermal index RWI as the core, comprehensively considers multiple influencing factors such as human metabolic rate, ambient temperature, and clothing thermal resistance, and can accurately evaluate the thermal comfort of passengers under different environmental conditions. By real-time monitoring of outdoor temperature changes and combining the thermal comfort needs of passengers during the ride, the system can intelligently adjust the temperature of the station hall and platform layer to match the physiological and psychological feelings of the passengers. This dynamic adjustment mechanism can not only significantly improve the passenger's riding experience and reduce passenger dissatisfaction caused by temperature discomfort, but also effectively reduce energy consumption and reduce operating costs. In addition, the intelligent design of the system also improves the system's adaptive ability and response speed, enabling it to quickly respond to rapid changes in indoor and outdoor environments and maintain the stability and comfort of the indoor environment. In short, the present invention realizes the personalization, intelligence and greening of subway station environmental control through scientific methods and advanced technologies, providing passengers with a more comfortable and healthy riding environment.

[0040] The relative thermal index RWI is selected as the passenger dynamic thermal comfort model, and its calculation formula is as follows: When Pa≤2269 Pa When Pa>2269 Pa Where: M--human metabolic rate, unit W / ㎡; τ--the time spent in the transition process, in seconds; t--temperature, unit ℃; I cw --Clothing thermal resistance, unit: clo; I a -- Thermal resistance of the air boundary layer outside the clothing, unit: clo; R--Average radiant heat per unit skin area, unit: W / ㎡.

[0041] S3: Determine the environmental control target temperature; S3.1: Obtain a comfortable indoor RWI value, i.e., a target RWI value, through a thermal comfort correlation survey, wherein the thermal comfort correlation survey includes establishing an ASHRAE thermal sensation scale model to obtain an ASHRAE thermal sensation calculation value, and also includes obtaining a subjective average value of the ASHRAE thermal sensation scale by asking the thermal sensation of the station personnel, and performing a weighted average of the ASHRAE thermal sensation calculation value and the subjective average value of the ASHRAE thermal sensation scale to obtain a target ASHRAE thermal sensation scale value, and then determining a target RWI value corresponding to the target ASHRAE thermal sensation scale value based on the corresponding relationship between the relative thermal index RWI and the ASHRAE thermal sensation scale; S3.2: Knowing the target RWI value, reversely calculate the temperature t through the dynamic thermal comfort model to obtain the optimum temperature in the station; RWI is formulated by American experts based on the relevant thermal comfort data of subways in the United States and other Western countries. Due to the differences in race, region, subway environment, etc. between China and Western countries, and the long time since the relevant indicators were formulated, it is impossible to correctly evaluate the current status of summer thermal comfort in a certain subway. The target RWI value of the present invention is obtained by the following method: In the thermal comfort correlation survey, the ASHRAE thermal sensation scale model is first introduced, and the ASHRAE thermal sensation scale calculation value is obtained by the following formula: ASHRAE Thermal Sensation Scale Calculated Values Where M is the human metabolic rate (W / m²); w is the body's metabolic rate (W / m²); Pa is the water vapor pressure of the environment (Pa); f cl is the dress factor; t r is the mean radiant temperature (°C); h c is the convective heat transfer coefficient (W / (m²·°C)); t cl is the garment surface temperature (°C); t a is the station air temperature (°C); The above parameters are obtained through the data collection step, and the calculated value of the ASHRAE thermal sensation scale is the target ASHRAE thermal sensation scale value; The ASHRAE thermal sensation scale is an integer range from -3 to +3, where -3 represents "very cold", -2 represents "cold", -1 represents "slightly cold", 0 represents "neutral", +1 represents "slightly warm", +2 represents "warm", and +3 represents "very warm", where "very cold", "cold", "slightly cold", "neutral", "slightly warm", "warm", and "very warm" are thermal sensations.

[0042] In the present invention, in order to realize fast and efficient investigation, the thermal sensation inquiry is specifically to let the people in the station select a thermal sensation from "very cold", "cold", "slightly cold", "neutral", "slightly warm", "warm" and "very warm".

[0043] The method for determining the target ASHRAE thermal sensation scale value also includes first conducting a site heat survey, obtaining the subjective average value of the ASHRAE thermal sensation scale by asking about thermal sensation, and obtaining the target ASHRAE thermal sensation scale value by weighted average of the ASHRAE thermal sensation calculated value and the subjective average value of the ASHRAE thermal sensation scale, and then obtaining the target RWI value by establishing a relationship between the RWI value and the ASHRAE thermal sensation scale value, as follows: In this embodiment, it is considered that the acceptable thermal sensation for passengers during riding in the station is slightly cooler to slightly warmer, so the following table is obtained: Table 1 Relationship between RWI value and ASHRAE thermal sensation scale value Therefore, the target RWI value is obtained through Table 1, and the passenger's metabolic rate M, clothing thermal resistance Icw, clothing outer air boundary layer thermal resistance Ia and average radiation heat gain per unit skin area R are known, then the station environmental control target temperature t can be obtained. It is used as one of the input parameters for indoor load calculation to guide the operation of the subway station environmental control system.

[0044] S4: Fresh air mode selection; The outdoor temperature parameters, humidity parameters and wind speed parameters collected by S1 are used as the basis for selecting the fresh air mode, where the fresh air modes include: minimum fresh air mode, maximum fresh air mode and non-mechanical fresh air mode; According to the comparison between the outdoor fresh air enthalpy value and the return air enthalpy value, and the monitoring results of the carbon dioxide concentration, when the outdoor fresh air enthalpy value is greater than or equal to the return air enthalpy value, monitor whether the carbon dioxide concentration exceeds the standard. If it exceeds the standard, the minimum fresh air mode is executed; if the carbon dioxide concentration does not exceed the standard, the non-mechanical fresh air operation is executed. If the outdoor fresh air enthalpy value is less than the return air enthalpy value, the fresh air mode is executed.

[0045] S5: Establish load forecasting model; The loads in subway stations include indoor loads, fresh air loads and fan heating loads. The indoor loads include personnel loads, lighting loads, elevator loads, enclosure structure loads, advertising light box loads and AFC equipment loads. Fresh air loads include mechanical fresh air loads, screen door infiltration loads and entrance and exit infiltration loads. In the present application, the daily variation pattern of indoor load is relatively stable compared with that of fresh air load, and its specific value can be obtained by verifying the recorded data of the subway operating company and the on-site measured data; the fresh air load is affected by the outdoor meteorological parameters and has a large volatility; the screen door infiltration load is mainly generated by the air flow interaction between the platform and the tunnel, and the air in the tunnel interacts with the outdoors through the piston air shaft, and the entrance and exit infiltration is mainly the air flow interaction between the station hall and the outdoors. Therefore, the screen door infiltration load and the entrance and exit infiltration load also have an obvious strong correlation with the outdoor climate state and have a large volatility.

[0046] The present invention achieves refined management of energy consumption by carefully dividing the load of the subway station into three parts: indoor load, fresh air load and fan temperature rise load, and further subdividing the sub-loads of each part. The stability of the indoor load and the volatility of the fresh air load are fully utilized, and the accuracy of load prediction is improved by combining historical data and real-time monitoring data. In addition, the system can dynamically respond to changes in outdoor meteorological parameters and adjust the fresh air volume in real time to adapt to the external environment, thereby significantly improving energy efficiency. For the screen door infiltration load and the entrance and exit infiltration load, the present invention achieves effective control of these loads by accurately calculating the air flow interaction between the platform and the tunnel and between the station hall and the outdoors. This optimization strategy not only reduces energy consumption, but also improves passenger comfort, and reduces the risk of system failure through precise load management, and enhances the reliability and stability of the entire environmental control system. In short, the present invention realizes the personalization, intelligence and greening of subway station environmental control through scientific methods and advanced technologies, providing passengers with a more comfortable and healthy riding environment, while bringing both economic and environmental benefits to subway operators.

[0047] The formula for calculating the passenger load is as follows: The passenger load in a subway station is related to the passenger flow of the station and the time the passengers stay in the station. The passenger load of the station can be calculated by the following formula: Where: Q p ——Full heat load of passengers, k·W; G c , G p ——The number of people in the station hall and platform, respectively; A1, A2——The number of people entering and leaving the station at each hour, respectively; a1, a2——the time passengers stay in the station hall and platform respectively, min; b1, b2 – the time passengers stay in the station hall and platform respectively after leaving the station, min.

[0048] The calculation formula of lighting load is as follows: The lighting load of subway stations mainly includes working lighting load and advertising lighting load. The calculation formula of lighting load of subway stations is as follows: Where: Q L ——Station lighting load (total power), W; m——Number of lamp types; n——the number of each type of lamp, units; W m ——The power of each lamp, W / unit; F——Total area of ​​the public area of ​​the subway station, m2; P L ——Lighting power per unit area in the station’s public areas, W / ㎡.

[0049] The advertising lighting load calculation formula is as follows: Where: Q A ——Station advertising lighting load (total power), W; n——Total number of advertising light boxes on the concourse floor, units; W A ——The power of the advertising light box, W / unit; F A ——The wall area of ​​the station hall level where advertising light boxes can be installed, m2; P A ——Advertising lighting power density per unit area at the station concourse level, W / ㎡.

[0050] The elevator load calculation formula is as follows: The down escalator load can be calculated according to the following formula: Where, QED is the load of the down escalator in the station, W; τ1——The ratio of the rated running time of the down escalator in this period to the length of the calculation period; P E1 ——Rated no-load power of down escalator, W; PE2 ——Low-speed no-load power of downward escalator, W.

[0051] The load of the upward escalator can be calculated according to the following formula: In the formula, Q EU ——Load of the upward escalator at the station, W; τ2——The ratio of the running time of the escalator at rated speed with load in this period to the length of the calculation period; P E3 ——Upward escalator load operating power, W; τ3——The ratio of the no-load running time of the escalator at rated speed in this period to the length of the calculation period; P E4 ——Rated no-load power of upward escalator, W; P E5 ——Low speed no-load power of upward escalator, W; P EF ——Full-load rated power of upward escalator, W; φ——average load rate during this period; n p ——The average number of passengers carried by a single upward escalator during this period, people; n E ——The maximum number of people who can stand on the escalator at the same time is people.

[0052] The frequency of use of vertical elevators is not high, and the load of vertical elevators can be calculated as 2kW / unit.

[0053] The AFC load calculation formula is as follows: It can be calculated based on the total power of the equipment as follows: In the formula, Q AFC ——Station AFC equipment load (total power), W; m——Number of AFC equipment types; n——the number of each type of equipment, units; W m ——The single-machine power of the equipment, W / unit.

[0054] The formula for calculating the load of the enclosure structure is as follows: The heat transfer load calculation formula of the shielding door is as follows: In the formula, Q psd ——Heat transfer of shielding door, W; K——comprehensive thermal conductivity on both sides of the shielding door, W / ㎡·K; F——area of ​​the shielding door, m2; Δt – temperature difference on both sides of the shielding door, °C.

[0055] The calculation formula of fresh air load is as follows: The fresh air load of the subway station with screen doors mainly includes mechanical fresh air load and unorganized infiltration load. Among them, the unorganized infiltration load includes tunnel infiltration load and entrance and exit infiltration load. Mechanical fresh air load and entrance and exit fresh air load are directly related to outdoor meteorological parameters; tunnel infiltration load is related to tunnel air state and station platform temperature state. The fresh air load can be calculated by the following formula: In the formula, Q vent , Q inf , Q tunnel ——They are mechanical fresh air load in the public area of ​​the subway station with screen doors, infiltration load at the entrances and exits, and infiltration load at the screen doors, kW; V vent 、V inf 、V tunnel ——They are the mechanical fresh air volume in public areas, inlet and outlet air infiltration volume, and screen door air infiltration volume, m³ / h; ρ——air density, kg / m³; C p ——Specific heat capacity of air, kJ / (kgK); Toutside, Tair, Ttunnel - outdoor air temperature, public area air temperature and tunnel air temperature respectively, ℃.

[0056] S6: Intelligent adjustment control; Intelligently adjust the chiller, water pump, fan frequency and water valve opening according to the load forecast results of S5 to maintain the indoor environment at the optimal temperature required by S3; S7: real-time feedback correction; Temperature and humidity sensors are set up in the station to monitor the indoor temperature and compare it with the optimal temperature in S3. If there is a deviation, it is fed back to S6 to execute the intelligent adjustment steps.

[0057] A subway station environmental control and temperature control system is used for data collection of S1, intelligent adjustment and control of S6 and real-time feedback correction of S7, characterized in that it includes an outdoor gas phase parameter collection module, an indoor temperature and humidity parameter collection module, and an intelligent control module, wherein the outdoor gas phase parameter collection module includes an outdoor temperature collection unit, an outdoor humidity collection unit, an outdoor fresh air enthalpy value collection unit, and an outdoor return air enthalpy value collection unit.

[0058] The intelligent control module controls the frequency of the chiller, the frequency of the water pump, the frequency of the fan and the opening of the water valve respectively.

[0059] The intelligent control module uses a deep learning algorithm to achieve precise real-time control of the chiller frequency, water pump frequency, fan frequency, and water valve opening. The deep learning algorithm specifically includes the following steps: S1: Data collection and preprocessing: divide the historical cooling load data, historical outdoor weather data, and historical heating load data in the station into training sets and test sets according to date, and standardize the data in the training sets respectively; S2: Establish an LSTM neural network model and train it. Then use the test set in S1 to verify the accuracy of the LSTM neural network prediction model. The LSTM neural network model outputs cooling load data by inputting target temperature and real-time heating load data. S3: Real-time control and optimization. According to the real-time predicted cooling load data, the frequency of the chiller, the frequency of the water pump, the frequency of the fan and the opening of the water valve are controlled in real time. According to the real-time outdoor weather data and real-time heat load data, the predicted output cooling load data is optimized.

[0060] In this embodiment, the mathematical expression of the loss function of the LSTM neural network model is: in: is the sample size; is the amount of cooling load; It is The sample The true value of the load; It is The sample The predicted value of the load; It is A set of weights is used to balance the importance of different loads.

[0061] In this embodiment, the cooling load data includes chiller load, water pump load, fan load and water load data, and the data is specifically cooling efficiency value.

[0062] The present invention realizes efficient control of the subway station environment by integrating an outdoor gas phase parameter acquisition module, an indoor temperature and humidity parameter acquisition module and an intelligent control module. The system uses a deep learning algorithm, especially an LSTM neural network model, to accurately control the operation of chillers, water pumps, fans and water valves in real time to adapt to real-time changing outdoor weather and heat load data. This data-driven intelligent control method not only improves energy efficiency, reduces operating costs, but also enhances passenger comfort. The system continuously optimizes the control strategy through a real-time feedback correction mechanism to ensure the stability and adaptability of the indoor environment. In addition, the automation and intelligence of the system reduce manual intervention, improve response speed and control efficiency. By reducing unnecessary energy consumption, the system also helps to achieve energy conservation and emission reduction, and contributes to environmental protection. In short, this system provides a reliable, energy-saving and comfortable environmental control solution for subway stations through its intelligent design.

[0063] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for controlling the environment and temperature of a subway station, characterized in that: include: S1: Data collection; S1.1: Measure outdoor temperature parameters, humidity parameters and wind speed parameters at subway stations; S1.2: Through thermal comfort research, obtain the human metabolic rate, clothing thermal resistance, clothing outer air boundary layer thermal resistance and average radiant heat gain per unit skin area; S2: establishing a dynamic thermal comfort model, wherein the dynamic thermal comfort model includes a corresponding relationship between a relative thermal index RWI and an ASHRAE thermal sensation scale; S3: Determine the environmental control target temperature; S3.1: Obtain a comfortable indoor RWI value, i.e., a target RWI value, through a thermal comfort correlation survey, wherein the thermal comfort correlation survey includes establishing an ASHRAE thermal sensation scale model to obtain an ASHRAE thermal sensation calculation value, and also includes obtaining a subjective average value of the ASHRAE thermal sensation scale by asking the thermal sensation of the station personnel, and performing a weighted average of the ASHRAE thermal sensation calculation value and the subjective average value of the ASHRAE thermal sensation scale to obtain a target ASHRAE thermal sensation scale value, and then determining a target RWI value corresponding to the target ASHRAE thermal sensation scale value based on the corresponding relationship between the relative thermal index RWI and the ASHRAE thermal sensation scale; S3.2: Knowing the target RWI value, reversely calculate the environmental control target temperature t through the dynamic thermal comfort model to obtain the optimal temperature in the station; S4: Fresh air mode selection; The outdoor temperature parameters, humidity parameters and wind speed parameters collected by S1 are used as the basis for selecting the fresh air mode, where the fresh air modes include: minimum fresh air mode, maximum fresh air mode and non-mechanical fresh air mode; According to the comparison between the outdoor fresh air enthalpy value and the return air enthalpy value, and the monitoring result of the carbon dioxide concentration, when the outdoor fresh air enthalpy value is greater than or equal to the return air enthalpy value, monitor whether the carbon dioxide concentration exceeds the standard. If it exceeds the standard, the minimum fresh air mode is executed; if the carbon dioxide concentration does not exceed the standard, the non-mechanical fresh air operation is executed; if the outdoor fresh air enthalpy value is less than the return air enthalpy value, the fresh air mode is executed; S5: Establish load forecasting model; The loads in subway stations include indoor loads, fresh air loads and fan heating loads. The indoor loads include personnel loads, lighting loads, elevator loads, enclosure structure loads, advertising light box loads and AFC equipment loads. Fresh air loads include mechanical fresh air loads, screen door infiltration loads and entrance and exit infiltration loads. S6: Intelligent adjustment control; Intelligently adjust the chiller, water pump, fan frequency and water valve opening according to the load forecast results of S5 to maintain the indoor environment at the optimal temperature required by S3; S7: real-time feedback correction; Temperature and humidity sensors are set up in the station to monitor the indoor temperature and compare it with the optimal temperature in S3. If there is a deviation, it is fed back to S6 to execute the intelligent adjustment steps.

2. A subway station environment control and temperature control method according to claim 1, characterized in that: The formula for calculating the passenger load in S5 is as follows: The passenger load in a subway station is related to the passenger flow of the station and the passenger's stay time in the station; the passenger load of the station is calculated by the following formula: ; ; ; Where: Q p ——Full heat load of passengers, kW; G c , G p ——The number of people in the station hall and platform, respectively; A1, A2——The number of people entering and leaving the station at each hour, respectively; a1, a2——the time passengers stay in the station hall and platform respectively, min; b1, b2 – the time passengers stay in the station hall and platform respectively after leaving the station, min.

3. A subway station environment control and temperature control method according to claim 1, characterized in that: The calculation formula of lighting load in S5 is as follows: The lighting load of subway stations mainly includes working lighting load and advertising lighting load, and the calculation formula of lighting load of subway stations is as follows: ; Where: Q L ——Station lighting load, W; m——Number of lamp types; n——the number of each type of lamp, units; W m ——The power of each lamp, W / unit; F——Total area of ​​the public area of ​​the subway station, m2; P L ——Lighting power per unit area of ​​the station public area, W / ㎡; The advertising lighting load calculation formula is as follows: ; ; Where: Q A ——Station advertising lighting load, W; n——Total number of advertising light boxes on the concourse floor, units; W A ——The power of the advertising light box, W / unit; F A ——The wall area of ​​the station hall that can be used to install advertising light boxes, m2; P A ——Advertising lighting power density per unit area at the station concourse level, W / ㎡.

4. A subway station environment control and temperature control method according to claim 1, characterized in that: The elevator load calculation formula in S5 is as follows: The down escalator load is calculated according to the following formula: ; In the formula, Q ED ——Downward escalator load at the station, W; τ1——The ratio of the rated running time of the down escalator in this period to the length of the calculation period; P E1 ——Rated no-load power of down escalator, W; P E2 ——Low speed no-load power of downward escalator, W; The load of the upward escalator is calculated according to the following formula: ; ; ; In the formula, Q EU ——Load of the upward escalator at the station, W; τ2——The ratio of the running time of the escalator at rated speed with load in this period to the length of the calculation period; P E3 ——Upward escalator load operating power, W; τ3——The ratio of the no-load running time of the escalator at rated speed in this period to the length of the calculation period; P E4 ——Rated no-load power of upward escalator, W; P E5 ——Low speed no-load power of upward escalator, W; P EF ——Full-load rated power of upward escalator, W; φ——average load rate during this period; n p ——The average number of passengers carried by a single upward escalator during this period, people; n E ——The maximum number of people who can stand on the escalator at the same time is people.

5. A subway station environment control and temperature control method according to claim 1, characterized in that: The AFC load calculation formula in S5 is as follows: Calculate according to the total power of the equipment, as follows: ; In the formula, Q AFC ——Station AFC equipment load, W; m——Number of AFC equipment types; n——the number of each type of equipment, units; W m ——The single machine power of the equipment, W / unit; The calculation formula of the enclosure structure load in S5 is as follows: The heat transfer load calculation formula of the shielding door is as follows: ; In the formula, Q psd ——Heat transfer of shielding door, W; K——comprehensive thermal conductivity on both sides of the shielding door, W / ㎡·K; F——area of ​​the shielding door, m2; Δt – temperature difference on both sides of the shielding door, °C.

6. A subway station environment control and temperature control method according to claim 1, characterized in that: The calculation formula of fresh air load in S5 is as follows: the fresh air load of the subway station with screen doors mainly includes mechanical fresh air load and unorganized infiltration load; among them, the unorganized infiltration load includes tunnel infiltration load and entrance and exit infiltration load; the mechanical fresh air load and entrance and exit fresh air load are directly related to outdoor meteorological parameters; the tunnel infiltration load is related to the tunnel air state and the station platform temperature state; the fresh air load is obtained by the following formula: ; ; ; In the formula, Q vent , Q inf , Q tunnel ——They are mechanical fresh air load in the public area of ​​the subway station with screen doors, infiltration load at the entrances and exits, and infiltration load at the screen doors, kW; V vent 、V inf 、V tunnel ——They are the mechanical fresh air volume in public areas, inlet and outlet air infiltration volume, and screen door air infiltration volume, m³ / h; ρ——air density, kg / m³; C p ——Specific heat capacity of air, kJ / (kg.K); Toutside, Tair, Ttunnel - outdoor air temperature, public area air temperature and tunnel air temperature respectively, ℃.

7. A subway station environmental control and temperature control system, used for data collection of S1 in claim 1, intelligent regulation and control of S6 and real-time feedback correction of S7, characterized in that: It includes an outdoor gas phase parameter acquisition module, an indoor temperature and humidity parameter acquisition module, and an intelligent control module. The outdoor gas phase parameter acquisition module includes an outdoor temperature acquisition unit, an outdoor humidity acquisition unit, an outdoor fresh air enthalpy value acquisition unit, and an outdoor return air enthalpy value acquisition unit. The intelligent control module controls the frequency of the chiller, the frequency of the water pump, the frequency of the fan, and the opening of the water valve respectively.

8. A subway station environment and temperature control system according to claim 7, characterized in that: The intelligent control module uses a deep learning algorithm to achieve precise real-time control of the chiller frequency, water pump frequency, fan frequency, and water valve opening. The deep learning algorithm specifically includes the following steps: S1: Data collection and preprocessing: divide the historical cooling load data, historical outdoor weather data, and historical heating load data in the station into training sets and test sets according to date, and standardize the data in the training sets respectively; S2: Establish an LSTM neural network model and train it. Then use the test set in S1 to verify the accuracy of the LSTM neural network prediction model. The LSTM neural network model outputs cooling load data by inputting target temperature and real-time heating load data. S3: Real-time control and optimization. According to the real-time predicted cooling load data, the frequency of the chiller, the frequency of the water pump, the frequency of the fan and the opening of the water valve are controlled in real time. According to the real-time outdoor weather data and real-time heat load data, the predicted output cooling load data is optimized.

9. A subway station environment and temperature control system according to claim 8, characterized in that: The mathematical expression of the loss function of the LSTM neural network model is: ; in: is the sample size; is the amount of cooling load; It is The sample The true value of the load; It is The sample The predicted value of the load; It is A set of weights is used to balance the importance of different loads.

10. A subway station environment and temperature control system according to claim 9, characterized in that: The cooling load data includes chiller load, water pump load, fan load and water load data, and the data is specifically the cooling efficiency value.

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