A subway station environmental control and temperature control method and system

Abstract

The present invention discloses a method for environmental control and temperature control of subway stations, including: the present invention introduces a dynamic thermal comfort model, which 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 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.

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 and temperature control method and system. Background Art

[0002] Traditional subway station environmental control systems typically use fixed temperature and humidity settings, which are unable to adapt to outdoor temperature fluctuations and the temperature requirements of different areas within the station. This design not only wastes energy but also affects passenger comfort. Specific issues include the following:

[0003] 1. Traditional subway station environmental control systems lack flexibility. They are often unable to adjust to real-time passenger flow and outdoor climate changes, resulting in overcooling or overheating in certain periods or areas.

[0004] 2. Due to the lack of an effective regulation mechanism, traditional systems may over-operate, for example, providing large amounts of cooling when not needed, resulting in unnecessary energy waste;

[0005] 3. Fixed settings for temperature and humidity in different areas may not meet the needs of all passengers, resulting in a mixed passenger experience. Finally, fixed-setting systems may require more manual intervention for maintenance and adjustments, increasing operating costs.

[0006] 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. In addition, the fixed fresh air volume may lead to poor indoor air quality, especially during peak hours when there are many passengers and the carbon dioxide concentration rises, affecting air quality.

[0007] 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 air infiltration, subway stations often have an excessive supply of fresh air, which increases the energy consumption of the air-conditioning system.

[0008] 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 that needs to be solved in subway operation and design. Summary of the Invention

[0009] The object of the present invention is to provide a method and system for environmental and temperature control in a subway station to solve the above-mentioned problems.

[0010] The present invention is achieved through the following technical solutions:

[0011] A method for controlling environmental and temperature control in a subway station, comprising:

[0012] S1: Data collection;

[0013] S1.1: Measure outdoor temperature, humidity, and wind speed parameters at subway stations;

[0014] S1.2: Through thermal comfort research, obtain the human metabolic rate, clothing thermal resistance, clothing boundary layer thermal resistance, and average radiant heat gain per unit skin area.

[0015] S2: establishing a dynamic thermal comfort model, wherein the dynamic thermal comfort model includes a correspondence between the relative thermal index (RWI) and the ASHRAE thermal sensation scale;

[0016] The present invention proposes a dynamic thermal comfort model for subway stations. The determination principle of the model is to formulate the changing temperatures of the subway station hall and platform layers according to the changes in outdoor temperature and taking into account the dynamic thermal comfort of passengers during the ride.

[0017] The relative thermal index (RWI) was selected as the dynamic thermal comfort model for passengers. The RWI is a dimensionless number that takes into account parameters such as metabolic rate, transition time, ambient temperature, and clothing thermal resistance. If the RWI values are the same in two different environments and activity states, the thermal sensation is considered similar in both situations.

[0018] This invention, by introducing a dynamic thermal comfort model, achieves a revolutionary advancement in subway station environmental control systems. This model, centered on the relative thermal index (RWI), comprehensively considers multiple influencing factors, including the human metabolic rate, ambient temperature, and clothing thermal resistance, enabling accurate assessment of passengers' thermal comfort under varying environmental conditions. By monitoring outdoor temperature changes in real time and incorporating passengers' thermal comfort needs during their journey, the system intelligently adjusts the temperatures in the station hall and platform levels to align with their physiological and psychological needs. This dynamic adjustment mechanism not only significantly improves the passenger experience and reduces dissatisfaction caused by thermal discomfort, but also effectively reduces energy consumption and operating costs. Furthermore, the system's intelligent design enhances its adaptability and response speed, enabling it to rapidly adapt to rapid changes in the indoor and outdoor environments, maintaining indoor stability and comfort. In summary, this invention, through scientific methods and advanced technologies, achieves personalized, intelligent, and green subway station environmental control, providing passengers with a more comfortable and healthy riding environment.

[0019] The relative thermal index RWI is selected as the passenger dynamic thermal comfort model, and its calculation formula is as follows:

[0020] When Pa≤2269 Pa

[0021] When Pa>2269 Pa

[0022] Where: M--human metabolic rate, unit W / ㎡;

[0023] τ - the time spent in the transition process, unit s;

[0024] t--temperature, unit ℃;

[0025] I cw --Clothing thermal resistance, unit: clo;

[0026] I a -- Thermal resistance of the air boundary layer outside the clothing, unit: clo;

[0027] R--average radiant heat per unit skin area, unit W / ㎡.

[0028] S3: Determine the environmental control target temperature;

[0029] S3.1: Obtain a comfortable indoor RWI value, i.e., a target RWI value, through a thermal comfort correlation survey. The thermal comfort correlation survey includes establishing an ASHRAE thermal sensation scale model to obtain an ASHRAE calculated thermal sensation value, and obtaining a subjective average of the ASHRAE thermal sensation scale by asking station personnel about their thermal sensations. The target ASHRAE thermal sensation scale value is obtained by taking a weighted average of the calculated ASHRAE thermal sensation value and the subjective average of the ASHRAE thermal sensation scale. The target RWI value corresponding to the target ASHRAE thermal sensation scale value is then determined based on the corresponding relationship between the relative thermal index (RWI) and the ASHRAE thermal sensation scale.

[0030] S3.2: Given the target RWI value, reversely calculate the temperature t through the dynamic thermal comfort model to obtain the optimal temperature in the station;

[0031] RWI was developed by American experts based on relevant thermal comfort data of subways in the United States and other Western countries. Due to differences in ethnicity, region, subway environment, etc. between China and Western countries, and the long time since the relevant indicators were developed, it is difficult to accurately evaluate the current status of summer thermal comfort in a particular subway. The target RWI value of the present invention is obtained by the following method:

[0032] In the thermal comfort correlation investigation, the ASHRAE thermal sensation scale model was first introduced, and the ASHRAE thermal sensation scale calculation value was obtained by the following formula:

[0033] ASHRAE Thermal Sensation Scale Calculated Values

[0034]

[0035] Where M is the human metabolic rate (W / m²);

[0036] w is the human metabolic rate (W / m²);

[0037] Pa is the water vapor pressure of the environment (Pa);

[0038] f cl is the dress factor;

[0039] t r is the mean radiant temperature (°C);

[0040] h c is the convective heat transfer coefficient (W / (m²·°C));

[0041] t cl is the garment surface temperature (°C);

[0042] t a is the station air temperature (°C);

[0043] The above parameters are obtained through the data acquisition step;

[0044] The ASHRAE thermal sensation scale is an integer ranging 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.

[0045] 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. The target ASHRAE Thermal Sensation Scale value is obtained by weighted averaging the calculated ASHRAE Thermal Sensation Value and the subjective average value of the ASHRAE Thermal Sensation Scale. The target RWI value is then obtained by establishing a relationship between the RWI value and the ASHRAE Thermal Sensation Scale value.

[0046] Finally, by comparing the target RWI value to the ASHRAE Thermal Perception Scale, the corresponding value is obtained. Given parameters such as the passenger's metabolic rate M, clothing thermal resistance Icw, the thermal resistance of the air boundary layer outside the clothing Ia, and the average radiant heat gain per unit skin area R, the station's environmental control target temperature t can be calculated. This serves as one of the input parameters for indoor load calculations and guides the operation of the subway station's environmental control system.

[0047] S4: Fresh air mode selection;

[0048] The outdoor temperature, humidity 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;

[0049] Based on the comparison of the outdoor fresh air enthalpy value and the return air enthalpy value, as well as 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, the carbon dioxide concentration is monitored to see if it exceeds the standard. If it does, the system will operate in minimum fresh air mode. If the carbon dioxide concentration does not exceed the standard, it will operate without mechanical fresh air. If the outdoor fresh air enthalpy value is less than the return air enthalpy value, it will operate in full fresh air mode.

[0050] S5: Establish a load forecasting model;

[0051] The loads in subway stations include indoor loads, fresh air loads, and fan heating loads. Indoor loads include personnel loads, lighting loads, elevator loads, enclosure 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.

[0052] In this application, the daily variation pattern of indoor load is relatively stable compared to the 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 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 outdoor air 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 conditions and have a large volatility.

[0053] This invention achieves refined management of energy consumption by meticulously dividing subway station loads into three main components: indoor load, fresh air load, and fan temperature rise load, and further subdividing each component into sub-loads. The stability of indoor loads and the volatility of fresh air loads are fully utilized, and the accuracy of load forecasting is improved by combining historical data with real-time monitoring data. Furthermore, the system dynamically responds to changes in outdoor meteorological parameters, adjusting the fresh air volume in real time to adapt to the external environment, significantly improving energy efficiency. For screen door infiltration loads and entrance and exit infiltration loads, this invention effectively controls these loads by precisely calculating the airflow 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. Precise load management reduces the risk of system failures and enhances the reliability and stability of the entire environmental control system. In summary, this invention, through scientific methods and advanced technologies, achieves personalized, intelligent, and green environmental control for subway stations, providing passengers with a more comfortable and healthy riding environment while simultaneously improving both economic and environmental benefits for subway operators.

[0054] The formula for calculating passenger load is as follows: The passenger load in a subway station is related to the passenger flow and the length of time passengers stay at the station. The passenger load of a station can be calculated using the following formula:

[0055]

[0056]

[0057]

[0058] Where: Q p ——Full heat load of passengers, kW;

[0059] G c , G p ——The number of people in the station hall and platform respectively, people;

[0060] A1, A2——number of people entering and leaving the station at each hour, respectively;

[0061] a1, a2——the time passengers stay in the station hall and platform respectively, in minutes;

[0062] b1, b2 – the time passengers spend in the station hall and on the platform after exiting the station, in minutes.

[0063] The calculation formula for lighting load is as follows: The lighting load of subway stations mainly includes working lighting load and advertising lighting load. The calculation formula for lighting load of subway stations is as follows:

[0064]

[0065] Where: Q L ——Station lighting load (total power), W;

[0066] m——Number of lamp types;

[0067] n——the number of each type of lamp, units;

[0068] W m ——The power of each lamp, W / unit;

[0069] F——Total area of public areas in subway stations, m2;

[0070] P L ——Lighting power per unit area in the station’s public areas, W / ㎡.

[0071] The calculation formula for advertising lighting load is as follows:

[0072]

[0073]

[0074] Where: Q A ——Station advertising lighting load (total power), W;

[0075] n——the total number of advertising light boxes on the concourse floor, units;

[0076] W A ——Power of advertising light box, W / unit;

[0077] F A ——The wall area of the station concourse level that can be used to install advertising light boxes, m2;

[0078] P A ——Advertising lighting power density per unit area at the station concourse level, W / ㎡.

[0079] The elevator load calculation formula is as follows: The down escalator load can be calculated according to the following formula:

[0080]

[0081] Where Q ED ——Load of the down escalator at the station, W;

[0082] τ1——the ratio of the rated running time of the down escalator in this period to the length of the calculation period;

[0083] P E1 ——Rated no-load power of down escalator, W;

[0084] P E2 ——Downward escalator low speed no-load power, W.

[0085] The load of the upward escalator can be calculated according to the following formula:

[0086]

[0087]

[0088]

[0089] Where Q EU ——Load of the upward escalator at the station, W;

[0090] τ2——the ratio of the duration of the upward escalator running at rated speed with load in this period to the duration of the calculation period;

[0091] P E3 ——Upward escalator load operating power, W;

[0092] τ3 - the ratio of the escalator's rated speed no-load running time in this period to the length of the calculation period;

[0093] P E4 ——Rated no-load power of upward escalator, W;

[0094] P E5 ——Low speed no-load power of upward escalator, W;

[0095] P EF ——Rated power of the upward escalator at full load, W;

[0096] φ——average load rate during this period;

[0097] n p ——The average number of passengers carried by a single upward escalator during this period, people;

[0098] n E ——The maximum number of people standing on the escalator at the same time, people.

[0099] Vertical elevators are not used frequently, and the load of vertical elevators can be calculated as 2kW / unit.

[0100] The AFC load calculation formula is as follows:

[0101] It can be calculated based on the total power of the equipment as follows:

[0102]

[0103] Where Q AFC ——Station AFC equipment load (total power), W;

[0104] m——Number of AFC equipment types;

[0105] n——the number of each type of equipment, units;

[0106] W m ——The power of a single device, W / unit.

[0107] The formula for calculating the load of the enclosure structure is as follows:

[0108] The heat transfer load calculation formula of shield door is as follows:

[0109]

[0110] Where Q psd ——heat transfer of shield door, W;

[0111] K - comprehensive thermal conductivity on both sides of the shield door, W / ㎡·K;

[0112] F——area of screen door, m2;

[0113] Δt——Temperature difference on both sides of the shielding door, ℃.

[0114] The formula for calculating fresh air load is as follows: The fresh air load in subway stations with platform screen doors primarily consists of mechanical fresh air load and unorganized infiltration load. Unorganized infiltration load includes tunnel infiltration load and entrance / exit infiltration load. Mechanical and entrance / exit fresh air loads are directly related to outdoor meteorological parameters; tunnel infiltration load is related to tunnel air conditions and station platform temperature. The fresh air load can be calculated using the following formula:

[0115]

[0116]

[0117]

[0118] Where Q ven t, Q inf , Q tunnel ——respectively, the mechanical fresh air load of the public area of the platform subway station with platform screen doors, the infiltration load of the entrance and exit, and the infiltration load of the platform screen doors, kW;

[0119] V ven t、V inf 、V tunnel ——They are the mechanical fresh air volume in public areas, infiltration volume at entrances and exits, and infiltration volume at screen doors, m³ / h;

[0120] ρ——air density, kg / m³;

[0121] Cp ——Specific heat capacity of air, kJ / (kg·K);

[0122] Toutside, Tair, Ttunnel—outdoor air temperature, public area air temperature, and tunnel air temperature, respectively, in °C.

[0123] S6: Intelligent adjustment control;

[0124] 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;

[0125] S7: real-time feedback correction;

[0126] 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.

[0127] 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. It is characterized by including 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.

[0128] 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.

[0129] 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:

[0130] S1: Data collection and preprocessing: divide the historical cooling load data, historical outdoor weather data, and historical heating load data of the station into training sets and test sets according to date, and standardize the data in the training sets respectively;

[0131] S2: Build and train an LSTM neural network model. The accuracy of the LSTM neural network prediction model is then verified using the test set in S1. The LSTM neural network model inputs the target temperature and real-time heating load data and outputs cooling load data.

[0132] S3: Real-time control and optimization: Based on 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. Based on the real-time outdoor weather data and real-time heat load data, the predicted output cooling load data is optimized.

[0133] Preferably, the mathematical expression of the loss function of the LSTM neural network model is:

[0134]

[0135] in: is the sample size;

[0136] is the amount of cooling load;

[0137] It is The first sample The true value of the load;

[0138] It is The first sample The predicted value of the load;

[0139] It is A weighted method is used to balance the importance of different loads.

[0140] 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.

[0141] This invention achieves efficient control of the subway station environment by integrating an outdoor gas parameter acquisition module, an indoor temperature and humidity parameter acquisition module, and an intelligent control module. The system utilizes deep learning algorithms, specifically an LSTM neural network model, to precisely control the operation of chillers, water pumps, fans, and water valves in real time, adapting to changing outdoor weather and heat load data. This data-driven intelligent control approach not only improves energy efficiency and reduces operating costs, but also enhances passenger comfort. The system continuously optimizes control strategies through a real-time feedback correction mechanism to ensure the stability and adaptability of the indoor environment. Furthermore, the system's automation and intelligence reduce manual intervention, improving response speed and control efficiency. By reducing unnecessary energy consumption, the system also contributes to energy conservation and emission reduction, thus contributing to environmental protection. In summary, through its intelligent design, this system provides a reliable, energy-efficient, and comfortable environmental control solution for subway stations.

[0142] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0143] 1. The present invention has achieved revolutionary progress in the environmental control system of subway stations by introducing a dynamic thermal comfort model. This model takes the relative thermal index RWI as its 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 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 adaptability 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 personalized, intelligent and green environmental control of subway stations through scientific methods and advanced technologies, providing passengers with a more comfortable and healthy riding environment;

[0144] 2. This invention achieves refined management of energy consumption by meticulously dividing subway station loads into three main components: indoor load, fresh air load, and fan temperature rise load, and further subdividing each component into sub-loads. The stability of indoor loads and the volatility of fresh air loads are fully utilized, and the accuracy of load forecasting is improved by combining historical data with real-time monitoring data. Furthermore, the system dynamically responds to changes in outdoor meteorological parameters, adjusting the fresh air volume in real time to adapt to the external environment, significantly improving energy efficiency. For screen door infiltration loads and entrance and exit infiltration loads, this invention effectively controls these loads by precisely calculating the airflow 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. Precise load management reduces the risk of system failures and enhances the reliability and stability of the entire environmental control system. In summary, this invention, through scientific methods and advanced technologies, achieves personalized, intelligent, and green environmental control for subway stations, providing passengers with a more comfortable and healthy riding environment while simultaneously improving both economic and environmental benefits for subway operators. BRIEF DESCRIPTION OF THE DRAWINGS

[0145] 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:

[0146] Figure 1 is a flow chart of the method of the present invention;

[0147] Figure 2 Schematic diagram of the relationship of the system of the present invention. DETAILED DESCRIPTION

[0148] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the examples and accompanying drawings. The exemplary embodiments of the present invention and their descriptions 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

[0149] like Figures 1 to 2 As shown, this embodiment includes.

[0150] A method for controlling environmental and temperature control in a subway station, comprising:

[0151] S1: Data collection;

[0152] S1.1: Measure outdoor temperature, humidity, and wind speed parameters at subway stations;

[0153] S1.2: Through thermal comfort research, obtain the human metabolic rate, clothing thermal resistance, clothing boundary layer thermal resistance, and average radiant heat gain per unit skin area.

[0154] S2: establishing a dynamic thermal comfort model, wherein the dynamic thermal comfort model includes a correspondence between the relative thermal index (RWI) and the ASHRAE thermal sensation scale;

[0155] The present invention proposes a dynamic thermal comfort model for subway stations. The determination principle of the model is to formulate the changing temperatures of the subway station hall and platform layers according to the changes in outdoor temperature and taking into account the dynamic thermal comfort of passengers during the ride.

[0156] The relative thermal index (RWI) was selected as the dynamic thermal comfort model for passengers. The RWI is a dimensionless number that takes into account parameters such as metabolic rate, transition time, ambient temperature, and clothing thermal resistance. If the RWI values are the same in two different environments and activity states, the thermal sensation is considered similar in both situations.

[0157] This invention, by introducing a dynamic thermal comfort model, achieves a revolutionary advancement in subway station environmental control systems. This model, centered on the relative thermal index (RWI), comprehensively considers multiple influencing factors, including the human metabolic rate, ambient temperature, and clothing thermal resistance, enabling accurate assessment of passengers' thermal comfort under varying environmental conditions. By monitoring outdoor temperature changes in real time and incorporating passengers' thermal comfort needs during their journey, the system intelligently adjusts the temperatures in the station hall and platform levels to align with their physiological and psychological needs. This dynamic adjustment mechanism not only significantly improves the passenger experience and reduces dissatisfaction caused by thermal discomfort, but also effectively reduces energy consumption and operating costs. Furthermore, the system's intelligent design enhances its adaptability and response speed, enabling it to rapidly adapt to rapid changes in the indoor and outdoor environments, maintaining indoor stability and comfort. In summary, this invention, through scientific methods and advanced technologies, achieves personalized, intelligent, and green subway station environmental control, providing passengers with a more comfortable and healthy riding environment.

[0158] The relative thermal index RWI is selected as the passenger dynamic thermal comfort model, and its calculation formula is as follows:

[0159] When Pa≤2269 Pa

[0160] When Pa>2269 Pa

[0161] Where: M--human metabolic rate, unit W / ㎡;

[0162] τ - the time spent in the transition process, unit s;

[0163] t--temperature, unit ℃;

[0164] I cw --Clothing thermal resistance, unit: clo;

[0165] I a -- Thermal resistance of the air boundary layer outside the clothing, unit: clo;

[0166] R--average radiant heat per unit skin area, unit W / ㎡.

[0167] S3: Determine the environmental control target temperature;

[0168] S3.1: Obtain a comfortable indoor RWI value, i.e., a target RWI value, through a thermal comfort correlation survey. The thermal comfort correlation survey includes establishing an ASHRAE thermal sensation scale model to obtain an ASHRAE calculated thermal sensation value, and obtaining a subjective average of the ASHRAE thermal sensation scale by asking station personnel about their thermal sensations. The target ASHRAE thermal sensation scale value is obtained by taking a weighted average of the calculated ASHRAE thermal sensation value and the subjective average of the ASHRAE thermal sensation scale. The target RWI value corresponding to the target ASHRAE thermal sensation scale value is then determined based on the corresponding relationship between the relative thermal index (RWI) and the ASHRAE thermal sensation scale.

[0169] S3.2: Given the target RWI value, reversely calculate the temperature t through the dynamic thermal comfort model to obtain the optimal temperature in the station;

[0170] RWI was developed by American experts based on relevant thermal comfort data of subways in the United States and other Western countries. Due to differences in ethnicity, region, subway environment, etc. between China and Western countries, and the long time since the relevant indicators were developed, it is difficult to accurately evaluate the current status of summer thermal comfort in a particular subway. The target RWI value of the present invention is obtained by the following method:

[0171] In the thermal comfort correlation investigation, the ASHRAE thermal sensation scale model was first introduced, and the ASHRAE thermal sensation scale calculation value was obtained by the following formula:

[0172] ASHRAE Thermal Sensation Scale Calculated Values

[0173]

[0174] Where M is the human metabolic rate (W / m²);

[0175] w is the human metabolic rate (W / m²);

[0176] Pa is the water vapor pressure of the environment (Pa);

[0177] f cl is the dress factor;

[0178] t r is the mean radiant temperature (°C);

[0179] h c is the convective heat transfer coefficient (W / (m²·°C));

[0180] t cl is the garment surface temperature (°C);

[0181] ta is the station air temperature (°C);

[0182] The above parameters are obtained through the data collection step, and the calculated ASHRAE thermal sensation scale value at this time is the target ASHRAE thermal sensation scale value;

[0183] The ASHRAE thermal sensation scale is an integer ranging 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.

[0184] 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".

[0185] The target ASHRAE Thermal Sensation Scale value is determined by first conducting an on-site heat survey, asking about thermal sensations, and obtaining the subjective average ASHRAE Thermal Sensation Scale value. The target ASHRAE Thermal Sensation Scale value is then determined by weighted averaging the calculated ASHRAE Thermal Sensation value and the subjective average ASHRAE Thermal Sensation Scale value. The target RWI value is then determined by establishing a relationship between the RWI value and the ASHRAE Thermal Sensation Scale value, as follows:

[0186] In this embodiment, it is considered that the acceptable thermal sensation for passengers while riding in a station is slightly cool to slightly warm, so the following table is obtained:

[0187] Table 1 Relationship between RWI value and ASHRAE thermal sensation scale value

[0188]

[0189] Therefore, using the target RWI value obtained from Table 1 and given parameters such as the passenger's metabolic rate M, clothing thermal resistance Icw, thermal resistance of the air boundary layer outside the clothing Ia, and average radiant heat gain per unit skin area R, the station's environmental control target temperature t can be calculated. This serves as one of the input parameters for indoor load calculations and guides the operation of the subway station's environmental control system.

[0190] S4: Fresh air mode selection;

[0191] The outdoor temperature, humidity 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;

[0192] Based on the comparison of the outdoor fresh air enthalpy value and the return air enthalpy value, as well as 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, the carbon dioxide concentration is monitored to see if it exceeds the standard. If it does, the system will operate in minimum fresh air mode. If the carbon dioxide concentration does not exceed the standard, it will operate without mechanical fresh air. If the outdoor fresh air enthalpy value is less than the return air enthalpy value, it will operate in full fresh air mode.

[0193] S5: Establish a load forecasting model;

[0194] The loads in subway stations include indoor loads, fresh air loads, and fan heating loads. Indoor loads include personnel loads, lighting loads, elevator loads, enclosure 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.

[0195] In this application, the daily variation pattern of indoor load is relatively stable compared to the 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 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 outdoor air 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 conditions and have a large volatility.

[0196] This invention achieves refined management of energy consumption by meticulously dividing subway station loads into three main components: indoor load, fresh air load, and fan temperature rise load, and further subdividing each component into sub-loads. The stability of indoor loads and the volatility of fresh air loads are fully utilized, and the accuracy of load forecasting is improved by combining historical data with real-time monitoring data. Furthermore, the system dynamically responds to changes in outdoor meteorological parameters, adjusting the fresh air volume in real time to adapt to the external environment, significantly improving energy efficiency. For screen door infiltration loads and entrance and exit infiltration loads, this invention effectively controls these loads by precisely calculating the airflow 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. Precise load management reduces the risk of system failures and enhances the reliability and stability of the entire environmental control system. In summary, this invention, through scientific methods and advanced technologies, achieves personalized, intelligent, and green environmental control for subway stations, providing passengers with a more comfortable and healthy riding environment while simultaneously improving both economic and environmental benefits for subway operators.

[0197] The formula for calculating passenger load is as follows: The passenger load in a subway station is related to the passenger flow and the length of time passengers stay at the station. The passenger load of a station can be calculated using the following formula:

[0198]

[0199]

[0200]

[0201] Where: Q p ——Full heat load of passengers, k·W;

[0202] G c , G p ——The number of people in the station hall and platform respectively, people;

[0203] A1, A2——number of people entering and leaving the station at each hour, respectively;

[0204] a1, a2——the time passengers stay in the station hall and platform respectively, in minutes;

[0205] b1, b2 – the time passengers spend in the station hall and on the platform after exiting the station, in minutes.

[0206] The calculation formula for lighting load is as follows: The lighting load of subway stations mainly includes working lighting load and advertising lighting load. The calculation formula for lighting load of subway stations is as follows:

[0207]

[0208] Where: Q L ——Station lighting load (total power), W;

[0209] m——Number of lamp types;

[0210] n——the number of each type of lamp, units;

[0211] W m ——The power of each lamp, W / unit;

[0212] F——Total area of public areas in subway stations, m2;

[0213] P L ——Lighting power per unit area in the station’s public areas, W / ㎡.

[0214] The calculation formula for advertising lighting load is as follows:

[0215]

[0216]

[0217] Where: Q A ——Station advertising lighting load (total power), W;

[0218] n——the total number of advertising light boxes on the concourse floor, units;

[0219] W A ——Power of advertising light box, W / unit;

[0220] F A ——The wall area of the station concourse level that can be used to install advertising light boxes, m2;

[0221] P A ——Advertising lighting power density per unit area at the station concourse level, W / ㎡.

[0222] The elevator load calculation formula is as follows: The down escalator load can be calculated according to the following formula:

[0223]

[0224] Where, QED is the load of the down escalator at the station, W;

[0225] τ1——the ratio of the rated running time of the down escalator in this period to the length of the calculation period;

[0226] P E1 ——Rated no-load power of down escalator, W;

[0227] P E2 ——Downward escalator low speed no-load power, W.

[0228] The load of the upward escalator can be calculated according to the following formula:

[0229]

[0230]

[0231]

[0232] Where Q EU ——Load of the upward escalator at the station, W;

[0233] τ2——the ratio of the duration of the upward escalator running at rated speed with load in this period to the duration of the calculation period;

[0234] P E3 ——Upward escalator load operating power, W;

[0235] τ3 - the ratio of the escalator's rated speed no-load running time in this period to the length of the calculation period;

[0236] P E4 ——Rated no-load power of upward escalator, W;

[0237] P E5 ——Low speed no-load power of upward escalator, W;

[0238] P EF ——Rated power of the upward escalator at full load, W;

[0239] φ——average load rate during this period;

[0240] n p ——The average number of passengers carried by a single upward escalator during this period, people;

[0241] n E ——The maximum number of people standing on the escalator at the same time, people.

[0242] Vertical elevators are not used frequently, and the load of vertical elevators can be calculated as 2kW / unit.

[0243] The AFC load calculation formula is as follows:

[0244] It can be calculated based on the total power of the equipment as follows:

[0245]

[0246] Where Q AFC ——Station AFC equipment load (total power), W;

[0247] m——Number of AFC equipment types;

[0248] n——the number of each type of equipment, units;

[0249] W m ——The power of a single device, W / unit.

[0250] The formula for calculating the load of the enclosure structure is as follows:

[0251] The heat transfer load calculation formula of shield door is as follows:

[0252]

[0253] Where Q psd ——heat transfer of shield door, W;

[0254] K - comprehensive thermal conductivity on both sides of the shield door, W / ㎡·K;

[0255] F——area of screen door, m2;

[0256] Δt——Temperature difference on both sides of the shielding door, ℃.

[0257] The formula for calculating fresh air load is as follows: The fresh air load in subway stations with platform screen doors primarily consists of mechanical fresh air load and unorganized infiltration load. Unorganized infiltration load includes tunnel infiltration load and entrance / exit infiltration load. Mechanical and entrance / exit fresh air loads are directly related to outdoor meteorological parameters; tunnel infiltration load is related to tunnel air conditions and station platform temperature. The fresh air load can be calculated using the following formula:

[0258]

[0259]

[0260]

[0261] Where Q vent , Q inf , Q tunnel ——respectively, the mechanical fresh air load of the public area of the platform subway station with platform screen doors, the infiltration load of the entrance and exit, and the infiltration load of the platform screen doors, kW;

[0262] V vent 、V inf 、V tunnel ——They are the mechanical fresh air volume in public areas, infiltration volume at entrances and exits, and infiltration volume at screen doors, m³ / h;

[0263] ρ——air density, kg / m³;

[0264] C p ——Specific heat capacity of air, kJ / (kgK);

[0265] Toutside, Tair, Ttunnel—outdoor air temperature, public area air temperature, and tunnel air temperature, respectively, in °C.

[0266] S6: Intelligent adjustment control;

[0267] 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;

[0268] S7: real-time feedback correction;

[0269] 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.

[0270] 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. It is characterized by including 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.

[0271] 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.

[0272] 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:

[0273] S1: Data collection and preprocessing: divide the historical cooling load data, historical outdoor weather data, and historical heating load data of the station into training sets and test sets according to date, and standardize the data in the training sets respectively;

[0274] S2: Build and train an LSTM neural network model. The accuracy of the LSTM neural network prediction model is then verified using the test set in S1. The LSTM neural network model inputs the target temperature and real-time heating load data and outputs cooling load data.

[0275] S3: Real-time control and optimization: Based on 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. Based on the real-time outdoor weather data and real-time heat load data, the predicted output cooling load data is optimized.

[0276] In this embodiment, the mathematical expression of the loss function of the LSTM neural network model is:

[0277]

[0278] in: is the sample size;

[0279] is the amount of cooling load;

[0280] It is The first sample The true value of the load;

[0281] It is The first sample The predicted value of the load;

[0282] It is A weighted method is used to balance the importance of different loads.

[0283] 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.

[0284] This invention achieves efficient control of the subway station environment by integrating an outdoor gas parameter acquisition module, an indoor temperature and humidity parameter acquisition module, and an intelligent control module. The system utilizes deep learning algorithms, specifically an LSTM neural network model, to precisely control the operation of chillers, water pumps, fans, and water valves in real time, adapting to changing outdoor weather and heat load data. This data-driven intelligent control approach not only improves energy efficiency and reduces operating costs, but also enhances passenger comfort. The system continuously optimizes control strategies through a real-time feedback correction mechanism to ensure the stability and adaptability of the indoor environment. Furthermore, the system's automation and intelligence reduce manual intervention, improving response speed and control efficiency. By reducing unnecessary energy consumption, the system also contributes to energy conservation and emission reduction, thus contributing to environmental protection. In summary, through its intelligent design, this system provides a reliable, energy-efficient, and comfortable environmental control solution for subway stations.

[0285] 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, humidity, and wind speed parameters at subway stations; S1.2: Through thermal comfort research, obtain the human metabolic rate, clothing thermal resistance, clothing 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 correspondence between the relative thermal index (RWI) and the 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. The thermal comfort correlation survey includes establishing an ASHRAE thermal sensation scale model to obtain an ASHRAE calculated thermal sensation value, and obtaining a subjective average of the ASHRAE thermal sensation scale by asking station personnel about their thermal sensations. The target ASHRAE thermal sensation scale value is obtained by taking a weighted average of the calculated ASHRAE thermal sensation value and the subjective average of the ASHRAE thermal sensation scale. The target RWI value corresponding to the target ASHRAE thermal sensation scale value is then determined based on the corresponding relationship between the relative thermal index (RWI) and the ASHRAE thermal sensation scale. S3.2: Given 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, humidity 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; Based on the comparison between the outdoor fresh air enthalpy value and the return air enthalpy value, as well as 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, the carbon dioxide concentration is monitored to see if it exceeds the standard. If it exceeds the standard, the minimum fresh air mode is implemented; if the carbon dioxide concentration does not exceed the standard, the non-mechanical fresh air operation is implemented; if the outdoor fresh air enthalpy value is less than the return air enthalpy value, the fresh air mode is implemented; S5: Establish a load forecasting model; The loads in subway stations include indoor loads, fresh air loads, and fan heating loads. Indoor loads include personnel loads, lighting loads, elevator loads, enclosure 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 environmental 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 and the length of time passengers stay in the station. The passenger load of the station can be calculated using 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, people; A1, A2——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, in minutes; b1, b2 – the time passengers spend in the station hall and on the platform after exiting the station, in minutes.

3. The method for controlling the environment and temperature of a subway station 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. 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 public areas in subway stations, m2; P L ——Lighting power per unit area of the station public area, W / ㎡; The calculation formula for advertising lighting load is as follows: ; ; Where: Q A ——Station advertising lighting load, W; n——the total number of advertising light boxes on the concourse floor, units; W A ——Power of advertising light box, W / unit; F A ——The wall area of the station concourse level 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. The method for controlling the environment and temperature of a subway station 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: ; Where Q ED ——Load of the down escalator at the station, W; τ1——the ratio of the rated running time of the down escalator in this period to the total running time in this period; P E1 ——Rated no-load power of down escalator, W; P E2 ——Downward escalator low speed no-load power, W; The load of the upward escalator is calculated according to the following formula: ; ; ; Where Q EU ——Load of the upward escalator at the station, W; τ2——the ratio of the running time of the escalator at rated speed and load in this period to the total running time of this period; P E3 ——Upward escalator load operating power, W; τ3 - the ratio of the escalator's rated speed no-load running time to the total running time of the escalator in this period; P E4 ——Rated no-load power of upward escalator, W; P E5 ——Low speed no-load power of upward escalator, W; P EF ——Rated power of the upward escalator at full load, 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 standing on the escalator at the same time, people.

5. The method for controlling environmental and temperature control of a subway station according to claim 1, characterized in that: The AFC load calculation formula in S5 is as follows: Calculate based on the total power of the equipment, as follows: ; Where 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 power of a single device, W / unit; The calculation formula for the enclosure structure load in S5 is as follows: The heat transfer load calculation formula of shield door is as follows: ; Where Q psd ——heat transfer of shielding door, W; K - comprehensive thermal conductivity on both sides of the shield door, W / ㎡·K; F——area of screen door, m2; Δt——Temperature difference on both sides of the shielding door, ℃.

6. The method for controlling environmental and temperature control in a subway station according to claim 1, characterized in that: The calculation formula for the fresh air load in S5 is as follows: The fresh air load in subway stations with platform screen doors mainly includes mechanical fresh air load and unorganized infiltration load. 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 conditions and the station platform temperature conditions. The fresh air load is calculated using the following formula: ; ; ; Where Q vent , Q inf , Q tunnel ——respectively, the mechanical fresh air load of the public area of the platform subway station, the entrance and exit air leakage load, and the platform door air leakage load, kW; V vent 、V inf 、V tunnel ——They are the mechanical fresh air volume in public areas, infiltration volume at entrances and exits, and infiltration volume at screen doors, m³ / h; ρ——air density, kg / m³; C p ——Specific heat capacity of air, kJ / ( ); Toutside, Tair, Ttunnel—outdoor air temperature, public area air temperature, and tunnel air temperature, respectively, in °C.

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. The subway station environmental control 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 of the station into training sets and test sets according to date, and standardize the data in the training sets respectively; S2: Build and train an LSTM neural network model. The accuracy of the LSTM neural network prediction model is then verified using the test set in S1. The LSTM neural network model inputs the target temperature and real-time heating load data and outputs cooling load data. S3: Real-time control and optimization: Based on 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. Based on the real-time outdoor weather data and real-time heat load data, the predicted output cooling load data is optimized.

9. The subway station environmental control 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 first sample The true value of the load; It is The first sample The predicted value of the load; It is A weighted method is used to balance the importance of different loads.

10. The subway station environmental control 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.

Images