A subway station environment self-adaptive energy-saving control method

Abstract

The present application belongs to the field of metro station environment regulation, and discloses a kind of metro station in environmental adaptive energy-saving control method;Including: using environmental parameter set to calculate outdoor enthalpy and indoor enthalpy, and using improved smoothing algorithm to optimize calculation indoor environment data, for the selection and control of operating mode;Predefine operating mode, and set the judgment standard of operating mode;Set operating mode switching logic and smooth switching mechanism, and adaptively adjust the mode running in the station;Based on environmental parameter set design dynamic correlation model, the parameters of environmental parameter set are associated with each other, based on dynamic correlation model design parameter gradual change optimization model, output optimized control parameters, output control parameters according to time sequence in adjustment cycle, rolling update is carried out, the switching of operating mode is completed by minimizing objective function, and the adaptive energy-saving control of metro station environment is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of subway station environment regulation, more particularly, the present application relates to a subway station environment self-adaptive energy-saving control method. BACKGROUND

[0002] With the rapid development of subway transportation, the energy consumption problem of subway station ventilation and air conditioning system is becoming more and more serious. When the traditional ventilation and air conditioning system is running, a fixed control mode is adopted. For example, in summer, no matter how the outdoor temperature changes, the constant refrigeration output is maintained, resulting in a large amount of energy waste; in the transition season, even if the outdoor temperature and humidity are suitable, it will not automatically switch to the natural ventilation mode, and still consume energy. And the operation mode is less, it is difficult to control the environment in the station, resulting in waste of resources, at the same time, due to the large difference in the environment under the rigid mode, the temperature and humidity control relies on simple temperature controller and humidity regulator, the precision is poor, the indoor temperature fluctuation is often more than ± 3℃, the humidity fluctuation is more than ± 10%, which makes the passengers feel uncomfortable, in addition, in the process of adjusting the control parameters, the longer the step length, the more energy will be consumed in a short time, and the one-step parameter control will also lead to poor adjustment accuracy.

[0003] In view of this, the present application provides a subway station environment self-adaptive energy-saving control method to solve the above problems. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, in order to achieve the above-mentioned purpose, the present application provides the following technical scheme: a subway station environment self-adaptive energy-saving control method, comprising: step S1: collecting environment parameter set in real time;

[0005] Step S2: using the environment parameter set to calculate the outdoor enthalpy and the indoor enthalpy, and using the improved smoothing algorithm to optimize the indoor environment data for the selection and control of the operation mode;

[0006] Step S3: predefining the operation mode, the operation mode includes natural ventilation mode, small fresh air mode, refrigeration and dehumidification mode, dehumidification priority mode, carbon dioxide control mode and energy-saving standby mode, and setting the judgment standard of the operation mode; setting the operation mode switching logic and the smooth switching mechanism, and adaptively adjusting the mode running in the station;

[0007] Step S4: based on the environment parameter set, a dynamic correlation model is designed, the parameters of the environment parameter set are associated and attached, a parameter gradual optimization model is designed based on the dynamic correlation model, and the optimized control parameters are output, the control parameters are output according to time sequence in the adjustment period, and the rolling update is carried out, and the switching of the operation mode is completed by minimizing the objective function;

[0008] Step S5: arranging the data acquisition and analysis module, the operation mode design module, and the operation mode control and adjustment module to the visual interface to interact with the user.

[0009] Preferably, the set of environmental parameters comprises outdoor environmental data, indoor environmental data, and system operation state data.

[0010] The outdoor environmental data comprises temperature, relative humidity, wind speed, PM2.5 concentration, and atmospheric pressure outside the station.

[0011] The indoor environmental data comprises temperature, relative humidity, carbon dioxide concentration, and passenger flow on the station hall floor and the station platform floor.

[0012] The system operation state data comprises supply and return water temperature of the chilled water system of the air conditioner, return cooling tower outlet water temperature, condenser heat exchange temperature, air volume, and flow.

[0013] Preferably, the method for calculating the outdoor enthalpy and the indoor enthalpy using the set of environmental parameters comprises:

[0014] Defining the outdoor enthalpy as H_out and the indoor enthalpy as H_in.

[0015] Step A1: obtaining atmospheric pressure P in the outdoor environmental data, and if the atmospheric pressure P ∈ [80 kPa, 110 kPa], marking it as normal data, otherwise, marking it as abnormal data, and if the data is abnormal, reacquiring and warning.

[0016] Step A2: calculating saturated water vapor pressure Ps according to temperature T, and when T ≥ 0℃, when T < 0℃,

[0017] Step A3: calculating air moisture content when , marking it as normal data, otherwise, marking it as abnormal data.

[0018] Step A4: calculating enthalpy H = 1.006 × T + W × (2501 + 1.86 × T), and assigning T to temperature T_out in the outdoor environmental data and temperature T_in in the indoor environmental data, respectively assigning relative humidity in the outdoor environmental data and relative humidity in the indoor environmental data,

[0019] if the enthalpy H ∈ [-10 kJ / kg, 150 kJ / kg], marking it as normal data, otherwise, marking it as abnormal data.

[0020] Preferably, the method for optimizing calculation of indoor environment data using improved smoothing algorithm comprises:

[0021] Setting dynamic sliding window Wherein, K_min is a predefined minimum sliding window, Cov(J_j, c_co2) represents the covariance of the degree of personnel gathering J_j and the carbon dioxide concentration c_co2, σ(J_j) is the standard deviation of the degree of personnel gathering J_j, and σ(c_co2) is the standard deviation of the carbon dioxide concentration c_co2.

[0022] Defining the current cycle time, averaging the values at the same collection point, and arranging them in time sequence, using dynamic sliding window to perform sliding average on the carbon dioxide concentration in time sequence, removing the average of the sliding average values, then arranging the average values in sequence, and taking the median as the final carbon dioxide concentration in the indoor environment data.

[0023] Using weighted average to calculate the indoor average temperature T_avg and average relative humidity And setting the weight of the station hall layer to 0.7 and the weight of the platform layer to 0.3 in the weighted average.

[0024] Preferably, the judgment standard for setting the operation mode comprises:

[0025] The judgment standard for natural ventilation mode is that the outdoor enthalpy is lower than the indoor enthalpy, the temperature T_out in the outdoor environment data is in the interval of 20 to 26℃, the PM2.5 concentration is less than 50μg / m3, and the wind speed is greater than 0.5m / s.

[0026] The judgment standard for small fresh air mode is that the outdoor enthalpy is higher than the indoor enthalpy, and the carbon dioxide concentration is less than 600ppm.

[0027] The judgment standard for refrigeration and dehumidification mode is that the average relative humidity is higher than 65%, and the average temperature is in the interval of 24 to 26℃.

[0028] The judgment standard for carbon dioxide control mode is that the carbon dioxide concentration is higher than 800ppm.

[0029] The judgment standard for energy-saving standby mode is that the degree of personnel gathering is lower than the preset standard value.

[0030] Preferably, the method for setting the operation mode switching logic and smoothing switching mechanism and adaptively adjusting the mode running in the station comprises:

[0031] Defining the priority order of the modes from high to low as: natural ventilation mode, small fresh air mode, refrigeration and dehumidification mode, dehumidification priority mode, carbon dioxide control mode, and energy-saving standby mode.

[0032] Identify the current operating mode and mode lock time, set the score threshold and cost threshold;

[0033] Calculate real-time score If the real-time score is greater than the score threshold, and the current operating mode is not natural ventilation mode, switch to natural ventilation mode, if the conditions of the current operating mode no longer meet, and meet the downgrade conditions, downgrade to a mode with lower priority,

[0034] Wherein, And V_wind is the wind speed, and c_PM2.5 is the PM2.5 concentration;

[0035] The lock time and hysteresis condition of the preset mode are set, and the switching is triggered by the condition;

[0036] The mode switching cost Cs = γ1 × ΔE + γ2 × ΔC is calculated, and if the switching cost Cs is greater than the cost threshold, wherein γ1 and γ2 are weight coefficients, ΔE is the energy consumption change caused by mode switching, and ΔC is the comfort change.

[0037] Preferably, the dynamic correlation model is designed based on the environmental parameter set, and the parameters of the environmental parameter set are associated and affiliated, and the method comprises:

[0038] The dynamic correlation model is formed by horizontally splicing a chilled water system model, an indoor temperature change amount model, an indoor dehumidification amount model, and a carbon dioxide concentration model;

[0039] The output of the chilled water system model is cold energy, so the cold energy Qc = δ × c p × mw × (T g -T h ), wherein δ is the density of water, c p is the specific heat capacity of water, mw is the flow of system operating state data, T g and T h are the supply and return water temperatures in the system operating state data, respectively;

[0040] The dynamic equation of the indoor temperature change amount model is Wherein, ρ is the density of air, C is the specific heat capacity of air, V in is the indoor volume in the station, ΔT in_b is the unit change of indoor temperature, Qlozd is the indoor heat load, and ma is the air volume in the system operating state data;

[0041] The dynamic equation of the indoor dehumidification amount model is Wherein, ΔWin_b M is a unit change of indoor relative humidity de M is a dehumidification amount

[0042] The dynamic equation of the carbon dioxide concentration model is V in ×ΔCO 2_in_b = c_co2-ma×(k_in(CO2)-S_out(CO2)), wherein k_in(CO2) is the amount of carbon dioxide generated by the passenger flow, S_out(CO2) is the outdoor carbon dioxide concentration, ΔCO 2_in_b M is a unit change of indoor carbon dioxide concentration.

[0043] Preferably, the dynamic correlation model-based parameter gradual change optimization model is designed to output the optimized control parameters, and the control parameters are output in time sequence in the adjustment period and are updated in a rolling manner, and the switching of the operation mode is completed by minimizing the objective function, and the method comprises the following steps:

[0044] The objective function of the parameter gradual change optimization model is set as wherein ∈1, ∈2, ∈3 and ∈4 are predefined weight coefficients, E(k) is the energy consumption of the air conditioner when the control parameters output by the dynamic correlation model at the kth time in the adjustment period are used for adjustment, M is the difference between the indoor relative humidity and the target relative humidity when the control parameters are used for adjustment, co2(k) is the difference between the indoor carbon dioxide concentration and the target carbon dioxide concentration when the control parameters are used for adjustment, and T(k) is the difference between the indoor temperature and the target temperature when the control parameters are used for adjustment;

[0045] The constraint condition is set as that the control parameters output by the dynamic correlation model at the kth time in the adjustment period are all within the numerical range of the operation mode specification;

[0046] The objective function is minimized in the adjustment period, the switching of the operation mode is completed when the objective function is minimized, and the switched operation mode is used for the environmental control in the station.

[0047] Preferably, the use of the control parameters comprises the following steps:

[0048] Step D1: obtaining the control parameters output by the dynamic correlation model at the kth time in the adjustment period;

[0049] Step D2: using the PID control to arrange the control parameters to the air conditioner and using the parameter gradual change optimization model to optimize the control parameters;

[0050] Step D3: repeating the step D1 and the step D2 until the adjustment period is consumed or the step is stopped when the objective function is minimized, and the switching of the operation mode is completed;

[0051] Step D4: Use the switched operating mode for station environment control.

[0052] Preferably, the visualization interface comprises a computer's end page or a mobile device's display interface.

[0053] The technical effects and advantages of the subway station environment adaptive energy-saving control method of the present application are:

[0054] 1. Energy-efficient

[0055] Through adaptive operating mode selection, natural ventilation mode and small fresh air mode are preferred, reducing air conditioning system running time and energy consumption. Dynamic correlation model and parameter gradual optimization model comprehensively consider multi-objective optimization of energy consumption, temperature, humidity and carbon dioxide concentration, etc., to ensure that the system meets the comfort requirements while saving energy. Smooth switching mechanism avoids frequent mode switching, reducing equipment wear and energy consumption fluctuations.

[0056] 2. Strong environmental adaptability

[0057] The system can dynamically adjust the operating mode according to outdoor environmental changes (such as temperature, humidity, PM2.5 concentration) and indoor environmental changes (such as passenger flow, carbon dioxide concentration), adapt to different climate conditions and passenger flow peaks. Improved smoothing algorithm and enthalpy calculation method improve the accuracy and reliability of environmental parameters, providing a scientific basis for mode selection.

[0058] 3. Comfort and health

[0059] Through carbon dioxide control mode and dehumidification priority mode, indoor air quality and humidity are adjusted in time to ensure passenger health and comfort. Weighted average calculation of indoor temperature and humidity (station hall weight 0.7, platform layer weight 0.3) is more in line with actual use scenarios, optimizing passenger experience.

[0060] 4. High intelligence and automation

[0061] The system realizes automatic control through dynamic correlation model and parameter gradual optimization model, reduces manual intervention, and improves operation efficiency. The mode switching logic and scoring mechanism have high intelligence, and can automatically adjust the operating mode according to real-time scoring and cost threshold.

[0062] 5. System stability and reliability

[0063] Data anomaly detection (such as enthalpy anomaly, atmospheric pressure anomaly) ensures the reliability of input data, avoiding control errors caused by data errors. Smooth switching mechanism and mode lock time design reduce the instability of system operation, prolonging the service life of equipment.

[0064] The scheme realizes intelligent control and energy-saving optimization of the environment in the subway station through accurate collection and optimization of environmental parameters, intelligent selection and self-adaptive adjustment of operation modes, dynamic correlation model and parameter gradual optimization, and visual interface design. The technical effects are reflected in the accuracy of environmental monitoring, the intelligence of operation modes, the optimization of control parameters, and the significant improvement of energy-saving effects. The advantages include high energy efficiency, strong environmental adaptability, consideration of comfort and health, high intelligence, good system stability, strong scalability, and high comprehensive cost benefit. The scheme has high application value in the field of subway station environment control, can effectively improve the operation efficiency and passenger experience of the station, and realizes the goal of energy saving and emission reduction. BRIEF DESCRIPTION OF DRAWINGS

[0065] Figure 1 FIG. 1 is a structural schematic diagram of a subway station environment adaptive energy-saving control method according to the present application;

[0066] Figure 2 FIG. 2 is a step schematic diagram of a subway station environment adaptive energy-saving control method according to the present application. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor fall within the scope of the present application.

[0068] Embodiment 1

[0069] Please refer to Figure 1 and Figure 2 , the subway station environment adaptive energy-saving control method described in the present embodiment includes:

[0070] With the rapid development of subway transportation, the energy consumption problem of subway station ventilation and air conditioning system is becoming more and more serious. In the traditional ventilation and air conditioning system, a fixed control mode is adopted during operation. For example, in summer, no matter how the outdoor temperature changes, the constant refrigeration output is maintained, resulting in a large amount of energy waste; in the transition season, even if the outdoor temperature and humidity are suitable, it will not automatically switch to natural ventilation mode, and still consume energy. The temperature and humidity control relies on simple temperature controller and humidity regulator, with poor precision, indoor temperature fluctuation often exceeds ± 3℃, humidity fluctuation exceeds ± 10%, causing passengers to feel uncomfortable. Compared with the prior art, the present application is committed to breaking these limitations and realizing the perfect combination of high efficiency, energy saving and precise comfort through the innovative energy saving control method. The system station air conditioning system (including large system, water system equipment) daily energy consumption is 1000 degrees (operation time is 17 hours), which is more than 50% than the traditional control system.

[0071] The data acquisition and analysis module is used for real-time acquisition of the environmental parameter set, calculation of the outdoor enthalpy and indoor enthalpy using the environmental parameter set, and optimization calculation of the indoor environmental data using the improved smoothing algorithm, for selection and control of the operation mode.

[0072] Step S1: Real-time acquisition of the environmental parameter set.

[0073] Step S2: Calculation of the outdoor enthalpy and indoor enthalpy, carbon dioxide concentration, and temperature and humidity in the indoor environmental data.

[0074] The environmental parameter set includes outdoor environmental data, indoor environmental data and system operation state data.

[0075] The outdoor environmental data includes temperature, relative humidity, wind speed, PM2.5 concentration and atmospheric pressure of the station outdoor.

[0076] The indoor environmental data includes temperature, relative humidity, carbon dioxide concentration and passenger flow of the station hall layer and platform layer.

[0077] The system operation state data includes the supply and return water temperature of the air conditioning refrigerated water system, the cooling tower outlet water temperature, the condenser heat exchange temperature, the air volume and the flow.

[0078] The sensors are arranged at the locations of the collected environmental data, for example, 10 temperature sensors are uniformly laid at each station outdoor, and so on. By arranging sensors at the data acquisition required place, and the sensors collect corresponding values, the accuracy of the sensors is required to be within a certain range, for example, temperature: ± 0.1℃; humidity: ± 2% RH; carbon dioxide concentration: ± 50ppm; other parameters: meet the industrial grade accuracy requirements.

[0079] The method for calculating the outdoor enthalpy and indoor enthalpy using the environmental parameter set includes:

[0080] Define the outdoor enthalpy as H_out, and the indoor enthalpy as H_in; the enthalpy is used to determine whether the outdoor air is suitable for natural ventilation.

[0081] Step A1: Obtain the atmospheric pressure P in the outdoor environment data. If P ∈ [80 kPa, 110 kPa], mark it as normal data, otherwise, mark the data as abnormal. If the data is abnormal, reacquire and issue a warning.

[0082] Step A2: Calculate the saturated water vapor pressure Ps according to the temperature T. When T ≥ 0℃, When T < 0℃,

[0083] Step A3: Calculate the air moisture content W = 622 Ps / (P - Ps) When , mark it as normal data, otherwise, mark the data as abnormal. P, Ps, and relative humidity abnormal, such as sensor reading error, reacquire and trigger an alarm, for example, the alarm prompt: "humidity data is abnormal, Please check the sensor data." The purpose is to ensure that W ≥ 0, otherwise mark the data as abnormal.

[0084] Step A4: Calculate the enthalpy H = 1.006 × T + W × (2501 + 1.86 × T), and assign T to the temperature T_out in the outdoor environment data and the temperature T_in in the indoor environment data, respectively, and assign the relative humidity in the indoor environment data to the relative humidity in the outdoor environment data and the relative humidity

[0085] If H ∈ [-10 kJ / kg, 150 kJ / kg], mark it as normal data, otherwise, mark the data as abnormal.

[0086] Among them, the humidity sensor is the most direct and commonly used method to obtain the relative humidity. The relative humidity can also be obtained through meteorological data;

[0087] When using enthalpy to determine whether the station is ventilated, due to the influence of air pressure on the inside and outside of the station, the pressure difference will affect the effect of ventilation, and if only temperature is used to determine enthalpy and the influence of air pressure is ignored, errors will occur in the selection of the operation mode, resulting in inaccurate determination. Therefore, real-time measurement of atmospheric pressure is introduced to improve the accuracy of enthalpy calculation. In addition, when the sensor collects data, there will be error data. Since a large number of sensors are arranged in the station, and real-time performance is required when the mode is selected and switched, a large amount of data will be generated when the sensor is continuously supervised. If the data is individually judged for abnormality, the data running memory will be occupied, the running efficiency will be reduced, and the networking speed of the station is generally slow. Therefore, the occupation of too much data memory will slow down the operation of other modules and affect the overall running efficiency. Therefore, after data calculation, the use effect of the data is considered, and the correlation between the data is amplified, which can greatly reduce the data processing power and improve the data processing effect. In addition, the data is reasonably checked, the data anomaly is marked and an alarm is given, the timely processing capacity is improved, and a log is formed for easy reference.

[0088] The method for optimizing calculation of indoor environment data using the improved smoothing algorithm comprises the following steps:

[0089] Due to the gathering of people in the station, the distribution of personnel is uneven, which leads to a higher degree of crowd gathering, and the higher the degree of crowd gathering, the faster the carbon dioxide concentration rises. In areas with fewer people, the carbon dioxide concentration is lower, and the uniform distribution of sensors will cause single-point data fluctuations, resulting in the output carbon dioxide concentration value not being representative and difficult to represent the overall situation of the carbon dioxide concentration in the station. Based on this, a smoothing algorithm is used for optimization calculation, and the specific method is as follows:

[0090] Set a dynamic sliding window Wherein, K_min is a predefined minimum sliding window, Cov(J_j, c_co2) represents the covariance of the degree of personnel gathering J_j and the carbon dioxide concentration c_co2, and σ(J_j) is the standard deviation of the degree of personnel gathering J_j, and σ(c_co2) is the standard deviation of the carbon dioxide concentration c_co2.

[0091] The degree of personnel gathering J_j can be obtained by the sensor network or the video monitoring system to obtain the number of people in different areas of the station. For example, a thermal imaging camera or a personnel counting system based on Wi-Fi signals is used. The carbon dioxide concentration c_co2 here can be the historical data in the last period, for example, a sliding window adjustment is performed every 3 minutes, and then in the current period, the carbon dioxide concentration c_co2 here is the value after optimization calculation of the historical data in the last 3-minute period.

[0092] The current cycle time is defined, the values at the same collection point are averaged, and then arranged in time sequence. The dynamic sliding window is used to slide average the carbon dioxide concentration in time sequence. The values after sliding average are averaged, and then arranged in sequence. The median is taken as the final indoor environment data of carbon dioxide concentration.

[0093] In which, from the time, it can be seen that the data collected in 3 minutes is arranged in time sequence horizontally, that is, each collection point has a value at each time point. First, the values at each collection point are arranged in time sequence. The dynamic sliding window is used for processing to obtain the processed data of each collection point in the cycle time. Then, the average value is calculated by averaging these data to obtain the representative value of each sampling point in the cycle time. Each sampling point has a representative carbon dioxide concentration. The carbon dioxide concentration here is related to the degree of crowd gathering. Then, the median is obtained. The median is better than the average to represent the main carbon dioxide concentration of each sampling point in the station. The sampling point here is the position of the sensor, that is, the place where the sensor is used for data collection.

[0094] The indoor average temperature T_avg and the average relative humidity are calculated using weighted average and the weight of the station hall layer is set to 0.7 and the weight of the platform layer is set to 0.3.

[0095] The specific calculation formula can be ω can be used to represent the weight of the station hall layer and the platform layer.

[0096] The operation mode design module is used to predefine the operation mode, which includes natural ventilation mode, small fresh air mode, refrigeration and dehumidification mode, dehumidification priority mode, carbon dioxide control mode and energy saving standby mode, and set the judgment standard of the operation mode; set the operation mode switching logic and smooth switching mechanism, and adaptively adjust the mode running in the station;

[0097] The judgment standard of the operation mode is set, and the method includes;

[0098] The judgment standard of the natural ventilation mode is that the outdoor enthalpy is lower than the indoor enthalpy; the temperature T_out in the outdoor environment data is in the interval of 20 to 26℃, the outdoor temperature is in the appropriate range, the PM2.5 concentration is less than 50μg / m 3 , the air quality is good, and the wind speed is greater than 0.5m / s; the wind speed is appropriate; the control target is to stop refrigeration and use natural wind to cool.

[0099] The judgment standard of the small fresh air mode is that the outdoor enthalpy is higher than the indoor enthalpy; the air quality demand is low, and the carbon dioxide concentration is less than 600ppm; the control target is to reduce the fresh air ratio and reduce the refrigeration load.

[0100] The judgment standard of the refrigeration and dehumidification mode is that the average relative humidity is higher than 65%; the average relative humidity is too high, but the average temperature is in the appropriate range, and the average temperature is in the interval of 24 to 26℃; the control target is to prefer dehumidification and avoid excessive refrigeration.

[0101] The judgment standard of the carbon dioxide control mode is that the carbon dioxide concentration is higher than 800ppm; the carbon dioxide concentration exceeds the standard, and the control target is to increase the fresh air volume and reduce the carbon dioxide concentration.

[0102] The judgment standard of the energy-saving standby mode is that the degree of personnel gathering is lower than the preset standard value; the operation ends or is in a non-peak period, and the environmental load is low, and the control target is to reduce the operation frequency and maintain the minimum environmental demand. The standard value can be set by the management personnel, for example, 3.

[0103] The application scenario analysis of the operation mode, for example, in different seasons, in the case of high temperature and high humidity in summer, the operation mode selects the refrigeration and dehumidification mode. In the case of low temperature and dryness in winter, the operation mode selects the energy-saving standby mode. In the case of transition season, the operation mode selects the natural ventilation mode. Also, for example, the operation scheme in different passenger flow periods, in the peak period, the operation mode is the carbon dioxide control mode. The fresh air volume is preferentially increased, and the refrigeration and dehumidification mode or the small fresh air mode is dynamically switched according to the temperature and humidity conditions. In the non-peak period, the passenger density is low, and the environmental load is small. The operation mode is the energy-saving standby mode, the operation frequency of the air conditioning equipment is reduced, and the minimum fresh air volume and temperature and humidity control are maintained. After the operation ends, there is no passenger, and only the basic state of the equipment and the station environment needs to be maintained. The operation mode is the energy-saving standby mode, most of the equipment is turned off, and only the monitoring system and the necessary fresh air supplement are reserved.

[0104] The switching logic and smooth switching mechanism of the operation mode are set, and the mode running in the station is adaptively adjusted, and the method comprises:

[0105] The priority order of the mode is defined from high to low as: the natural ventilation mode, the small fresh air mode, the refrigeration and dehumidification mode, the dehumidification priority mode, the carbon dioxide control mode, and the energy-saving standby mode;

[0106] The current operation mode and the mode locking time are identified, and the score threshold and the cost threshold are set;

[0107] The real-time score is calculated If the real-time score is greater than the score threshold, and the current operation mode is not the natural ventilation mode, the natural ventilation mode is switched to, if the conditions of the current operation mode no longer meet, that is, the real-time score is less than or equal to the score threshold, and the downgrade condition is met, the mode is downgraded to a mode with lower priority.

[0108] Wherein, and are weight coefficients, adjusted according to actual needs, for example and V_wind is the wind speed, and c PM2.5 is the PM2.5 concentration;

[0109] Among them, the upgrade condition is to switch to a higher priority mode if the applicable condition of the upper mode is met. The downgrade condition is to downgrade to a lower priority mode if the condition of the current mode is no longer met.

[0110] The preset mode lock time (for example, 5 minutes, 3 minutes, etc., to avoid frequent mode switching.) and hysteresis condition (such as temperature difference greater than 1 degree Celsius or carbon dioxide concentration greater than 800 ppm) are switched by condition triggering (such as temperature difference greater than 1 degree Celsius for more than 2 minutes or carbon dioxide concentration greater than 800 ppm for more than 3 minutes);

[0111] The switching cost Cs = γ1 × ΔE + γ2 × ΔC is calculated, and if the switching cost Cs is greater than the cost threshold, the switching is suspended to avoid equipment wear and energy consumption fluctuations caused by frequent switching, where γ1 and γ2 are weight coefficients, adjusted according to energy saving priority or comfort priority. ΔE is the energy consumption change caused by mode switching (unit: kWh). ΔC is the comfort change (such as the weighted sum of temperature and humidity deviation);

[0112] Running mode control and regulation module: based on the environmental parameter set, a dynamic correlation model is designed, and the parameters of the environmental parameter set are associated and attached. Based on the dynamic correlation model, a parameter gradual change optimization model is designed, and the optimized control parameters are output. The control parameters are output in time sequence according to the adjustment period, and are updated rolling. The switching of the running mode is completed by minimizing the objective function;

[0113] The dynamic behavior of the ventilation and air conditioning system can be described by thermodynamic and fluid mechanics models, covering key parameters such as chilled water system, cooling tower, condenser, air volume and flow, as well as the relationship between indoor temperature, relative humidity, carbon dioxide concentration and passenger flow. The goal of the dynamic model is to predict the energy consumption and comfort level of the system under different parameters.

[0114] Based on the environmental parameter set, a dynamic correlation model is designed, and the parameters of the environmental parameter set are associated and attached. The method comprises:

[0115] The dynamic correlation model is formed by horizontally splicing the chilled water system model, the indoor temperature change amount model, the indoor dehumidification amount model and the carbon dioxide concentration model;

[0116] The output of the chilled water system model is cold energy. The cold energy of the chilled water system is related to the temperature difference and flow of the supply and return water. Therefore, the cold energy Qc = δ × c p × mw × (T g-T h ), where δ is the density of water, c p Where is the specific heat capacity of water, mw is the flow rate of the system operating data, i.e., the mass flow rate of chilled water, and T is the flow rate of the chilled water. g and T h These are the supply and return water temperatures in the system operating status data, namely the chilled water supply temperature and the return water temperature.

[0117] The change in indoor temperature is related to the cooling input, air volume, and indoor heat load. Therefore, the dynamic equation for the indoor temperature change model is: in, For the density of air, V is the specific heat capacity of air. in Let ΔT be the indoor volume of the station. in_b Qlozd represents the unit change in indoor temperature. The indoor heat load can be obtained through the heat balance method, the heat index method, or the empirical formula method. ma represents the air volume in the system operating status data, which is the supply air mass flow rate.

[0118] The change in indoor relative humidity (RH) is related to the dehumidification capacity and the fresh air volume. Therefore, the dynamic equation of the indoor dehumidification capacity model is: Wherein, ΔW in_b M represents the unit change in indoor relative humidity. de This refers to the dehumidification capacity;

[0119] The change in carbon dioxide concentration is related to the fresh air volume and passenger flow, so the dynamic equation of the carbon dioxide concentration model is: Where k_in(CO2) is the amount of carbon dioxide generated by passenger flow, S_out(CO2) is the outdoor carbon dioxide concentration, and ΔCO 2_in_b This represents the unit change in indoor carbon dioxide concentration.

[0120] The dynamic model of the ventilation and air conditioning system encompasses the interrelationships between the chilled water system, indoor temperature, humidity, and carbon dioxide concentration. These models allow for the prediction of energy consumption and comfort levels under different parameters, providing a theoretical basis for optimized control.

[0121] A parameter gradual optimization model is designed based on a dynamic correlation model, and the optimized control parameters are output. The output control parameters are iteratively updated according to a time series within the adjustment period, and the switching of operating modes is achieved by minimizing the objective function. The method includes:

[0122] The objective function of the parameter gradual optimization model is set as follows: Wherein, ∈1, ∈2, ∈3 and ∈4 are predefined weight coefficients, i.e. preset by the management personnel, E(k) is the difference between the indoor relative humidity and the target relative humidity when the control parameter is used for adjustment, co2(k) is the difference between the indoor carbon dioxide concentration and the target carbon dioxide concentration when the control parameter is used for adjustment, and T(k) is the difference between the indoor temperature and the target temperature when the control parameter is used for adjustment; the target relative humidity refers to the value of the relative humidity under the selected operation mode, and the target carbon dioxide concentration and the target temperature are similar.

[0123] The constraint condition is that the control parameter output by the dynamic correlation model at the kth time within the adjustment time is within the value range specified by the operation mode;

[0124] By minimizing the objective function within the adjustment cycle time, when the objective function is minimized, the operation mode switching is completed, and the switched operation mode is used for the station indoor environment control.

[0125] The use of the control parameter includes:

[0126] Step D1: obtaining the control parameter output by the dynamic correlation model at the kth time within the adjustment time;

[0127] Step D2: using the PID control to arrange the control parameter to the air conditioner, and using the parameter gradual change optimization model to optimize the control parameter;

[0128] Step D3: repeating the step D1 and the step D2 until the adjustment time consumption is completed or the objective function is minimized to stop, and the operation mode switching is completed;

[0129] Step D4: using the switched operation mode for the station indoor environment control.

[0130] The visualization module: arranging the data acquisition and analysis module, the operation mode design module, the operation mode control and adjustment module to the visualization interface, and performing the interface interaction with the user.

[0131] The visualization interface includes the end page of the computer or the display interface of the mobile device.

[0132] The user performs the interactive operation such as clicking, inquiring, downloading or value setting on the entire adjustment through the computer end page (computer display screen, the computer here refers to the smart device, and the mobile device can be a mobile phone or a tablet).

[0133] Embodiment 2

[0134] Please refer to Figure 1As shown, the embodiments not described in detail see example 1, provide a subway station environment adaptive energy-saving control system, comprising:

[0135] Data acquisition and analysis module: for real-time acquisition of environmental parameter set, using environmental parameter set to calculate outdoor enthalpy and indoor enthalpy, and using improved smoothing algorithm to optimize the calculation of indoor environment data, for the selection and control of operation mode;

[0136] Operation mode design module: for predefining operation mode, operation mode includes natural ventilation mode, small fresh air mode, refrigeration dehumidification mode, dehumidification priority mode, carbon dioxide control mode and energy-saving standby mode, and setting the judgment standard of operation mode; Set the switching logic and smooth switching mechanism of operation mode, and adaptively adjust the mode running in the station;

[0137] Operation mode control and adjustment module: based on the environmental parameter set, design a dynamic correlation model, associate the parameters of the environmental parameter set, design a parameter gradual optimization model based on the dynamic correlation model, output the optimized control parameters, output the control parameters according to the time sequence in the adjustment cycle, and update rolling, complete the switching of operation mode by minimizing the objective function;

[0138] Visual module: arrange the data acquisition and analysis module, operation mode design module, operation mode control and adjustment module to the visual interface, and interact with the user through the interface.

[0139] By real-time acquisition of outdoor environment data (temperature, humidity, wind speed, PM2.5 concentration, atmospheric pressure), indoor environment data (temperature, humidity, carbon dioxide concentration, passenger flow) and system running state data (refrigerated water supply and return water temperature, air volume, etc.), the environmental state inside and outside the station is comprehensively mastered.

[0140] The improved smoothing algorithm is used to optimize the indoor environment data (such as dynamic sliding window processing of carbon dioxide concentration, weighted average calculation of temperature and humidity), which effectively reduces the data noise and improves the data reliability, providing accurate basis for subsequent operation mode selection and control.

[0141] By segmenting the calculation of saturated water vapor pressure and air moisture content, the outdoor and indoor enthalpy are accurately calculated, and the abnormal data are marked and warned to ensure the accuracy and stability of the calculation results.

[0142] Natural ventilation mode, small fresh air mode, refrigeration dehumidification mode, dehumidification priority mode, carbon dioxide control mode and energy-saving standby mode are predefined, and clear judgment criteria are set (such as enabling natural ventilation mode when outdoor enthalpy is lower than indoor enthalpy).

[0143] Through the priority order, score threshold and cost threshold, combined with mode locking time and hysteresis conditions, smooth switching of operating modes is realized, avoiding the increase in energy consumption and device wear caused by frequent switching.

[0144] According to the changes of real-time environmental parameters, the operating mode is dynamically adjusted to ensure that the system always runs in the optimal mode under different passenger flow, climate conditions and indoor environmental state.

[0145] Through the dynamic correlation of the chilled water system model, indoor temperature change model, indoor dehumidification amount model and carbon dioxide concentration model, the physical change law of the station environment is comprehensively reflected.

[0146] Based on the dynamic correlation model, a parameter gradual optimization model is designed, the optimized control parameters are output by minimizing the objective function (comprehensively considering energy consumption, temperature deviation, humidity deviation and carbon dioxide concentration deviation), and the balance between energy consumption and comfort is realized. The control parameters are output in time sequence in the adjustment period to dynamically adjust the air conditioning operating state, ensuring the stability and efficiency of the system operation.

[0147] Through PID control and parameter gradual optimization model, the optimized control parameters are applied to the air conditioning system to realize smooth switching of operating modes. The mode switching is completed when the objective function is minimized, the low-energy mode such as natural ventilation mode is preferentially selected, the air conditioning system running time and energy consumption are reduced, and the indoor comfort requirement is met at the same time.

[0148] The data acquisition and analysis module, operating mode design module, operating mode control and adjustment module are integrated into the visual interface to facilitate users to monitor the station environment state, operating mode and control parameters in real time. Users can adjust parameters and view operating state through the interface to improve the operability and transparency of the system.

[0149] Embodiment 3

[0150] The embodiment discloses an electronic device, comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to realize the operation mode of the subway station environment adaptive energy-saving control method provided by the above-mentioned.

[0151] Since the electronic device introduced in the embodiment is the electronic device used for implementing the metro station indoor environment adaptive energy-saving control method in the embodiment, based on the metro station indoor environment adaptive energy-saving control method introduced in the embodiment, those skilled in the art can understand the specific implementation of the electronic device of the embodiment and various changes thereof, so the implementation of the electronic device in the method of the embodiment will not be introduced in detail. As long as the electronic device used for implementing the metro station indoor environment adaptive energy-saving control method in the embodiment is implemented by those skilled in the art, it belongs to the scope of protection of the present application.

[0152] The above formulas are dimensionless values, and the formulas are obtained by collecting a large amount of data to simulate the most recent real situation. The preset parameters and threshold values in the formulas are set by those skilled in the art according to the actual situation.

[0153] The above is only the preferred embodiment of the present application, and the protection scope of the present application is not limited to the above-mentioned embodiments. Any technical solution falling within the concept of the present application shall be considered as falling within the protection scope of the present application. It should be noted that, for ordinary technical users in the technical field, some improvements and refinements without departing from the principles of the present application are also considered as falling within the protection scope of the present application.

Claims

1. A subway station indoor environment adaptive energy-saving control method, characterized in that, The application relates to a dynamic running mode selection and control method for a station air conditioning system. Step S1: collecting an environmental parameter set in real time; Step S2: calculating outdoor enthalpy and indoor enthalpy by using the environmental parameter set, and performing optimized calculation on indoor environmental data by using an improved smoothing algorithm, which is used for running mode selection and control; Step S3: predefining a running mode, the running mode comprising a natural ventilation mode, a small fresh air mode, a refrigeration and dehumidification mode, a dehumidification priority mode, a carbon dioxide control mode and an energy-saving standby mode, and setting a judgment standard of the running mode; setting a running mode switching logic and a smoothing switching mechanism, and adaptively adjusting the mode running in the station; The method for setting the running mode switching logic and the smoothing switching mechanism and adaptively adjusting the mode running in the station comprises the following steps. The priority order of the modes is arranged from high to low as follows: the natural ventilation mode, the small fresh air mode, the refrigeration and dehumidification mode, the dehumidification priority mode, the carbon dioxide control mode and the energy-saving standby mode; The current running mode and the mode locking time are identified, and a score threshold value and a cost threshold value are set; Calculating real-time score If the real-time score is greater than the score threshold and the current operating mode is not the natural ventilation mode, switching to the natural ventilation mode, if the conditions for the current operating mode are no longer met, i.e. the real-time score is less than or equal to the score threshold and the downgrade condition is met, downgrading to a mode with a lower priority, wherein, , , and are weight coefficients, is the wind speed, is the PM2.5 concentration, is the outdoor enthalpy, is the indoor enthalpy, is the temperature in the outdoor environmental data, is the temperature in the indoor environmental data; The locking time and the lag condition of the mode are pre-set, and switching is performed through condition triggering; computing a mode switching cost suspending the switching if the switching cost is greater than a cost threshold, wherein and are weight coefficients, is a change in energy consumption resulting from the mode switching, is a change in comfort; Step S4: designing a dynamic correlation model based on the environmental parameter set, correlating parameters of the environmental parameter set, designing a parameter gradual change optimization model based on the dynamic correlation model, outputting optimized control parameters, outputting the control parameters in a time sequence in a regulation cycle, rolling updating, and completing running mode switching by minimizing an objective function; Step S5: arranging the data acquisition and analysis module, the running mode design module and the running mode control and regulation module on a visual interface, and performing interface interaction with a user.

2. The method according to claim 1, wherein, The environmental parameter set comprises outdoor environmental data, indoor environmental data and system running state data; The outdoor environmental data comprises temperature, relative humidity, wind speed, PM2.5 concentration and atmospheric pressure of the station outdoor environment; The indoor environmental data comprises temperature, relative humidity, carbon dioxide concentration and passenger flow of the station hall layer and the station platform layer; The system running state data comprises the supply and return water temperature of the refrigerated water system of the air conditioner, the return cooling tower outlet water temperature, the condenser heat exchange temperature, the air volume and the flow.

3. The method according to claim 2, wherein, The method for calculating the outdoor enthalpy and the indoor enthalpy by using the environmental parameter set comprises the following steps. Step A1: acquire atmospheric pressure in outdoor environment data , if atmospheric pressure , mark as normal data, otherwise, mark data as abnormal, if data is abnormal, re-acquire and give warning; Step A2: According to temperature Segmented calculation of saturated water vapor pressure then when , ; when , ; Step A3: Calculate air moisture content When is true, then mark the data as normal, otherwise, mark the data as abnormal; Step A4: Calculate enthalpy value , , relative humidity in indoor environment data , then the obtained enthalpy value is the outdoor enthalpy value and the indoor enthalpy value ; If the enthalpy value is marked as normal data, otherwise, the data is marked as abnormal.

4. The method according to claim 3, wherein, The method for performing optimized calculation on the indoor environmental data by using the improved smoothing algorithm comprises the following steps. Setting a dynamic sliding window wherein, is a predefined minimum sliding window, represents a degree of people gathering a covariance of the carbon dioxide concentration , is a standard deviation of the degree of people gathering , is a standard deviation of the carbon dioxide concentration ; The current cycle time is defined, the time sequence is arranged, the dynamic sliding window is used to slide average the carbon dioxide concentration in the time sequence, the average value of the slide-averaged values is obtained, the average values are sequentially arranged, the median is taken as the final carbon dioxide concentration in the indoor environmental data, and the carbon dioxide concentration in the indoor environmental data is obtained. The indoor average temperature is calculated using a weighted average and the average relative humidity and the weighting of the mezzanine level is set to 0.7 and the weighting of the platform level is set to 0.3 in the weighted average.

5. The method according to claim 4, wherein, The method for setting the judgment standard of the running mode comprises the following steps. The judgment standard of natural ventilation mode is that the outdoor enthalpy is lower than the indoor enthalpy; the temperature in the outdoor environment data is lower than the temperature in the indoor environment data In 20 to 26 Intervals, PM2.5 concentration is less than 50 μg / m³, and the wind speed is greater than 0.5 m / s; The judgment standard of the small fresh air mode is that the outdoor enthalpy is higher than the indoor enthalpy, and the carbon dioxide concentration is less than 600 ppm; The judgment standard of the refrigeration and dehumidification mode is that the average relative humidity is higher than 65% and the average temperature is between 24 and 26 Intervals; The judgment standard of the carbon dioxide control mode is that the carbon dioxide concentration is higher than 800 ppm; The judgment standard of the energy-saving standby mode is that the personnel gathering degree is lower than a preset standard value.

6. The method according to claim 5, wherein, The method for designing the dynamic correlation model based on the environmental parameter set and correlating parameters of the environmental parameter set comprises the following steps. The dynamic correlation model is formed by horizontally splicing a chilled water system model, an indoor temperature change amount model, an indoor dehumidification amount model and a carbon dioxide concentration model; The output of the chilled water system model is chilled energy, then chilled energy where, is the density of water, is the specific heat capacity of water, is the flow rate of system operating condition data, and are the supply and return water temperatures, respectively, in the system operating condition data. The dynamic equation of the indoor temperature change amount model is wherein, the density of air, the specific heat capacity of air, the indoor volume in the station, the unit change amount of the indoor temperature, the indoor heat load, the air volume in the system operation state data; The dynamic equation of the indoor dehumidification amount model is wherein, is a unit change amount of the indoor relative humidity, is the dehumidification amount; The dynamic equation of the carbon dioxide concentration model is wherein, is the amount of carbon dioxide generated by the flow of visitors, is the outdoor carbon dioxide concentration, is the unit change of the indoor carbon dioxide concentration.

7. The method according to claim 6, wherein, The parameter gradual change optimization model based on the dynamic correlation model is used to output the optimized control parameters, the control parameters are output according to the time sequence in the adjustment period, and the rolling update is performed, the switching of the operation mode is completed by minimizing the objective function, and the method comprises the following steps: The objective function of the parameter gradual optimization model is set as follows: ,in, , , and For predefined weight coefficients, the dynamic association model at the settling time... When outputting control parameters This refers to the energy consumption of the air conditioner when adjusted using control parameters. This represents the difference between the indoor relative humidity and the target relative humidity when adjusting using control parameters. This represents the difference between the indoor carbon dioxide concentration and the target carbon dioxide concentration when adjusted using control parameters. The difference between the indoor temperature and the target temperature when adjusted using control parameters; The constraint condition is set as the control parameters of the dynamic correlation model at the first time output in the adjustment time are within the numerical range of the operating mode specification; By minimizing the objective function in the adjustment period, when the objective function is minimized, the switching of the operation mode is completed, and then the switched operation mode is used for the station indoor environment control.

8. The method according to claim 7, wherein, The use of the control parameters comprises the following steps: Step D1 : Obtain the control parameters of the dynamic correlation model for the first output in the adjustment time ​ Step D2: using the PID control to arrange the control parameters to the air conditioner, and using the parameter gradual change optimization model to optimize the control parameters; Step D3: repeating the step D1 and the step D2 until the adjustment time consumption is completed or the objective function is minimized, and then the switching of the operation mode is completed; Step D4: using the switched operation mode for the station indoor environment control.

9. The method according to claim 8, wherein, The visual interface comprises a computer end page or a mobile device display interface.

Images