Energy-saving control method and system for chiller units in subway stations

Abstract

The present invention provides an energy-saving control method and system for chillers in subway stations, specifically relating to the technical field of air-conditioning systems. The method comprises: obtaining passenger flow data, indoor and outdoor environmental data, and equipment data, performing load forecasting, and generating equipment operating parameters based on the load forecast results; the passenger flow data includes personnel retention data; obtaining forecast temperature data within a preset time period, and determining whether to shut down at least one chiller based on the forecast temperature data and personnel retention data; if the chiller is shut down, using a phase change cold storage system for auxiliary cooling; and regenerating equipment operating parameters based on the equipment data, passenger flow data, and indoor and outdoor environmental data after auxiliary cooling. This method uses outdoor temperature and the number of personnel retained in the station as factors for determining whether to shut down the chiller; and uses a phase change cold storage system to adapt to subsequent load fluctuations caused by changes in passenger flow, thereby extending the chiller's downtime and avoiding frequent starts and stops of the chiller.

Description

Technical Field

[0001] The present invention relates to the technical field of air-conditioning systems, and in particular to an energy-saving control method and system for a chiller unit in a subway station. Background Art

[0002] With the rapid development of urban railways, the subway has become one of the most important means of transportation in modern society. According to surveys, the subway's air-conditioning system consumes 1 / 3-1 / 2 of the total energy consumed during subway operation. Among the energy consumed by the air-conditioning system, the energy consumption of the chiller accounts for about 1 / 2. Traditional air-conditioning system control methods are mostly based on experience or fixed operating modes. During non-peak passenger flow periods, the temperature in the station is often too low, resulting in energy waste.

[0003] To achieve energy savings in subway environmental control systems, a method has been proposed to control air conditioning systems based on predicted cooling loads, adjusting chiller operating parameters in real time to achieve energy savings. Subway stations typically have multiple chillers, and existing technologies can determine whether to shut down one or more chillers based on the predicted cooling load. However, the cooling load within a station is easily affected by passenger flow, which is highly variable and difficult to predict. Shutting down a chiller can lead to insufficient cooling due to increased passenger flow. Restarting the chiller, resulting in frequent starts and stops, increases energy consumption and shortens the chiller's lifespan. Summary of the Invention

[0004] The purpose of the present invention is to provide a method and system for energy-saving control of a chiller unit in a subway station to improve the above-mentioned problem. To achieve the above-mentioned purpose, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect, the present application provides an energy-saving control method for a chiller unit in a subway station, comprising:

[0006] Obtaining passenger flow data, indoor and outdoor environmental data, and equipment data, performing load forecasting based on the passenger flow data, indoor and outdoor environmental data, and equipment data, and generating equipment operating parameters based on the load forecast results to maintain the indoor temperature within a target range; the passenger flow data includes passenger retention data;

[0007] During a preset time period, forecast temperature data is obtained. Based on the forecast temperature data and personnel retention data, it is determined whether to shut down at least one chiller. If the chiller is shut down, a phase change cold storage system is used for auxiliary cooling.

[0008] The equipment operating parameters are regenerated based on the equipment data after auxiliary cooling, passenger flow data, and indoor and outdoor environmental data.

[0009] In a second aspect, the present application further provides an energy-saving control system for a chiller unit in a subway station, comprising:

[0010] A prediction control module is configured to obtain passenger flow data, indoor and outdoor environmental data, and equipment data, perform load forecasting based on the passenger flow data, indoor and outdoor environmental data, and equipment data, and generate equipment operating parameters based on the load forecast results to maintain the indoor temperature within a target range; the passenger flow data includes data on the number of people stranded;

[0011] The start-stop control module is used to obtain forecast temperature data within a preset time period and determine whether to shut down at least one chiller based on the forecast temperature data and personnel retention data. If the chiller is shut down, the phase change cold storage system is used for auxiliary cooling;

[0012] The parameter updating module is used to regenerate the equipment operating parameters according to the equipment data after the auxiliary cooling, the passenger flow data and the indoor and outdoor environmental data.

[0013] The beneficial effects of the present invention are:

[0014] The method of the present application no longer determines whether to shut down the chiller based on the predicted cooling load as in the traditional method, but makes a judgment based on the outdoor temperature and the number of people stranded in the station; the future outdoor temperature and the number of people stranded can be predicted relatively accurately; and the passenger flow data of those who only stay in the station for a short time is not considered as a factor in shutting down the chiller. When the present application determines that a chiller can be shut down, the phase change cold storage system is turned on for auxiliary cooling, and the cooling capacity of the phase change cold storage system is controlled to adapt to subsequent load fluctuations caused by changes in passenger flow. The energy consumption of the phase change cold storage system is significantly lower than that of the chiller, which can extend the downtime of the chiller while ensuring the cooling effect in the station, and avoid frequent start and stop of the chiller, thus achieving energy-saving control of the chiller.

[0015] Other features and advantages of the present invention will be set forth in the following description, and in part will become apparent from the description, or may be learned by practicing embodiments of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0017] Figure 1 This is a flow chart of the energy-saving control method for chillers in subway stations according to an embodiment of the present invention;

[0018] Figure 2Schematic diagram of the energy-saving control system for a subway station chiller unit according to an embodiment of the present invention.

[0019] Explanation of symbols: 100, prediction control module; 110, retention data acquisition unit; 111, first acquisition unit; 112, second acquisition unit; 113, first identification unit; 114, third acquisition unit; 120, station hall load prediction unit; 130, platform load prediction unit; 140, total load calculation unit; 200, start-stop control module; 300, parameter update module. DETAILED DESCRIPTION

[0020] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein in the specification of this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0021] In this application, the technical features described in an open manner include closed technical solutions composed of the listed features, and also include open technical solutions containing the listed features.

[0022] The factors that have the greatest impact on station temperature are outdoor temperature and passenger flow. Traditional load forecasting methods mostly build prediction models based on historical data on the environment and passenger flow to predict future loads. However, passenger flow during non-peak commuting hours is actually a highly variable factor that is difficult to accurately predict. Current load changes may also differ significantly from historical load changes. Therefore, after shutting down the chiller based on the predicted load, if passenger flow increases, insufficient cooling may occur. At this time, the chiller needs to be turned on again, resulting in frequent start-up and shutdown of the chiller and poor energy-saving effects.

[0023] like Figure 1 As shown, the present application provides an energy-saving control method for a chiller unit in a subway station, comprising step S100, step S200 and step S300.

[0024] Step S100: Obtain passenger flow data, indoor and outdoor environmental data, and equipment data, perform load forecasting based on the passenger flow data, indoor and outdoor environmental data, and equipment data, generate equipment operating parameters based on the load forecast results, and maintain the indoor temperature within a target range; the passenger flow data includes personnel retention data;

[0025] This step is the basic control method of the chiller. During the entire working period, the chiller operates according to the above method.

[0026] The indoor and outdoor environmental data include outdoor dry-bulb temperature, outdoor wet-bulb temperature, outdoor relative humidity, CO2 concentration, temperature and humidity at the concourse and platform levels, etc.

[0027] The equipment data includes chilled water temperature difference, return air temperature difference, cooling water temperature difference and number of units in operation;

[0028] The passenger flow data includes the number of people entering and leaving the station acquired by an image acquisition device, and also includes the number of people entering and leaving the station recorded by the gate;

[0029] This step uses a pre-trained load forecasting model, preferably constructed using an LSTM network, with historical passenger flow, equipment parameter, and environmental data as training inputs and the corresponding historical cooling load as training outputs. Based on the predicted load, the chilled water flow rate and temperature difference required to meet that load are calculated. The control strategy is then optimized under various constraints, including dynamically adjusting the chiller's control parameters to maintain the indoor temperature within the target range, based on the set chilled water temperature range and the maximum and minimum outputs of each device.

[0030] This step establishes different load prediction models for the station hall layer and the platform layer respectively. The main difference between the station hall layer load prediction model and the platform layer load prediction model is that the passenger flow data are considered differently. In addition, since the platform layer is deeper than the platform layer, its temperature fluctuation is smaller and it is less affected by the outdoor temperature. Therefore, establishing different prediction models for the station hall layer and the platform layer can make the prediction value more accurate.

[0031] As an optional implementation, obtaining passenger flow data, indoor and outdoor environmental data, and equipment data, and performing load forecasting based on the passenger flow data, indoor and outdoor environmental data, and equipment data, includes:

[0032] Step S110: Using monitoring equipment to collect images of the station hall, obtaining first passenger flow data based on the image collection results, and using face recognition technology and behavior recognition technology to obtain data on the number of people staying in the station;

[0033] Specifically, based on the surveillance video of the subway entrance and exit, a passenger flow counting system based on visual recognition is used to obtain the number of people entering and leaving the station, and the number of people currently in the station is calculated based on the data as the first passenger flow data;

[0034] The facial recognition technology and behavior recognition technology are used to obtain the data of people staying in the station, specifically:

[0035] Use facial recognition technology to process all images collected in the station hall to obtain facial information of everyone in the station hall. Remove the facial information of subway staff and obtain valid facial information. Generate a corresponding ID for each valid facial information, store it in the cache database, and record the corresponding storage time;

[0036] Based on the valid facial information, behavior recognition technology is used to obtain the behavior information of the person, and obtain their location and stay time as follows:

[0037] When obtaining valid facial information, if the corresponding ID is retrieved and already exists in the cache database, it will not be stored again. The difference between the storage time of the ID and the current time is calculated to obtain the residence time.

[0038] When the stay time exceeds the preset value, the person is classified as a monitored person, and then the monitored person is tracked and behavior recognition technology is used to identify the monitored person's behavior information. For example, Openpose's posture recognition technology is used to identify human behaviors such as standing, sitting, walking, and looking at mobile phones. At the same time, face recognition technology can be combined to identify behaviors such as conversation to obtain the behavior information of each monitored person.

[0039] Generally, people stay in the station concourse for too long because they are resting, cooling off, or waiting in the station. In recent years, in order to reduce urban electricity consumption in summer, more and more city subways have set up cooling-off areas for citizens. As a result, there have been cases of people staying in the station concourse for a long time. The behaviors of such people are highly overlapping, such as sitting, talking, and looking at mobile phones, and their activities are concentrated in a small area. Therefore, the behavioral information of people can be used to predict whether they will stay for a long time.

[0040] While obtaining behavioral information, the location of the monitored person is obtained based on the collected images, and the range of their activities during the current stay time is obtained.

[0041] Based on the behavior information, location and current stay time of the monitored personnel, the deep learning network model is used to identify the stranded personnel, obtain the number of stranded personnel, store the stranded personnel's facial information in the database and record their corresponding stay time, which is the total time the person stays in the station hall;

[0042] The deep learning network model is pre-trained using a large amount of historical data. Specifically, it constructs a GA-BP neural network model, assigning quantified values to a person's behavior information, current stay time, and activity range as input, and training the GA-BP neural network model using the person's total stay time as output. This model can predict the total stay time of a monitored person. If the predicted total stay time exceeds a preset value, the person is identified as stranded.

[0043] As a preferred embodiment, when quantifying and assigning values to a person's behavioral information, the behavioral information is first classified into features. For example, standing, sitting, walking, and running are considered posture features, and each posture feature is assigned a value: standing: 0, sitting: 1, walking: 2, and running: 3. Conversation, looking at a phone, and answering a phone are considered behavior features, and each feature is also assigned a value: talking: 4, looking at a phone: 5, and answering a phone: 6. Simultaneous posture features and behavior features are combined. For example, if sitting and talking occur simultaneously, feature combination 14 is obtained. Feature combinations of a person are obtained at preset time intervals (e.g., 1 minute). For example, after obtaining five feature combinations, [04, 15, 15, 15, 05] is obtained. This time series data is used as model input. In other embodiments, more feature classifications can be expanded, for example, by also including a person's age or expression as a feature and combining it with other features.

[0044] The facial information and ID of each stranded person are stored in the stranded database, and the corresponding actual stranded time is recorded.

[0045] According to the effective facial information of the stranded person, the corresponding historical stranded time is retrieved in the stranded database.

[0046] If the ID of the stranded person is a newly added ID in the stranded database, that is, there is no historical data of his / her stranded time in the stranded database, the total predicted stay time will be used as his / her current stranded time for load forecasting. If there is historical data of his / her stranded time in the stranded database, the average of his / her historical data of stranded time and the total predicted stay time will be used as the average of his / her current stranded time for load forecasting.

[0047] Step S120: predicting the load of the concourse layer based on the environmental data, the first passenger flow data, and the passenger retention data;

[0048] This step constructs a concourse load prediction model. This model uses historical environmental data, first passenger flow data, and passenger retention data as input, and is trained using the concourse's historical cooling load as output. The retention data includes the number of passengers and the duration of each passenger's stay. The concourse load prediction model is used to determine the concourse load.

[0049] Step S130: predicting the platform load based on the environmental data and the second passenger flow data obtained by the gate system;

[0050] The second passenger flow data obtained by the gate system is the entry data recorded by the gate system. The passenger flow reflected by this data does not include stranded passengers, but rather passengers traveling normally. Therefore, the second passenger flow data is the actual passenger flow that affects the platform temperature.

[0051] Therefore, the concourse load prediction model constructed in this step is trained using historical environmental data and the second passenger flow data as input and the platform layer historical cooling load as output. The platform layer load is obtained through the platform layer load prediction model.

[0052] Step S140: Calculate the total load of the subway station based on the hall load and the platform load. That is, the total load of the station can be obtained by calculating the sum of the hall load and the platform load.

[0053] Step S200: within a preset time period, obtain forecast temperature data, and determine whether to shut down at least one chiller based on the forecast temperature data and the passenger retention data. If the chiller is shut down, use a phase change cold storage system for auxiliary cooling; the preset time period is a non-peak passenger flow period;

[0054] Specifically, determining whether to shut down at least one chiller according to the forecast temperature data and the personnel retention data includes:

[0055] During a preset time period, the estimated difference between the indoor temperature and the forecast temperature at at least one subsequent time point is calculated; in this embodiment, the indoor temperature uses the average of the station hall temperature and the platform temperature, and the temperatures at the subsequent two time points are selected (each time point is 1 hour apart). For example, the current time is 9:20, and the subsequent time points are 10:00 and 11:00, respectively. The estimated difference between the indoor temperature and the forecast temperature at the second subsequent time point, i.e., 11:00, is calculated to be 5.6°C. Preferably, a threshold value can be set in advance. When the estimated difference value is higher than the threshold value, it indicates that the outdoor temperature will be very high. At this time, the calculation and judgment of the subsequent unit demand value will no longer be performed, and the chiller will not be shut down. Alternatively, the number of chillers that need to be shut down can be determined based on the preset threshold value.

[0056] The detention impact is calculated based on the number of detained personnel and the corresponding detention time. Specifically, the historical detention time (i.e., the number of historical detentions) of the person is first queried in the detention database according to the ID of the person, and a weight is generated for each person based on the number of historical detentions. The more detentions, the greater the weight.

[0057] Multiply each person's weight by their current detention time, and then calculate the total of all people to get the detention impact.

[0058] The unit demand value is calculated based on the estimated difference and the retention impact; the calculation method of the unit demand value is:

[0059] During off-peak hours, we selected two hours with minimal passenger flow fluctuations, calculated the total load value, detention impact, and maximum indoor and outdoor temperature difference of the station hall within those two hours, and collected multiple sets of data as samples.

[0060] With the detention influence and the maximum temperature difference between indoor and outdoor as independent variables, and the total load value of the station hall layer as the dependent variable, Matlab was used for nonlinear fitting to obtain the fitting function. The fitting function was then used as the calculation formula for the unit demand value. The estimated difference and the detention influence were substituted into the formula to obtain the unit demand value.

[0061] If the unit demand value is less than the preset shutdown threshold, at least one chiller will be shut down.

[0062] After the calculation formula of the unit demand value is preliminarily determined, the shutdown threshold can be adjusted to achieve a reasonable judgment on whether to shut down. In general, the shutdown judgment conditions of the present application method are more relaxed than those of traditional judgment methods, which means that the shutdown time can be extended.

[0063] The auxiliary cooling using the phase change cold storage system includes:

[0064] Based on the forecast temperature data, the auxiliary cooling time for the day is predicted; specifically, the time period suitable for using auxiliary cooling can be estimated based on the forecast temperature, combined with historical daily auxiliary cooling time predictions.

[0065] The maximum flow rate of the phase change material is calculated based on the auxiliary cooling time and the total amount of cold storage in the phase change cold storage system; the total amount of cold storage is the volume of the phase change material that has stored cold in the current phase change system, and the total amount of cold storage may vary from day to day.

[0066] Maximum flow rate = total amount of cold storage / auxiliary cooling time;

[0067] To adjust the auxiliary cooling strategy in real time based on the current load and minimize excessive energy consumption by the still-operating chillers, a suitable target temperature is first set for the chilled water return based on the selected chiller model. The difference between the chilled water return temperature and the preset target temperature is calculated. If the difference is less than a preset first threshold, the flow rate of the phase change material is calculated based on the difference. The principle for setting the first threshold is that when the difference is less than the first threshold, adjusting the flow rate of the phase change material is sufficient to reduce the chilled water return temperature to the target temperature.

[0068] If the difference is greater than or equal to the preset first threshold, the maximum flow rate is used as the flow rate of the phase change material; at this time, the chilled water return cannot be reduced to the target temperature through the phase change material, and the working energy consumption of the chiller will increase slightly, but the pre-cooling effect of the phase change material also greatly alleviates the working pressure of the chiller.

[0069] The formula for calculating the flow rate of the phase change material based on the difference is:

[0070]

[0071] Among them, L 相 is the flow rate of phase change material, L 水 is the chilled water flow rate, ρ 水 is the density of frozen water, c 水 is the constant pressure specific heat of chilled water, Δt is the difference between the return temperature of chilled water and the preset target temperature, λ is the phase change heat transfer coefficient, K is the unit cooling capacity of the phase change material (kJ / m 3 );

[0072] The K value is determined according to the selected phase change material, and the λ value is related to factors such as the selection of the heat exchanger and the type of phase change material, and can be determined through multiple experiments.

[0073] To implement this method, a heat exchanger needs to be installed on the chilled water return pipeline. The phase change material cools the chilled water return in the heat exchanger. The cooled chilled water return enters the evaporator, and the phase change material after absorbing heat is sent to the material storage tank for storage.

[0074] As an optional embodiment, the material storage tank includes a plurality of storage tank units arranged in parallel, each storage tank unit is provided with a temperature sensor, and the cold storage method of the phase change cold storage system includes:

[0075] The chilled water flow rate is obtained. When the system is operating at low load, the unit uses a frequency converter to adjust the chilled water flow rate to reduce energy consumption. However, the chilled water flow rate cannot fall below the unit's minimum allowable value. Therefore, when the obtained chilled water flow rate approaches the minimum allowable value, for example, below a preset second threshold, the chilled water pump operating frequency is maintained or maintained, and some chilled water output is sent to the material storage tank to cool the phase change material in each storage tank unit in turn, thereby storing cold. When the pump operating frequency rises to a certain value or the chilled water return temperature rises to a certain value, cold storage ends and normal chilled water output resumes.

[0076] During periods when passenger flow is far below design values, the temperature inside subway stations is generally low. This method utilizes the cooling capacity during low-load periods to store cold in the phase-change material, effectively utilizing energy. Low-load operation times may vary daily, and therefore the amount of cold stored in the phase-change material will also vary. To further ensure sufficient cold storage, cold storage can be implemented at night when electricity prices are low.

[0077] The phase change material is obtained by dispersing phase change capsule material or phase change metal particles in a fluid carrier, so the phase change material can be transported through pipelines. During auxiliary cooling, the phase change material is output from the cold storage tank unit and exchanges heat with the chilled water in a convection manner.

[0078] Step S300: regenerate equipment operating parameters based on the equipment data after auxiliary cooling, passenger flow data, and indoor and outdoor environmental data.

[0079] Measure the chilled water return temperature after phase change material cooling, use this temperature to update the chilled water temperature difference, and simultaneously update the number of operating units. Use passenger flow data, indoor and outdoor environmental data, and updated equipment data to perform load forecasting and regenerate equipment operating parameters.

[0080] On the other hand, the present application also provides an energy-saving control system for a chiller in a subway station, see Figure 2 ,include:

[0081] The prediction control module 100 is configured to obtain passenger flow data, indoor and outdoor environmental data, and equipment data, perform load forecasting based on the passenger flow data, indoor and outdoor environmental data, and equipment data, and generate equipment operating parameters based on the load forecast results to maintain the indoor temperature within a target range; the passenger flow data includes passenger retention data;

[0082] The start-stop control module 200 is used to obtain forecast temperature data within a preset time period and determine whether to shut down at least one chiller based on the forecast temperature data and personnel retention data. If the chiller is shut down, the phase change cold storage system is used for auxiliary cooling;

[0083] The parameter updating module 300 is used to regenerate the equipment operating parameters according to the equipment data after auxiliary cooling, the passenger flow data and the indoor and outdoor environmental data.

[0084] As an optional implementation, the prediction control module 100 includes:

[0085] The retention data acquisition unit 110 is used to collect images of the station hall layer, obtain first passenger flow data based on the image collection results, and obtain retention data of people in the station using face recognition technology and behavior recognition technology;

[0086] The station hall load prediction unit 120 is used to predict the station hall load based on the environmental data, equipment data, first passenger flow data and passenger retention data;

[0087] The platform load prediction unit 130 is used to predict the platform load based on the environmental data, the equipment data and the second passenger flow data obtained by the gate system;

[0088] The total load calculation unit 140 is used to calculate the total load of the subway station based on the hall load and the platform load.

[0089] As an optional implementation, the retention data acquisition unit 110 includes:

[0090] The first acquisition unit 111 is used to acquire facial information of people in the station, remove facial information belonging to subway staff, and obtain valid facial information;

[0091] The second acquisition unit 112 is configured to obtain the behavior information of the person based on the valid facial information by using behavior recognition technology, and obtain the person's location and stay time;

[0092] The first recognition unit 113 is configured to identify stranded persons based on the deep learning network model according to their behavior information, location, and length of stay, obtain the number of stranded persons, store their facial information in a database, and record their corresponding length of stay;

[0093] The third obtaining unit 114 is configured to retrieve the corresponding historical detention time from the database according to the effective facial information of the detained person.

[0094] It should be noted that, regarding the system in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated on here.

[0095] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for energy-saving control of a chiller unit in a subway station, characterized in that: include: Obtaining passenger flow data, indoor and outdoor environmental data, and equipment data, performing load forecasting based on the passenger flow data, indoor and outdoor environmental data, and equipment data, and generating equipment operating parameters based on the load forecast results to maintain the indoor temperature within a target range; the passenger flow data includes passenger retention data; Obtain forecast temperature data within a preset time period and determine whether to shut down at least one chiller based on the forecast temperature data and personnel retention data, including: Calculating the estimated difference between the indoor temperature and the predicted temperature at at least one subsequent time point within a preset time period; The detention impact is calculated based on the number of detained people and the corresponding historical detention time; The unit demand value is calculated based on the estimated difference and the retention impact; If the unit demand value is less than the preset shutdown threshold, at least one chiller will be shut down; If the chiller is shut down, the phase change cold storage system is used for auxiliary cooling; The calculation method of the unit demand value is: During off-peak hours, the total load value, retention impact, and maximum indoor and outdoor temperature difference of the station hall are counted, and multiple sets of data are collected as samples; With the retention effect and the maximum temperature difference between indoor and outdoor as independent variables and the total load value of the station hall as the dependent variable, nonlinear fitting was performed using MATLAB to obtain the fitting function. The fitting function is used as the calculation formula for the unit demand value, and the estimated difference and the retention impact are substituted into the calculation to obtain the unit demand value; Based on the number of stranded personnel and the corresponding historical stranded time, the stranded impact is calculated, including: According to the effective facial information of the detained person, the number of his / her historical detention time is queried in the database to obtain his / her historical detention times; generating a weight for each detained person according to the historical number of detentions; Based on the weight of each detained person and the historical detention time, the detention impact is obtained; The equipment operating parameters are regenerated based on the equipment data after auxiliary cooling, passenger flow data, and indoor and outdoor environmental data.

2. The energy-saving control method for a chiller unit in a subway station according to claim 1, characterized in that: Obtaining passenger flow data, indoor and outdoor environmental data, and equipment data, and performing load forecasting based on the passenger flow data, indoor and outdoor environmental data, and equipment data, including: Capture images of the station hall, obtain the first passenger flow data based on the image acquisition results, and use facial recognition technology and behavior recognition technology to obtain the data on the number of people stranded in the station; Predict the concourse load based on environmental data, equipment data, first passenger flow data, and passenger retention data; Predict platform load based on environmental data, equipment data, and secondary passenger flow data obtained from the gate system; The total load of the subway station is calculated based on the hall load and the platform load.

3. The energy-saving control method for a chiller unit in a subway station according to claim 2, characterized in that: The data on people staying in the station is obtained by using facial recognition technology and behavior recognition technology, including: Obtain facial information of people in the station, remove facial information belonging to subway staff, and obtain valid facial information; Based on the effective facial information, behavior recognition technology is used to obtain the behavior information of the person and obtain their location and stay time; Based on the behavior information, location and length of stay of the personnel, the deep learning network model is used to identify the stranded personnel, obtain the number of stranded personnel, store the facial information of the stranded personnel in the database and record their corresponding length of stay; According to the effective facial information of the stranded person, the corresponding historical stranded time is retrieved from the database.

4. The energy-saving control method for a chiller unit in a subway station according to claim 1, characterized in that: The auxiliary cooling using the phase change cold storage system includes: According to the forecast temperature data, the auxiliary cooling time of the day is predicted; Calculating the maximum flow rate of the phase change material based on the auxiliary cooling time and the total amount of cold storage of the phase change cold storage system; Calculating the difference between the return temperature of the chilled water and the preset target temperature; if the difference is less than a preset second threshold, calculating the flow rate of the phase change material based on the difference; if the difference is greater than or equal to the preset second threshold, using the maximum flow rate as the flow rate of the phase change material; The phase change material is used to cool the chilled water return water, and the cooled chilled water return water enters the evaporator.

5. The energy-saving control method for a chiller unit in a subway station according to claim 1, characterized in that: The phase change cold storage system includes a material storage tank, and the cold storage method of the phase change cold storage system includes: The chilled water flow rate is obtained. When the chilled water flow rate is lower than a preset second threshold, the operating frequency of the chilled water pump is kept unchanged, and part of the chilled water output is transported to the material storage tank to cool the phase change material in the material storage tank for cold storage.

6. An energy-saving control system for a chiller unit in a subway station, characterized in that: The subway station chiller energy-saving control system is used to implement the method according to any one of claims 1 to 5, comprising: A prediction control module is configured to obtain passenger flow data, indoor and outdoor environmental data, and equipment data, perform load forecasting based on the passenger flow data, indoor and outdoor environmental data, and equipment data, and generate equipment operating parameters based on the load forecast results to maintain the indoor temperature within a target range; the passenger flow data includes data on the number of people stranded; The start-stop control module is used to obtain forecast temperature data within a preset time period and determine whether to shut down at least one chiller based on the forecast temperature data and personnel retention data. If the chiller is shut down, the phase change cold storage system is used for auxiliary cooling; The parameter updating module is used to regenerate the equipment operating parameters according to the equipment data after the auxiliary cooling, the passenger flow data and the indoor and outdoor environmental data.

7. The energy-saving control system for chillers in subway stations according to claim 6, characterized in that: The predictive control module includes: A retention data acquisition unit is used to collect images of the station hall layer, obtain first passenger flow data based on the image collection results, and obtain retention data of people in the station using face recognition technology and behavior recognition technology; The station hall load prediction unit is used to predict the station hall load based on environmental data, equipment data, first passenger flow data and passenger retention data; A platform load prediction unit, configured to predict the platform load based on environmental data, equipment data, and second passenger flow data obtained from the gate system; The total load calculation unit is used to calculate the total load of the subway station based on the station hall load and the platform load.

8. The energy-saving control system for chillers in subway stations according to claim 7, characterized in that: The retention data acquisition unit includes: The first acquisition unit is configured to acquire facial information of people in the station, remove facial information belonging to subway staff, and obtain valid facial information; A second acquisition unit is configured to obtain the behavior information of the person based on the valid facial information by using behavior recognition technology, and obtain the person's location and stay time; The first recognition unit is used to identify stranded persons based on the deep learning network model according to the behavior information, location and residence time of the persons, obtain the number of stranded persons, store the facial information of the stranded persons in the database and record their corresponding residence time; The third obtaining unit is configured to retrieve the corresponding historical detention time from the database according to the effective facial information of the detained person.

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