Temperature control method and device of railway vehicle, computer equipment and storage medium

Abstract

The embodiment of the invention provides a temperature control method of a rail vehicle, a temperature control device of the rail vehicle, computer equipment and a computer storage medium, and relates to the technical field of rail transit. The method comprises the following steps: acquiring multi-source sensing data corresponding to a railway vehicle; wherein the multi-source perception data comprises environment perception data and passenger perception data; inputting the multi-source sensing data into a pre-trained thermal comfort prediction model to obtain a comfort value of a passenger in the railway vehicle, and determining a target comfort level matched with the comfort value; and a first temperature adjustment strategy corresponding to the target comfort level is determined, and temperature control network equipment of the railway vehicle is controlled based on the first temperature adjustment strategy. The method can adapt to complex and changeable actual operation requirements, the intelligence and energy-saving performance of the air conditioning system are improved, and the comfort level of passengers is met.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of rail transit, in particular, to a temperature control method of a rail vehicle, a temperature control device of a rail vehicle, a computer device and a computer storage medium. BACKGROUND

[0002] As the core carrier of public transportation, the comfort of the operation environment of the urban rail vehicle directly affects the travel experience of passengers, and the temperature control system is the key core component for ensuring the appropriate temperature in the vehicle compartment. However, the operation scene of urban rail transit has significant complexity and dynamic variability, and these dynamic changes have put very high requirements on the real-time perception ability, response speed and adjustment accuracy of the temperature control system.

[0003] In the related technical solutions, the preset temperature range is usually combined with the feedback control mechanism, but the above-mentioned static control logic not only is difficult to adapt to the complex and variable actual operation requirements, thereby leading to poor passenger experience, but also causes energy waste due to frequent invalid temperature adjustment, and increases the operation cost, which does not meet the energy saving and emission reduction demand. SUMMARY

[0004] The present application provides a temperature control method of a rail vehicle, a temperature control device of a rail vehicle, a computer device and a computer storage medium, thereby at least to some extent overcoming the series of technical problems that due to the limitations and defects of the related art, it is difficult to adapt to the complex and variable actual operation requirements, thereby leading to poor passenger experience, and causing energy waste due to frequent invalid temperature adjustment, and increasing the operation cost, which does not meet the energy saving and emission reduction demand.

[0005] The first aspect of the present application provides a temperature control method of a rail vehicle, the method comprising: acquiring multi-source perception data corresponding to the rail vehicle; wherein the multi-source perception data includes environment perception data and passenger perception data; inputting the multi-source perception data into a pre-trained thermal comfort prediction model to obtain a comfort value of the passengers in the rail vehicle, and determining a target comfort level matched with the comfort value; determining a first temperature adjustment strategy corresponding to the target comfort level, and controlling the temperature control network device of the rail vehicle based on the first temperature adjustment strategy.

[0006] In a second aspect, the present application provides a temperature control device for a rail vehicle, comprising: a data acquisition module configured to acquire multi-source perception data corresponding to the rail vehicle; wherein the multi-source perception data comprises environmental perception data and passenger perception data; a comfort output module configured to input the multi-source perception data into a pre-trained thermal comfort prediction model to obtain a comfort value of a passenger in the rail vehicle and determine a target comfort level matching the comfort value; and a temperature control module configured to determine a first temperature adjustment strategy corresponding to the target comfort level and control a temperature control network device of the rail vehicle based on the first temperature adjustment strategy.

[0007] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the above rail vehicle temperature control methods when executing the computer program.

[0008] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program implements the steps of any one of the above rail vehicle temperature control methods when executed by a processor.

[0009] In a fifth aspect, the present application provides a computer program product, comprising a computer program, and the computer program implements the steps of any one of the above rail vehicle temperature control methods when executed by a processor.

[0010] The technical solution of the present application has the following advantages: Through the above rail vehicle temperature control method, multi-source perception data corresponding to the rail vehicle is acquired; wherein the multi-source perception data comprises environmental perception data and passenger perception data; the multi-source perception data is input into a pre-trained thermal comfort prediction model to obtain a comfort value of a passenger in the rail vehicle and determine a target comfort level matching the comfort value; a first temperature adjustment strategy corresponding to the target comfort level is determined, and a temperature control network device of the rail vehicle is controlled based on the first temperature adjustment strategy.

[0011] The method realizes environment and human parameter coupling modeling by acquiring multi-source perception data including environment perception data containing external environment dimensions and passenger perception data such as passenger physiological dimensions and passenger behavior dimensions, so as to determine the comfort value of passenger feeling based on the thermal comfort prediction model. Then, the temperature adjustment strategy matched with the comfort value of passenger feeling is adjusted according to different comfort levels of the comfort value of passenger feeling, so as to ensure that the thermal comfort feeling of passengers in the car is optimized under various environmental conditions. Moreover, the method realizes "on-demand regulation" through the hierarchical strategy and introduces an intelligent temperature control system adapted thereto, so as to intelligently adjust the air conditioning setting in the car, avoid excessive refrigeration / heating, and enable the indoor environment to maintain good comfort and energy-saving effect under variable external conditions. BRIEF DESCRIPTION OF DRAWINGS

[0012] The accompanying drawings, which are included to provide a further understanding of the present application, constitute a part of this application and illustrate embodiments of the present application and together with the description serve to explain the present application. In the drawings: Figure 1 A flowchart of a temperature control method of a rail vehicle provided by an embodiment of the present application; Figure 2 A schematic diagram of a method for determining a first temperature adjustment strategy corresponding to a target comfort level provided by an embodiment of the present application; Figure 3 A flowchart of another temperature control method of a rail vehicle provided by an embodiment of the present application; Figure 4 A flowchart of a method for determining a temperature adjustment strategy provided by an embodiment of the present application; Figure 5 A flowchart of another method for determining a temperature adjustment strategy provided by an embodiment of the present application; Figure 6 A structural schematic diagram of a temperature control device of a rail vehicle provided by an embodiment of the present application; Figure 7 A structural schematic diagram of a computer device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0013] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations can be implemented in any

[0014] Moreover, the drawings are not necessarily to scale. Like numbers refer to like, similar or analogous items throughout the drawings. Like numbers also can be used to denote like claimed elements among multiple drawings. Some of the blocks in the flowchart illustrations can be implemented with software, hardware, or a combination of software and hardware. Further, some of the blocks can be combined with other blocks and / or divided into sub-blocks. Some of the blocks can be omitted or not implemented. Some of the blocks can be performed or implemented in a different order than as shown in the flowcharts. Some of the blocks can be performed or implemented concurrently, in parallel, or sequentially.

[0015] The flowcharts shown in the drawings are merely illustrative examples and do not necessarily include all steps. For example, some steps can be further divided, and some steps can be combined or partially combined, so the actual execution order can be changed according to the actual situation.

[0016] Urban rail vehicles are the core carriers of modern urban public transportation, and the comfort of their operating environment directly affects the travel experience of passengers. The temperature control system (hereinafter also referred to as the temperature control system) installed on the rail vehicle is the key component to ensure the appropriate temperature in the vehicle compartment. At the same time, under the background of global advocacy of sustainable development and energy saving and emission reduction, the energy consumption control of the rail vehicle has become an important link to reduce the operating cost of the operator and practice the concept of green transportation. Therefore, the energy efficiency performance of the temperature control system is of great concern.

[0017] It can be understood that the operation scene of urban rail transit has significant complexity and dynamic variability. On the one hand, the temperature in the car is affected by external climate (for example, seasonal changes, solar radiation intensity, etc.), vehicle operating state (for example, air exchange when driving at high speed), and other factors, and the car environment parameters present nonlinear changes; on the other hand, the passenger load of the rail train fluctuates with distinct multidimensional characteristics - for example, due to the commuter tidal phenomenon, the number of passengers during peak hours and off-peak hours is very different; the passenger flow distribution during holidays is significantly different from weekdays; due to the differences in surrounding functional areas (for example, business district, residential area, office area), the number of passengers getting on and off the train also presents obvious fluctuations. These dynamic characteristics of the rail train in the actual operation process put high requirements on the real-time sensing ability, response speed and adjustment accuracy of the vehicle temperature control system.

[0018] In related technical solutions, the following technical solutions are mainly used: The first traditional technical solution is a mechanism that relies on a preset temperature range and combines feedback control based on data collected by an external temperature sensor, and then compares it with a preset temperature threshold to achieve adjustment of the air conditioner in the rail vehicle.

[0019] However, the above technical solution lacks comprehensive perception of the complex environmental factors in the car, and the control method based on the preset temperature threshold not only lags in response speed, making it difficult to respond to sudden changes in passenger flow, resulting in poor passenger experience; moreover, frequent and ineffective temperature adjustment can easily increase energy consumption, leading to energy waste that does not meet the energy saving and emission reduction requirements, and also increases operating costs.

[0020] The second traditional technical solution is to introduce a fuzzy control algorithm to fuzz multiple environmental factors such as temperature and humidity, and then establish a fuzzy rule base to control the air conditioning system.

[0021] However, in the above method, the formulation of fuzzy rules usually relies on a large amount of experience, which makes it difficult for the method to comprehensively cover various complex working conditions, making the reliability of the temperature control system insufficient. Moreover, in extreme cases such as a large fluctuation in the number of passengers and a sharp change in solar radiation intensity, precise adjustment cannot be achieved.

[0022] Therefore, there is an urgent need for a temperature control method that can adapt to the complex and variable actual operation requirements in the rail transit scene, reduce energy waste and operating costs, and thus meet the energy saving and emission reduction requirements and practice the green transportation concept. That is, the above related technology cannot balance the vehicle comfort and energy saving performance of the vehicle air conditioner.

[0023] The exemplary embodiments of the present application consider the above problems and propose a temperature control method for a rail vehicle, which realizes environment and human parameter coupling modeling by acquiring multi-source perception data including environment perception data containing an external environment dimension and passenger perception data such as a passenger physiological dimension and a passenger behavior dimension, so as to determine a comfort value of passenger feeling based on a thermal comfort prediction model. Then, a temperature adjustment strategy matched with different comfort levels of the comfort value of passenger feeling is adjusted to ensure that the thermal comfort feeling of passengers in the vehicle cabin under various environmental conditions is optimized. Moreover, the method realizes "on-demand regulation" through a grading strategy and introduces an intelligent temperature control system adapted thereto, so as to intelligently adjust the in-vehicle air conditioning settings to avoid excessive cooling / heating, so that the in-vehicle environment can still maintain good comfort and energy-saving effect under variable external conditions.

[0024] Figure 1 A flowchart of a temperature control method for a rail vehicle is provided for an embodiment of the present application. The method can be applied to a controller of an air conditioner in a rail vehicle cabin. As shown in Figure 1 , the method comprises the following steps 101-103: Step 101, acquiring multi-source perception data corresponding to the rail vehicle; wherein the multi-source perception data includes environment perception data and passenger perception data.

[0025] Step 102, inputting the multi-source perception data into a pre-trained thermal comfort prediction model to obtain a comfort value of passengers in the rail vehicle, and determining a target comfort level matched with the comfort value.

[0026] Step 103, determining a first temperature adjustment strategy corresponding to the target comfort level, and controlling a temperature control network device of the rail vehicle based on the first temperature adjustment strategy.

[0027] In Figure 1 the technical solution provided, the method realizes environment and human parameter coupling modeling by acquiring multi-source perception data including environment perception data containing an external environment dimension and passenger perception data such as a passenger physiological dimension and a passenger behavior dimension, so as to determine a comfort value of passenger feeling based on a thermal comfort prediction model. Then, a temperature adjustment strategy matched with different comfort levels of the comfort value of passenger feeling is adjusted to ensure that the thermal comfort feeling of passengers in the vehicle cabin under various environmental conditions is optimized. Moreover, the method realizes "on-demand regulation" through a grading strategy and introduces an intelligent temperature control system adapted thereto, so as to intelligently adjust the in-vehicle air conditioning settings to avoid excessive cooling / heating, so that the in-vehicle environment can still maintain good comfort and energy-saving effect under variable external conditions. The specific implementation of each step in the embodiment shown in Figure 1 will be described in detail below: In step 101, the multi-source perception data corresponding to the rail vehicle is acquired; wherein the multi-source perception data includes environmental perception data and passenger perception data.

[0028] For example, various types of sensors, infrared thermal imaging arrays, millimeter wave radars, and environmental sensor networks can be arranged inside and outside the rail vehicle to collect the multi-source perception data corresponding to the rail vehicle.

[0029] The multi-source perception data includes not only environmental perception data but also passenger perception data, so that the individual comfort differences of passengers are considered in addition to the comfort changes caused by the external environment, not only providing a reliable data basis for improving the accuracy of the calculated comfort value, but also facilitating subsequent rapid and accurate temperature adjustment according to the comfort needs of different passengers and changes in the external environment.

[0030] In an optional embodiment of the present application, the environmental perception data includes one or more of the following: air temperature, radiation humidity, relative air humidity, wind speed, light radiation intensity, ultraviolet index, target gas concentration; and the passenger perception data includes one or more of the following: clothing thermal resistance, passenger density, passenger activity intensity, passenger body surface temperature, and metabolic rate.

[0031] The multi-source perception data includes environmental perception data of the external environment dimension and passenger perception data of the passenger dimension. The environmental perception data includes at least one of the following: air temperature, radiation humidity, relative air humidity, wind speed, light radiation intensity, ultraviolet index, and target gas concentration. The passenger perception data includes at least one of the following: clothing thermal resistance, passenger density, passenger activity intensity, passenger body surface temperature, and metabolic rate.

[0032] For example, the target gas concentration in the above environmental perception data refers to a gas that affects environmental temperature, humidity, dryness, etc., such as carbon dioxide concentration. It should be noted that the relative air humidity refers to the percentage (RH) of the actual water vapor pressure in the air to the saturated water vapor pressure at the same temperature, which is used to reflect the degree of air approaching saturation. And the relative air humidity is directly affected by temperature, for example, when the temperature rises, the saturated water vapor pressure increases, and if the absolute humidity remains unchanged, the relative humidity will decrease. The air humidity is the core index of the rail train air conditioning system regulation and control, and directly affects the passenger comfort. For example, the relative humidity on the subway platform in summer is required to be 40%~70%. The radiation humidity refers to the indirect effect of local humidity perception affected by thermal radiation (such as train equipment heat dissipation). It is related to the thermal radiation intensity; high-temperature radiation environment (for example, near the train engine) may accelerate water evaporation.

[0033] The thermal resistance of the clothes refers to the degree of hindering of heat flow of the clothes material, and reflects the heat insulation performance of the clothes. Generally, the higher the thermal resistance value, the stronger the warmth retention of the clothes.

[0034] By the above embodiments, the multi-source perception data corresponding to the rail vehicle is collected from two dimensions of external environment and individual passenger, which can provide more accurate and diverse input data basis for subsequent steps. Moreover, the prior art relies on a single environmental parameter (temperature) to control the air conditioner, while the embodiments of the present application combine environmental parameters (relative humidity, radiant temperature, wind speed), physiological parameters (metabolic rate, clothing thermal resistance), and behavior data (passenger density / passenger flow density, activity intensity) to realize multi-dimensional environment-human coupling modeling, and then facilitate accurate evaluation of the thermal comfort of passengers to adjust the air conditioning system in real time and provide a comfortable environment meeting the needs of passengers.

[0035] Further, in order to improve the effectiveness of the collected multi-source perception data corresponding to the rail vehicle and reduce the waste of computing resources caused by invalid data, the multi-source perception data corresponding to the rail vehicle can be preprocessed based on the following embodiments: In an optional embodiment of the present application, when the multi-source perception data corresponding to the rail vehicle is acquired in step 101, the initial multi-source perception data corresponding to the rail vehicle can be collected, the initial multi-source perception data is preprocessed, and the preprocessed multi-source perception data is obtained.

[0036] The preprocessing of the initial multi-source perception data at least includes at least one of the following: The preprocessing of the initial multi-source perception data at least includes at least one of the following:

[0037] For example, after the initial multi-source perception data corresponding to the rail vehicle is collected, the abnormal values and noise data can be removed, and then normalized to simplify the calculation process.

[0038] In step 102, the multi-source perception data is input into a pre-trained thermal comfort prediction model to obtain a comfort value of the passenger in the rail vehicle, and a target comfort level matching the comfort value is determined.

[0039] The thermal comfort prediction model is pre-trained and is a model for calculating the comfort value of the passenger. The model is a network model constructed by combining environmental parameters (relative humidity, radiant temperature, wind speed, etc.), passenger physiological parameters (metabolic rate, clothing thermal resistance / clothes thermal resistance), and behavior data (passenger flow density, passenger activity intensity, etc.) to realize multi-dimensional environment-human coupling.

[0040] Optionally, the thermal comfort prediction model can be a Predicted Percentage of Dissatisfied (PWV) based thermal comfort model. It is to be explained that the PWV is mainly used to predict the percentage of people who are dissatisfied with the thermal environment under a specific thermal environment.

[0041] Exemplarily, after obtaining the multi-source perception data, the multi-source perception data can be input into the pre-trained thermal comfort prediction model, so as to obtain the comfort value of the passenger in the rail vehicle.

[0042] In an optional embodiment of the present application, in response to the multi-source perception data at least including the metabolic rate, the relative humidity, the air temperature, the mean radiant temperature, the clothing thermal resistance, a thermal comfort prediction model is constructed based on the following formula (1): Formula (1) In formula (1), characterizes the comfort value determined based on the thermal comfort prediction model; characterizes the metabolic rate; characterizes the mechanical power of the human body; characterizes the relative humidity; characterizes the air water vapor partial pressure around the human body; characterizes the air temperature around the human body; characterizes the clothing area and the bare area of the human body; characterizes the outer surface temperature of the clothing; characterizes the air temperature around the clothing; characterizes the mean radiant temperature; characterizes the heat transfer coefficient of the clothing surface.

[0043] Exemplarily, the thermal comfort prediction model is established by integrating the environmental parameters (air temperature, humidity, wind speed, radiant temperature), the human body parameters (metabolic rate, clothing thermal resistance) and the passenger physiological parameters, to establish the mapping relationship between the passenger thermal sensation and the environmental variables, so as to obtain the comfort value output by the PWV model.

[0044] For the above formula (1), is the energy metabolic rate of the human body, i.e. the metabolic rate, for example, 1.0 met for sitting and 2.0 met for walking, and the unit can be W / m 2 ; is the mechanical power of the human body, and the unit can be W / m 2 ; is the air temperature around the human body, and the unit is ℃; is the clothing area and the bare area of the human body, and the unit can be m 2 ; Tc is the temperature of the clothing surface, which can be in units of °C; Tr is the mean radiant temperature, which can be in units of °C; h is the surface heat transfer coefficient, which can be in units of W / m 2 k.

[0045] It can be seen from the above formula (1) that the thermal comfort prediction model can comprehensively consider human parameter factors, environmental factors, and other related factors, so as to more accurately construct the thermal comfort prediction model of the passenger. The human parameter factors include metabolic rate, clothing thermal resistance, and external mechanical work. The environmental factors include environmental temperature, wind speed, relative humidity, and mean radiant temperature. The other related factors include water vapor partial pressure, clothing surface coefficient, convective heat transfer coefficient, and clothing surface temperature.

[0046] Further, after obtaining the comfort value of the passenger based on the above embodiment, the target comfort level matching the comfort value can be determined.

[0047] For example, if a comfort interval is [-0.5, 0.5], and assuming that the comfort value output by the thermal comfort prediction model is 0.3, then the target comfort interval to which the comfort value belongs is [-0.5, 0.5].

[0048] The temperature control system can store the pre-configured comfort interval in the thermal comfort information library, and after obtaining the comfort value output by the thermal comfort prediction model, match the thermal comfort state information corresponding to the target comfort interval from the thermal comfort information library, and then use it as the thermal comfort state information felt by the passenger. Based on this scheme, the thermal comfort feeling of the passenger can be accurately obtained according to the comfort value output by the thermal comfort prediction model, thereby providing accurate data basis for the subsequent environmental regulation process of the temperature control network equipment such as the air conditioner in the rail vehicle.

[0049] Further, in the case where the system obtains the comfort value output by the thermal comfort prediction model, the thermal comfort state level of the passenger for evaluating the current feeling of the passenger can be determined in combination with the comfort value and the evaluation index. In this scheme, the system can obtain the thermal comfort information library, and the thermal comfort information library stores the thermal comfort levels corresponding to different comfort intervals respectively.

[0050] In an optional embodiment of the present application, the plurality of comfort levels can include a first comfort level, a second comfort level, and a third comfort level, the first comfort level is greater than the second comfort level, and the second comfort level is greater than the third comfort level.

[0051] It needs to be explained that the first comfort level, the second comfort level and the third comfort level shown in the embodiments of the present disclosure are not a limitation on the number of set comfort levels, and are only used to illustrate that different temperature adjustment strategies can be set for different comfort levels. The number of comfort levels can be configured according to the actual running environment, and the embodiments of the present disclosure do not make special limitations and enumerations.

[0052] In step 103, a first temperature adjustment strategy corresponding to the target comfort level is determined, and the temperature control network device of the rail vehicle is controlled based on the first temperature adjustment strategy.

[0053] Figure 2 A schematic diagram of a method for determining a first temperature adjustment strategy corresponding to a target comfort level is provided for an embodiment of the present application, as shown in Figure 2 In an optional embodiment of the present application, determining a first temperature adjustment strategy corresponding to the target comfort level includes steps 201-204 as follows: Step 201, obtaining a plurality of pre-configured comfort levels; wherein the plurality of comfort levels include a first comfort level, a second comfort level and a third comfort level, the first comfort level is greater than the second comfort level, and the second comfort level is greater than the third comfort level.

[0054] Step 202, in response to the target comfort level being the first comfort level and the first comfort level corresponding to a first comfort interval, determining the first temperature adjustment strategy to be reducing a first preset value of a cooling parameter or increasing a first preset value of a heating parameter, and reaching a target temperature according to a first wind speed, and then switching to a second wind speed, the first wind speed being greater than the second wind speed.

[0055] Wherein, the first comfort interval represents a comfort value region in which the comfort value is not less than a first comfort value and the comfort value is not greater than a second comfort value.

[0056] For example, the first comfort interval determined by the comfort value region in which the comfort value is greater than or equal to the first comfort value and the comfort value is less than or equal to the second comfort value is determined as the first comfort level of high level.

[0057] Step 203, in response to the target comfort level being the second comfort level and the second comfort level corresponding to a second comfort interval, determining the first temperature adjustment strategy to be using a preset temperature value and wind speed.

[0058] Wherein, the second comfort interval represents a comfort value region in which the comfort value is not less than a third comfort value and the comfort value is not greater than a first comfort value, or the second comfort interval represents a comfort value region in which the comfort value is not less than a second comfort value and the comfort value is not greater than a fourth comfort value.

[0059] For example, the comfort value region where the comfort value is greater than or equal to the third comfort value and the comfort value is less than or equal to the first comfort value, or the second comfort interval determined by the comfort value region where the comfort value is greater than or equal to the second comfort value and the comfort value is less than or equal to the fourth comfort value, is determined as the second comfort level of the middle level.

[0060] In step 204, in response to the target comfort level being the third comfort level and the third comfort level corresponding to the third comfort interval, the first temperature adjustment strategy is determined as reducing the second preset value of the cooling parameter or increasing the second preset value of the heating parameter, and reducing the preset proportion of the air speed.

[0061] The third comfort interval represents a comfort value region where the comfort value is not less than the fifth comfort value and the comfort value is not greater than the third comfort value, or the second comfort interval represents a comfort value region where the comfort value is not less than the fourth comfort value and the comfort value is not greater than the sixth comfort value.

[0062] For example, the third comfort interval is determined as the third comfort level of the lowest level, by the comfort value region where the comfort value is greater than or equal to the fifth comfort value and the comfort value is less than or equal to the third comfort value, or the comfort value region where the comfort value is greater than or equal to the fourth comfort value and the comfort value is less than or equal to the sixth comfort value.

[0063] For ease of understanding, the following will be more clearly explained in combination with the embodiment shown in Table 1.

[0064] Table 1

[0065] Referring to Table 1, if the comfort interval is [-0.5, 0.5], the corresponding thermal comfort level is the first comfort level; at this time, the first comfort value and the second comfort value are -0.5 and 0.5 respectively.

[0066] If the comfort interval is [-0.7, -0.5] or [0.5, 0.7], the corresponding thermal comfort level is the second comfort level; at this time, the third comfort value and the fourth comfort value are -0.7 and 0.7 respectively.

[0067] If the comfort interval is [-1.0, -0.7] or [0.7, 1.0], the corresponding thermal comfort level is the third comfort level; at this time, the fifth comfort value and the sixth comfort value are -1.0 and 1.0 respectively.

[0068] The temperature adjustment strategies for different comfort levels are different. This is because the temperature adjustment strategy is based on the model parameters of the thermal comfort prediction model, and the thermal comfort prediction model usually includes six parameters: air temperature, radiant temperature, relative humidity, wind speed, clothing thermal resistance, and activity (passenger density and passenger movement intensity, etc.). Since clothing thermal resistance and activity are determined by passengers, and radiant temperature is determined by weather, vehicle insulation performance, K value, etc., which are all uncontrollable factors for air conditioning, the temperature adjustment strategy is usually controlled from the perspective of air temperature, relative humidity, and wind speed.

[0069] However, the inventors consider that the humidity requirement in the car of the rail vehicle usually needs to be matched according to the actual temperature in the car, so the control of humidity can be converted into the temperature control of the air conditioner. In general, the apparent temperature will rise by 0.3 ℃ for every 10% increase in humidity. In high-temperature and high-humidity seasons, the transition dehumidification operation will cause energy waste, so the influence of excessive humidity on human comfort can be compensated by reducing the temperature.

[0070] Similarly, the temperature adjustment strategy increases the influence of the ambient temperature on the fresh air volume, and considering that the supply of fresh air volume can be reduced after ensuring the human demand in extreme environments. Therefore, by adjusting the size of the fresh air volume, the over-supply of fresh air volume can be avoided, which can improve the energy-saving effect of the air conditioner.

[0071] For this purpose, for the first comfort level (for example, -0.5≦PMV≦0.5 shown in Table 1), the temperature can be reduced by 1℃ for cooling or increased by 1℃ for heating, and for the air volume adjustment, high wind speed (for example, 3-5 gears of the air conditioner) can be used in the initial preset time period to quickly reach the target temperature in the car of the rail vehicle, and then after stabilizing to the target temperature, low wind speed (for example, 1-2 gears) can be switched to.

[0072] For the second comfort level (for example, -0.7≤PMV≤-0.5 or +0.5≤PMV≤+0.7 shown in Table 1), the original temperature control mode can be maintained.

[0073] For the third comfort level (for example, -1.0≤PMV≤-0.7 or +0.7≤PMV≤+1.0 shown in Table 1), the cooling temperature is set to be reduced by 1℃, or the heating temperature is set to be increased by 1℃; at the same time, for the air volume adjustment, the air supply volume can be reduced by 30%.

[0074] In this embodiment, the influence of humidity on passenger comfort can be compensated by adjusting the temperature appropriately in seasons with high temperature and humidity, thereby avoiding energy waste caused by excessive dehumidification. At the same time, the system can adjust the fresh air volume according to the actual temperature to further optimize the energy saving effect.

[0075] Further, the operation line of the rail vehicle includes a plurality of transfer stations, and the plurality of transfer stations at least include a target transfer station, the passenger density of the target transfer station is greater than the first passenger density threshold, and the passenger activity intensity of the target transfer station is greater than the first activity intensity threshold.

[0076] Figure 3 Another flowchart of a temperature control method of a rail vehicle is provided for an embodiment of the present application; referring to Figure 3 As shown in the figure, in an optional embodiment of the present application, the method comprises the following steps: Step 101, acquiring multi-source perception data corresponding to the rail vehicle; wherein the multi-source perception data includes environmental perception data and passenger perception data.

[0077] Step 102, inputting the multi-source perception data into a pre-trained thermal comfort prediction model to obtain a comfort value of the passengers in the rail vehicle, and determining a target comfort level matched with the comfort value.

[0078] Step 103, determining a first temperature adjustment strategy corresponding to the target comfort level, and controlling the temperature control network equipment of the rail vehicle based on the first temperature adjustment strategy.

[0079] Step 104, in response to the rail vehicle running to a distance of a preset distance from the target transfer station, switching the first temperature adjustment strategy to a second temperature adjustment strategy, and adjusting the temperature control network equipment of the rail vehicle based on the second temperature adjustment strategy; wherein the comfort level corresponding to the second temperature adjustment strategy is greater than the comfort level corresponding to the first temperature adjustment strategy.

[0080] For example, considering the dynamic changes of the rail vehicle in actual operation, such as the difference between different line stations due to the difference in surrounding functional areas (such as commercial areas, residential areas, and office areas), the number of passengers getting on and off also fluctuates obviously, therefore, the target transfer station with large transfer passenger flow in the operation line of the rail vehicle can be determined, so that the temperature adjustment strategy currently used is adjusted before entering the target transfer station, to adapt to the situation that the passenger flow will increase soon. This pre-cooling / pre-heating method can reduce the instantaneous increase of energy consumption caused by the instantaneous load fluctuation of the temperature control network equipment, thereby increasing the operating cost, and can also avoid the problem of slow response speed, thereby improving the applicability to complex situations such as sudden passenger flow changes, and further improving the comfort experience of passengers.

[0081] In this embodiment, the temperature control system of the rail train takes the target transfer station as the judgment point, and automatically switches the air conditioning control mode when the vehicle approaches these stations, so as to ensure that the passengers have higher comfort when transferring. This strategy enhances the intelligent level of the air conditioning system, and enables it to dynamically adjust the control mode according to the actual demand.

[0082] In an optional embodiment of the present application, the rail vehicle generally includes a plurality of passenger carriages. In this case, the carriages can be divided into independent temperature control sub-regions, and differential temperature control strategies are dynamically generated according to the decision instructions to adjust the operating parameters of the air conditioning system.

[0083] Optionally, Figure 4 A method flowchart for determining a temperature adjustment strategy is provided for an embodiment of the present application; refer to Figure 4 As shown in the figure, in an optional embodiment of the present application, the method includes the following steps 401-403: Step 401, obtain the passenger density and passenger activity intensity in each passenger carriage.

[0084] Step 402, determine the passenger state type of each passenger carriage according to the passenger density and passenger activity intensity in each passenger carriage.

[0085] Step 403, determine the third temperature adjustment strategy corresponding to each passenger carriage according to the passenger state type of each passenger carriage, and adjust the temperature control network equipment of the rail vehicle based on the third temperature adjustment strategy corresponding to each passenger carriage.

[0086] In this embodiment, the passenger density and passenger activity intensity in each passenger carriage can be obtained, so that the carriages can be divided into different passenger state types according to the values of the two core parameters of passenger density and passenger activity intensity in each passenger carriage, so that different temperature adjustment strategies can be executed for different passenger state types.

[0087] For example, in this scheme, the system can obtain regional state division indicators, and different division indicators correspond to respective state types.

[0088] Figure 5 Another method flowchart for determining a temperature adjustment strategy is provided for an embodiment of the present application; refer to Figure 5 As shown in the figure, in an optional embodiment of the present application, when executing the above-mentioned step 403, determining the third temperature adjustment strategy corresponding to each passenger carriage according to the passenger state type of each passenger carriage, the following steps 501-505 can be referred to: Step 501, determine whether the passenger state type is a dense state.

[0089] The dense state represents a passenger state in which the passenger density is greater than the second passenger density threshold and the passenger activity intensity is greater than the second activity intensity threshold.

[0090] In an optional embodiment, if yes, i.e. in response to the passenger state type being the dense state, step 502 is performed to determine that the third temperature adjustment strategy corresponding to each passenger compartment is the temperature adjustment strategy corresponding to the first comfort level.

[0091] On the contrary, if no, step 503 is continued to determine whether the passenger state type is the sparse state.

[0092] The sparse state represents a passenger state in which the passenger density is greater than the third passenger density threshold and not greater than the second passenger density threshold, and the passenger activity intensity is greater than the third activity intensity threshold and not greater than the second activity intensity threshold.

[0093] In an optional embodiment, if yes, i.e. in response to the passenger state type being the sparse state, step 504 is performed to determine that the third temperature adjustment strategy corresponding to each passenger compartment is the temperature adjustment strategy corresponding to the second comfort level.

[0094] On the contrary, step 505 is performed to determine that the third temperature adjustment strategy corresponding to each passenger compartment is the temperature adjustment strategy corresponding to the third comfort level.

[0095] For example, when the passenger state type of each passenger compartment is neither the dense state nor the sparse state, the passenger state type of each passenger compartment is determined to be the idle state, and the third temperature adjustment strategy corresponding to each passenger compartment is determined to be the temperature adjustment strategy corresponding to the third comfort level.

[0096] The idle state represents a passenger state in which the passenger density is less than or equal to the third passenger density threshold, and the passenger activity intensity is less than the third activity intensity threshold.

[0097] The determination method of the passenger state type will be described below in conjunction with Table 2.

[0098] Table 2

[0099] In Table 2, the second passenger density threshold is 2 people / m 2 , and the third passenger density threshold is 0.5 people / m 2 .

[0100] In this embodiment, the compartment is divided into an idle area, a sparse area, and a dense area according to the passenger density and the activity intensity, which correspond to different air conditioning control strategies (for example, the idle area uses three-level control, and the dense area uses one-level control). This differentiated control method based on the area state effectively balances passenger comfort and energy saving.

[0101] Through the above-mentioned embodiments, by intelligently identifying the state of the carriage area and optimizing the control strategy, energy waste can be significantly reduced without reducing comfort. For example, in the idle area and sparse area, the air conditioning system reduces the power output according to the number and activity intensity of the passengers, avoiding unnecessary energy consumption. By optimizing the temperature control strategy, the application significantly reduces energy consumption and reduces energy waste. This not only reduces the operating cost of the rail vehicle, but also meets the needs of sustainable development and energy saving and emission reduction in the era.

[0102] It should be understood that although each step in the flowchart is shown in sequence according to the direction of the arrow, these steps are not necessarily executed in the order indicated by the arrow. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least part of the steps in the figure can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with at least part of other steps or sub-steps or stages of other steps.

[0103] To implement the above-mentioned temperature control method of the rail vehicle, please refer to Figure 6 An embodiment of the present application provides a temperature control device of a rail vehicle, which can include a data acquisition module 601, a comfort output module 602 and a temperature control module 603.

[0104] The data acquisition module 601 is configured to acquire multi-source perception data corresponding to the rail vehicle, wherein the multi-source perception data includes environmental perception data and passenger perception data. The comfort output module 602 is configured to input the multi-source perception data into a pre-trained thermal comfort prediction model to obtain a comfort value of the passengers in the rail vehicle, and determine a target comfort level matched with the comfort value. The temperature control module 603 is configured to determine a first temperature adjustment strategy corresponding to the target comfort level, and control a temperature control network device of the rail vehicle based on the first temperature adjustment strategy.

[0105] In an optional embodiment of the present application, the environmental perception data includes one or more of the following: air temperature, radiation humidity, relative air humidity, wind speed, light radiation intensity, ultraviolet index, target gas concentration; the passenger perception data includes one or more of the following: clothing thermal resistance, passenger density, passenger activity intensity, passenger body surface temperature, metabolic rate.

[0106] In an optional embodiment of the present application, the device further comprises a model construction module, which can be configured to construct a thermal comfort prediction model based on the following formula, on the premise that the multi-source perception data at least comprises metabolic rate, relative humidity, air temperature, mean radiant temperature, clothing thermal resistance:

[0107] wherein, characterize the comfort value determined based on the thermal comfort prediction model; characterize the metabolic rate; characterize the mechanical power of the human body; characterize the relative humidity; characterize the air water vapor partial pressure around the human body; characterize the air temperature around the human body; characterize the clothing area and the bare area of the human body; characterize the outer surface temperature of the clothing; characterize the air temperature around the clothing; characterize the mean radiant temperature; characterize the heat transfer coefficient of the clothing surface.

[0108] In an optional embodiment of the present application, the device further comprises a strategy determination module, which is configured to obtain a plurality of comfort levels preconfigured, wherein the plurality of comfort levels comprises a first comfort level, a second comfort level and a third comfort level, the first comfort level is greater than the second comfort level, and the second comfort level is greater than the third comfort level; in response to the target comfort level being the first comfort level and the first comfort level corresponding to a first comfort interval, determining that the first temperature adjustment strategy is to reduce the cooling parameter by a first preset value, or to increase the heating parameter by a first preset value, and to reach the target temperature according to a first wind speed, and then switching to a second wind speed, wherein the first wind speed is greater than the second wind speed; wherein the first comfort interval characterizes a comfort value region in which the comfort value is not less than a first comfort value and the comfort value is not greater than a second comfort value. The strategy determination module is configured to, in response to the target comfort level being the second comfort level and the second comfort level corresponding to a second comfort interval, determine that the first temperature adjustment strategy is to use a preset temperature value and wind speed; wherein the second comfort interval characterizes a comfort value region in which the comfort value is not less than a third comfort value and the comfort value is not greater than a first comfort value, or the second comfort interval characterizes a comfort value region in which the comfort value is not less than a second comfort value and the comfort value is not greater than a fourth comfort value. The strategy determination module is configured to determine, in response to the target comfort level being a third comfort level and the third comfort level corresponding to a third comfort interval, that the first temperature adjustment strategy is to reduce a second preset value of a cooling parameter or to increase a second preset value of a heating parameter and to reduce a preset proportion of a wind speed; and wherein the third comfort interval represents a comfort value region in which the comfort value is not less than a fifth comfort value and the comfort value is not greater than a third comfort value, or the second comfort interval represents a comfort value region in which the comfort value is not less than a fourth comfort value and the comfort value is not greater than a sixth comfort value.

[0109] In an optional embodiment of the present application, the operation line of the rail vehicle includes a plurality of transfer stations, and the plurality of transfer stations at least include a target transfer station, the passenger density of the target transfer station being greater than a first passenger density threshold value and the passenger activity intensity of the target transfer station being greater than a first activity intensity threshold value; the device further includes a strategy switching module, the strategy switching module being configured to switch the first temperature adjustment strategy to a second temperature adjustment strategy in response to the rail vehicle operating to a preset distance from the target transfer station, and adjust the temperature control network equipment of the rail vehicle based on the second temperature adjustment strategy; wherein the comfort level corresponding to the second temperature adjustment strategy is greater than the comfort level corresponding to the first temperature adjustment strategy.

[0110] In an optional embodiment of the present application, the rail vehicle includes a plurality of passenger carriages, and the device further includes a state type determination module, the data acquisition module 601 can also be configured to acquire the passenger density and the passenger activity intensity in each passenger carriage; the state type determination module is configured to determine the passenger carrying state type of each passenger carriage according to the passenger density and the passenger activity intensity in each passenger carriage; and the strategy determination module is configured to determine the third temperature adjustment strategy corresponding to each passenger carriage according to the passenger carrying state type of each passenger carriage, so as to adjust the temperature control network equipment of the rail vehicle based on the third temperature adjustment strategy corresponding to each passenger carriage.

[0111] In an optional embodiment of the present application, the strategy determination module is further configured to determine, in response to the passenger carrying state type being a dense state, that the third temperature adjustment strategy corresponding to each passenger carriage is a temperature adjustment strategy corresponding to a first comfort level; wherein the dense state represents a passenger carrying state in which the passenger density is greater than a second passenger density threshold value and the passenger activity intensity is greater than a second activity intensity threshold value. The strategy determination module is further configured to determine, in response to the passenger carrying state type being a sparse state, that the third temperature adjustment strategy corresponding to each passenger carriage is a temperature adjustment strategy corresponding to a second comfort level; wherein the sparse state represents a passenger carrying state in which the passenger density is greater than a third passenger density threshold value and not greater than the second passenger density threshold value, and the passenger activity intensity is greater than a third activity intensity threshold value and not greater than the second activity intensity threshold value. The strategy determining module is further configured to, in response to the passenger state type being an idle state, determine the third temperature adjustment strategy corresponding to each passenger compartment as a temperature adjustment strategy corresponding to a third comfort level; wherein the idle state represents a passenger state in which the passenger density is less than the second passenger density threshold and the passenger activity intensity is less than the second activity intensity threshold.

[0112] The specific definitions of the temperature control device of the rail vehicle can be referred to the definitions of the temperature control method of the rail vehicle, which will not be repeated here. Each module in the temperature control device of the rail vehicle can be realized by software, hardware, or a combination thereof. Each module can be embedded in or independent of the processor in the computer device in hardware form, or stored in the memory in the computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0113] In one embodiment, a computer device is provided, and an internal structure diagram of the computer device can be as shown in Figure 7 The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium to run. The database of the computer device is configured to store data. The network interface of the computer device is configured to communicate with an external terminal through a network connection. The computer program is executed by the processor to implement a temperature control method of a rail vehicle as described above. The computer program includes a memory and a processor, and the memory stores a computer program. When the processor executes the computer program, it implements any step in the temperature control method of the rail vehicle as described above.

[0114] In one embodiment, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program. When the computer program is executed by a processor, it can implement any step in the temperature control method of the rail vehicle as described above.

[0115] Those skilled in the art will appreciate that embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) containing computer usable program code.

[0116] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0117] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0118] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.

[0119] While the preferred embodiments of the application have been described, additional variations and modifications can be employed by those skilled in the art. Therefore, the appended claims intend to cover all such modifications and variations as fall within the true spirit and scope of the application.

[0120] Obviously, numerous modifications and variations of the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims and their equivalents, the application can be practiced otherwise than as specifically described.

Claims

1. A temperature control method for rail vehicles, characterized in that, include: Acquire multi-source sensing data corresponding to the rail vehicle; wherein, the multi-source sensing data includes environmental sensing data and passenger sensing data; The multi-source sensing data is input into a pre-trained thermal comfort prediction model to obtain the comfort value of passengers in the rail vehicle, and a target comfort level matching the comfort value is determined. A first temperature adjustment strategy corresponding to the target comfort level is determined, and the temperature control network equipment of the rail vehicle is controlled based on the first temperature adjustment strategy.

2. The method according to claim 1, characterized in that, The environmental sensing data includes one or more of the following: air temperature, radiative humidity, relative air humidity, wind speed, light radiation intensity, ultraviolet index, and target gas concentration; the passenger sensing data includes one or more of the following: clothing thermal resistance, passenger density, passenger activity intensity, passenger body surface temperature, and metabolic rate.

3. The method according to claim 1 or 2, characterized in that, The method further includes: In response to the multi-source sensing data including at least metabolic rate, relative humidity, air temperature, average radiative humidity, and clothing thermal resistance, the thermal comfort prediction model is constructed based on the following formula: ； in, Characterizes the comfort value determined based on the thermal comfort prediction model; Characterizing the metabolic rate; Characterizing the mechanical power of the human body; Characterizing the relative humidity; Characterizes the partial pressure of water vapor in the air surrounding the human body; The air temperature surrounding the human body; Characterizes the area covered by clothing and the area of ​​bare skin on the human body; Characterizes the outer surface temperature of clothing; The temperature of the air surrounding the garment is characterized. Characterizing the average radiation temperature; The heat transfer coefficient characterizes the surface of clothing.

4. The method according to claim 1, characterized in that, The first temperature adjustment strategy for determining the target comfort level includes: Obtain multiple pre-configured comfort levels; wherein the multiple comfort levels include a first comfort level, a second comfort level, and a third comfort level, the first comfort level being greater than the second comfort level, and the second comfort level being greater than the third comfort level; In response to the target comfort level being the first comfort level, and the first comfort level corresponding to a first comfort range, the first temperature adjustment strategy is determined to be to reduce the cooling parameter by a first preset value or increase the heating parameter by a first preset value, and to reach the target temperature at a first fan speed, and then switch to a second fan speed, wherein the first fan speed is greater than the second fan speed; wherein, the first comfort range represents a comfort value range in which the comfort value is not less than the first comfort value and the comfort value is not greater than the second comfort value; In response to the target comfort level being the second comfort level, and the second comfort level corresponding to the second comfort range, the first temperature adjustment strategy is determined to be using a preset temperature value and wind speed; wherein, the second comfort range represents a comfort value range where the comfort value is not less than the third comfort value and the comfort value is not greater than the first comfort value, or, the second comfort range represents a comfort value range where the comfort value is not less than the second comfort value and the comfort value is not greater than the fourth comfort value; In response to the target comfort level being the third comfort level, and the third comfort level corresponding to the third comfort range, the first temperature adjustment strategy is determined to be either reducing the cooling parameter to a second preset value or increasing the heating parameter to a second preset value, and reducing the fan speed by a preset percentage; wherein, the third comfort range represents a comfort value range where the comfort value is not less than the fifth comfort value and the comfort value is not greater than the third comfort value, or, the second comfort range represents a comfort value range where the comfort value is not less than the fourth comfort value and the comfort value is not greater than the sixth comfort value.

5. The method according to claim 4, characterized in that, The rail vehicle's operating line includes multiple transfer stations, and the multiple transfer stations include at least a target transfer station, where the passenger density of the target transfer station is greater than a first passenger density threshold and the passenger activity intensity is greater than a first activity intensity threshold. The method further includes, after adjusting the temperature control network equipment of the rail vehicle based on the first temperature adjustment strategy: In response to the fact that the distance between the rail vehicle and the target transfer station is a preset distance, the first temperature adjustment strategy is switched to the second temperature adjustment strategy, and the temperature control network equipment of the rail vehicle is adjusted based on the second temperature adjustment strategy. The comfort level corresponding to the second temperature adjustment strategy is higher than that corresponding to the first temperature adjustment strategy.

6. The method according to claim 4, characterized in that, The rail vehicle comprises multiple passenger carriages, and the method further includes: Obtain passenger density and passenger activity intensity in each passenger carriage; The passenger carrying status type of each passenger car is determined based on the passenger density and passenger activity intensity in each passenger car. Based on the passenger-carrying status type of each passenger car, a third temperature adjustment strategy corresponding to each passenger car is determined, so as to adjust the temperature control network equipment of the rail vehicle based on the third temperature adjustment strategy corresponding to each passenger car.

7. The method according to claim 6, characterized in that, Based on the passenger-carrying status type of each passenger car, a third temperature adjustment strategy is determined for each passenger car, including: In response to the passenger-carrying state type being a dense state, the third temperature adjustment strategy corresponding to each passenger-carrying carriage is determined to be the temperature adjustment strategy corresponding to the first comfort level; wherein, the dense state represents a passenger-carrying state in which the passenger density is greater than a second passenger density threshold and the passenger activity intensity is greater than a second activity intensity threshold. In response to the passenger-carrying state type being sparse, the third temperature adjustment strategy corresponding to each passenger-carrying carriage is determined to be the temperature adjustment strategy corresponding to the second comfort level; wherein, the sparse state represents a passenger-carrying state in which the passenger density is greater than a third passenger density threshold but not greater than a second passenger density threshold, and the passenger activity intensity is greater than a third activity intensity threshold but not greater than a second activity intensity threshold. In response to the passenger-carrying state type being idle, the third temperature adjustment strategy corresponding to each passenger-carrying carriage is determined to be the temperature adjustment strategy corresponding to the third comfort level; wherein, the idle state represents a passenger-carrying state in which the passenger density is less than the second passenger density threshold and the passenger activity intensity is less than the second activity intensity threshold.

8. A temperature control device for a rail vehicle, characterized in that, include: The data acquisition module is used to acquire multi-source sensing data corresponding to the rail vehicle; wherein, the multi-source sensing data includes environmental sensing data and passenger sensing data; The comfort output module is used to input the multi-source sensing data into a pre-trained thermal comfort prediction model to obtain the comfort value of passengers in the rail vehicle and determine the target comfort level that matches the comfort value. The temperature control module is used to determine a first temperature adjustment strategy corresponding to the target comfort level, and to control the temperature control network equipment of the rail vehicle based on the first temperature adjustment strategy.

9. A computer device, comprising: The system includes a memory and a processor, the memory storing a computer program, characterized in that the processor executes the computer program to implement the steps of the temperature control method for the rail vehicle according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the temperature control method for rail vehicles according to any one of claims 1 to 7.

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