A method and device for controlling the environment of a cab of a railway vehicle in a tunnel

Abstract

The application provides a tunnel railway vehicle cab environment control method and device, relates to the technical field of cab environment control, and comprises the following steps: obtaining first information and second information, wherein the first information comprises air data information and personnel information in the cab, and the second information comprises historical air data information and personnel information of the cab; sending the second information to a preset environment control strategy prediction model to obtain first environment control strategy information before personnel enter the cab; obtaining body index changes of the personnel entering the cab and air data difference values inside and outside the cab; real-time prediction of at least two second environment control strategy information, energy consumption score processing and comfort assessment based on a human body comfort assessment model; and selection of the environment control strategy of the cab with the highest comprehensive score through comprehensive analysis of energy consumption and comfort scores. The application intelligently balances energy consumption and passenger comfort, and improves the efficiency of cab environment control.

Description

Technical Field

[0001] This invention relates to the field of driver's cab environmental control technology, and more specifically, to a method and device for controlling the environment of a railway vehicle driver's cab in a tunnel. Background Technology

[0002] Currently, environmental control in railway vehicle driver's cabs within tunnels faces several challenges. Traditional environmental control systems often employ fixed control strategies, failing to intelligently adjust based on real-time conditions inside and outside the cab, as well as the physical condition of the personnel, resulting in high energy consumption and low comfort levels. Furthermore, existing technologies rarely consider the difference in air quality data between the cab and the outside, or changes in personnel's physical indicators, lacking the ability to predict and respond to environmental changes in real time.

[0003] Furthermore, existing technologies often fail to adequately consider the trade-off between energy consumption and comfort during environmental control, lacking a comprehensive analytical approach to select the optimal environmental control strategy. Therefore, it is necessary to propose a more intelligent and efficient environmental control method for railway vehicle driver's cabs within tunnels to address the problems inherent in traditional systems. Summary of the Invention

[0004] The purpose of this invention is to provide a method and apparatus for controlling the environment of a railway vehicle driver's cab in a tunnel, so as to improve the above-mentioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0005] On the one hand, this application provides a method for environmental control of a railway vehicle driver's cab in a tunnel, including:

[0006] Obtain first information and second information. The first information includes air data information and personnel information in the driver's cab. The second information includes historical air data information and personnel information in the driver's cab in the database. The air data information includes air temperature information and air humidity information.

[0007] The second information is sent to the preset environmental control strategy prediction model for processing to obtain the first environmental control strategy information. The first environmental control strategy information is the air temperature control strategy information and air humidity control strategy information in the driver's cab before personnel enter the driver's cab.

[0008] The third information is obtained, including the change information of the physical indicators of the person entering the driver's cab and the difference information of the air data between the driver's cab and the outside of the driver's cab. Based on the third information, the environmental control strategy is predicted in real time to obtain at least two second environmental control strategy information. The second environmental control strategy information is the real-time air temperature control strategy information and the real-time air humidity control strategy information in the driver's cab after the person enters the driver's cab.

[0009] The energy consumption score of the second environmental control strategy information is processed to determine the energy consumption score corresponding to each second environmental control strategy information.

[0010] The second environment control strategy information is evaluated based on a preset human comfort assessment model to obtain a comfort score corresponding to each second environment control strategy information.

[0011] Based on the comprehensive analysis of the energy consumption score and the comfort score, the second environmental control strategy information with the highest comprehensive score is selected as the driver's cab environmental control strategy.

[0012] On the other hand, this application also provides an environmental control device for a railway vehicle driver's cab in a tunnel, characterized in that it includes:

[0013] The first acquisition unit is used to acquire first information and second information. The first information includes air data information and personnel information in the driver's cab. The second information includes historical air data information and personnel information in the driver's cab in the database. The air data information includes air temperature information and air humidity information.

[0014] The first processing unit is used to send the second information to a preset environmental control strategy prediction model for processing to obtain the first environmental control strategy information. The first environmental control strategy information is the air temperature control strategy information and air humidity control strategy information in the driver's cab before personnel enter the driver's cab.

[0015] The second acquisition unit is used to acquire third information, which includes information on changes in the physical indicators of the personnel entering the driver's cab and information on the difference in air data between the driver's cab and the outside of the driver's cab. Based on the third information, the unit performs real-time prediction of environmental control strategies to obtain at least two second environmental control strategy information, which are real-time air temperature control strategy information and real-time air humidity control strategy information in the driver's cab after the personnel enter the driver's cab.

[0016] The second processing unit is used to perform energy consumption scoring processing on the second environmental control strategy information and determine the energy consumption score corresponding to each second environmental control strategy information.

[0017] The third processing unit is used to evaluate the second environment control strategy information based on a preset human comfort assessment model, and obtain a comfort score corresponding to each second environment control strategy information.

[0018] The fourth processing unit is used to perform a comprehensive analysis based on the energy consumption score and the comfort score, and select the second environmental control strategy information with the highest comprehensive score as the driver's cab environmental control strategy.

[0019] The beneficial effects of this invention are as follows:

[0020] This invention, by combining historical and real-time data, enables the system to provide appropriate environmental control strategies before and after personnel enter the driver's cab, ensuring comfortable temperature and humidity under different conditions. The introduction of energy consumption scoring allows the system to consider energy consumption, resulting in more energy-efficient selection of the optimal strategy. The consideration of comfort scoring ensures that the system's selected strategy meets the comfort needs of the crew, improving driver efficiency and safety. This intelligent environmental control system has potential applications in the transportation sector, improving driver working conditions and overall operational efficiency.

[0021] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a schematic diagram of the process for controlling the environment of a railway vehicle driver's cab in a tunnel, as described in an embodiment of the present invention.

[0024] Figure 2 This is a schematic diagram of the environmental control device for the driver's cab of a railway vehicle inside a tunnel, as described in an embodiment of the present invention.

[0025] In the diagram: 701, First Acquisition Unit; 702, First Processing Unit; 703, Second Acquisition Unit; 704, Second Processing Unit; 705, Third Processing Unit; 706, Fourth Processing Unit; 7021, First Processing Subunit; 7022, Second Processing Subunit; 7023, First Calculation Subunit; 7024, Third Processing Subunit; 7031, First Clustering Subunit; 7032, Second Clustering Subunit; 7033, First Analysis Subunit; 7034, Second Analysis Subunit; 7035, Third Analysis Subunit; 7036, Fourth Processing Subunit; 7041, Fourth Analysis Subunit; 7042, Fifth Processing Subunit; 7051, Sixth Processing Subunit; 7052, Seventh Processing Subunit; 7061, Eighth Processing Subunit; 7062, Second Calculation Subunit. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0027] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0028] Example 1:

[0029] This embodiment provides a method for controlling the environment of a railway vehicle driver's cab inside a tunnel.

[0030] See Figure 1 The figure shows that the method includes steps S100, S200, S300, S400, S500 and S600.

[0031] Step S100: Obtain first information and second information. The first information includes air data information and personnel information in the driver's cab. The second information includes historical air data information and personnel information in the driver's cab in the database. The air data information includes air temperature information and air humidity information.

[0032] It is understood that this step acquires real-time air quality and personnel information within the driver's cabin using sensors. These sensors may be installed at appropriate locations within the driver's cabin to monitor air temperature and humidity. This data is real-time, providing the system with basic parameters of the current environment. Furthermore, this invention can also acquire air quality data such as PM2.5 and PM10 for environmental control within the driver's cabin.

[0033] Step S200: Send the second information to the preset environmental control strategy prediction model for processing to obtain the first environmental control strategy information. The first environmental control strategy information is the air temperature control strategy information and air humidity control strategy information in the driver's cab before personnel enter the driver's cab.

[0034] It is understandable that this step predicts the ideal temperature and humidity range for the driver's cab within a certain time period based on historical data and crew feedback under different environmental conditions. If hot weather is predicted, the system may activate the air conditioning system in advance to ensure that the temperature in the driver's cab remains at a comfortable level before personnel enter. In this step, step S200 includes steps S201, S202, S203, and S204.

[0035] Step S201: Obtain the training set and validation set by combining the preset historical first environment control strategy information and second information;

[0036] Understandably, this step involves associating historical control strategy information with corresponding historical air data and personnel information from the driver's cab in order to train a neural network model.

[0037] Step S202: Input the training set into the BP neural network model for training, wherein the driver's cab historical air data and personnel information for a preset time period are used based on the second information to obtain the prediction result;

[0038] It is understandable that this step involves the model learning historical first-environment control strategy information, thereby enabling the model to perform more accurately in future predictions.

[0039] Step S203: Calculate the matching degree between the driver's cab historical air data and personnel information in the prediction results and the preset historical first environmental control strategy information, and determine the matching degree between the driver's cab historical air data and personnel information and the historical first environmental control strategy information.

[0040] Understandably, this step involves calculating the matching degree between the historical air data and personnel information in the driver's cab and the preset historical first environmental control strategy information. This matching degree can be evaluated using various methods, such as correlation coefficients or other similarity measures, to assess the consistency between the predicted data and the actual historical control strategy.

[0041] Step S204: Use the historical first environment control strategy information with a matching degree greater than a preset threshold as the predicted first environment control strategy information.

[0042] It is understandable that this step introduces a matching degree calculation and threshold screening mechanism to ensure the consistency between the predicted environmental information and historical control strategies, thereby improving the reliability and applicability of the prediction.

[0043] Step S300: Obtain third information, which includes information on changes in the physical indicators of the person entering the driver's cab and information on the difference in air data between the driver's cab and the outside of the driver's cab. Based on the third information, perform real-time prediction of environmental control strategies to obtain at least two second environmental control strategy information. The second environmental control strategy information is real-time air temperature control strategy information and real-time air humidity control strategy information in the driver's cab after the person enters the driver's cab.

[0044] It is understandable that by considering changes in personnel's physical indicators, this step allows the system to more individually adjust the driver's cab environment to meet different individual needs. Utilizing real-time acquired physical indicators and environmental difference information, the system can adjust the environment in real time, improving its ability to respond quickly to changes. In this step, step S300 includes steps S301, S302, S303, S304, S305, and S306.

[0045] Step S301: Cluster the physical index change information of the people in the driver's cab to obtain at least two clusters, and each cluster includes the physical index change information of at least two people in the driver's cab.

[0046] Understandably, this step involves cluster analysis of the physiological indicator changes of the people in the driver's cab. K-means clustering can be used to obtain at least two clusters, each containing individuals with similar patterns of physiological indicator changes.

[0047] Step S302: Calculate the range of each cluster based on the Laida criterion, and take the cluster with the largest range as the feature cluster;

[0048] Understandably, this step calculates the range of each cluster using the Laida criterion, which measures the sample similarity within a cluster. The cluster with the largest range is selected as the feature cluster, implying that the changes in physical indicators of individuals within this cluster have relatively high similarity.

[0049] Step S303: Perform correlation analysis between the feature clusters and the first environmental control strategy information to obtain the first correlation value between the feature clusters and each strategy in the first environmental control strategy information;

[0050] Understandably, this step uses association analysis to assess the relationship between clusters and environmental control strategies, thereby determining the connection between the two.

[0051] Step S304: Perform correlation analysis on the air data difference information between the driver's cab and the outside air and the first environmental control strategy information to determine the second correlation value between the air data difference information and the first environmental control strategy information;

[0052] Understandably, this step uses correlation analysis to assess the relationship between the air quality difference between the driver's cabin and the outside environment and environmental control strategies, thereby determining the connection between the two.

[0053] Step S305: Perform regression analysis on the feature cluster corresponding to the largest first correlation value and the air data difference information corresponding to the largest second correlation value with the first environmental control strategy information to obtain the linear relationship between the first environmental control strategy information and the feature cluster corresponding to the largest first correlation value and the air data difference information corresponding to the largest second correlation value.

[0054] It is understandable that this step establishes a linear relationship model between feature clusters and air data difference information and environmental control strategies, thereby determining the connection between the two and providing a basis for subsequent adjustments to the first environmental strategy.

[0055] Step S306: Adjust the first environmental control strategy information based on the linear relationship until the range of the feature cluster corresponding to the largest first correlation value is within a preset range, and the air data difference information corresponding to the largest second correlation value is less than a preset threshold, to obtain the second environmental control strategy information.

[0056] It is understood that this step determines the control strategy that needs to be adjusted, and then adjusts it based on the linear relationship, thereby more precisely meeting the requirements.

[0057] Step S400: Perform energy consumption scoring processing on the second environmental control strategy information to determine the energy consumption score corresponding to each second environmental control strategy information;

[0058] It is understandable that this step, through energy consumption scoring, allows the system to comprehensively consider the energy utilization efficiency of different environmental control strategies, thereby improving the overall energy efficiency of the system. In this step, step S400 includes steps S401 and S402.

[0059] Step S401: Perform a correlation analysis between the strategy information and the preset average lifespan of the equipment in the driver's cab to obtain a third correlation value;

[0060] Step S402: Multiply the third correlation value with the average lifespan of the equipment in the driver's cab, and use the result as the energy consumption score corresponding to each second environmental control strategy information.

[0061] Understandably, this step, through correlation analysis with equipment lifespan, considers the impact of environmental control strategies on the lifespan of equipment in the driver's cab, which helps maintain the long-term healthy operation of the equipment. Incorporating equipment lifespan factors into energy consumption scoring calculations allows the system to comprehensively consider multiple factors such as comfort, energy efficiency, and equipment lifespan.

[0062] Step S500: Evaluate the second environment control strategy information based on the preset human comfort assessment model to obtain the comfort score corresponding to each second environment control strategy information.

[0063] It is understandable that this step, through comfort assessment, allows the system to consider the user's subjective feelings and ensure that the selected environmental control strategy meets the user's expectations in terms of comfort. In this step, step S500 includes steps S501 and S502.

[0064] Step S501: Control the equipment according to the preset second environmental control strategy information to obtain the environmental control information of the driver's cab. The environmental control information of the driver's cab includes the temperature control information and the humidity control information of the driver's cab.

[0065] Step S502: Based on a preset human comfort assessment table, score the environmental control information of the driver's cab to obtain a comfort score corresponding to each second environmental strategy control strategy information.

[0066] The human comfort assessment form in this step is understandable; it's a table containing various environmental factors and comfort scores, used to quantify the impact of the environment on human comfort. By acquiring actual driver's cab environmental control information, this step allows the system to consider the actual effects of equipment control, thus more accurately assessing comfort. The use of the comfort assessment form helps the system gain a more comprehensive understanding of the impact of different environmental parameters on user comfort, optimizing the user experience within the driver's cab.

[0067] Step S600: Based on the energy consumption score and the comfort score, a comprehensive analysis is performed, and the second environmental control strategy information with the highest comprehensive score is selected as the driver's cab environmental control strategy.

[0068] Understandably, by comprehensively considering the two important factors of energy consumption and comfort in this step, the system can find a balance between different needs to provide a more comprehensive environmental control strategy. In this step, step S600 includes steps S601 and S602.

[0069] Step S601: Assign weights to the comfort score and energy consumption score corresponding to each second environmental strategy control strategy information, wherein the comfort score and energy consumption score corresponding to the second environmental strategy control strategy information are weighted according to the principle of minimum discrimination information, and the comfort score is weighted to obtain the weight information corresponding to each score.

[0070] Step S602: Based on the weight information corresponding to each score, perform weighted calculation on the comfort score and energy consumption score of each second environmental control strategy information to obtain the score value corresponding to each second environmental control strategy information, and select the second environmental control strategy information with the highest comprehensive score as the driver's cab environmental control strategy.

[0071] It is understandable that this step involves weighting and calculating to comprehensively consider the comfort score and energy consumption score of each second environmental control strategy, and selects the strategy with the highest comprehensive score as the driver's cab environmental control strategy. Specifically, by utilizing the principle of least discriminant information for weighting, the system can intelligently balance the two important factors of comfort and energy consumption.

[0072] Example 2:

[0073] like Figure 2 As shown, this embodiment provides an environmental control device for a railway vehicle driver's cab inside a tunnel. See [link / reference]. Figure 2 The device includes a first acquisition unit 701, a first processing unit 702, a second acquisition unit 703, a second processing unit 704, a third processing unit 705, and a fourth processing unit 706.

[0074] The first acquisition unit 701 is used to acquire first information and second information. The first information includes air data information and personnel information in the driver's cab. The second information includes historical air data information and personnel information in the driver's cab in the database. The air data information includes air temperature information and air humidity information.

[0075] The first processing unit 702 is used to send the second information to a preset environmental control strategy prediction model for processing to obtain the first environmental control strategy information. The first environmental control strategy information is the air temperature control strategy information and air humidity control strategy information in the driver's cab before personnel enter the driver's cab.

[0076] The first processing unit 702 includes a first processing subunit 7021, a second processing subunit 7022, a first calculation subunit 7023, and a third processing subunit 7024.

[0077] The first processing subunit 7021 is used to obtain a training set and a validation set from the preset historical first environment control strategy information and second information;

[0078] The second processing subunit 7022 is used to input the training set into the BP neural network model for training, wherein the driver's cab historical air data and personnel information for a preset time period are used based on the second information to obtain the prediction result.

[0079] The first calculation subunit 7023 is used to calculate the matching degree between the driver's cab historical air data information and personnel information in the prediction result and the preset historical first environmental control strategy information, and to determine the matching degree between the driver's cab historical air data information and personnel information and the historical first environmental control strategy information.

[0080] The third processing subunit 7024 is used to use historical first environmental control strategy information with a matching degree greater than a preset threshold as the predicted first environmental control strategy information.

[0081] The second acquisition unit 703 is used to acquire third information, which includes information on changes in the physical indicators of the person entering the driver's cab and information on the difference in air data between the driver's cab and the outside of the driver's cab. Based on the third information, it performs real-time prediction of environmental control strategies to obtain at least two second environmental control strategy information. The second environmental control strategy information is real-time air temperature control strategy information and real-time air humidity control strategy information in the driver's cab after the person enters the driver's cab.

[0082] The second acquisition unit 703 includes a first clustering subunit 7031, a second clustering subunit 7032, a first analysis subunit 7033, a second analysis subunit 7034, a third analysis subunit 7035, and a fourth processing subunit 7036.

[0083] The first clustering subunit 7031 is used to cluster the physical index change information of the people in the driver's cab to obtain at least two clusters, and each cluster includes the physical index change information of at least two people in the driver's cab.

[0084] The second clustering subunit 7032 is used to calculate the range of each cluster based on the Laida criterion, and to take the cluster with the largest cluster range as the feature cluster.

[0085] The first analysis subunit 7033 is used to perform correlation analysis between the feature clusters and the first environmental control strategy information to obtain the first correlation value between the feature clusters and each strategy in the first environmental control strategy information.

[0086] The second analysis subunit 7034 is used to perform correlation analysis on the air data difference information between the driver's cab and the outside and the first environmental control strategy information to determine the second correlation value between the air data difference information and the first environmental control strategy information.

[0087] The third analysis subunit 7035 is used to perform regression analysis on the feature cluster corresponding to the largest first correlation value and the air data difference information corresponding to the largest second correlation value with the first environmental control strategy information, respectively, to obtain the linear relationship between the first environmental control strategy information and the feature cluster corresponding to the largest first correlation value and the air data difference information corresponding to the largest second correlation value.

[0088] The fourth processing subunit 7036 is used to adjust the first environmental control strategy information based on the linear relationship until the range of the feature cluster corresponding to the largest first correlation value is within a preset range, and the air data difference information corresponding to the largest second correlation value is less than a preset threshold, so as to obtain the second environmental control strategy information.

[0089] The second processing unit 704 is used to perform energy consumption scoring processing on the second environmental control strategy information and determine the energy consumption score corresponding to each second environmental control strategy information.

[0090] The second processing unit 704 includes a fourth analysis subunit 7041 and a fifth processing subunit 7042.

[0091] The fourth analysis subunit 7041 is used to perform correlation analysis between the strategy information and the preset average lifespan of the equipment in the driver's cab to obtain a third correlation value.

[0092] The fifth processing subunit 7042 is used to multiply the third correlation value with the average lifespan of the equipment in the driver's cab, and use the result as the energy consumption score corresponding to each second environmental control strategy information.

[0093] The third processing unit 705 is used to evaluate the second environmental control strategy information based on a preset human comfort assessment model, and obtain a comfort score corresponding to each second environmental control strategy information.

[0094] The third processing unit 705 includes a sixth processing subunit 7051 and a seventh processing subunit 7052.

[0095] The sixth processing subunit 7051 is used to control the equipment according to the preset second environmental control strategy information to obtain the environmental control information of the driver's cab, which includes the temperature control information and humidity control information of the driver's cab.

[0096] The seventh processing subunit 7052 is used to score the environmental control information of the driver's cab based on a preset human comfort assessment table, and obtain the comfort score corresponding to each second environmental strategy control strategy information.

[0097] The fourth processing unit 706 is used to perform a comprehensive analysis based on the energy consumption score and the comfort score, and select the second environmental control strategy information with the highest comprehensive score as the driver's cab environmental control strategy.

[0098] The fourth processing unit 706 includes an eighth processing subunit 7061 and a second calculation subunit 7062.

[0099] The eighth processing subunit 7061 is used to assign weights to the comfort score and energy consumption score corresponding to each second environmental strategy control strategy information, wherein the comfort score and energy consumption score corresponding to the second environmental strategy control strategy information are weighted according to the principle of minimum discrimination information to obtain the weight information corresponding to each score.

[0100] The second calculation subunit 7062 is used to perform weighted calculations on the comfort score and energy consumption score of each second environmental control strategy information based on the weight information corresponding to each score, to obtain the score value corresponding to each second environmental control strategy information, and to select the second environmental control strategy information with the highest comprehensive score as the driver's cab environmental control strategy.

[0101] It should be noted that the specific manner in which each module performs its operation in the apparatus described in the above embodiments has been described in detail in the embodiments of the method, and will not be elaborated here.

[0102] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0103] 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 variations or substitutions that can be easily conceived by those 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 determined by the scope of the claims.

Claims

1. A method for environmental control in a railway vehicle driver's cab within a tunnel, characterized in that, include: Obtain first information and second information. The first information includes air data information and personnel information in the driver's cab. The second information includes historical air data information and personnel information in the driver's cab in the database. The air data information includes air temperature information and air humidity information. The second information is sent to the preset environmental control strategy prediction model for processing to obtain the first environmental control strategy information. The first environmental control strategy information is the air temperature control strategy information and air humidity control strategy information in the driver's cab before personnel enter the driver's cab. The third information is obtained, including the change information of the physical indicators of the person entering the driver's cab and the difference information of the air data between the driver's cab and the outside of the driver's cab. Based on the third information, the environmental control strategy is predicted in real time to obtain at least two second environmental control strategy information. The second environmental control strategy information is the real-time air temperature control strategy information and the real-time air humidity control strategy information in the driver's cab after the person enters the driver's cab. The energy consumption score of the second environmental control strategy information is processed to determine the energy consumption score corresponding to each second environmental control strategy information. The second environmental control strategy information is evaluated based on a preset human comfort assessment model to obtain a comfort score corresponding to each second environmental control strategy information. Based on the energy consumption score and the comfort score, a comprehensive analysis is performed, and the second environmental control strategy information with the highest comprehensive score is selected as the driver's cab environmental control strategy. The acquisition of third information includes changes in the physical indicators of personnel entering the driver's cab and differences in air quality data between the driver's cab and the outside air. Real-time prediction of environmental control strategies based on this third information includes: The physical indicator change information of the people in the driver's cab is clustered to obtain at least two clusters, and each cluster includes the physical indicator change information of at least two people in the driver's cab. The range of each cluster is calculated based on the Laida criterion, and the cluster with the largest range is selected as the feature cluster. The feature clusters are correlated with the first environmental control strategy information to obtain the first correlation value between the feature clusters and each strategy in the first environmental control strategy information. The air data difference information between the driver's cab and the outside is correlated with the first environmental control strategy information to determine the second correlation value between the air data difference information and the first environmental control strategy information. Regression analysis is performed on the feature cluster corresponding to the largest first correlation value and the air data difference information corresponding to the largest second correlation value with the first environmental control strategy information to obtain the linear relationship between the first environmental control strategy information and the feature cluster corresponding to the largest first correlation value and the air data difference information corresponding to the largest second correlation value. The first environmental control strategy information is adjusted based on the linear relationship until the range of the feature cluster corresponding to the largest first correlation value is within a preset range, and the air data difference information corresponding to the largest second correlation value is less than a preset threshold, thereby obtaining the second environmental control strategy information.

2. The method for environmental control of railway vehicle driver's cab in a tunnel according to claim 1, characterized in that, The step of sending the second information to a preset environmental control strategy prediction model for processing to obtain the first environmental control strategy information includes: Based on the preset historical first environmental control strategy information and second information, a training set and a validation set are obtained; The training set is input into the BP neural network model for training, where the prediction result is obtained based on the historical air data and personnel information of the driver's cab over a preset time period. The matching degree between the driver's cab historical air data and personnel information in the prediction results and the preset historical first environmental control strategy information is calculated to determine the matching degree between the driver's cab historical air data and personnel information and the historical first environmental control strategy information. The historical first environmental control strategy information with a matching degree greater than a preset threshold is used as the predicted first environmental control strategy information.

3. The method for environmental control of railway vehicle driver's cab in a tunnel according to claim 1, characterized in that, The energy consumption score is processed on the second environmental control strategy information to determine the energy consumption score corresponding to each second environmental control strategy information, including: The second environmental control strategy information is correlated with the preset average lifespan of equipment in the driver's cab to obtain a third correlation value. The third correlation value is multiplied by the average lifespan of the equipment in the driver's cab, and the result is used as the energy consumption score corresponding to each second environmental control strategy information.

4. The method for environmental control of railway vehicle driver's cab in a tunnel according to claim 1, characterized in that, The second environmental control strategy information is evaluated based on a pre-set human comfort assessment model, including: The equipment is controlled according to the preset second environmental control strategy information to obtain the environmental control information of the driver's cab, which includes the temperature control information and the humidity control information of the driver's cab. The environmental control information in the driver's cab is scored based on a preset human comfort assessment table to obtain a comfort score corresponding to each second environmental strategy control strategy.

5. An environmental control device for a railway vehicle driver's cab in a tunnel, characterized in that, include: The first acquisition unit is used to acquire first information and second information. The first information includes air data information and personnel information in the driver's cab. The second information includes historical air data information and personnel information in the driver's cab in the database. The air data information includes air temperature information and air humidity information. The first processing unit is used to send the second information to a preset environmental control strategy prediction model for processing to obtain the first environmental control strategy information. The first environmental control strategy information is the air temperature control strategy information and air humidity control strategy information in the driver's cab before personnel enter the driver's cab. The second acquisition unit is used to acquire third information, which includes information on changes in the physical indicators of the person entering the driver's cab and information on the difference in air data between the driver's cab and the outside of the driver's cab. Based on the third information, the unit performs real-time prediction of environmental control strategies to obtain at least two second environmental control strategy information. The second environmental control strategy information is real-time air temperature control strategy information and real-time air humidity control strategy information in the driver's cab after the person enters the driver's cab. The second processing unit is used to perform energy consumption scoring processing on the second environmental control strategy information and determine the energy consumption score corresponding to each second environmental control strategy information. The third processing unit is used to evaluate the second environmental control strategy information based on a preset human comfort assessment model, and obtain a comfort score corresponding to each second environmental control strategy information. The fourth processing unit is used to perform a comprehensive analysis based on the energy consumption score and the comfort score, and select the second environmental control strategy information with the highest comprehensive score as the driver's cab environmental control strategy. The second acquisition unit includes: The first clustering subunit is used to cluster the physical index change information of the people in the driver's cab to obtain at least two clusters, and each cluster includes the physical index change information of at least two people in the driver's cab. The second clustering subunit is used to calculate the range of each cluster based on the Laida criterion, and the cluster with the largest cluster range is taken as the feature cluster. The first analysis subunit is used to perform correlation analysis between the feature clusters and the first environmental control strategy information to obtain a first correlation value between the feature clusters and each strategy in the first environmental control strategy information. The second analysis subunit is used to perform correlation analysis on the air data difference information between the driver's cab and the outside air and the first environmental control strategy information to determine the second correlation value between the air data difference information and the first environmental control strategy information. The third analysis subunit is used to perform regression analysis on the feature cluster corresponding to the largest first correlation value and the air data difference information corresponding to the largest second correlation value with the first environmental control strategy information, respectively, to obtain the linear relationship between the first environmental control strategy information and the feature cluster corresponding to the largest first correlation value and the air data difference information corresponding to the largest second correlation value. The fourth processing subunit is used to adjust the first environmental control strategy information based on the linear relationship until the range of the feature cluster corresponding to the largest first correlation value is within a preset range, and the air data difference information corresponding to the largest second correlation value is less than a preset threshold, so as to obtain the second environmental control strategy information.

6. The environmental control device for railway vehicle driver's cab in a tunnel according to claim 5, characterized in that, The first processing unit includes: The first processing subunit is used to obtain a training set and a validation set based on preset historical first environmental control strategy information and second information. The second processing subunit is used to input the training set into the BP neural network model for training, wherein the prediction result is obtained based on the historical air data information and personnel information of the driver's cab within a preset time period. The first calculation subunit is used to calculate the matching degree between the driver's cab historical air data information and personnel information in the prediction result and the preset historical first environmental control strategy information, and to determine the matching degree between the driver's cab historical air data information and personnel information and the historical first environmental control strategy information. The third processing subunit is used to take the historical first environment control strategy information with a matching degree greater than a preset threshold as the predicted first environment control strategy information.

7. The environmental control device for railway vehicle driver's cab in a tunnel according to claim 5, characterized in that, The second processing unit includes: The fourth analysis subunit is used to perform correlation analysis between the second environmental control strategy information and the preset average lifespan of equipment in the driver's cab to obtain a third correlation value. The fifth processing subunit is used to multiply the third correlation value with the average lifespan of the equipment in the driver's cab, and use the result as the energy consumption score corresponding to each second environmental control strategy information.

8. The environmental control device for railway vehicle driver's cab in a tunnel according to claim 5, characterized in that, The third processing unit includes: The sixth processing subunit is used to control the equipment according to the preset second environmental control strategy information to obtain the environmental control information of the driver's cab, which includes the temperature control information and humidity control information of the driver's cab. The seventh processing subunit is used to score the environmental control information of the driver's cab based on a preset human comfort assessment table, and obtain the comfort score corresponding to each second environmental strategy control strategy information.

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