Locomotive winter and summer ventilation switching control method, device, equipment, medium and program

Abstract

The invention relates to the technical field of ventilation conversion, and provides a winter and summer ventilation conversion control method and device for a locomotive, and the method comprises the steps: collecting the temperature inside and outside a locomotive workshop through a thermal infrared imager; generating scatter diagrams inside and outside the locomotive workshop according to the temperatures inside and outside the locomotive workshop; performing feature convolution on the scatter diagrams inside and outside the locomotive workshop according to a preset step length by using a pre-acquired convolution kernel to obtain images of the scatter diagrams inside and outside the locomotive workshop; respectively pooling the images of the scatter diagram convolution inside and outside the locomotive workshop by using a convolutional neural network to obtain images of the scatter diagram convolution inside and outside the locomotive workshop; performing full connection on the images of the scatter diagram pooling inside and outside the locomotive workshop by using a convolutional neural network to obtain images of full connection of the scatter diagrams inside and outside the locomotive workshop; and controlling the state of the ventilation opening according to the scatter diagrams inside and outside the locomotive workshop. The method can improve the accuracy of temperature control of the ventilation opening.

Description

Technical Field

[0001] The present disclosure relates to the field of ventilation conversion technology, and in particular to a locomotive winter-summer ventilation conversion control method, device, equipment, medium and program. Background Art

[0003] The winter-summer conversion function of existing locomotives is mainly achieved by manually adjusting the installation position of the air inlet cover and mesh plate. Based on past experience, it is manually determined whether the locomotive uses summer ventilation or winter ventilation. In actual applications, the winter-summer conversion of existing locomotives is only judged based on the external ambient temperature and human perception, and cannot accurately reflect the actual temperature changes, so the accuracy of the vent control state is low. Summary of the invention

[0004] In view of the above problems, embodiments of the present invention provide a locomotive winter-summer ventilation conversion control method, device, equipment, medium and program.

[0005] In a first aspect, an embodiment of the present invention provides a locomotive winter-summer ventilation conversion control method, comprising:

[0006] The temperature inside the locomotive workshop is collected by infrared thermal imager, and the temperature outside the locomotive workshop is collected by infrared thermal imager;

[0007] Generating a scatter plot inside the locomotive workshop and a scatter plot outside the locomotive workshop according to the temperature inside the locomotive workshop and the temperature outside the locomotive workshop respectively;

[0008] Using the pre-acquired convolution kernel to perform feature convolution on the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop according to a preset step length, respectively, to obtain an image of the convolution of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop;

[0009] Using a convolutional neural network, respectively, pooling the images of the convolution of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop to obtain pooled images of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop;

[0010] Using a convolutional neural network, fully connect the pooled images of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop respectively to obtain a fully connected image of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop;

[0011] The state of the vent is controlled according to the scatter plot image inside the locomotive workshop and the scatter plot image outside the locomotive workshop.

[0012] According to an embodiment of the present invention, collecting the temperature inside the locomotive workshop by using an infrared thermal imager, and collecting the temperature outside the locomotive workshop by using an infrared thermal imager, comprises:

[0013] Collect infrared radiation energy in the locomotive workshop through infrared thermal imager;

[0014] Convert infrared radiation energy into infrared radiation images;

[0015] Assigning a temperature value to each pixel on the infrared radiation image according to a preset assignment rule;

[0016] Selecting the median temperature value of the temperature values ​​assigned to each pixel in the infrared radiation image;

[0017] The median temperature value of each pixel in the infrared radiation image is sampled at preset time intervals to obtain the temperature in the locomotive workshop.

[0018] According to an embodiment of the present invention, allocating a temperature value to each pixel on the infrared radiation image according to a preset allocation rule includes:

[0019] Selecting pixel points in the infrared radiation image one by one as target pixel points;

[0020] Performing a linear transformation on the brightness value of the target pixel to obtain an initial temperature value of the target pixel;

[0021] The initial temperature value is error corrected using a preset error value to obtain the temperature value of the target pixel point.

[0022] According to an embodiment of the present invention, generating a scatter plot in a locomotive workshop according to the temperature in the locomotive workshop includes:

[0023] A first temperature coordinate system is established with the preset time when the infrared thermal imager collects the temperature in the locomotive workshop as the x-axis and the temperature value in the locomotive workshop collected by the infrared thermal imager as the y-axis;

[0024] The temperature in the locomotive workshop is mapped to the first temperature coordinate system to obtain a scatter plot in the locomotive workshop.

[0025] According to an embodiment of the present invention, the step of performing feature convolution on the scatter plot in the locomotive workshop according to a preset step length using a pre-acquired convolution kernel to obtain a convolved image of the scatter plot in the locomotive workshop includes:

[0026] Generate a first matrix array according to the scatter plot in the locomotive workshop;

[0027] Splitting the first matrix array into a plurality of sub-matrix arrays according to a preset convolution kernel size and a preset step size;

[0028] Perform feature convolution on each sub-matrix array with a preset convolution kernel one by one to obtain a number of result data to be spliced;

[0029] All the result data to be stitched are stitched into an image of the scatter graph convolution in the locomotive workshop.

[0030] According to an embodiment of the present invention, controlling the state of the vent according to the scattergram inside the locomotive workshop and the scattergram image outside the locomotive workshop includes:

[0031] When the temperature outside the locomotive workshop is greater than a first threshold, the vent is controlled to be in an open state;

[0032] When the temperature outside the locomotive workshop is greater than the second threshold and less than the first threshold, the vent is controlled to be in an open state;

[0033] When the temperature in the locomotive workshop is lower than the third threshold, the vent is controlled to be in a closed state;

[0034] When the temperature outside the locomotive workshop is less than or equal to the second threshold, the vent is controlled to be in a closed state.

[0035] In a second aspect, an embodiment of the present invention provides a locomotive winter-summer ventilation conversion control device, characterized in that it includes:

[0036] A temperature acquisition module, used to acquire the temperature inside the locomotive workshop through an infrared thermal imager, and to acquire the temperature outside the locomotive workshop through an infrared thermal imager;

[0037] A scatter plot generating module, used to generate a scatter plot inside the locomotive workshop and a scatter plot outside the locomotive workshop according to the temperature inside the locomotive workshop and the temperature outside the locomotive workshop respectively;

[0038] An image convolution module, used to perform feature convolution on the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop respectively according to a preset step length using a pre-acquired convolution kernel, to obtain an image of the convolution of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop;

[0039] An image pooling module, used for pooling the images of the convolution of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop respectively using a convolutional neural network to obtain pooled images of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop;

[0040] An image full connection module is used to fully connect the pooled images of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop respectively by using a convolutional neural network to obtain a fully connected image of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop;

[0041] The vent state control module is used to control the state of the vent according to the scatter plot image inside the locomotive workshop and the scatter plot image outside the locomotive workshop.

[0042] In a third aspect, the present disclosure provides a computer device including a memory, a processor and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of the locomotive winter-summer ventilation conversion control method described in the above aspect.

[0043] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the locomotive winter-summer ventilation conversion control method described in the above aspect.

[0044] In a fifth aspect, the present disclosure provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the locomotive winter-summer ventilation conversion control method described in the above aspects.

[0045] Compared with the prior art, the above technical solution of the present invention has the following beneficial effects:

[0046] The temperature inside and outside the locomotive workshop can be collected continuously and in real time by an infrared thermal imager, which greatly improves work efficiency. The temperature can also be represented by a scatter plot, which can well display the temperature distribution area and the temperature fluctuation, which is helpful to quickly predict trends and improve the accuracy and efficiency of data analysis. The convolutional neural network is used to convolve, pool, and fully connect the scatter plots inside and outside the locomotive workshop to extract image features, so as to automatically control the state of the vents according to the temperature inside and outside the locomotive workshop, thereby improving work efficiency while ensuring the health and comfort of the indoor environment, reducing errors caused by manual judgment, improving accuracy, efficiency, scalability, and adaptability, helping to reduce operating costs and improve the accuracy and practicality of temperature-controlled vents. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The present disclosure will be described in more detail below based on embodiments and with reference to the accompanying drawings:

[0048] Figure 1 A flow chart of a locomotive winter-summer ventilation conversion control method provided in the first embodiment of the present disclosure.

[0049] Figure 2 This is a functional module diagram of the locomotive winter-summer ventilation conversion control device provided in Example 3 of the present disclosure.

[0050] Figure 3 A schematic diagram of the process judgment of the locomotive winter-summer ventilation conversion control method provided in the first embodiment of the present disclosure.

[0051] Figure 4 A schematic diagram of the composition structure of an electronic device of a locomotive winter-summer ventilation conversion control method provided in Embodiment 4 of the present disclosure.

[0052] In the drawings, the same reference numerals are used for the same components, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0053] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, and to fully understand and implement how the present disclosure applies technical means to solve technical problems and achieve the corresponding technical effects, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. The embodiments of the present disclosure and the various features in the embodiments can be combined with each other without conflict, and the technical solutions formed are all within the scope of protection of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0054] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0055] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0056] Embodiment 1

[0057] Figure 1 The flowchart of a locomotive winter-summer ventilation conversion control method provided by the embodiment of the present disclosure is as follows. Figure 1 As shown, the locomotive winter and summer ventilation conversion control method includes the following steps:

[0058] S1. Collect the temperature inside the locomotive workshop by using an infrared thermal imager, and collect the temperature outside the locomotive workshop by using an infrared thermal imager.

[0059] In the embodiment of the present invention, the infrared thermal imager is an instrument that uses the heat emitted from the surface of an object to acquire images; the inside of the locomotive workshop refers to the interior space of the locomotive compartment, and the outside of the locomotive workshop refers to the exterior space of the locomotive compartment.

[0060] In the embodiment of the present invention, the collecting the temperature in the locomotive workshop by using an infrared thermal imager includes:

[0061] Collect infrared radiation energy in the locomotive workshop through infrared thermal imager;

[0062] Convert infrared radiation energy into infrared radiation images;

[0063] Assigning a temperature value to each pixel on the infrared radiation image according to a preset assignment rule;

[0064] Selecting the median temperature value of the temperature values ​​assigned to each pixel in the infrared radiation image;

[0065] The median temperature value of each pixel in the infrared radiation image is sampled at preset time intervals to obtain the temperature in the locomotive workshop.

[0066] In the example of the present invention, the step of collecting the temperature outside the locomotive workshop by using an infrared thermal imager is consistent with the step of collecting the temperature inside the locomotive workshop by using an infrared thermal imager, and will not be described in detail here.

[0067] In detail, the infrared thermal imager calculates the infrared radiation energy in the locomotive workshop according to the following formula:

[0068] E=PSΔτ

[0069] Among them, E represents the infrared radiation energy inside and outside the locomotive workshop, Δτ represents the emission time inside and outside the locomotive workshop, P represents the infrared radiation emittance inside and outside the locomotive workshop (which can be obtained according to Planck's law), and S represents the surface area of ​​the infrared radiation energy range collected inside and outside the locomotive workshop.

[0070] Furthermore, the infrared radiation energy can be converted into an infrared radiation image according to the following formula:

[0071] I=k*(E-Emin) / (Emax-Emin)

[0072] Among them, I is the grayscale value of each pixel in the image, E is the infrared radiation energy, Emin and Emax are the minimum and maximum values ​​of the brightness range of the infrared thermal imager, and k is a constant used to flexibly adjust the proportional relationship of the grayscale values. The grayscale values ​​of each pixel in the image are combined to form an infrared radiation image.

[0073] Specifically, the infrared thermal imager is installed in the middle of the locomotive compartment, and the appropriate temperature measurement range is limited by adjusting the field of view and focal length of the infrared thermal imager.

[0074] Furthermore, multiple temperature values ​​displayed in the images inside and outside the locomotive workshop are arranged from low to high, and the median is selected as the temperature value inside and outside the locomotive workshop. If the data amount is an even number, the average of the two middle numbers is taken as the median.

[0075] In the embodiment of the present invention, allocating a temperature value to each pixel on the infrared radiation image according to a preset allocation rule includes:

[0076] Selecting pixel points in the infrared radiation image one by one as target pixel points;

[0077] Performing a linear transformation on the brightness value of the target pixel to obtain an initial temperature value of the target pixel;

[0078] The initial temperature value is error corrected using a preset error value to obtain the temperature value of the target pixel point.

[0079] On the other hand, the allocation rule can be expressed as:

[0080] f(x)=ax+b

[0081] Wherein, f(x) is the temperature value assigned to the i-th pixel on the infrared radiation image, x is the brightness value of the i-th pixel on the infrared radiation image, a is the coefficient of the pixel on the infrared radiation image, and b is the error value of the temperature value assigned to the infrared radiation image;

[0082] In detail, the brightness value x of the i-th pixel on the infrared radiation image can be converted by the following formula:

[0083] ax+b

[0084] Wherein, x is the brightness value of the i-th pixel on the infrared radiation image, a is the coefficient of the pixel on the infrared radiation image, and b is the error value of the temperature value assigned to the infrared radiation image;

[0085] In detail, after the conversion, the conversion result can be mapped to the temperature value assigned to the i-th pixel point on the infrared radiation image.

[0086] In the embodiment of the present invention, the infrared thermal imager can measure the infrared radiation energy of the target surface in real time and quickly output the thermal image; by assigning a temperature value to each pixel on the image according to a preset allocation rule, the reliability and accuracy of the image can be improved; by a reasonable algorithm and measurement calculation, the temperature information can be more accurately extracted from the infrared radiation image to better predict or judge the distribution of hot spots and cold spots, thereby improving the accuracy of subsequent data analysis; by the median, the influence of local abnormal points in the image can be eliminated, thereby improving the stability and accuracy of temperature measurement; by comparing multiple groups of temperatures and taking the median, errors caused by environmental, interference and other factors can be avoided, thereby improving the reliability of temperature measurement.

[0087] In the embodiment of the present invention, when using an infrared thermal imager to collect temperature, there is no need to contact the object to be measured. This measurement method can reduce interference with the object to be measured and avoid heat transfer losses between the instrument and the object to be measured and the influence of the complex surface of the object on the measurement in traditional measurement methods; when using an infrared thermal imager to collect temperature, measurements can be performed uninterruptedly and in real time, greatly improving work efficiency; when using an infrared thermal imager to collect temperature, various complex analysis, processing, storage and other operations can be performed on the temperature data through computers and other processing equipment, thereby making subsequent data processing and analysis more convenient.

[0088] S2, generating a scatter plot inside the locomotive workshop and a scatter plot outside the locomotive workshop according to the temperature inside the locomotive workshop and the temperature outside the locomotive workshop respectively;

[0089] In an embodiment of the present invention, the scatter plot is a graphical expression in statistics and data visualization; the scatter plot in the locomotive workshop refers to a scatter plot in the locomotive workshop formed by marking the collected temperature values ​​in the locomotive workshop at corresponding positions with the time point as the horizontal axis and the temperature as the vertical axis; similarly, the collected temperature values ​​outside the locomotive workshop are marked at corresponding positions to form a scatter plot outside the locomotive workshop.

[0090] In the embodiment of the present invention, generating a scatter plot in the locomotive workshop according to the temperature in the locomotive workshop includes:

[0091] A first temperature coordinate system is established with the preset time when the infrared thermal imager collects the temperature in the locomotive workshop as the x-axis and the temperature value in the locomotive workshop collected by the infrared thermal imager as the y-axis;

[0092] The temperature in the locomotive workshop is mapped to the first temperature coordinate system to obtain a scatter plot in the locomotive workshop.

[0093] In the embodiment of the present invention, the step of generating a scatter plot outside the locomotive workshop based on the temperature outside the locomotive workshop is consistent with the step of generating a scatter plot inside the locomotive workshop based on the temperature inside the locomotive workshop, and will not be described in detail herein.

[0094] In detail, the program in the electronic controller inside the infrared thermal imager is used to set the start time of the infrared thermal imager. The program automatically starts the infrared thermal imager according to the set time and conditions, captures and processes infrared image information in real time, and imports the temperature data inside and outside the locomotive workshop collected by the infrared thermal imager into the statistical software. The data is processed in the statistical software, and the scatter plot drawing option is selected in the statistical software. The preset time point of collection is used as the horizontal axis, and the temperature data is used as the variable value of the coordinate axis to draw a scatter plot.

[0095] Furthermore, statistical software uses a mathematical software that can also be used for data processing and graphing.

[0096] Collecting the temperature at regular intervals can form a temperature change curve graph, so as to better understand the temperature changes of the object being measured and better grasp the physical characteristics and status of the object being measured; measuring the temperature at regular intervals can timely grasp the temperature change trend of the object being measured and take corresponding control measures when necessary; in addition, multiple measurements can reduce errors and improve measurement accuracy.

[0097] Plotting the temperature as a scatter plot can very intuitively present the temperature distribution of the measured object and understand the trend and law of temperature change; the scatter plot can well display the temperature distribution area and the high and low temperature fluctuations; it helps to quickly predict trends and improve the accuracy and efficiency of data analysis; it is convenient to compare with other related data to better show the relationship between data.

[0098] S3, using the pre-acquired convolution kernel to perform feature convolution on the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop according to a preset step size, to obtain a convolved image of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop;

[0099] In an embodiment of the present invention, the convolution kernel refers to an important parameter in a convolutional neural network, which is used to perform convolution operations on input images inside and outside the locomotive workshop and extract feature information; the step size refers to the distance that the set convolution kernel advances forward each time a convolution operation is performed on the scatter plot images inside and outside the locomotive workshop; the point multiplication operation refers to the multiplication and addition operation of corresponding values.

[0100] In an embodiment of the present invention, the scatter plot in the locomotive workshop is feature convolved using a pre-acquired convolution kernel according to a preset step length to obtain an image of the convolution of the scatter plot in the locomotive workshop, including:

[0101] Generate a first matrix array according to the scatter plot in the locomotive workshop;

[0102] Splitting the first matrix array into a plurality of sub-matrix arrays according to a preset convolution kernel size and a preset step size;

[0103] Perform feature convolution on each sub-matrix array with a preset convolution kernel one by one to obtain a number of result data to be spliced;

[0104] All the result data to be stitched are stitched into an image of the scatter graph convolution in the locomotive workshop.

[0105] In an embodiment of the present invention, the step of using a pre-acquired convolution kernel to perform feature convolution on the scatter map outside the locomotive workshop according to a preset step size to obtain an image of the convolution of the scatter map outside the locomotive workshop is consistent with the step of using a pre-acquired convolution kernel to perform feature convolution on the scatter map inside the locomotive workshop according to a preset step size to obtain an image of the convolution of the scatter map inside the locomotive workshop, and will not be repeated here.

[0106] In detail, the scatter plots inside and outside the locomotive workshop are generated into a matrix array through the existing algorithm. For example, the height of the matrix array is 5, the width is 5, and the convolution kernel size is 3 in height and 3 in width. If the step size is set to 1, the matrix array can be split into 9 sub-matrix arrays, and the 9 sub-matrix arrays are multiplied by the values ​​corresponding to the convolution kernel respectively and then added to obtain the result data to be spliced.

[0107] Specifically, if the input channels of the matrix array are set to 3 and the number of convolution kernels is 3, a point multiplication operation is performed on each channel of the matrix array and each convolution kernel, and the obtained results are multiplied and added to obtain the result data to be spliced.

[0108] In the embodiment of the present invention, feature convolution is performed on each sub-matrix array with a preset convolution kernel one by one to obtain a number of result data to be spliced, including:

[0109] Performing instance normalization processing on each of the submatrix arrays according to the size of a preset convolution kernel to obtain a normalized submatrix corresponding to each of the submatrix arrays;

[0110] The standardized sub-matrices are used one by one with preset convolution kernels to perform feature convolution to obtain a number of result data to be spliced.

[0111] In detail, for example, the width of the first matrix array is 3 and the height is 4; the convolution kernel size is 3 and 3, and the step size is set to 1, and the boundary of the first matrix array is padded with 0, so that when the convolution kernel splits the first matrix array, each sub-matrix array split is the same size as the convolution kernel, so that the width of the first matrix array is 4 and the height is 4, and then the sub-matrix array split by the convolution kernel is point multiplied with the convolution kernel size to obtain a number of result data to be spliced, and the several result data to be spliced ​​are spliced ​​to form an image of the scatter plot convolution in the locomotive workshop.

[0112] In the example of the present invention, by selecting the several sub-matrix arrays one by one and performing feature convolution with the preset convolution kernel, image features can be captured more quickly, the image can be processed more finely, the local features in the image can be captured more quickly, and the image characterization ability is enhanced; splicing several result data to be spliced ​​can reduce information loss, and at the same time can also maintain the pattern and structure of the image; the computational complexity is small, and the time and computing resources for model evaluation can be reduced; it is more flexible to adapt to different images, and image features can be captured more accurately, and at the same time, the features of edges and corners can be better processed, thereby improving the robustness of convolution.

[0113] In detail, random scattered points can be converted into images with more regularity and continuity, thereby improving image clarity and observability; the image after convolution processing has more pixel information, which can more accurately show the temperature distribution of the object under test, thereby enhancing the image contrast and making the temperature distribution more obvious; the noise and cluttered information of the temperature scatter plot are eliminated, the image quality is improved, and the temperature distribution of the object under test is clearer, which is conducive to better data analysis and processing.

[0114] S4, using a convolutional neural network to pool the images of the convolution of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop, respectively, to obtain pooled images of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop;

[0115] In the embodiment of the present invention, the pooling is to compress and reduce the dimension of the output data of the convolutional layer to prevent overfitting and reduce the number of parameters.

[0116] In the embodiment of the present invention, the convolutional neural network is used to pool the image of the scatter plot convolved in the locomotive workshop to obtain the pooled image of the scatter plot in the locomotive workshop, including:

[0117] Generate a first matrix array in the locomotive workshop according to the image of the scattergram convolution in the locomotive workshop;

[0118] Splitting the first matrix array into a plurality of sub-matrix arrays according to a preset pooling window size and a preset step size;

[0119] The maximum data values ​​in the plurality of sub-matrix arrays are selected one by one, and the selected maximum data values ​​are spliced ​​into a scatter plot pooled image in a locomotive workshop.

[0120] In the example of the present invention, the step of using a convolutional neural network to pool the scatter plot outside the locomotive workshop to obtain a pooled image of the scatter plot outside the locomotive workshop is consistent with the step of using a convolutional neural network to pool the scatter plot inside the locomotive workshop to obtain a pooled image of the scatter plot inside the locomotive workshop, and will not be repeated here.

[0121] In detail, the scatter plots inside and outside the locomotive workshop are generated into a matrix array through the existing algorithm. For example, the height of the matrix array is 5, the width is 5, and the pooling window size is 3 in height and 3 in width; if the step size is set to 1, the matrix array can be split into 9 sub-matrix arrays. The values ​​of the first sub-matrix array are 2, 4, 3, and 6, respectively, then 6 is selected as an output data, and the other sub-matrix arrays are operated in the same way to obtain multiple groups of output data.

[0122] Specifically, the maximum pooling operation strengthens the edges and features in the image by retaining the maximum value in the image, thereby improving the image's discernibility and making the image easier to accurately classify and identify; it eliminates outliers and noise, reduces the interference of image noise, makes the image cleaner, and helps factors affecting image recognition to be more objective; and it reduces the complexity of the model structure and the difficulty of training while ensuring the extraction of the maximum value of the image, which is conducive to the implementation and promotion of the model.

[0123] In the example of the present invention, after the scatter plot is pooled, the number of image pixels can be reduced, thereby reducing the difficulty of storing and processing image data, which is conducive to accelerating the speed of image processing; the image can be smoothed and the changes in image pixels can be reduced, thereby reducing the impact of noise and overfitting, and improving the generalization ability and accuracy of the model; image information can be aggregated and the potential features of the image can be extracted, which is conducive to improving the model's ability to understand the image and reducing the complexity of the model; the impact of image pixels on the model can be reduced to a certain extent, and the robustness of the image can be enhanced, so that a good match can still be maintained for some slight deformations; the size of the feature map is reduced, thereby reducing the computing cost and storage requirements, which is conducive to improving the performance and efficiency of the system.

[0124] S5. Using a convolutional neural network, fully connect the pooled images of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop respectively to obtain a fully connected image of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop;

[0125] In the embodiment of the present invention, the full connection is to combine the feature information extracted from the convolution layer, the pooling layer, etc. to obtain a global feature representation, and then pass this global feature to the final output layer for classification or regression tasks.

[0126] In the embodiment of the present invention, a convolutional neural network is used to fully connect the pooled image of the scatter plot in the locomotive workshop to obtain a fully connected image of the scatter plot in the locomotive workshop, including:

[0127] Generate a first matrix array in the locomotive workshop according to the image of the scatter plot pooling in the locomotive workshop;

[0128] Stretching the first matrix arrays to a one-dimensional space according to a preset order for merging, and splicing the stretched first matrix arrays into a one-dimensional feature vector;

[0129] Perform feature connection between the one-dimensional feature vector and the preset hidden layer to obtain multiple groups of output vectors;

[0130] Multiple sets of output vectors are concatenated into a fully connected image of the scatter plot in the locomotive workshop.

[0131] In the example of the present invention, the step of using a convolutional neural network to fully connect the image of the scatter map pooling outside the locomotive workshop to obtain a fully connected image of the scatter map outside the locomotive workshop is consistent with the step of using a convolutional neural network to fully connect the image of the scatter map pooling inside the locomotive workshop to obtain a fully connected image of the scatter map inside the locomotive workshop, and will not be repeated here.

[0132] In detail, the scatter plots inside and outside the locomotive workshop are generated into a matrix array through the existing algorithm. For example, the height of the matrix array is 5 and the width is 5. If it is 16@5*5, there are 400 pixels, which form a one-dimensional feature vector with 400 features, corresponding to 400 neurons. The hidden layer neurons are set to 120 and 84, and finally the data is output.

[0133] Specifically, multiple sets of hidden layers can improve the expressive power of the model and enhance the ability to extract and understand image features; can reduce error transmission and improve the stability and robustness of the model; at the same time, can avoid the problem of too many parameters and reduce the complexity of the model, thereby reducing computing time and computing resources; can more fully extract and utilize image features, thereby improving the overall accuracy of the model for tasks such as image classification.

[0134] In the examples of the present invention, the full connection operation can achieve a high-level abstraction of semantics on the image interface, thereby better extracting the features of the image, which is conducive to improving the classification accuracy of the image; the information can be represented by numbers in the last layer and processed by the classifier, so that the information in the image can be fully mined and utilized, and the impact of information reduction can be reduced; the spatial relationship of image pixels that cannot be captured in the scatter plot can be established through a neural network, so as to more comprehensively consider the spatial information and truly reflect the features and details in the image.

[0135] S6, controlling the state of the vents according to the scatter plot image inside the locomotive workshop and the scatter plot image outside the locomotive workshop;

[0136] In the embodiment of the present invention, the scatter plot inside the locomotive workshop and the scatter plot outside the workshop images refer to forming the scatter plot inside the locomotive workshop and the scatter plot outside the workshop into a standard image format.

[0137] In the embodiment of the present invention, the controlling the state of the vent according to the scatter plot image inside the locomotive workshop and the scatter plot image outside the locomotive workshop includes:

[0138] When the temperature outside the locomotive workshop is greater than a first threshold, the vent is controlled to be in an open state;

[0139] When the temperature outside the locomotive workshop is greater than the second threshold and less than the first threshold, the vent is controlled to be in an open state;

[0140] When the temperature in the locomotive workshop is lower than the third threshold, the vent is controlled to be in a closed state;

[0141] When the temperature outside the locomotive workshop is less than or equal to the second threshold, the vent is controlled to be in a closed state.

[0142] In detail, for example, the first threshold is set to 0°, the second threshold is set to -25°, and the third threshold is set to 15°. At 9 o'clock in the morning, if the collected temperature outside the locomotive workshop is greater than 0°, the vent state is open; if -25° is less than the collected temperature outside the locomotive workshop and less than 0°, the vent state is open; if the temperature inside the locomotive workshop is less than 15°, the vent state is closed; if the temperature outside the locomotive workshop is ≤-25°, the vent state is closed; at 21 o'clock in the evening, the temperature inside and outside the locomotive workshop is monitored and the vent state is controlled corresponding to the threshold.

[0143] Specifically, according to the status of the temperature control vents on the scatter plot images inside and outside the locomotive workshop, convolutional layers, pooling algorithms, fully connected layers, and back propagation algorithms are used to realize the recognition of the status of the temperature control vents.

[0144] In the examples of the present invention, by setting a suitable temperature range, the indoor temperature can be controlled within a certain range to avoid the temperature being too high or too low, thereby maintaining a stable indoor temperature, improving the comfort and productivity of the accommodation or staff, improving ventilation efficiency, and ensuring the health and comfort of the indoor environment.

[0145] Automatic identification of the status of temperature control vents through scatter plot images inside and outside the locomotive workshop can reduce the errors caused by manual judgment, improve accuracy, and improve efficiency, real-time performance, scalability, and adaptability, which helps to reduce operating costs and improve the accuracy and practicality of temperature control vents.

[0146] Embodiment 2

[0147] In order to understand the present invention more clearly, a second embodiment is used below to further explain the situation of convolving scatter plots inside and outside a locomotive workshop in an embodiment of the present invention.

[0148] In an example of the present invention, by convolving the scatter plots inside and outside the locomotive workshop, an image of the convolution of the scatter plots inside and outside the locomotive workshop is obtained. Before the convolution, the scatter plots inside and outside the locomotive workshop are converted into a matrix array pattern, and then the convolution is performed. When converting the scatter plots inside and outside the locomotive workshop into the matrix array pattern, the correlation coefficient needs to be calculated first. In order to more accurately judge the correlation between different variables, the correlation coefficient can be used to describe the linear correlation between the variables. For two variables X and Y, commonly used correlation coefficients include the Pearson correlation coefficient and the Spearman rank correlation coefficient, and the calculation of the correlation coefficient can be achieved by statistical software or a calculator.

[0149] Embodiment 3

[0150] like Figure 2 , which is a functional module diagram of the locomotive winter-summer ventilation conversion control device provided for this embodiment.

[0151] The locomotive winter-summer ventilation conversion control device 100 described in this embodiment can be installed in an electronic device. According to the functions to be implemented, the locomotive winter-summer ventilation conversion control device 100 may include a temperature acquisition module 101, a scatter plot generation module 102, an image convolution module 103, an image pooling module 104, an image full connection module 105 and a vent state control module 106. The module described in the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0152] In this embodiment, the functions of each module / unit are as follows:

[0153] The temperature acquisition module 101 is used to acquire the temperature inside the locomotive workshop through the infrared thermal imager, and to acquire the temperature outside the locomotive workshop through the infrared thermal imager;

[0154] The scatter plot generating module 102 is used to generate a scatter plot inside the locomotive workshop and a scatter plot outside the locomotive workshop according to the temperature inside the locomotive workshop and the temperature outside the locomotive workshop respectively;

[0155] The image convolution module 103 is used to perform feature convolution on the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop respectively according to a preset step length using the pre-acquired convolution kernel to obtain an image of the convolution of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop;

[0156] The image pooling module 104 is used to pool the images convolved with the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop respectively using the convolutional neural network to obtain pooled images of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop;

[0157] The image full connection module 105 is used to fully connect the images of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop respectively pooled by using the convolutional neural network to obtain an image of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop fully connected;

[0158] The vent state control module 106 is used to control the state of the vent according to the scatter diagram inside the locomotive workshop and the scatter diagram image outside the locomotive workshop.

[0159] Embodiment 4

[0160] like Figure 4 As shown, this embodiment also provides a computer device, which may include a processor 10, a memory 11, a communication bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a locomotive winter and summer ventilation conversion control program.

[0161] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory 11 (for example, executing a locomotive winter and summer ventilation conversion control program, etc.), and calls data stored in the memory 11 to execute various functions of the electronic device and process data.

[0162] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of the locomotive winter and summer ventilation conversion control program, but also can be used to temporarily store data that has been output or is to be output.

[0163] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0164] The communication interface 13 is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0165] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device, and may include fewer or more components than shown in the figure, or combine certain components, or arrange the components differently.

[0166] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0167] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0168] The locomotive winter-summer ventilation conversion control program stored in the memory 11 in the electronic device is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0169] The temperature inside the locomotive workshop is collected by infrared thermal imager, and the temperature outside the locomotive workshop is collected by infrared thermal imager;

[0170] Generating a scatter plot inside the locomotive workshop and a scatter plot outside the locomotive workshop according to the temperature inside the locomotive workshop and the temperature outside the locomotive workshop respectively;

[0171] Using the pre-acquired convolution kernel to perform feature convolution on the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop according to a preset step length, respectively, to obtain an image of the convolution of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop;

[0172] Using a convolutional neural network, respectively, pooling the images of the convolution of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop to obtain pooled images of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop;

[0173] Using a convolutional neural network, fully connect the pooled images of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop respectively to obtain a fully connected image of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop;

[0174] The state difference of the vent is controlled according to the scatter plot image inside the locomotive workshop and the scatter plot image outside the locomotive workshop.

[0175] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0176] Furthermore, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0177] Embodiment 5

[0178] This embodiment provides a storage medium storing a computer program. When the computer program is executed by a processor, the steps of the locomotive winter-summer ventilation conversion control method as described above are implemented.

[0179] This embodiment provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the locomotive winter-summer ventilation conversion control method described in the above embodiment.

[0180] These program codes can also be loaded onto a computer or other programmable data processing device so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions executed on the computer or other programmable device for implementing the process. Figure 1 The steps of a specified function in a process or multiple processes.

[0181] Storage media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, modules of programs or other data. Examples of storage media can include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission medium that can be used to store information that can be accessed by a computing device.

[0182] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0183] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0184] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0185] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0186] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is not limited only according to the above description, and it is intended that all changes within the meaning and scope of equivalent elements within the scope of protection are included in the present invention.

[0187] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0188] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0189] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A locomotive winter-summer ventilation conversion control method, characterized in that: The method comprises: The temperature inside the locomotive workshop is collected by infrared thermal imager, and the temperature outside the locomotive workshop is collected by infrared thermal imager; Generating a scatter plot inside the locomotive workshop and a scatter plot outside the locomotive workshop according to the temperature inside the locomotive workshop and the temperature outside the locomotive workshop respectively; Using the pre-acquired convolution kernel to perform feature convolution on the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop according to a preset step length, respectively, to obtain an image of the convolution of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop; Using a convolutional neural network, respectively, pooling the images of the convolution of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop to obtain pooled images of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop; Using a convolutional neural network, fully connect the pooled images of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop respectively to obtain a fully connected image of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop; The state of the vent is controlled according to the scatter plot image inside the locomotive workshop and the scatter plot image outside the locomotive workshop.

2. The locomotive winter-summer ventilation conversion control method according to claim 1, characterized in that: The method of collecting the temperature in the locomotive workshop by using an infrared thermal imager includes: Collect infrared radiation energy in the locomotive workshop through infrared thermal imager; Convert infrared radiation energy into infrared radiation images; Assigning a temperature value to each pixel on the infrared radiation image according to a preset assignment rule; Selecting the median temperature value of the temperature values ​​assigned to each pixel in the infrared radiation image; The median temperature value of each pixel in the infrared radiation image is sampled at preset time intervals to obtain the temperature in the locomotive workshop.

3. The locomotive winter-summer ventilation conversion control method according to claim 2, characterized in that: The assigning of a temperature value to each pixel on the infrared radiation image according to a preset assignment rule comprises: Selecting pixel points in the infrared radiation image one by one as target pixel points; Performing a linear transformation on the brightness value of the target pixel to obtain an initial temperature value of the target pixel; The initial temperature value is error corrected using a preset error value to obtain the temperature value of the target pixel point.

4. The locomotive winter-summer ventilation conversion control method according to claim 1, characterized in that: The step of generating a scatter plot in the locomotive workshop according to the temperature in the locomotive workshop comprises: A first temperature coordinate system is established with the preset time when the infrared thermal imager collects the temperature in the locomotive workshop as the x-axis and the temperature value in the locomotive workshop collected by the infrared thermal imager as the y-axis; The temperature in the locomotive workshop is mapped to the first temperature coordinate system to obtain a scatter plot in the locomotive workshop.

5. The locomotive winter-summer ventilation conversion control method according to claim 1, characterized in that: The method of performing feature convolution on the scatter plot in the locomotive workshop using the pre-acquired convolution kernel according to a preset step length to obtain a convolved image of the scatter plot in the locomotive workshop includes: Generate a first matrix array according to the scatter plot in the locomotive workshop; Splitting the first matrix array into a plurality of sub-matrix arrays according to a preset convolution kernel size and a preset step size; Perform feature convolution on each sub-matrix array with a preset convolution kernel one by one to obtain a number of result data to be spliced; All the result data to be stitched are stitched into an image of the scatter graph convolution in the locomotive workshop.

6. The locomotive winter-summer ventilation conversion control method according to claim 1, characterized in that: The controlling the state of the vent according to the scatter plot image inside the locomotive workshop and the scatter plot image outside the locomotive workshop includes: When the temperature outside the locomotive workshop is greater than a first threshold, the vent is controlled to be in an open state; When the temperature outside the locomotive workshop is greater than the second threshold and less than the first threshold, the vent is controlled to be in an open state; When the temperature in the locomotive workshop is lower than the third threshold, the vent is controlled to be in a closed state; When the temperature outside the locomotive workshop is less than or equal to the second threshold, the vent is controlled to be in a closed state.

7. A locomotive winter and summer ventilation conversion control device, characterized in that: The device comprises: A temperature acquisition module, used for acquiring the temperature inside the locomotive workshop through the infrared thermal imager, and acquiring the temperature outside the locomotive workshop through the infrared thermal imager; A scatter plot generating module, used for generating a scatter plot inside the locomotive workshop and a scatter plot outside the locomotive workshop according to the temperature inside the locomotive workshop and the temperature outside the locomotive workshop respectively; An image convolution module, used for performing feature convolution on the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop respectively according to a preset step length using the pre-acquired convolution kernel to obtain an image of the convolution of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop; An image pooling module, used for pooling the images of the convolution of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop respectively by using a convolutional neural network to obtain pooled images of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop; An image full connection module, used for fully connecting the pooled images of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop respectively by using a convolutional neural network to obtain a fully connected image of the scatter plot inside the locomotive workshop and the scatter plot outside the locomotive workshop; The vent state control module is used to control the state of the vent according to the scatter plot image inside the locomotive workshop and the scatter plot image outside the locomotive workshop.

8. A computer device comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the locomotive winter-summer ventilation conversion control method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the locomotive winter-summer ventilation conversion control method described in any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the locomotive winter-summer ventilation conversion control method described in any one of claims 1 to 6 are implemented.

Images