A Train Air Conditioning Control Method and System Based on Passenger Flow Density

Abstract

The present invention relates to the field of intelligent control technology, and discloses a train air-conditioning control method and system based on passenger flow density. The method includes: deploying temperature sensors, image acquisition devices, and infrared sensors inside the carriage; collecting the internal image of the carriage to identify the number of passengers, and collecting the external environmental temperature to determine whether to turn on temperature regulation; when temperature regulation is turned on, determining a regulation value according to the number of passengers in the carriage, recording the regulation time, collecting the actual temperature inside the carriage to construct a temperature data set, and determining whether to adjust the regulation value; when the regulation value is adjusted, calculating a regulation influence factor according to the heat source density and the temperature data set, comparing the regulation influence factor with historical data, and determining an adjustment coefficient to adjust the regulation value. This application improves the response speed and regulation accuracy of air-conditioning regulation, and effectively reduces energy consumption.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent control, and in particular, to a train air-conditioning control method and system based on passenger flow density. Background Art

[0002] With the increasing congestion in transportation and public places, the comfort and safety inside the carriage have become the focus of people's attention. When the traditional air-conditioning control system adjusts the temperature inside the carriage, it often relies on fixed set values, ignoring the actual number of people inside the carriage and changes in the external environment. This method may lead to insufficient air-conditioning cooling when the number of people is dense, and energy waste when the number of people is scarce, thus affecting the comfort of passengers and the energy efficiency of the carriage.

[0003] In addition, the current air-conditioning control system lacks real-time monitoring of the heat source distribution inside the carriage, and cannot accurately judge the temperature demand, resulting in poor overall regulation effect. The lack of a feedback regulation mechanism for temperature regulation inside the carriage may cause discomfort to some passengers, and the system cannot correct the adjustment in real time, thus exacerbating the negative experience of passengers.

[0004] Therefore, it is necessary to design a train air-conditioning control method and system based on passenger flow density to solve the problems existing in the current technology. Summary of the Invention

[0005] In view of this, the present invention proposes a train air-conditioning control method and system based on passenger flow density, aiming to solve the problem of poor temperature regulation effect caused by the lack of a feedback regulation mechanism in the current air-conditioning regulation process.

[0006] On the one hand, the present invention proposes a train air-conditioning control method based on passenger flow density, including:

[0007] Deploy temperature sensors, image acquisition devices and infrared sensors inside the carriage, and at least three temperature sensors are set;

[0008] Based on the image acquisition device to collect the internal image of the carriage, identify the number of people in the carriage according to the internal image of the carriage, collect the external environmental temperature based on big data, and judge whether to start temperature regulation according to the number of people in the carriage and the external environmental temperature;

[0009] When it is determined to start the temperature regulation, determine the regulation value according to the number of people in the carriage, record the regulation time when the temperature regulation is completed and the temperature inside the carriage is stable, construct a temperature data set based on the actual temperature inside the carriage collected by the temperature sensor, and analyze the temperature data set to judge whether to adjust the regulation value;

[0010] When it is determined to adjust the regulation value, the carriage is divided into several sub-regions according to the position of the temperature sensor. Based on the heat source density collected by the infrared sensor in each sub-region, a regulation influence factor is calculated according to the heat source density and the temperature data set. The regulation influence factor is compared with the historical data, and an adjustment coefficient is determined according to the comparison result to adjust the regulation value.

[0011] Further, when determining whether to turn on the temperature regulation according to the number of people in the carriage and the external environmental temperature, it includes:

[0012] When the number of people in the carriage is greater than the number threshold and the external environmental temperature is higher than the temperature threshold, it is determined to turn on the temperature regulation;

[0013] When the number of people in the carriage is less than or equal to the number threshold, or the external environmental temperature is lower than or equal to the temperature threshold, it is determined not to turn on the temperature regulation.

[0014] Further, when determining the regulation value according to the number of people in the carriage, it includes:

[0015] Obtain the stable value of the carriage personnel according to the number of people in the carriage;

[0016] Obtain the number of people at each moment in the carriage according to the internal image of the carriage, form a number-of-people - time data set, intercept the data within the first preset period forward from the current moment in the number-of-people - time data set, and analyze the intercepted data based on the Gaussian kernel density function to determine the stable value of the personnel;

[0017]

[0018] Among them, is the stable value of the personnel, n is the number of samples in the intercepted data, h represents the smoothing bandwidth, s i represents the i-th data point in the intercepted data, and x represents any variable in the overall data.

[0019] Further, when determining the regulation value according to the number of people in the carriage, it also includes: determining the regulation value according to the stable value of the carriage personnel:

[0020] Compare the stable value of the carriage personnel with a preset first preset stable value and a second preset stable value respectively, and determine the regulation value according to the comparison result. The first preset stable value is less than the second preset stable value;

[0021] When the stable value of the passengers in the carriage is less than or equal to the first preset stable value, determine that the regulation value is the first preset regulation value; when the stable value of the passengers in the carriage is greater than the first preset stable value and less than or equal to the second preset regulation value, determine that the regulation value is the second preset regulation value; when the stable value of the passengers in the carriage is greater than the second preset stable value, determine that the regulation value is the third preset regulation value; the first preset regulation value is greater than the second preset regulation value, the second preset regulation value is greater than the third preset regulation value, and the regulation value is less than zero.

[0022] Further, when analyzing the temperature data set to determine whether to adjust the regulation value, it includes:

[0023] When there are data in the temperature data set that are greater than the internally set temperature, it is determined that the regulation value is to be adjusted;

[0024] When all the data in the temperature data set are less than or equal to the internally set temperature, it is determined that the regulation value is not to be adjusted.

[0025] Further, when calculating the regulation influence factor according to the heat source density and the temperature data set, it includes:

[0026] Taking the center point of the temperature sensor as the origin to establish a coordinate system, recording the positions (aj, bj) of each data in the temperature data set, each temperature data in the temperature data set is denoted as Sj, and the origin is denoted as (a0, b0);

[0027] Calculating the distance from each data in the temperature data set to the origin;

[0028]

[0029] Among them, Lj represents the distance from the j-th data in the temperature data set to the origin;

[0030] Calculating the weights of each data in the temperature data set:

[0031]

[0032] Among them, qj represents the weight of the j-th data in the temperature data set, and w is the standard deviation of the data in the temperature data set;

[0033] Calculating the overall temperature data:

[0034]

[0035] Among them, W represents the overall temperature data, and n represents the total number of data in the temperature data set.

[0036] Further, when calculating the regulation influence factor according to the heat source density and the temperature data set, the regulation influence factor is calculated by the following formula:

[0037]

[0038] where X represents the regulation influence factor, M0 represents the standard value of the heat source density, a and b are weights, and a + b = 1, Mu represents the heat source density of the u-th sub-region, and N represents the number of sub-regions.

[0039] Further, when adjusting the regulation value according to the comparison result to determine the adjustment coefficient, it includes:

[0040] When there is data in the historical data that is the same as the regulation influence factor, the regulation value is adjusted with the recorded adjustment coefficient;

[0041] When there is no data in the historical data that is the same as the regulation influence factor, the regulation influence factor is stored, and the adjustment coefficient is determined according to the regulation time and the temperature data set.

[0042] Further, when determining the adjustment coefficient according to the regulation time and the temperature data set, it includes:

[0043] The actual temperature average value is obtained according to the temperature data set, the target temperature is determined according to the regulation value, the temperature change value is obtained according to the target temperature and the actual temperature average value, the temperature change rate is obtained in combination with the regulation time, the temperature change rate is compared with a first preset change rate and a second preset change rate set in advance respectively, and the adjustment coefficient is determined according to the comparison result. The first preset change rate is less than the second preset change rate;

[0044] When the temperature change rate is less than or equal to the first preset change rate, the adjustment coefficient is determined to be the first adjustment coefficient; when the temperature change rate is greater than the first preset change rate and less than or equal to the second preset change rate, the adjustment coefficient is determined to be the second adjustment coefficient; when the temperature change rate is greater than the second preset change rate, the adjustment coefficient is determined to be the third adjustment coefficient; the first adjustment coefficient is greater than the second adjustment coefficient, the second adjustment coefficient is greater than the third adjustment coefficient, and the adjustment coefficient is greater than 1.

[0045] Compared with the prior art, the beneficial effects of the present invention are as follows: By deploying multiple temperature sensors, image acquisition devices, and infrared sensors inside the carriage, comprehensive monitoring and intelligent regulation of the internal environment of the carriage are achieved. It can identify the number of people in the carriage in real time, and combined with the external environmental temperature, intelligently determine whether to turn on the temperature regulation to ensure the effective operation of the air-conditioning system under different passenger densities. By recording the regulation time and analyzing the temperature data set, dynamic adjustment of the regulation value is realized to accurately meet the temperature requirements. In addition, based on the analysis of the heat source density, the temperature regulation strategy is further optimized, reducing energy waste while improving the comfort of passengers. The response speed and regulation accuracy of the air-conditioning system are improved, and the energy consumption is effectively reduced.

[0046] On the other hand, the present application also provides a train air-conditioning control system based on passenger flow density for applying the above-mentioned train air-conditioning control method based on passenger flow density, including:

[0047] A sensor assembly, including a temperature sensor, an image acquisition device, and an infrared sensor;

[0048] An acquisition unit configured to collect an internal image of the carriage based on the image acquisition device, identify the number of people in the carriage according to the internal image of the carriage, collect the external environmental temperature based on big data, and determine whether to turn on the temperature regulation according to the number of people in the carriage and the external environmental temperature;

[0049] A judgment unit configured to, when it is determined to turn on the temperature regulation, determine the regulation value according to the number of people in the carriage, record the regulation time when the temperature regulation is completed and the temperature in the carriage is stable, construct a temperature data set based on the actual temperature in the carriage collected by the temperature sensor, analyze the temperature data set, and determine whether to adjust the regulation value;

[0050] A processing unit configured to, when it is determined to adjust the regulation value, divide the carriage into several sub-regions according to the position of the temperature sensor, collect the heat source density in each sub-region based on the infrared sensor, calculate a regulation influence factor according to the heat source density and the temperature data set, compare the regulation influence factor with historical data, and determine an adjustment coefficient according to the comparison result to adjust the regulation value.

[0051] It can be understood that the above-mentioned train air-conditioning control method and system based on passenger flow density have the same beneficial effects, which will not be elaborated here. Description of the Drawings

[0052] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Also, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0053] Figure 1 It is a flowchart of a train air-conditioning control method based on passenger flow density provided by an embodiment of the present invention;

[0054] Figure 2 It is a structural block diagram of a train air-conditioning control system based on passenger flow density provided by an embodiment of the present invention. Specific Embodiments

[0055] Hereinafter, exemplary embodiments of the present disclosure will be described in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. Hereinafter, the present invention will be described in detail with reference to the drawings and in conjunction with the embodiments.

[0056] In some embodiments of the present application, referring to Figure 1 as shown, a train air-conditioning control method based on passenger flow density includes:

[0057] S100: Deploy temperature sensors, image acquisition devices, and infrared sensors inside the carriage, with at least three temperature sensors;

[0058] S200: Collect the interior image of the carriage based on the image acquisition device, identify the number of passengers in the carriage according to the interior image of the carriage, collect the external environmental temperature based on big data, and determine whether to turn on temperature regulation according to the number of passengers in the carriage and the external environmental temperature;

[0059] S300: When it is determined to turn on temperature regulation, determine the regulation value according to the number of passengers in the carriage, record the regulation time when the temperature regulation is completed and the temperature in the carriage is stable, construct a temperature data set based on the actual temperature collected by the temperature sensors in the carriage, and analyze the temperature data set to determine whether to adjust the regulation value;

[0060] S400: When it is determined that the regulation value needs to be adjusted, the carriage is divided into several sub-regions according to the positions of the temperature sensors. Based on the heat source density collected by the infrared sensors in each sub-region, the regulation influence factor is calculated according to the heat source density and the temperature data set. The regulation influence factor is compared with the historical data, and the adjustment coefficient is determined according to the comparison result to adjust the regulation value.

[0061] Specifically, at least three temperature sensors, an image acquisition device, and infrared sensors are deployed inside the carriage. These sensors together form a comprehensive perception network that can obtain the temperature, the number of people, and the heat source distribution information inside the carriage in real time. The image of the inside of the carriage is obtained through the image acquisition device, and the number of people in the current carriage is identified by using image recognition technology. This provides real-time dynamic data for temperature regulation, enabling the regulation process to adjust the air-conditioning settings according to the actual situation. The external environmental temperature is collected through big data technology to provide a reference for the temperature control inside the carriage. When it is determined that temperature regulation needs to be started, the regulation value is calculated according to the number of people, and the regulation time is recorded. By analyzing the actual temperature data collected by the temperature sensors, a temperature data set is constructed to determine whether the regulation value needs to be further adjusted. When the regulation value needs to be adjusted, the carriage is divided into several sub-regions according to the positions of the temperature sensors, and the heat source density in each sub-region is measured by using the infrared sensors. By calculating the heat source density and the temperature data set, a regulation influence factor is generated and compared with the historical data, and finally the adjustment coefficient is determined. Depending on the sensing technology and big data analysis, a closed-loop feedback regulation mechanism is formed. Through real-time monitoring and analysis, it can dynamically respond to the changes in the internal and external environments of the carriage, ensuring the timeliness and effectiveness of temperature regulation. The multi-level data collection and analysis enable the air-conditioning regulation process to make corresponding adjustments according to the actual number of people.

[0062] It can be understood that by comprehensively monitoring the temperature, the number of people, and the heat source density inside the carriage, the working state of the air conditioner can be adjusted in real time to achieve precise temperature control. This improves the comfort of passengers, reduces the discomfort caused by uneven temperature, and at the same time effectively reduces energy consumption and improves the overall energy efficiency.

[0063] In some embodiments of the present application, when determining whether to start temperature regulation according to the number of people in the carriage and the external environmental temperature, it includes: when the number of people in the carriage is greater than the number threshold and the external environmental temperature is higher than the temperature threshold, it is determined to start temperature regulation; when the number of people in the carriage is less than or equal to the number threshold, or the external environmental temperature is lower than or equal to the temperature threshold, it is determined not to start temperature regulation.

[0064] In some embodiments of the present application, when determining the regulation value according to the number of people in the carriage, it includes:

[0065] Obtain the passenger stability value of the carriage according to the number of passengers in the carriage;

[0066] Obtain the number of passengers at each moment inside the carriage based on the internal image of the carriage, form a dataset of number of passengers - time, intercept the data within the first preset time period forward from the current moment in the dataset of number of passengers - time, and analyze the intercepted data based on the Gaussian kernel density function to determine the passenger stability value;

[0067]

[0068] Wherein, is the passenger stability value, n is the number of samples in the intercepted data, h represents the smoothing bandwidth, s i represents the i-th data point in the intercepted data, and x represents any variable in the overall data.

[0069] In some embodiments of the present application, when determining the regulation value according to the number of passengers in the carriage, it further includes: determining the regulation value according to the passenger stability value of the carriage: comparing the passenger stability value of the carriage with a preset first preset stability value and a second preset stability value respectively, and determining the regulation value according to the comparison result, where the first preset stability value is less than the second preset stability value;

[0070] Specifically, when the passenger stability value of the carriage is less than or equal to the first preset stability value, determine the regulation value as the first preset regulation value; when the passenger stability value of the carriage is greater than the first preset stability value and less than or equal to the second preset regulation value, determine the regulation value as the second preset regulation value; when the passenger stability value of the carriage is greater than the second preset stability value, determine the regulation value as the third preset regulation value; the first preset regulation value is greater than the second preset regulation value, the second preset regulation value is greater than the third preset regulation value, and the regulation value is less than zero.

[0071] It can be understood that by setting a quantity threshold and a temperature threshold, it is possible to accurately determine when temperature regulation needs to be activated. When the number of people in the carriage exceeds the preset quantity threshold and the external environmental temperature is higher than the temperature threshold, it means that heat accumulation may occur in the carriage, and it is necessary to start regulation to maintain passenger comfort. On the contrary, when the number of people is small or the external temperature is low, the air conditioner can maintain the current settings to avoid unnecessary energy consumption. To ensure the scientific nature of the regulation values, by analyzing the change situation of the number of people in the carriage, a number-of-people - time data set is constructed. Through the analysis of this data set using the Gaussian kernel density function, the personnel stability within a period of time can be reflected, ensuring adaptation to fluctuations within a short period of time and making accurate regulation. Based on the calculated personnel stability value, by comparing it with multiple groups of preset stability values, the regulation values are reasonably set. Ensure that under different personnel densities, intelligent adjustment can be made to achieve the best temperature control effect. The Gaussian kernel density function is used to analyze the intercepted number-of-people data, which can effectively smooth the data fluctuations and capture the change trend of the number of people, so as to better evaluate the personnel stability in the carriage.

[0072] It can be understood that through the calculation of the set thresholds and stability values, the flexibility and intelligence level of the air conditioner regulation process are enhanced. By analyzing the change of the number of people in the carriage in real time, the regulation values can be dynamically adjusted to ensure effective refrigeration in the case of a high-density crowd and improve the comfort of passengers. At the same time, this embodiment avoids energy waste of the air conditioner when the number of people is small.

[0073] In some embodiments of the present application, when analyzing the temperature data set to determine whether to adjust the regulation values, it includes: when there are data in the temperature data set that are greater than the internally set temperature, it is determined to adjust the regulation values; when all the data in the temperature data set are less than or equal to the internally set temperature, it is determined not to adjust the regulation values.

[0074] In some embodiments of the present application, when calculating the regulation influence factor according to the heat source density and the temperature data set, it includes: establishing a coordinate system with the center point of the temperature sensor as the origin, recording the positions (aj, bj) of each data in the temperature data set, each temperature data in the temperature data set is denoted as Sj, and the origin is denoted as (a0, b0);

[0075] Calculate the distance from each data in the temperature data set to the origin;

[0076]

[0077] Among them, Lj represents the distance from the jth data in the temperature data set to the origin;

[0078] Calculate the weights of each data in the temperature data set:

[0079]

[0080] Among them, qj represents the weight of the j-th data in the temperature dataset, and w is the standard deviation of the data in the temperature dataset;

[0081] Calculate the overall temperature data:

[0082]

[0083] Among them, W represents the overall temperature data, and n represents the total number of data in the temperature dataset.

[0084] In some embodiments of the present application, when calculating the regulation influence factor according to the heat source density and the temperature dataset, the regulation influence factor is obtained by the following formula:

[0085]

[0086] Among them, X represents the regulation influence factor, M0 represents the standard value of the heat source density, a and b are weights, and a + b = 1, Mu represents the heat source density of the u-th sub-region, and N represents the number of sub-regions.

[0087] It can be understood that first, it is judged whether there is a value higher than the internally set temperature in the temperature dataset, so as to determine whether it is necessary to adjust the regulation value. If all data are lower than or equal to the set temperature, it means that the current temperature state is good and no adjustment is needed, which can effectively avoid unnecessary intervention and resource waste. The regulation influence factor is an important index for evaluating the influence of temperature changes on the regulation value. By establishing a coordinate system and calculating the distance from each data point in the temperature dataset to the origin, the position and importance of each data point in the temperature distribution can be quantified. At the same time, combined with the standard value of the heat source density, the influence of the heat sources in different regions on the overall temperature regulation can be effectively analyzed. The standard value of the heat source density can be set according to the actual application scenario. By taking the center point of the temperature sensor as the origin and recording the position of each temperature data point, the spatial analysis ability of the data is enhanced. It helps to understand the spatial change trend of the temperature distribution. Using the standard deviation to calculate the weight of each data point makes the temperature values deviating from the mean in the temperature dataset be appropriately emphasized when affecting the regulation influence factor. Ensure the accuracy of data analysis, considering the diversity of temperature distributions. Combining the standard value of the heat source density and the heat source density of each sub-region, through the set weight ratio, the final regulation influence factor is calculated. This embodiment integrates multi-dimensional information of temperature and heat source.

[0088] It can be understood that by analyzing the temperature dataset and calculating the influencing factors for regulation, the accuracy and flexibility of air-conditioning regulation are improved. By real-time monitoring and analyzing the temperature state inside the vehicle compartment, it can respond quickly and dynamically adjust the regulation value, thus effectively coping with the challenges brought by temperature changes. Especially in the case of crowded people or uneven heat source distribution, it ensures the comfortable experience of passengers, reduces energy consumption, and improves the overall energy efficiency.

[0089] In some embodiments of the present application, when determining the adjustment coefficient based on the comparison result to adjust the regulation value, it includes: when there is the same data as the influencing factor for regulation in the historical data, adjusting the regulation value with the recorded adjustment coefficient; when there is no same data as the influencing factor for regulation in the historical data, storing the influencing factor for regulation and determining the adjustment coefficient according to the regulation time and the temperature dataset.

[0090] In some embodiments of the present application, when determining the adjustment coefficient according to the regulation time and the temperature dataset, it includes: obtaining the actual temperature average value from the temperature dataset, determining the target temperature according to the regulation value, obtaining the temperature change value from the target temperature and the actual temperature average value, obtaining the temperature change rate in combination with the regulation time, comparing the temperature change rate with a first preset change rate and a second preset change rate set in advance respectively, and determining the adjustment coefficient according to the comparison result, where the first preset change rate is less than the second preset change rate;

[0091] Specifically, when the temperature change rate is less than or equal to the first preset change rate, determining the adjustment coefficient as the first adjustment coefficient; when the temperature change rate is greater than the first preset change rate and less than or equal to the second preset change rate, determining the adjustment coefficient as the second adjustment coefficient; when the temperature change rate is greater than the second preset change rate, determining the adjustment coefficient as the third adjustment coefficient; the first adjustment coefficient is greater than the second adjustment coefficient, the second adjustment coefficient is greater than the third adjustment coefficient, and the adjustment coefficient is greater than 1.

[0092] It can be understood that when adjusting the regulation value, first judge whether there is the same data as the current influencing factor for regulation in the historical data. If it exists, directly adjust it with the recorded adjustment coefficient; if it does not exist, store the current influencing factor and deduce a new adjustment coefficient according to the relevant data. By calculating the difference between the actual temperature average value and the target temperature, the temperature change value is obtained, and the temperature change rate is calculated in combination with the regulation time. This change rate reflects the speed of temperature adjustment and is an important indicator for judging the adjustment range. By comparing with the preset change rate, the speed of temperature change is clarified, and thus a suitable adjustment coefficient is determined. By storing and comparing historical data and using the existing regulation experience, it is ensured that it can respond quickly in similar situations, reducing the debugging time and resource consumption. By setting multiple adjustment coefficients according to the temperature change rate, it is possible to flexibly select the most suitable response strategy in different regulation situations, thus realizing refined management.

[0093] In the above embodiments, by deploying multiple temperature sensors, image acquisition devices, and infrared sensors inside the carriage, comprehensive monitoring and intelligent control of the internal environment of the carriage are achieved. It can identify the number of people in the carriage in real time, and combined with the external environmental temperature, intelligently determine whether to turn on the temperature control to ensure the effective operation of the air-conditioning system under different passenger densities. By recording the control time and analyzing the temperature data set, dynamic adjustment of the control value is realized to accurately meet the temperature requirements. In addition, based on the analysis of the heat source density, the temperature control strategy is further optimized, reducing energy waste while improving the comfort of passengers. The response speed and control accuracy of the air-conditioning system are improved, and the energy consumption is effectively reduced.

[0094] In another preferred manner based on the above embodiments, referring to Figure 2 As shown, this embodiment provides a train air-conditioning control system based on passenger flow density for applying the above train air-conditioning control method based on passenger flow density, including:

[0095] A sensor assembly, including a temperature sensor, an image acquisition device, and an infrared sensor;

[0096] An acquisition unit, configured to acquire an internal image of the carriage based on the image acquisition device, identify the number of people in the carriage according to the internal image of the carriage, acquire the external environmental temperature based on big data, and determine whether to turn on the temperature control according to the number of people in the carriage and the external environmental temperature;

[0097] A judgment unit, configured to determine the control value according to the number of people in the carriage when it is determined to turn on the temperature control, record the control time when the temperature control is completed and the temperature in the carriage is stable, construct a temperature data set based on the actual temperature in the carriage acquired by the temperature sensor, analyze the temperature data set, and determine whether to adjust the control value;

[0098] A processing unit, configured to divide the carriage into several sub-regions according to the position of the temperature sensor when it is determined to adjust the control value, acquire the heat source density in each sub-region based on the infrared sensor, calculate a control influence factor according to the heat source density and the temperature data set, compare the control influence factor with historical data, and determine an adjustment coefficient to adjust the control value according to the comparison result.

[0099] It is understandable that by deploying multiple temperature sensors, image acquisition devices, and infrared sensors inside the carriage, comprehensive monitoring and intelligent control of the internal environment of the carriage are achieved. It can identify the number of people in the carriage in real time, and combined with the external environmental temperature, it can intelligently judge whether to turn on the temperature control to ensure the effective operation of the air-conditioning system under different passenger densities. By recording the control time and analyzing the temperature data set, dynamic adjustment of the control value is realized to accurately meet the temperature requirements. In addition, based on the analysis of the heat source density, the temperature control strategy is further optimized, reducing energy waste while improving the comfort of passengers. The response speed and control accuracy of the air-conditioning system are improved, and the energy consumption is effectively reduced.

[0100] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0101] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the processes and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0102] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the specified functions in Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0103] These computer program instructions 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 generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide means for implementing the specified functions in Figure 1Steps of a process or multiple processes and / or boxes Figure 1 Steps of the functions specified in a box or multiple boxes.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent substitutions can still be made to the specific implementation manners of the present invention, and any modification or equivalent substitution that does not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. A train air-conditioning control method based on passenger flow density, characterized in that, Including: Deploy temperature sensors, image acquisition devices and infrared sensors inside the carriage, with at least three temperature sensors provided; Based on the images acquired by the image acquisition device, identify the number of people in the carriage according to the images inside the carriage, collect the external environmental temperature based on big data, and determine whether to activate temperature regulation according to the number of people in the carriage and the external environmental temperature; When it is determined to activate the temperature regulation, determine the regulation value according to the number of people in the carriage, record the regulation time when the temperature regulation is completed and the temperature inside the carriage is stable, construct a temperature data set based on the actual temperature inside the carriage collected by the temperature sensors, and analyze the temperature data set to determine whether to adjust the regulation value; When it is determined to adjust the regulation value, divide the carriage into several sub-regions according to the positions of the temperature sensors, collect the heat source density in each sub-region based on the infrared sensors, calculate the regulation influence factor according to the heat source density and the temperature data set, compare the regulation influence factor with historical data, and determine the adjustment coefficient according to the comparison result to adjust the regulation value.

2. The train air-conditioning control method based on passenger flow density according to claim 1, wherein When determining whether to activate temperature regulation according to the number of people in the carriage and the external environmental temperature, it includes: When the number of people in the carriage is greater than the number threshold and the external environmental temperature is higher than the temperature threshold, it is determined to activate the temperature regulation; When the number of people in the carriage is less than or equal to the number threshold, or the external environmental temperature is lower than or equal to the temperature threshold, it is determined not to activate the temperature regulation.

3. The train air-conditioning control method based on passenger flow density according to claim 2, wherein When determining the regulation value according to the number of people in the carriage, it includes: Obtain the stable value of the people in the carriage according to the number of people in the carriage; Obtain the number of people at each moment inside the carriage according to the images inside the carriage, form a people number-time data set, intercept the data within the first preset time period forward from the current moment in the people number-time data set, and analyze the intercepted data based on the Gaussian kernel density function to determine the stable value of the people; Among them, is the personnel stability value, n is the number of samples in the intercepted data, h represents the smoothing bandwidth, s i represents the number of personnel at the i-th data point in the intercepted data, and x represents any variable in the overall data.

4. The train air-conditioning control method based on passenger flow density according to claim 3, wherein, When determining the regulation value according to the number of people in the carriage, it also includes: determining the regulation value according to the stable value of the people in the carriage: Compare the stable value of the people in the carriage with a preset first preset stable value and a second preset stable value respectively, and determine the regulation value according to the comparison result, where the first preset stable value is less than the second preset stable value; When the stable value of the people in the carriage is less than or equal to the first preset stable value, determine the regulation value as the first preset regulation value; when the stable value of the people in the carriage is greater than the first preset stable value and less than or equal to the second preset regulation value, determine the regulation value as the second preset regulation value; when the stable value of the people in the carriage is greater than the second preset stable value, determine the regulation value as the third preset regulation value; the first preset regulation value is greater than the second preset regulation value, the second preset regulation value is greater than the third preset regulation value, and the regulation value is less than zero.

5. The train air-conditioning control method based on passenger flow density according to claim 1, characterized in that When analyzing the temperature data set to determine whether to adjust the regulation value, it includes: When there is data in the temperature dataset that is greater than the internally set temperature, it is determined that the regulation value needs to be adjusted; When all the data in the temperature dataset is less than or equal to the internally set temperature, it is determined that the regulation value does not need to be adjusted.

6. The train air-conditioning control method based on passenger flow density according to claim 1, wherein When calculating the regulation influence factor based on the heat source density and the temperature dataset, it includes: Establish a coordinate system with the center point of the temperature sensor as the origin, record the positions (aj, bj) of each data in the temperature dataset, each temperature data in the temperature dataset is denoted as Sj, and the origin is denoted as (a0, b0); Calculate the distance from each data in the temperature dataset to the origin; Among them, Lj represents the distance from the j-th data in the temperature dataset to the origin; Calculate the weights of each data in the temperature dataset: Among them, qj represents the weight of the j-th data in the temperature dataset, and w is the standard deviation of the data in the temperature dataset; Calculate the overall temperature data: Among them, W represents the overall temperature data, and n represents the total number of data in the temperature dataset.

7. The train air-conditioning control method based on passenger flow density according to claim 6, characterized in that When calculating the regulation influence factor based on the heat source density and the temperature dataset, the regulation influence factor is obtained through the following formula: Among them, X represents the regulation influence factor, M0 represents the standard value of the heat source density, a and b are weights, and a + b = 1, Mu represents the heat source density of the u-th sub-region, and N represents the number of sub-regions.

8. The train air-conditioning control method based on passenger flow density according to claim 7, characterized in that When determining the adjustment coefficient based on the comparison result to adjust the regulation value, it includes: When there is data in the historical data that is the same as the regulation influence factor, adjust the regulation value with the recorded adjustment coefficient; When there is no data in the historical data that is the same as the regulation influence factor, store the regulation influence factor, and determine the adjustment coefficient according to the regulation time and the temperature dataset.

9. The train air-conditioning control method based on passenger flow density according to claim 8, wherein, When determining the adjustment coefficient according to the regulation time and the temperature dataset, it includes: Obtain the actual temperature average value according to the temperature dataset, determine the target temperature according to the regulation value, obtain the temperature change value according to the target temperature and the actual temperature average value, combine the regulation time to obtain the temperature change rate, compare the temperature change rate with the preset first preset change rate and the second preset change rate respectively, and determine the adjustment coefficient according to the comparison result. The first preset change rate is less than the second preset change rate; When the temperature change rate is less than or equal to the first preset change rate, determine that the adjustment coefficient is the first adjustment coefficient; when the temperature change rate is greater than the first preset change rate and less than or equal to the second preset change rate, determine that the adjustment coefficient is the second adjustment coefficient; when the temperature change rate is greater than the second preset change rate, determine that the adjustment coefficient is the third adjustment coefficient; the first adjustment coefficient is greater than the second adjustment coefficient, the second adjustment coefficient is greater than the third adjustment coefficient, and the adjustment coefficient is greater than 1.

10. A train air-conditioning control system based on passenger flow density, which is used to apply the train air-conditioning control method based on passenger flow density according to any one of claims 1-9, characterized in that, It includes: A sensor assembly, including a temperature sensor, an image acquisition device, and an infrared sensor; The acquisition unit is configured to collect the interior image of the carriage based on the image acquisition device, identify the number of passengers in the carriage according to the interior image of the carriage, collect the external environmental temperature based on big data, and determine whether to turn on the temperature regulation according to the number of passengers in the carriage and the external environmental temperature; The judgment unit is configured to, when it is determined to turn on the temperature regulation, determine the regulation value according to the number of passengers in the carriage, record the regulation time when the temperature regulation is completed and the temperature in the carriage is stable, construct a temperature data set based on the actual temperature in the carriage collected by the temperature sensor, and analyze the temperature data set to determine whether to adjust the regulation value; The processing unit is configured to, when it is determined to adjust the regulation value, divide the carriage into several sub-regions according to the position of the temperature sensor, collect the heat source density in each sub-region based on the infrared sensor, calculate the regulation influence factor according to the heat source density and the temperature data set, compare the regulation influence factor with the historical data, and determine the adjustment coefficient according to the comparison result to adjust the regulation value.

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