Train air conditioner control method and system based on passenger flow density

Abstract

The invention relates to the technical field of intelligent control, and discloses a train air conditioner control method and system based on passenger flow density, and the method comprises the steps: deploying a temperature sensor, an image acquisition device and an infrared sensor in a compartment; acquiring an image in the compartment to identify the number of passengers in the compartment, and acquiring the external environment temperature to judge whether temperature regulation is started or not; when temperature regulation and control are started, a regulation and control value is determined according to the number of passengers in the compartment, regulation and control time is recorded, actual temperature in the compartment is collected to construct a temperature data set, and whether the regulation and control value is adjusted or not is judged; when the regulation and control value is adjusted, a regulation and control influence factor is calculated according to the heat source density and the temperature data set, the regulation and control influence factor is compared with historical data, and an adjustment coefficient is determined to adjust the regulation and control value. The response speed and the regulation and control precision of air conditioner regulation and control are improved, and energy consumption is effectively reduced.

Description

Technical Field

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

[0002] As transportation and public places become increasingly crowded, the comfort and safety of vehicles have become a focus of attention. Traditional air conditioning control systems often rely on fixed set points when adjusting the temperature inside the vehicle, ignoring the actual number of people in the vehicle and changes in the external environment. This approach may lead to insufficient cooling when there are many people, and energy waste when there are few people, which in turn affects passenger comfort and the energy efficiency of the vehicle.

[0003] In addition, the current air conditioning control system lacks real-time monitoring of the heat source distribution inside the car, and cannot accurately determine the temperature demand, resulting in poor overall control effect. The lack of feedback adjustment mechanism for temperature control in the car may cause some passengers to feel uncomfortable, and the system cannot make corrections 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 control effect caused by lack of feedback adjustment mechanism in the current air-conditioning control process.

[0006] In one aspect, the present invention provides a train air conditioning control method based on passenger flow density, comprising:

[0007] Deploy a temperature sensor, an image acquisition device and an infrared sensor in the vehicle compartment, wherein at least three temperature sensors are provided;

[0008] Based on the image acquisition device, an image of the interior of the carriage is collected, the number of people in the carriage is identified according to the image of the interior of the carriage, the external environment temperature is collected based on big data, and whether to turn on temperature control is determined according to the number of people in the carriage and the external environment temperature;

[0009] When it is determined that the temperature control is turned on, the control value is determined according to the number of people in the compartment, the control time when the temperature control is completed and the temperature in the compartment is stable is recorded, the actual temperature in the compartment is collected by the temperature sensor to construct a temperature data set, and the temperature data set is analyzed to determine whether to adjust the control value;

[0010] When it is determined to adjust the control value, the car is divided into several sub-areas according to the position of the temperature sensor, the heat source density in each sub-area is collected based on the infrared sensor, the control influence factor is calculated according to the heat source density and the temperature data set, the control influence factor is compared with the historical data, and the adjustment coefficient is determined according to the comparison result to adjust the control value.

[0011] Furthermore, when judging whether to turn on the temperature control according to the number of passengers in the compartment and the external ambient temperature, it includes:

[0012] When the number of passengers in the compartment is greater than a number threshold and the external ambient temperature is higher than a temperature threshold, determining to turn on the temperature control;

[0013] When the number of people in the compartment is less than or equal to a number threshold, or the external ambient temperature is lower than or equal to a temperature threshold, it is determined not to start the temperature control.

[0014] Furthermore, when determining the control value according to the number of passengers in the carriage, it includes:

[0015] Obtaining a stable value of the carriage personnel according to the number of the carriage personnel;

[0016] According to the interior image of the carriage, the number of people in the carriage at each moment is obtained to form a number-of-people-time data set, in which data within a first preset time period is intercepted from the current moment as the starting point, and the intercepted data is analyzed based on the Gaussian kernel density function to determine the stable value of the people;

[0017]

[0018] in, is the personnel stability value, n is the number of samples in the intercepted data, h is 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] Furthermore, when determining the control value according to the number of passengers in the carriage, it also includes: determining the control value according to the stability value of passengers in the carriage:

[0020] The passenger stability value is compared with a first preset stability value and a second preset stability value respectively, and the control value is determined according to the comparison result, wherein the first preset stability value is less than the second preset stability value;

[0021] When the stability value of the car occupants is less than or equal to the first preset stability value, the control value is determined to be the first preset control value; when the stability value of the car occupants is greater than the first preset stability value and less than or equal to the second preset control value, the control value is determined to be the second preset control value; when the stability value of the car occupants is greater than the second preset stability value, the control value is determined to be the third preset control value; the first preset control value is greater than the second preset control value, the second preset control value is greater than the third preset control value, and the control value is less than zero.

[0022] Furthermore, analyzing the temperature data set to determine whether to adjust the control value includes:

[0023] When there is data in the temperature data set that is greater than the internal set temperature, it is determined to adjust the control value;

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

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

[0026] A coordinate system is established with the center point of the temperature sensor as the origin, and the position (aj, bj) of each data in the temperature data set is recorded. Each temperature data in the temperature data set is recorded as Sj, and the origin is recorded as (a0, b0);

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

[0028]

[0029] Wherein, Lj represents the distance from the jth data in the temperature data set to the origin;

[0030] Calculate the weight of each data in the temperature data set:

[0031]

[0032] Wherein, qj represents the weight of the jth data in the temperature data set, and w is the standard deviation of the data in the temperature data set;

[0033] Calculate the overall temperature data:

[0034]

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

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

[0037]

[0038] Among them, X represents the regulation influencing factor, M0 represents the standard value of 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] Furthermore, when the adjustment coefficient is determined according to the comparison result to adjust the control value, it includes:

[0040] When the historical data contains data identical to the control impact factor, the control value is adjusted with the recorded adjustment coefficient;

[0041] When the historical data does not contain data identical to the control influencing factor, the control influencing factor is stored, and the adjustment coefficient is determined according to the control time and temperature data set.

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

[0043] Obtaining an actual temperature average value according to the temperature data set, determining a target temperature according to the control value, obtaining a temperature change value according to the target temperature and the actual temperature average value, obtaining a temperature change rate in combination with the control time, comparing the temperature change rate with a first preset change rate and a second preset change rate respectively, determining an adjustment coefficient according to the comparison result, the first preset change rate being 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 in the car, comprehensive monitoring and intelligent control of the internal environment of the car is achieved. It can identify the number of people in the car in real time, and combine the external ambient temperature to intelligently judge whether it is necessary to turn on temperature control, so as 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, the control value can be dynamically adjusted to accurately meet the temperature requirements. In addition, based on the analysis of heat source density, the temperature control strategy is further optimized, energy waste is reduced, and passenger comfort is improved. The response speed and control accuracy of the air-conditioning system are improved, and 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, which is used to apply 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] A collection unit is configured to collect an image of the interior of the carriage based on the image collection device, identify the number of people in the carriage based on the image of the interior of the carriage, collect the external environment temperature based on big data, and determine whether to turn on temperature control based on the number of people in the carriage and the external environment temperature;

[0049] a judgment unit configured to, when determining to start the temperature control, determine a control value according to the number of people in the compartment, record the control time when the temperature control is completed and the temperature in the compartment is stable, collect the actual temperature in the compartment based on the temperature sensor to build a temperature data set, analyze the temperature data set, and judge whether to adjust the control value;

[0050] The processing unit is configured to, when determining to adjust the control value, divide the car into a number of sub-areas according to the position of the temperature sensor, collect the heat source density in each sub-area based on the infrared sensor, calculate the control influence factor according to the heat source density and the temperature data set, compare the control influence factor with the historical data, and determine the adjustment coefficient according to the comparison result to adjust the control 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 repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

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

[0054] Figure 2 A structural block diagram of a train air conditioning control system based on passenger flow density provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying 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 in order to enable a more thorough understanding of the present disclosure and to be able to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

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

[0057] S100: deploying a temperature sensor, an image acquisition device, and an infrared sensor in the vehicle compartment, with at least three temperature sensors being provided;

[0058] S200: collecting an image of the interior of the carriage based on an image acquisition device, identifying the number of people in the carriage based on the image of the interior of the carriage, collecting the external environment temperature based on big data, and determining whether to turn on temperature control based on the number of people in the carriage and the external environment temperature;

[0059] S300: when it is determined that the temperature control is turned on, the control value is determined according to the number of passengers in the carriage, the control time when the temperature control is completed and the temperature in the carriage is stable is recorded, the actual temperature in the carriage is collected by the temperature sensor to build a temperature data set, the temperature data set is analyzed, and it is determined whether to adjust the control value;

[0060] S400: When it is determined that the control value needs to be adjusted, the car is divided into several sub-areas according to the position of the temperature sensor, the heat source density in each sub-area is collected based on the infrared sensor, the control influence factor is calculated based on the heat source density and the temperature data set, the control influence factor is compared with the historical data, and the adjustment coefficient is determined according to the comparison result to adjust the control value.

[0061] Specifically, at least three temperature sensors, image acquisition devices and infrared sensors are deployed in the car. These sensors together form a comprehensive perception network that can obtain the temperature, number of people and heat source distribution information inside the car in real time. The image acquisition device obtains the image of the interior of the car, and the image recognition technology is used to identify the number of people in the current car. Real-time dynamic data is provided for temperature control, so that the control process can adjust the air conditioning settings according to actual conditions. The external environment temperature is collected through big data technology to provide a reference for temperature control in the car. When it is judged that the temperature control needs to be turned on, the control value will be calculated according to the number of people, and the control time will be recorded. By analyzing the actual temperature data collected by the temperature sensor, a temperature data set is constructed to determine whether the control value needs to be further adjusted. When the control value needs to be adjusted, the car is divided into several sub-areas according to the position of the temperature sensor, and the heat source density in each sub-area is measured by an infrared sensor. By calculating the heat source density and temperature data set, the control influencing factor is generated, and it is compared with the historical data to finally determine the adjustment coefficient. Relying on 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 changes in the environment inside and outside the car, ensuring the timeliness and effectiveness of temperature control. Multi-level data collection and analysis enables the air conditioning control process to be adjusted accordingly according to the actual number of people.

[0062] It is understandable that by comprehensively monitoring the internal temperature, number of people and heat source density of the carriage, the working state of the air conditioner can be adjusted in real time to achieve precise control of the temperature, thereby improving the comfort of passengers and reducing the discomfort caused by uneven temperature, while effectively reducing energy consumption and improving overall energy efficiency.

[0063] In some embodiments of the present application, whether to turn on temperature control is determined based on the number of people in the car and the external ambient temperature, including: when the number of people in the car is greater than the number threshold and the external ambient temperature is higher than the temperature threshold, it is determined to turn on temperature control; when the number of people in the car is less than or equal to the number threshold, or the external ambient temperature is lower than or equal to the temperature threshold, it is determined not to turn on temperature control.

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

[0065] Obtain the stable value of carriage personnel according to the number of carriage personnel;

[0066] According to the image inside the carriage, the number of people in the carriage at each moment is obtained to form a personnel number-time data set, in which the data within a first preset time period is intercepted from the current moment as the starting point, and the intercepted data is analyzed based on the Gaussian kernel density function to determine the personnel stability value;

[0067]

[0068] in, is the personnel stability value, n is the number of samples in the intercepted data, h is 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 control value according to the number of passengers in the carriage, the method further includes: determining the control value according to the stability value of passengers in the carriage: comparing the stability value of passengers in the carriage with a first preset stability value and a second preset stability value respectively, and determining the control value according to the comparison result, the first preset stability value is less than the second preset stability value;

[0070] Specifically, when the stability value of the car occupants is less than or equal to the first preset stability value, the control value is determined to be the first preset control value; when the stability value of the car occupants is greater than the first preset stability value and less than or equal to the second preset control value, the control value is determined to be the second preset control value; when the stability value of the car occupants is greater than the second preset stability value, the control value is determined to be the third preset control value; the first preset control value is greater than the second preset control value, the second preset control value is greater than the third preset control value, and the control value is less than zero.

[0071] It is understandable that by setting the number threshold and temperature threshold, it is possible to accurately determine when the temperature control needs to be turned on. When the number of people in the car exceeds the preset number threshold and the external ambient temperature is higher than the temperature threshold, it means that heat accumulation may occur in the car, and it is necessary to start the control 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 setting to avoid unnecessary energy consumption. In order to ensure the scientific nature of the control value, the number of people-time data set is constructed by analyzing the changes in the number of people in the car. This data set can reflect the stability of people over a period of time through the Gaussian kernel density function analysis, ensuring that it adapts to fluctuations in a short period of time and makes accurate control. Based on the calculated stable value of the people, the control value is reasonably set by comparing it with multiple sets of preset stable values. Ensure that under different population densities, it can be adjusted intelligently 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 data fluctuations and capture the changing trend of the number of people, so as to better evaluate the stability of people in the car.

[0072] It can be understood that the flexibility and intelligence level of the air conditioning control process are enhanced by calculating the set threshold and stable value. By analyzing the change in the number of people in the car in real time, the control value can be dynamically adjusted to ensure effective cooling in high-density crowd conditions and improve passenger comfort. At the same time, this embodiment avoids energy waste of air conditioning when there are fewer people.

[0073] In some embodiments of the present application, the temperature data set is analyzed to determine whether to adjust the control value, including: when there is data in the temperature data set that is greater than the internal set temperature, it is determined that the control value is adjusted; when the data in the temperature data set is less than or equal to the internal set temperature, it is determined that the control value is not adjusted.

[0074] In some embodiments of the present application, when calculating the control influencing 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 position (aj, bj) of each data in the temperature data set, each temperature data in the temperature data set is recorded as Sj, and the origin is recorded as (a0, b0);

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

[0076]

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

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

[0079]

[0080] Among them, qj represents the weight of the jth data in the temperature data set, and w is the standard deviation of the data in the temperature data set;

[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 data set.

[0084] In some embodiments of the present application, when calculating the control influence factor according to the heat source density and the temperature data set, the control influence factor is calculated by the following formula:

[0085]

[0086] Among them, X represents the regulation influencing factor, M0 represents the standard value of 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 is understandable that first, it is determined whether there is a value higher than the internal set temperature in the temperature data set to determine whether the control value needs to be adjusted. 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 required, which can effectively avoid unnecessary intervention and waste of resources. The control impact factor is an important indicator for evaluating the impact of temperature change on the control value. By establishing a coordinate system and calculating the distance from each data point of the temperature data set 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 impact of heat sources in different regions on the overall temperature control 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 capability of the data is enhanced. It helps to understand the trend of temperature distribution in space. The standard deviation is used to calculate the weight of each data point, so that the temperature value that deviates from the mean in the temperature data set is appropriately emphasized when affecting the control impact factor. Ensure the accuracy of data analysis, taking into account the diversity of temperature distribution. Combined with the standard value of the heat source density and the heat source density of each sub-region, the final control impact factor is calculated by the set weight ratio. This embodiment integrates multi-dimensional information of temperature and heat source.

[0088] It is understandable that the accuracy and flexibility of air conditioning control are improved through the analysis of temperature data sets and the calculation of control influencing factors. By real-time monitoring and analysis of the temperature status in the car, it is possible to respond quickly and dynamically adjust the control value, thereby effectively responding to the challenges brought by temperature changes. Especially in the case of dense crowds or uneven distribution of heat sources, it ensures the comfort of passengers, reduces energy consumption, and improves overall energy efficiency.

[0089] In some embodiments of the present application, when the adjustment coefficient is determined based on the comparison result to adjust the control value, it includes: when there is data identical to the control influence factor in the historical data, the control value is adjusted with the recorded adjustment coefficient; when there is no data identical to the control influence factor in the historical data, the control influence factor is stored, and the adjustment coefficient is determined based on the control time and temperature data set.

[0090] In some embodiments of the present application, when determining the adjustment coefficient according to the control time and the temperature data set, it includes: obtaining an actual temperature average value according to the temperature data set, determining a target temperature according to the control value, obtaining a temperature change value according to the target temperature and the actual temperature average value, obtaining a temperature change rate in combination with the control time, comparing the temperature change rate with a first preset change rate and a second preset change rate respectively, determining the adjustment coefficient according to the comparison result, and 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, 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.

[0092] It is understandable that when adjusting the control value, first determine whether there is data in the historical data that is the same as the current control influencing factor. If it exists, the recorded adjustment coefficient is directly used for adjustment; if it does not exist, the current influencing factor is stored and a new adjustment coefficient is derived based on the relevant data. The temperature change value is obtained by calculating the difference between the actual temperature mean and the target temperature, and the temperature change rate is calculated in combination with the control time. This change rate reflects the speed of temperature adjustment and is an important indicator for judging the adjustment range. By comparing with the pre-set change rate, the speed of temperature change is clarified, so as to determine the appropriate adjustment coefficient. By storing and comparing historical data and using the existing control experience, it is ensured that a quick response can be made in similar situations, reducing debugging time and resource consumption. Setting multiple adjustment coefficients according to the temperature change rate enables the most appropriate response strategy to be flexibly selected in different control situations, thereby achieving refined management.

[0093] In the above embodiment, by deploying multiple temperature sensors, image acquisition devices and infrared sensors in the car, comprehensive monitoring and intelligent control of the internal environment of the car are achieved. It can identify the number of people in the car in real time, and combine the external ambient temperature to intelligently determine whether to turn on 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, the control value is dynamically adjusted to accurately meet the temperature requirements. In addition, based on the analysis of heat source density, the temperature control strategy is further optimized, energy waste is reduced, and passenger comfort is improved. The response speed and control accuracy of the air-conditioning system are improved, and energy consumption is effectively reduced.

[0094] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides a train air conditioning control system based on passenger flow density, which is used to apply the above-mentioned 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] The acquisition unit is configured to acquire an image of the interior of the carriage based on the image acquisition device, identify the number of people in the carriage based on the image of the interior of the carriage, acquire the external environment 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 environment temperature;

[0097] A judgment unit is configured to, when determining to start temperature control, determine a control value according to the number of passengers in the carriage, record the control time when the temperature control is completed and the temperature in the carriage is stable, collect the actual temperature in the carriage based on the temperature sensor to build a temperature data set, analyze the temperature data set, and determine whether to adjust the control value;

[0098] The processing unit is configured to, when determining to adjust the control value, divide the car into several sub-areas according to the position of the temperature sensor, collect the heat source density in each sub-area based on the infrared sensor, calculate the control influence factor according to the heat source density and the temperature data set, compare the control influence factor with the historical data, and determine the adjustment coefficient according to the comparison result to adjust the control value.

[0099] It is understandable that by deploying multiple temperature sensors, image acquisition devices and infrared sensors in the car, comprehensive monitoring and intelligent control of the internal environment of the car are achieved. It can identify the number of people in the car in real time, and combine the external ambient temperature to intelligently determine whether to turn on 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, the control value is dynamically adjusted to accurately meet the temperature requirements. In addition, based on the analysis of heat source density, the temperature control strategy is further optimized, energy waste is reduced, and passenger comfort is improved. The response speed and control accuracy of the air-conditioning system are improved, and energy consumption is effectively reduced.

[0100] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may 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 codes.

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

[0102] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0103] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A train air conditioning control method based on passenger flow density, characterized in that: include: Deploy a temperature sensor, an image acquisition device and an infrared sensor in the vehicle compartment, wherein at least three temperature sensors are provided; Based on the image acquisition device, an image of the interior of the carriage is collected, the number of people in the carriage is identified according to the image of the interior of the carriage, the external environment temperature is collected based on big data, and whether to turn on temperature control is determined according to the number of people in the carriage and the external environment temperature; When it is determined that the temperature control is turned on, the control value is determined according to the number of people in the compartment, the control time when the temperature control is completed and the temperature in the compartment is stable is recorded, the actual temperature in the compartment is collected by the temperature sensor to construct a temperature data set, and the temperature data set is analyzed to determine whether to adjust the control value; When it is determined to adjust the control value, the car is divided into several sub-areas according to the position of the temperature sensor, the heat source density in each sub-area is collected based on the infrared sensor, the control influence factor is calculated according to the heat source density and the temperature data set, the control influence factor is compared with the historical data, and the adjustment coefficient is determined according to the comparison result to adjust the control value.

2. The train air conditioning control method based on passenger flow density according to claim 1, characterized in that: The method of determining whether to turn on the temperature control according to the number of passengers in the compartment and the external ambient temperature includes: When the number of passengers in the compartment is greater than a number threshold and the external ambient temperature is higher than a temperature threshold, determining to turn on the temperature control; When the number of people in the compartment is less than or equal to a number threshold, or the external ambient temperature is lower than or equal to a temperature threshold, it is determined not to start the temperature control.

3. The train air conditioning control method based on passenger flow density according to claim 2, characterized in that: When the control value is determined according to the number of passengers in the carriage, it includes: Obtaining a stable value of the carriage personnel according to the number of the carriage personnel; According to the interior image of the carriage, the number of people in the carriage at each moment is obtained to form a number-of-people-time data set, in which data within a first preset time period is intercepted from the current moment as the starting point, and the intercepted data is analyzed based on the Gaussian kernel density function to determine the stable value of the people; in, is the personnel stability value, n is the number of samples in the intercepted data, h is the smoothing bandwidth, s i represents 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, characterized in that: When the control value is determined according to the number of passengers in the carriage, the method further includes: determining the control value according to the stability value of passengers in the carriage: The passenger stability value is compared with a first preset stability value and a second preset stability value respectively, and the control value is determined according to the comparison result, wherein the first preset stability value is less than the second preset stability value; When the stability value of the car occupants is less than or equal to the first preset stability value, the control value is determined to be the first preset control value; when the stability value of the car occupants is greater than the first preset stability value and less than or equal to the second preset control value, the control value is determined to be the second preset control value; when the stability value of the car occupants is greater than the second preset stability value, the control value is determined to be the third preset control value; the first preset control value is greater than the second preset control value, the second preset control value is greater than the third preset control value, and the control value is less than zero.

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

6. The train air conditioning control method based on passenger flow density according to claim 1, characterized in that: When calculating the control influence factor according to the heat source density and temperature data set, it includes: A coordinate system is established with the center point of the temperature sensor as the origin, and the position (aj, bj) of each data in the temperature data set is recorded. Each temperature data in the temperature data set is recorded as Sj, and the origin is recorded as (a0, b0); Calculate the distance from each data in the temperature data set to the origin; Wherein, Lj represents the distance from the jth data in the temperature data set to the origin; Calculate the weight of each data in the temperature data set: Wherein, qj represents the weight of the jth data in the temperature data set, and w is the standard deviation of the data in the temperature data set; Calculate the overall temperature data: Wherein, W represents the overall temperature data, and n represents the total number of data in the temperature data set.

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

8. The train air conditioning control method based on passenger flow density according to claim 7, characterized in that: When the adjustment coefficient is determined according to the comparison result to adjust the control value, it includes: When the historical data contains data identical to the control impact factor, the control value is adjusted with the recorded adjustment coefficient; When the historical data does not contain data identical to the control influencing factor, the control influencing factor is stored, and the adjustment coefficient is determined according to the control time and temperature data set.

9. The train air conditioning control method based on passenger flow density according to claim 8, characterized in that: When determining the adjustment coefficient according to the control time and temperature data set, it includes: Obtaining an actual temperature average value according to the temperature data set, determining a target temperature according to the control value, obtaining a temperature change value according to the target temperature and the actual temperature average value, obtaining a temperature change rate in combination with the control time, comparing the temperature change rate with a first preset change rate and a second preset change rate respectively, determining an adjustment coefficient according to the comparison result, the first preset change rate being less than the second preset change rate; 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.

10. A train air conditioning control system based on passenger flow density, used for applying the train air conditioning control method based on passenger flow density as claimed in any one of claims 1 to 9, characterized in that: include: A sensor assembly, including a temperature sensor, an image acquisition device, and an infrared sensor; A collection unit is configured to collect an image of the interior of the carriage based on the image collection device, identify the number of people in the carriage based on the image of the interior of the carriage, collect the external environment temperature based on big data, and determine whether to turn on temperature control based on the number of people in the carriage and the external environment temperature; a judgment unit configured to, when determining to start the temperature control, determine a control value according to the number of people in the compartment, record the control time when the temperature control is completed and the temperature in the compartment is stable, collect the actual temperature in the compartment based on the temperature sensor to build a temperature data set, analyze the temperature data set, and judge whether to adjust the control value; The processing unit is configured to, when determining to adjust the control value, divide the car into a number of sub-areas according to the position of the temperature sensor, collect the heat source density in each sub-area based on the infrared sensor, calculate the control influence factor according to the heat source density and the temperature data set, compare the control influence factor with the historical data, and determine the adjustment coefficient according to the comparison result to adjust the control value.

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