A method and system for controlling air conditioning of urban rail train based on FPGA

Abstract

This application relates to the field of train air conditioning control technology, specifically to a method and system for controlling the air conditioning of urban rail trains based on FPGA. The method includes: acquiring two-dimensional coordinate information of passengers in each carriage during the operation of the urban rail train, as well as the return air temperature and humidity at the air vents of each unit's air conditioning unit within the carriage; constructing the distribution concentration and air density uniformity, and based on this, obtaining the ventilation adjustment intensity in each carriage at each sampling time; analyzing the changes in the ventilation adjustment intensity in each carriage to determine the ventilation demand coefficient, and then determining the ventilation adjustment weight of each carriage at the current time; combining the number of passengers in each carriage at the current time and the differences in the maximum and minimum fresh air volumes preset by each passenger to obtain the fresh air demand in each carriage at the current time; and controlling the fresh air volume of the air conditioning in each carriage of the urban rail train through an FPGA processor. This application can achieve accurate control of the fresh air volume in the carriages.

Description

Technical Field

[0001] The present application relates to the technical field of train air conditioning control, and in particular to an urban rail train air conditioning control method and system based on FPGA. Background Art

[0002] As the core component of urban rail trains, air conditioning units have higher requirements for the control accuracy and intelligence of urban rail train air conditioning units in order to improve the comfort of passengers traveling on urban rail trains. At present, the requirements for urban rail train air conditioning units are not limited to traditional functions, but more importantly, they are expected to be able to perform various automatic adjustments according to the actual needs of passengers, so as to better serve passengers.

[0003] However, due to the limited volume of urban rail train carriages, the passengers getting on and off during the movement, and the constantly changing environmental factors, most existing air-conditioning systems have a relatively simple control and adjustment method for the fresh air volume. They are unable to accurately control and adjust the air-conditioning fresh air volume according to the actual changes in the environmental factors in the carriage, which can easily cause too much or too little fresh air to be introduced, resulting in a poor comfort experience for urban rail train passengers and failing to meet the comfort requirements of urban rail trains. Summary of the invention

[0004] In order to solve the above technical problems, the purpose of this application is to provide an urban rail train air conditioning control method and system based on FPGA. The technical solutions adopted are as follows:

[0005] The present application provides an urban rail train air conditioning control method based on FPGA, comprising the following steps:

[0006] Obtain the two-dimensional coordinate information of passengers in each carriage during the operation of the urban rail train, and the return air temperature and return air humidity at the air outlet of each unit air conditioning unit in the carriage;

[0007] Determine the distribution concentration of passengers in each compartment at each sampling time according to the difference in passenger coordinate information in each compartment at each sampling time, analyze the difference in return air temperature and return air humidity at the air outlets of different unit air conditioning units in the compartment at the adjacent sampling time of each sampling time, so as to determine the air density uniformity in each compartment at each sampling time, and obtain the ventilation adjustment strength in each compartment at each sampling time by combining the distribution concentration and the air density uniformity;

[0008] Determine the ventilation demand coefficient in each compartment at the current moment according to the change of the ventilation adjustment strength in each compartment at all sampling moments before the current moment;

[0009] According to the average ventilation adjustment strength in the carriage at all sampling moments before the current moment and the ventilation demand coefficient in the carriage, the ventilation adjustment weight of each carriage at the current moment is determined, and the fresh air demand in each carriage at the current moment is obtained by combining the number of passengers in each carriage at the current moment and the difference between the maximum and minimum fresh air volumes preset by each passenger;

[0010] Based on the fresh air demand, the fresh air volume of the air conditioner in each compartment of the urban rail train is controlled and adjusted through the FPGA processor.

[0011] Preferably, the calculation formula of the distribution concentration is: , where is the distribution concentration of passengers in carriage i at sampling time t, is the number of all passengers in carriage i at sampling time t, is the Euclidean distance function, They are the two-dimensional coordinate positions of the j-th and j-1-th passengers in carriage i at sampling time t respectively.

[0012] Preferably, the neighboring sampling moments of each sampling moment are: a plurality of sampling moments with the shortest time intervals between each sampling moment are used as the neighboring sampling moments of each sampling moment.

[0013] Preferably, the calculation formula for the uniformity of air density in each compartment at each sampling time is:

[0014] , where is the uniformity of air density in compartment i at sampling time t, is an exponential function with a natural constant as base, is the number of neighboring sampling times of sampling time t, are the return air temperatures of the first and second air conditioning units in compartment i at the sth nearest sampling time of sampling time t, They are respectively the return air humidity values ​​at the return air outlet of the first and second unit air-conditioning units in compartment i at the sth nearest sampling time of sampling time t.

[0015] Preferably, the ventilation adjustment intensity in each compartment at each sampling moment is the ratio of the distribution concentration to the air density uniformity.

[0016] Preferably, the calculation formula for the ventilation demand coefficient in each compartment at the current moment is:

[0017] , where is the ventilation demand coefficient in compartment i at the current moment, is the number of positive values ​​in the first-order difference sequence of the ventilation adjustment intensity sequence of compartment i at the current moment, is the mean of all positive values ​​in the first-order difference sequence of the ventilation adjustment intensity sequence of compartment i at the current moment, wherein the ventilation adjustment intensity sequence is composed of the ventilation adjustment intensity at all sampling moments before the current moment.

[0018] Preferably, the ventilation adjustment strengths in each compartment at all sampling moments before the current moment are arranged in chronological order to form a ventilation adjustment strength sequence for each compartment at the current moment.

[0019] Preferably, the calculation formula for the ventilation adjustment weight of each compartment at the current moment is:

[0020] , where , are the ventilation adjustment weight and ventilation demand coefficient in compartment i at the current moment, is an exponential function with a natural constant as base, is the mean of the ventilation adjustment intensity sequence of compartment i at the current moment.

[0021] Preferably, the calculation formula for the fresh air demand in each compartment at the current moment is:

[0022] , where are the fresh air demand and ventilation adjustment weight in compartment i at the current moment, is the number of passengers in carriage i at the current moment, The minimum amount of fresh air required for each passenger, The maximum amount of fresh air required for each passenger.

[0023] The present application also provides an urban rail train air conditioning control system implemented based on FPGA, including a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the above methods when executing the computer program.

[0024] From the above, it can be seen that the urban rail train air conditioning control method and system based on FPGA provided by the present application have at least the following beneficial effects:

[0025] The present application constructs a distribution concentration based on the analysis of the distribution characteristics of passengers in the car and the characteristics of the air density in the car, characterizes the concentration density of passengers in the car at different times, and constructs an air density uniformity to characterize the air uniformity in different areas in the car, so as to improve the accuracy of the subsequent analysis of the demand for fresh air in the car; constructs the ventilation adjustment strength in the car based on the distribution concentration and the air density uniformity, and further constructs the ventilation demand characteristics, so as to obtain the ventilation adjustment weight according to the ventilation adjustment strength and the ventilation demand characteristics in the car, reflecting the size of the adjustment weight for the fresh air volume in the car at the current moment, and accurately obtain the fresh air demand through the ventilation adjustment weight and the number of passengers in the car; controls and adjusts the opening of the fresh air regulating valve in the air-conditioning equipment in the car based on the fresh air demand, so as to achieve accurate control of the fresh air volume of the air-conditioning units in different cars.

[0026] Compared with the prior art, the present application analyzes the actual changes in environmental factors in each carriage of the urban rail train, constructs the ventilation adjustment weights and fresh air demand in the carriage, thereby accurately controlling and adjusting the fresh air volume in the carriage, avoiding the problem of too much or too little fresh air volume introduced by the air-conditioning unit, improving the comfort experience of urban rail train passengers, and meeting the comfort requirements of urban rail trains. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 A flowchart of the steps of an urban rail train air conditioning control method implemented based on FPGA provided in this application. DETAILED DESCRIPTION

[0029] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following, in combination with the accompanying drawings and preferred embodiments, describes in detail the specific implementation method, structure, features and effects of an urban rail train air conditioning control method and system based on FPGA implemented in accordance with the present application. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0030] Unless otherwise specified and limited, terms such as "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such articles or devices. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the article or device including the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of this application.

[0031] The following is a detailed description of a specific solution of an urban rail train air conditioning control method and system based on FPGA implemented in the present application with reference to the accompanying drawings.

[0032] See also Figure 1 , which shows a flowchart of a method for controlling air conditioning of a city rail train based on FPGA provided by an embodiment of the present application, comprising the following steps:

[0033] Step 1: Obtain the two-dimensional coordinate information of passengers in each carriage during the operation of the urban rail train, and the return air temperature and return air humidity at the air outlet of each unit air-conditioning unit in the carriage.

[0034] The purpose of the present application is to control and adjust the opening of the fresh air regulating valve in the air-conditioning equipment in the carriage by analyzing the actual changes in environmental factors in each carriage of the urban rail train, so as to accurately control the fresh air volume of the air-conditioning units in different carriages, avoid the problem of too much or too little fresh air volume introduced by the air-conditioning units, improve the comfort experience of the urban rail train passengers, and meet the comfort requirements of the urban rail train.

[0035] In this embodiment, the urban rail train is provided with 8 carriages, each of which is equipped with two unit air-conditioning units, and the unit air-conditioning units in each carriage are equipped with an air-conditioning control system, wherein each unit air-conditioning unit includes a condensing fan, a compressor, a fresh air regulating valve, a return air regulating valve, a return air inlet, an air supply inlet, a blower, an evaporator, and a condenser; at the same time, a radar sensor is provided on an air-conditioning unit in each carriage for radar detection of the two-dimensional coordinate position of each passenger in each carriage.

[0036] An FPGA processor is provided inside the train air conditioning controller, and the FPGA processor is used to receive environmental parameter information, analyze and process the environmental parameter information, and extract air conditioning control information. During the operation of the urban rail train, the return air temperature and return air humidity at the return air outlet of each unit air conditioning unit in each car are sampled by temperature and humidity sensors. In this embodiment, the sampling time interval is 1s and the sampling time is 5min. At the same time, the two-dimensional coordinate information of all passengers in each car at each sampling time is obtained by radar detection. The sampled environmental parameter information is transmitted to the FPGA processor inside the train air conditioning controller, and the environmental parameter information is analyzed and processed. The environmental parameter information described in this embodiment includes the return air temperature value, the return air humidity value and the two-dimensional coordinate information of the passengers.

[0037] Step 2: Determine the distribution concentration of passengers in each compartment at each sampling moment based on the difference in passenger coordinate information in each compartment at each sampling moment, analyze the return air temperature difference and return air humidity difference at the air outlets of different unit air-conditioning units in the compartments at the adjacent sampling moments of each sampling moment, so as to determine the air density uniformity in each compartment at each sampling moment, and obtain the ventilation adjustment intensity in each compartment at each sampling moment by combining the distribution concentration and the air density uniformity.

[0038] Normally, the amount of fresh air supplied by the air conditioner in the carriage is determined by the number of passengers in the carriage. The amount of fresh air supplied to each passenger is generally in the range of 10-20 Therefore, the more passengers there are in the car, the greater the amount of fresh air supply required in the car. However, this method ignores the position distribution of passengers in the car. When the number of passengers in the car is fixed, if the position distribution of passengers in the car is relatively concentrated, a larger amount of fresh air supply is often required to ensure that passengers have a good comfort experience; if the position distribution of passengers in the car is relatively dispersed, a smaller amount of fresh air supply is often selected to ensure that passengers have a good comfort experience and at the same time avoid the problem of introducing too much fresh air flow and increasing the energy consumption of the air conditioning system.

[0039] Based on the above analysis, for each carriage, the distribution concentration of passengers in the carriage at each sampling time is calculated:

[0040] , where is the distribution concentration of passengers in carriage i at sampling time t, is the number of all passengers in carriage i at sampling time t, is the Euclidean distance function, They are the two-dimensional coordinate positions of the j-th and j-1-th passengers in carriage i at sampling time t respectively.

[0041] It can be understood that the smaller the two-dimensional Euclidean distance between different passengers in the car at the sampling moment, to a certain extent, indicates that the concentration of passengers in the corresponding car at this time is greater. At this time, a larger fresh air supply should be selected to avoid the problem of poor passenger comfort experience in local intervals when the passengers in the car are concentrated. The larger the distribution concentration; conversely, the smaller the distribution concentration, that is, a smaller fresh air supply can be selected at this time to ensure that passengers have a good comfort experience and avoid the problem of increasing the energy consumption of the air-conditioning system due to excessive fresh air flow.

[0042] In addition, in general, the return air vents of air conditioners have the function of achieving air circulation, and the air density in the car is closely related to the temperature and humidity. If the temperature difference and humidity difference between the return air vents of two different air conditioner units in the same car are greater, it can be said that the temperature and airflow distribution in the car are uneven, resulting in significant differences in air density in different areas of the corresponding car. At this time, it is more necessary to increase the fresh air supply in the car to ensure the comfort experience of passengers.

[0043] In order to analyze the air density characteristics between different areas in the compartment at different sampling moments in a local short time, in this embodiment, the 30 sampling moments with the closest time intervals to each sampling moment are used as the neighboring sampling moments of each sampling moment.

[0044] Based on the above analysis, for each carriage, the air density uniformity in the carriage at each sampling moment is calculated:

[0045] , where is the uniformity of air density in compartment i at sampling time t, is an exponential function with a natural constant as base, is the number of neighboring sampling moments of sampling moment t, and in this embodiment, the value is 30. are the return air temperatures of the first and second air conditioning units in compartment i at the sth nearest sampling time of sampling time t, They are respectively the return air humidity values ​​at the return air outlet of the first and second unit air-conditioning units in compartment i at the sth nearest sampling time of sampling time t.

[0046] It is understandable that the air density in different areas of the car will change due to changes in temperature and humidity. The greater the temperature difference and humidity difference between the return air outlets of the two air-conditioning units in the car, to a certain extent, it indicates that there are differences in air density in different areas of the car. At this time, it can be reflected that the circulation effect of fresh air in the car is poor, and the more likely it is to affect the passenger comfort experience in the local area, the smaller the air density uniformity.

[0047] Furthermore, this embodiment takes into account that under normal circumstances, the greater the distribution concentration of passengers in a certain carriage, the more concentrated the passengers in the carriage, and the passenger-concentrated area often consumes a higher level of fresh air. Therefore, the fresh air supply in the carriage should be appropriately increased to ensure the comfort experience of the passengers in the carriage. At the same time, if the air density uniformity in the carriage is smaller, it means that the difference in air density between different areas in the carriage is greater, reflecting that the air circulation effect in the carriage is worse. Therefore, the fresh air supply in the carriage should be appropriately increased to improve the air circulation effect in the carriage and ensure the comfort experience of the passengers in the carriage.

[0048] Based on the above analysis, for each carriage, the ventilation adjustment strength in the carriage at each sampling moment is calculated. The ventilation adjustment strength in each carriage at each sampling moment is the ratio of the distribution concentration to the air density uniformity. In this embodiment, for ease of understanding, the expression of the ventilation adjustment strength is: , where is the ventilation adjustment intensity in compartment i at sampling time t.

[0049] The greater the concentration of passenger distribution in the car and the smaller the uniformity of air density, to a certain extent, it means that the passenger density in the car is high and the difference in air density between different areas is greater, which is more unfavorable for the air circulation in the car. In order to improve the comfort experience of passengers, the fresh air supply in the car should be increased at this time, and the air circulation effect in the car should be strengthened, and the ventilation adjustment intensity will be greater.

[0050] Step 3: Determine the ventilation demand coefficient in each compartment at the current moment according to the changes in the ventilation adjustment intensity in each compartment at all sampling moments before the current moment.

[0051] Normally, during the operation of urban rail trains, due to the passengers getting on and off the train, passengers walking in the corresponding carriages, and passengers walking between different carriages, the ventilation adjustment intensity in the carriages will change to different levels at different sampling times. If the change in the ventilation adjustment intensity in the carriage at the current moment has a stronger upward trend, it means to a certain extent that the demand for fresh air supply at the current moment is more urgent. At this time, the amount of fresh air in the carriage should be increased to ensure the air quality in the carriage and the comfort requirements of passengers.

[0052] In order to control and adjust the fresh air supply in different carriages of the urban rail train, the ventilation adjustment intensity corresponding to all sampling moments before the current moment in each carriage is sequenced in chronological order and recorded as the ventilation adjustment intensity sequence of each carriage at the current moment.

[0053] In order to extract the urgency of the demand for fresh air supply in the car at the current moment, the first-order difference sequence of the ventilation adjustment intensity sequence of each car at the current moment is calculated. The first-order difference sequence is the difference between the ventilation adjustment intensities at all adjacent sampling moments. The more positive values ​​there are and the larger the positive values ​​are, the stronger the upward trend of the change in the ventilation adjustment intensity in the car at the current moment.

[0054] Based on the above analysis, according to the analysis of the first-order difference sequence of the ventilation adjustment intensity sequence of each carriage at the current moment, the ventilation demand coefficient in each carriage at the current moment is calculated:

[0055] , where is the ventilation demand coefficient in compartment i at the current moment, is the number of positive values ​​in the first-order difference sequence of the ventilation adjustment intensity sequence of compartment i at the current moment, is the mean of all positive values ​​in the first-order difference sequence of the ventilation adjustment intensity sequence of compartment i at the current moment. The more positive values ​​there are and the larger the mean of the positive values, the stronger the upward trend of the change in the ventilation adjustment intensity in the compartment at the current moment. To a certain extent, it means that the more urgent the demand for fresh air supply at the current moment, the greater the ventilation demand coefficient, that is, the more fresh air volume in the compartment should be increased at this time to ensure the air quality in the compartment and the comfort requirements of passengers.

[0056] Step 4: Determine the ventilation adjustment weight of each compartment at the current moment based on the average ventilation adjustment intensity in the compartment at all sampling moments before the current moment and the ventilation demand coefficient in the compartment. Combined with the number of passengers in each compartment at the current moment and the difference between the maximum and minimum fresh air volumes preset by each passenger, obtain the fresh air demand in each compartment at the current moment.

[0057] Generally speaking, if the average level of ventilation adjustment intensity in the car during the current time period is higher and the ventilation demand coefficient in the car at the current moment is greater, in order to improve the passengers' comfort experience, the fresh air supply in the car should be increased at the current moment to enhance the air circulation effect in the car.

[0058] Therefore, this embodiment calculates the ventilation adjustment weight in each compartment at the current moment based on the average level of ventilation adjustment intensity and the ventilation demand coefficient:

[0059] , where is the ventilation adjustment weight in compartment i at the current moment, is an exponential function with a natural constant as base, is the mean of the ventilation adjustment intensity sequence of compartment i at the current moment. The larger the ventilation demand coefficient and the larger the mean of ventilation adjustment intensity, the more urgent the demand for fresh air supply in the compartment at the current moment, and the greater the required ventilation adjustment intensity. Selecting a larger ventilation adjustment weight and increasing the fresh air supply in the compartment can quickly improve the air circulation effect in the compartment and improve the comfort experience of passengers, and the larger the ventilation adjustment weight.

[0060] Furthermore, according to the ventilation adjustment weight of each compartment at the current moment and the difference between the maximum and minimum fresh air volumes required by each passenger in the compartment, and combined with the number of passengers in each compartment at the current moment, the fresh air demand in each compartment at the current moment is calculated:

[0061] , where is the fresh air demand in compartment i at the current moment, is the number of passengers in carriage i at the current moment, is the minimum fresh air volume required for each passenger. In this embodiment, the value is 10 , is the maximum fresh air volume required for each passenger. In this embodiment, the value is 20 The greater the ventilation adjustment weight, the greater the demand for fresh air, indicating that the amount of fresh air supply required by the passengers in the car at the current moment is more and more urgent. Therefore, the average amount of fresh air required for each person is adjusted to be larger, so that the fresh air supply in the car can meet the needs of passengers without excessive waste.

[0062] Step 5: Based on the fresh air demand, the FPGA processor is used to control and adjust the fresh air volume of the air conditioners in each compartment of the urban rail train.

[0063] According to the above process, the environmental parameter information is analyzed and processed by the FPGA processor inside the air conditioning controller, and the air conditioning control information is extracted to obtain the fresh air demand in the compartment at the current moment, which is used as the fresh air volume adjusted in each compartment. In this embodiment, the FPGA processor outputs a control signal corresponding to the fresh air demand in the compartment at the current moment, and controls the opening of the fresh air regulating valve through the air conditioning equipment in the compartment to realize the control and adjustment of the fresh air volume of the air conditioning in the compartment of the urban rail train.

[0064] Based on the same inventive concept as the above method, an embodiment of the present application also provides an urban rail train air-conditioning control system implemented based on FPGA, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, the steps of any one of the above-mentioned urban rail train air-conditioning control methods implemented based on FPGA are implemented.

[0065] It is to be understood that the sequence of the embodiments of the present application described above is for description only and does not represent the advantages and disadvantages of the embodiments. The above describes specific embodiments of the present specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0066] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0067] The above content is only an implementation method of the present application and is not intended to limit the scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly used in other related technical fields, are also included in the protection scope of the present application.

Claims

1. A method for controlling urban rail train air conditioning based on FPGA, characterized in that: The following steps are involved: Obtain the two-dimensional coordinate information of passengers in each carriage during the operation of the urban rail train, and the return air temperature and return air humidity at the air outlet of each unit air conditioning unit in the carriage; Determine the distribution concentration of passengers in each compartment at each sampling time according to the difference in the two-dimensional coordinate information of passengers in each compartment at each sampling time, analyze the difference in return air temperature and return air humidity at the air outlets of different unit air conditioning units in the compartment at the adjacent sampling time of each sampling time, so as to determine the air density uniformity in each compartment at each sampling time, and obtain the ventilation adjustment strength in each compartment at each sampling time by combining the distribution concentration and the air density uniformity; Determine the ventilation demand coefficient in each compartment at the current moment according to the change of the ventilation adjustment strength in each compartment at all sampling moments before the current moment; According to the average ventilation adjustment strength in the carriage at all sampling moments before the current moment and the ventilation demand coefficient in the carriage, the ventilation adjustment weight of each carriage at the current moment is determined, and the fresh air demand in each carriage at the current moment is obtained by combining the number of passengers in each carriage at the current moment and the difference between the maximum and minimum fresh air volumes preset by each passenger; Based on the fresh air demand, the fresh air volume of the air conditioner in each compartment of the urban rail train is controlled and adjusted through the FPGA processor.

2. The urban rail train air conditioning control method based on FPGA as claimed in claim 1, characterized in that: The calculation formula of the distribution concentration is: , where is the distribution concentration of passengers in carriage i at sampling time t, is the number of all passengers in carriage i at sampling time t, is the Euclidean distance function, They are the two-dimensional coordinate positions of the j-th and j-1-th passengers in carriage i at sampling time t respectively.

3. The urban rail train air conditioning control method based on FPGA as claimed in claim 1, characterized in that: The neighboring sampling moments of each sampling moment are: a plurality of sampling moments with the shortest time intervals between each sampling moment are used as the neighboring sampling moments of each sampling moment.

4. The urban rail train air conditioning control method based on FPGA as claimed in claim 1, characterized in that: The calculation formula for the uniformity of air density in each compartment at each sampling time is: , where is the uniformity of air density in compartment i at sampling time t, is an exponential function with a natural constant as base, is the number of neighboring sampling times of sampling time t, are the return air temperatures of the first and second air conditioning units in compartment i at the sth nearest sampling time of sampling time t, They are respectively the return air humidity values ​​at the return air outlet of the first and second unit air-conditioning units in compartment i at the sth nearest sampling time of sampling time t.

5. The urban rail train air conditioning control method based on FPGA as claimed in claim 1, characterized in that: The ventilation adjustment intensity in each compartment at each sampling moment is the ratio of the distribution concentration to the air density uniformity.

6. The urban rail train air conditioning control method based on FPGA as claimed in claim 1, characterized in that: The calculation formula of the ventilation demand coefficient in each compartment at the current moment is: , where is the ventilation demand coefficient in compartment i at the current moment, is the number of positive values ​​in the first-order difference sequence of the ventilation adjustment intensity sequence of compartment i at the current moment, is the mean of all positive values ​​in the first-order difference sequence of the ventilation adjustment intensity sequence of compartment i at the current moment, wherein the ventilation adjustment intensity sequence is composed of the ventilation adjustment intensity at all sampling moments before the current moment.

7. The urban rail train air conditioning control method based on FPGA as claimed in claim 6, characterized in that: The ventilation adjustment strength in each compartment at all sampling moments before the current moment is arranged in chronological order to form a ventilation adjustment strength sequence for each compartment at the current moment.

8. The urban rail train air conditioning control method based on FPGA as claimed in claim 7, characterized in that: The calculation formula of the ventilation adjustment weight of each compartment at the current moment is: , where , are the ventilation adjustment weight and ventilation demand coefficient in compartment i at the current moment, is an exponential function with a natural constant as base, is the mean of the ventilation adjustment intensity sequence of compartment i at the current moment.

9. The urban rail train air conditioning control method based on FPGA as claimed in claim 1, characterized in that: The calculation formula for the fresh air demand in each compartment at the current moment is: , where are the fresh air demand and ventilation adjustment weight in compartment i at the current moment, is the number of passengers in carriage i at the current moment, The minimum amount of fresh air required for each passenger, The maximum amount of fresh air required for each passenger.

10. An urban rail train air conditioning control system based on FPGA, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 9 are implemented.

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