Air quality optimization system, method, device and equipment for rail transit and medium

Abstract

The invention provides an air quality optimization system, method, device and equipment for rail transit and a medium, and the system comprises an air quality monitoring terminal which is used for collecting the air quality data of a to-be-monitored position, and transmitting the air quality data and the collection time to a cloud platform; the cloud platform is used for determining target passenger flow data of the to-be-monitored position at the acquisition time based on a first relationship between the passenger flow data of the to-be-monitored position and the time; determining target operation state data of target air optimization equipment around the to-be-monitored position at the acquisition time; acquiring a second relationship based on the target passenger flow data, the air quality data and the target operation state data; predicting the predicted air quality data of any to-be-monitored position at the current time based on the second relationship; and if the predicted air quality data does not meet the preset air condition, the operation state of the air optimization equipment is adjusted, the air quality of the to-be-monitored position is optimized, and the air optimization equipment is controlled to purify the air of the rail transit based on the air quality data.

Description

Technical Field

[0001] The present invention relates to the field of rail transit technology, and in particular to an air quality optimization system in rail transit, an air quality optimization method in rail transit, an air quality optimization device in rail transit, an electronic device and a computer-readable medium. Background Art

[0002] With the vigorous development of the urban rail transit industry, the subway has become an important means of transportation for people's daily travel.

[0003] The air quality monitoring and analysis equipment used in the rail transit industry is relatively expensive, which limits the possibility of deploying these devices in multiple locations. In addition, existing air quality monitoring equipment does not support networking functions, so even if these devices are deployed in multiple locations, they cannot be networked and cannot provide centralized digital monitoring capabilities. This means that these devices cannot communicate with each other or share data, making it impossible to build a centralized monitoring system to monitor and manage air quality data collected at various monitoring points in real time. Due to the lack of centralized digital monitoring capabilities, it is impossible to evaluate and assess the quality of the rail transit environment based on air quality indicators, nor can decisions be made based on these indicators to control equipment such as fans, air conditioners and purifiers to purify the air in the rail transit environment and save energy consumption of equipment. Summary of the invention

[0004] Embodiments of the present invention provide an air quality optimization system, method, device, electronic device and computer-readable storage medium in rail transit to solve the problem that air quality monitoring equipment lacks centralized digital monitoring capabilities, cannot evaluate and assess the quality of the rail transit environment based on air quality indicators, and cannot make decisions based on these indicators to control equipment such as fans, air conditioners and purifiers to purify the air in the rail transit environment and save energy consumption of the equipment.

[0005] The embodiment of the present invention discloses an air quality optimization system in rail transit, the air quality optimization system in rail transit comprising: an air quality monitoring terminal, a cloud platform and at least one air optimization device;

[0006] The air quality monitoring terminal is used to collect air quality data of at least one location to be monitored in the rail transit, and send the air quality data and the collection time of the air quality data to the cloud platform; the cloud platform stores a first relationship between the passenger flow data at the location to be monitored and the time;

[0007] The air optimization device is used to send the operating status data of the air optimization device to the cloud platform;

[0008] The cloud platform is used to determine the target passenger flow data of the monitored location at the collection time based on the first relationship; determine the target operating status data of the target air optimization equipment around the monitored location at the collection time based on the operating status data; obtain the second relationship between the air quality data and the target passenger flow data and the target operating status data based on the target passenger flow data, the air quality data and the target operating status data; predict the predicted air quality data of any of the monitored locations at the current time based on the second relationship; and adjust the operating status of the air optimization equipment to optimize the air quality of the monitored location if the predicted air quality data does not meet the preset air conditions.

[0009] Optionally, the cloud platform is used to determine the passenger flow data to be processed at the monitored location at the current time based on the first relationship; determine the operating status data to be processed of the target air optimization equipment at the current time based on the operating status data; and determine the predicted air quality data of the monitored location at the current time based on the second relationship, the passenger flow data to be processed and the operating status data to be processed.

[0010] Optionally, for any of the locations to be monitored, the air quality monitoring terminal is used to collect air quality data for at least one collection time; obtain the mean of the air quality data; based on the mean, obtain a first variance and a second variance between the air quality data of the at least one location to be monitored; based on the first variance and the second variance, determine whether there is a difference between the air quality data of the at least one location to be monitored.

[0011] Optionally, in the case that there are differences between the air quality data of at least one of the locations to be monitored, the cloud platform is used to obtain the standard error between the air quality data of the at least one location to be monitored based on the first variance; based on the standard error, obtain the minimum significant difference value between the air quality data of the at least one location to be monitored; based on the minimum significant difference value, determine the target location to be monitored where there are differences in the air quality data; analyze the target location to be monitored to obtain at least one influencing factor causing the difference; the influencing factors include the passenger flow data of the target location to be monitored and / or the operating status data of the air optimization equipment around the target location to be monitored.

[0012] Optionally, the air quality optimization system further comprises a gateway; the gateway is connected to the air quality monitoring terminal by a preset long-distance radio communication technology;

[0013] The air quality monitoring terminal is used to send the air quality data to the gateway;

[0014] The gateway is used to determine whether the air quality data is greater than a preset air quality threshold; when the air quality data is greater than the air quality threshold, an alarm is issued through a preset sound and light alarm.

[0015] Optionally, the gateway and the cloud platform are connected to each other by a preset narrowband Internet of Things communication technology;

[0016] The gateway is used to send the air quality data to the cloud platform;

[0017] The cloud platform is used to generate a three-dimensional image of the location to be monitored, and send the three-dimensional image and the air quality data to a preset user terminal.

[0018] The embodiment of the present invention further discloses an air quality optimization method in rail transit, which is applied to an air quality optimization system in rail transit. The air quality optimization system in rail transit includes: an air quality monitoring terminal, a cloud platform, and at least one air optimization device; the method includes:

[0019] Using the air quality monitoring terminal to collect air quality data of at least one location to be monitored in the rail transit, and sending the air quality data and the collection time of the air quality data to the cloud platform; the cloud platform stores a first relationship between the passenger flow data at the location to be monitored and the time;

[0020] Using the air optimization device to send the operating status data of the air optimization device to the cloud platform;

[0021] Based on the first relationship, determining the target passenger flow data of the location to be monitored at the collection time using the cloud platform;

[0022] According to the operating status data, using the cloud platform to determine the target operating status data of the target air optimization equipment around the location to be monitored at the collection time;

[0023] Based on the target passenger flow data, the air quality data, and the target operating status data, using the cloud platform to obtain a second relationship between the air quality data and the target passenger flow data and the target operating status data;

[0024] Based on the second relationship, the cloud platform is used to predict the predicted air quality data of any of the monitored locations at the current time, and when the predicted air quality data does not meet the preset air conditions, the operating state of the air optimization device is adjusted to optimize the air quality of the monitored location.

[0025] Optionally, based on the second relationship, using the cloud platform to predict the predicted air quality data of any of the locations to be monitored at the current time includes:

[0026] Based on the first relationship, using the cloud platform to determine the passenger flow data to be processed at the location to be monitored at the current time;

[0027] According to the operating status data, using the cloud platform to determine the operating status data to be processed of the target air optimization device at the current time;

[0028] Based on the second relationship, the passenger flow data to be processed and the operating status data to be processed, the predicted air quality data of the location to be monitored at the current time is determined using the cloud platform.

[0029] Optionally, the collecting air quality data of at least one to-be-monitored location in the rail transit by using the air quality monitoring terminal includes:

[0030] For any of the locations to be monitored, using the air quality monitoring terminal to collect air quality data of at least one collection time;

[0031] The method comprises:

[0032] Using the cloud platform to obtain the mean value of the air quality data;

[0033] Based on the mean, using the cloud platform to obtain a first variance and a second variance between the air quality data of the at least one location to be monitored;

[0034] Based on the first variance and the second variance, the cloud platform is used to determine whether there is a difference between the air quality data of the at least one location to be monitored.

[0035] Optionally, the method comprises:

[0036] In the case where there is a difference between the air quality data of the at least one location to be monitored, using the cloud platform to obtain a standard error between the air quality data of the at least one location to be monitored based on the first variance;

[0037] Based on the standard error, using the cloud platform to obtain a minimum significant difference value between the air quality data of the at least one location to be monitored;

[0038] Based on the minimum significant difference value, the cloud platform is used to determine the target monitored location where there is a difference in air quality data, and the target monitored location is analyzed to obtain at least one influencing factor causing the difference; the influencing factors include the passenger flow data of the target monitored location and / or the operating status data of the air optimization equipment around the target monitored location.

[0039] Optionally, the air quality optimization system further includes a gateway; the gateway is connected to the air quality monitoring terminal via a preset long-distance radio communication technology; the sending of the air quality data and the collection time of the air quality data to the cloud platform includes:

[0040] Using the air quality monitoring terminal to send the air quality data to the gateway;

[0041] The method comprises:

[0042] The gateway is used to determine whether the air quality data is greater than a preset air quality threshold, and when the air quality data is greater than the air quality threshold, an alarm is sounded through a preset sound and light alarm.

[0043] Optionally, the gateway and the cloud platform are connected to each other through a preset narrowband Internet of Things communication technology; sending the air quality data and the collection time of the air quality data to the cloud platform includes:

[0044] Using the gateway to send the air quality data to the cloud platform;

[0045] The method comprises:

[0046] The cloud platform is used to generate a three-dimensional image of the location to be monitored, and the three-dimensional image and the air quality data are sent to a preset user terminal.

[0047] The embodiment of the present invention further discloses an air quality optimization device in rail transit, which is applied to an air quality optimization system in rail transit. The air quality optimization system in rail transit includes: an air quality monitoring terminal, a cloud platform, and at least one air optimization device; the device includes:

[0048] A collection module, used for collecting air quality data of at least one to-be-monitored position in the rail transit by using the air quality monitoring terminal, and sending the air quality data and the collection time of the air quality data to the cloud platform; the cloud platform stores a first relationship between the passenger flow data and time at the to-be-monitored position;

[0049] A sending module, used to send the operating status data of the air optimization device to the cloud platform using the air optimization device;

[0050] A target passenger flow data determination module, configured to determine, based on the first relationship, the target passenger flow data of the location to be monitored at the collection time using the cloud platform;

[0051] A target operating state data determination module, configured to determine, based on the operating state data, the target operating state data of the target air optimization equipment around the location to be monitored at the collection time using the cloud platform;

[0052] A second relationship acquisition module, configured to acquire, based on the target passenger flow data, the air quality data, and the target operating status data, a second relationship between the air quality data and the target passenger flow data and the target operating status data using the cloud platform;

[0053] A prediction module is used to predict the predicted air quality data of any of the monitored locations at the current time based on the second relationship using the cloud platform, and to adjust the operating state of the air optimization device to optimize the air quality of the monitored location when the predicted air quality data does not meet the preset air conditions.

[0054] Optionally, the prediction module includes:

[0055] A passenger flow data to be processed determination submodule, configured to determine the passenger flow data to be processed at the location to be monitored at the current time using the cloud platform based on the first relationship;

[0056] A submodule for determining the operating status data to be processed, used to determine the operating status data to be processed of the target air optimization device at the current time using the cloud platform according to the operating status data;

[0057] The predicted air quality data determination submodule is used to determine the predicted air quality data of the monitored location at the current time using the cloud platform based on the second relationship, the passenger flow data to be processed and the operating status data to be processed.

[0058] Optionally, the acquisition module includes:

[0059] A collection submodule, configured to collect air quality data of at least one collection time using the air quality monitoring terminal for any of the locations to be monitored;

[0060] The device comprises:

[0061] A mean value acquisition module, used for acquiring the mean value of the air quality data using the cloud platform;

[0062] A variance acquisition module, configured to acquire, based on the mean, a first variance and a second variance between the air quality data of the at least one location to be monitored using the cloud platform;

[0063] A difference judgment module is used to judge whether there is a difference between the air quality data of the at least one monitored location based on the first variance and the second variance using the cloud platform.

[0064] Optionally, the device comprises:

[0065] A standard error acquisition module, configured to acquire, using the cloud platform based on the first variance, a standard error between the air quality data of the at least one location to be monitored when there is a difference between the air quality data of the at least one location to be monitored;

[0066] A minimum significant difference value acquisition module, used to acquire the minimum significant difference value between the air quality data of the at least one monitored location using the cloud platform based on the standard error;

[0067] A target monitored location determination module is used to determine the target monitored location where there is a difference in air quality data based on the minimum significant difference value using the cloud platform, and analyze the target monitored location to obtain at least one influencing factor causing the difference; the influencing factor includes the passenger flow data of the target monitored location and / or the operating status data of the air optimization equipment around the target monitored location.

[0068] Optionally, the air quality optimization system further includes a gateway; the gateway is connected to the air quality monitoring terminal via a preset long-distance radio communication technology; the acquisition module includes:

[0069] A first sending submodule, configured to send the air quality data to the gateway using the air quality monitoring terminal;

[0070] The device comprises:

[0071] The judgment module is used to use the gateway to judge whether the air quality data is greater than a preset air quality threshold, and when the air quality data is greater than the air quality threshold, issue an alarm through a preset sound and light alarm.

[0072] Optionally, the gateway and the cloud platform are connected to each other by a preset narrowband Internet of Things communication technology; and the acquisition module includes:

[0073] A second sending submodule, used to send the air quality data to the cloud platform using the gateway;

[0074] The device comprises:

[0075] A generation module is used to generate a three-dimensional image of the location to be monitored using the cloud platform, and send the three-dimensional image and the air quality data to a preset user terminal.

[0076] The embodiment of the present invention further discloses an electronic device, comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0077] The memory is used to store computer programs;

[0078] The processor is used to implement the method described in the embodiment of the present invention when executing the program stored in the memory.

[0079] The embodiment of the present invention further discloses one or more computer-readable media on which instructions are stored. When executed by one or more processors, the processors are enabled to execute the method described in the embodiment of the present invention.

[0080] The embodiments of the present invention include the following advantages:

[0081] In an embodiment of the present invention, an air quality optimization system in rail transit includes: an air quality monitoring terminal, a cloud platform and at least one air optimization device; the air quality monitoring terminal is used to collect air quality data of at least one location to be monitored in rail transit, and send the air quality data and the collection time of the air quality data to the cloud platform; the cloud platform stores a first relationship between passenger flow data and time at the location to be monitored; the air optimization device is used to send the operating status data of the air optimization device to the cloud platform; the cloud platform is used to determine the target passenger flow data of the location to be monitored at the collection time based on the first relationship; determine the target operating status data of the target air optimization device around the location to be monitored at the collection time based on the operating status data; based on the target passenger flow data, air quality data and target operating status data, obtain the second relationship between the air quality data and the target passenger flow data and the target operating status data; based on the second relationship, predict the predicted air quality data of any location to be monitored at the current time; when the predicted air quality data does not meet the preset air conditions, adjust the operating status of the air optimization device to optimize the air quality of the location to be monitored. The cloud platform can obtain air quality data collected by air quality monitoring terminals deployed at multiple locations to be monitored, realizing centralized digital monitoring; at the same time, based on the target passenger flow data, air quality data of the locations to be monitored, and the target operating status data of the target air optimization equipment around the locations to be monitored, the cloud platform can obtain the second relationship between the air quality data and the target passenger flow data and the target operating status data. Based on the second relationship, the cloud platform can predict the predicted air quality data of any location to be monitored at the current time; when the predicted air quality data does not meet the preset air conditions, the operating status of the air optimization equipment is adjusted to optimize the air quality of the locations to be monitored, thereby realizing decision-making based on air quality data, controlling equipment such as fans, air conditioners and purifiers, purifying the air in the rail transit environment and saving the energy consumption of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 is a structural block diagram of an air quality optimization system in rail transit provided in an embodiment of the present invention;

[0083] Figure 2 is a structural block diagram of another air quality optimization system in rail transit provided in an embodiment of the present invention;

[0084] Figure 3 is a structural block diagram of another air quality optimization system in rail transit provided in an embodiment of the present invention;

[0085] Figure 4 is a flowchart of the steps of a method for optimizing air quality in rail transit provided in an embodiment of the present invention;

[0086] Figure 5 is a structural block diagram of an air quality optimization device in rail transit provided in an embodiment of the present invention;

[0087] Figure 6 is a block diagram of an electronic device provided in an embodiment of the present invention;

[0088] Figure 7 is a schematic diagram of a computer-readable medium provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0089] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0090] To facilitate understanding of the technical solutions and technical effects of the embodiments of the present invention, the prior art of the present invention is briefly described below.

[0091] With the booming development of the urban rail transit industry, the subway has become an important means of transportation for people's daily travel. Since the subway runs in underground space, the subway station and vehicle environment are relatively closed, the ventilation is poor, and the subway station and the car have a large flow of people. Therefore, the ambient air of the subway station and the car is mainly regulated by the air-conditioning system, resulting in limited comfort. In the subway system, the braking system, power facilities, passenger activities, and building materials will produce various gaseous pollutants, including arsine (AsH3), ammonia (NH3), total volatile organic compounds (TVOCs, Total Volatile Organic Compounds), nitric oxide (NO) and nitrogen dioxide (NO2). These pollutants may pose a serious threat to the passengers' riding comfort experience and health.

[0092] Reference Figure 1 , shows a structural block diagram of an air quality optimization system in rail transit provided in an embodiment of the present invention, the air quality optimization system in rail transit comprising: an air quality monitoring terminal, a cloud platform and at least one air optimization device;

[0093] The air quality monitoring terminal is used to collect air quality data of at least one location to be monitored in the rail transit, and send the air quality data and the collection time of the air quality data to the cloud platform; the cloud platform stores a first relationship between the passenger flow data at the location to be monitored and the time;

[0094] The air optimization device is used to send the operating status data of the air optimization device to the cloud platform;

[0095] The cloud platform is used to determine the target passenger flow data of the monitored location at the collection time based on the first relationship; determine the target operating status data of the target air optimization equipment around the monitored location at the collection time based on the operating status data; obtain the second relationship between the air quality data and the target passenger flow data and the target operating status data based on the target passenger flow data, the air quality data and the target operating status data; predict the predicted air quality data of any of the monitored locations at the current time based on the second relationship; and adjust the operating status of the air optimization equipment to optimize the air quality of the monitored location if the predicted air quality data does not meet the preset air conditions.

[0096] In an embodiment of the present invention, an air quality optimization system in rail transit includes: an air quality monitoring terminal, a cloud platform, and at least one air optimization device. The air quality monitoring terminal is communicatively connected to the cloud platform, and the cloud platform is communicatively connected to the air optimization device.

[0097] In an embodiment of the present invention, the air quality monitoring terminal can collect air quality data of at least one location to be monitored in rail transit, and send the air quality data and the time of collecting the air quality data to the cloud platform. It should be noted that the cloud platform stores the first relationship between the passenger flow data and time at the location to be monitored. In an embodiment of the present invention, the air optimization device can send the operating status data of the air optimization device to the cloud platform.

[0098] In an embodiment of the present invention, the cloud platform can determine the target passenger flow data of the location to be monitored at the collection time based on the first relationship. Then, based on the operating status data sent by the air optimization device, the cloud platform can determine the target operating status data of the target air optimization device around the location to be monitored at the collection time. Based on the target passenger flow data, air quality data, and target operating status data, the cloud platform can obtain a second relationship between the air quality data and the target passenger flow data and the target operating status data. Based on the second relationship, the cloud platform can predict the predicted air quality data of any location to be monitored at the current time. When the predicted air quality data does not meet the preset air conditions, the cloud platform can adjust the operating status of the air optimization device to optimize the air quality of the location to be monitored.

[0099] In a specific example, the air quality data includes data such as PM10, CO2 (carbon dioxide), harmful gases, and the average concentration of total bacteria. Among them, PM10 refers to particulate matter in the ambient air with an aerodynamic equivalent diameter less than or equal to 10 microns. The target air optimization device may refer to an air conditioner, and the target operating status data refers to the fresh air volume of the air conditioner. The second relationship includes the relationship between the air quality data at the monitored location and the target passenger flow data, and the relationship between the air quality data at the monitored location and the fresh air volume of the air conditioner. Therefore, a simple linear regression analysis can be performed with the fresh air volume of the air conditioner as the independent variable (x) and the air quality data as the dependent variable (y) to obtain at least one regression equation corresponding to the air quality data:

[0100] y1=-0.002x+0.16;

[0101] y2=-0.001x+0.09;

[0102] y3=-0.026x+22.31;

[0103] y4=-0.956x+34.52.

[0104] Among them, y1, y2, y3, and y4 are the mean concentrations of PM10, CO2, harmful gases, and total bacteria, respectively. According to the regression equation, a linear regression chart can also be used to show that each air quality data is negatively correlated with the fresh air volume of the air conditioner. The regression analysis between the target passenger flow data and the air quality data is similar to the regression analysis between the fresh air volume of the air conditioner and the air quality data, and the present invention will not be repeated here.

[0105] In some embodiments of the present invention, the cloud platform is used to determine the passenger flow data to be processed at the monitored location at the current time based on the first relationship; determine the operating status data to be processed of the target air optimization equipment at the current time based on the operating status data; and determine the predicted air quality data of the monitored location at the current time based on the second relationship, the passenger flow data to be processed and the operating status data to be processed.

[0106] In an embodiment of the present invention, when predicting the predicted air quality data of any location to be monitored at the current time, the cloud platform can first determine the pending passenger flow data of the location to be monitored at the current time based on the first relationship between the passenger flow data and time at the location to be monitored. Then, based on the operating status data sent by the air optimization device, determine the pending operating status data of the target air optimization device at the current time. Based on the second relationship between the air quality data and the target passenger flow data and the target operating status data, the pending passenger flow data and the pending operating status data, the cloud platform can determine the predicted air quality data of the location to be monitored at the current time.

[0107] In some embodiments of the present invention, for any of the locations to be monitored, the air quality monitoring terminal is used to collect air quality data of at least one collection time; obtain the mean of the air quality data; based on the mean, obtain a first variance and a second variance between the air quality data of the at least one location to be monitored; based on the first variance and the second variance, determine whether there is a difference between the air quality data of the at least one location to be monitored.

[0108] In an embodiment of the present invention, the rail transit includes at least one location to be monitored. For any location to be monitored, the air quality monitoring terminal can collect air quality data of at least one collection time. The cloud platform can use a single factor analysis method to group and compare the air quality data of multiple locations to be monitored according to different locations, and determine whether there are significant differences between the air quality data of multiple locations to be monitored. Specifically, for any location to be monitored, the cloud platform can obtain the mean of the air quality data of at least one collection time. Based on the mean, the cloud platform can obtain the first variance and the second variance between the air quality data of at least one collection time corresponding to at least one location to be monitored. Based on the first variance and the second variance, the cloud platform can determine whether there are differences between the air quality data of at least one location to be monitored.

[0109] In a specific example, the positions to be monitored include the head, middle and tail of the carriage. The air quality data refers to the CO2 concentration. For any position to be monitored, the cloud platform can calculate the mean of the CO2 concentration at at least one acquisition time, and obtain the mean of all CO2 concentrations at the head of the carriage, the mean of all CO2 concentrations in the middle of the carriage, and the mean of all CO2 concentrations at the tail of the carriage. Then, the square of the difference between each CO2 concentration at the head of the carriage and the mean of all CO2 concentrations at the head of the carriage is calculated, the square of the difference between each CO2 concentration in the middle of the carriage and the mean of all CO2 concentrations in the middle of the carriage is calculated, and the square of the difference between each CO2 concentration at the tail of the carriage and the mean of all CO2 concentrations at the tail of the carriage is calculated. Add the squares of the differences between all CO2 concentrations at the head and the mean to obtain the sum of squares at the head; add the squares of the differences between all CO2 concentrations in the middle and the mean to obtain the sum of squares in the middle; add the squares of the differences between all CO2 concentrations at the tail and the mean to obtain the sum of squares at the tail. Add the sum of the squares of the head, middle, and tail, and use the summed value to calculate the first variance. The first variance is also called the within-group variance, which is the average level of CO2 concentration differences at each location to be monitored. The calculation formula for the first variance is:

[0110]

[0111] Among them, MS within is the first variance, WSS is the sum of the squares of the head, middle and tail, and k is the number of locations to be monitored, which is 3.

[0112] The cloud platform can also calculate the total mean of the CO2 concentrations at all monitored locations, and then calculate the first square of the difference between the mean of all CO2 concentrations at the head of the car and the total mean, calculate the second square of the difference between the mean of all CO2 concentrations in the middle of the car and the total mean, and calculate the third square of the difference between the mean of all CO2 concentrations at the rear of the car and the total mean. Then, calculate the first product of the first square and the number of numerical values ​​of the CO2 concentration at the head of the car, calculate the second product of the second square and the number of numerical values ​​of the CO2 concentration in the middle of the car, and calculate the third product of the third square and the number of numerical values ​​of the CO2 concentration at the rear of the car. Add the first product, the second product, and the third product, and use the value obtained after the addition to calculate the second variance. The second variance is also called the between-group variance. The calculation formula for the second variance is:

[0113]

[0114] Among them, MS between is the second variance, BSS is the value obtained by adding the first product, the second product, and the third product, and k is the number of locations to be monitored, which is 3. If the second variance is significantly greater than the first variance, this indicates that there is a significant difference in the mean CO2 concentration of different locations to be monitored.

[0115] In some embodiments of the present invention, when there are differences between the air quality data of at least one of the locations to be monitored, the cloud platform is used to obtain the standard error between the air quality data of the at least one location to be monitored based on the first variance; obtain the minimum significant difference value between the air quality data of the at least one location to be monitored based on the standard error; determine the target location to be monitored where there are differences in the air quality data based on the minimum significant difference value; analyze the target location to be monitored to obtain at least one influencing factor causing the difference; the influencing factors include the passenger flow data of the target location to be monitored and / or the operating status data of the air optimization equipment around the target location to be monitored.

[0116] In an embodiment of the present invention, when there is a difference between the air quality data of at least one position to be monitored, the cloud platform can use the minimum significant difference method to analyze and determine the influencing factors affecting the air quality. Based on the first variance, the standard error between the air quality data of at least one position to be monitored is obtained. Then, based on the standard error, the cloud platform can obtain the minimum significant difference value between the air quality data of at least one position to be monitored. Based on the minimum significant difference value, the cloud platform can determine two target positions to be monitored where there are differences in the air quality data in at least one position to be monitored. The target position to be monitored is analyzed, and the cloud platform can obtain at least one influencing factor causing the difference. Specifically, the cloud platform can use methods such as the control variable method to analyze whether different factors will affect the air quality of the position to be monitored to determine the influencing factors causing the difference. The influencing factors include the passenger flow data of the target position to be monitored and / or the operating status data of the air optimization equipment around the target position to be monitored. In addition, the cloud platform can also compare the difference in the mean of different positions to be monitored according to the minimum significant difference value to analyze the degree of influence of each air quality data on the air quality.

[0117] In a specific example, the positions to be monitored include the head, middle and tail of the car. The air quality data refers to the CO2 concentration. In the case where there is a difference between the CO2 concentrations at the head, middle and tail of the car, the cloud platform can obtain the standard error between the CO2 concentrations at the head, middle and tail of the car based on the first variance. The number of data on the CO2 concentrations at the head, middle and tail of the car is the same. The calculation formula for the standard error is:

[0118] SE=√(s 2 / (n·k))

[0119] Among them, SE is the standard error, s 2is the first variance, n is the number of data on CO2 concentration at the head, middle or tail of the carriage, and k is the number of locations to be monitored, which is 3.

[0120] The cloud platform can calculate the minimum significant difference value between the air quality data of at least one monitored location based on the standard error and using the calculation formula of the minimum significant difference value. The calculation formula of the minimum significant difference value is:

[0121] LSD=t·SE

[0122] Where LSD is the least significant difference, and t is the critical value found from the distribution table based on the degrees of freedom and the preset significance level. The significance level can be 0.05.

[0123] For any two monitored positions in the head, middle and tail of the car, if the difference between the means of the two monitored positions is greater than the minimum significant difference value, the cloud platform confirms that there is a significant difference between the two monitored positions; if the difference between the means of the two monitored positions is not greater than the minimum significant difference value, the cloud platform confirms that there is no significant difference between the two monitored positions.

[0124] In some embodiments of the present invention, the air quality optimization system further comprises a gateway; the gateway is connected to the air quality monitoring terminal by a preset long-distance radio communication technology;

[0125] The air quality monitoring terminal is used to send the air quality data to the gateway;

[0126] The gateway is used to determine whether the air quality data is greater than a preset air quality threshold; when the air quality data is greater than the air quality threshold, an alarm is issued through a preset sound and light alarm.

[0127] In an embodiment of the present invention, the air quality optimization system further includes a gateway; the gateway and the air quality monitoring terminal are connected to each other through a preset long-distance radio communication technology. The air quality monitoring terminal can send air quality data to the gateway, and the gateway can determine whether the air quality data is greater than a preset air quality threshold, and when the air quality data is greater than the air quality threshold, an alarm is issued through a preset sound and light alarm.

[0128] In some embodiments of the present invention, the gateway and the cloud platform are connected in communication via a preset narrowband Internet of Things communication technology;

[0129] The gateway is used to send the air quality data to the cloud platform;

[0130] The cloud platform is used to generate a three-dimensional image of the location to be monitored, and send the three-dimensional image and the air quality data to a preset user terminal.

[0131] In the embodiment of the present invention, the gateway and the cloud platform are connected by a preset narrowband Internet of Things communication technology. The gateway can send the air quality data to the cloud platform, and the cloud platform can generate a three-dimensional image of the location to be monitored, and send the three-dimensional image and the air quality data to a preset user terminal.

[0132] Reference Figure 2 , shows a structural block diagram of another air quality optimization system in rail transit provided in an embodiment of the present invention.

[0133] Figure 2 In the figure, number 1 indicates a subway station, number 2 indicates a subway vehicle, number 3 indicates a gateway, and number 4 indicates an air quality monitoring terminal. Therefore, the deployment of the air quality optimization system is divided into three levels: the first level is the cloud platform, which is mainly deployed on a public cloud or private cloud server, and the cloud platform is also connected to the air optimization equipment; the second level is the gateway, which is deployed on a vehicle or station, and only one gateway is needed for one vehicle or station; the third level is the air quality monitoring terminal. According to the actual scenario, multiple air quality monitoring terminals are usually deployed, which are mainly installed in each compartment of the vehicle or in various areas of the subway station.

[0134] The gateway and the cloud platform communicate through NB-IoT (Narrow Band Internet of Things) technology, and the air quality monitoring terminal is connected to the gateway through LoRa (Long Range Radio) wireless communication technology, thus forming a hybrid network communication system as a whole. Among them, NB-IoT is a standard for low-power wide-area (LPWA) network technology, which is based on cellular technology. This technology is mainly suitable for the low-power Internet of Things market and is an emerging technology that can be widely used worldwide. LoRa is a long-distance radio transmission technology based on spread spectrum technology. Its biggest advantages are low power consumption, wide signal coverage and strong penetration. LoRa has strong anti-interference ability and is particularly suitable for rail transportation environments such as subways.

[0135] Reference Figure 3, shows a structural block diagram of another air quality optimization system in rail transit provided in an embodiment of the present invention. The air quality optimization system includes an ambient air quality monitoring intelligent terminal 100, an ambient air quality monitoring intelligent gateway 200, and an ambient air quality observation cloud platform 300. Among them, the ambient air quality monitoring intelligent terminal is the air quality monitoring terminal, the ambient air quality monitoring intelligent gateway is the gateway, and the ambient air quality observation cloud platform is the cloud platform.

[0136] The air quality monitoring terminal includes a power module 107, a clock synchronization module 106, a control module 104, a memory module 105, a sensor hub module 102, a sensor module 103 and a communication module 101. The gateway includes a display module 201, a northbound communication module 202, a southbound communication module 203, a collection module 209, a data processing module 206, a data temporary storage module 208, a data upload module 205, a data analysis module 207, an alarm notification 204 and a power module 210. The cloud platform includes an Internet of Things base 301, a data collection module 302, a data processing module 303, a data storage module 304, a data analysis module 305, a data display module 306 and a device linkage module 307.

[0137] The communication module of the air quality monitoring terminal and the southbound communication module of the gateway are connected through LoRa wireless communication technology. When the air quality monitoring terminal and the gateway are powered on, the communication module and the southbound communication module will automatically perform the addressing and networking process, automatically allocate or determine the address in the network, and form a network without external intervention. Once the networking is completed, the communication module and the southbound communication module can communicate independently without external control or configuration. The air quality monitoring terminal and the gateway communicate through the communication module and the southbound communication module.

[0138] After the air quality monitoring terminal is powered on, the clock will be synchronized to obtain the collection frequency and collection period of the air quality data collected by the air quality monitoring terminal from the cloud platform.

[0139] The clock synchronization module can be connected to the Beidou clock system at regular intervals to synchronize time, ensuring that all air quality monitoring terminals are in step with each other without deviation.

[0140] The control module is mainly composed of an MCU (Microcontroller Unit) control module. The control module can control clock synchronization, control the communication module to send air quality data and instructions, and control the sensor module to collect air quality data according to the collection cycle and collection frequency.

[0141] The control module can also convert the data format collected by the sensor module, convert the binary data into recognizable air index values, and encapsulate the recognizable air index values ​​into data frames using the LoRaWAN protocol, and send the data frames to the gateway through the communication module. Encapsulating the data frames using the LoRaWAN protocol means packaging the collected data according to specific formats and rules to meet the requirements of the LoRaWAN protocol.

[0142] The control module can enter the sleep state during the non-operation period of rail transit according to the preset sleep instruction. The memory module is used to store data such as device information, collection frequency, collection cycle, device location information, networking information and sleep setting time.

[0143] The sensor hub module is responsible for connecting the sensor modules and realizing the pluggable function of various sensors, allowing users to freely select or replace sensors as needed. Sensor types include: temperature sensor, humidity sensor, smoke concentration sensor, harmful gas detection sensor, PM2.5 sensor and PM10 sensor. Specifically, the temperature sensor is used to monitor the temperature of a certain area in real time, the humidity sensor is used to monitor the humidity in real time, the smoke concentration sensor is used to monitor the smoke concentration in real time, the harmful gas detection sensor is used to detect the types and contents of various harmful gases in the air, the PM2.5 sensor is used to monitor the concentration value of PM2.5 in real time, and the PM10 sensor is used to monitor the concentration value of PM10 in real time.

[0144] The sensor hub module is a device that acts as a central node to manage and connect multiple sensor modules. It provides a physical and electrical interface so that sensor modules can be plugged in or out at any time without powering off or performing complex configurations.

[0145] The southbound communication module is responsible for wireless communication with the air quality monitoring terminal and receiving the air quality data sent from the air quality monitoring terminal. The southbound communication module can verify the legitimacy of the received data frame, parse the data, distinguish the type of collected data, and perform corresponding conversion processing.

[0146] The acquisition module is responsible for receiving the air quality data sent by the air quality monitoring terminal and storing, processing or statistically analyzing the data. The processed data will be stored in the data temporary storage module or directly output to the display module.

[0147] The data storage module is used to store short-term (can be set to 30 days, 15 days, 7 days, etc.) air quality data, historical data and processed data.

[0148] The data upload module synchronously transmits the collected air quality data to the cloud platform through the northbound communication module.

[0149] When the air quality data does not meet the preset air conditions, the alarm notification module will issue a mild alarm or a severe alarm according to the standard range, and remind the staff through the sound and light alarm.

[0150] The display module can display the overall environmental conditions of subway stations or vehicles and the real-time data collected by each monitoring node device on the display screen, and also display the statistical analysis report of recent data. In addition, the display module can also display the ambient air quality monitoring board through other display terminals, such as large-screen devices or tablet computers, to facilitate staff observation.

[0151] The main functions of the IoT base include device registration, device authentication, device security, device connection, device object model management, and device full life cycle management.

[0152] The data acquisition module can receive air quality data sent by gateways from various stations and vehicles through the MQTT protocol.

[0153] The cloud platform can collect data in real time and online by using the Internet of Things technology to monitor and remotely control the equipment. Specifically, the cloud platform first needs to create the device model of the gateway and air quality monitoring terminal through the Internet of Things base and configure the device parameters. Each device has a unique ID (identity) for identity comparison with the connected device (including smart gateways and smart terminals). After the physical model is created, the cloud platform will configure the device parameters, such as the sensor's operating range, data reporting frequency, alarm threshold, etc., to ensure that the device works as expected. The registration process involves adding the device to the cloud platform's device management system, collecting basic information about the device, such as device model, manufacturer, serial number, etc., and assigning a unique identity ID to each device. When the device is connected to the cloud platform, this ID will be used to verify the device identity to ensure that only registered devices can access the system and prevent unauthorized devices from accessing.

[0154] When the gateway or air quality monitoring terminal is powered on and connected to the Internet, the cloud platform will perform security authentication. Only gateways or air quality monitoring terminals that have passed security authentication can connect to the cloud platform.

[0155] In the full life cycle management of IoT base devices, it can be observed that the device connection status changes from offline to online. At this time, the gateway or air quality monitoring terminal is in operation and can obtain real-time collected air quality data.

[0156] The data acquisition module is interconnected with the gateway through NB-IoT communication technology, and uses the MQTT communication protocol for data exchange, receiving data sent from each subway station or vehicle gateway.

[0157] The data processing module performs real-time calculations on the collected air quality data, which is used to display dynamic analysis charts of air quality indicators in real time, and simultaneously stores the data in the data storage module. The data analysis module performs multi-dimensional analysis (such as by time, station, train, line, etc.) and correlation analysis (such as the relationship between subway passenger flow and changes in ambient air quality).

[0158] The data display module can be used to observe the real-time air quality status and statistical analysis reports of each station, vehicle, and even each point, and combine 3D technology to enhance the display effect and improve intuitiveness.

[0159] The equipment linkage module monitors the sample data reported by the intelligent terminal of environmental air quality monitoring. When the temperature and air quality reach the trigger linkage threshold, the platform will automatically adjust the air-conditioning temperature and air volume, the gear of the fresh air fan in the carriage or station, and start the air purification equipment.

[0160] Based on the above modules included in the air quality monitoring terminal, gateway and cloud platform, these three parts can respectively achieve the following functions:

[0161] The air quality monitoring terminal is responsible for collecting air quality data at subway stations and vehicle locations to be tested, and is deployed in various areas of the subway and locations to be monitored in the carriages as needed. After the air quality monitoring terminal is powered on, the device will be initialized. The clock synchronization module needs to synchronize the clock with the clock service system, and will synchronize regularly to prevent clock deviation and ensure the time synchronization of all air quality monitoring terminals, thereby ensuring the accuracy of the collected air quality data time.

[0162] The gateway receives air quality data collected by air quality monitoring terminals at various locations to be monitored, stores them locally for a short period of time, and analyzes the data in real time to generate real-time or recent air quality monitoring reports. The gateway can display these reports in the form of reports and transmit them to the mobile phones, pads or computers of subway station staff. The report includes the overall air quality status, the monitoring status of each area, whether each air quality indicator is normal and whether it exceeds the preset threshold, and also includes real-time air quality curves and historical curve analysis charts. The gateway does not have the ability to store massive data, and only retains data for a preset number of days (configurable to 30 days), and regularly clears expired data.

[0163] The gateway also transmits the air quality data collected by multiple air quality monitoring terminals at a subway station or vehicle to the cloud platform synchronously. The cloud platform can display the data in real time and process it, including multi-dimensional analysis and correlation analysis. The scene model built with 3D (three-dimensional) technology can more intuitively display the air quality status in rail transit.

[0164] The data collection strategy of the air quality monitoring terminal is obtained by the gateway from the cloud platform. The specific operation is that the air quality monitoring terminal sends an air quality data collection strategy request to the gateway, and the gateway forwards the request to the cloud platform. After receiving the request, the cloud platform puts it into the queue for processing, and sends the processed air quality data collection strategy to the air quality monitoring terminal through the gateway. The air quality monitoring terminal saves the received collection strategy to the memory and collects air quality data according to the strategy.

[0165] If the cloud platform modifies the air quality data collection strategy, the cloud platform can send batch instructions to the gateway. The gateway receives these instructions and forwards them to all air quality monitoring terminals in the domain (referring to a station or vehicle). The air quality monitoring terminal can send a synchronization collection strategy request to obtain the modified air quality data collection strategy from the cloud platform to ensure that the data collection strategy executed by the air quality monitoring terminal is consistent with the settings on the cloud platform.

[0166] When the gateway is powered on, it sends a warning threshold synchronization request to the cloud platform. After receiving the request, the cloud platform puts it into a queue for processing and sends the preset thresholds of the air quality data to the gateway. The gateway can store these preset thresholds in the local database. If the preset thresholds on the cloud platform change, all gateways will be notified, and the gateway can send a request to the cloud platform again to obtain the updated preset thresholds.

[0167] The gateway can display the air quality data sent by the air quality monitoring terminal on the preset screen in real time and analyze the data in real time. If the data exceeds the preset threshold, the gateway will display it in different colors according to different warning levels, and in serious cases, it will alert the staff through the sound and light alarm. In addition, the gateway can also synchronize the alarm information to the cloud platform in real time, and the cloud platform will then send the alarm information to the user terminal and the large screen in the rail transit environment.

[0168] After the gateway aggregates and stores the air quality data in the local database, it can also forward the data to the cloud platform in real time through the MQTT (Message Queuing Telemetry Transport) protocol.

[0169] The IoT base is mainly responsible for the registration, connection, communication and management of IoT devices, and is a key component for realizing the IoT of devices. The data acquisition module is based on the IoT base and is used to collect air quality data sent from each gateway. The data storage module has a large amount of data storage capacity and can store the data collected by the air quality monitoring terminal for a long time. The data processing module is responsible for processing, processing, summarizing and statistics of air quality data, and performing real-time and offline calculations. The data analysis module can analyze air quality data according to dimensions such as subway lines, subway stations, vehicles, regions, and time (year, month, day, hour, minute, 7 days, 30 days), and store the analysis results in a multidimensional model; at the same time, access passenger flow data to establish an association analysis between subway stations or vehicles and changes in passenger flow (weekdays, holidays, morning and evening peaks). The data display module can display the air quality situation and data analysis results in real time through large-screen devices and user terminals in the rail transit environment, and display the data collected by the terminal in real time, including the temperature, humidity, PM2.5, PM10, toxic gases and other detection index values ​​at the station or vehicle to be monitored. The air quality status and data analysis results can be refreshed regularly, with a refresh cycle of 5 seconds or 10 seconds. Users can also configure the gateway and air quality monitoring terminal on the user terminal through the web interface.

[0170] The data display module can use 3D (three-dimensional) modeling technology to visualize subway stations and vehicle scenes, and intuitively display the locations of all air quality monitoring terminals and gateways in the 3D model. Users can interact with the 3D model through the user terminal and view the air quality data collected by the air quality monitoring terminal.

[0171] When the air quality data of the monitored location exceeds the preset threshold, the equipment linkage module will analyze and make decisions to determine whether it is necessary to adjust the operating status of air-conditioning, fresh air fans, air purifiers and other air-optimization equipment around the monitored location. It should be noted that these air-optimization equipment have remote control functions.

[0172] In a specific example, if the real-time calculation results of the cloud platform show that a certain air quality index at the location to be monitored exceeds the preset threshold, the device linkage module will obtain the ID (identification number) of the air quality monitoring device at the location to be monitored to determine the location and area information. Subsequently, the device linkage module will send a linkage instruction to the air optimization equipment such as the fresh air fan around the location to be monitored. After receiving the instruction, the air optimization equipment will start running to promote air circulation around the location to be monitored so that the air quality reaches the normal range. When the values ​​of various indicators of the air environment around the location to be monitored are continuously controlled within the standard range, the linkage module of the cloud platform will decide whether to start the device linkage mechanism based on the calculation results of the air quality data. If the linkage mechanism is started, the device linkage module will issue instructions to the air optimization equipment around the location to be monitored, and use the fresh air fan, air conditioner and other equipment in a linked manner by suspending the operation of the fresh air fan, etc., to save energy and achieve the goal of reducing costs and increasing efficiency.

[0173] In an embodiment of the present invention, an air quality optimization system in rail transit includes: an air quality monitoring terminal, a cloud platform and at least one air optimization device; the air quality monitoring terminal is used to collect air quality data of at least one location to be monitored in rail transit, and send the air quality data and the collection time of the air quality data to the cloud platform; the cloud platform stores a first relationship between passenger flow data and time at the location to be monitored; the air optimization device is used to send the operating status data of the air optimization device to the cloud platform; the cloud platform is used to determine the target passenger flow data of the location to be monitored at the collection time based on the first relationship; determine the target operating status data of the target air optimization device around the location to be monitored at the collection time based on the operating status data; based on the target passenger flow data, air quality data and target operating status data, obtain the second relationship between the air quality data and the target passenger flow data and the target operating status data; based on the second relationship, predict the predicted air quality data of any location to be monitored at the current time; when the predicted air quality data does not meet the preset air conditions, adjust the operating status of the air optimization device to optimize the air quality of the location to be monitored. The cloud platform can obtain air quality data collected by air quality monitoring terminals deployed at multiple locations to be monitored, realizing centralized digital monitoring; at the same time, based on the target passenger flow data, air quality data of the locations to be monitored, and the target operating status data of the target air optimization equipment around the locations to be monitored, the cloud platform can obtain the second relationship between the air quality data and the target passenger flow data and the target operating status data. Based on the second relationship, the cloud platform can predict the predicted air quality data of any location to be monitored at the current time; when the predicted air quality data does not meet the preset air conditions, the operating status of the air optimization equipment is adjusted to optimize the air quality of the locations to be monitored, thereby realizing decision-making based on air quality data, controlling equipment such as fans, air conditioners and purifiers, purifying the air in the rail transit environment and saving the energy consumption of the equipment.

[0174] In an embodiment of the present invention, the air quality optimization system of rail transit may include: an air quality monitoring terminal, a gateway, and a cloud platform. The air quality monitoring terminal is connected to the gateway through LoRa wireless communication technology, and the gateway is connected to the cloud platform through NB-IoT Internet of Things technology. The air quality monitoring terminal can be deployed at multiple points, and the gateway can be used as a transfer station between the cloud platform and the air quality monitoring terminal to achieve data aggregation, upload, and command issuance. The present invention monitors the air quality in the rail transit environment in real time through the cloud platform, edge gateway, and terminal at three levels, and transfers the computing power from a single device of the terminal to the cloud platform through the integrated deployment of cloud, edge, and terminal, solving the problem of high installation cost of air quality monitoring and analysis equipment at multiple points, and improving the intelligent and digital management level of environmental air quality detection in the urban rail transit industry.

[0175] The air quality optimization system in the embodiment of the present invention includes an air quality monitoring terminal, a gateway and a cloud platform, which has the advantages of lightweight, intelligent and low-cost terminal equipment. The air quality monitoring terminal is connected to the gateway through LoRa wireless communication technology, and the gateway is connected to the cloud platform through NB-IoT Internet of Things technology. It has the characteristics of long distance, low power consumption, reliable transmission, strong penetration, etc., and is suitable for the complex environment of rail transit. The present invention realizes the interconnection between the air quality monitoring terminal and the cloud platform through the Internet of Things technology, improves the overall management and control capabilities of the data collected by the air quality monitoring terminal, and fully taps the data value through the computing power of the cloud platform, real-time data calculation, offline calculation, multi-dimensional analysis, correlation analysis and other technologies, providing a scientific basis and reasonable suggestions for the air environmental sanitation work of rail transit.

[0176] In an embodiment of the present invention, the air quality monitoring terminal is mainly installed at the location to be monitored, such as subway entrances A, B, C, D, elevators, station halls, platforms, toilets, the head, tail, and middle of the carriage, etc. The air quality monitoring terminal connects to the gateway via a wireless network and transmits the collected air quality data to the gateway. The gateway is responsible for obtaining the data collected by the air quality monitoring terminals from each location to be monitored, storing the data in a local database, and performing statistics, analysis, and processing to generate an air monitoring analysis report for a subway station or a certain vehicle, and issuing a voice or photoelectric alarm for data that exceeds a preset threshold limit. The gateway can also synchronously transmit the data collected by the air quality monitoring terminal to the cloud platform, which can process, store, and statistically analyze the data, and display the ambient air quality status and analysis reports of subway stations and vehicles in real time, providing multi-dimensional analysis and correlation analysis. Staff can view the ambient air quality real-time and analysis dashboard through Web pages, mobile phones, or Pads.

[0177] Reference Figure 4 , shows a flow chart of the steps of an air quality optimization method in rail transit provided in an embodiment of the present invention, which is applied to an air quality optimization system in rail transit, and the air quality optimization system in rail transit includes: an air quality monitoring terminal, a cloud platform and at least one air optimization device; specifically, it may include the following steps:

[0178] Step 401, using the air quality monitoring terminal to collect air quality data of at least one location to be monitored in the rail transit, and sending the air quality data and the collection time of the air quality data to the cloud platform; the cloud platform stores a first relationship between the passenger flow data at the location to be monitored and the time;

[0179] Step 402, using the air optimization device to send the operating status data of the air optimization device to the cloud platform;

[0180] Step 403, based on the first relationship, using the cloud platform to determine the target passenger flow data of the location to be monitored at the collection time;

[0181] Step 404, determining target operating status data of the target air optimization equipment around the location to be monitored at the collection time using the cloud platform according to the operating status data;

[0182] Step 405, based on the target passenger flow data, the air quality data and the target operating status data, using the cloud platform to obtain a second relationship between the air quality data and the target passenger flow data and the target operating status data;

[0183] Step 406, based on the second relationship, use the cloud platform to predict the predicted air quality data of any of the monitored locations at the current time, and when the predicted air quality data does not meet the preset air conditions, adjust the operating state of the air optimization device to optimize the air quality of the monitored location.

[0184] In any of the above embodiments, step 406 includes:

[0185] Sub-step S11, based on the first relationship, using the cloud platform to determine the passenger flow data to be processed at the location to be monitored at the current time;

[0186] Sub-step S12, determining the to-be-processed operating status data of the target air optimization device at the current time using the cloud platform according to the operating status data;

[0187] Sub-step S13, based on the second relationship, the passenger flow data to be processed and the operating status data to be processed, using the cloud platform to determine the predicted air quality data of the location to be monitored at the current time.

[0188] In any of the above embodiments, step 401 includes:

[0189] Sub-step S21, for any of the locations to be monitored, using the air quality monitoring terminal to collect air quality data for at least one collection time;

[0190] The method comprises:

[0191] Sub-step S31, using the cloud platform to obtain the mean of the air quality data;

[0192] Sub-step S32, based on the mean, using the cloud platform to obtain a first variance and a second variance between the air quality data of the at least one location to be monitored;

[0193] Sub-step S33: Based on the first variance and the second variance, using the cloud platform to determine whether there is a difference between the air quality data of the at least one monitored location.

[0194] In any of the above embodiments, the method includes:

[0195] Sub-step S41, when there is a difference between the air quality data of the at least one location to be monitored, using the cloud platform to obtain the standard error between the air quality data of the at least one location to be monitored based on the first variance;

[0196] Sub-step S42, based on the standard error, using the cloud platform to obtain the minimum significant difference value between the air quality data of the at least one location to be monitored;

[0197] Sub-step S43, based on the minimum significant difference value, using the cloud platform to determine the target monitored location where there is a difference in air quality data, and analyzing the target monitored location to obtain at least one influencing factor causing the difference; the influencing factors include the passenger flow data of the target monitored location and / or the operating status data of the air optimization equipment around the target monitored location.

[0198] In any of the above embodiments, the air quality optimization system further includes a gateway; the gateway and the air quality monitoring terminal are connected to each other through a preset long-distance radio communication technology; step 401 includes:

[0199] Sub-step S51, using the air quality monitoring terminal to send the air quality data to the gateway;

[0200] The method comprises:

[0201] Sub-step S61, using the gateway to determine whether the air quality data is greater than a preset air quality threshold, and when the air quality data is greater than the air quality threshold, an alarm is issued through a preset sound and light alarm.

[0202] In any of the above embodiments, the gateway and the cloud platform are connected to each other through a preset narrowband Internet of Things communication technology; step 401 includes:

[0203] Sub-step S71, using the gateway to send the air quality data to the cloud platform;

[0204] The method comprises:

[0205] Sub-step S81, using the cloud platform to generate a three-dimensional image of the location to be monitored, and sending the three-dimensional image and the air quality data to a preset user terminal.

[0206] As for the method embodiment, since it is basically similar to the system embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the system embodiment.

[0207] It should be noted that, for the sake of simplicity, the method embodiments are described as a series of action combinations, but those skilled in the art should be aware that the embodiments of the present invention are not limited by the order of the actions described, because according to the embodiments of the present invention, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily required by the embodiments of the present invention.

[0208] Reference Figure 5 , shows a structural block diagram of an air quality optimization device in rail transit provided in an embodiment of the present invention, which is applied to an air quality optimization system in rail transit, and the air quality optimization system in rail transit includes: an air quality monitoring terminal, a cloud platform, and at least one air optimization device; specifically, it may include the following modules:

[0209] The collection module 501 is used to collect air quality data of at least one location to be monitored in the rail transit by using the air quality monitoring terminal, and send the air quality data and the collection time of the air quality data to the cloud platform; the cloud platform stores the first relationship between the passenger flow data and time at the location to be monitored;

[0210] A sending module 502 is used to send the operating status data of the air optimization device to the cloud platform using the air optimization device;

[0211] A target passenger flow data determination module 503, configured to determine the target passenger flow data of the location to be monitored at the collection time using the cloud platform based on the first relationship;

[0212] A target operating state data determination module 504 is used to determine the target operating state data of the target air optimization device around the location to be monitored at the collection time using the cloud platform according to the operating state data;

[0213] A second relationship acquisition module 505, for acquiring, based on the target passenger flow data, the air quality data and the target operating status data, a second relationship between the air quality data and the target passenger flow data and the target operating status data using the cloud platform;

[0214] The prediction module 506 is used to predict the predicted air quality data of any of the monitored locations at the current time based on the second relationship using the cloud platform, and to adjust the operating state of the air optimization device to optimize the air quality of the monitored location when the predicted air quality data does not meet the preset air conditions.

[0215] In an optional embodiment of the present invention, the prediction module includes:

[0216] A passenger flow data to be processed determination submodule, configured to determine the passenger flow data to be processed at the location to be monitored at the current time using the cloud platform based on the first relationship;

[0217] A submodule for determining the operating status data to be processed, used to determine the operating status data to be processed of the target air optimization device at the current time using the cloud platform according to the operating status data;

[0218] The predicted air quality data determination submodule is used to determine the predicted air quality data of the monitored location at the current time using the cloud platform based on the second relationship, the passenger flow data to be processed and the operating status data to be processed.

[0219] In an optional embodiment of the present invention, the acquisition module includes:

[0220] A collection submodule, configured to collect air quality data of at least one collection time using the air quality monitoring terminal for any of the locations to be monitored;

[0221] The device comprises:

[0222] A mean value acquisition module, used for acquiring the mean value of the air quality data using the cloud platform;

[0223] A variance acquisition module, configured to acquire, based on the mean, a first variance and a second variance between the air quality data of the at least one location to be monitored using the cloud platform;

[0224] A difference judgment module is used to judge whether there is a difference between the air quality data of the at least one monitored location based on the first variance and the second variance using the cloud platform.

[0225] In an optional embodiment of the present invention, the device comprises:

[0226] A standard error acquisition module, configured to acquire, using the cloud platform based on the first variance, a standard error between the air quality data of the at least one location to be monitored when there is a difference between the air quality data of the at least one location to be monitored;

[0227] A minimum significant difference value acquisition module, used to acquire the minimum significant difference value between the air quality data of the at least one monitored location using the cloud platform based on the standard error;

[0228] A target monitored location determination module is used to determine the target monitored location where there is a difference in air quality data based on the minimum significant difference value using the cloud platform, and analyze the target monitored location to obtain at least one influencing factor causing the difference; the influencing factor includes the passenger flow data of the target monitored location and / or the operating status data of the air optimization equipment around the target monitored location.

[0229] In an optional embodiment of the present invention, the air quality optimization system further includes a gateway; the gateway and the air quality monitoring terminal are connected to each other through a preset long-distance radio communication technology; the acquisition module includes:

[0230] A first sending submodule, configured to send the air quality data to the gateway using the air quality monitoring terminal;

[0231] The device comprises:

[0232] The judgment module is used to use the gateway to judge whether the air quality data is greater than a preset air quality threshold, and when the air quality data is greater than the air quality threshold, issue an alarm through a preset sound and light alarm.

[0233] In an optional embodiment of the present invention, the gateway and the cloud platform are connected to each other through a preset narrowband Internet of Things communication technology; the acquisition module includes:

[0234] A second sending submodule, used to send the air quality data to the cloud platform using the gateway;

[0235] The device comprises:

[0236] A generation module is used to generate a three-dimensional image of the location to be monitored using the cloud platform, and send the three-dimensional image and the air quality data to a preset user terminal.

[0237] As for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0238] In addition, an embodiment of the present invention further provides an electronic device, such as Figure 6As shown, it includes a processor 601, a communication interface 602, a memory 603 and a communication bus 604, wherein the processor 601, the communication interface 602, and the memory 603 communicate with each other through the communication bus 604.

[0239] Memory 603, used for storing computer programs;

[0240] The processor 601 is used to execute the program stored in the memory 603 to implement the following steps:

[0241] Using the air quality monitoring terminal to collect air quality data of at least one location to be monitored in the rail transit, and sending the air quality data and the collection time of the air quality data to the cloud platform; the cloud platform stores a first relationship between the passenger flow data at the location to be monitored and the time;

[0242] Using the air optimization device to send the operating status data of the air optimization device to the cloud platform;

[0243] Based on the first relationship, determining the target passenger flow data of the location to be monitored at the collection time using the cloud platform;

[0244] According to the operating status data, using the cloud platform to determine the target operating status data of the target air optimization equipment around the location to be monitored at the collection time;

[0245] Based on the target passenger flow data, the air quality data, and the target operating status data, using the cloud platform to obtain a second relationship between the air quality data and the target passenger flow data and the target operating status data;

[0246] Based on the second relationship, the cloud platform is used to predict the predicted air quality data of any of the monitored locations at the current time, and when the predicted air quality data does not meet the preset air conditions, the operating state of the air optimization device is adjusted to optimize the air quality of the monitored location.

[0247] In an optional embodiment of the present invention, the predicting of the predicted air quality data of any of the locations to be monitored at the current time using the cloud platform based on the second relationship includes:

[0248] Based on the first relationship, using the cloud platform to determine the passenger flow data to be processed at the location to be monitored at the current time;

[0249] According to the operating status data, using the cloud platform to determine the operating status data to be processed of the target air optimization device at the current time;

[0250] Based on the second relationship, the passenger flow data to be processed and the operating status data to be processed, the predicted air quality data of the location to be monitored at the current time is determined using the cloud platform.

[0251] In an optional embodiment of the present invention, the collecting air quality data of at least one to-be-monitored location in the rail transit by using the air quality monitoring terminal includes:

[0252] For any of the locations to be monitored, using the air quality monitoring terminal to collect air quality data of at least one collection time;

[0253] The method comprises:

[0254] Using the cloud platform to obtain the mean value of the air quality data;

[0255] Based on the mean, using the cloud platform to obtain a first variance and a second variance between the air quality data of the at least one location to be monitored;

[0256] Based on the first variance and the second variance, the cloud platform is used to determine whether there is a difference between the air quality data of the at least one location to be monitored.

[0257] In an optional embodiment of the present invention, the method includes:

[0258] In the case where there is a difference between the air quality data of the at least one location to be monitored, using the cloud platform to obtain a standard error between the air quality data of the at least one location to be monitored based on the first variance;

[0259] Based on the standard error, using the cloud platform to obtain a minimum significant difference value between the air quality data of the at least one location to be monitored;

[0260] Based on the minimum significant difference value, the cloud platform is used to determine the target monitored location where there is a difference in air quality data, and the target monitored location is analyzed to obtain at least one influencing factor causing the difference; the influencing factors include the passenger flow data of the target monitored location and / or the operating status data of the air optimization equipment around the target monitored location.

[0261] In an optional embodiment of the present invention, the air quality optimization system further includes a gateway; the gateway is connected to the air quality monitoring terminal via a preset long-distance radio communication technology; the air quality data and the collection time of the air quality data are sent to the cloud platform, including:

[0262] Using the air quality monitoring terminal to send the air quality data to the gateway;

[0263] The method comprises:

[0264] The gateway is used to determine whether the air quality data is greater than a preset air quality threshold, and when the air quality data is greater than the air quality threshold, an alarm is sounded through a preset sound and light alarm.

[0265] In an optional embodiment of the present invention, the gateway and the cloud platform are connected to each other through a preset narrowband Internet of Things communication technology; sending the air quality data and the collection time of the air quality data to the cloud platform includes:

[0266] Using the gateway to send the air quality data to the cloud platform;

[0267] The method comprises:

[0268] The cloud platform is used to generate a three-dimensional image of the location to be monitored, and the three-dimensional image and the air quality data are sent to a preset user terminal.

[0269] The communication bus mentioned in the above terminal can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0270] The communication interface is used for communication between the above terminal and other devices.

[0271] The memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0272] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0273] like Figure 7 As shown, in another embodiment provided by the present invention, a computer-readable storage medium 701 is also provided, in which instructions are stored. When the computer-readable storage medium 701 is run on a computer, the computer executes an air quality optimization method in rail transit described in the above embodiment.

[0274] In another embodiment provided by the present invention, a computer program product including instructions is also provided, which, when executed on a computer, enables the computer to execute the method for optimizing air quality in rail transit described in the above embodiment.

[0275] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented by software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state hard disk Solid State Disk (SSD)), etc.

[0276] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0277] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0278] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention are included in the protection scope of the present invention.

Claims

1. An air quality optimization system in rail transit, characterized in that: The air quality optimization system in rail transit includes: an air quality monitoring terminal, a cloud platform and at least one air optimization device; The air quality monitoring terminal is used to collect air quality data of at least one location to be monitored in the rail transit, and send the air quality data and the collection time of the air quality data to the cloud platform; the cloud platform stores a first relationship between the passenger flow data at the location to be monitored and the time; The air optimization device is used to send the operating status data of the air optimization device to the cloud platform; The cloud platform is used to determine the target passenger flow data of the monitored location at the collection time based on the first relationship; determine the target operating status data of the target air optimization equipment around the monitored location at the collection time based on the operating status data; obtain the second relationship between the air quality data and the target passenger flow data and the target operating status data based on the target passenger flow data, the air quality data and the target operating status data; predict the predicted air quality data of any of the monitored locations at the current time based on the second relationship; and adjust the operating status of the air optimization equipment to optimize the air quality of the monitored location if the predicted air quality data does not meet the preset air conditions.

2. The air quality optimization system according to claim 1, characterized in that: The cloud platform is used to determine the passenger flow data to be processed at the location to be monitored at the current time based on the first relationship; Determining, according to the operating status data, the operating status data to be processed of the target air optimization device at the current time; The predicted air quality data of the location to be monitored at the current time is determined based on the second relationship, the passenger flow data to be processed, and the operating status data to be processed.

3. The air quality optimization system according to claim 1, characterized in that: For any of the locations to be monitored, the air quality monitoring terminal is used to collect air quality data of at least one collection time; and obtain a mean value of the air quality data; Based on the mean, a first variance and a second variance between the air quality data of the at least one location to be monitored are obtained; based on the first variance and the second variance, it is determined whether there is a difference between the air quality data of the at least one location to be monitored.

4. The air quality optimization system according to claim 3, characterized in that: In the case where there is a difference between the air quality data of the at least one location to be monitored, the cloud platform is used to obtain a standard error between the air quality data of the at least one location to be monitored based on the first variance; Based on the standard error, obtain the minimum significant difference value between the air quality data of at least one of the monitored locations; based on the minimum significant difference value, determine the target monitored location where there is a difference in air quality data; analyze the target monitored location to obtain at least one influencing factor causing the difference; the influencing factors include the passenger flow data of the target monitored location and / or the operating status data of the air optimization equipment around the target monitored location.

5. The air quality optimization system according to claim 1, characterized in that: The air quality optimization system further includes a gateway; the gateway is connected to the air quality monitoring terminal via a preset long-distance radio communication technology; The air quality monitoring terminal is used to send the air quality data to the gateway; The gateway is used to determine whether the air quality data is greater than a preset air quality threshold; when the air quality data is greater than the air quality threshold, an alarm is issued through a preset sound and light alarm.

6. The air quality optimization system according to claim 5, characterized in that: The gateway and the cloud platform are connected to each other by a preset narrowband Internet of Things communication technology; The gateway is used to send the air quality data to the cloud platform; The cloud platform is used to generate a three-dimensional image of the location to be monitored, and send the three-dimensional image and the air quality data to a preset user terminal.

7. A method for optimizing air quality in rail transit, characterized in that: An air quality optimization system applied to rail transit, the air quality optimization system in rail transit comprising: an air quality monitoring terminal, a cloud platform and at least one air optimization device; the method comprising: Using the air quality monitoring terminal to collect air quality data of at least one location to be monitored in the rail transit, and sending the air quality data and the collection time of the air quality data to the cloud platform; the cloud platform stores a first relationship between the passenger flow data at the location to be monitored and the time; Using the air optimization device to send the operating status data of the air optimization device to the cloud platform; Based on the first relationship, determining the target passenger flow data of the location to be monitored at the collection time using the cloud platform; According to the operating status data, using the cloud platform to determine the target operating status data of the target air optimization equipment around the location to be monitored at the collection time; Based on the target passenger flow data, the air quality data, and the target operating status data, using the cloud platform to obtain a second relationship between the air quality data and the target passenger flow data and the target operating status data; Based on the second relationship, the cloud platform is used to predict the predicted air quality data of any of the monitored locations at the current time, and when the predicted air quality data does not meet the preset air conditions, the operating state of the air optimization device is adjusted to optimize the air quality of the monitored location.

8. An air quality optimization device in rail transit, characterized in that: An air quality optimization system applied to rail transit, the air quality optimization system in rail transit comprising: an air quality monitoring terminal, a cloud platform and at least one air optimization device; the device comprises: A collection module, used for collecting air quality data of at least one to-be-monitored position in the rail transit by using the air quality monitoring terminal, and sending the air quality data and the collection time of the air quality data to the cloud platform; the cloud platform stores a first relationship between the passenger flow data and time at the to-be-monitored position; A sending module, used to send the operating status data of the air optimization device to the cloud platform using the air optimization device; A target passenger flow data determination module, configured to determine, based on the first relationship, the target passenger flow data of the location to be monitored at the collection time using the cloud platform; A target operating state data determination module, configured to determine, based on the operating state data, the target operating state data of the target air optimization equipment around the location to be monitored at the collection time using the cloud platform; A second relationship acquisition module, configured to acquire, based on the target passenger flow data, the air quality data, and the target operating status data, a second relationship between the air quality data and the target passenger flow data and the target operating status data using the cloud platform; A prediction module is used to predict the predicted air quality data of any of the monitored locations at the current time based on the second relationship using the cloud platform, and to adjust the operating state of the air optimization device to optimize the air quality of the monitored location when the predicted air quality data does not meet the preset air conditions.

9. An electronic device, characterized in that: It includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other through the communication bus; The memory is used to store computer programs; The processor is used to implement the method according to claim 7 when executing the program stored in the memory.

10. One or more computer readable media having instructions stored thereon which, when executed by one or more processors, cause the processors to perform the method of claim 7.

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