Artificial Intelligence-Based Indoor Air Quality Monitoring Method and System

Abstract

The present invention discloses an indoor air quality monitoring method and system based on artificial intelligence, which relates to the technical field of air quality monitoring, and includes: obtaining the mainstream gas path and the conventional gas path in the monitored indoor area based on gas tracers; obtaining the quality monitoring points; obtaining the monitoring interval time based on the quality monitoring points; performing air quality monitoring on the monitored indoor area based on artificial intelligence and the artificial intelligence execution method α. The present invention is used to solve the problem that in the existing indoor air quality monitoring methods, it is impossible to obtain the areas with large changes in air quality in the monitored space based on the air circulation in the monitored space, resulting in the inability to flexibly change the placement points of monitoring devices and the monitoring intervals of monitoring devices during air quality monitoring, leading to inaccurate monitoring results.

Description

Technical Field

[0001] The present invention relates to the technical field of air quality monitoring, and specifically to an indoor air quality monitoring method and system based on artificial intelligence. Background Technique

[0002] Air quality monitoring refers to the activity of sampling and measuring pollutants existing in the air at fixed points, continuously or at regular intervals to evaluate the air quality status; its main purpose is to understand the air quality status and provide data support for protecting human health and the environment; the air quality monitoring methods mainly include methods such as unmanned aerial vehicle aerial survey, on-line monitoring instruments, chemical analysis methods, and sensor technology.

[0003] The existing methods for indoor air quality monitoring usually focus on improving the monitoring accuracy of air quality, such as analyzing the density and emission amount of a certain substance emission in the monitoring area and evaluating the overall air quality in the monitoring area through the analysis results, so as to make the monitoring results more accurate. Although this improvement method can monitor the air quality in the monitoring area from the emission data of one substance by taking a point as a guide to an area, and thus improve the accuracy of air quality monitoring in the monitoring area, it cannot obtain the areas with large changes in air quality in the monitoring space based on the air circulation in the monitoring space. As a result, it is impossible to flexibly change the placement points of the monitoring devices and the monitoring intervals of the monitoring devices during air quality monitoring, and further cause the monitoring devices to be unable to accurately obtain effective monitoring data based on the gas flow in the monitoring space, resulting in inaccurate monitoring results. For example, in the patent application with the publication number CN118518824A, an air quality monitoring analysis method and system based on carbon emission analysis are disclosed. This solution is to evaluate and analyze the distribution density of carbon emission sources and statistically analyze the carbon emission amounts in the air quality area to be monitored, and conduct statistical analysis and correlation impact evaluation analysis on the air quality indicators in the air quality area to be monitored, so as to judge the air quality in the air quality area to be monitored. Other improvements in indoor air quality monitoring usually focus on improving the calibration accuracy of monitoring data transmission, and still cannot obtain the areas with large changes in air quality in the monitoring space based on the air circulation in the monitoring space. As a result, it is impossible to flexibly change the placement points of the monitoring devices and the monitoring intervals of the monitoring devices during air quality monitoring, and further cause the monitoring devices to be unable to accurately obtain effective monitoring data based on the gas flow in the monitoring space, resulting in inaccurate monitoring results. In view of this, it is necessary to improve the existing indoor air quality monitoring methods. Summary of the Invention

[0004] The present invention aims to solve at least one of the technical problems in the prior art to some extent. By providing an indoor air quality monitoring method and system based on artificial intelligence, it is used to solve the problem in the existing indoor air quality monitoring methods that it is impossible to obtain the areas with large changes in air quality in the monitoring space based on the air circulation in the monitoring space, resulting in the inability to flexibly change the placement points of monitoring devices and the monitoring intervals of monitoring devices during air quality monitoring. Furthermore, it causes the monitoring devices to be unable to accurately obtain effective monitoring data based on the gas flow in the monitoring space, leading to inaccurate monitoring results.

[0005] To achieve the above object, in the first aspect, the present application provides an indoor air quality monitoring method based on artificial intelligence, including the following steps:

[0006] Obtain the spatial model of the indoor area for air quality detection, denoted as the monitoring spatial model, and denote the indoor area for air quality detection as the monitoring indoor area; Place gas tracers in the monitoring indoor area, and use the air flow analysis method based on the gas tracers to obtain the mainstream gas path and the conventional gas path in the monitoring spatial model;

[0007] Based on the mainstream gas path and the conventional gas path, use a wind speed sensor to detect the air flow velocity in the monitoring indoor area, and obtain the quality monitoring points in the monitoring spatial model; Monitor the air quality in the monitoring indoor area based on the quality monitoring points, and use the interval analysis method based on the monitoring results to obtain the monitoring interval time;

[0008] Input the sensing data of the monitoring spatial model, the air flow analysis method, and the wind speed sensor into artificial intelligence, and use artificial intelligence to execute method α. Based on artificial intelligence and artificial intelligence execution method α, monitor the air quality in the monitoring indoor area.

[0009] Furthermore, placing gas tracers in the monitoring indoor area includes:

[0010] Obtain the gas tracers that are allowed to be placed in the monitoring indoor area based on the spatial environment in the monitoring indoor area, and denote the device for monitoring the gas tracers as the tracer detection device;

[0011] Establish a spatial coordinate system with the unit of m for the X-axis, Y-axis, and Z-axis, denoted as the monitoring analysis coordinate system; Place the monitoring spatial model into the monitoring analysis coordinate system;

[0012] Based on the state when the monitoring indoor area is used by people, open the doors and windows of the monitoring indoor area, and mark the opened doors and windows in the monitoring spatial model; Place the tracer detection device in the monitoring indoor area, and release gas tracers in the monitoring indoor area, where the position where the gas tracers are released is denoted as the release position.

[0013] Furthermore, the air flow analysis method includes:

[0014] Based on the detection results of the gas tracer by the missing detection device, obtain the diffusion area of the gas tracer in the monitoring space model, and denote it as the gas diffusion area;

[0015] Denote the straight line connecting the release position and the point farthest from the release position in the gas diffusion area as the fastest diffusion straight line; Denote the door or window closest to the fastest diffusion straight line in the monitoring space model as the gas influence point; Take the gas influence point as the center of the sphere, and the length between the gas influence point and the point farthest from the release position in the gas diffusion area as the radius to make a sphere, and denote it as the gas influence sphere;

[0016] Based on the tracer detection device, monitor the gas tracer in the area where the gas influence sphere coincides with the gas diffusion area, and denote the area with the fastest moving speed of the gas tracer as the fastest influence area, and denote the connection line between the fastest influence area and the gas influence point as the mainstream gas path.

[0017] Furthermore, the air flow analysis method further includes:

[0018] Denote the number of independent areas where the gas influence sphere coincides with the gas diffusion area as n; Based on the tracer detection device, divide the n areas with relatively fast moving speeds of the gas tracer in the gas diffusion area except the fastest influence area into conventional influence areas;

[0019] For any conventional influence area, denote the door or window closest to the conventional influence area in the monitoring space model as the conventional influence point, and denote the connection line between the conventional influence area and the conventional influence point as the conventional gas path.

[0020] Furthermore, based on the mainstream gas path and the conventional gas path, use a wind speed sensor to detect the air flow velocity in the monitoring room, and obtain mass monitoring points in the monitoring space model, including:

[0021] For the mainstream gas path or any conventional gas path: Denote the midpoint of the path as the wind speed sensing point A, and place a wind speed sensor A at the wind speed sensing point A; Denote the door or window in the path as the wind speed sensing point B, and place a wind speed sensor B at the wind speed sensing point B;

[0022] Denote as the offset length, denote the point at a distance of the offset length in the direction from the wind speed sensing point A to the wind speed sensing point B as the mass monitoring point. Among them, when the mass monitoring point is outside the monitoring space model, relocate the mass monitoring point to the wind speed sensing point B, L is the distance between the wind speed sensing point A and the wind speed sensing point B, AA1 is the wind speed detected by the wind speed sensor A, and BB1 is the wind speed detected by the wind speed sensor B.

[0023] Further, monitoring the air quality in the monitoring room based on the quality monitoring points includes:

[0024] Obtain the quality monitoring points corresponding to the mainstream gas path and all conventional gas paths;

[0025] Place air quality monitors at all quality monitoring points in the monitoring room and conduct monitoring for T hours; record the monitoring data of the air quality monitors placed in the mainstream gas path as the main monitoring data, and record the monitoring data of the air quality monitors placed in the conventional gas paths as the secondary monitoring data.

[0026] Further, the interval analysis method includes:

[0027] Obtain the monitoring data of each gas type in the main monitoring data respectively, and obtain the relationship curve of the concentration and time corresponding to each gas type respectively, where the abscissa in the relationship curve is time and the ordinate is the gas concentration; record the value of [(ordinate of the highest point - ordinate of the lowest point) / ordinate of the lowest point] in all the relationship curves as the concentration fluctuation difference, and record the gas type corresponding to the relationship curve with the largest concentration fluctuation difference as the main monitoring type;

[0028] Obtain the concentration fluctuation differences corresponding to the main monitoring type in all the secondary monitoring data, and record them as the secondary fluctuation differences CB1 to CB c , where c is the number of conventional gas paths;

[0029] Use the monitoring interval algorithm to obtain the monitoring interval time, and the monitoring interval algorithm is: , where F is the monitoring interval time, c1 is the number of the secondary fluctuation differences CB that are greater than the concentration fluctuation difference corresponding to the main monitoring data, and c2 is the number of the secondary fluctuation differences CB that are less than or equal to the concentration fluctuation difference corresponding to the main monitoring data.

[0030] Further, monitoring the air quality in the monitoring room based on artificial intelligence and the artificial intelligence execution method α includes:

[0031] Input the monitoring space model, the air flow analysis method, and the sensing data of the wind speed sensor into the artificial intelligence, and use the artificial intelligence execution method α;

[0032] Artificial intelligence execution method α: The artificial intelligence uses gas tracers to re-obtain the quality monitoring points at each monitoring interval time, continues to monitor the air quality in the monitoring room based on the newly obtained quality monitoring points, and uploads the monitoring results. The artificial intelligence obtains the re-obtained monitoring interval time based on the interval analysis method; repeat the artificial intelligence execution method α.

[0033] Further, the air quality monitoring of the monitoring room based on artificial intelligence and the artificial intelligence execution method α further includes: whenever the artificial intelligence obtains a monitoring interval time, repeating the artificial intelligence execution method α.

[0034] In a second aspect, the present application further provides an indoor air quality monitoring system based on artificial intelligence, including a gas path analysis module, an air positioning monitoring module, and an interval azimuth change module;

[0035] The gas path analysis module obtains a spatial model of the indoor area for air quality detection, denoted as the monitoring spatial model, and denotes the indoor area for air quality detection as the monitoring room; place a gas tracer in the monitoring room, and use the air flow analysis method based on the gas tracer to obtain the mainstream gas path and the conventional gas path in the monitoring spatial model;

[0036] The air positioning monitoring module is used to detect the air flow velocity in the monitoring room using a wind speed sensor based on the mainstream gas path and the conventional gas path, and obtain the quality monitoring points in the monitoring spatial model; monitor the air quality in the monitoring room based on the quality monitoring points, and obtain the monitoring interval time using the interval analysis method based on the monitoring results;

[0037] The interval azimuth change module is used to input the sensing data of the monitoring spatial model, the air flow analysis method, and the wind speed sensor into the artificial intelligence, and use the artificial intelligence execution method α to monitor the air quality in the monitoring room based on the artificial intelligence and the artificial intelligence execution method α.

[0038] Advantages of the present invention: The present application first obtains the monitoring spatial model and the monitoring room; places a gas tracer in the monitoring room, and uses the air flow analysis method based on the gas tracer to obtain the mainstream gas path and the conventional gas path in the monitoring spatial model; then uses a wind speed sensor to detect the air flow velocity in the monitoring room based on the mainstream gas path and the conventional gas path. The advantage of this is that by using the gas tracer, the air flow direction in the monitoring room can be effectively obtained, so that the obtained mainstream gas path is the gas flow path with the largest air flow change in the monitoring room, and the conventional gas path is the gas flow path with a relatively large gas flow change in the monitoring room, thus ensuring that the air quality in the monitoring room can be accurately monitored during subsequent analysis and improving the accuracy of data analysis;

[0039] This application also obtains quality monitoring points in the monitoring space model; monitors the air quality in the monitoring room based on the quality monitoring points, and uses interval analysis to obtain the monitoring interval time based on the monitoring results; finally, inputs the monitoring space model, the air flow analysis method, and the sensing data of the wind speed sensor into artificial intelligence, and uses artificial intelligence to execute method α. Based on artificial intelligence and artificial intelligence execution method α, the air quality in the monitoring room is monitored. The advantage of this is that by obtaining the monitoring interval time based on the monitoring results of the quality monitoring points, the monitoring interval of the monitoring device can be made to match the air flow rate in the monitoring room, thereby ensuring that the monitoring device can increase the monitoring frequency when the air flow rate in the monitoring room is fast, so as to ensure that the change of the air quality in the monitoring room can be accurately obtained; by using artificial intelligence, the data processing rate can be increased while saving manpower, thereby making the air quality monitoring more efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a schematic block diagram of the principle of the system of the present invention;

[0041] Figure 2 is a flowchart of the steps of the method of the present invention;

[0042] Figure 3 is a schematic diagram of obtaining the quality monitoring points of the present invention;

[0043] Figure 4 is a schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present invention.

[0045] Embodiment 1, please refer to Figure 1 As shown, this application provides an indoor air quality monitoring system based on artificial intelligence, including a gas path analysis module, an air positioning monitoring module, and an interval azimuth change module;

[0046] The gas path analysis module obtains the space model of the room for air quality detection, denoted as the monitoring space model, and denotes the room for air quality detection as the monitoring room; places a gas tracer in the monitoring room, and obtains the main gas path and the conventional gas path in the monitoring space model based on the gas tracer using the air flow analysis method;

[0047] The gas path analysis module includes a gas path analysis unit, and the gas path analysis unit is configured with a gas path acquisition strategy, which includes:

[0048] Based on the spatial environment in the monitoring room, obtain the gas tracers that are allowed to be placed in the monitoring room, and record the devices used to monitor the gas tracers as tracer detection devices;

[0049] Establish a spatial coordinate system with the units of the X-axis, Y-axis, and Z-axis all being m, and record it as the monitoring and analysis coordinate system; place the monitoring space model in the monitoring and analysis coordinate system;

[0050] Based on the state when the monitoring room is used by personnel, open the doors and windows of the monitoring room, and mark the opened doors and windows in the monitoring space model; place the tracer detection devices in the monitoring room, and release the gas tracers in the monitoring room. Among them, record the position where the gas tracers are released as the release position;

[0051] In the specific implementation process, the gas tracers can include hydrogen, helium, sulfur hexafluoride gas, etc. The gas tracers can be selected according to the gas tracers allowed to be released in the environment actually used for air quality monitoring, so as to prevent the released gas tracers from affecting the environment in the monitoring room or the objects in the monitoring room.

[0052] The air flow analysis method includes: based on the detection results of the gas tracers by the missing detection devices, obtain the diffusion area of the gas tracers in the monitoring space model, and record it as the gas diffusion area;

[0053] Record the straight line connecting the release position and the point farthest from the release position in the gas diffusion area as the fastest diffusion straight line; record the door or window closest to the fastest diffusion straight line in the monitoring space model as the gas influence point; take the gas influence point as the center of the sphere, and the length between the gas influence point and the point farthest from the release position in the gas diffusion area as the radius to make a sphere, and record it as the gas influence sphere;

[0054] In the specific implementation process, by obtaining the gas influence point, the position where the point most affected by the air flow in the monitoring room after the gas tracers are released is located after diffusion can be obtained. And through the gas influence sphere established based on the gas influence point, it is the area associated with the gas influence point and the door or window that causes the gas influence point; by obtaining the fastest influence area in the overlapping area of the gas influence sphere and the gas diffusion area during subsequent analysis, the area with the maximum air flow velocity after the gas tracers are released and affected by the air flow in the detection room can be obtained, thus providing data support for the subsequent acquisition of the quality monitoring points and the acquisition of the monitoring interval time;

[0055] Based on the tracer detection device, monitor the gas tracer in the area where the gas influence sphere coincides with the gas diffusion area, record the area with the fastest moving speed of the gas tracer as the fastest influence area, and record the line connecting the fastest influence area and the gas influence point as the mainstream gas path;

[0056] Record the number of independent areas in the gas influence sphere that coincide with the gas diffusion area as n; based on the tracer detection device, evenly divide the n areas with relatively fast moving speeds of the gas tracer in the gas diffusion area except the fastest influence area into conventional influence areas;

[0057] In the specific implementation process, by obtaining the conventional influence areas, it is possible to more comprehensively obtain the areas with relatively fast air flow speeds in the monitoring room, so as to ensure that the monitoring data is more comprehensive and effective when monitoring the air quality subsequently;

[0058] For any conventional influence area, record the door or window closest to the conventional influence area in the monitoring space model as the conventional influence point, and record the line connecting the conventional influence area and the conventional influence point as the conventional gas path.

[0059] The air positioning monitoring module is used to detect the air flow speed in the monitoring room using a wind speed sensor based on the mainstream gas path and the conventional gas path, and obtain the air quality monitoring points in the monitoring space model; monitor the air quality in the monitoring room based on the air quality monitoring points, and use the interval analysis method to obtain the monitoring interval time based on the monitoring results;

[0060] The air positioning monitoring module includes a monitoring interval determination unit, and the monitoring interval determination unit is configured with a monitoring interval determination strategy, and the monitoring interval determination strategy includes:

[0061] For the mainstream gas path or any conventional gas path: record the midpoint of the path as the wind speed sensing point A, and place the wind speed sensor A at the wind speed sensing point A; record the door or window in the path as the wind speed sensing point B, and place the wind speed sensor B at the wind speed sensing point B;

[0062] Record as the offset length, record the point with the direction from the wind speed sensing point A to the wind speed sensing point B and the distance of the offset length as the air quality monitoring point. Among them, when the air quality monitoring point is outside the monitoring space model, relocate the air quality monitoring point to the wind speed sensing point B. L is the distance between the wind speed sensing point A and the wind speed sensing point B, AA1 is the wind speed detected by the wind speed sensor A, and BB1 is the wind speed detected by the wind speed sensor B;

[0063] In the specific implementation process, for example, in a data analysis, the obtained conventional gas path is as Figure 3As shown in the figure, where CQ is the conventional gas path, FC1 is the wind speed sensing point A, FC2 is the wind speed sensing point B. Through data analysis, AA1 is 5 m / s, BB1 is 10 m / s, and L is 2 m. Then, through calculation, the offset length is -2.5. Then, it can be obtained in Figure 3 that the mass monitoring points corresponding to FC1 and FC2 are point ZJ; in this embodiment, when the offset length is a positive number, the mass monitoring point is at the offset length from the wind speed sensing point A to the wind speed sensing point B; when the offset length is a negative number, the mass monitoring point is at the absolute value of the offset length from the wind speed sensing point B to the wind speed sensing point A.

[0064] Obtain the mass monitoring points corresponding to the mainstream gas path and all conventional gas paths;

[0065] Place air quality monitors at all mass monitoring points in the monitoring room and conduct monitoring for T hours; record the monitoring data of the air quality monitors placed in the mainstream gas path as the main monitoring data, and record the monitoring data of the air quality monitors placed in the conventional gas paths as the secondary monitoring data;

[0066] In the specific implementation process, the value of T can be set according to the data analysis ability during actual data analysis. When the actual data analysis ability is relatively large, the value of T can be increased; in this embodiment, the value of T is set to 3.

[0067] The interval analysis method includes: obtaining the monitoring data of each gas type from the main monitoring data respectively, and obtaining the relationship curve between the concentration and time corresponding to each gas type respectively. Among them, the abscissa in the relationship curve is time, and the ordinate is the gas concentration; record [ (ordinate of the highest point - ordinate of the lowest point) / ordinate of the lowest point ] in all relationship curves as the concentration fluctuation difference, and record the gas type corresponding to the relationship curve with the largest concentration fluctuation difference as the main monitoring type;

[0068] In the specific implementation process, for example, in a data processing, the ordinates of the highest point and the lowest point in the relationship curve of oxygen obtained are 23% and 18% respectively. Then, the concentration fluctuation difference can be recorded as 0.278; in this embodiment, the unit of the concentration of all gases is analyzed as the percentage of the gas in the air. The purpose is to ensure the unity of the calculation unit. In actual situations, the unit of the gas concentration can be uniformly replaced according to the actually monitored gas to ensure the accuracy of the data;

[0069] Obtain the concentration fluctuation differences corresponding to the main monitoring type in all secondary monitoring data, and record them as the secondary fluctuation differences CB1 to CB c , where c is the number of conventional gas paths;

[0070] The monitoring interval algorithm is used to obtain the monitoring interval time, and the monitoring interval algorithm is as follows: , where F is the monitoring interval time, c1 is the number of all fluctuations CB greater than the concentration fluctuation corresponding to the main monitoring data, and c2 is the number of all fluctuations CB less than or equal to the concentration fluctuation corresponding to the main monitoring data;

[0071] In the specific implementation process, for example, in one data processing, the obtained T is 3, c is 10, and through data analysis, the number of all fluctuations CB greater than the concentration fluctuation corresponding to the main monitoring data is 1, and the number of all fluctuations CB less than or equal to the concentration fluctuation corresponding to the main monitoring data is 9. Then, through calculation, the value of F is 5.16; in this embodiment, when the number of all fluctuations CB less than or equal to the concentration fluctuation corresponding to the main monitoring data is large, it indicates that the air quality fluctuation in the monitoring room is small. Therefore, the monitoring interval time can be increased to reduce the monitoring frequency and energy consumption; while when the number of all fluctuations CB greater than the concentration fluctuation corresponding to the main monitoring data is large or even all fluctuations CB are greater than the concentration fluctuation corresponding to the main monitoring data, it indicates that the air quality fluctuation in the monitoring room is large, and the monitoring interval time should be reduced to increase the monitoring frequency, so as to accurately obtain the air quality change in the monitoring room. When the monitoring interval time is 0, it means that the air quality monitor should be kept on all the time to perform real-time monitoring on the monitoring room with fast air quality change.

[0072] The interval azimuth change module is used to input the monitoring space model, air flow analysis method, and sensing data of the wind speed sensor into the artificial intelligence, and use the artificial intelligence to execute method α. Based on the artificial intelligence and the artificial intelligence execution method α, the air quality in the monitoring room is monitored;

[0073] The interval azimuth change module includes an artificial intelligence monitoring unit, and the artificial intelligence monitoring unit is configured with an artificial intelligence monitoring strategy. The artificial intelligence monitoring strategy includes: inputting the monitoring space model, air flow analysis method, and sensing data of the wind speed sensor into the artificial intelligence, and using the artificial intelligence execution method α;

[0074] Artificial intelligence execution method α: The artificial intelligence uses a gas tracer to re-obtain the quality monitoring points every monitoring interval time, continues to monitor the air quality in the monitoring room based on the newly obtained quality monitoring points, and uploads the monitoring results. The artificial intelligence obtains the re-obtained monitoring interval time based on the interval analysis method; repeat the artificial intelligence execution method α;

[0075] Whenever the artificial intelligence obtains a monitoring interval time, repeat the artificial intelligence execution method α;

[0076] In the specific implementation process, by using artificial intelligence, it is possible to save manpower while increasing the rate of data processing, thereby making air quality monitoring more efficient.

[0077] Example 2, please refer to Figure 2 As shown, the present application also provides an indoor air quality monitoring method based on artificial intelligence, including the following steps:

[0078] Step S1, obtain the spatial model of the indoor area for air quality detection, denoted as the monitoring spatial model, and denote the indoor area for air quality detection as the monitoring indoor area; place a gas tracer in the monitoring indoor area, and use the air flow analysis method based on the gas tracer to obtain the mainstream gas path and the conventional gas path within the monitoring spatial model;

[0079] Step S1 includes: Step S101, obtain the gas tracer that is allowed to be placed in the monitoring indoor area based on the spatial environment of the monitoring indoor area, and denote the device for monitoring the gas tracer as the tracer detection device;

[0080] Step S102, establish a spatial coordinate system with the units of the X-axis, Y-axis, and Z-axis all being m, denoted as the monitoring analysis coordinate system; place the monitoring spatial model into the monitoring analysis coordinate system;

[0081] Step S103, open the doors and windows of the monitoring indoor area based on the state when the monitoring indoor area is used by people, and mark the opened doors and windows in the monitoring spatial model; place the tracer detection device in the monitoring indoor area, and release the gas tracer in the monitoring indoor area, where the position of releasing the gas tracer is denoted as the release position;

[0082] Step S104, the air flow analysis method includes: Step S1041, based on the detection result of the gas tracer by the missing detection device, obtain the diffusion area of the gas tracer in the monitoring spatial model, denoted as the gas diffusion area;

[0083] Step S1042, denote the straight line connecting the release position and the point farthest from the release position in the gas diffusion area as the fastest diffusion straight line; denote the door or window in the monitoring spatial model that is closest to the fastest diffusion straight line as the gas influence point; use the gas influence point as the center of the sphere, and use the length between the gas influence point and the point farthest from the release position in the gas diffusion area as the radius to make a sphere, denoted as the gas influence sphere;

[0084] Step S1043, monitor the gas tracer in the area where the gas influence sphere coincides with the gas diffusion area based on the tracer detection device, and denote the area where the gas tracer has the fastest moving speed as the fastest influence area, and denote the connection line between the fastest influence area and the gas influence point as the mainstream gas path;

[0085] Step S1044: Denote the number of independent regions in the gas influence sphere that coincide with the gas diffusion region as n; based on the tracer detection device, divide the n regions with relatively fast moving speeds of gas tracers in the gas diffusion region except for the fastest influence region into conventional influence regions respectively.

[0086] Step S1045: For any conventional influence region, denote the door or window in the monitoring space model that is closest to the conventional influence region as the conventional influence point, and denote the line connecting the conventional influence region and the conventional influence point as the conventional gas path.

[0087] Step S2: Based on the mainstream gas path and the conventional gas path, use a wind speed sensor to detect the air flow velocity in the monitoring room, and obtain mass monitoring points in the monitoring space model; monitor the air quality in the monitoring room based on the mass monitoring points, and use the interval analysis method to obtain the monitoring interval time based on the monitoring results.

[0088] Step S2 includes: Step S201: For the mainstream gas path or any conventional gas path: Denote the midpoint of the path as the wind speed sensing point A, and place a wind speed sensor A at the wind speed sensing point A; denote the door or window in the path as the wind speed sensing point B, and place a wind speed sensor B at the wind speed sensing point B.

[0089] Step S202: Denote as the offset length, and denote the point at a distance of the offset length in the direction from the wind speed sensing point A to the wind speed sensing point B as the mass monitoring point. Among them, when the mass monitoring point is outside the monitoring space model, relocate the mass monitoring point to the wind speed sensing point B. L is the distance between the wind speed sensing point A and the wind speed sensing point B, AA1 is the wind speed detected by the wind speed sensor A, and BB1 is the wind speed detected by the wind speed sensor B.

[0090] Step S203: Obtain the mass monitoring points corresponding to the mainstream gas path and all conventional gas paths.

[0091] Step S204: Place air quality monitors at all mass monitoring points in the monitoring room and conduct monitoring for T hours; denote the monitoring data of the air quality monitors placed in the mainstream gas path as the main monitoring data, and denote the monitoring data of the air quality monitors placed in the conventional gas path as the secondary monitoring data.

[0092] Step S205. The interval analysis method includes: Step S2051, obtaining the monitoring data of each gas type from the main monitoring data respectively, and obtaining the relationship curve between the concentration and time corresponding to each gas type respectively. In the relationship curve, the abscissa is time and the ordinate is gas concentration; record the value of [(ordinate of the highest point - ordinate of the lowest point) / ordinate of the lowest point] in all the relationship curves as the concentration fluctuation difference, and record the gas type corresponding to the relationship curve with the largest concentration fluctuation difference as the main monitoring type;

[0093] Step S2052, obtaining the concentration fluctuation differences corresponding to the main monitoring type in all the secondary monitoring data, and recording them as the secondary fluctuation differences CB1 to CB c , where c is the number of conventional gas paths;

[0094] Step S2053, using the monitoring interval algorithm to obtain the monitoring interval time. The monitoring interval algorithm is: , where F is the monitoring interval time, c1 is the number of the secondary fluctuation differences CB that are greater than the concentration fluctuation difference corresponding to the main monitoring data, and c2 is the number of the secondary fluctuation differences CB that are less than or equal to the concentration fluctuation difference corresponding to the main monitoring data.

[0095] Step S3, input the monitoring space model, the air flow analysis method, and the sensing data of the wind speed sensor into the artificial intelligence, and use the artificial intelligence to execute method α. Based on the artificial intelligence and the artificial intelligence execution method α, conduct air quality monitoring in the monitoring room;

[0096] Step S3 includes: Step S301, input the monitoring space model, the air flow analysis method, and the sensing data of the wind speed sensor into the artificial intelligence, and use the artificial intelligence to execute method α;

[0097] Step S302. The artificial intelligence execution method α: The artificial intelligence uses a gas tracer to re-obtain the quality monitoring points at each monitoring interval time, continues to conduct air quality monitoring in the monitoring room based on the newly obtained quality monitoring points, and uploads the monitoring results. The artificial intelligence obtains the re-obtained monitoring interval time based on the interval analysis method; repeat the artificial intelligence execution method α;

[0098] Step S303, whenever the artificial intelligence obtains a monitoring interval time, repeat the artificial intelligence execution method α.

[0099] Example 3, please refer to Figure 4 as shown in Figure 4The structural schematic diagram of an electronic device is exemplified. The electronic device may include: a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete mutual communication through the communication bus. The memory stores computer-readable instructions. The processor can call the instructions in the memory. When the computer-readable instructions are executed by the processor, the steps in the indoor air quality monitoring method based on artificial intelligence are run to achieve the following functions: First, obtain the monitoring space model and the monitored indoor area; Place a gas tracer in the monitored indoor area, and use the air flow analysis method based on the gas tracer to obtain the mainstream gas path and the conventional gas path in the monitoring space model; Then, based on the mainstream gas path and the conventional gas path, use a wind speed sensor to detect the air flow velocity in the monitored indoor area, and obtain the quality monitoring points in the monitoring space model; Monitor the air quality in the monitored indoor area based on the quality monitoring points, and use the interval analysis method to obtain the monitoring interval time based on the monitoring results; Finally, input the monitoring space model, the air flow analysis method, and the sensing data of the wind speed sensor into artificial intelligence, and use artificial intelligence to execute method α. Based on artificial intelligence and artificial intelligence execution method α, monitor the air quality in the monitored indoor area.

[0100] In addition, when the logical instructions in the above-mentioned memory are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0101] Embodiment 4. The present application also provides a computer-readable storage medium. The present application provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, it runs the steps in the above-mentioned indoor air quality monitoring method based on artificial intelligence to achieve the following functions: First, obtain the monitoring space model and the monitored indoor area; Place a gas tracer in the monitored indoor area, and use the air flow analysis method based on the gas tracer to obtain the main gas path and the conventional gas path in the monitoring space model; Then, based on the main gas path and the conventional gas path, use a wind speed sensor to detect the air flow velocity in the monitored indoor area, and obtain the quality monitoring points in the monitoring space model; Monitor the air quality in the monitored indoor area based on the quality monitoring points, and use the interval analysis method to obtain the monitoring interval time based on the monitoring results; Finally, input the sensing data of the monitoring space model, the air flow analysis method, and the wind speed sensor into the artificial intelligence, and use the artificial intelligence to execute Method α, and monitor the air quality in the monitored indoor area based on the artificial intelligence and the artificial intelligence execution method α.

[0102] Through the description of the above embodiments, the embodiments of the present invention can be provided as a method, a system, or a computer program product. Based on such an understanding, the above technical solution, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0103] In the embodiments provided by the present application, it should be understood that the disclosed system or method can be implemented in other ways. The above-described embodiments are only illustrative. For example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation. Another example is that multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some communication interfaces. The indirect coupling or communication connection of the system, module, and unit can be electrical, mechanical, or other forms.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: They can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; And these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. An indoor air quality monitoring method based on artificial intelligence, characterized in that, The method includes the following steps: Obtain the spatial model of the indoor area for air quality detection, denoted as the monitoring spatial model, and denote the indoor area for air quality detection as the monitoring indoor area. Place a gas tracer in the monitoring indoor area, and based on the gas tracer, use the air flow analysis method to obtain the main gas path and the conventional gas path within the monitoring spatial model. Based on the main gas path and the conventional gas path, use a wind speed sensor to detect the air flow velocity in the monitoring indoor area, and obtain the quality monitoring points in the monitoring spatial model. Monitor the air quality in the monitoring indoor area based on the quality monitoring points, and use the interval analysis method to obtain the monitoring interval time based on the monitoring results. Input the sensing data of the monitoring spatial model, the air flow analysis method, and the wind speed sensor into artificial intelligence, and use artificial intelligence to execute method α. Based on artificial intelligence and artificial intelligence execution method α, monitor the air quality in the monitoring indoor area. Placing a gas tracer in the monitoring indoor area includes: Obtain the gas tracer that is allowed to be placed in the monitoring indoor area based on the spatial environment of the monitoring indoor area, and denote the device for monitoring the gas tracer as the tracer detection device. Establish a spatial coordinate system with the units of the X-axis, Y-axis, and Z-axis all being m, denoted as the monitoring analysis coordinate system. Place the monitoring spatial model into the monitoring analysis coordinate system. Based on the state when the monitoring indoor area is used by people, open the doors and windows of the monitoring indoor area, and mark the opened doors and windows in the monitoring spatial model. Place the tracer detection device in the monitoring indoor area, and release the gas tracer in the monitoring indoor area, where the position of releasing the gas tracer is denoted as the release position. The air flow analysis method includes: Based on the detection result of the gas tracer by the tracer detection device, obtain the diffusion area of the gas tracer in the monitoring spatial model, denoted as the gas diffusion area. Denote the straight line connecting the release position and the point farthest from the release position in the gas diffusion area as the fastest diffusion straight line. Denote the door or window in the monitoring spatial model that is closest to the fastest diffusion straight line as the gas influence point. Take the gas influence point as the center of the sphere, and take the length between the gas influence point and the point farthest from the release position in the gas diffusion area as the radius to make a sphere, denoted as the gas influence sphere. Based on the tracer detection device, monitor the gas tracer in the area where the gas influence sphere coincides with the gas diffusion area, and denote the area where the gas tracer has the fastest moving speed as the fastest influence area. Denote the connection line between the fastest influence area and the gas influence point as the main gas path. The air flow analysis method further includes: Denote the number of independent areas in the gas influence sphere that coincide with the gas diffusion area as n. Based on the tracer detection device, denote the n areas in the gas diffusion area where the gas tracer has a relatively fast moving speed except for the fastest influence area as the conventional influence areas. For any one of the conventional influence areas, denote the door or window in the monitoring spatial model that is closest to the conventional influence area as the conventional influence point, and denote the connection line between the conventional influence area and the conventional influence point as the conventional gas path. Artificial Intelligence Execution Method α: The artificial intelligence uses a gas tracer to re-acquire the mass monitoring points at each monitoring interval, continues to monitor the air quality in the monitoring room based on the latest acquired mass monitoring points, and uploads the monitoring results. The artificial intelligence re-acquires the monitoring interval based on the interval analysis method; Repeat the Artificial Intelligence Execution Method α.

2. The indoor air quality monitoring method based on artificial intelligence according to claim 1, characterized in that, Based on the mainstream gas path and the conventional gas path, use a wind speed sensor to detect the air flow velocity in the monitoring room, and obtain the mass monitoring points in the monitoring space model, including: For the mainstream gas path or any one of the conventional gas paths: Denote the midpoint of the path as the wind speed sensing point A, and place a wind speed sensor A at the wind speed sensing point A; Denote the door or window in the path as the wind speed sensing point B, and place a wind speed sensor B at the wind speed sensing point B; Let be denoted as the offset length. The direction is from the wind speed sensing point A to the wind speed sensing point B, and the point at a distance of the offset length is denoted as the mass monitoring point. Among them, when the mass monitoring point is outside the monitoring space model, the mass monitoring point is repositioned at the wind speed sensing point B. L is the distance between the wind speed sensing point A and the wind speed sensing point B, AA1 is the wind speed detected by the wind speed sensor A, and BB1 is the wind speed detected by the wind speed sensor B.

3. The indoor air quality monitoring method based on artificial intelligence according to claim 2, wherein Monitoring the air quality in the monitoring room based on the mass monitoring points includes: Obtain the mass monitoring points corresponding to the mainstream gas path and all conventional gas paths; Place air quality monitors at all mass monitoring points in the monitoring room and conduct monitoring for T hours; Denote the monitoring data of the air quality monitors placed in the mainstream gas path as the main monitoring data, and denote the monitoring data of the air quality monitors placed in the conventional gas paths as the secondary monitoring data.

4. The indoor air quality monitoring method based on artificial intelligence according to claim 3, characterized in that, The interval analysis method includes: Obtain the monitoring data of each gas type in the main monitoring data respectively, and obtain the relationship curve between the concentration and time corresponding to each gas type respectively. Among them, the abscissa in the relationship curve is time, and the ordinate is the gas concentration; Denote the value of [(ordinate of the highest point - ordinate of the lowest point) / ordinate of the lowest point] in all the relationship curves as the concentration fluctuation difference, and denote the gas type corresponding to the relationship curve with the largest concentration fluctuation difference as the main monitoring type; Obtain the concentration fluctuation differences corresponding to the main monitoring types in all secondary monitoring data, and record them as secondary fluctuation differences CB1 to secondary fluctuation difference CB respectively c , where c is the number of conventional gas paths; Obtain the monitoring interval time using the monitoring interval algorithm, and the monitoring interval algorithm is as follows: , where F is the monitoring interval time, c1 is the number of concentration fluctuations greater than the concentration fluctuation corresponding to the main monitoring data among all fluctuations, and c2 is the number of concentration fluctuations less than or equal to the concentration fluctuation corresponding to the main monitoring data among all fluctuations.

5. The indoor air quality monitoring method based on artificial intelligence according to claim 4, characterized in that Monitoring the air quality in the monitoring room based on the artificial intelligence and the Artificial Intelligence Execution Method α includes: Input the monitoring space model, the air flow analysis method, and the sensing data of the wind speed sensor into the artificial intelligence, and use the Artificial Intelligence Execution Method α.

6. The indoor air quality monitoring method based on artificial intelligence according to claim 5, wherein, Monitoring the air quality in the monitoring room based on the artificial intelligence and the Artificial Intelligence Execution Method α also includes: Whenever the artificial intelligence obtains a monitoring interval, repeat the Artificial Intelligence Execution Method α.

7. An indoor air quality monitoring system based on artificial intelligence, which is used to implement the indoor air quality monitoring method based on artificial intelligence according to any one of claims 1-6, characterized in that Including a gas path analysis module, an air positioning monitoring module, and an interval azimuth change module; The gas path analysis module obtains the space model of the indoor for air quality detection, denoted as the monitoring space model, and denotes the indoor for air quality detection as the monitoring room; Place a gas tracer in the monitoring room, and based on the gas tracer, use the air flow analysis method to obtain the mainstream gas path and the conventional gas paths in the monitoring space model; The air positioning monitoring module is used to detect the air flow velocity in the monitoring room based on the mainstream gas path and the conventional gas path using a wind speed sensor, and obtain the mass monitoring points in the monitoring space model; Monitor the air quality in the monitoring room based on the mass monitoring points, and obtain the monitoring interval based on the monitoring results using the interval analysis method; The interval azimuth change module is used to input the sensing data of the monitoring space model, the air flow analysis method, and the wind speed sensor into artificial intelligence, and use the artificial intelligence to execute method α. Based on the artificial intelligence and method α executed by the artificial intelligence, air quality monitoring is carried out in the monitored room.

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