Atmospheric particulate monitoring system and monitoring method thereof

Abstract

The invention discloses an atmospheric particulate monitoring system and a monitoring method thereof, and relates to the technical field of environmental monitoring, and the system comprises the following modules: a monitoring information processing module for establishing a plurality of monitoring stations, respectively obtaining the atmospheric particulate concentration of each monitoring station through an air quality model, respectively obtaining the environmental value of each monitoring station, and sending the environmental value to a server; the cause value of each monitoring station is obtained; according to the scheme, in the early warning control module, according to the change value of each monitoring station, the atmospheric particulate concentration coefficient, the atmospheric particulate concentration prediction coefficient and the atmospheric particulate trend, an alarm is controlled to be started to remind a manager; therefore, the future change of the atmospheric particulates is predicted by combining the currently measured atmospheric particulate concentration with different real conditions of each monitoring station, so that the management personnel can be pre-warned according to the prediction result.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and in particular to an atmospheric particulate matter monitoring system and a monitoring method thereof. Background Art

[0002] Atmospheric particulate matter monitoring is an important environmental monitoring activity for solid or liquid particles suspended in the atmosphere. Atmospheric particulate matter monitoring is becoming increasingly important. It can grasp the status of atmospheric environmental quality in real time, especially the monitoring of inhalable particulate matter and fine particulate matter. Through professional monitoring equipment and technology, it can accurately measure parameters such as the concentration and particle size distribution of particulate matter in the atmosphere. These data are of key significance for assessing air quality, formulating environmental protection policies, and protecting public health. They can help us understand the extent and source of air pollution so as to take effective governance measures to reduce the harm of atmospheric particulate matter to the environment and human body. At the same time, atmospheric particulate matter monitoring also provides important data support for scientific research, which is helpful for in-depth research on the formation mechanism and transmission law of air pollution.

[0003] In the process of monitoring atmospheric particulate matter, due to the high cost of building monitoring stations, the monitoring results within the monitoring range of the monitoring station are mostly based on the monitoring center location. However, during the monitoring process, the actual situation in the edge areas of the monitoring range will be different from the monitoring results in the central area of ​​the monitoring station. There will be intersections at the edges of the monitoring ranges of multiple adjacent monitoring stations. Therefore, the monitoring results of the intersection areas by different monitoring stations are often different, which affects the accuracy and authenticity of the atmospheric particulate matter monitoring results. In addition, the atmospheric particulate matter monitoring system lacks intelligence, and it is difficult to intuitively deduce and predict future changes and trends in atmospheric particulate matter concentrations based on current data, which causes the atmospheric particulate matter early warning function at the intersection to lag. For this reason, we propose an atmospheric particulate matter monitoring system and a monitoring method thereof. Summary of the invention

[0004] 1. Technical issues to be resolved In view of the deficiencies of the prior art, the present invention provides an atmospheric particulate matter monitoring system and a monitoring method thereof to solve the above-mentioned problems in the prior art.

[0005] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: an atmospheric particulate matter monitoring system, comprising the following modules: The monitoring information processing module establishes multiple monitoring stations, obtains the atmospheric particulate matter concentration of each monitoring station through the air quality model, obtains the environmental value of each monitoring station, and obtains the cause value of each monitoring station; The concentration distribution prediction module obtains the atmospheric particle concentration coefficient of each monitoring station through the atmospheric particle concentration, environmental value and cause value of each monitoring station, establishes an atmospheric particle digital twin model, and marks the atmospheric particle concentration prediction coefficient in the atmospheric particle digital twin model according to the atmospheric particle concentration coefficient of each monitoring station; The mobile trend prediction module obtains the change value of each monitoring station, obtains the wind direction of each monitoring station, and marks the trend of atmospheric particulate matter in the digital twin model of atmospheric particulate matter according to the change value and wind direction of each monitoring station; The monitoring and early warning control module activates alarms to remind management personnel based on the change values ​​of each monitoring station, the atmospheric particulate matter concentration coefficient, the atmospheric particulate matter concentration prediction coefficient and the atmospheric particulate matter trend control alarm.

[0006] Preferably, the environmental value of each monitoring station is obtained in the monitoring information processing module, specifically: Step 1: Get the current outdoor temperature of each monitoring station, get the current outdoor humidity of each environmental station, and get the current outdoor air pressure of each monitoring station; Step 2: Obtain the environmental value of each monitoring station through the current outdoor temperature, outdoor humidity and outdoor air pressure of each monitoring station; The calculation method of the environmental value is as follows: Among them, Hj represents the environmental value, Qy represents the outdoor air pressure, Wd represents the outdoor temperature, and Sd represents the outdoor temperature. , and are weights, , , .

[0007] Preferably, the cause value of each monitoring station is obtained in the monitoring information processing module, specifically: Step 1: Set the influence distance, define the surrounding area of ​​each monitoring station with each monitoring station as the center and the influence distance as the radius, obtain the number of factories within the surrounding area of ​​each monitoring station, obtain the traffic density within the surrounding area of ​​each monitoring station, and obtain the number of construction sites within the surrounding area of ​​each monitoring station; Step 2: Obtain the current number of motor vehicles in the surrounding area of ​​each monitoring station, obtain the surrounding area, and obtain the traffic flow density by dividing the number of motor vehicles by the surrounding area; Step 3: Obtain the cause value of each monitoring station through the number of factories, traffic density and number of construction sites within the surrounding area of ​​each monitoring station; The calculation method of the cause value is as follows: Among them, Cy represents the cause value, Cm represents the traffic density, Jc represents the number of construction sites, and Gs represents the number of factories. , and are weights, , , .

[0008] Preferably, the calculation method of the atmospheric particulate matter concentration coefficient in the concentration distribution prediction module is specifically as follows: Among them, Nx represents the atmospheric particulate matter concentration coefficient, Kn represents the atmospheric particulate matter concentration, Hj represents the environmental value, and Cy represents the cause value. , and are weights, , , .

[0009] Preferably, a digital twin model of atmospheric particulate matter is established in the concentration distribution prediction module, specifically: Step 1: Obtain the geographic information of all monitoring stations and use the GIS model to establish a spatial model for atmospheric particulate matter monitoring; Step 2: Mark the locations of all monitoring stations in the atmospheric particulate matter monitoring spatial model, and mark the current atmospheric particulate matter concentration coefficient and current atmospheric particulate matter concentration at each monitoring station; Step 3: Get the surrounding area of ​​each monitoring station, and fill the surrounding area of ​​each monitoring station with red of different brightness according to the atmospheric particulate matter concentration coefficient marked on each monitoring station, that is, the higher the atmospheric particulate matter concentration coefficient, the lower the brightness.

[0010] Preferably, in the concentration distribution prediction module, the atmospheric particulate matter concentration prediction coefficient is marked in the atmospheric particulate matter digital twin model according to the atmospheric particulate matter concentration coefficient of each monitoring station, specifically: Step 1: Obtain the overlapping areas around all monitoring stations in the atmospheric particulate matter digital twin model and mark them as prediction areas, and obtain the atmospheric particulate matter concentration coefficient of each monitoring station in the prediction area; Step 2: Sum and average the atmospheric particulate matter concentration coefficients of all monitoring stations in the prediction area to obtain the atmospheric particulate matter concentration prediction coefficient, and mark the atmospheric particulate matter concentration prediction coefficient in the prediction area.

[0011] Preferably, the change value of each monitoring station is obtained in the moving trend prediction module, specifically: Step 1: Set a preset height threshold, obtain the heights of all mountains within the surrounding area of ​​each monitoring station, mark the mountains whose heights are greater than the preset height threshold among all the mountains, and count the mountains whose heights are greater than the preset height threshold to obtain the number of high mountains; Step 2: Obtain wind speed measurements at three different edge locations and one central location within the perimeter of each monitoring station, and sum and average the four measured wind speeds to obtain the average wind speed; Step 3: Obtain the change value through the number of high mountains and average wind speed at each monitoring station; The calculation method of the change value is as follows: Among them, Dm represents the change value, Fs represents the average wind speed, and Sg represents the number of high mountains. and are weights, , .

[0012] Preferably, in the mobile trend prediction module, the trend of atmospheric particulate matter is marked in the atmospheric particulate matter digital twin model according to the change value and wind direction of each monitoring station, specifically: Step 1: Obtain the wind direction of each monitoring station and mark the wind direction of each monitoring station with an arrow in the atmospheric particulate matter digital twin model; Step 2: Obtain the change value of each monitoring station and mark the change value above the wind direction arrow of each monitoring station in the digital twin model of atmospheric particulate matter.

[0013] Preferably, the monitoring and early warning control module is specifically: Step 1: Determine whether the wind directions between two adjacent monitoring stations are opposite or in the same direction. If the wind directions between two adjacent monitoring stations are opposite, mark the area between the two monitoring stations as the area to be predicted. Step 2: Determine whether there is a monitoring intersection area between two adjacent monitoring stations in the area to be measured. If there is an intersection area between the two adjacent monitoring stations, obtain the atmospheric particulate matter concentration prediction coefficient of the intersection area, obtain the change values ​​of the two monitoring stations, and obtain the change sum value by summing the change values ​​of the two monitoring stations. Then, multiply the change sum value by the atmospheric particulate matter concentration prediction coefficient to obtain the atmospheric particulate matter concentration assessment coefficient. If there is no intersection area between adjacent monitoring stations, the atmospheric particle concentration coefficients of the two monitoring stations are obtained, the change values ​​of the two monitoring stations are obtained, the atmospheric particle concentration coefficients are multiplied by the corresponding change values ​​to obtain the atmospheric particle concentration transfer values, the two atmospheric particle transfer values ​​are summed and averaged to obtain the atmospheric particle transfer mean, the two atmospheric particle transfer values ​​are subtracted to obtain the atmospheric particle transfer difference, and the atmospheric particle transfer mean and the atmospheric particle transfer difference are summed to obtain the atmospheric particle concentration assessment coefficient; Step 3: Set a preset threshold value for the atmospheric particulate matter assessment coefficient, and compare the preset threshold value for the atmospheric particulate matter assessment coefficient with the atmospheric particulate matter concentration assessment coefficient. If the atmospheric particulate matter concentration assessment coefficient is greater than the preset threshold value for the atmospheric particulate matter assessment coefficient, activate an alarm to alert the management personnel.

[0014] A method for monitoring atmospheric particulate matter comprises the following steps: S1: Establish multiple monitoring stations, and obtain the atmospheric particulate matter concentration, environmental value, and cause value of each monitoring station through the air quality model; S2: Obtain the atmospheric particle concentration coefficient of each monitoring station through the atmospheric particle concentration, environmental value and cause value of each monitoring station, establish an atmospheric particle digital twin model, and mark the atmospheric particle concentration prediction coefficient in the atmospheric particle digital twin model according to the atmospheric particle concentration coefficient of each monitoring station; S3: Obtain the change value of each monitoring station, obtain the wind direction of each monitoring station, and mark the trend of atmospheric particulate matter in the atmospheric particulate matter digital twin model according to the change value and wind direction of each monitoring station; S4: Based on the change value of each monitoring station, the atmospheric particulate matter concentration coefficient, the atmospheric particulate matter concentration prediction coefficient and the atmospheric particulate matter trend control alarm, the management personnel are alerted.

[0015] (III) Beneficial effects The present invention provides an atmospheric particulate matter monitoring system and a monitoring method thereof, which have the following beneficial effects: (1) In the concentration distribution prediction module, this scheme obtains the atmospheric particle concentration coefficient of each monitoring station through the atmospheric particle concentration, environmental value and cause value, which is beneficial to accurately monitor the monitored atmospheric particle concentration according to the specific conditions of each monitoring station, and further helps to improve the accuracy of the atmospheric particle concentration prediction results. By establishing an atmospheric particle digital twin model, it is convenient to intuitively see the distribution of atmospheric particle concentration at each monitoring station, and then mark the atmospheric particle concentration prediction coefficient in the atmospheric particle digital twin model according to the atmospheric particle concentration coefficient of each monitoring station, so as to facilitate the calibration of monitoring results in the monitoring overlapping areas between multiple monitoring stations, which is further beneficial to improve the accuracy of atmospheric particle concentration prediction.

[0016] (2) In the mobile trend prediction module, this solution marks the trend of atmospheric particulate matter in the digital twin model of atmospheric particulate matter according to the change value and wind direction of each monitoring station, so as to facilitate the intuitive display of the trend of atmospheric particulate matter in the digital twin model of atmospheric particulate matter according to the actual situation of each monitoring station, and then facilitate the subsequent prediction of future atmospheric particulate matter concentration, and then facilitate the early warning of management personnel based on the prediction results.

[0017] (3) In the early warning control module, this solution uses the change value of each monitoring station, the atmospheric particulate matter concentration coefficient, the atmospheric particulate matter concentration prediction coefficient, and the atmospheric particulate matter trend control alarm to alert the management personnel, thereby combining the currently measured atmospheric particulate matter concentration with the different actual conditions of each monitoring station to predict the future changes of atmospheric particulate matter, and then facilitating early warning to the management personnel based on the prediction results. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a structural schematic diagram of an atmospheric particulate matter monitoring system of the present invention; Figure 2 The present invention is a flowchart of an atmospheric particulate matter monitoring method. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1-Figure 2 The present invention provides an atmospheric particulate matter monitoring system, comprising the following modules: The monitoring information processing module establishes multiple monitoring stations, obtains the atmospheric particulate matter concentration of each monitoring station through the air quality model, obtains the environmental value of each monitoring station, and obtains the cause value of each monitoring station; The concentration distribution prediction module obtains the atmospheric particle concentration coefficient of each monitoring station through the atmospheric particle concentration, environmental value and cause value of each monitoring station, establishes an atmospheric particle digital twin model, and marks the atmospheric particle concentration prediction coefficient in the atmospheric particle digital twin model according to the atmospheric particle concentration coefficient of each monitoring station; The mobile trend prediction module obtains the change value of each monitoring station, obtains the wind direction of each monitoring station, and marks the trend of atmospheric particulate matter in the digital twin model of atmospheric particulate matter according to the change value and wind direction of each monitoring station; The monitoring and early warning control module starts alerting management personnel based on the change value of each monitoring station, the atmospheric particulate matter concentration coefficient, the atmospheric particulate matter concentration prediction coefficient and the atmospheric particulate matter trend control alarm. The calculation method of atmospheric particulate matter concentration coefficient in the concentration distribution prediction module is as follows: Among them, Nx represents the atmospheric particulate matter concentration coefficient, Kn represents the atmospheric particulate matter concentration, Hj represents the environmental value, and Cy represents the cause value. , and are weights, , , .

[0021] In this embodiment, this solution obtains the atmospheric particle concentration, environmental value and cause value of each monitoring station in the monitoring information processing module, so as to facilitate the subsequent prediction of the atmospheric particle concentration distribution of each monitoring station based on the atmospheric particle concentration, environmental value and cause value; In the concentration distribution prediction module, this solution obtains the atmospheric particle concentration coefficient of each monitoring station through the atmospheric particle concentration, environmental value and cause value, which is beneficial to accurately monitor the monitored atmospheric particle concentration according to the specific situation of each monitoring station, and further to improve the accuracy of the atmospheric particle concentration prediction results. By establishing an atmospheric particle digital twin model, it is convenient to intuitively see the distribution of atmospheric particle concentration at each monitoring station, and then mark the atmospheric particle concentration prediction coefficient in the atmospheric particle digital twin model according to the atmospheric particle concentration coefficient of each monitoring station, so as to facilitate the calibration of monitoring results in the monitoring overlapping area between multiple monitoring stations, which is further beneficial to improve the accuracy of atmospheric particle concentration prediction; In the mobile trend prediction module, this solution marks the trend of atmospheric particulate matter in the atmospheric particulate matter digital twin model according to the change value and wind direction of each monitoring station, so as to facilitate the intuitive display of the trend of atmospheric particulate matter in the atmospheric particulate matter digital twin model according to the actual situation of each monitoring station, and then facilitate the subsequent prediction of future atmospheric particulate matter concentration, and then facilitate the early warning of management personnel according to the prediction results; In the early warning control module, this solution starts to remind the management personnel according to the change value of each monitoring station, the atmospheric particulate matter concentration coefficient, the atmospheric particulate matter concentration prediction coefficient and the atmospheric particulate matter trend control alarm, thereby combining the currently measured atmospheric particulate matter concentration with the different actual conditions of each monitoring station to predict the future changes of atmospheric particulate matter, and then it is convenient to warn the management personnel according to the prediction results; It is worth mentioning that the method of obtaining the atmospheric particulate matter concentration of the monitoring station through the air quality model in this scheme is an existing technology. Specifically, the atmospheric particulate matter concentration can be obtained by combining one or more of the following methods: direct measurement by sensors, β-ray absorption method, light scattering method and micro-oscillation balance method. The specific operation steps will not be described in detail here. The weight values ​​in this scheme can be obtained through the hierarchical analysis method, and the value of the preset threshold can be obtained through the weight analysis method, which will not be elaborated here.

[0022] In the monitoring information processing module, the environmental values ​​of each monitoring station are obtained respectively, specifically: Step 1: Get the current outdoor temperature of each monitoring station, get the current outdoor humidity of each environmental station, and get the current outdoor air pressure of each monitoring station; Step 2: Obtain the environmental value of each monitoring station through the current outdoor temperature, outdoor humidity and outdoor air pressure of each monitoring station; The calculation method of the environmental value is as follows: Among them, Hj represents the environmental value, Qy represents the outdoor air pressure, Wd represents the outdoor temperature, and Sd represents the outdoor temperature. , and are weights, , , .

[0023] In this embodiment, the environmental value is obtained by combining the ambient temperature, humidity and air pressure of each monitoring station, so as to accurately grasp the change of the atmospheric particulate matter concentration of the monitoring station, and then facilitate the subsequent prediction and trend deduction of the atmospheric particulate matter concentration.

[0024] In the monitoring information processing module, the cause value of each monitoring station is obtained respectively, specifically: Step 1: Set the influence distance, define the surrounding area of ​​each monitoring station with each monitoring station as the center and the influence distance as the radius, obtain the number of factories within the surrounding area of ​​each monitoring station, obtain the traffic density within the surrounding area of ​​each monitoring station, and obtain the number of construction sites within the surrounding area of ​​each monitoring station; Step 2: Obtain the current number of motor vehicles in the surrounding area of ​​each monitoring station, obtain the surrounding area, and obtain the traffic flow density by dividing the number of motor vehicles by the surrounding area; Step 3: Obtain the cause value of each monitoring station through the number of factories, traffic density and number of construction sites within the surrounding area of ​​each monitoring station; The calculation method of the cause value is as follows: Among them, Cy represents the cause value, Cm represents the traffic density, Jc represents the number of construction sites, and Gs represents the number of factories. , and are weights, , , .

[0025] In this embodiment, the cause value is obtained by combining the number of construction sites, traffic density and construction sites at each monitoring station, so as to facilitate further accurate grasp of the changes in the atmospheric particulate matter concentration at the monitoring station, and then facilitate subsequent prediction and trend deduction of the atmospheric particulate matter concentration.

[0026] A digital twin model of atmospheric particulate matter is established in the concentration distribution prediction module, specifically: Step 1: Obtain the geographic information of all monitoring stations and use the GIS model to establish a spatial model for atmospheric particulate matter monitoring; Step 2: Mark the locations of all monitoring stations in the atmospheric particulate matter monitoring spatial model, and mark the current atmospheric particulate matter concentration coefficient and current atmospheric particulate matter concentration at each monitoring station; Step 3: Get the surrounding area of ​​each monitoring station, and fill the surrounding area of ​​each monitoring station with red of different brightness according to the atmospheric particulate matter concentration coefficient marked on each monitoring station, that is, the higher the atmospheric particulate matter concentration coefficient, the lower the brightness.

[0027] In this embodiment, a spatial model for atmospheric particulate matter monitoring is established through a GIS model, and the monitored data is fed back into the model in real time, and the atmospheric particulate matter concentration is marked with red of different brightness, so as to facilitate a more intuitive understanding of the distribution and changes of the atmospheric particulate matter concentration within the monitoring range of all monitoring stations.

[0028] In the concentration distribution prediction module, the atmospheric particulate matter concentration prediction coefficient is marked in the atmospheric particulate matter digital twin model according to the atmospheric particulate matter concentration coefficient of each monitoring station, specifically: Step 1: Obtain the overlapping areas around all monitoring stations in the atmospheric particulate matter digital twin model and mark them as prediction areas, and obtain the atmospheric particulate matter concentration coefficient of each monitoring station in the prediction area; Step 2: Sum and average the atmospheric particulate matter concentration coefficients of all monitoring stations in the prediction area to obtain the atmospheric particulate matter concentration prediction coefficient, and mark the atmospheric particulate matter concentration prediction coefficient in the prediction area.

[0029] In this embodiment, by marking the overlapping areas of the monitoring ranges of the monitoring stations, and then calculating the atmospheric particulate matter concentration in the overlapping areas based on the monitoring results of adjacent monitoring stations, the uniqueness and authenticity of the atmospheric particulate matter concentration in the overlapping areas are facilitated, thereby facilitating subsequent early warning based on the predicted atmospheric particulate matter concentration in the overlapping areas.

[0030] In the moving trend prediction module, the change value of each monitoring station is obtained, specifically: Step 1: Set a preset height threshold, obtain the heights of all mountains within the surrounding area of ​​each monitoring station, mark the mountains whose heights are greater than the preset height threshold among all the mountains, and count the mountains whose heights are greater than the preset height threshold to obtain the number of high mountains; Step 2: Obtain wind speed measurements at three different edge locations and one central location within the perimeter of each monitoring station, and sum and average the four measured wind speeds to obtain the average wind speed; Step 3: Obtain the change value through the number of high mountains and average wind speed at each monitoring station; The calculation method of the change value is as follows: Among them, Dm represents the change value, Fs represents the average wind speed, and Sg represents the number of high mountains. and are weights, , .

[0031] In this embodiment, the change value is obtained by combining the number of high mountains and wind speed conditions in the monitoring area of ​​each monitoring station, so as to facilitate the subsequent prediction of the change trend of the atmospheric particulate matter concentration based on the change value, and then facilitate the early warning of the predicted atmospheric particulate matter concentration at the intersection.

[0032] In the mobile trend prediction module, the trend of atmospheric particulate matter is marked in the atmospheric particulate matter digital twin model according to the change value and wind direction of each monitoring station, specifically: Step 1: Obtain the wind direction of each monitoring station and mark the wind direction of each monitoring station with an arrow in the atmospheric particulate matter digital twin model; Step 2: Obtain the change value of each monitoring station and mark the change value above the wind direction arrow of each monitoring station in the digital twin model of atmospheric particulate matter.

[0033] In this embodiment, by marking the wind direction and change value in the digital twin model of atmospheric particulate matter, it is convenient to more intuitively understand the current trend of atmospheric particulate matter concentration in all monitoring stations and the future change trend, and then facilitate early warning of the predicted atmospheric particulate matter concentration at the intersection.

[0034] Monitoring and early warning control module, specifically: Step 1: Determine whether the wind directions between two adjacent monitoring stations are opposite or in the same direction. If the wind directions between two adjacent monitoring stations are opposite, mark the area between the two monitoring stations as the area to be predicted. Step 2: Determine whether there is a monitoring intersection area between two adjacent monitoring stations in the area to be measured. If there is an intersection area between the two adjacent monitoring stations, obtain the atmospheric particulate matter concentration prediction coefficient of the intersection area, obtain the change values ​​of the two monitoring stations, and obtain the change sum value by summing the change values ​​of the two monitoring stations. Then, multiply the change sum value by the atmospheric particulate matter concentration prediction coefficient to obtain the atmospheric particulate matter concentration assessment coefficient. If there is no intersection area between adjacent monitoring stations, the atmospheric particle concentration coefficients of the two monitoring stations are obtained, the change values ​​of the two monitoring stations are obtained, the atmospheric particle concentration coefficients are multiplied by the corresponding change values ​​to obtain the atmospheric particle concentration transfer values, the two atmospheric particle transfer values ​​are summed and averaged to obtain the atmospheric particle transfer mean, the two atmospheric particle transfer values ​​are subtracted to obtain the atmospheric particle transfer difference, and the atmospheric particle transfer mean and the atmospheric particle transfer difference are summed to obtain the atmospheric particle concentration assessment coefficient; Step 3: Set a preset threshold value for the atmospheric particulate matter assessment coefficient, and compare the preset threshold value for the atmospheric particulate matter assessment coefficient with the atmospheric particulate matter concentration assessment coefficient. If the atmospheric particulate matter concentration assessment coefficient is greater than the preset threshold value for the atmospheric particulate matter assessment coefficient, activate an alarm to alert the management personnel.

[0035] In this embodiment, the atmospheric particle concentration assessment coefficient at the intersection is obtained through the wind direction, change value, atmospheric particle concentration coefficient and atmospheric particle concentration prediction coefficient of two adjacent monitoring stations, and then the prediction result of the atmospheric particle concentration assessment coefficient is compared with the preset threshold value of the atmospheric particle assessment coefficient. Finally, the alarm is controlled to start to remind the management personnel according to the comparison result, so as to facilitate the deduction, evaluation and prediction of the trend of atmospheric particles according to the actual situation of each monitoring station, and finally it is convenient to remind the management personnel in advance of the atmospheric particle concentration at the intersection of the monitoring stations, so as to facilitate the management personnel to make preparations in advance.

[0036] See also Figure 1-Figure 2 The present invention provides a method for monitoring atmospheric particulate matter, comprising the following steps: S1: Establish multiple monitoring stations, and obtain the atmospheric particulate matter concentration, environmental value, and cause value of each monitoring station through the air quality model; S2: Obtain the atmospheric particle concentration coefficient of each monitoring station through the atmospheric particle concentration, environmental value and cause value of each monitoring station, establish an atmospheric particle digital twin model, and mark the atmospheric particle concentration prediction coefficient in the atmospheric particle digital twin model according to the atmospheric particle concentration coefficient of each monitoring station; S3: Obtain the change value of each monitoring station, obtain the wind direction of each monitoring station, and mark the trend of atmospheric particulate matter in the atmospheric particulate matter digital twin model according to the change value and wind direction of each monitoring station; S4: Based on the change value of each monitoring station, the atmospheric particulate matter concentration coefficient, the atmospheric particulate matter concentration prediction coefficient and the atmospheric particulate matter trend control alarm, the management personnel are alerted.

[0037] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0038] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0039] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. An atmospheric particulate matter monitoring system, characterized in that: Includes the following modules: The monitoring information processing module establishes multiple monitoring stations, obtains the atmospheric particulate matter concentration of each monitoring station through the air quality model, obtains the environmental value of each monitoring station, and obtains the cause value of each monitoring station; The concentration distribution prediction module obtains the atmospheric particle concentration coefficient of each monitoring station through the atmospheric particle concentration, environmental value and cause value of each monitoring station, establishes an atmospheric particle digital twin model, and marks the atmospheric particle concentration prediction coefficient in the atmospheric particle digital twin model according to the atmospheric particle concentration coefficient of each monitoring station; The mobile trend prediction module obtains the change value of each monitoring station, obtains the wind direction of each monitoring station, and marks the trend of atmospheric particulate matter in the digital twin model of atmospheric particulate matter according to the change value and wind direction of each monitoring station; The monitoring and early warning control module activates alarms to remind management personnel based on the change values ​​of each monitoring station, the atmospheric particulate matter concentration coefficient, the atmospheric particulate matter concentration prediction coefficient and the atmospheric particulate matter trend control alarm.

2. An atmospheric particulate matter monitoring system according to claim 1, characterized in that: In the monitoring information processing module, the environmental values ​​of each monitoring station are obtained respectively, specifically: Step 1: Get the current outdoor temperature of each monitoring station, get the current outdoor humidity of each environmental station, and get the current outdoor air pressure of each monitoring station; Step 2: Obtain the environmental value of each monitoring station through the current outdoor temperature, outdoor humidity and outdoor air pressure of each monitoring station; The calculation method of the environmental value is as follows: Among them, Hj represents the environmental value, Qy represents the outdoor air pressure, Wd represents the outdoor temperature, and Sd represents the outdoor temperature. , and are weights, , , .

3. The atmospheric particulate matter monitoring system according to claim 1, characterized in that: In the monitoring information processing module, the cause value of each monitoring station is obtained respectively, specifically: Step 1: Set the influence distance, define the surrounding area of ​​each monitoring station with each monitoring station as the center and the influence distance as the radius, obtain the number of factories within the surrounding area of ​​each monitoring station, obtain the traffic density within the surrounding area of ​​each monitoring station, and obtain the number of construction sites within the surrounding area of ​​each monitoring station; Step 2: Obtain the current number of motor vehicles in the surrounding area of ​​each monitoring station, obtain the surrounding area, and obtain the traffic flow density by dividing the number of motor vehicles by the surrounding area; Step 3: Obtain the cause value of each monitoring station through the number of factories, traffic density and number of construction sites within the surrounding area of ​​each monitoring station; The calculation method of the cause value is as follows: Among them, Cy represents the cause value, Cm represents the traffic density, Jc represents the number of construction sites, and Gs represents the number of factories. , and are weights, , , .

4. The atmospheric particulate matter monitoring system according to claim 1, characterized in that: The calculation method of atmospheric particulate matter concentration coefficient in the concentration distribution prediction module is as follows: Among them, Nx represents the atmospheric particulate matter concentration coefficient, Kn represents the atmospheric particulate matter concentration, Hj represents the environmental value, and Cy represents the cause value. , and are weights, , , .

5. The atmospheric particulate matter monitoring system according to claim 3, characterized in that: A digital twin model of atmospheric particulate matter is established in the concentration distribution prediction module, specifically: Step 1: Obtain the geographic information of all monitoring stations and use the GIS model to establish a spatial model for atmospheric particulate matter monitoring; Step 2: Mark the locations of all monitoring stations in the atmospheric particulate matter monitoring spatial model, and mark the current atmospheric particulate matter concentration coefficient and current atmospheric particulate matter concentration at each monitoring station; Step 3: Get the surrounding area of ​​each monitoring station, and fill the surrounding area of ​​each monitoring station with red of different brightness according to the atmospheric particulate matter concentration coefficient marked on each monitoring station, that is, the higher the atmospheric particulate matter concentration coefficient, the lower the brightness.

6. The atmospheric particulate matter monitoring system according to claim 5, characterized in that: In the concentration distribution prediction module, the atmospheric particulate matter concentration prediction coefficient is marked in the atmospheric particulate matter digital twin model according to the atmospheric particulate matter concentration coefficient of each monitoring station, specifically: Step 1: Obtain the overlapping areas around all monitoring stations in the atmospheric particulate matter digital twin model and mark them as prediction areas, and obtain the atmospheric particulate matter concentration coefficient of each monitoring station in the prediction area; Step 2: Sum and average the atmospheric particulate matter concentration coefficients of all monitoring stations in the prediction area to obtain the atmospheric particulate matter concentration prediction coefficient, and mark the atmospheric particulate matter concentration prediction coefficient in the prediction area.

7. The atmospheric particulate matter monitoring system according to claim 3, characterized in that: In the moving trend prediction module, the change value of each monitoring station is obtained, specifically: Step 1: Set a preset height threshold, obtain the heights of all mountains within the surrounding area of ​​each monitoring station, mark the mountains whose heights are greater than the preset height threshold among all the mountains, and count the mountains whose heights are greater than the preset height threshold to obtain the number of high mountains; Step 2: Obtain wind speed measurements at three different edge locations and one central location within the perimeter of each monitoring station, and sum and average the four measured wind speeds to obtain the average wind speed; Step 3: Obtain the change value through the number of high mountains and average wind speed at each monitoring station; The calculation method of the change value is as follows: Among them, Dm represents the change value, Fs represents the average wind speed, and Sg represents the number of high mountains. and are weights, , .

8. The atmospheric particulate matter monitoring system according to claim 1, characterized in that: In the mobile trend prediction module, the trend of atmospheric particulate matter is marked in the atmospheric particulate matter digital twin model according to the change value and wind direction of each monitoring station, specifically: Step 1: Obtain the wind direction of each monitoring station and mark the wind direction of each monitoring station with an arrow in the atmospheric particulate matter digital twin model; Step 2: Obtain the change value of each monitoring station and mark the change value above the wind direction arrow of each monitoring station in the digital twin model of atmospheric particulate matter.

9. An atmospheric particulate matter monitoring system according to claim 8, characterized in that: Monitoring and early warning control module, specifically: Step 1: Determine whether the wind directions between two adjacent monitoring stations are opposite or in the same direction. If the wind directions between two adjacent monitoring stations are opposite, mark the area between the two monitoring stations as the area to be predicted. Step 2: Determine whether there is a monitoring intersection area between two adjacent monitoring stations in the area to be measured. If there is an intersection area between the two adjacent monitoring stations, obtain the atmospheric particulate matter concentration prediction coefficient of the intersection area, obtain the change values ​​of the two monitoring stations, and obtain the change sum value by summing the change values ​​of the two monitoring stations. Then, multiply the change sum value by the atmospheric particulate matter concentration prediction coefficient to obtain the atmospheric particulate matter concentration assessment coefficient. If there is no intersection area between adjacent monitoring stations, the atmospheric particle concentration coefficients of the two monitoring stations are obtained, the change values ​​of the two monitoring stations are obtained, the atmospheric particle concentration coefficients are multiplied by the corresponding change values ​​to obtain the atmospheric particle concentration transfer values, the two atmospheric particle transfer values ​​are summed and averaged to obtain the atmospheric particle transfer mean, the two atmospheric particle transfer values ​​are subtracted to obtain the atmospheric particle transfer difference, and the atmospheric particle transfer mean and the atmospheric particle transfer difference are summed to obtain the atmospheric particle concentration assessment coefficient; Step 3: Set a preset threshold value for the atmospheric particulate matter assessment coefficient, and compare the preset threshold value for the atmospheric particulate matter assessment coefficient with the atmospheric particulate matter concentration assessment coefficient. If the atmospheric particulate matter concentration assessment coefficient is greater than the preset threshold value for the atmospheric particulate matter assessment coefficient, an alarm is activated to alert the management personnel.

10. An atmospheric particulate matter monitoring method, applied to an atmospheric particulate matter monitoring system according to any one of claims 1 to 9, characterized in that: The steps include: S1: Establish multiple monitoring stations, and obtain the atmospheric particulate matter concentration, environmental value, and cause value of each monitoring station through the air quality model; S2: Obtain the atmospheric particle concentration coefficient of each monitoring station through the atmospheric particle concentration, environmental value and cause value of each monitoring station, establish an atmospheric particle digital twin model, and mark the atmospheric particle concentration prediction coefficient in the atmospheric particle digital twin model according to the atmospheric particle concentration coefficient of each monitoring station; S3: Obtain the change value of each monitoring station, obtain the wind direction of each monitoring station, and mark the trend of atmospheric particulate matter in the atmospheric particulate matter digital twin model according to the change value and wind direction of each monitoring station; S4: Based on the change value of each monitoring station, the atmospheric particulate matter concentration coefficient, the atmospheric particulate matter concentration prediction coefficient and the atmospheric particulate matter trend control alarm, the management personnel are alerted.

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