Dynamic monitoring system and method for dust distribution and diffusion

Abstract

The invention relates to the technical field of dust monitoring, and discloses a dynamic monitoring system and method for dust distribution and diffusion, and the method comprises the steps: determining a monitoring region through a site positioning module, and configuring a ground monitoring site; the data acquisition module is used for acquiring numerical data of a ground monitoring station and integrating data of different dimensions; the data display module processes and displays the obtained numerical data and displays a trend curve of the analog data; the safety early warning module analyzes the numerical data, judges the standard exceeding condition of the numerical data, triggers early warning and extracts and displays radar data and meteorological data of the ground monitoring station when the numerical data of the ground monitoring station exceeds the standard, so that the response time to potential pollution events is shortened; detailed data of related monitoring stations are extracted in real time to assist in decision making; and the control execution module generates a spraying strategy according to the standard exceeding condition of the numerical data, and controls the spraying group to execute the spraying strategy. According to the invention, the resource use is optimized, and the purification efficiency is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of dust monitoring, and relates to a supporting sliding device, in particular to a dynamic monitoring system and method for dust distribution and diffusion. Background Art

[0002] As industrial activities, especially mining, have an increasingly serious impact on the environment, dust is one of the important pollutants. Dust monitoring and control has become an important task for environmental protection. Traditional dust monitoring mainly relies on manual sampling and analysis. This method is time-consuming and labor-intensive, and it is difficult to achieve real-time monitoring and large-scale spatial coverage. The monitoring system based on machine vision can quickly identify and locate dust and quickly respond to dust pollution events. However, most of them have not solved how to determine the monitoring area, configure ground monitoring stations and obtain numerical data from ground monitoring stations, display the obtained numerical data, analyze the numerical data and judge the exceeding of the numerical data to trigger an early warning, and generate a spray strategy to control the spray group to execute the spray strategy.

[0003] For example, the Chinese patent application with publication number CN115683966A discloses a method and system for dynamic monitoring of dust distribution in an open-pit mine, which belongs to the field of dust monitoring technology. First, the collection board is determined according to the physical and chemical properties, particle size and observation field area of ​​the dust in the mine; secondly, the dust collection point is determined according to the collection conditions; then the light reflected on the collection board at each collection point is monitored to obtain the spectral reflectivity; finally, the amount of dust falling is determined according to the spectral reflectivity and a dust distribution area map is drawn. By reasonably setting the collection points and selecting the collection boards, the collected dust can reflect the environmental status of the workplace as much as possible, and the spectral reflectivity is obtained through light collection, and then the dust distribution area map is automatically drawn. The operation is simple, and large-area open-pit mines can be equipped with drones for fixed-point monitoring, which is not affected by the scope of the test area, and overcomes the problems of traditional manual monitoring, such as large dust monitoring workload, long collection time, and low data reliability.

[0004] For example, the Chinese patent with the authorization announcement number CN116087042B discloses a real-time monitoring system and method for the spatial distribution of dust deposition. First, the real-time monitoring unit of dust deposition is calibrated, and the calibration coefficient of each unit is obtained and uploaded to the cloud server; multiple calibrated monitoring units are used to realize real-time and continuous monitoring of dust deposition at multiple monitoring points in the workplace, and positioning marking is performed based on the GPS position sensor in the real-time monitoring unit of dust deposition, marking the relative positions of different monitoring units, and uploading the data collected by different monitoring units to the cloud server. A real-time monitoring database for the spatial distribution of dust deposition is established to realize the uploading of real-time monitoring data, cloud storage and data call; the cloud decision-making platform predicts the dust deposition of the entire spatial scale of the workplace based on the real-time data of dust deposition of monitoring units at different locations and the temperature, humidity, ground wind speed, and relative position of the monitoring units according to the deep learning spatial prediction model.

[0005] For example, a Chinese patent with authorization announcement number CN114264582B discloses an ultra-low dust online monitoring and analysis control system, including: a setting module, used to set dust measurement points according to the dust monitoring area; a monitoring module, used to collect dust gas at the dust measurement points, enter the analysis space, and obtain the dust concentration at each dust measurement point; an analysis module, based on the dust concentration of each dust measurement point, determines the spatial dust dynamic law in the dust monitoring area; a control module, based on the spatial dust dynamic law, determines the spray scheme and controls the dust concentration in the dust monitoring area; by setting the dust measurement points according to the dust monitoring area, all-round dust concentration detection of the dust monitoring area is achieved, thereby determining the spatial dust dynamic law, ensuring the accuracy of the dust monitoring area, and determining a suitable spray scheme to achieve dust concentration control in the dust monitoring area.

[0006] The above-mentioned open-pit mine dust distribution dynamic monitoring method determines the collection board through the physical and chemical properties, particle size and observation field area of ​​the mine dust, determines the dust collection point according to the collection conditions, monitors the light reflected on the collection board at each collection point to obtain the spectral reflectivity, determines the dust amount according to the spectral reflectivity and draws the dust distribution area; the above-mentioned dust deposition spatial distribution real-time monitoring system calibrates the dust deposition real-time monitoring unit, obtains the correction coefficient of each unit and uploads it to the cloud server, and uses the calibrated multiple monitoring units to realize real-time and continuous monitoring of dust deposition at multiple monitoring points in the workplace, and performs positioning marking based on the GPS position sensor in the dust deposition real-time monitoring unit to realize the marking of the relative positions of different monitoring units, and uploads the data collected by different monitoring units to the cloud server.

[0007] The three public patents listed above do not solve the problem of how to determine the monitored area, configure ground monitoring stations and obtain numerical data of ground monitoring stations, display the obtained numerical data, analyze the numerical data to reflect the trajectory of pollutants, judge the exceeding of the numerical data to trigger an early warning, and generate a spray strategy according to the predicted trajectory to control the spray group to execute the spray strategy. To solve this problem, the present invention proposes a dynamic monitoring system and method for dust distribution and diffusion. Summary of the invention

[0008] The purpose of the present invention is to overcome the deficiencies of the prior art and to provide a system and method for dynamic monitoring of dust distribution and diffusion, so as to overcome the problems raised in the above-mentioned background technology.

[0009] The present invention solves the technical problem by adopting the following technical solutions:

[0010] A dynamic monitoring system for dust distribution and diffusion, characterized by comprising a site positioning module, a data acquisition module, a data display module, a safety warning module and a control execution module;

[0011] The site positioning module is used to determine the monitoring area and configure the ground monitoring site;

[0012] The data acquisition module is used to acquire numerical data of ground monitoring sites;

[0013] The data display module is used to process and display the acquired numerical data and display the trend curve of the simulation data;

[0014] The safety warning module is used to analyze the numerical data, determine the exceeding of the numerical data, and trigger the warning when the numerical data of the ground monitoring site exceeds the standard, and extract and display the radar data and meteorological data of the ground monitoring site;

[0015] The control execution module is used to generate a spray strategy according to the exceeding of the standard of the numerical data, and control the spray group to execute the spray strategy.

[0016] Moreover, the numerical data includes meteorological data, radar data and simulation data. The meteorological data includes temperature, humidity, wind speed and wind direction. The radar data includes simulation data exceeding the standard and location longitude and latitude. The simulation data includes PM2.5, PM10 and TSP.

[0017] Furthermore, the specific steps of obtaining the numerical data of the ground monitoring site include:

[0018] Automatically adjust the acquisition frequency of numerical data according to the changes of meteorological data and simulation data, configure the change threshold and frequency increment, compare the changes of meteorological data and simulation data with the change threshold, and use the basic acquisition frequency of numerical data if the changes of meteorological data and simulation data are less than the change threshold;

[0019] If the change between the meteorological data and the simulation data is greater than or equal to the change threshold, the base acquisition frequency of the numerical data plus the frequency increment is used.

[0020] Furthermore, the specific steps of processing and displaying the acquired numerical data include:

[0021] Intelligent interpolation is performed on missing or blank numerical data. A complete numerical data is selected, the missing or blank numerical data is compared with the complete numerical data, and a similarity threshold is configured. If the similarity between the missing or blank numerical data and the complete numerical data is greater than or equal to the similarity threshold, the complete numerical data is filled into the missing or blank numerical data.

[0022] If the similarity between the missing or blank numerical data and the complete numerical data is less than the similarity threshold, another complete numerical data is selected.

[0023] Moreover, the function expression of the similarity between the missing or blank numerical data and the complete numerical data is as follows:

[0024]

[0025] Where k(x,x') represents the similarity between missing or blank numerical data and complete numerical data, σ represents the amplitude adjustment factor, l represents the speed adjustment factor, x represents missing or blank numerical data, and x' represents complete numerical data.

[0026] Furthermore, the specific steps of determining whether the numerical data exceeds the standard include:

[0027] Analyze the numerical data, calculate the dynamic safety threshold according to the meteorological data, compare the concentration of the simulated data with the dynamic safety threshold, and if the concentration of the simulated data is greater than or equal to the dynamic safety threshold, it is determined that the numerical data exceeds the standard;

[0028] If the concentration of the simulated data is less than the dynamic safety threshold, the numerical data is judged to be within the limit;

[0029] The function expression of the dynamic safety threshold is as follows:

[0030] m=m 0 ×e α×w ×e β×h ×(e t +λ);

[0031] In the formula, m represents the dynamic safety threshold, m 0 represents the basic safety threshold, e represents a constant, w represents the wind speed in the meteorological data, α represents the wind speed adjustment factor, h represents the humidity in the meteorological data, β represents the humidity adjustment factor, t represents the temperature in the meteorological data, and λ represents the temperature adjustment factor.

[0032] Furthermore, the spraying strategy includes:

[0033] Determine the spray intensity of the spray group according to the concentration of the simulation data, the basic safety threshold and the dynamic safety threshold, configure the basic spray range, the high spray range and the extreme spray range, and configure the spray range of the spray group according to the comparison between the concentration of the simulation data and different multiples of the basic safety threshold;

[0034] The function expression of the spray intensity of the spray group is as follows:

[0035] s=max(0,(pm 0 )×m);

[0036] Where s represents the spray intensity of the spray group, max(·) represents the maximum value, and p represents the concentration of the simulated data.

[0037] Moreover, the function expression of the spray range of the spray group is as follows:

[0038]

[0039] In the formula, r represents the spray range of the spray group, r 1 Indicates the basic range of the spray, r 2 Indicates the high value range of spraying, r 3 Indicates the spray extreme range.

[0040] A dynamic monitoring method for dust distribution and diffusion, comprising:

[0041] S1. Determine the monitoring area and configure ground monitoring sites;

[0042] S2, obtaining numerical data of ground monitoring stations;

[0043] S3, processing and displaying the acquired numerical data, and displaying the trend curve of the simulation data;

[0044] S4, analyzing the numerical data, determining whether the numerical data exceeds the standard, and triggering an early warning when the numerical data of the ground monitoring station exceeds the standard, extracting and displaying the radar data and meteorological data of the ground monitoring station;

[0045] S5. Generate a spray strategy based on the exceeding of the numerical data, and control the spray group to execute the spray strategy.

[0046] A computer device includes a memory for storing instructions and a processor for executing the instructions, so that the computer device implements a dynamic monitoring method for dust distribution and diffusion.

[0047] A computer-readable storage medium stores a computer program, which, when executed, implements a dynamic monitoring method for dust distribution and diffusion.

[0048] The advantages and positive effects of the present invention are:

[0049] The present invention is a dynamic monitoring system and method for dust distribution and diffusion. The monitoring area is determined by a site positioning module, and a ground monitoring site is configured. The data acquisition module acquires the numerical data of the ground monitoring site, integrates data of different dimensions, and provides comprehensive data support for decision-making. The data display module processes and displays the acquired numerical data, and displays the trend curve of the simulated data at the same time, so that users can timely understand the current environmental conditions, provide data-based decision support, and help managers better understand the behavior of pollutants and make scientific decisions. The safety warning module analyzes the numerical data, determines the exceeding of the numerical data, and triggers the warning when the numerical data of the ground monitoring site exceeds the standard, extracts and displays the radar data and meteorological data of the ground monitoring site, shortens the response time to potential pollution events, and assists in decision-making by extracting detailed data of relevant ground monitoring sites in real time. The control execution module generates a spraying strategy according to the exceeding of the numerical data, controls the spraying group to execute the spraying strategy, intelligently generates the spraying strategy according to the exceeding numerical data and distribution, and automatically adjusts the spraying intensity and area, which not only optimizes resource use, but also improves purification efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a system structure diagram of a dynamic monitoring system for dust distribution and diffusion of the present invention;

[0051] Figure 2 A flow chart of the steps of acquiring data for a dynamic monitoring system for dust distribution and diffusion of the present invention;

[0052] Figure 3 A flow chart of the steps of judging data exceeding the standard in a dynamic monitoring system for dust distribution and diffusion of the present invention;

[0053] Figure 4 The present invention is a method flow chart of a method for dynamic monitoring of dust distribution and diffusion. DETAILED DESCRIPTION

[0054] The embodiments of the present invention are further described in detail below with reference to the accompanying drawings:

[0055] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein, and those skilled in the art can make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0056] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0057] Example 1

[0058] In this embodiment, a system structure diagram of a dynamic monitoring system for dust distribution and diffusion is provided, such as Figure 1 As shown, a dynamic monitoring system for dust distribution and diffusion includes a site positioning module, a data acquisition module, a data display module, a safety warning module and a control execution module.

[0059] The site positioning module is used to determine the monitoring area and configure ground monitoring sites.

[0060] Determine the areas that need to be monitored based on the historical dust pollution situation, plan the number and approximate locations of ground monitoring stations according to the shape and size of the area, use GIS tools to analyze terrain and population density, ensure that ground monitoring stations can cover all key areas, especially areas where dust pollution incidents have occurred before, conduct on-site surveys of the scheduled ground monitoring stations, evaluate the actual environment and feasibility of the ground monitoring stations, including power supply, network connection and security conditions, deploy necessary monitoring equipment at each ground monitoring station, including sensors for PM2.5, PM10 and total suspended particulate matter TSP and meteorological instruments, regularly maintain monitoring equipment to ensure that the monitoring data obtained can be maintained at a high standard for a long time.

[0061] The data acquisition module is used to obtain numerical data from ground monitoring sites.

[0062] Numerical data include meteorological data, radar data and simulation data. Meteorological data include temperature, humidity, wind speed and wind direction. Radar data include simulation data exceeding the standard and location longitude and latitude. Simulation data include PM2.5, PM10 and TSP.

[0063] The specific steps to obtain numerical data from ground monitoring stations are as follows: Figure 2 As shown, including:

[0064] Automatically adjust the acquisition frequency of numerical data according to the changes of meteorological data and simulation data, configure the change threshold and frequency increment, compare the changes of meteorological data and simulation data with the change threshold, and use the basic acquisition frequency of numerical data if the changes of meteorological data and simulation data are less than the change threshold;

[0065] If the change between the meteorological data and the simulation data is greater than or equal to the change threshold, the base acquisition frequency of the numerical data plus the frequency increment is used.

[0066] The function expression for automatically adjusting the frequency of obtaining numerical data is as follows:

[0067]

[0068] In the formula, f represents the frequency of obtaining numerical data, f 0 represents the basic acquisition frequency of numerical data, Δf represents the frequency increment, c represents the standard deviation of meteorological data and simulation data, and c 0 Indicates the change threshold.

[0069] It should be explained that the standard deviation c of the meteorological data and the simulation data is used to represent the variation of the meteorological data and the simulation data.

[0070] When acquiring numerical data from ground monitoring sites, the frequency of acquiring numerical data is automatically adjusted according to changes in meteorological data and simulation data. For example, the frequency of acquiring numerical data may be increased during pollution peaks or when meteorological data is unstable, and may be reduced in places where meteorological data is stable or pollution incidents do not occur frequently.

[0071] The data display module is used to process and display the acquired numerical data and to display the trend curve of the simulation data.

[0072] The specific steps for processing and displaying the acquired numerical data include:

[0073] Intelligent interpolation is performed on missing or blank numerical data. A complete numerical data is selected, the missing or blank numerical data is compared with the complete numerical data, and a similarity threshold is configured. If the similarity between the missing or blank numerical data and the complete numerical data is greater than or equal to the similarity threshold, the complete numerical data is filled into the missing or blank numerical data.

[0074] If the similarity between the missing or blank numerical data and the complete numerical data is less than the similarity threshold, another complete numerical data is selected.

[0075] The function expression for the similarity between missing or blank numerical data and complete numerical data is as follows:

[0076]

[0077] Where k(x,x') represents the similarity between missing or blank numerical data and complete numerical data, σ represents the amplitude adjustment factor, l represents the speed adjustment factor, x represents missing or blank numerical data, and x' represents complete numerical data.

[0078] It needs to be explained that the amplitude adjustment factor σ is used to control the smoothness of filling missing or blank numerical data. The larger the amplitude adjustment factor σ is, the smoother the filled numerical data is. The speed adjustment factor l is used to control the speed at which the similarity between the missing or blank numerical data and the complete numerical data decays with the distance between the missing or blank numerical data and the complete numerical data. The smaller the speed adjustment factor l is, the missing or blank numerical data can find a complete numerical data with high similarity more quickly.

[0079] After receiving the numerical data, the numerical data is preprocessed, including format conversion and timestamp alignment, to ensure the consistency and comparability of the numerical data. According to the above specific steps, the missing or blank numerical data is intelligently interpolated, and the acquired numerical data, including meteorological data and simulation data, are displayed in real time. The radar data will be displayed when the numerical data of a certain ground monitoring station exceeds the standard, and the trend curve of the simulation data will be displayed at the same time. The trend curve of the simulation data here is a trend curve chart of PM2.5, PM10 and TSP drawn according to the time series, which is convenient for observing and analyzing the changing trend of the simulation data. At the same time, user interaction functions are provided to facilitate users to view the numerical data of a certain ground monitoring station in a certain time period in detail. At the same time, combined with GIS technology, the spatial distribution characteristics of numerical data are displayed, such as displaying the pollution degree of different ground monitoring stations through heat maps.

[0080] The safety warning module is used to analyze numerical data and determine whether the numerical data exceeds the standard. When the numerical data of the ground monitoring station exceeds the standard, an early warning is triggered to extract and display the radar data and meteorological data of the ground monitoring station.

[0081] The specific steps for judging the exceeding of numerical data are as follows: Figure 3 As shown, including:

[0082] Analyze the numerical data, calculate the dynamic safety threshold according to the meteorological data, compare the concentration of the simulated data with the dynamic safety threshold, and if the concentration of the simulated data is greater than or equal to the dynamic safety threshold, it is determined that the numerical data exceeds the standard;

[0083] If the concentration of the simulation data is less than the dynamic safety threshold, it is determined that the numerical data does not exceed the standard.

[0084] The function expression of the dynamic safety threshold is as follows:

[0085] m=m0 ×e α×w ×e β×h ×(e t +λ);

[0086] In the formula, m represents the dynamic safety threshold, m 0 represents the basic safety threshold, e represents a constant, w represents the wind speed in the meteorological data, α represents the wind speed adjustment factor, h represents the humidity in the meteorological data, β represents the humidity adjustment factor, t represents the temperature in the meteorological data, and λ represents the temperature adjustment factor.

[0087] It should be explained that the wind speed adjustment factor α is a negative coefficient, which means that an increase in wind speed will lead to a decrease in the wind speed adjustment factor. The humidity adjustment factor β is a positive coefficient, which means that an increase in humidity will lead to an increase in the humidity adjustment factor. The temperature adjustment factor λ is used to fit the actual temperature data.

[0088] Meteorological data and simulation data are combined to more accurately determine the exceeding of numerical data and trigger an early warning. For meteorological data, low wind speed and high humidity usually make it difficult for dust pollutants to spread. When this situation is detected, the dynamic safety threshold needs to be lowered to make the early warning operation more sensitive. At the same time, temperature also affects the diffusion of dust pollutants, and wind direction determines the propagation path of dust pollution. The dynamic safety threshold is calculated by the above formula, and the dynamic safety threshold is compared with the concentration of simulation data to determine the exceeding of numerical data. Here, the concentration of simulation data is used for comparison, and the final comparison is the concentration corresponding to PM2.5, PM10 or TSP. When the numerical data of the ground monitoring station exceeds the standard, an early warning is triggered, and the radar data and meteorological data of the ground monitoring station are extracted and displayed, and they are displayed emphatically.

[0089] The control execution module is used to generate a spray strategy according to the exceeding of the numerical data and control the spray group to execute the spray strategy.

[0090] Spraying strategies include:

[0091] The spray intensity of the spray group is determined according to the concentration of the simulation data, the basic safety threshold and the dynamic safety threshold, and the basic spray range, high spray range and extreme spray range are configured. The spray range of the spray group is configured respectively according to the comparison between the concentration of the simulation data and different multiples of the basic safety threshold.

[0092] The function expression of the spray intensity of the spray group is as follows:

[0093] s=max(0,(pm 0 )×m);

[0094] Where s represents the spray intensity of the spray group, max(·) represents the maximum value, and p represents the concentration of the simulated data.

[0095] The function expression of the spray range of the spray group is as follows:

[0096]

[0097] In the formula, r represents the spray range of the spray group, r 1 Indicates the basic range of the spray, r 2 Indicates the high value range of spraying, r 3 Indicates the spray extreme range.

[0098] Different levels of spraying intensity and spraying range are adopted according to the degree of dust pollutants exceeding the standard. For example, under conditions of low wind speed and high humidity, the spraying intensity and spraying range are increased to promote the sedimentation of dust pollutants. After the implementation of the spraying strategy, meteorological data and simulation data are continuously monitored to evaluate the spraying effect of the spray group. The spray group here is used to spray water to cover dust pollutants, and the spraying strategy is adjusted according to feedback.

[0099] Example 2

[0100] In this embodiment, a method flow chart for a dynamic monitoring method for dust distribution and diffusion is provided, such as Figure 4 As shown, a dynamic monitoring method for dust distribution and diffusion includes:

[0101] S1. Determine the monitoring area and configure ground monitoring stations.

[0102] S2. Obtain numerical data of ground monitoring sites.

[0103] S3. Process and display the acquired numerical data, and display the trend curve of the simulation data.

[0104] S4. Analyze the numerical data and determine whether the numerical data exceeds the standard. When the numerical data of the ground monitoring station exceeds the standard, trigger an early warning, extract and display the radar data and meteorological data of the ground monitoring station.

[0105] S5. Generate a spray strategy based on the exceeding of the numerical data, and control the spray group to execute the spray strategy.

[0106] The specific contents of a dynamic monitoring method for dust distribution and diffusion are referred to a dynamic monitoring system for dust distribution and diffusion, which will not be described in detail here.

[0107] Example 3

[0108] In this embodiment, a computer device is provided, including a memory and a processor, the memory is used to store instructions, and the processor is used to execute the instructions, so that the computer device executes the steps of implementing the above-mentioned method for dynamic monitoring of dust distribution and diffusion.

[0109] Example 4

[0110] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed, the steps of the above-mentioned method for dynamic monitoring of dust distribution and diffusion are implemented.

[0111] The computer-readable storage medium includes: a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk, and other media for storing program codes.

[0112] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A dynamic monitoring system for dust distribution and diffusion, characterized in that: It includes site positioning module, data acquisition module, data display module, safety warning module and control execution module; The site positioning module is used to determine the monitoring area and configure the ground monitoring site; The data acquisition module is used to acquire numerical data of ground monitoring sites; The data display module is used to process and display the acquired numerical data, and simultaneously display the trend curve of the simulation data and the pollutant trajectory; The safety warning module is used to analyze the numerical data, determine the exceeding of the numerical data, and trigger the warning when the numerical data of the ground monitoring site exceeds the standard, and extract and display the radar data and meteorological data of the ground monitoring site; The control execution module is used to generate a spray strategy according to the exceeding of the standard of the numerical data, and control the spray group to execute the spray strategy.

2. A dynamic monitoring system for dust distribution and diffusion as claimed in claim 1, characterized in that: The numerical data includes meteorological data, radar data and simulation data; the meteorological data includes temperature, humidity, wind speed and wind direction; the radar data includes simulation data exceeding the standard, location longitude and latitude; the simulation data includes PM2.5, PM10 and TSP; The specific steps of obtaining the numerical data of the ground monitoring site include: Automatically adjust the acquisition frequency of numerical data according to the changes of meteorological data and simulation data, configure the change threshold and frequency increment, compare the changes of meteorological data and simulation data with the change threshold, and use the basic acquisition frequency of numerical data if the changes of meteorological data and simulation data are less than the change threshold; If the change between the meteorological data and the simulation data is greater than or equal to the change threshold, the base acquisition frequency of the numerical data plus the frequency increment is used.

3. A dynamic monitoring system for dust distribution and diffusion as claimed in claim 2, characterized in that: The specific steps of processing and displaying the acquired numerical data include: Intelligent interpolation is performed on missing or blank numerical data. A complete numerical data is selected, the missing or blank numerical data is compared with the complete numerical data, and a similarity threshold is configured. If the similarity between the missing or blank numerical data and the complete numerical data is greater than or equal to the similarity threshold, the complete numerical data is filled into the missing or blank numerical data. If the similarity between the missing or blank numerical data and the complete numerical data is less than the similarity threshold, another complete numerical data is selected.

4. A dynamic monitoring system for dust distribution and diffusion as claimed in claim 3, characterized in that: The function expression of the similarity between the missing or blank numerical data and the complete numerical data is as follows: Where k(x,x') represents the similarity between the missing or blank numerical data and the complete numerical data, σ represents the amplitude adjustment factor, l represents the speed adjustment factor, x represents the missing or blank numerical data, and x' represents the complete numerical data.

5. A dynamic monitoring system for dust distribution and diffusion as claimed in claim 4, characterized in that: The specific steps of determining the exceeding of the numerical data include: Analyze the numerical data, calculate the dynamic safety threshold according to the meteorological data, compare the concentration of the simulated data with the dynamic safety threshold, and if the concentration of the simulated data is greater than or equal to the dynamic safety threshold, it is determined that the numerical data exceeds the standard; If the concentration of the simulated data is less than the dynamic safety threshold, the numerical data is judged to be within the limit; The function expression of the dynamic safety threshold is as follows: m=m0×e α×w ×e β×h ×(e t +λ); Where m represents the dynamic safety threshold, m0 represents the basic safety threshold, e represents a constant, w represents the wind speed in the meteorological data, α represents the wind speed adjustment factor, h represents the humidity in the meteorological data, β represents the humidity adjustment factor, t represents the temperature in the meteorological data, and λ represents the temperature adjustment factor.

6. A dynamic monitoring system for dust distribution and diffusion as claimed in claim 5, characterized in that: Pollutant tracks include: Pollutant particles in the atmosphere are the result of the combined influence of wind force, wind direction, humidity, etc. Diffusion is the forward prediction, and tracing is the backward trajectory. The trajectory analysis method is based on the meteorological field obtained by observation interpolation or simulation to estimate the most likely transmission path from the source to the receptor area at a specific time. The backward trajectory problem is studied using the hybrid single-particle Lagrangian integral transmission diffusion model calculation. The trajectory calculation principle assumes that a passive tracer particle moves with the wind and is at position P(t) at time t. The position after the time step Δt is: P(t+Δt)=P(t)+0.5[V(P,t)+V(P′,t+Δt)]Δt In the formula, P' is an intermediate guess position, and the requirement for the time step Δt during the calculation process is: P(t+Δt)=P(t)+V(P, t)Δt.

7. A dynamic monitoring system for dust distribution and diffusion as claimed in claim 6, characterized in that: The spraying strategy includes: Determine the spray intensity of the spray group according to the concentration of the simulation data, the basic safety threshold and the dynamic safety threshold, configure the basic spray range, the high spray range and the extreme spray range, and configure the spray range of the spray group according to the comparison between the concentration of the simulation data and different multiples of the basic safety threshold; The function expression of the spray intensity of the spray group is as follows: s=max(0,(p-m0)×m); In the formula, s represents the spray intensity of the spray group, max(·) represents the maximum value, and p represents the concentration of the simulated data; The function expression of the spray range of the spray group is as follows: In the formula, r represents the spraying range of the spray group, r1 represents the spraying basic range, r2 represents the spraying high value range, and r3 represents the spraying extreme value range.

8. A method for dynamic monitoring of dust distribution and diffusion, implemented based on a dynamic monitoring system for dust distribution and diffusion according to any one of claims 1 to 7, characterized in that: include, S1. Determine the monitoring area and configure ground monitoring sites; S2, obtaining numerical data of ground monitoring stations; S3, processing and displaying the acquired numerical data, and displaying the trend curve of the simulation data; S4, analyzing the numerical data, determining whether the numerical data exceeds the standard, and triggering an early warning when the numerical data of the ground monitoring station exceeds the standard, extracting and displaying the radar data and meteorological data of the ground monitoring station; S5. Generate a spray strategy based on the exceeding of the numerical data, and control the spray group to execute the spray strategy.

9. A computer device, characterized in that: include: A memory for storing instructions; The processor is used to execute the instructions so that the computer device implements the dynamic monitoring method for dust distribution and diffusion as described in claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed, a dynamic monitoring method for dust distribution and diffusion as described in claim 8 is implemented.

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