Dust multi-dimensional cloud monitoring and intelligent control system

Abstract

The invention discloses a dust multi-dimensional cloud monitoring and intelligent control system, which belongs to the technical field of environment monitoring and comprises scanning equipment, fixed monitoring equipment and dust suppression equipment. The system generates a quantized dust cloud picture by fusing the scanning distribution map and the real-time concentration value, and performs gridding analysis; performing reverse traceability on the standard exceeding points by using a reverse diffusion model, and determining a main pollution source grid; and automatically controlling the dust suppression equipment of the corresponding grid to start based on comparison between the real-time index and the threshold value. According to the invention, a monitoring blind area is eliminated, and global quantitative monitoring, accurate traceability and intelligent closed-loop treatment of dust are realized.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring technology, and more specifically, to a multi-dimensional cloud monitoring and intelligent control system for dust. Background Technology

[0002] Currently, dust pollution control in industrial settings such as bulk cargo ports, open-pit mines, and large storage yards primarily relies on traditional single-point fixed monitoring equipment to obtain dust concentration data. However, this traditional "point-based" monitoring method has significant limitations. Due to the fixed locations and limited number of monitoring devices, it can only reflect the local air quality at discrete points, failing to cover a wide operational area. This results in a large spatiotemporal blind spot in the monitoring data, making it difficult to capture the overall spatial distribution, diffusion path, and evolution trend of dust clouds under complex meteorological conditions. Although some technologies attempt to introduce scanning equipment such as lidar to obtain large-scale dust distribution data, simple scanning techniques often only provide qualitative or semi-quantitative data such as echo intensity, lacking real-time, accurate absolute concentration calibration. This makes it difficult to directly use the measurement results for stringent environmental compliance assessments. Furthermore, in terms of dust suppression control, existing systems mostly adopt a simple and crude "spray when exceeding the limit" linkage logic. That is, when the reading of a certain monitoring point exceeds the limit, the dust suppression equipment near that point is mechanically activated. This method ignores the physical characteristics of dust drifting with the wind, often resulting in a misalignment phenomenon where "the monitoring point alarms, but the dust source is upwind." This not only fails to accurately locate and control the real dust source, but also leads to delayed dust suppression operations, waste of water resources, and ineffective spraying in non-operational areas. There is a lack of a comprehensive monitoring and control system that can combine macroscopic spatial distribution with precise microscopic quantification, and based on this, perform grid-based source tracing and intelligent decision-making. Summary of the Invention

[0003] To address the aforementioned problems in the existing technology, the purpose of this invention is to provide a multi-dimensional cloud monitoring and intelligent control system for dust, used for multi-dimensional monitoring of dust.

[0004] To solve the above problems, the technical solution adopted by the present invention is as follows: a multi-dimensional cloud monitoring and intelligent control system for dust, comprising: Long-range scanning equipment is used to acquire spatial distribution maps of dust.

[0005] Multiple fixed-point monitoring devices are used to obtain real-time absolute dust concentration values.

[0006] Multiple dust suppression devices, each of which is associated with one or more preset work areas.

[0007] A processing server, wherein the processing server is deployed with: The data fusion and calibration module is used to receive the spatial distribution map and the absolute concentration value, and generate a quantified dust concentration cloud map.

[0008] The gridded analysis module is used to divide the port area into grid cells and calculate the real-time dust concentration index of each grid cell.

[0009] The intelligent decision-making and control module is used to compare the real-time dust concentration index with the dust suppression start threshold, and automatically send a start command to the dust suppression equipment corresponding to the over-limit grid unit when the limit is exceeded.

[0010] Preferably, the intelligent decision-making and control module further includes a source resolution computing engine, which is used to perform the following operations when receiving an out-of-range signal from a fixed-point monitoring device: The system receives the geographic coordinates of the monitoring points exceeding the standard, the geographic coordinates of all grid cells, and real-time meteorological data as model inputs; based on a preset reverse diffusion model, it calculates the source strength contribution coefficient of each grid cell to the monitoring points exceeding the standard; and sorts the grid cells according to the calculation results of the source strength contribution coefficients to generate and output a list of grid cells that identify the main pollution contribution sources.

[0011] Preferably, the processing server also stores the actual geographical location data of each grid cell; the system also includes a visualization management platform, which renders the color layer of each grid cell into a user-selectable graphic object, and in response to the user's selection operation, retrieves and displays the concentration time series chart of the grid cell associated with the selected graphic object within a preset period. Preferably, the visualization management platform also provides a timeline navigation control, which is used to receive the user's time range selection input and drive the platform to continuously render the dust concentration cloud map in time sequence to realize the visualization and retrospection of its spatiotemporal evolution process.

[0012] Preferably, the system further includes a meteorological data acquisition unit; when executing the reverse tracing algorithm, the intelligent decision-making and control module is used to: use the real-time wind direction data obtained from the meteorological data acquisition unit as the dominant vector for advection transport of pollutants in the reverse diffusion model; and, based on the real-time wind speed, query and determine the horizontal and vertical diffusion coefficients applicable to the model under the current conditions from a preset "atmospheric stability-diffusion parameter" mapping table.

[0013] Preferably, the system performs a monitoring method including: step S1, acquiring a dust spatial distribution map covering the entire port area using a long-distance scanning device; and simultaneously acquiring real-time absolute dust concentration values ​​at monitoring point locations using multiple fixed-point monitoring devices deployed within the port area.

[0014] Step S2: Using the real-time absolute dust concentration value, the dust spatial distribution map is quantitatively calibrated to generate a quantitative dust concentration cloud map covering the entire port area.

[0015] Step S3: Divide the port area into multiple grid units on the quantitative dust concentration cloud map, and assign a real-time dust concentration index to each grid unit.

[0016] Step S4: Compare the real-time dust concentration index of each grid cell with the preset dust suppression activation threshold.

[0017] Step S5: When it is determined that the real-time dust concentration index of a target grid cell exceeds the dust suppression activation threshold, an activation command is automatically sent to the dust suppression equipment covering the target grid cell to carry out dust suppression operations on the target grid cell.

[0018] Preferably, the fixed-point monitoring device is a plurality of light scattering dust monitors, and the plurality of fixed-point monitors are geographically deployed within the scanning coverage area of ​​the lidar.

[0019] Preferably, the quantitative calibration step in S2 includes: establishing a spatial correspondence between the echo signal intensity at different locations in the dust spatial distribution map and the physical location of the fixed-point monitoring device; generating a conversion function to convert the signal intensity into a concentration value by associating the echo signal intensity value at the corresponding location with the absolute concentration value; and applying the conversion function to the entire dust spatial distribution map.

[0020] Preferably, when the dust concentration value at a certain monitoring point exceeds the standard, a reverse calculation is performed based on the Gaussian diffusion model to quantify the contribution rate of the emission source strength of each grid unit to the concentration value at the monitoring point, and the grid unit with the highest contribution rate is identified as the main pollution source grid.

[0021] Preferably, the method further includes pre-establishing a mapping relationship between the identifier of each grid cell and the control address of at least one dust suppression device; the start command sent in S5 includes the control address corresponding to the target grid cell, and the dust suppression device is a zone-controlled automated spray system.

[0022] Preferably, the dust suppression activation threshold is determined by a preset rule set, which defines multiple thresholds corresponding to different wind speed and wind direction ranges. The method includes selecting the currently applicable threshold from the rule set based on real-time acquired meteorological data.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) This invention integrates the macroscopic spatial distribution data of long-distance scanning equipment with the precise absolute concentration values ​​of fixed-point monitoring equipment, and uses data fusion and calibration technology to generate a full-field quantitative dust concentration cloud map. This effectively solves the problems of "spatial blind spots" in single fixed monitoring methods and "quantitative difficulties" in simple scanning technology, and realizes the leap from local qualitative observation to global quantitative analysis of dust pollution in port areas, greatly improving the spatiotemporal resolution and accuracy of monitoring data.

[0024] (2) This invention introduces a source analysis calculation engine based on meteorological data and a reverse diffusion model. When the monitoring point values ​​exceed the standard, it can combine real-time wind direction, wind speed and atmospheric stability to perform reverse inference, accurately calculate and sort the source strength contribution of each grid unit. This mechanism can penetrate the appearance and trace the downstream exceedance phenomenon back to the actual dust source in the upwind direction, thereby avoiding the misplaced phenomenon of "treating the head when it hurts" in traditional governance, and providing a scientific basis for precise law enforcement and targeted governance.

[0025] (3) The present invention constructs a grid-based intelligent closed-loop control system. By establishing a precise mapping relationship between grid units and dust suppression equipment, and comparing real-time concentration indicators with dynamic thresholds containing meteorological correction rules, the automatic decision-making and zonal distribution of dust suppression instructions are realized. This not only realizes the refined operation of "treating dust wherever it occurs", but also effectively avoids ineffective spraying in non-operation areas, and significantly saves water resources and energy consumption while ensuring dust suppression efficiency. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of the system module structure of the present invention; Figure 2 This is a schematic diagram illustrating the exemplary steps of the monitoring method of the present invention. Detailed Implementation

[0027] The present invention will be further described below with reference to specific embodiments.

[0028] Example 1 like Figure 1 As shown, this embodiment provides a multi-dimensional cloud monitoring and intelligent control system for dust, including: Long-range scanning equipment is used to acquire spatial distribution maps of dust.

[0029] Multiple fixed-point monitoring devices are used to acquire real-time absolute dust concentration values. These devices are typically deployed at key nodes in the port area, such as yard boundaries, road intersections, and areas with frequent operations. Each device integrates a high-precision particulate matter sensor, capable of uploading PM2.5 and PM10 mass concentration data (unit: micrograms per cubic meter) every minute. Although these absolute concentration values ​​only reflect the situation at a single point, the data has legal validity and metrological accuracy, serving as a benchmark anchor for subsequent "calibration" of radar cloud images.

[0030] Multiple dust suppression devices are used, each associated with one or more pre-defined work areas. These devices include, but are not limited to, high-pole spray guns deployed around the storage yard, micro-mist nozzles on windbreak and dust suppression nets, and automatic water cannons along roadsides. Each dust suppression device is equipped with an independent PLC control unit and wireless communication module, and its physical coordinates, coverage radius, and associated work area ID are registered in the system backend, thus achieving a logical binding with a specific geographic space.

[0031] The processing server, which has the following deployed: The data fusion and calibration module is used to receive spatial distribution maps and absolute concentration values, and generate quantified dust concentration cloud maps.

[0032] The gridded analysis module is used to divide the port area into grid cells and calculate the real-time dust concentration index for each grid cell.

[0033] The intelligent decision-making and control module compares real-time dust concentration indicators with dust suppression activation thresholds and automatically sends activation commands to the dust suppression equipment corresponding to the exceeding grid unit when limits are exceeded. This module incorporates a complex rule engine. When the concentration index of a certain grid exceeds a set threshold (e.g., 150 μg / m³) for M consecutive minutes (e.g., 5 minutes), the system determines that dust pollution exists in that area. Subsequently, the module searches for all dust suppression equipment IDs covering that grid according to a preset mapping relationship and generates control commands containing parameters such as activation duration and spray angle, which are then sent to the field equipment via the Internet of Things (IoT) to execute the spraying operation.

[0034] The intelligent decision-making and control module also includes a source resolution computing engine, which performs the following operations when it receives a signal indicating that the fixed-point monitoring equipment has exceeded the limit: The system receives the geographic coordinates of the monitoring points exceeding the standard, the geographic coordinates of all grid cells, and real-time meteorological data as model inputs. Based on a pre-set reverse diffusion model, it calculates the source strength contribution coefficient of each grid cell to the monitoring points exceeding the standard. The grid cells are sorted according to the calculation results of the source strength contribution coefficients, and a list of grid cells identifying the main pollution sources is generated and output.

[0035] The processing server also stores the actual geographic location data of each grid cell; the system also includes a visualization management platform, which renders the color layer of each grid cell as a user-selectable graphic object, and in response to the user's selection operation, retrieves and displays the concentration time series chart of the grid cell associated with the selected graphic object within a preset period.

[0036] The visualization management platform also provides a timeline navigation control, which receives the user's time range selection input and drives the platform to continuously render dust concentration cloud maps in time sequence to achieve visualized backtracking of its spatiotemporal evolution process.

[0037] The system also includes a meteorological data acquisition unit; when executing the reverse tracing algorithm, the intelligent decision and control module is used to: use the real-time wind direction data obtained from the meteorological data acquisition unit as the dominant vector for advection transport of pollutants in the reverse diffusion model; and, based on the real-time wind speed, query and determine the horizontal and vertical diffusion coefficients applicable to the model under the current conditions from the preset "atmospheric stability-diffusion parameter" mapping table.

[0038] like Figure 2 As shown, the system executes monitoring methods, including: Step S1: A spatial distribution map of dust covering the entire port area is acquired using a long-range scanning device. Simultaneously, real-time absolute dust concentration values ​​are obtained at multiple fixed monitoring points deployed within the port area. The long-range scanning device operates in horizontal scanning mode, completing a 360-degree scan every 5-10 minutes to generate raw echo data in polar coordinate format. Meanwhile, the fixed monitoring points collect PM2.5 / PM10 data at a rate of one second and upload it to the server database via 4G / 5G networks.

[0039] Step S2: Quantitatively calibrate the dust spatial distribution map using real-time absolute dust concentration values ​​to generate a quantitative dust concentration cloud map covering the entire port area.

[0040] Step S3: Divide the port area into multiple grid units on the quantitative dust concentration cloud map, and assign a real-time dust concentration index to each grid unit.

[0041] Step S4: Compare the real-time dust concentration index of each grid cell with the preset dust suppression activation threshold.

[0042] Step S5: When it is determined that the real-time dust concentration index of a target grid cell exceeds the dust suppression start threshold, an start command is automatically sent to the dust suppression equipment covering the target grid cell to carry out dust suppression operations on the target grid cell.

[0043] The fixed-point monitoring equipment consists of multiple light scattering dust monitors, geographically deployed within the scanning coverage area of ​​the lidar. Light scattering monitors offer advantages such as fast response, small size, and low cost, making them suitable for large-scale grid deployment. Deploying them within the radar coverage area ensures that at least one ground truth reference point can be found for calibration in each sector scanned by the radar, thereby improving the accuracy of the overall inversion.

[0044] The quantitative calibration steps in S2 include establishing a spatial correspondence between the echo signal intensity at different locations in the dust spatial distribution map and the physical location of the fixed-point monitoring device; generating a conversion function to convert the signal intensity into a concentration value by associating the echo signal intensity value with the absolute concentration value at the corresponding location; and applying the conversion function to the entire dust spatial distribution map.

[0045] When the dust concentration at a certain monitoring point exceeds the standard, a reverse calculation is performed based on the Gaussian diffusion model to quantify the contribution rate of the emission source strength of each grid unit to the concentration value at the monitoring point, and the grid unit with the highest contribution rate is identified as the main pollution source grid.

[0046] In this embodiment, a mapping relationship is pre-established between the identifier of each grid cell and the control address of at least one dust suppression device. The start command sent in S5 contains the control address corresponding to the target grid cell, and the dust suppression device is an automated sprinkler system with zone control. The mapping relationship is stored in a table structure in a relational database, for example, {Grid_ID: A01, Device_IP: 192.168.1.10, Valve_ID: 2}. The start command is a data packet containing the target device IP and valve number. Zone control means that the system can precisely control the opening of only the sprinklers in the corresponding grid area without affecting the surrounding area, achieving precise smog control and water conservation.

[0047] The dust suppression activation threshold is determined by a preset rule set. This rule set defines multiple thresholds corresponding to different wind speed and direction ranges. The method involves selecting the applicable threshold from the rule set based on real-time acquired meteorological data. For example, the rule set specifies: when the wind speed is less than level 3, the threshold is 150; when the wind speed is greater than level 5, due to strong natural diffusion capabilities, the threshold can be relaxed to 200; or when the wind direction is towards residential areas, the threshold automatically tightens to 100. The system reads meteorological data every minute and dynamically updates the current judgment threshold by looking up a table, making the control strategy more scientific and user-friendly.

[0048] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A multi-dimensional cloud monitoring and intelligent control system for dust, characterized in that, include: Long-range scanning equipment is used to acquire spatial distribution maps of dust. Multiple fixed-point monitoring devices are used to obtain real-time absolute dust concentration values; Multiple dust suppression devices, each of which is associated with one or more preset work areas; A processing server, wherein the processing server is deployed with: The data fusion and calibration module is used to receive the spatial distribution map and the absolute concentration value, and generate a quantified dust concentration cloud map; The grid analysis module is used to divide the port area into grid cells and calculate the real-time dust concentration index of each grid cell. The intelligent decision-making and control module is used to compare the real-time dust concentration index with the dust suppression start threshold, and automatically send a start command to the dust suppression equipment corresponding to the over-limit grid unit when the limit is exceeded.

2. The multi-dimensional cloud monitoring and intelligent control system for dust according to claim 1, characterized in that: The intelligent decision-making and control module also includes a source resolution computing engine, which is used to perform the following operations when receiving an out-of-range signal from a fixed-point monitoring device: The system receives the geographic coordinates of the monitoring points exceeding the standard, the geographic coordinates of all grid cells, and real-time meteorological data as model inputs; based on a preset reverse diffusion model, it calculates the source strength contribution coefficient of each grid cell to the monitoring points exceeding the standard; and sorts the grid cells according to the calculation results of the source strength contribution coefficients to generate and output a list of grid cells that identify the main pollution contribution sources.

3. The multi-dimensional cloud monitoring and intelligent control system for dust according to claim 1, characterized in that: The processing server also stores the actual geographical location data of each grid cell; the system also includes a visualization management platform, which renders the color layer of each grid cell into a user-selectable graphic object, and in response to the user's selection operation, retrieves and displays the concentration time series chart of the grid cell associated with the selected graphic object within a preset period. The visualization management platform also provides a timeline navigation control, which is used to receive the user's time range selection input and drive the platform to continuously render the dust concentration cloud map in time sequence to realize the visualization and retrospection of its spatiotemporal evolution process.

4. The multi-dimensional cloud monitoring and intelligent control system for dust according to claim 1, characterized in that: The system also includes a meteorological data acquisition unit; when the intelligent decision-making and control module executes the reverse tracing algorithm, it is used to: use the real-time wind direction data obtained from the meteorological data acquisition unit as the dominant vector for advection transport of pollutants in the reverse diffusion model; Based on the real-time wind speed, the horizontal and vertical diffusion coefficients applicable to the model under the current conditions are queried and determined from the preset "Atmospheric Stability-Diffusion Parameter" mapping table.

5. The multi-dimensional cloud monitoring and intelligent control system for dust according to claim 1, characterized in that: The system executes a monitoring method, including: Step S1: Obtain a spatial distribution map of dust covering the entire port area using a long-distance scanning device; simultaneously, obtain real-time absolute dust concentration values ​​at monitoring points using multiple fixed-point monitoring devices deployed within the port area. Step S2: Using the real-time absolute dust concentration value, the dust spatial distribution map is quantitatively calibrated to generate a quantitative dust concentration cloud map covering the entire port area. Step S3: Divide the port area into multiple grid units on the quantitative dust concentration cloud map, and assign a real-time dust concentration index to each grid unit. Step S4: Compare the real-time dust concentration index of each grid cell with the preset dust suppression activation threshold. Step S5: When it is determined that the real-time dust concentration index of a target grid cell exceeds the dust suppression activation threshold, an activation command is automatically sent to the dust suppression equipment covering the target grid cell to carry out dust suppression operations on the target grid cell.

6. The multi-dimensional cloud monitoring and intelligent control system for dust according to claim 1, characterized in that: The fixed-point monitoring equipment consists of multiple light scattering dust monitors, and these multiple fixed-point monitors are geographically deployed within the scanning coverage area of ​​the lidar.

7. The multi-dimensional cloud monitoring and intelligent control system for dust according to claim 5, characterized in that: The quantitative calibration step in S2 includes establishing a spatial correspondence between the echo signal intensity at different locations in the dust spatial distribution map and the physical location of the fixed-point monitoring device; generating a conversion function to convert the signal intensity into a concentration value by associating the echo signal intensity value at the corresponding location with the absolute concentration value; and applying the conversion function to the entire dust spatial distribution map.

8. The multi-dimensional cloud monitoring and intelligent control system for dust according to claim 1, characterized in that: When the dust concentration at a certain monitoring point exceeds the standard, a reverse calculation is performed based on the Gaussian diffusion model to quantify the contribution rate of the emission source strength of each grid unit to the concentration value of the monitoring point, and the grid unit with the highest contribution rate is identified as the main pollution source grid.

9. The multi-dimensional cloud monitoring and intelligent control system for dust according to claim 1, characterized in that: It also includes pre-establishing a mapping relationship between the identifier of each grid cell and the control address of at least one dust suppression device; the start command sent in S5 includes the control address corresponding to the target grid cell, and the dust suppression device is a zone-controlled automated spray system.

10. The multi-dimensional cloud monitoring and intelligent control system for dust according to claim 1, characterized in that: The dust suppression activation threshold is determined by a preset rule set, which defines multiple thresholds corresponding to different wind speed and wind direction ranges. The method includes selecting the currently applicable threshold from the rule set based on real-time acquired meteorological data.

Images