A comprehensive management system for dust pollution level classification and intelligent early warning

Abstract

This invention belongs to the field of environmental monitoring and intelligent early warning technology, specifically a comprehensive management system for dust pollution level classification and intelligent early warning. It includes a multi-source environmental sensing layer, a pollution cause identification module, a spatiotemporal evolution modeling unit, a risk weight configuration module, a pollution level determination core, and an early warning strategy generation module. By integrating multi-source sensing data to identify the dominant causes of dust pollution, combining a physical diffusion model to deduce spatiotemporal evolution trends, and overlaying a regional sensitivity weight matrix to generate a weighted pollution heat map, the invention ultimately determines the pollution level based on a three-dimensional coupling of the area exceeding the standard, the causal risk coefficient, and the duration, driving a graded early warning response. By coupling pollution cause identification with the physical evolution model, misjudgments caused by relying solely on instantaneous concentration values ​​are avoided.

Description

Technical Field

[0001] This invention belongs to the field of environmental monitoring and intelligent early warning technology, specifically a comprehensive management system for classifying dust pollution levels and providing intelligent early warning. Background Technology

[0003] Current mainstream dust pollution monitoring solutions build a network of fixed or mobile monitoring devices with particulate matter concentration sensors as the core. They combine meteorological parameters such as wind speed and humidity to compare thresholds and achieve preliminary assessment of dust levels in local areas and alarms for exceeding limits. This solution is based on the linear mapping logic of concentration and risk, which assumes that the higher the particulate matter concentration, the more serious the pollution level. It has certain practicality in static and isolated scenarios (such as construction site entrances and key road sections) and has been widely adopted as a common industry practice due to the improved visibility of supervision.

[0004] In related technologies, over-reliance on instantaneous concentration values ​​to determine pollution levels ignores the spatiotemporal dynamic evolution characteristics and causal heterogeneity of dust pollution, resulting in early warnings lacking physical interpretability and decision support. Dust pollution is a complex process involving the coupling of multiple factors such as emission intensity and diffusion conditions; the environmental and social risks of the same concentration vary significantly under different meteorological conditions and in different regions. Due to a lack of modeling capabilities for key dimensions such as pollution causes, duration, and receptor sensitivity, it is easy to produce misjudgments such as high reporting of low risk and low reporting of high risk, leading to early warning fatigue, delayed response, and misallocation of regulatory resources; even increasing sensor density or optimizing algorithms cannot solve the cognitive misalignment between underlying static indicators and dynamic processes.

[0005] Therefore, the present invention provides a comprehensive management system for classifying dust pollution levels and providing intelligent early warning. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0007] The technical solution adopted by the present invention to solve its technical problem is as follows: The dust pollution level classification and intelligent early warning integrated management system of the present invention includes a multi-source environmental perception layer, a pollution cause identification module, a spatiotemporal evolution modeling unit, a risk weight configuration module, a pollution level determination core, an early warning strategy generation module, and an execution feedback interface. The modules are interconnected through a standardized communication protocol to form a three-in-one collaborative working mechanism of data flow, control flow, and feedback flow.

[0008] The multi-source environmental perception layer consists of distributed sensing nodes deployed within the target area. Each sensing node integrates a particulate matter concentration sensor, a meteorological parameter acquisition unit, a video image acquisition device, and a geolocation module. The particulate matter concentration sensor is used to acquire PM10 mass concentration data in real time. The meteorological parameter acquisition unit includes an anemometer, a humidity sensor, and an atmospheric pressure gauge, used to simultaneously acquire wind speed, relative humidity, and air pressure information. The video image acquisition device uses a dual-mode camera with visible light and near-infrared capabilities to capture surface disturbances, signs of construction activities, and material coverage. The geolocation module uses the Global Navigation Satellite System to accurately calibrate the node's location and uploads all sensing data, along with timestamps and geographic coordinates, to the edge computing gateway. The edge computing gateway connects to each sensing node via power line carrier or RS485 bus, performs local filtering, anomaly removal, and time-series alignment on the raw data, and then transmits structured data packets to the central processing platform via a wireless communication link conforming to the TLS 1.3 security protocol.

[0009] The pollution cause identification module receives structured data packets from the multi-source environmental perception layer and classifies the dominant causes of the current dust event based on a preset pollution source feature rule base and machine learning discrimination model. The pollution source feature rule base stores multi-dimensional feature vectors of four typical dust sources: natural disturbance, construction emissions, road transportation, and material storage. Each feature vector consists of five elements: the slope of particulate matter concentration change, the correlation coefficient between wind speed and concentration, the texture complexity index of video images, the proportion of exposed surface area, and the time distribution pattern. The machine learning discrimination model adopts a multi-classifier structure based on support vector machines. Its training samples come from a historical labeled event database. The input is the normalized five-element observation values ​​within the current time period, and the output is the probability distribution of the four causes. The pollution cause identification module selects the cause category with the highest confidence as the dominant cause identifier of the current event by comparing the matching degree of the rule base with the output probability of the model, and transmits this identifier along with the original observation data to the spatiotemporal evolution modeling unit.

[0010] The spatiotemporal evolution modeling unit, based on the dominant cause identifiers output by the pollution cause identification module, calls the corresponding physical diffusion-deposition coupling model to dynamically extrapolate the spatial coverage and concentration decay trend of dust pollution in subsequent time periods. For causes related to construction emissions and road transportation, a Gaussian plume expansion model combined with a turbulent diffusion coefficient correction term is used to simulate the concentration field. For causes related to natural disturbances, the surface dust flux function and wind erosion deposition balance equation are introduced. For causes related to material storage, a semi-empirical power-law decay model is used to describe the concentration decay characteristics of fugitive emissions over time. All models use the measured concentration field, meteorological field, and topographic elevation data at the current moment as initial boundary conditions, and iteratively solve them on a two-dimensional grid using the finite difference method to generate predicted concentration distribution maps for multiple future time steps. This unit uses motion vector analysis in video image sequences to extract the actual movement trajectory and diffusion velocity of dust clouds, and performs online correction of the model prediction results to ensure consistency between the extrapolation process and physical reality. The corrected spatiotemporal evolution data is stored in raster form, and the effective duration and peak concentration of each raster cell are marked.

[0011] The risk weight configuration module pre-stores a regional sensitivity weight matrix, which is constructed based on urban planning land use, population density distribution, ecological protection zone boundaries, and key protection target locations. Each geographic unit in the matrix is ​​assigned a fixed risk weight value, which reflects the comprehensive level of population exposure sensitivity and ecological vulnerability within that unit. High-sensitivity areas such as schools, hospitals, and residential areas are assigned high weight values, while low-sensitivity areas such as industrial wastelands and undeveloped plots are assigned low weight values. The risk weight configuration module receives the predicted concentration distribution map output by the spatiotemporal evolution modeling unit, performs spatial overlay calculations on it and the regional sensitivity weight matrix, and generates a weighted pollution impact heat map. The value of each grid cell in this heat map is equal to the product of the predicted concentration value and the corresponding regional risk weight value, representing the comprehensive pollution load of that location after considering receptor sensitivity.

[0012] The pollution level determination core receives a weighted pollution impact heat map and combines it with the dominant cause identifier output by the pollution cause identification module to execute a multi-dimensional coupled determination logic. This logic defines four pollution levels: Level 1: Slight, Level 2: Moderate, Level 3: Relatively Severe, and Level 4: Severe.

[0013] The classification is based on three criteria: The total area of ​​grid cells exceeding a preset baseline threshold in the weighted pollution impact heatmap; The inherent risk coefficient corresponding to the dominant cause category; Whether the expected duration of the pollution incident exceeds the preset persistence criteria; Among them, the inherent risk coefficient is a preset constant, with higher values ​​for construction emissions and road transportation, lower values ​​for natural disturbances, and medium values ​​for material storage. The core of pollution level determination is to first calculate the total area of ​​the grid exceeding the standard. If the area is lower than the first area threshold, it is directly determined to be level one. If the area is higher than the first area threshold but lower than the second area threshold, a second or third level judgment will be made based on the inherent risk coefficient and duration conditions. If the area exceeds the second area threshold, it will be classified as level four regardless of the cause category. All decision conditions are fixed in the decision tree structure of the decision core as Boolean logic expressions, ensuring the determinism and traceability of the decision process.

[0014] The early warning strategy generation module retrieves the corresponding response instruction set from the preset early warning response strategy library based on the final level result output by the pollution level determination core. The strategy library is configured with differentiated response measures for each level, including information release level, enforcement patrol priority, water spraying and dust suppression dispatch instructions, and public health tips. Level 1 event triggers internal recording and trend tracking; Level 2 incidents trigger on-site verification by regional regulators; Level 3 incidents automatically send enforcement task orders to the local management department and coordinate with nearby fog cannons to carry out targeted dust suppression. A Level 4 incident activates a cross-departmental emergency response mechanism, issues health protection advice to the public, and imposes temporary work restrictions on the affected area; All response instructions are encapsulated in a standard message format and distributed to downstream execution units through a message queue mechanism.

[0015] The execution feedback interface is responsible for receiving execution status feedback information from law enforcement terminals, dust suppression equipment and public service platforms, and inputting this information as feedback signals into the pollution cause identification module and the spatiotemporal evolution modeling unit. For example, after a fog cannon truck completes its dust suppression operation, its operating location, water volume, and operation duration information are transmitted back. Based on this, the spatiotemporal evolution modeling unit adjusts the sedimentation rate parameters in the subsequent concentration decay model. If the on-site verification results uploaded by the enforcement terminal confirm illegal emissions, the pollution cause identification module increases the confidence level of the cause label for that event and updates it to the historical event database for model retraining. This feedback mechanism forms a closed-loop control, enabling the system to have continuous self-optimization capabilities.

[0016] Preferably, the system's central processing platform is deployed on a secure server with a trusted execution environment. All sensitive data is processed in an encrypted storage area to ensure that pollution source identification results and law enforcement-related information are not leaked to the general operating system. The edge computing gateway of the multi-source environmental perception layer has a built-in security element chip for end-to-end signature verification of sensor data to prevent data tampering. The communication link adopts a two-way certificate authentication and session key rotation mechanism throughout to ensure the integrity and confidentiality of data transmission.

[0017] Preferably, the video image acquisition device is equipped with an infrared supplementary lighting module, enabling it to effectively identify surface disturbance characteristics even at night or under low light conditions. Meanwhile, the particulate matter concentration sensor adopts the β-ray absorption method and has an automatic temperature and humidity compensation function to ensure measurement stability in high humidity environments. The meteorological parameter acquisition unit and the particulate matter sensor are housed together in a windproof and rainproof louvered box to avoid direct sunlight and precipitation interference, ensuring the accuracy of synchronous acquisition of environmental parameters.

[0018] Preferably, the spatiotemporal evolution modeling unit adopts a multi-model switching mechanism. When a sudden change in meteorological conditions is detected (such as a sudden increase in wind speed or the start of rainfall), the current model operation is automatically terminated, and a sub-model suitable for the new meteorological scenario is loaded to re-initialize the inference process, ensuring the physical rationality of the evolution prediction.

[0019] Preferably, the risk weight configuration module supports a dynamic update mechanism. When the local government issues a new announcement on the adjustment of urban functional zoning, the system can automatically download the updated land use layer through the government data interface and regenerate the regional sensitivity weight matrix to ensure that the risk assessment is synchronized with the actual development of the city.

[0020] Preferably, the decision tree structure of the pollution level determination core supports remote configuration and updates. Environmental protection authorities can adjust the area threshold, persistence determination conditions, and inherent risk coefficients through a secure authorization channel according to seasonal changes in pollution characteristics or the needs of major events, so that the system can adapt to the differentiated requirements of different management stages.

[0021] The beneficial effects of this invention are as follows: The dust pollution level classification and intelligent early warning integrated management system described in this invention avoids misjudgment caused by relying solely on instantaneous concentration values ​​by coupling pollution cause identification with physical evolution models. It enables the use of regional sensitivity weighting matrices to achieve differentiated risk assessments for the same concentration at different geographical locations; it also enables multi-dimensional judgment logic to accurately characterize the comprehensive harm of pollution events, ensuring that the warning level matches the actual environmental impact; This ensures that the system can maintain the physical consistency of its judgment logic even under complex weather and multi-source interference scenarios; and through the execution of a feedback closed-loop mechanism, the ability to identify pollution causes and predict their evolution is continuously enhanced over time. Through the collaborative protection of secure element chips and trusted execution environments, the security of sensitive regulatory data is ensured throughout the entire chain of collection, transmission and processing. In addition, the dynamic weight update and remote policy configuration mechanism enables the system to adapt to the long-term evolution of urban spatial structure and management policies, maintaining the long-term applicability of the early warning system. Attached Figure Description

[0022] The invention will now be further described with reference to the accompanying drawings.

[0023] Figure 1 This is a schematic diagram of the overall structure of a comprehensive management system for classifying dust pollution levels and providing intelligent early warning, based on the present invention.

[0024] Figure 2 This is a schematic diagram of the decision-making process for the core of pollution level determination in this invention, which executes multi-dimensional coupled determination logic. Detailed Implementation

[0025] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0026] like Figures 1-2 As shown in the embodiment of the present invention, a comprehensive management system for dust pollution level classification and intelligent early warning includes seven functional modules: a multi-source environmental perception layer, a pollution cause identification module, a spatiotemporal evolution modeling unit, a risk weight configuration module, a pollution level determination core, an early warning strategy generation module, and an execution feedback interface. The modules are interconnected through a standardized communication protocol to form a collaborative working mechanism integrating data flow, control flow, and feedback flow.

[0027] The multi-source environmental perception layer consists of distributed sensing nodes deployed in the target area. Each sensing node integrates a particulate matter concentration sensor, a meteorological parameter acquisition unit, a video image acquisition device, and a geolocation module. The particulate matter concentration sensor uses the β-ray absorption method and has a built-in automatic temperature and humidity compensation algorithm to correct measurement drift in high humidity environments. The meteorological parameter acquisition unit includes a three-cup anemometer, a capacitive humidity sensor, and a piezoresistive atmospheric pressure gauge; The video image acquisition device uses a dual-mode camera with visible light and near-infrared capabilities. The visible light channel has a resolution of 1920×1080@30fps, and the near-infrared channel has a wavelength range of 850±20nm. It is equipped with an infrared supplementary light module, and the minimum illumination can reach 0.01 lux. It is used to capture the state of ground disturbance, signs of construction activities, and material coverage at night or under low illumination conditions. The geolocation module is based on the BeiDou / GPS dual-mode global satellite navigation system, with a positioning accuracy better than 2.5 meters (CEP50), and supports RTK differential enhancement mode to achieve centimeter-level calibration; All sensing data are accompanied by a UTC timestamp and latitude and longitude information in the WGS-84 coordinate system, and are transmitted to the edge computing gateway via RS485 bus. The edge computing gateway runs an embedded Linux system and has a built-in security element chip to perform local filtering, anomaly removal, and timing alignment on data from each sensor node. The filtering algorithm uses a sliding window midpoint filter combined with the 3σ criterion to remove outliers; the temporal alignment uses the timestamp of particulate matter concentration data as a reference and performs linear interpolation to synchronize meteorological and video data. The processed structured data packets are encapsulated in JSON format and uploaded to the central processing platform via a 4G / 5G wireless communication link that conforms to the TLS 1.3 security protocol.

[0028] The pollution cause identification module is deployed in the application service layer of the central processing platform, receiving structured data packets from the multi-source environmental perception layer. This engine comprises two sub-components: a pollution source feature rule base and a machine learning discrimination model. The pollution source feature rule base stores multi-dimensional feature vectors for four typical dust sources: natural disturbances (e.g., sandstorms, wind erosion of bare soil), construction emissions (e.g., earthwork excavation, waste soil transportation), road transportation (e.g., unpaved road vehicle traffic), and material storage (e.g., uncovered coal piles, sand and gravel yards). Each type of feature vector consists of five elements: slope of particulate matter concentration change , in units of μg / (m³·min); Correlation coefficient between wind speed and concentration The correlation between wind speed and PM10 concentration over the past 30 minutes was calculated using the Pearson correlation coefficient. Video image texture complexity index The weighted sum of four indicators—contrast, energy, entropy, and homogeneity—is calculated using the gray-level co-occurrence matrix (GLCM). percentage of exposed surface area The vegetation coverage area is identified by a near-infrared image segmentation algorithm and then calculated in reverse. Time distribution pattern The statistics are calculated on an hourly basis to determine whether the events occur during weekday daytime (7:00–19:00). The above five elements are normalized to the [0,1] interval by Min-Max and then input into the machine learning discriminant model; The machine learning discriminative model employs a support vector machine (SVM) multi-classifier structure based on radial basis function (RBF) kernels, and the output is a probability distribution of four causes. ; The training samples are from a historical labeled event database, which contains 2,178 dust storm events that have been manually verified in the past three years. Each event is labeled with the dominant cause category and the corresponding five-element observation values. The pollution cause identification module first calculates the Euclidean distance between the current observation vector and various feature vectors in the rule base to obtain the matching score. ( Simultaneously obtain the probability of the SVM model output. ; The final cause determination adopts a weighted fusion strategy: in, These are empirical weighting coefficients, reflecting the relative importance of prior rule knowledge and the data-driven model. They are selected as... The category corresponding to the largest value serves as the dominant cause identifier for the current event. and will Raw observation data and confidence level It is then transmitted to the spatiotemporal evolution modeling unit.

[0029] Spatiotemporal evolution modeling unit according to Call the corresponding physical diffusion-settlement coupling model, when (Construction emissions) or (For road transport, the modified Gaussian plume extension model is used:) in, Downwind position PM10 concentration at time t (μg / m³); The instantaneous emission intensity (μg / s) is estimated by inversion from the current measured concentration; The average wind speed is (m / s). For effective source height (m), take 2.5m for construction projects and 0.5m for roads; , The lateral and vertical diffusion parameters are determined by looking up a table based on the Pasquill-Gifford stability classification, and a turbulence correction term is introduced. ,in For turbulence intensity, This is an empirical coefficient; when (For natural disturbances), the surface dust flux function and wind erosion subsidence balance equation are used: in, Dust flux (μg / (m²·s)) is defined as the flux of dust. The threshold wind speed for sand-raising. , These are empirical parameters. The angle between wind direction and ground slope; This refers to the dry settling velocity; when (For material storage), a semi-empirical power-law decay model is used: in, The initial concentration is (μg / m³). For reference time, The decay index; All models use the measured concentration field, meteorological field (wind speed, wind direction, stability) and digital elevation model at the current moment as initial boundary conditions, and are divided into two-dimensional grids within the domain. The explicit finite difference method is used for time-step solution with a time step of Δt=1min to predict the concentration distribution in the next 60 minutes. The spatiotemporal evolution modeling unit extracts motion vector fields from video image sequences using optical flow to calculate the centroid movement velocity of dust cloud clusters. and the advection velocity predicted by the model. ( Compare the angle of wind direction; If the deviation between the two exceeds 15%, an online correction mechanism is triggered, replacing the advection term in the model with the measured cloud velocity. The corrected spatiotemporal evolution data is stored in GeoTIFF raster format, with each raster cell recording the concentration values ​​for the next 60 time steps and marking the effective duration. (defined as the duration of a concentration consistently above 50 μg / m³) and peak concentration .

[0030] The risk weight configuration module pre-stores the region sensitivity weight matrix. This matrix is ​​constructed based on data from the three zones and three lines of urban land spatial planning, gridded data from the seventh national population census (100m resolution), ecological protection red line layers, and key protection targets (schools, hospitals, and nursing homes). The weight values ​​are calculated using the following formula: in, The normalized value for population density is (0–1). The weights for land use are as follows: (Residential / Public Management Land = 1.0, Industrial Land = 0.3, Green Space = 0.2, Unused Land = 0.1). Ecological sensitivity (within the ecological protection red line = 1.0, buffer zone = 0.6, others = 0). The impact factor is the proximity to key targets (if there is a school / hospital within 500m, it = 1.0; otherwise, it = 0). Weighting coefficient The matrix resolution is consistent with the spatiotemporal evolution model (10m). The risk weight configuration module receives the predicted concentration distribution map output by the spatiotemporal evolution modeling unit. Perform spatial superposition operation at each time step: Generate a weighted heatmap of pollution impacts , with units of μg·weight / (m³); right Integrating over time yields the cumulative impact value: And statistics Exceeding the benchmark threshold Total grid area .

[0031] Pollution Level Determination Core Receiver , and Execute multi-dimensional coupled decision logic; The multi-dimensional coupled decision-making logic is embedded in the decision tree structure, defining four pollution levels: Level 1: Mild; Level 2: Moderate; Level 3: More serious; Level 4: Severe. The determination process is as follows: First, determine... Is it less than the first area threshold? If yes, output level 1; otherwise, check if it is less than the second area threshold. ; like Then, further determine the inherent risk coefficient of the dominant cause. With duration condition. Preset as: Construction emissions ( ) and road transport ( )Pick Material storage type ( )Pick Natural disturbances ( )Pick .like and If so, it is judged as Level 3; otherwise, it is judged as Level 2. If the cause is classified as Level 4, then regardless of the type of cause, it will be classified as Level 4. The above logic is implemented using Boolean expressions: Rank = 1 if and only if Rank = 2, if and only if and Rank = 3, if and only if and and Level = 4, if and only if The early warning strategy generation module retrieves the corresponding instruction set from the preset early warning response strategy library based on the final level result; The strategy library is configured as follows: Level 1: Generate internal event logs, push them to the regulatory platform trend dashboard, and initiate 72-hour concentration tracking; Level 2: Push on-site verification tasks to the mobile terminals of regional grid workers, requiring them to upload verification photos and text descriptions within 2 hours; Level 3: Automatically generate law enforcement task orders, push them to the local ecological and environmental sub-bureau through the government affairs collaboration platform, and send fixed-point dust suppression instructions to the fog cannon dispatch system within 5km of the incident center; Level 4: Activate the cross-departmental emergency response mechanism and simultaneously send red alert notices to the Municipal Emergency Management Bureau, Housing and Construction Bureau, and Urban Management Bureau; issue health protection advice to the public through the city's broadcasting system, SMS platform, and city service APP, such as advising sensitive groups to avoid outdoor activities; Send temporary work restriction instructions to the smart monitoring platform of the construction site or road section involved, such as suspending earthwork operations and waste transportation, until the concentration drops below 80μg / m³.

[0032] The execution feedback interface subscribes to the status feedback topics of downstream execution units via the MQTT protocol; After completing its operation, the fog cannon truck sends back a message containing the device ID, operation center coordinates, and actual operation duration. Total water consumption ; The spatiotemporal evolution modeling unit updates the settlement rate parameters accordingly: in, For the working area, This is an empirical coefficient. If the verification results uploaded by the law enforcement terminal confirm the existence of violations (such as uncovered bare soil or lack of a washing platform), the pollution cause identification module will increase the confidence of the cause label of the event by 0.2 and store the complete event data (including the five elements, cause label, and verification conclusion) in the historical event database for incremental training of the SVM model. The model is updated online once a month, and performance is evaluated using leave-one-out cross-validation. If the accuracy improves by more than 2%, a new model is deployed.

[0033] The system's central processing platform is deployed on a secure server with a Trusted Execution Environment (TEE), using Intel SGX or domestically produced trusted computing modules. All sensitive data (including causal identification results, law enforcement-related information, and weight matrices) are processed within an encrypted enclave, with memory data encrypted throughout the process to prevent side-channel attacks. The edge computing gateway of the multi-source environmental perception layer has a built-in security element chip that digitally signs each sensor data packet. The central platform verifies the validity of the signature using a public key and rejects unsigned or invalid data packets. The communication link uses bidirectional X.509 certificate authentication, the session key is rotated every 24 hours, and the key negotiation uses the ECDHE algorithm to ensure forward security.

[0034] Furthermore, the infrared supplementary lighting module of the video image acquisition device, in conjunction with the high-sensitivity CMOS sensor in the near-infrared channel, can identify changes in ground texture and effectively support the identification of dust sources at night. The temperature and humidity compensation algorithm for particulate matter concentration sensors is based on the following formula: in, For the original readings, , For real-time temperature and humidity, , As the standard reference point, , The compensation coefficient was determined through laboratory calibration.

[0035] Furthermore, the spatiotemporal evolution modeling unit incorporates a meteorological abrupt change detection module to monitor the wind speed change rate in real time. With rainfall intensity .like or If the current model is not found, the current model will be terminated immediately, and a sub-model suitable for strong wind or rain scenarios will be loaded. The strong wind sub-model introduces an unsteady turbulence term, while the precipitation sub-model adds a wet deposition term. ,in The rain removal coefficient.

[0036] Furthermore, the risk weight configuration module automatically synchronizes the latest land use change data released by the Natural Resources Bureau every morning through the government data sharing interface. The system parses the planning adjustment layer in Shapefile format and recalculates the data. and update This ensures that the weight matrix is ​​consistent with the actual functional layout of the city.

[0037] Furthermore, the core decision tree parameters for pollution level determination ( Threshold, The coefficient supports remote configuration. Environmental protection authorities can log in to the security configuration interface through a dedicated management terminal, modify the parameters, generate a digital signature configuration package, and dynamically load it into the judgment core after the central platform verifies the signature, without needing to restart the service.

[0038] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A comprehensive management system for classifying and intelligently warning about dust pollution levels, characterized in that, include: The system includes a multi-source environmental perception layer, a pollution cause identification module, a spatiotemporal evolution modeling unit, a risk weight configuration module, a pollution level determination core, an early warning strategy generation module, and an execution feedback interface. The multi-source environmental perception layer is used to collect particulate matter concentration, meteorological parameters, video images and geographic location information in the target area, and upload the structured data package with timestamp and geographic coordinates to the central processing platform. The pollution cause identification module receives the structured data packet and outputs the dominant cause identifier of the current dust event based on the preset pollution source feature rule base and machine learning discrimination model. The spatiotemporal evolution modeling unit calls the corresponding physical coupling model according to the dominant cause identifier, combines the measured concentration field, meteorological field and topographic data, and infers the spatial coverage range and concentration decay trend of dust pollution in the future period. It also uses motion vectors in the video image sequence to correct the inference results online and generate corrected spatiotemporal evolution data. The risk weight configuration module stores a regional sensitivity weight matrix, which is constructed based on urban land use, population density, ecological protection zone boundaries, and the location of key protection targets. The risk weight configuration module performs spatial overlay operations on the spatiotemporal evolution data and the regional sensitivity weight matrix to generate a weighted pollution impact heat map. The pollution level determination core receives the weighted pollution impact heat map and the dominant cause identifier. Based on the three dimensions of the total area of ​​the exceeding grid, the inherent risk coefficient corresponding to the dominant cause, and the expected duration of the pollution event, it executes multi-dimensional coupled determination logic and outputs the pollution level determination result. The early warning strategy generation module retrieves the corresponding response instruction set from the preset early warning response strategy library based on the pollution level determination result. The execution feedback interface receives execution status feedback information from law enforcement terminals, dust suppression equipment, and public service platforms, and inputs the information as feedback signals to the pollution cause identification module and the spatiotemporal evolution modeling unit to achieve system closed-loop self-optimization.

2. The integrated management system for dust pollution level classification and intelligent early warning according to claim 1, characterized in that, The multi-source environmental perception layer includes distributed sensing nodes and edge computing gateways; the sensing nodes integrate particulate matter concentration sensors, meteorological parameter acquisition units, visible light and near-infrared dual-mode video image acquisition devices, and BeiDou / GPS dual-mode geolocation modules. The edge computing gateway connects each sensor node via a wired bus, performs local filtering, anomaly removal, and timing alignment on the raw data, and transmits structured data packets to the central processing platform via a wireless link.

3. The integrated management system for dust pollution level classification and intelligent early warning according to claim 1, characterized in that, The pollution source feature rule base in the pollution cause identification module stores multi-dimensional feature vectors of four types of dust sources: natural disturbance, construction emissions, road transportation, and material storage. Each feature vector consists of the slope of particulate matter concentration change, the correlation coefficient between wind speed and concentration, the texture complexity index of video images, the proportion of exposed surface area, and the temporal distribution pattern elements. The machine learning discrimination model is a multi-classifier based on support vector machines. Its input is the normalized five-factor observation values, and its output is the probability distribution of four types of causes. The pollution cause identification module selects the cause category with the highest confidence level as the dominant cause identifier by integrating the rule base matching degree and the model output probability.

4. The integrated management system for dust pollution level classification and intelligent early warning according to claim 1, characterized in that, The spatiotemporal evolution modeling unit invokes differentiated physical models for different dominant causes: For causes such as construction emissions or road transport, a modified Gaussian plume extension model is adopted. For natural disturbance-related causes, the surface dust flux function and the wind erosion subsidence balance equation are adopted. For the causes of material accumulation, a semi-empirical power-law decay model is adopted; Using measured data as initial boundary conditions, the model is solved iteratively on a two-dimensional grid using the finite difference method. The advection term of the differentiated physical model is then corrected online based on the actual moving speed of the dust cloud extracted from video optical flow analysis.

5. The integrated management system for dust pollution level classification and intelligent early warning according to claim 1, characterized in that, The risk weight value of each geographic unit in the regional sensitivity weight matrix is ​​obtained by weighted summation of the normalized population density value, land use weight, ecological sensitivity, and the influence factor of adjacent key protection targets; The risk weight configuration module multiplies the predicted concentration distribution map with the regional sensitivity weight matrix grid by grid to generate a weighted pollution impact heat map, and calculates the cumulative impact value to count the total area of ​​grids that exceed the baseline threshold.

6. The integrated management system for dust pollution level classification and intelligent early warning according to claim 1, characterized in that, The multi-dimensional coupled judgment logic of the core pollution level determination is as follows: If the total area of ​​the grid cells exceeding the standard is less than the first area threshold, it is judged as Level 1; If the area is greater than or equal to the first area threshold but less than the second area threshold, it will be classified as Level 2 or Level 3 based on the inherent risk coefficient. If the area is greater than or equal to the second area threshold, it is directly classified as level four.

7. The integrated management system for dust pollution level classification and intelligent early warning according to claim 1, characterized in that, The response instruction set configured in the early warning strategy generation module includes: Level 1 event triggers internal recording and trend tracking; Level 2 events push on-site verification tasks to the regional supervisor's terminal; Level 3 incidents automatically generate law enforcement task orders and coordinate with fog cannons for targeted dust suppression. The Level 4 incident activated the cross-departmental emergency response mechanism, issued health protection recommendations, and implemented temporary work restrictions in the affected areas.

8. The integrated management system for dust pollution level classification and intelligent early warning according to claim 1, characterized in that, After receiving the operation feedback information from the fog cannon vehicle, the spatiotemporal evolution modeling unit dynamically updates the settlement rate parameters accordingly. When the verification results confirm the existence of illegal emissions, the pollution cause identification module increases the confidence level of the event cause label and stores the complete event data in the historical event database for incremental training of the machine learning model.

9. The integrated management system for dust pollution level classification and intelligent early warning according to claim 1, characterized in that, The spatiotemporal evolution modeling unit has a built-in meteorological change detection module. When the meteorological change detection module detects that a meteorological element has a preset change characteristic, it automatically terminates the current model and loads a sub-model adapted to the severe weather scenario to reinitialize the inference process.

10. The integrated management system for dust pollution level classification and intelligent early warning according to claim 1, characterized in that, The risk weight configuration module automatically synchronizes urban functional zoning adjustment data through the government data interface to dynamically update the regional sensitivity weight matrix. The decision tree parameters of the pollution level determination core include area threshold, duration determination conditions, and inherent risk coefficient, and support remote configuration and updates via a secure authorized channel.

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