A smart ecological environment monitoring system and method

Abstract

This invention discloses a smart ecological environment monitoring system and method, relating to the field of urban ecological environment monitoring technology. It includes an air quality monitoring subsystem, which deploys multiple air quality monitoring nodes in an urban area and collects air pollution parameters at corresponding locations; a pollution analysis module, which performs frequency clustering, pollution intensity statistics, and rate of change assessment on historical pollution data collected by the air quality monitoring nodes to obtain the frequency, intensity, and fluctuation characteristics of pollution. These three characteristics are then normalized and weighted to obtain a pollution sensitivity factor; and a monitoring node adjustment module, which dynamically calculates the optimal density layout strategy for air quality monitoring nodes within the urban area. This invention dynamically adjusts the monitoring density distribution in different urban areas by constructing a pollution sensitivity factor and introducing a regional weighting mechanism, combined with the limitation on the number of air quality monitoring nodes.

Description

Technical Field

[0001] This invention relates to the field of urban ecological environment monitoring technology, specifically to a smart ecological environment monitoring system and method. Background Technology

[0002] With the accelerating pace of urbanization, urban ecological and environmental issues have increasingly attracted widespread social attention. Air pollution, as a significant factor affecting urban ecological security, is directly related to public health, urban operations, and sustainable social development. Currently, various urban air quality monitoring systems are widely used, primarily relying on fixed stations to continuously monitor key pollutants and combining meteorological data to conduct regional pollution trend analysis and risk warnings.

[0003] In recent years, some technical solutions have attempted to incorporate elements such as wind speed and direction, and Geographic Information System (GIS) data to optimize monitoring station deployment, simulate pollution diffusion, or provide early warnings for sensitive targets; other studies have utilized machine learning to improve the accuracy of pollution prediction. These solutions have provided valuable insights into enhancing local prediction capabilities and improving response efficiency.

[0004] A search revealed a Chinese patent (publication number: CN119576054A) that discloses an urban area ecological environment monitoring system. The patent includes a data acquisition module, a control module, a database, and an alarm. The output of the data acquisition module is connected to the input of the controller. The controller and the database are bidirectionally connected. The output of the controller is connected to the input of the alarm. The output of the controller is also connected to the input of the control module.

[0005] In existing technologies, monitoring points are mostly deployed in a fixed manner for a long period of time, making it difficult to optimize the configuration of sensing density based on the historical change characteristics or real-time risks of pollution-sensitive areas. This results in insufficient resource coverage efficiency. Furthermore, traditional models focus more on the continuous changes of pollution concentration in time and space, and rarely abstractly model the pollutant transmission process from the perspective of the propagation path structure. They also lack a systematic expression of the volatility of pollution diffusion boundaries. Therefore, this application discloses a smart ecological environment monitoring system and method. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent ecological environment monitoring system and method to solve the problems mentioned in the background art.

[0007] This invention can be achieved through the following technical solution: a smart ecological environment monitoring system, comprising an air quality monitoring subsystem, a pollution analysis module, a monitoring node adjustment module, a pollution diffusion modeling module, a target risk analysis module, and a risk response module;

[0008] The air quality monitoring subsystem is used to deploy multiple air quality monitoring nodes in urban areas. Each air quality monitoring node is used to collect air pollution parameters at the corresponding location. The air pollution parameters include pollutants such as inhalable particulate matter (PM2.5, PM10), nitrogen dioxide, ozone, and carbon monoxide. The air quality monitoring nodes have unified time synchronization and global positioning functions.

[0009] The pollution analysis module is used to perform frequency clustering, pollution intensity statistics, and rate of change assessment on historical pollution data collected from various air quality monitoring nodes. By statistically analyzing the frequency of pollution events, average pollution concentration, and rate of change of pollution concentration over time at each air quality monitoring node within a set period, the frequency, intensity, and fluctuation characteristics of pollution are obtained. These three characteristics are then normalized and weighted to form a pollution sensitivity factor. The calculation basis is used to characterize the response priority of each region to the monitoring deployment;

[0010] The monitoring node adjustment module dynamically calculates the optimal density layout strategy of air quality monitoring nodes in different urban areas based on the pollution sensitivity factor provided by the pollution analysis module, and dynamically distributes the air quality monitoring nodes to achieve dense deployment of air quality monitoring nodes in high-pollution-risk areas and sparse deployment of air quality monitoring nodes in low-risk areas, thereby optimizing the efficiency of full-area monitoring coverage without increasing the total number of nodes.

[0011] The pollution diffusion modeling module is used to establish a pollution diffusion prediction model based on air pollution parameters, meteorological factor data and urban topographic information obtained by the air quality monitoring subsystem. It models and analyzes the spread trend of pollutants in urban space and outputs the pollution impact area, including the pollution diffusion path, the scope of impact and the confidence level of pollutant concentration distribution in each area.

[0012] The target risk analysis module spatially matches the confidence level pollution impact area output by the pollution diffusion modeling module with the preset target area and calculates the exposure risk score for each target.

[0013] The exposure risk scoring function comprehensively considers pollutant concentration, predicted arrival time, tolerance threshold of the target area, and response time window factor, and outputs priority ranking results for use by the risk response module.

[0014] The risk response module determines the response level based on the ranking results output by the target risk analysis module, and links with the urban management system, including the traffic dispatch system, pollution control equipment control system, and public early warning information release system, to perform priority response operations.

[0015] A further technical improvement of the present invention is that the method for dynamically distributing air quality monitoring nodes by the monitoring node adjustment module includes:

[0016] A1. Region Grid Division:

[0017] First, the entire city's monitoring area is divided into several spatial grid units, each denoted as . ;

[0018] A2. Receive and match contamination sensitivity factors:

[0019] Each spatial grid unit Corresponding pollution sensitivity factors Perform a match;

[0020] A3. Determine the constraint on the total number of monitoring nodes:

[0021] The total number of air quality monitoring nodes allowed to be deployed in urban areas is set as follows: ;

[0022] A4. Calculate the expected number of nodes to be allocated to each region:

[0023] Each spatial grid unit Pollution sensitivity factor Perform normalization processing;

[0024] Then calculate the number of nodes that should be allocated to each region. , This represents the pollution sensitivity factor of the j-th spatial grid cell among all spatial grid cells.

[0025] A further technical improvement of the present invention lies in: completing each spatial grid unit After calculating the number of monitoring nodes, the system performs a node location and constraint correction phase, which includes the following steps:

[0026] B1. Deployment Feasibility Assessment:

[0027] First, determine each spatial grid unit. Physical deployable capacity;

[0028] If spatial grid unit The area is insufficient to support The minimum deployment spacing between each air quality monitoring node, or the area where deployment restrictions exist, is recorded as a "deployment-restricted area".

[0029] The system records its actual deployable capacity. < And calculate its node deficit. ;

[0030] B2. For all spatial grid cells with missing nodes Based on the principle of spatial proximity, identify its adjacent region set. ;

[0031] Identify pollution-sensitive factors in adjacent areas. Spatial grid cells with deployment capacity still available , to fill the node gap Pollution sensitivity factor assigned to its neighborhood Spatial grid cells exceeding a preset sensitivity threshold;

[0032] B3. In each spatial grid cell, the system loads its urban basic geographic information layer, excludes all preset non-deployable points, and marks the remaining points as a set of valid deployment candidate points.

[0033] B4. In each spatial grid unit Spatial clustering algorithm is used to optimize the actual deployment location of each air quality monitoring node from the effective deployment candidate point set;

[0034] B5. After the deployment locations of all air quality monitoring nodes are optimized, the system outputs the final deployment layer, including each spatial grid unit. The number of nodes, their coordinates, and the correlation sensitivity factor. .

[0035] A further technical improvement of the present invention is that the method for the pollution diffusion modeling module to output the pollution diffusion path and its impact range includes:

[0036] Z1. Collect air pollution parameter data, meteorological factor data, and urban topographic information data as model inputs;

[0037] Z2. Construct a two-dimensional pollution diffusion vector field within the urban area to describe the speed and direction of pollutant propagation in geographic space;

[0038] Z3. Based on the pollution diffusion vector field in Z2, a boundary uncertainty learning mechanism is introduced to predict the future diffusion path of pollutants and model the uncertainty of the propagation boundary.

[0039] Z4. The pollution diffusion prediction path and its confidence boundary zone are jointly defined as the "pollution impact area".

[0040] A further technical improvement of the present invention is that the working method of the target risk analysis module includes:

[0041] Y1. The system presets several target area sets, including but not limited to schools, medical institutions, elderly care facilities, parks and other functional areas sensitive to air pollution;

[0042] Each target region has the following attribute information:

[0043] Regional boundary coordinate information;

[0044] Function type identifier;

[0045] Tolerable pollutant concentration threshold;

[0046] Minimum response time requirement (i.e., early response time window);

[0047] Y2. The system receives pollution impact area information output by the pollution diffusion modeling module. The pollution impact area is composed of the predicted pollution diffusion path and its confidence boundary zone, representing the geographic spatial range that pollutants may affect within a set prediction time range in the future.

[0048] The system uses a spatial overlay algorithm to determine whether there is a spatial overlap between each target area and the pollution-affected area. If the boundary of a target area intersects with the pollution-affected area, the target area is determined to be a "potential exposure target".

[0049] Y3. For each target area identified as a potential exposure target, the system extracts the following pollution exposure parameters from the pollution diffusion model:

[0050] The predicted maximum pollutant concentration within the target area;

[0051] The estimated time when pollutants are expected to begin affecting the target area;

[0052] The time difference between the current system time and the predicted arrival time is used to represent the responsive time window;

[0053] The corresponding target area attribute values ​​include the tolerance concentration threshold and the minimum response time requirement;

[0054] The above parameters are used to construct a comprehensive assessment basis for the target's exposure status;

[0055] Y4. The system compares the extracted pollution exposure parameters with preset attribute parameters and performs a target exposure risk level determination operation, with the specific rules as follows:

[0056] When the predicted pollution concentration is higher than the tolerable concentration threshold of the target area and the remaining response time is less than the minimum response time requirement, it is judged as high risk;

[0057] When the predicted pollution concentration is close to the tolerance threshold, or the response time is approaching but still meets the requirements, it is judged as medium risk;

[0058] When the predicted pollution concentration is significantly lower than the tolerance threshold and there is sufficient response time, it is judged as low risk;

[0059] Based on the above judgment logic, the system assigns a risk level label to each potential exposure target and generates a target response priority ranking.

[0060] Y5. Output the target risk assessment results in a structured format, including the following fields:

[0061] Unique identifier for the target area;

[0062] The determined level of exposure risk;

[0063] Suggested response priorities;

[0064] Additional fields include the expected duration of impact and the intensity of pollution;

[0065] The assessment results are used to drive operations in the risk response module, such as law enforcement dispatch, information dissemination, and intervention resource allocation, to support proactive responses in urban ecological pollution prevention and control.

[0066] A further technical improvement of the present invention is that the implementation process of the risk response module includes the following steps:

[0067] N1. Response Level Determination:

[0068] The risk response module receives the risk level and response priority ranking results for each target area output by the target risk analysis module, and classifies the target areas into response levels according to the response level determination mechanism set within the system; the response level determination mechanism is based on the following determination conditions:

[0069] When the risk level of the target area is high and it ranks high in the response ranking, it corresponds to a Level 1 response target;

[0070] When the risk level of the target area is medium, or it is in the middle of the ranking, it corresponds to a level 2 response target;

[0071] When the target area is a low-risk area, or is ranked lower, it corresponds to a Level III response target or an early warning monitoring target.

[0072] The system determines the response level for each target area based on the above judgment results;

[0073] N2, Linkage Response Control:

[0074] Based on the response level determined in step one, the risk response module calls the following subsystems in the city management system to perform the corresponding response operations:

[0075] 1. Traffic dispatching system response control:

[0076] For target areas at Level 1 response, the system sends control instructions to the traffic dispatch system;

[0077] The instructions include, but are not limited to: traffic flow guidance on roads surrounding the polluted area, setting up restricted traffic zones, and reserving emergency lanes;

[0078] Supports dynamic adjustment of traffic scheduling strategies based on predicted changes in pollution diffusion paths;

[0079] 2. Response control of pollution control equipment control system:

[0080] The system calls upon the urban pollution control equipment management platform to allocate mobile or fixed pollution control equipment to the target area;

[0081] The control content includes: equipment start / stop commands, work position instructions, and operating parameter settings;

[0082] The control logic determines the priority areas for equipment deployment based on the location, shape, and diffusion trend of the pollution-affected area;

[0083] 3. Response control of the public early warning information release system:

[0084] The system calls the information dissemination interface to release early warning information to the public within the pollution-affected area;

[0085] The information released includes the type of pollutant, the expected period of impact, the pollution intensity level, and health protection recommendations.

[0086] Information dissemination channels include: mobile terminal push notifications, electronic information screens, public broadcasting systems, and government website platforms;

[0087] N3. Response execution status tracking and feedback:

[0088] The system continuously receives real-time data from air quality monitoring nodes to assess the actual effectiveness of response measures;

[0089] If the pollution spread trend exceeds the original prediction range, or if the pollution in the response area is not effectively controlled, the system will automatically adjust the response level and expand the control range.

[0090] The system records the execution status data of each response operation, including response start time, response duration, response coverage area, and actual pollution concentration changes, which are used for subsequent model calibration and strategy evaluation.

[0091] The system determines the response level for each target area based on the above judgment results.

[0092] A further technical improvement of the present invention is that the target risk analysis module further includes a pollution diffusion path map constructed based on the pollution diffusion modeling module, which identifies multiple propagation paths between pollution source nodes and representative nodes of sensitive targets, and calculates the path exposure level based on the pollution propagation intensity, propagation time and geographical damping factors of each side of the path.

[0093] When a target area is located at the end of a high-risk transmission path among multiple high-intensity paths, the system automatically upgrades the exposure risk level of the target area and uses the updated risk level for priority response ranking in subsequent risk response modules.

[0094] A further technical improvement of the present invention is that: the target risk analysis module identifies the propagation path between pollution source nodes and sensitive target areas based on the pollution diffusion path map structure, and extracts the pollutant concentration data of each air quality monitoring node on the path to form a pollution concentration sequence;

[0095] The target risk analysis module performs sequential analysis of the concentration sequence to determine whether the pollutant concentration continues to rise during the propagation of the path.

[0096] If a path is found to show a continuous increase in concentration in a segment near the target area, and the concentration at the endpoint exceeds a set threshold, the path is marked as having a "pollution shock trend".

[0097] Furthermore, the target risk analysis module determines whether the target area is in a state of potential pollution impact based on whether there is a path with a pollution impact trend;

[0098] If the condition is met, the system will automatically raise the exposure risk level of the target area and transmit the updated level to the risk response module for scheduling, prioritization, and early warning push.

[0099] On the other hand, the present invention also discloses a smart ecological environment monitoring method, which includes the following steps:

[0100] Step 1: Deploy multiple air quality monitoring nodes in the urban area, with each node collecting air pollution parameters for its corresponding location;

[0101] Step 2: Perform frequency clustering, pollution intensity statistics, and rate of change assessment on the historical pollution data collected by the air quality monitoring nodes. Statistically analyze the frequency of pollution events, the average pollution concentration, and the rate of change of pollution concentration over time within a set period to obtain three characteristics: pollution frequency, intensity, and volatility.

[0102] After normalizing the above three features, a weighted fusion is performed to obtain the pollution sensitivity factor for each region. The pollution sensitivity factor is used to characterize the region's response priority to the monitoring deployment.

[0103] Step 3: Calculate the weighting coefficients for different areas of the city based on the pollution sensitivity factors obtained in Step 2;

[0104] Under the constraint of the total number of monitoring nodes, the optimal number of monitoring nodes for each region is dynamically calculated, and the number of air quality monitoring nodes is dynamically adjusted based on the optimization results.

[0105] To optimize the efficiency of comprehensive monitoring coverage, dense deployment in highly polluted and sensitive areas and sparse deployment in low-risk areas are achieved.

[0106] Step 4: Based on real-time data collected from air quality monitoring nodes, integrate meteorological factors and urban topographic information to construct a pollution diffusion prediction model;

[0107] The predictive model outputs the propagation path trends and impact range of pollutants in urban space;

[0108] Step 5: Spatial matching of the pollution impact area predicted by the pollution diffusion model with the preset sensitive target area;

[0109] For each sensitive target, the exposure risk score is calculated by taking into account factors such as pollutant concentration, predicted arrival time, target tolerance threshold, and response time window.

[0110] All sensitive targets were prioritized based on their exposure risk scores.

[0111] Step 6: Determine the response level based on the sorting results of step S5;

[0112] In conjunction with the city management system, implement response measures of corresponding priority to complete pollution early warning and response scheduling operations.

[0113] Compared with the prior art, the present invention has the following beneficial effects:

[0114] This invention constructs a pollution sensitivity factor and introduces a regional weighting mechanism. Combined with the limitation on the number of air quality monitoring nodes, it dynamically adjusts the monitoring density distribution in different urban areas to achieve the optimal strategy of "densified deployment in high-risk areas and sparse deployment in low-risk areas". Without increasing the hardware cost of monitoring nodes, it improves the regional coverage effectiveness and scientific resource allocation of air quality monitoring. It is suitable for application scenarios with complex urban environments and frequent multi-source pollution.

[0115] Furthermore, this invention constructs a pollution diffusion prediction model by integrating meteorological factors, topographic information, and monitoring data. This model can not only identify the propagation path of pollutants, but also output the confidence boundary zone of the pollution-affected area by combining historical and real-time error data. This enables the modeling of the uncertainty of pollution trends, making the pollution diffusion simulation closer to the real urban propagation behavior, improving the interpretability and timeliness of the prediction model, and meeting the forward-looking needs of intelligent urban management.

[0116] On the other hand, the target exposure risk scoring mechanism proposed in this invention quantitatively integrates pollution concentration, predicted arrival time, target tolerance threshold and response window to scientifically classify the exposure risk level of different targets and allocate corresponding response priorities accordingly. This mechanism can guide traffic scheduling, the activation of treatment devices and the public early warning release system to carry out graded responses, build an efficient linkage channel between pollution early warning and urban governance, and significantly improve the accuracy and execution efficiency of emergency management. Attached Figure Description

[0117] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0118] Figure 1 This is the system logic diagram of the present invention;

[0119] Figure 2 This is a system logic diagram of the air quality monitoring subsystem in this invention;

[0120] Figure 3 This is a system logic diagram of the monitoring node adjustment module in this invention;

[0121] Figure 4 This is a system logic diagram of the target risk analysis module in this invention;

[0122] Figure 5 This is a system logic diagram of the pollution analysis module in this invention;

[0123] Figure 6 This is a system logic diagram of the pollution diffusion modeling module in this invention;

[0124] Figure 7 This is a system logic diagram of the risk response module in this invention. Detailed Implementation

[0125] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0126] Please see Figure 1-7As shown, the present invention provides an intelligent ecological environment monitoring system, including an air quality monitoring subsystem, a pollution analysis module, a monitoring node adjustment module, a pollution diffusion modeling module, a target risk analysis module, and a risk response module;

[0127] The air quality monitoring subsystem is used to deploy multiple air quality monitoring nodes in urban areas. Each air quality monitoring node is used to collect air pollution parameters at the corresponding location. The air pollution parameters include pollutants such as inhalable particulate matter (PM2.5, PM10), nitrogen dioxide, ozone, and carbon monoxide. The air quality monitoring nodes have unified time synchronization and global positioning functions.

[0128] The pollution analysis module is used to perform frequency clustering, pollution intensity statistics, and rate of change assessment on historical pollution data collected from various air quality monitoring nodes. By statistically analyzing the frequency of pollution events, average pollution concentration, and rate of change of pollution concentration over time at each air quality monitoring node within a set period, it obtains the frequency, intensity, and fluctuation characteristics of pollution. These three characteristics are then normalized and weighted to form a pollution sensitivity factor. The calculation basis is used to characterize the response priority of each region to the monitoring deployment, specifically including:

[0129] Pollution frequency statistics:

[0130] For each air quality monitoring node, the number of times any pollutant exceeds its set pollution threshold within a set time window (e.g., the past 30 days) is recorded as the pollution time frequency. Furthermore, pollution thresholds can be set using the daily average concentration limits in national environmental standards.

[0131] Calculation of average pollution intensity:

[0132] The average concentration of pollutants during the period of the pollution incident is recorded as the average pollution intensity. ;

[0133] If multiple pollutants exceed their set pollution thresholds, their values ​​are calculated separately. Weighted average pollution intensity Used as the average pollution intensity ;

[0134] Pollution change rate assessment:

[0135] A sliding time window was used to statistically analyze the rate of change of pollutant concentration over time, and the standard deviation of the daily average variation gradient was taken as an indicator of pollution fluctuation. This is used to reflect the instability and risk of rapid deterioration of pollution levels in the region;

[0136] Pollution sensitivity factor calculation:

[0137] For each monitoring node region i, the pollution sensitivity factor The calculation is as follows:

[0138] ;

[0139] in, , and Let be the weighting coefficients, and satisfy . + + =1, and its specific value can be preset based on actual scenario experience or optimization algorithm;

[0140] , , The maximum value within the historical statistical period is used for normalization.

[0141] Regional weighting coefficient output:

[0142] Pollution sensitivity factors Corresponding to the area or grid unit where the air quality monitoring node is located, it is used as a regional weighting coefficient in the deployment optimization calculation of the monitoring node adjustment module, so as to realize the weighted optimization of node deployment in areas with frequent pollution, forming a dynamic deployment pattern of "dense in hot areas and sparse in cold areas".

[0143] Based on the pollution sensitivity factor provided by the pollution analysis module, the monitoring node adjustment module dynamically calculates the optimal density layout strategy for air quality monitoring nodes in different urban areas and dynamically distributes the air quality monitoring nodes to achieve denser deployment of air quality monitoring nodes in high-pollution-risk areas and sparse deployment of air quality monitoring nodes in low-risk areas, thereby optimizing the efficiency of full-area monitoring coverage without increasing the total number of nodes.

[0144] The method for dynamically distributing air quality monitoring nodes using the monitoring node adjustment module includes:

[0145] A1. Region Grid Division:

[0146] First, the entire city monitoring area is divided into several spatial grid units (such as a regular grid of 500m × 500m or an adaptive area based on roads / functional zones). Each spatial grid unit is denoted as... ;

[0147] A2. Receive and match contamination sensitivity factors:

[0148] Each spatial grid unit Corresponding pollution sensitivity factors Perform a match;

[0149] A3. Determine the constraint on the total number of monitoring nodes:

[0150] The total number of air quality monitoring nodes allowed to be deployed in urban areas is set as follows: And the number of nodes remains constant during dynamic distribution;

[0151] A4. Calculate the expected number of nodes to be allocated to each region:

[0152] Each spatial grid unit Pollution sensitivity factor Normalization is performed:

[0153] ;

[0154] Then calculate the number of nodes that should be allocated to each region: This represents the pollution sensitivity factor of the j-th spatial grid cell among all spatial grid cells;

[0155] Its meaning is the normalized weight value corresponding to the i-th spatial grid cell. Total number of monitoring nodes Multiply by these to obtain the theoretical value of the number of nodes that should be allocated to the grid.

[0156] The theoretical value is then rounded to obtain the final integer number of monitoring nodes allocated to that spatial grid cell. ;

[0157] In the formula, Represents spatial grid cells The number of monitoring nodes to be allocated;

[0158] After completing each spatial grid unit After calculating the number of monitoring nodes, the system performs a node location and constraint correction phase, which includes the following steps:

[0159] B1. Deployment Feasibility Assessment:

[0160] First, determine each spatial grid unit. Physical deployable capacity;

[0161] If spatial grid unit The area is insufficient to support The minimum deployment spacing between each air quality monitoring node, or the area where deployment is restricted (such as green space, lake, or area without power), is recorded as a "restricted deployment area".

[0162] The system records its actual deployable capacity. < And calculate its node deficit. ;

[0163] B2. For all spatial grid cells with missing nodes Based on the principle of spatial proximity, identify its adjacent region set. ;

[0164] Identify pollution-sensitive factors in adjacent areas. Spatial grid cells with deployment capacity still available , to fill the node gap The air quality monitoring nodes are allocated to high-sensitivity areas within the region to ensure that the allocation of air quality monitoring nodes between regions complies with both pollution risk and spatial feasibility constraints.

[0165] Among them, the highly sensitive area is its pollution sensitivity factor. Spatial grid cells exceeding a preset sensitivity threshold;

[0166] B3. In each spatial grid cell, the system loads its urban basic geographic information layer (including green space, water body, traffic isolation zone, building shadow area, etc.), excludes all preset undeployable points, and marks the remaining points as a set of valid deployment candidate points;

[0167] B4. In each spatial grid unit Spatial clustering algorithms are used in the effective deployment candidate point set. In this embodiment, the Voronoi centroid distribution algorithm is used to optimize the actual deployment location of each air quality monitoring node. Specifically, this includes:

[0168] The system is based on spatial grid units Actual deployable capacity Construct within the set of effective deployment candidate points An initial reference point;

[0169] The initial reference point is used to construct the Voronoi polygon structure. Each Voronoi polygon is called a "deployment unit" and is used to divide the node optimization space.

[0170] Among them, a deployment unit refers to an equipotential space region generated based on an initial reference point, and each deployment unit corresponds uniquely to its generated reference point.

[0171] The system extracts a set of candidate deployment points within each deployment unit. That is, spatial grid unit All candidate deployment points that fall into the j-th deployment unit;

[0172] Candidate deployment points refer to the effective deployment locations allowed by deployment constraints, which are generated in advance based on conditions such as minimum spacing and infrastructure accessibility;

[0173] For each deployment unit, the system uses its corresponding set of candidate deployment points. In this context, the distance from each candidate deployment point in the set to the geometric centroid of the deployment unit is calculated.

[0174] Select the candidate deployment point closest to the geometric centroid as the final deployment location for the deployment unit;

[0175] Among them, the geometric centroid refers to the centroid point of the internal space of the deployment unit, which is used to maintain spatial uniformity to the greatest extent.

[0176] Finally, boundary anomaly correction is performed, and the mechanism includes:

[0177] If there are no candidate deployment points in a certain deployment unit, the system performs a minimum distance search within the boundary of the deployment unit, selects the external candidate deployment point closest to the geometric centroid of the deployment unit for alternative deployment, and records the offset of the alternative deployment point as the basis for spatial coverage compensation.

[0178] B5. After the deployment locations of all air quality monitoring nodes are optimized, the system outputs the final deployment layer, including each spatial grid unit. The number of nodes, their coordinates, and the correlation sensitivity factor. It is used for actual construction deployment or dynamic deployment execution scheduling;

[0179] The pollution diffusion modeling module is used to establish a pollution diffusion prediction model based on air pollution parameters, meteorological factor data and urban topographic information obtained by the air quality monitoring subsystem. It models and analyzes the spread trend of pollutants in urban space and outputs the pollution impact area, including the pollution diffusion path, the scope of impact and the confidence level of pollutant concentration distribution in each area.

[0180] The pollution diffusion modeling module outputs methods for pollution diffusion paths and their impact range, including:

[0181] Z1. Data Preparation Stage:

[0182] Collect the following three types of input data as model input:

[0183] Air pollution parameter data: provided by the air quality monitoring subsystem, including time-series data of pollutants such as inhalable particulate matter (PM2.5, PM10), nitrogen dioxide, ozone, and carbon monoxide collected by each air quality monitoring node;

[0184] Meteorological factor data: including wind speed, wind direction, air pressure, temperature and humidity, etc., are obtained through meteorological monitoring modules or third-party interfaces, representing the external driving conditions for pollution diffusion;

[0185] Urban topographic information data: including digital elevation models, building boundaries, road structures, etc., used to simulate obstacles and pathways in the process of pollution diffusion;

[0186] Z2, Construction of the pollution diffusion vector field model:

[0187] The system constructs a two-dimensional pollution diffusion vector field within the urban area to describe the propagation speed and direction of pollutants in geospatial space. This model consists of the following variables:

[0188] Each air quality monitoring node serves as the initial pollution source point;

[0189] By combining meteorological factors, velocity vectors (such as wind speed direction) are generated in space to form a diffusion-driven vector field;

[0190] By combining topographical barriers to set regional damping factors, actual flow channels of pollution in the city are constructed;

[0191] Furthermore, the "pollution diffusion vector field" refers to the comprehensive vector representation of the possible propagation direction and speed of pollutants at each spatial location;

[0192] Specifically, including:

[0193] Spatial gridding processing:

[0194] First, the urban monitoring area is divided into regular two-dimensional spatial grid units, each denoted as . Its center point coordinates are , serving as spatial sampling points for vector calculation;

[0195] Pollution source initialization:

[0196] Let the coordinates of each air quality monitoring node at the current moment be denoted as the pollution source point set P={ };

[0197] Each pollution source Corresponding to a pollutant concentration value , as the initial pollution intensity;

[0198] Furthermore, the system employs a radial influence decay function (such as a Gaussian diffusion kernel) to diffuse the initial contamination intensity from each source point to the surrounding grid.

[0199] Superposition of meteorological factor vector fields:

[0200] At the center of each grid cell The system superimposes meteorological driving factors to calculate the dominant vector of pollution propagation. To obtain the dominant diffusion direction and velocity of pollutants at each spatial point.

[0201] ;

[0202] In the formula, This is a meteorological vector constructed based on wind speed and direction, sourced from urban meteorological monitoring systems or third-party open meteorological platforms. Specific data includes wind speed (unit: m / s) and wind direction (unit: angle or direction vector). The generation method involves the system mapping the collected wind speed and direction data to the center point of the corresponding grid cell. This is converted into a two-dimensional wind direction vector, which serves as the dominant migration direction of pollutants at that spatial point.

[0203] The thermal convection vector is constructed based on temperature differences. Its source is spatial temperature distribution data collected from the same meteorological monitoring system, including the temperature values ​​(unit: °C) of each grid cell and its neighboring areas. Generation method: The system constructs a temperature difference direction vector (i.e. thermal convection driving direction) by calculating the temperature gradient between adjacent grids, and normalizes it into a two-dimensional thermal convection vector to simulate the direction of heat rise or conduction.

[0204] and These are empirical weighting parameters (which can be determined through historical fitting);

[0205] Simultaneously, the system loads the city's digital elevation model and building boundary layers, identifying the obstruction zone D. { };

[0206] The system has a center point for each grid cell. Define damping coefficient ,in:

[0207] =0: Complete blockage (such as high walls or enclosed buildings);

[0208] =1: Unobstructed free diffusion area;

[0209] The median value represents partial resistance (such as green spaces and low-rise buildings);

[0210] Finally, at the center point of each grid cell At this point, the diffusion vector is corrected to obtain the final pollution propagation vector. ;

[0211] The system will center the point of each grid cell. The corresponding final pollution propagation vector The output is a diffusion vector field;

[0212] Z3. Training of the pollution diffusion prediction model:

[0213] Based on the pollution diffusion vector field in Z2, a boundary uncertainty learning mechanism is introduced to predict the future diffusion path of pollutants and model the uncertainty of the propagation boundary, specifically including:

[0214] Pollution diffusion prediction path modeling: The system uses the pollution center trajectory formed by pollution monitoring data as the set of real sample trajectories. Combined with the currently constructed pollution diffusion vector field, it uses streamline tracking (such as Runge-Kutta integral) or particle simulation to construct the set of central paths of pollutants from each pollution source to the boundary, which serve as the training target for pollution diffusion prediction paths.

[0215] Boundary uncertainty modeling: The system uses historical path points ( , The training samples are composed of the corresponding pollution intensities. Gaussian process regression is used to predict the confidence interval of the pollution intensity of each path point at future time t+Δt. The width of the confidence band is automatically estimated by the covariance function of GPR and serves as the boundary band of the pollution impact range.

[0216] The system sets a confidence threshold (e.g., 95%) and uses the boundary fluctuation range around the center of the diffusion path as the "confidence boundary zone of pollution impact". Specifically, the system takes the pollution diffusion path as the center line and generates buffer zones on both sides of it according to the confidence boundary. This area is output in GeoJSON or GeoTIFF format for recognition by GIS platform or scheduling system.

[0217] Z4. Pollution impact area output:

[0218] The system defines the pollution diffusion prediction path and its confidence boundary zone together as the "pollution impact area";

[0219] The pollution impact area represents the geographical range that pollutants may reach within a given time frame T.

[0220] The pollution-affected area serves as the input basis for subsequent modules (such as the target risk analysis module and the response scheduling module);

[0221] The target risk analysis module spatially matches the confidence level pollution impact area output by the pollution diffusion modeling module with the preset target areas (including schools, medical institutions, parks, elderly care institutions, etc.) and calculates the exposure risk score for each target.

[0222] The exposure risk scoring function comprehensively considers pollutant concentration, predicted arrival time, tolerance threshold of the target area, and response time window factor, and outputs priority ranking results for use by the risk response module.

[0223] The working method of the target risk analysis module includes:

[0224] Y1. The system pre-sets several target areas, including but not limited to schools, medical institutions, elderly care facilities, parks and other functional areas sensitive to air pollution;

[0225] Each target region has the following attribute information:

[0226] Regional boundary coordinate information;

[0227] Function type identifier;

[0228] Tolerable pollutant concentration threshold;

[0229] Minimum response time requirement (i.e., early response time window);

[0230] Y2. The system receives pollution impact area information output by the pollution diffusion modeling module. The pollution impact area is composed of the predicted pollution diffusion path and its confidence boundary zone, representing the geographic spatial range that pollutants may affect within a set prediction time range in the future.

[0231] The system uses a spatial overlay algorithm to determine whether there is a spatial overlap between each target area and the pollution-affected area. If the boundary of a target area intersects with the pollution-affected area, the target area is determined to be a "potential exposure target".

[0232] Y3. For each target area identified as a potential exposure target, the system extracts the following pollution exposure parameters from the pollution diffusion model:

[0233] The predicted maximum pollutant concentration within the target area;

[0234] The estimated time when pollutants are expected to begin affecting the target area;

[0235] The time difference between the current system time and the predicted arrival time is used to represent the responsive time window;

[0236] The corresponding target area attribute values ​​include the tolerance concentration threshold and the minimum response time requirement;

[0237] The above parameters are used to construct a comprehensive assessment basis for the target's exposure status;

[0238] Y4. The system compares the extracted pollution exposure parameters with preset attribute parameters and performs a target exposure risk level determination operation, with the specific rules as follows:

[0239] When the predicted pollution concentration is higher than the tolerable concentration threshold of the target area and the remaining response time is less than the minimum response time requirement, it is judged as high risk;

[0240] When the predicted pollution concentration is close to the tolerance threshold, or the response time is approaching but still meets the requirements, it is judged as medium risk;

[0241] When the predicted pollution concentration is significantly lower than the tolerance threshold and there is sufficient response time, it is judged as low risk;

[0242] Based on the above judgment logic, the system assigns a risk level label to each potential exposure target and generates a target response priority ranking.

[0243] Y5. Output the target risk assessment results in a structured format, including the following fields:

[0244] Unique identifier for the target area;

[0245] The determined exposure risk level (high, medium, low);

[0246] Suggested response priorities;

[0247] Additional fields include the expected duration of impact and the intensity of pollution;

[0248] The assessment results are used to drive operations in the risk response module, such as law enforcement dispatch, information dissemination, and intervention resource allocation, to support proactive responses in urban ecological pollution prevention and control.

[0249] The target risk analysis module further includes a pollution diffusion path map constructed based on the pollution diffusion modeling module, which identifies multiple propagation paths between pollution source nodes and representative nodes of sensitive targets, and calculates the path exposure level based on the pollution propagation intensity, propagation time and geographical damping factors of each side of the path.

[0250] When a target area is located at the end of a high-risk transmission path among multiple high-intensity paths, the system automatically upgrades the exposure risk level of the target area and uses the updated risk level for priority response ranking in subsequent risk response modules.

[0251] Specifically, it includes:

[0252] H1. Constructing the pollution diffusion pathway map structure:

[0253] In the pollution diffusion path map structure, the map nodes are the various air quality monitoring nodes deployed in the system;

[0254] Each map node has geographic coordinates (latitude and longitude), a data collection timestamp, the type of pollutant, and a pollution concentration value. The map node is denoted as N={ , ,..., };

[0255] H2. Graph edge construction and edge weight calculation:

[0256] For any two graph nodes and If the spatial distance between them is less than the set communication radius R, then a directed edge is established between them. ;

[0257] Towards The direction is determined by the pollution diffusion trend (such as upwind nodes and downwind nodes);

[0258] Directed edge Boundary rights Indicates pollutants from map nodes to The ability to spread;

[0259] In this embodiment, The method for obtaining it is as follows:

[0260] ;

[0261] In the formula, This represents a real-time or historical pollution concentration gradient. Spatial distance (used for propagation time correction); This is the wind direction matching coefficient, calculated based on the consistency of wind direction angles; This is a geographic damping factor, obtained by matching layers (such as buildings, green spaces, and water bodies);

[0262] Weighting factors , , and Obtained based on empirical parameters or model training results;

[0263] H3. Determination of pollution source nodes:

[0264] Based on the pollution sensitivity factor output by the pollution analysis module, the system performs trend analysis on the time series data of node concentrations and selects nodes whose pollution concentrations continuously rise and exceed a set threshold as the pollution source node set S={ , ,..., };

[0265] H4. Let Y = {...} , ,..., };

[0266] Each target area is associated with the nearest monitoring node in the map structure, which serves as its representative node. The distance can be determined by geographic distance or Voronoi adjacency.

[0267] Construction of the pollution path set:

[0268] For each pair of pollution source nodes Target represents node A graph traversal algorithm is used to search for all reachable paths in the path graph. ;

[0269] Each path p∈ It contains a sequence of nodes and edges;

[0270] H5. Path propagation intensity calculation:

[0271] Propagation intensity of each path Weights of all edges on the path The weighted average of the values, the propagation intensity is used to characterize the effectiveness of pollutants propagating to the target area via this path;

[0272] Path propagation time estimation:

[0273] Based on the propagation speed model of pollutants in the wind field, the propagation time is calculated by combining the path length. The propagation speed model can be fitted by historical events or preset as a constant.

[0274] H6. Dynamic Exposure Level Update and Sorting Output for Target Area:

[0275] Exposure risk assessment criteria:

[0276] If the propagation strength of a certain path p If the risk level is higher than the preset risk threshold and the propagation time is less than the response time window of the target area, then the path is marked as a high-exposure-risk path.

[0277] If there are multiple high-exposure-risk pathways in a target area, the propagation time of the strongest pathway is extracted for level assessment.

[0278] Risk level update mechanism:

[0279] The system updates the exposure level of the target area based on a cross-tabulation of transmission intensity level and transmission time;

[0280] The updated results are recorded in the output structure of the target risk analysis module and used as the sorting basis for the response scheduling module;

[0281] The target risk analysis module is based on the pollution diffusion path map structure to identify the propagation path between pollution source nodes and sensitive target areas, and extracts the pollutant concentration data of each air quality monitoring node on the path to form a pollution concentration sequence;

[0282] The target risk analysis module performs sequential analysis of the concentration sequence to determine whether the pollutant concentration continues to rise during the propagation of the path.

[0283] If a path is found to show a continuous increase in concentration in a segment near the target area, and the concentration at the endpoint exceeds a set threshold, the path is marked as having a "pollution shock trend".

[0284] Furthermore, the target risk analysis module determines whether the target area is in a state of potential pollution impact based on whether there is a path with a pollution impact trend;

[0285] If the condition is met, the system will automatically raise the exposure risk level of the target area and transmit the updated level to the risk response module for scheduling, prioritization, and early warning push.

[0286] Based on the ranking results output by the target risk analysis module, the risk response module determines the response level and links with the urban management system, including the traffic dispatch system, pollution control equipment control system, and public early warning information release system, to perform priority response operations.

[0287] The implementation process of the risk response module includes the following steps:

[0288] N1. Response Level Determination:

[0289] The risk response module receives the risk level and response priority ranking results for each target area from the target risk analysis module, and classifies the target areas into response levels according to the response level determination mechanism set within the system. The response level determination mechanism is based on the following judgment conditions:

[0290] When the risk level of the target area is high and it ranks high in the response ranking, it corresponds to a Level 1 response target;

[0291] When the risk level of the target area is medium, or it is in the middle of the ranking, it corresponds to a level 2 response target;

[0292] When the target area is a low-risk area, or is ranked lower, it corresponds to a Level III response target or an early warning monitoring target.

[0293] The system determines the response level for each target area based on the above judgment results;

[0294] N2, Linkage Response Control:

[0295] Based on the response level determined in step one, the risk response module calls the following subsystems in the city management system to perform the corresponding response operations:

[0296] 1. Traffic dispatching system response control:

[0297] For target areas at Level 1 response, the system sends control instructions to the traffic dispatch system;

[0298] The instructions include, but are not limited to: traffic flow guidance on roads surrounding the polluted area, setting up restricted traffic zones, and reserving emergency lanes;

[0299] Supports dynamic adjustment of traffic scheduling strategies based on predicted changes in pollution diffusion paths;

[0300] 2. Response control of pollution control equipment control system:

[0301] The system calls upon the urban pollution control equipment management platform to allocate mobile or fixed pollution control equipment to the target area;

[0302] The control content includes: equipment start / stop commands, work position instructions, and operating parameter settings (such as spray volume and work duration).

[0303] The control logic determines the priority areas for equipment deployment based on the location, shape, and diffusion trend of the pollution-affected area;

[0304] 3. Response control of the public early warning information release system:

[0305] The system calls the information dissemination interface to release early warning information to the public within the pollution-affected area;

[0306] The information released includes the type of pollutant, the expected period of impact, the pollution intensity level, and health protection recommendations.

[0307] Information dissemination channels include: mobile terminal push notifications, electronic information screens, public broadcasting systems, and government website platforms;

[0308] N3. Response execution status tracking and feedback:

[0309] The system continuously receives real-time data from air quality monitoring nodes to assess the actual effectiveness of response measures;

[0310] If the pollution spread trend exceeds the original prediction range, or if the pollution in the response area is not effectively controlled, the system will automatically adjust the response level and expand the control range.

[0311] The system records the execution status data of each response operation, including response start time, response duration, response coverage area, and actual pollution concentration changes, which are used for subsequent model calibration and strategy evaluation.

[0312] The system determines the response level for each target area based on the above judgment results.

[0313] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0314] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A smart ecological environment monitoring system, characterized in that, The method comprises the following steps: An air quality monitoring subsystem is used to arrange a plurality of air quality monitoring nodes in an urban area and collect air pollution parameters at corresponding positions; A pollution analysis module is used to perform frequency clustering, pollution intensity statistics and change rate evaluation on historical pollution data collected by the air quality monitoring nodes, to obtain pollution frequency, intensity and volatility characteristics, and to obtain a pollution sensitivity factor by weighting and fusing the three characteristics after normalization; A monitoring node adjustment module dynamically calculates an optimal density layout strategy of the air quality monitoring nodes in the urban area based on the pollution sensitivity factor; The method for dynamically distributing the air quality monitoring nodes by the monitoring node adjustment module comprises the following steps: A1, regional grid division: First, the whole city monitoring range is divided into several spatial grid units, each spatial grid unit is recorded as ; A2, receiving and matching the pollution sensitivity factor: each spatial grid cell with a corresponding pollution sensitivity factor is matched; A3, determining a total number constraint of the monitoring nodes: The total number of air quality monitoring nodes allowed to be deployed in the set urban area is set to ; A4, calculating an expected number of node distribution in each region: normalizing the pollution sensitivity factor of each spatial grid cell of the pollution sensitivity factor to obtain a normalized weight value ; The number of nodes that each region should be allocated is then calculated as ; A pollution diffusion modeling module is used to establish a pollution diffusion prediction model based on air pollution parameters, meteorological factor data and urban terrain information, to output a pollution impact area including a pollution diffusion path, an impact range and a confidence degree of pollutant concentration distribution in each region; A target risk analysis module is used to perform spatial matching between the pollution impact area and a preset target area, and to calculate an exposure risk score based on pollutant concentration, predicted arrival time, target area tolerance threshold and response time window factor; A risk response module is used to determine a response level according to a sorting result output by the target risk analysis module, and to link the urban management system to perform a corresponding response operation. 2.The intelligent ecological environment monitoring system according to claim 1, characterized in that, After the calculation of the number of monitoring nodes for each spatial grid cell is completed, the system proceeds to the node position and constraint correction phase, which includes the following steps: B1, deployment feasibility determination: First, the physical deployable capacity of each spatial grid cell is judged ; If the area of the spatial grid cell is not sufficient to support the minimum deployment spacing of each air quality monitoring node, or there is a deployment restriction in the area, it is recorded as a "deployment restricted area"; The system records its actual deployable capacity and calculates its node deficit ; B2. For all spatial grid cells that have a node deficit , identify its set of contiguous regions according to the spatial proximity principle ; Finding pollution sensitivity factors in adjacent regions Space grid cells with room for deployment , node deficits to spatial grid cells within its neighborhood whose pollution sensitivity factors are above a preset sensitivity threshold; B3, in each spatial grid unit, the system loads a city basic geographic information layer thereof, excludes all preset non-deployable points, and marks the remaining points as a set of valid deployment candidate points; B4、In each spatial grid cell The spatial clustering algorithm is used in the effective deployment candidate point set of each spatial grid cell to optimize the actual deployment position of each air quality monitoring node. B5、All air quality monitoring node deployment location optimization is completed, the system outputs the final deployment layer, including each spatial grid unit The number of nodes, coordinate positions and associated sensitivity factors . 3.The intelligent ecological environment monitoring system according to claim 2, characterized in that, The B4 step comprises: The system constructs an initial set of reference points within the set of valid deployment candidate points based on the actual deployable capacity of the spatial grid cells ​​​ The system extracts a set of candidate deployment points within each deployment unit ; For each deployment unit, the system calculates the distance from each candidate deployment point in its corresponding set of candidate deployment points to the geometric center of the deployment unit In some embodiments, the system calculates the distance from each candidate deployment point in the set of candidate deployment points to the geometric center of the deployment unit. Selecting a candidate deployment point closest to the geometric center as a final deployment position of the deployment unit; Finally, if there is no candidate deployment point in a deployment unit, the system performs a minimum distance search within the boundary of the deployment unit to select an external candidate deployment point closest to the geometric center of the deployment unit for replacement deployment. 4.The intelligent ecological environment monitoring system according to claim 3, characterized in that, The method for outputting a pollution diffusion path and its impact range by the pollution diffusion modeling module comprises the following steps: Z1, collecting air pollution parameter data, meteorological factor data and urban terrain information data as model input; Z2, constructing a two-dimensional pollution diffusion vector field in the urban area to describe the propagation speed and direction of the pollutant in the geographical space; Z3, on the basis of the pollution diffusion vector field in Z2, introducing a boundary uncertainty learning mechanism to predict the future diffusion path of the pollutant and model the uncertainty of the propagation boundary; Z4, defining the pollution diffusion prediction path and its confidence boundary band as a "pollution impact area" together. 5.The intelligent ecological environment monitoring system according to claim 4, characterized in that, The working method of the target risk analysis module comprises the following steps: Y1, the system presets a plurality of target area sets; Y2, the system receives pollution impact area information output by the pollution diffusion modeling module, and the pollution impact area is composed of the predicted pollution diffusion path and its confidence boundary band together; The system determines whether each target region and the pollution influence region have a spatial overlap relationship. If the boundary of a target region intersects with the pollution influence region, the target region is determined as a "potential exposure target"; Y3. For each target region determined as a potential exposure target, the system extracts the following pollution exposure parameters from the pollution diffusion model: The predicted maximum pollutant concentration value in the target region; The time when the pollutant is expected to start affecting the target region; The time difference between the current system time and the predicted arrival time, which represents the response time window; The corresponding target region attribute values, including the tolerance concentration threshold and the minimum response time requirement; Y4. The system compares the extracted pollution exposure parameters with the preset attribute parameters and performs target exposure risk level determination operation based on the preset rules; Y5. The target risk assessment result is output in a structured form. 6.The intelligent ecological environment monitoring system according to claim 1, characterized in that, The target risk analysis module further includes a pollution diffusion path graph constructed based on the pollution diffusion modeling module, which identifies multiple propagation paths between the pollution source node and the sensitive target representative node, and calculates the path exposure level based on the pollution propagation strength, propagation time, and geographic damping factor of each edge in the path. When a target region is at the end point of a high-risk propagation path in multiple high-intensity paths, the system automatically raises the exposure risk level of the target region. 7.The intelligent ecological environment monitoring system according to claim 6, characterized in that, The target risk analysis module identifies the propagation path between the pollution source node and the sensitive target region based on the pollution diffusion path graph structure, and extracts the pollutant concentration data of each air quality monitoring node on the path to form a pollution concentration sequence. The target risk analysis module sequentially analyzes the concentration sequence to determine whether the pollutant concentration is continuously increasing during the propagation process on the path. If the concentration is found to continuously increase on the path segment close to the target region, and the end point concentration exceeds the set threshold, the path is marked as having a "pollution impact trend"; And the target risk analysis module determines whether the target region is in a potential pollution impact state according to whether there is a path with a pollution impact trend; If so, the system automatically raises the exposure risk level of the target region and transmits the updated level to the risk response module for dispatching and warning pushing. 8.A method for monitoring an intelligent ecological environment, characterized in that, The method uses the monitoring system of any one of claims 1-7, comprising the following steps: Step one: multiple air quality monitoring nodes are arranged in the urban area, and each air quality monitoring node collects air pollution parameters at its corresponding location; Step two: the historical pollution data collected by the air quality monitoring nodes are subjected to frequency clustering, pollution intensity statistics, and change rate evaluation, and the pollution event occurrence frequency, pollution concentration average value, and pollution concentration change rate with time in a set period are respectively calculated to obtain the pollution frequency, intensity, and volatility characteristics; The above three characteristics are normalized and weighted to obtain the pollution sensitivity factor of each region; Step three: the pollution sensitivity factor obtained in step two is used to calculate the weighted coefficient of different regions in the city; Under the constraint of the total number of monitoring nodes, the optimal number of monitoring nodes for each region is dynamically calculated, and the air quality monitoring nodes are dynamically adjusted according to the optimization results; Realize the encryption deployment in the high pollution sensitive area and the sparse deployment in the low risk area, in order to optimize the global monitoring coverage efficiency; Step four: based on the real-time collection data of air quality monitoring nodes, fuse meteorological factors and urban terrain information, and construct a pollution diffusion prediction model; The prediction model outputs the propagation path trend and influence range of pollutants in the urban space; Step five, match the pollution influence area predicted by the pollution diffusion model with the preset sensitive target area in space; For each sensitive target, the exposure risk score of the sensitive target is calculated by comprehensively considering the factors of pollutant concentration, predicted arrival time, target tolerance threshold and response time window; According to the exposure risk score result, all sensitive targets are prioritized; Step six: according to the sorting result of step S5, determine the response level; Linkage city management system, complete pollution early warning and response scheduling operation.

Images