Optimization Method for Regional Atmospheric Environment Risk Monitoring Site Layout Based on Multi-Objective Planning

Abstract

This application document provides an optimization method for regional atmospheric environment risk monitoring site layout based on multi-objective programming, including: collecting initial data of environmental monitoring, establishing a multi-objective optimization model according to the initial environmental data, defining the constraint conditions and objective function for the layout of monitoring sites, using a multi-objective hybrid programming algorithm to solve the objective function, outputting a preliminary monitoring site layout plan, dynamically adjusting the monitoring site layout plan, and performing feedback optimization on the monitoring site layout plan to ensure that the distribution of monitoring sites adapts to environmental changes. Among them, the initial data includes pollutant sources, diffusion models, meteorological conditions, and risk assessment data. Through multi-objective optimization, this application minimizes the number of monitoring sites, reduces the layout cost and maintenance cost on the premise of ensuring the monitoring coverage rate and monitoring efficiency. Through the dynamic adjustment mechanism, this application document can respond to the impacts of meteorological conditions and pollution source changes in real time, ensuring the flexibility and high efficiency of the monitoring system.

Description

Technical Field

[0001] This application relates to the technical field of environmental monitoring, and particularly to an optimization method for the layout of regional atmospheric environmental risk monitoring points based on multi-objective programming. Background Art

[0002] Currently, the atmospheric environmental monitoring system usually relies on a fixed monitoring point layout to collect environmental data for analyzing the diffusion of pollutants. However, the layout optimization of such systems often only considers a single objective, such as minimizing the monitoring cost or maximizing the monitoring range, ignoring the balance between multi-dimensional factors (such as monitoring cost, risk level, pollution source diffusion model, etc.). More importantly, the current monitoring point layout scheme lacks a dynamic adjustment mechanism and cannot cope with the dynamic changes in the regional atmospheric environment, resulting in insufficient monitoring of key pollution areas and unreasonable resource allocation.

[0003] Therefore, in view of the multi-objective requirements in a complex environment, there is an urgent need for a new monitoring point layout optimization method that can achieve the intelligent layout of monitoring points in the context of the dynamic changes in the atmospheric environment and effectively balance the monitoring cost, coverage range, and risk assessment accuracy. Summary of the Invention

[0004] The purpose of this application is to provide an optimization method for the layout of regional atmospheric environmental risk monitoring points based on multi-objective programming to cope with the dynamic changes in the regional atmospheric environment and avoid insufficient monitoring of key pollution areas and unreasonable resource allocation.

[0005] To achieve the above object, the technical solution adopted in this application is: to provide an optimization method for the layout of regional atmospheric environmental risk monitoring points based on multi-objective programming, including: collecting the initial data of environmental monitoring, establishing a multi-objective optimization model according to the initial environmental data; defining the constraint conditions and objective function for the layout of monitoring points; using a multi-objective hybrid programming algorithm to solve the objective function and output a preliminary monitoring point layout scheme; dynamically adjusting the monitoring point layout scheme and performing feedback optimization on the monitoring point layout scheme to ensure that the distribution of monitoring points adapts to environmental changes.

[0006] As a preference, the initial data includes pollution source data, diffusion model data, meteorological condition data, and risk assessment data.

[0007] As another preference, the multi-objective hybrid programming algorithm comprehensively considers the following objectives: minimizing the layout cost of monitoring points, maximizing the monitoring coverage rate, and minimizing the environmental risk within the region.

[0008] Further preferably, the pollutant concentration P i , the population density ρ i , and the wind speed v iThe weight ratio is dynamically adjusted according to regional characteristics to meet the risk requirements of different regions.

[0009] Further preferably, the values of the weight coefficients ω1, ω2, ω3 are flexibly adjusted according to different monitoring requirements, and decision-makers can adjust the weight priorities based on the assessment of the actual environmental risks.

[0010] Further preferably, the multi-objective hybrid programming algorithm can dynamically obtain the real-time data of the monitoring points, and readjust the layout of the monitoring points based on the changes in environmental risks to adapt to the dynamic diffusion of air pollution within the region.

[0011] Compared with the prior art, the beneficial effects of this application are as follows: The dynamic adjustment mechanism in this application document ensures the flexibility of the monitoring system. For example, when the wind direction in a certain area suddenly changes, causing pollutants to spread towards the densely populated area, the system will immediately recalculate the environmental risks, adjust the layout of the monitoring points, increase the monitoring points in high-risk areas, and reduce the monitoring density in low-risk areas, so as to maximize the efficiency of the entire monitoring network.

[0012] Furthermore, through the assessment of environmental risks, this application can dynamically adjust the layout of the monitoring points according to the risk situations of different regions, ensuring more accurate monitoring of high-risk areas, and greatly improving the accuracy and reliability of the environmental monitoring system.

[0013] Furthermore, through multi-objective optimization, this application document minimizes the number of monitoring points on the premise of ensuring the monitoring coverage rate and monitoring efficiency, reducing the layout cost and maintenance cost. Through the dynamic adjustment mechanism, this application document can respond to the impacts of meteorological conditions and pollution source changes in real time, ensuring the flexibility and high efficiency of the monitoring system, and being applicable to various complex environmental monitoring scenarios. Generally speaking, this application document has significant innovation and practicability in terms of the coverage rate of monitoring point layout, cost control, and environmental risk assessment, can effectively improve the intelligent level of the environmental monitoring system, and greatly improve the ability to respond to environmental risks and the efficiency of resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a schematic flow chart of the steps of the regional air environment risk monitoring point layout optimization method based on multi-objective programming. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0015] Next, in combination with the specific embodiments, the present application will be further described. It should be noted that, on the premise of non-conflict, any combination of the following-described embodiments or technical features can form a new embodiment.

[0016] In the description of the present application, it should be noted that for orientation terms, such as the terms "center", "horizontal", "vertical", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc., indicating the orientation and positional relationship are based on the orientation or positional relationship shown in the drawings. This is only for the convenience of describing the present application and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and should not be construed as limiting the specific protection scope of the present application. It should be noted that the terms "first", "second", etc. in the description and claims of the present application are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0017] The terms "comprising" and "having" and any variations thereof in the description and claims of the present application are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0018] Therefore, in the multi-objective optimization model of the present application document, the objective function Z is used to balance three key factors: monitoring cost, coverage rate, and environmental risk.

[0019] In a preferred embodiment, refer to Figure 1 , the present application document provides a method for optimizing the layout of regional atmospheric environmental risk monitoring points based on multi-objective programming, including: step S10 collecting initial data of environmental monitoring, step S20 establishing a multi-objective optimization model according to the initial environmental data, defining the constraint conditions and objective function for the layout of monitoring points, step S30 using a multi-objective hybrid programming algorithm to solve the objective function, outputting a preliminary layout plan of monitoring points, and step S40 dynamically adjusting the layout plan of monitoring points, performing feedback optimization on the layout plan of monitoring points to ensure that the distribution of monitoring points adapts to environmental changes. Among them, the initial data includes pollutant sources, diffusion models, meteorological conditions, and risk assessment data.

[0020] First, it is necessary to collect environmental monitoring data within the region. These data include, but are not limited to: regional division data, the total area of the monitoring region, and the geographical location, area size, etc. of different sub-regions; pollutant diffusion models, emission data of different pollution sources, prediction of pollutant diffusion paths, etc.; meteorological conditions, such as meteorological factors directly affecting pollutant diffusion, such as wind speed, wind direction, temperature, humidity, etc.; population density, the population distribution density of different sub-regions, used to evaluate risks. By collecting the above data, it can provide basic support for subsequent optimization calculations.

[0021] Furthermore, a multi-objective optimization model can be established. The core is based on the above detection data. This model optimizes the layout of monitoring points by balancing the following key objectives: 1. Monitoring cost C, so as to minimize the number of monitoring points and equipment costs as much as possible; 2. Monitoring coverage rate O, to maximize the coverage of the monitoring network for polluted areas and ensure that there are no monitoring blind spots in key areas; 3. Environmental risk R, giving priority to monitoring high-risk areas to prevent the spread of pollutants in areas with high population density.

[0022] Furthermore, the multi-objective hybrid programming algorithm comprehensively considers the following objectives: minimizing the layout cost of monitoring points, maximizing the monitoring coverage rate, and minimizing the environmental risk within the region.

[0023] Therefore, preferably, in order to accurately evaluate the coverage of monitoring points, the following monitoring coverage formula is adopted in this application document: , where A i represents the coverage area of the i-th monitoring point, which depends on the monitoring radius r of the device i , f(d i ) is a distance function, preferably a distance attenuation function, to represent the distance attenuation effect of the monitoring point to the center of the target area, and at the same time to reflect the influence of the distance between the monitoring point and the center of the pollution source on the coverage effect, A total is the total area of the reference monitoring area, and O is the monitoring coverage rate. Among them, the form of the function f(d i ) is: , d i is the distance from the i-th monitoring point to the center of the target area, and α is the distance attenuation factor. Through the calculation of the monitoring coverage formula, the coverage effect of the monitoring point layout can be quantified, and the influence of distance on the coverage rate can be considered, thus helping to dynamically adjust the layout of monitoring points.

[0024] Furthermore, environmental risk R is an important factor affecting the optimization of the monitoring point layout. In order to more accurately evaluate the environmental risks of each sub-region, this application combines factors such as pollutant concentration, population density, and wind speed, and proposes the following environmental risk calculation formula: ; where P i is the pollutant concentration of the i-th sub-region, ρ i is the population density, v i is the wind speed of this sub-region. The greater the wind speed, the faster the pollutant diffusion speed, and the smaller the environmental risk within the region. λ i is the risk level weight, used to dynamically adjust the risk priorities of each sub-region, and m is the number of sub-regions, comprehensively considering the influence of pollutant diffusion, population density, and wind speed on environmental risk, so as to be able to reasonably evaluate the risks of each sub-region and ensure key monitoring of high-risk areas.

[0025] Furthermore, in order to comprehensively optimize the three objectives of monitoring cost, coverage rate, and environmental risk, an objective function Z for multi-objective optimization is proposed. Among them, , it should be noted that Z is the objective function, C is the monitoring cost, O is the monitoring coverage rate, R is the environmental risk value, ω1, ω2, and ω3 are weight coefficients. Users can balance and adjust these three objectives to different degrees according to actual needs. The design of the objective function takes into account the trade-off relationships among the three optimization objectives. For example, when it is necessary to preferentially reduce the cost of monitoring points, the value of ω1 can be appropriately increased during actual operation. When more attention needs to be paid to environmental risks, the value of ω3 can be appropriately increased during actual operation. By adjusting the weight coefficients, different monitoring requirements can be flexibly met.

[0026] Therefore, for industrial parks, the emission intensity of pollution sources is relatively large and is easily affected by meteorological conditions. The optimization method in this application document can layout monitoring points according to the positions and diffusion paths of emission sources in the initial stage, and through a real-time adjustment mechanism, ensure that the monitoring points can respond dynamically when the wind speed or wind direction changes, and preferentially cover high-risk areas. For example, assume that the area of an industrial park is 50 km 2 , and the emission sources are mainly concentrated in the east. The system arranges several monitoring points through the above optimization model and monitors the pollutant concentrations in each sub-region in real time. When the wind speed increases or the wind direction changes, the system will dynamically adjust the layout of the monitoring points in the west to prevent the diffusion of pollutants to residential areas.

[0027] Furthermore, for the urban environment, air quality is affected by various factors, including vehicle exhaust emissions, industrial emissions, and weather conditions, etc. This application document can divide urban areas and reasonably layout monitoring points according to the air pollution status and population density of each area. For example, in a certain city, the pollution concentration in the city center is relatively high and the population is dense. The optimization model will increase the density of monitoring points in the city center according to the pollutant concentration and population density to ensure accurate monitoring of high-risk areas.

[0028] Among them, in a specific embodiment, assume that an urban area needs to layout air quality monitoring points. The area of this region is 100 km 2 , and 5 monitoring points are initially arranged. The coverage radius of each monitoring point is 5 km 2 , that is, the coverage area A of each monitoring point i = 78.54 km 2 (calculated according to A = πr 2 ). The attenuation factor α = 0.05, and the distances from the monitoring points to the center of the target area are d1 = 2 km, d2 = 3 km, d3 = 4 km, d4 = 5 km, and d5 = 6 km respectively.

[0029] Furthermore, according to the distance attenuation function f(d i ) calculate the attenuation coefficient of each monitoring point. Substituting the data, we get: f(d1)=1 / 1 + 0.05×2 2 = 0.909; f(d2)=1 / 1 + 0.05×3 2 = 0.816; f(d3)=1 / 1 + 0.05×4 2 = 0.714; f(d4)=1 / 1 + 0.05×5 2 = 0.615; f(d5)=1 / 1 + 0.05×6 2 = 0.526. Further, calculate the total coverage rate O. Substituting the above data into the monitoring coverage formula, we get: O=(78.54x0.909)+(78.54x0.816)+(78.54 x0.714)+(78.54x0.615)+(78.54 x 0.526) / 100 = 2.81. Therefore, the monitoring coverage rate O = 2.81, that is, the total coverage area is 281.15km 2 , far exceeding the total area of the benchmark monitoring area of 100km 2 , and then the result shows that the current layout of monitoring points can already cover the target area and has a certain redundancy, and some monitoring points can be reduced to lower costs. This data is used for comparison with the total area of the benchmark monitoring area, and the total area of the benchmark monitoring area can be set by the operator according to the actual working conditions to meet the actual needs. If the monitoring coverage rate is too high, it indicates redundancy; if it is too low, more monitoring points need to be added.

[0030] Further, there are three sub - regions divided in the above - mentioned city. The pollutant concentration, population density, and wind speed detection in each sub - region are the following data. In the first sub - region, P1 = 150μg / m 3 , ρ 1= 500 people / km 2 , v1 = 3m / s, weight λ1 = 0.5; In the second sub - region, P2 = 200μg / m 3 , ρ2 = 700 people / km 2 , v2 = 4m / s, weight λ2 = 1; In the third sub - region, P3 = 100μg / m 3 , ρ3 = 400 people / km 2, v3 = 5 m / s, weight λ3 = 0.8. Then calculate the environmental risk of each sub-region, and we can get: R1 = 0.5×(150×500 / 3) = 12500; R2 = 1×(2000×700 / 4) = 35000; R3 = 0.8×(100×400 / 5) = 6400; Therefore, the total environmental risk R = 12500 + 35000 + 6400 = 53900, and the total environmental risk value R = 53900, indicating that the risk level in the current city is relatively high, especially the risk contribution of the second sub-region is the largest. This result is used to evaluate whether the layout of the monitoring points is reasonable. By comparing with the benchmark risk value, for example, the benchmark value of the above-mentioned city is 50000, and the benchmark value can be set according to the specific situation of the actual city. Since the current risk value is relatively high, it is necessary to increase monitoring points in high-risk areas, especially the second sub-region, to reduce the risk.

[0031] Therefore, further, the layout budget of the monitoring system in the above-mentioned city is 500000 yuan, and the layout cost of each monitoring point is 50000 yuan. Currently, 8 monitoring points are arranged, so the cost C = 50000×8 = 400000 yuan. The monitoring coverage rate O = 0.85, the environmental risk value R = 53900, and the weight coefficients ω1 = 0.4, ω2 = 0.3, ω3 = 0.3. Therefore, substituting into the objective function, we can get Z = 0.4×400000 + 0.3×(1 - 0.85) + 0.3×53900 = 180670. The objective function value Z = 180670 is used to comprehensively evaluate the effectiveness of the current layout plan. At the same time, the effective benchmark value is set to 160000, and the effective benchmark value can be adjusted according to the actual working conditions. Therefore, the current plan is slightly redundant, especially in terms of monitoring coverage rate and cost. It is possible to consider reducing the layout of inefficient monitoring points and re-evaluating the weight allocation to achieve a better resource allocation and monitoring effect.

[0032] Therefore, this application document provides an efficient solution for urban or industrial park atmospheric environment monitoring. This solution solves the problems existing in the traditional monitoring system, such as layout blind spots, low monitoring efficiency, and uneven resource allocation. Through multi-objective optimization, it realizes the balance among cost, monitoring coverage rate, and environmental risk, enabling the monitoring network layout to maximize cost and resource utilization while meeting environmental monitoring requirements. The regional atmospheric environment risk monitoring layout optimization method based on multi-objective programming provided in this application document can balance multiple objectives, including the layout cost of monitoring points, monitoring coverage rate, environmental risk assessment, etc. Different from the traditional single-objective optimization model, the multi-objective optimization model can consider the requirements of multiple dimensions simultaneously, making the layout of monitoring points more flexible and efficient.

[0033] At the same time, since traditional atmospheric environment monitoring site layout usually only focuses on one goal, such as maximizing the coverage area or minimizing the cost, while ignoring the comprehensive impact of other factors. The model proposed in this application not only focuses on the monitoring coverage rate, but also combines the assessment of environmental risks, so that after the monitoring sites are arranged, high-risk areas can be given priority, ensuring that important areas are fully monitored.

[0034] Therefore, the monitoring coverage rate is an important optimization goal in this application document. In environmental monitoring, a low coverage rate will result in monitoring blind spots. For the monitoring coverage rate in this application, through the proposal of the monitoring coverage rate formula, the layout of monitoring sites is optimized, enabling the coverage range of monitoring sites in different regions to be dynamically adjusted according to the distance from the pollution source. At the same time, an attenuation factor is introduced to consider the distance factor, and a reasonable quantification process is carried out on the distance between the monitoring site and the pollution source. Through this formula, the effective coverage range of each monitoring site can be calculated during the actual operation process, and dynamically adjusted according to the importance of the region, thus effectively avoiding the emergence of monitoring blind spots.

[0035] Similarly, the cost control of the monitoring system is also an important aspect of multi-objective optimization. The cost includes the purchase cost, installation cost, and later maintenance cost of the equipment. To optimize the cost, this application document introduces the monitoring cost into the objective function and controls the proportion of the cost in the overall optimization through the weight coefficient.

[0036] This application also proposes an important dynamic adjustment mechanism, that is, the dynamic optimization of the monitoring site layout. Traditional monitoring site layout plans often do not make adjustments after the initial planning is completed and cannot cope with changes in pollution sources and meteorological conditions. However, this application document can dynamically adjust the monitoring site layout through the feedback of real-time data, combined with meteorological conditions and pollution diffusion models, to respond to environmental changes.

[0037] In practical applications, the diffusion path of pollutants may be affected by weather changes. For example, changes in wind speed and wind direction will directly change the diffusion range and speed of pollutants, resulting in a reduction in the risk of some originally monitored areas and an increase in the risk of other areas. To address this situation, this application document designs a dynamic adjustment mechanism. The system can regularly obtain the real-time data of the monitoring site and update the regional risk assessment in real time according to meteorological changes and pollutant diffusion prediction models.

[0038] This dynamic adjustment mechanism ensures the flexibility of the monitoring system. For example, when the wind direction in a certain area suddenly changes, causing pollutants to spread towards the densely populated area, the system will immediately recalculate the environmental risk, adjust the layout of the monitoring sites, increase the monitoring sites in high-risk areas, and reduce the monitoring density in low-risk areas, so as to ensure the maximization of the efficiency of the entire monitoring network.

[0039] Through the assessment of environmental risks, the present application can dynamically adjust the layout of monitoring points according to the risk situations in different regions, ensuring more accurate monitoring of high-risk areas and greatly improving the accuracy and reliability of the environmental monitoring system.

[0040] Through multi-objective optimization, the present application document minimizes the number of monitoring points on the premise of ensuring monitoring coverage and monitoring efficiency, reducing the layout cost and maintenance cost. Through the dynamic adjustment mechanism, the present application document can respond to the impacts of meteorological conditions and changes in pollution sources in real time, ensuring the flexibility and high efficiency of the monitoring system and being applicable to various complex environmental monitoring scenarios. Generally speaking, the present application document has remarkable innovation and practicability in terms of the coverage rate of monitoring point layout, cost control and environmental risk assessment, can effectively improve the intelligent level of the environmental monitoring system, and greatly improve the ability to respond to environmental risks and the efficiency of resource utilization.

[0041] The basic principle, main features and advantages of the present application have been described above. Those skilled in the art should understand that the present application is not limited by the above embodiments. What is described in the above embodiments and the specification is only the principle of the present application. Without departing from the spirit and scope of the present application, the present application will have various changes and improvements, and these changes and improvements all fall within the scope of the present application claimed. The scope of protection claimed by the present application is defined by the appended claims and their equivalents.

Claims

1. An optimization method for regional atmospheric environment risk monitoring site layout based on multi-objective programming, characterized in that Including: S10. Collect the initial data of environmental monitoring; S20. Establish a multi-objective optimization model according to the initial data, and define the constraint conditions and objective function for the layout of monitoring points; S30. Solve the objective function using a multi-objective hybrid programming algorithm, and output a preliminary monitoring point layout plan; S40. Dynamically adjust the monitoring point layout plan, and perform feedback optimization on the monitoring point layout plan to ensure that the distribution of monitoring points adapts to environmental changes; The multi-objective hybrid programming algorithm comprehensively considers the following objectives: minimizing the layout cost of monitoring points, maximizing the monitoring coverage rate, and minimizing the environmental risk within the region; The monitoring coverage rate is calculated by the following formula: ； Among them, represents the coverage area of the i-th monitoring point, is a distance function, representing the distance attenuation effect from the monitoring point to the center of the target area, is the total area of the reference monitoring area, and O is the monitoring coverage rate; the distance function is related to the distance from the i-th monitoring point to the center of the target area and the distance attenuation factor; among them, the function f(d i ) has the following form: , d i is the distance from the i-th monitoring point to the center of the target area, and α is the distance attenuation factor; Environmental risk calculation formula: ; where P i is the pollutant concentration in the i-th sub-region, ρ i is the population density, v i is the wind speed in this sub-region. The greater the wind speed, the faster the pollutant diffusion speed, and the smaller the environmental risk within the region. λ i is the risk level weight, used to dynamically adjust the risk priority of each sub-region, and m is the number of sub-regions.

2. The regional atmospheric environment risk monitoring site layout optimization method based on multi-objective programming according to claim 1, characterized in that The expression of the objective function is related to the monitoring cost C, the monitoring coverage rate O, the environmental risk value R, and the weight coefficients ω1, ω2, ω3.

3. The method for optimizing the layout of regional atmospheric environmental risk monitoring points based on multi-objective programming according to claim 2, wherein The values of the weight coefficients ω1, ω2, ω3 are flexibly adjusted according to different monitoring requirements, and decision-makers can adjust the weight priorities according to the assessment of actual environmental risks.

4. The regional atmospheric environment risk monitoring site layout optimization method based on multi-objective programming according to any one of claims 1-3, characterized in that, The multi-objective hybrid programming algorithm can dynamically obtain the real-time data of monitoring points, and re-adjust the layout of monitoring points based on the changes in environmental risks to adapt to the dynamic diffusion of air pollution within the region.

5. The method for optimizing the layout of regional atmospheric environmental risk monitoring points based on multi-objective programming according to claim 1, wherein The initial data includes pollutant sources, diffusion models, meteorological conditions, and risk assessment data.

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