Regional atmospheric environment risk monitoring stationing optimization method based on multi-objective planning

Abstract

The invention provides a regional atmospheric environment risk monitoring stationing optimization method based on multi-objective planning, and the method comprises the steps: collecting the initial data of environment monitoring, building a multi-objective optimization model according to the initial environment data, defining the constraint condition and objective function of monitoring point layout, and carrying out the optimization of the monitoring point layout. And solving the target function by using a multi-target hybrid planning algorithm, outputting 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 monitoring point distribution adapts to the environmental change. Wherein the initial data comprises pollutant sources, diffusion models, meteorological conditions and risk assessment data. Through multi-objective optimization, on the premise that the monitoring coverage rate and the monitoring efficiency are guaranteed, the number of monitoring points is reduced to the maximum extent, and the layout cost and the maintenance cost are reduced. According to the application file, through a dynamic adjustment mechanism, the influence of meteorological conditions and pollution source changes can be dealt with in real time, and the flexibility and high efficiency of a monitoring system are ensured.

Description

Technical Field

[0001] The present application relates to the field of environmental monitoring technology, and in particular to a method for optimizing the distribution of regional atmospheric environmental risk monitoring points based on multi-objective planning. Background Art

[0002] At present, atmospheric environment monitoring systems usually rely on a fixed monitoring point layout to collect environmental data for analyzing pollutant diffusion. However, the layout optimization of such systems often only considers a single goal, such as minimizing monitoring costs or maximizing monitoring range, ignoring the balance between multidimensional factors (such as monitoring costs, risk levels, pollution source diffusion models, etc.). More importantly, the current monitoring point layout scheme lacks a dynamic adjustment mechanism and cannot cope with the dynamic changes in the atmospheric environment in the region, resulting in insufficient monitoring of key pollution areas and unreasonable resource allocation.

[0003] Therefore, in response to the multi-target needs in complex environments, there is an urgent need for a new monitoring point optimization method that can realize the intelligent layout of monitoring points under the background of dynamic changes in the atmospheric environment and effectively balance the monitoring cost, coverage and risk assessment accuracy. Summary of the invention

[0004] The purpose of this application is to provide a method for optimizing the distribution of regional atmospheric environmental risk monitoring points based on multi-objective planning, so as to cope with the dynamic changes of the atmospheric environment in the region and avoid insufficient monitoring of key pollution areas and unreasonable resource allocation.

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

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

[0007] As another preferred embodiment, the multi-objective hybrid planning algorithm comprehensively considers the following objectives: minimizing the cost of deploying monitoring points, maximizing monitoring coverage, and minimizing environmental risks in 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 adapt to the risk needs 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 priority according to the assessment of actual environmental risks.

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

[0011] Compared with the prior art, the beneficial effect of the present application is that the dynamic adjustment mechanism in the present 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 to densely populated areas, the system will immediately recalculate the environmental risks, adjust the layout of monitoring points, increase monitoring points in high-risk areas, and reduce the monitoring density in low-risk areas, thereby ensuring that the efficiency of the entire monitoring network is maximized.

[0012] Furthermore, through the assessment of environmental risks, this application can dynamically adjust the layout of monitoring points according to the risk conditions in different areas, ensuring that high-risk areas are monitored more accurately, 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 while ensuring monitoring coverage and monitoring efficiency, and reduces deployment and maintenance costs. Through a dynamic adjustment mechanism, this application document can respond to the impact of changes in meteorological conditions and pollution sources in real time, ensure the flexibility and efficiency of the monitoring system, and is suitable for a variety of complex environmental monitoring scenarios. In summary, this application document has significant innovation and practicality in terms of monitoring point deployment coverage, cost control, and environmental risk assessment, and can effectively improve the intelligence level of the environmental monitoring system, and improve the ability to respond to environmental risks and the efficiency of resource utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flowchart of the steps of the regional atmospheric environment risk monitoring point optimization method based on multi-objective planning. DETAILED DESCRIPTION

[0015] Below, the present application is further described in conjunction with specific implementation methods. It should be noted that, under the premise of no conflict, the various embodiments or technical features described below can be arbitrarily combined to form new embodiments.

[0016] In the description of the present application, it should be noted that the directional words, such as the terms "center", "lateral", "longitudinal", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", etc., indicating the directional and positional relationship are based on the directional or positional relationship shown in the drawings, which is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a specific directional orientation, be constructed and operated in a specific directional orientation, and cannot be understood as limiting the specific protection scope of the present application. It should be noted that the terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0017] The terms "including" and "having" and any variations thereof in the specification and claims of this application are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

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

[0019] In a preferred embodiment, see Figure 1 This application document provides a method for optimizing the layout of regional atmospheric environmental risk monitoring points based on multi-objective planning, including: step S10 to collect initial data of environmental monitoring, step S20 to establish a multi-objective optimization model based on the initial environmental data, define the constraints and objective function of the monitoring point layout, step S30 to solve the objective function using a multi-objective hybrid planning algorithm, output a preliminary monitoring point layout plan, step S40 to 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. 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 in the area. These data include but are not limited to: regional division data, the total area of ​​the monitoring area and the geographical location and size of different sub-areas; pollutant diffusion model, emission data of different pollution sources, pollutant diffusion path prediction, etc.; meteorological conditions, such as wind speed, wind direction, temperature, humidity and other meteorological factors that have a direct impact on pollutant diffusion; population density, population distribution density in different sub-areas, used to assess risks. The collection of the above data can provide basic support for subsequent optimization calculations.

[0021] Furthermore, a multi-objective optimization model can be established, the core of which is based on the above detection data. The 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; 2. Monitoring coverage O, to maximize the coverage of the monitoring network to polluted areas and ensure that there are no monitoring blind spots in key areas; 3. Environmental risk R, give priority to monitoring high-risk areas to prevent the spread of pollutants in high-density population areas.

[0022] Furthermore, the multi-objective hybrid planning algorithm comprehensively considers the following objectives: minimizing the deployment cost of monitoring points, maximizing the monitoring coverage, and minimizing the environmental risks in the region.

[0023] Therefore, preferably, in order to accurately evaluate the coverage of monitoring points, this application document adopts the following monitoring coverage formula: Among them, A i Represents the coverage area of ​​the ith 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, which is used to represent the distance attenuation effect from the monitoring point to the center of the target area, and is also used to reflect the influence of the distance from the monitoring point to the center of the pollution source on the coverage effect. total is the total area of ​​the benchmark monitoring area, and O is the monitoring coverage rate. i ) is in the form of: d i is the distance from the ith monitoring point to the center of the target area, and α is the distance attenuation factor. By calculating the monitoring coverage formula, the coverage effect of the monitoring point layout can be quantified, and the impact of distance on the coverage can be taken into account, which helps to dynamically adjust the layout of the monitoring points.

[0024] Furthermore, environmental risk R is an important factor affecting the optimization of monitoring point layout. In order to more accurately evaluate the environmental risk of each sub-area, this application combines factors such as pollutant concentration, population density, and wind speed to propose the following environmental risk calculation formula: Among them, P i is the pollutant concentration in the ith sub-area, ρ i is the population density, v i is the wind speed of the sub-area. The greater the wind speed, the faster the pollutants diffuse and the smaller the environmental risk in the area. i is the risk level weight, which is used to dynamically adjust the risk priority of each sub-region. m is the number of sub-regions. The impact of pollutant diffusion, population density and wind speed on environmental risks is comprehensively considered, so that the risk of each sub-region can be reasonably assessed and key monitoring of high-risk areas can be ensured.

[0025] Furthermore, in order to comprehensively optimize the three objectives of monitoring cost, coverage and environmental risk, a multi-objective optimization objective function Z is proposed, where Z = ω1·C+ω2·(1-O)+ω3·R. It should be noted that Z is the objective function, C is the monitoring cost, O is the monitoring coverage, R is the environmental risk value, ω1, ω2, and ω3 are weight coefficients. Users can balance and adjust these three objectives to varying degrees according to actual needs. The design of the objective function takes into account the trade-off relationship between the three optimization objectives. For example, when it is necessary to give priority to reducing the cost of monitoring points, the value of ω1 can be appropriately increased in the actual operation process. When it is necessary to pay more attention to environmental risks, the value of ω3 can be appropriately increased in the actual operation process. By adjusting the weight coefficient, different monitoring needs can be flexibly responded to.

[0026] Therefore, for industrial parks, the emission intensity of pollution sources is relatively large and is easily affected by meteorological conditions. The optimization method of this application document can initially arrange monitoring points according to the location and diffusion path of the emission sources, and through a real-time adjustment mechanism, ensure that the monitoring points can respond dynamically when the wind speed or wind direction changes, giving priority to covering high-risk areas. For example, assuming that an industrial park covers an area of ​​50km 2 , the emission sources are mainly concentrated in the east. The system arranges several monitoring points through the above optimization model and monitors the pollutant concentration in each sub-area 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 pollutants from spreading to residential areas.

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

[0028] In a specific embodiment, suppose that an urban area needs to deploy air quality monitoring points, and the area is 100km 2 Initially, 5 monitoring points were deployed, and the coverage radius of each monitoring point was 5km. 2 , that is, the coverage area A of each monitoring point i =78.54km 2 (According to A=πr 2 Calculation). The attenuation factor α = 0.05, the distances from the monitoring point to the center of the target area are d1 = 2km, d2 = 3km, d3 = 4km, d4 = 5km, d5 = 6km.

[0029] Furthermore, according to the distance decay function f(d i ) Calculate the attenuation coefficient of each monitoring point and substitute the data into: 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, the total coverage rate O is calculated. Substituting the above data into the monitoring coverage rate formula, we can get: O = (78.54x0.909) + (78.54x0.816) + (78.54x0.714) + (78.54x0.615) + (78.54x0.526) / 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 baseline monitoring area of ​​100km 2 , and the result shows that the current monitoring point layout can already cover the target area and has a certain redundancy, so some monitoring points can be reduced to reduce costs. This data is used to compare with the total area of ​​the benchmark monitoring area. 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 is too high, it indicates redundancy; if it is too low, it is necessary to increase the number of monitoring points.

[0030] Furthermore, the above city is divided into three sub-areas. The pollutant concentration, population density and wind speed detection of each sub-area are as follows: In the first sub-area, P1 = 150 μg / m 3 ,ρ1=500 persons / km 2 , v1 = 3 m / s, weight λ1 = 0.5; in the second sub-area, P2 = 200 μg / m 3 ,ρ2=700 persons / km 2 , v2 = 4 m / s, weight λ2 = 1; in the third sub-area, P3 = 100 μg / m 3 ,ρ3=400 persons / km 2, v3 = 5m / s, weight λ3 = 0.8. Then calculate the environmental risk of each sub-area, and 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 current risk level in the city is high, especially the second sub-area has the largest risk contribution. This result is used to evaluate whether the layout of monitoring points is reasonable and compare the benchmark risk value. For example, the benchmark value of the above city is 50000. The benchmark value can be set according to the specific situation of the actual city. The current risk value is high, indicating that it is necessary to increase monitoring points in high-risk areas, especially in the second sub-area, to reduce the risk.

[0031] Therefore, the deployment budget of the monitoring system in the above city is 500,000 yuan, and the deployment cost of each monitoring point is 50,000 yuan. Currently, 8 monitoring points are deployed, so the cost C = 50,000 x 8 = 400,000 yuan. The monitoring coverage O = 0.85, the environmental risk value R = 53,900, the weight coefficient ω1 = 0.4, ω2 = 0.3, ω3 = 0.3, therefore, substituting into the objective function, Z = 0.4 × 400,000 + 0.3 × (1-0.85) + 0.3 × 53,900 = 180,670. The objective function value Z = 180,670 is used to comprehensively evaluate the effectiveness of the current deployment plan. At the same time, the effective benchmark value is set to 160000. The effective benchmark value can be adjusted according to the actual working conditions. Therefore, the current solution is slightly redundant, especially in terms of monitoring coverage and cost. You can consider reducing inefficient monitoring point layout and re-evaluating weight distribution to achieve better resource allocation and monitoring effects.

[0032] Therefore, an efficient solution for atmospheric environmental monitoring in cities or industrial parks is provided in this application document. This solution solves the problems of layout blind spots, low monitoring efficiency, and uneven resource allocation in traditional monitoring systems. Through multi-objective optimization, a balance is achieved between cost, monitoring coverage, and environmental risk, so that the monitoring network layout can maximize cost and resource utilization while meeting environmental monitoring needs. The regional atmospheric environmental risk monitoring point optimization method based on multi-objective planning provided in this application document can balance multiple objectives, including the layout cost of monitoring points, monitoring coverage, environmental risk assessment, etc. Unlike traditional single-objective optimization models, multi-objective optimization models can consider the needs of multiple dimensions at the same time, making the layout of monitoring points more flexible and efficient.

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

[0034] Therefore, monitoring coverage is an important optimization goal of this application document. In environmental monitoring, coverage will lead to monitoring blind spots. For monitoring coverage in this application, the layout of monitoring points is optimized by proposing a monitoring coverage formula, so that the layout of monitoring points in different areas can dynamically adjust the coverage range according to the distance from the pollution source. At the same time, an attenuation factor is introduced to consider the distance factor, and the distance between the monitoring point and the pollution source is reasonably quantified. Through this formula, the effective coverage range of each monitoring point can be calculated in the actual operation process, and dynamically adjusted according to the importance of the area, thereby effectively avoiding the occurrence of monitoring blind spots.

[0035] Similarly, cost control of the monitoring system is also an important aspect of multi-objective optimization. Cost includes the purchase cost, installation cost and subsequent maintenance cost of the equipment. In order 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, namely the dynamic optimization of monitoring point layout. Traditional monitoring point layout plans are often no longer adjusted 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 point layout to cope with environmental changes through real-time data feedback, combined with meteorological conditions and pollution diffusion models.

[0037] In actual applications, the diffusion path of pollutants may be affected by weather changes. For example, changes in wind speed and direction will directly change the scope and speed of pollutant diffusion, resulting in reduced risks in some originally monitored areas and increased risks in other areas. In order to cope with this situation, this application document designs a dynamic adjustment mechanism, where the system can regularly obtain real-time data from monitoring points and update regional risk assessments in real time based on 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 changes suddenly, causing pollutants to spread to densely populated areas, the system will immediately recalculate the environmental risks, adjust the layout of monitoring points, increase monitoring points in high-risk areas, and reduce monitoring density in low-risk areas, thereby ensuring that the efficiency of the entire monitoring network is maximized.

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

[0040] Through multi-objective optimization, this application document minimizes the number of monitoring points while ensuring monitoring coverage and monitoring efficiency, and reduces deployment and maintenance costs. Through a dynamic adjustment mechanism, this application document can respond to the impact of changes in meteorological conditions and pollution sources in real time, ensure the flexibility and efficiency of the monitoring system, and is suitable for a variety of complex environmental monitoring scenarios. In summary, this application document has significant innovation and practicality in terms of monitoring point deployment coverage, cost control, and environmental risk assessment, and can effectively improve the intelligence level of the environmental monitoring system, and improve the ability to respond to environmental risks and the efficiency of resource utilization.

[0041] The above describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited by the above embodiments, and the above embodiments and the specification only describe the principles of the present application. The present application may have various changes and improvements without departing from the spirit and scope of the present application, and these changes and improvements fall within the scope of the present application for which protection is sought. The scope of protection claimed by the present application is defined by the attached claims and their equivalents.

Claims

1. A method for optimizing the regional atmospheric environment risk monitoring points based on multi-objective programming, characterized in that: include: Collect initial data for environmental monitoring; Establishing a multi-objective optimization model based on the initial environmental data, defining the constraint conditions and objective function of the monitoring point layout; The objective function is solved using a multi-objective hybrid programming algorithm to output a preliminary monitoring point layout plan; The monitoring point layout scheme is dynamically adjusted, and feedback optimization is performed on the monitoring point layout scheme to ensure that the distribution of monitoring points adapts to environmental changes.

2. The method for optimizing the regional atmospheric environment risk monitoring points based on multi-objective programming according to claim 1, characterized in that: The initial data include pollutant sources, diffusion models, meteorological conditions and risk assessment data.

3. The method for optimizing the regional atmospheric environment risk monitoring points based on multi-objective programming according to claim 1, characterized in that: The multi-objective hybrid planning algorithm comprehensively considers the following objectives: minimizing the deployment cost of monitoring points, maximizing the monitoring coverage, and minimizing the environmental risks in the area.

4. The method for optimizing the regional atmospheric environment risk monitoring points based on multi-objective programming according to claim 3, characterized in that: The monitoring coverage is obtained by calculation.

5. The method for optimizing the regional atmospheric environment risk monitoring points based on multi-objective programming according to claim 2, characterized in that: The distance function is related to the distance from the ith monitoring point to the center of the target area and the distance attenuation factor.

6. The method for optimizing the regional atmospheric environment risk monitoring points based on multi-objective programming according to claim 1, characterized in that: The environmental risk is obtained by calculation.

7. The method for optimizing the regional atmospheric environment risk monitoring points based on multi-objective programming according to claim 6, characterized in that: Pollutant concentration P i , population density ρ i , and wind speed v i The weight ratio is dynamically adjusted according to regional characteristics to adapt to the risk needs of different regions.

8. The method for optimizing the regional atmospheric environment risk monitoring points based on multi-objective programming according to any one of claims 1 to 7, characterized in that: The expression of the objective function is related to C being the monitoring cost, O being the monitoring coverage, R being the environmental risk value, and ω1, ω2, and ω3 being the weight coefficients.

9. The method for optimizing the regional atmospheric environment risk monitoring points based on multi-objective programming according to claim 8, characterized in that: The values ​​of the weight coefficients ω1, ω2, and ω3 can be flexibly adjusted according to different monitoring requirements, and decision makers can adjust the weight priority according to the assessment of actual environmental risks.

10. The method for optimizing regional atmospheric environment risk monitoring points based on multi-objective programming according to any one of claims 1 to 9, characterized in that: The multi-objective hybrid planning algorithm can dynamically obtain real-time data of monitoring points and readjust the layout of monitoring points based on changes in environmental risks to adapt to the dynamic diffusion of air pollution in the region.

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