Climate adaptation city monitoring and early warning method

Abstract

The invention relates to the technical field of climate monitoring, in particular to a climate adaptation city monitoring and early warning method, which comprises the following steps: S1, monitoring distribution: taking each administrative region of a city as a division standard, and according to the proportion a of the occupied area of a building in each administrative region to the occupied area of the city, determining the occupied area of the city; marking each administrative region as a first region, a second region and a third region; grids with the length-width ratio of 1: 1 are uniformly distributed in each administrative region, a plurality of circles are drawn with the building center of each administrative region as the circle center, and the intersection points of all the circles and the grids and the intersection points of the grids in the region outside the circles serve as monitoring point positions; arranging climate monitoring sensors at the monitoring points; s2, monitoring and early warning; the method is divided into three types of areas according to the building density, intersection points of grids and circles serve as monitoring points, a dual-coverage network is formed, monitoring blind areas are avoided, global coverage and key area strengthening are considered, and real climate gradient changes can be better reflected.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of climate monitoring, in particular to a climate adaptation city monitoring and early warning method. BACKGROUND

[0002] With the increasing impact of global climate change, cities, as the centers of population and economy, are facing unprecedented challenges. Predictive research on urban climate change is crucial for developing effective coping strategies. By analyzing historical climate data and simulating future climate change trends, we can predict the potential extreme weather events that cities may face in the future, such as high-temperature heatwaves, heavy rain and floods, droughts, and strong winds. These extreme weather events not only cause damage to urban infrastructure, but also have a serious impact on the lives and health of residents. For example, high-temperature heatwaves can lead to a surge in electricity demand, causing power supply shortages; heavy rain and floods can flood urban roads and underground facilities, causing traffic paralysis and property losses.

[0003] Therefore, the present application decides to design a climate adaptation city monitoring and early warning method. SUMMARY

[0004] To solve the above problems, the present application provides a climate adaptation city monitoring and early warning method.

[0005] A climate adaptation city monitoring and early warning method, comprising the following steps:

[0006] S1, monitoring points

[0007] Taking each administrative district of the city as the division standard, according to the proportion a of the building area in each administrative district to the building area of the city, each administrative district is marked as the first region a>40%, the second region a=20~40%, and the rest as the third region;

[0008] A grid with a length-width ratio of 1:1 is evenly distributed in each administrative district, and a plurality of circles are drawn with the building center of each administrative district as the center, the radius R of the largest circle max ≤the radius of the inscribed circle of the corresponding administrative district, all the intersections of the circles and the grid, and the intersections of the grid and the grid in the area outside the circles are taken as monitoring points; wherein the radius R min of the smallest circle is calculated according to the following formula, and the radius difference of the adjacent two circles is 0.2~0.4R min ;

[0009]

[0010] Wherein, k is the adjustment coefficient, taking 0.4~1.4, S is the building area of each administrative district, unit is m 2 , ρ is the daily average of PM2.5 in each administrative district, ρ标 The PM2.5 secondary standard limit value is 35ug / m3;

[0011] The climate monitoring sensor is arranged at the monitoring point;

[0012] S2, monitoring and early warning

[0013] Based on the data analysis system and the SPSS software, the disaster climate risk index threshold is determined, and based on the data analysis system, the current climate data monitored by the climate monitoring instrument is analyzed to obtain a current climate risk index; when the current climate risk index is greater than or equal to the disaster climate risk index threshold, an early warning alarm is issued.

[0014] Further, the grid size of the first area is 100*100m, and the grid size ratio of the first area, the second area and the third area is 1:2:4-6.

[0015] Description: The first area grid is the smallest, matches the complex microclimate of high-density built-up area, and improves the monitoring accuracy; the second area grid is expanded by 20%, covers the traffic pollution source and the ventilation corridor, and captures the dynamic transmission path of the pollutants; the third area grid is the largest, reduces the redundant distribution of the ecological green land and the like low-risk area.

[0016] Further, the monitoring indexes of the climate monitoring instrument include PM2.5, air pressure, solar radiation, air temperature, relative humidity, wind speed and precipitation.

[0017] Description: The above indexes can more comprehensively obtain the climate data, thereby improving the accuracy of the urban climate adaptation and early warning.

[0018] Further, the abnormal data needs to be removed from the climate data monitored by the climate monitoring instrument.

[0019] Description: Removing the abnormal data can improve the reliability of the final data analysis.

[0020] Further, in the first area, k is 0.4-0.6, in the second area, k is 0.8-1, and in the third area, k is 1.2-1.4.

[0021] Description: The first area has a large building density, the k value is the smallest, the radius is also small, the monitoring point distribution is more dense, and the complex microclimate is matched; the third area has a small building density, the k value is the largest, the monitoring point is sparse, and resource waste is avoided.

[0022] Further, the disaster climate risk index threshold is divided into a low disaster climate risk index threshold, a medium disaster climate risk index threshold and a high disaster climate risk index threshold, when the low disaster climate risk index threshold≤the current climate risk index<the medium disaster climate risk index threshold, the early warning alarm is low, when the medium disaster climate risk index threshold≤the current climate risk index<the high disaster climate risk index threshold, the early warning alarm is medium; and when the current climate risk index≥the high disaster climate risk index threshold, the early warning alarm is high.

[0023] Description: Threshold classification can reduce false positives of low-risk events and is targeted.

[0024] Further, the building includes a civil building, an industrial building and an agricultural building.

[0025] Description: Covering a variety of buildings, the monitoring is more accurate.

[0026] Further, the installation height of the climate monitoring instrument is 5-15 m from the ground or the top of the building.

[0027] Description: The height of 5-15 m can avoid ground turbulence interference and capture urban canopy mainstream meteorological parameters; being higher than the building can reduce shielding and improve data accuracy.

[0028] Further, the data analysis system includes a first data collection module for collecting historical disaster climate data, a second data collection module for collecting current climate data monitored by the climate monitoring instrument, a data cleaning module and a risk assessment module.

[0029] The method for determining the disaster climate risk index threshold is: the historical disaster climate data collected by the first data collection module is cleaned by the data cleaning module, the cleaned historical disaster climate data is analyzed by the risk assessment module to obtain a historical disaster climate risk index; and finally, the historical disaster climate risk index is subjected to cluster analysis by using SPSS software to determine the disaster climate risk index threshold.

[0030] The method for determining the current climate risk index is: the current climate data collected by the second data collection module is cleaned by the data cleaning module, and the cleaned current climate data is analyzed by the risk assessment module to obtain a current climate risk index.

[0031] The data cleaning includes processing missing values, deleting duplicate data, correcting error data and unifying data formats.

[0032] Description: The above modules can collect data in real time through the data collection module, clean abnormal data, align time and space through the data integration module, and perform dynamic index calculation and threshold comparison through the risk assessment module.

[0033] Compared with the existing urban climate monitoring and early warning, the beneficial effects of the present application are:

[0034] (1) The monitoring method of the present application is divided into three regions according to building density, which accurately matches the spatial characteristics of the city. The first region has high building density, usually the core area of the city, and the heat island effect, energy consumption and pollution emission are concentrated, so high-density monitoring points are needed. The second and third regions are ecological space, suburb or mixed function area, and the monitoring point density decreases, avoiding resource waste, breaking the traditional uniform point distribution mode, focusing on the high vulnerability of the built-up area, improving the monitoring efficiency, and through the intersection of the grid and the circle as the monitoring point, forming a double coverage network, avoiding the monitoring blind area caused by the terrain or building shelter, considering the overall coverage and the strengthening of the key area, which can better reflect the real climate gradient change than the single point distribution mode.

[0035] (2) The monitoring method of the present application calculates R min , the absolute value of the numerator and the standard value ensures that pollution exceeding the standard and low pollution are captured, and the monitoring sensitivity is enhanced. (1.4-0.02a) makes the coefficient linearly decrease from 1.4 when a (building density ratio) increases, automatically compresses the minimum radius in high-density areas, makes the concentric circles more dense, and equivalent to the area S of the administrative region as the circular radius, eliminating the influence of irregular boundaries, providing a standardized spatial reference, and through the adjustment coefficient k, the monitoring density can be dynamically adjusted according to the importance of the region. BRIEF DESCRIPTION OF DRAWINGS

[0036] Figure 1 is the point distribution diagram of the first region of the monitoring and early warning method of the present application. DETAILED DESCRIPTION

[0037] In order to further illustrate the mode adopted by the present application and the effects achieved, the technical solutions of the present application will be described in detail below in conjunction with experiments.

[0038] Example 1: A climate-adaptive urban monitoring and early warning method, comprising the following steps:

[0039] S1, monitoring point distribution

[0040] Taking each administrative district of the city as the division standard, according to the proportion a of the building area in each administrative district to the building area of the city, the buildings include civil buildings, industrial buildings and agricultural buildings, each administrative district is marked as a> 40% of the first region, a=20~40% of the second region, and the rest is the third region;

[0041] From Figure 1It can be seen that the grid with a length-width ratio of 1:1 is uniformly distributed in each administrative region, the grid size of the first region is 100*100m, the grid size ratio of the first region, the second region and the third region is 1:2:5, a plurality of circles are drawn with the building center of each administrative region as the center, the radius R max ≤the radius of the inscribed circle of the corresponding administrative region, all the intersections of the circles and the grids, and the intersections of the grids in the regions outside the circles are taken as monitoring points; wherein the radius R min of the smallest circle is calculated according to the following formula, and the radius difference of the adjacent two circles is 0.3R min ;

[0042]

[0043] Wherein, k is an adjustment coefficient, in the first region, k is 0.5, in the second region, k is 0.9, and in the third region, k is 1.3, S is the land area of each administrative region, with a unit of m 2 , and p is the daily average value of PM2.5 of each administrative region, p 标 is the limit value of PM2.5 secondary standard;

[0044] A climate monitoring sensor is arranged at the monitoring point, the installation height of the climate monitoring instrument is 5-15m from the ground or the top of the building, and the monitoring indexes of the climate monitoring instrument include PM2.5, air pressure, solar radiation, air temperature, relative humidity, wind speed and precipitation;

[0045] S2, monitoring and early warning

[0046] Based on the data analysis system and the SPSS software, the disaster climate risk index threshold is determined, and based on the data analysis system, the current climate data monitored by the climate monitoring instrument is analyzed to obtain the current climate risk index. In the climate data monitored by the climate monitoring instrument, abnormal data needs to be removed, and when the current climate risk index is greater than or equal to the disaster climate risk index threshold, an early warning alarm is issued.

[0047] The data analysis system includes a first data collection module for collecting historical disaster climate data, a second data collection module for collecting current climate data monitored by the climate monitoring instrument, a data cleaning module and a risk assessment module.

[0048] The method for determining the disaster climate risk index threshold value is: the historical disaster climate data collected by the first data collection module is cleaned by a data cleaning module, the cleaned historical disaster climate data is analyzed by a risk assessment module to obtain a historical disaster climate risk index, and finally, the historical disaster climate risk index is subjected to cluster analysis by using SPSS software to determine the disaster climate risk index threshold value. The disaster climate risk index threshold value is divided into a low disaster climate risk index threshold value, a medium disaster climate risk index threshold value and a high disaster climate risk index threshold value. When the low disaster climate risk index threshold value ≤ the current climate risk index < the medium disaster climate risk index threshold value, the early warning alarm is low; when the medium disaster climate risk index threshold value ≤ the current climate risk index < the high disaster climate risk index threshold value, the early warning alarm is medium; and when the current climate risk index ≥ the high disaster climate risk index threshold value, the early warning alarm is high. (It should be noted that the specific method for determining the disaster climate risk index threshold value and the current climate risk index in the scheme of the present application is prior art, for example, reference can be made to the article China Climate Security Evaluation Index System Construction, Risk Assessment and Adaptation Strategy published by Liu Changsong)

[0049] The method for determining the current climate risk index is: the current climate data collected by the second data collection module is cleaned by a data cleaning module, and the cleaned current climate data is analyzed by a risk assessment module to obtain a current climate risk index.

[0050] The data cleaning includes processing missing values, deleting duplicate data, correcting error data and unifying data formats.

[0051] The risk assessment module includes standardizing data, determining index weights and generating a climate risk index. For specific contents, reference can be made to the article China Climate Security Evaluation Index System Construction, Risk Assessment and Adaptation Strategy published by Liu Changsong.

[0052] Embodiment 2: The difference between this embodiment and embodiment 1 is that the radius difference of the two adjacent circles is 0.2R min .

[0053] Embodiment 3: The difference between this embodiment and embodiment 1 is that the radius difference of the two adjacent circles is 0.4R min .

[0054] Embodiment 4: The difference between this embodiment and embodiment 1 is that the grid size ratio of the first region, the second region and the third region is 1:2:4.

[0055] Embodiment 5: The difference between this embodiment and embodiment 1 is that the grid size ratio of the first region, the second region and the third region is 1:2:6.

[0056] Example 6: The difference between this example and Example 1 is that in the first area, k is 0.4, in the second area, k is 1, and in the third area, k is 1.2.

[0057] Example 7: The difference between this example and Example 1 is that in the first area, k is 0.6, in the second area, k is 0.8, and in the third area, k is 1.4.

[0058] Example 8: The difference between this example and Example 1 is that the installation height of the climate monitoring instrument is 5m from the ground or the top of the building.

[0059] Example 9: The difference between this example and Example 1 is that the installation height of the climate monitoring instrument is 15m from the ground or the top of the building.

[0060] Experimental Example: The description of this experimental example is based on the description of Example 1, and is intended to illustrate the practical application effect of the present application.

[0061] To explore the influence of each example on the blind area rate and false alarm rate of the monitoring and early warning method of the present application.

[0062] Table 1 Blind area rate and false alarm rate of Example 1-Example 9

[0063]

[0064] From the results in Table 1, it can be seen that too large a difference in the radii of the two adjacent circles, too large a grid size ratio of the first area, the second area, and the third area, too small or too large a k adjustment coefficient, and too small or too large an installation height of the climate monitoring instrument can all increase the blind area rate and the false alarm rate. In Example 2, the difference in the radii of the two adjacent circles is smaller, and in Example 4, the grid size ratio of the first area, the second area, and the third area is smaller, so the monitoring point distribution is more intensive, but the decline in the blind area rate and the false alarm rate is smaller than the complexity of the distribution, so from the perspective of economy, the parameter effect of Example 1 is relatively better.

Claims

1. A climate adaptation urban monitoring and early warning method, characterized in that, The method comprises the following steps: S1, monitoring points Taking each administrative district of a city as a division standard, marking each administrative district as a first region, a second region, and a third region according to the proportion a of the building area in each administrative district in the city area, wherein a > 40% for the first region, a = 20-40% for the second region, and the rest for the third region; The grid with a length-width ratio of 1:1 is evenly distributed in each administrative region, and a plurality of circles are drawn with the building centers of each administrative region as the centers, the radius R of the largest circle is max ≤ the radius of the inscribed circle of the corresponding administrative region, and all the intersections of the circles and the grid and the intersections of the grid and the grid in the area outside the circles are taken as monitoring points; wherein the radius R min of the smallest circle is calculated according to the following formula, and the radius difference of the adjacent two circles is 0.2-0.4R min . Wherein, k is the adjustment coefficient, taking 0.4-1.4, S is the land area of each administrative district, unit is m 2 , ρ is the daily average of PM2.5 of each administrative district, ρ 标 is the limit value of PM2.5 secondary standard; A climate monitoring sensor is arranged at the monitoring point; S2, monitoring and early warning Based on a data analysis system and SPSS software, a disaster climate risk index threshold is determined, and based on the data analysis system, current climate data monitored by the climate monitoring instrument is analyzed to obtain a current climate risk index; when the current climate risk index is greater than or equal to the disaster climate risk index threshold, an early warning alarm is issued.

2. The climate adaptive city monitoring and warning method of claim 1, wherein, The grid size of the first region is 100*100 m, and the grid size ratio of the first region, the second region, and the third region is 1:2:4-6.

3. The climate adaptive city monitoring and warning method of claim 1, wherein, The monitoring indexes of the climate monitoring instrument include PM2.5, air pressure, solar radiation, air temperature, relative humidity, wind speed, and precipitation.

4. The climate adaptive city monitoring and warning method of claim 1, wherein, In the current climate data monitored by the climate monitoring instrument, abnormal data needs to be removed.

5. The climate adaptive city monitoring and warning method of claim 1, wherein, In the first region, k is 0.4-0.6, in the second region, k is 0.8-1, and in the third region, k is 1.2-1.

4.

6. The climate adaptive city monitoring and warning method of claim 1, wherein, The disaster climate risk index threshold is divided into a low disaster climate risk index threshold, a medium disaster climate risk index threshold, and a high disaster climate risk index threshold; when the low disaster climate risk index threshold is less than or equal to the current climate risk index and less than the medium disaster climate risk index threshold, the early warning alarm is low-level; when the medium disaster climate risk index threshold is less than or equal to the current climate risk index and less than the high disaster climate risk index threshold, the early warning alarm is medium-level; and when the current climate risk index is greater than or equal to the high disaster climate risk index threshold, the early warning alarm is high-level. In the first region, k is 0.4-0.6, in the second region, k is 0.8-1, and in the third region, k is 1.2-1.4, and the building includes civil buildings, industrial buildings, and agricultural buildings.

7. The climate adaptive city monitoring and warning method of claim 1, wherein, The installation height of the climate monitoring instrument is 5-15 m from the ground or the top of the building.

8. The climate adaptive city monitoring and warning method of claim 1, wherein, The data analysis system comprises a first data collection module for collecting historical disaster climate data, a second data collection module for collecting current climate data monitored by the climate monitoring instrument, a data cleaning module, and a risk assessment module.

9. The climate adaptive city monitoring and warning method of claim 1, wherein, The method for determining the disaster climate risk index threshold is as follows: the historical disaster climate data collected by the first data collection module is cleaned by the data cleaning module, the cleaned historical disaster climate data is analyzed by the risk assessment module to obtain a historical disaster climate risk index, and finally, the historical disaster climate risk index is clustered and analyzed by using the SPSS software to determine the disaster climate risk index threshold. The method for determining the current climate risk index is as follows: the current climate data collected by the second data collection module is cleaned by the data cleaning module, and the cleaned current climate data is analyzed by the risk assessment module to obtain the current climate risk index. ​ The data cleaning includes processing missing values, deleting duplicate data, correcting error data and unifying data formats. The data cleaning includes processing missing values, deleting duplicate data, correcting error data and unifying data formats. The data cleaning includes processing missing values, deleting duplicate data, correcting error

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