Meteorological data intelligent acquisition method and system

Abstract

The invention relates to the technical field of meteorological data, and particularly discloses a meteorological data intelligent acquisition method and system, and the method comprises the following steps: S1, determining a city region, setting a height threshold value, and obtaining a three-dimensional region; dividing the three-dimensional area to obtain a plurality of acquisition points; selecting time nodes in a set acquisition time period, and acquiring the air temperature of each acquisition point on the time nodes; s2, obtaining the number of people of the rectangular grids of the bottom layer under each time node, and if the air temperature of the rectangular grids of the bottom layer under each time node is positively correlated with the number of people and the air temperature of the rectangular grids under each time node in the rectangular columns is negatively correlated with the number, obtaining a heat island area from all the heat island columns; the areas except the heat island area in the three-dimensional area are marked as non-heat island areas; and S3, when the air temperature of the monitoring point is obtained, if the monitoring point belongs to the heat island area, obtaining an influence site, and obtaining the air temperature of the monitoring point.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological data collection, and in particular to a meteorological data intelligent collection method and system. Background Art

[0002] With the development of science and technology, meteorological data collection technology is also constantly improving. Automated and intelligent meteorological stations are gradually replacing traditional manual observation methods, improving the efficiency and accuracy of data collection.

[0003] In the existing technology, collecting meteorological data in urban areas faces many challenges. Among them, the urban heat island effect is a particularly prominent problem, which often leads to inaccurate meteorological data collected, thus affecting the accuracy and reliability of weather forecasts and climate research; the urban heat island effect refers to the phenomenon that the temperature in the central area of ​​the city is significantly higher than that in the surrounding suburbs, so that the temperature data collected by the urban meteorological station is often higher than the actual value, while the relative humidity data is lower. The inaccuracy of this data not only affects the quality of daily weather forecasts, but may also mislead the long-term monitoring and analysis of climate change. Therefore, how to effectively overcome the data collection difficulties brought about by the urban heat island effect and improve the accuracy and representativeness of urban meteorological data is an urgent problem to be solved in the current meteorological science research and technology development. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for intelligently collecting meteorological data to solve the above technical problems.

[0005] The purpose of the present invention can be achieved through the following technical solutions:

[0006] A method for intelligently collecting meteorological data comprises the following steps:

[0007] Step S1: obtaining an urban area and setting a collection height threshold, taking the urban area as the bottom surface and the collection height threshold as the height to obtain a three-dimensional area; dividing the three-dimensional area into grids to obtain a number of rectangular grids, obtaining the center point of each rectangular grid, and recording it as a collection point;

[0008] Set a collection time period, select a time node in the collection time period, obtain the temperature at the collection point at each time node, and record it as the temperature of the corresponding rectangular grid;

[0009] Step S2: Obtain all rectangular grids at the bottom layer of the three-dimensional area, recorded as bottom rectangular grids; obtain the population of the bottom rectangular grid at each time node, if the temperature of the bottom rectangular grid at each time node is positively correlated with the population, then obtain the bottom rectangular grid and all rectangular grids directly above it to obtain a rectangular column;

[0010] Number the rectangular squares on the rectangular column from bottom to top, obtain the temperature of each numbered rectangular square, and if the temperature of the rectangular square at each time node is negatively correlated with the number, record the rectangular column as a heat island column; obtain all heat island columns in the three-dimensional area to obtain a heat island area, and record the area other than the heat island area in the three-dimensional area as a non-heat island area;

[0011] Step S3: several stations are set in the non-heat island area, and the stations are used to obtain the temperature; when obtaining the temperature at any location in the city as a monitoring point, if the monitoring point belongs to the heat island area, the station closest to the monitoring point in each direction is obtained and recorded as the influencing station;

[0012] The temperature at the monitoring point is obtained according to the temperature at the influencing site: Where T i represents the temperature at the i-th impact site, P is the location of the monitoring point, and P i represents the location of the i-th impact site, |PP i | for P and P i , n is the total number of affected sites, k is the preset correction coefficient and k>0.

[0013] As a further solution of the present invention: the setting range of the acquisition height threshold is [150m, 500m].

[0014] As a further solution of the present invention: the process of meshing the three-dimensional area includes:

[0015] An interval threshold is set, the setting range of the interval threshold is [5km, 10km], and the three-dimensional area is grid-divided according to the interval threshold.

[0016] As a further solution of the present invention: the process of selecting the time node includes:

[0017] The setting range of the collection time period is [3 months, 12 months], and a selection time interval threshold is set. A node is selected every time interval threshold in the collection time period and recorded as a time node. The selection of the time node includes the start and end points of the collection time period.

[0018] As a further solution of the present invention: the process of judging whether the temperature of the bottom rectangular grid at each time node is positively correlated with the population includes:

[0019] According to the temperature of the bottom rectangular grid at each time node, the average temperature t is obtained. ave , and according to the population of the bottom rectangular grid at each time node, the average number of people N is obtained ave; According to the average temperature and the average number of people, the covariance is obtained Where m is the total number of time nodes, t x Represents the temperature of the bottom rectangular grid at the xth time node, N x Indicates the population of the bottom rectangular grid at the xth time node;

[0020] And according to the temperature of the bottom rectangular grid at each time node, the standard deviation of the temperature is obtained According to the population of the bottom rectangular grid at each time node, the standard deviation of the population is obtained. According to the covariance, temperature standard deviation and number standard deviation, the correlation coefficient is obtained. At this time, if r>0, the temperature of the bottom rectangular grid at each time node is positively correlated with the population; if r≤0, the temperature of the bottom rectangular grid at each time node is not positively correlated with the population.

[0021] As a further solution of the present invention: the process of judging whether the temperature of the rectangular grid at each time node is negatively correlated with the number includes:

[0022] According to the above steps, the second correlation coefficient r′ is obtained according to the temperature and number of the rectangular grid at each time node. If r′<0, the temperature and number of the rectangular grid at each time node are negatively correlated; if r′≥0, the temperature and number of the rectangular grid at each time node are not negatively correlated.

[0023] As a further solution of the present invention: the process of obtaining the station closest to the monitoring point in each direction includes:

[0024] An angle interval threshold is set, and any direction of the monitoring point is selected as the initial direction, and the angle of the initial direction is recorded as 0°; and the clockwise direction is taken as positive, then the angle interval [0°, 360°] is obtained, and every other angle interval threshold in the angle interval is recorded as a sub-node, and the sub-nodes include angles of 0° and 360°; an angle sub-interval is obtained from every two adjacent sub-nodes, and a site closest to the monitoring point is selected in each angle sub-interval, then the site closest to the monitoring point in each direction is obtained, and a number of influencing sites are obtained.

[0025] As a further solution of the present invention: a meteorological data intelligent collection system, comprising:

[0026] Area division preprocessing module: obtain the urban area, set the acquisition height threshold, take the urban area as the bottom surface, take the acquisition height threshold as the height, and obtain the three-dimensional area; divide the three-dimensional area into grids to obtain a number of rectangular grids, obtain the center point of each rectangular grid, and record it as the acquisition point;

[0027] Set a collection time period, select a time node in the collection time period, obtain the temperature at the collection point at each time node, and record it as the temperature of the corresponding rectangular grid;

[0028] Area division module: obtain all rectangular grids at the bottom layer of the three-dimensional area, record them as bottom rectangular grids; obtain the population of the bottom rectangular grid at each time node, if the temperature of the bottom rectangular grid at each time node is positively correlated with the population, then obtain the bottom rectangular grid and all rectangular grids directly above it to obtain a rectangular column;

[0029] Number the rectangular squares on the rectangular column from bottom to top, obtain the temperature of each numbered rectangular square, and if the temperature of the rectangular square at each time node is negatively correlated with the number, record the rectangular column as a heat island column; obtain all heat island columns in the three-dimensional area to obtain a heat island area, and record the area other than the heat island area in the three-dimensional area as a non-heat island area;

[0030] Temperature estimation module: several stations are set in the non-heat island area, and the stations are used to obtain the temperature; when obtaining the temperature at any location in the city as a monitoring point, if the monitoring point belongs to the heat island area, the station closest to the monitoring point in each direction is obtained and recorded as the influencing station;

[0031] The temperature at the monitoring point is obtained according to the temperature at the influencing site: Where T i represents the temperature at the i-th impact site, P is the location of the monitoring point, and P i represents the location of the i-th impact site, |PP i | for P and P i , n is the total number of affected sites, k is the preset correction coefficient and k>0.

[0032] Beneficial effects of the present invention:

[0033] The present invention can systematically cover the entire area and ensure the comprehensiveness and representativeness of the data by setting the collection height threshold and dividing the grid in the urban area. In addition, the use of automation technology for real-time monitoring of temperature and other meteorological parameters reduces the errors and delays of human operations and improves the efficiency and accuracy of data collection. By analyzing the relationship between the population and temperature of the bottom rectangular grid, as well as the temperature change trend with height (i.e., the formation of heat island columns), the heat island area in the city can be accurately identified. This is of great significance for urban planning and environmental protection, and helps to take targeted measures to alleviate the heat island effect. Several stations are set up in non-heat island areas to obtain temperature data, and the nearest station is selected as the influencing station according to the location of the monitoring point to calculate the temperature. This method not only saves resources, but also improves the practicality and accuracy of the monitoring data. A mathematical model is used to calculate the temperature of the monitoring point. This weighted average method takes into account the degree of influence of data from different stations, making the final temperature estimation more scientific and reasonable. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below in conjunction with the accompanying drawings.

[0035] Figure 1 The present invention is a schematic diagram of a flow chart of a method for intelligently collecting meteorological data. DETAILED DESCRIPTION

[0036] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0037] See also Figure 1 As shown, the present invention is a method for intelligently collecting meteorological data, comprising the following steps:

[0038] Step S1: obtaining an urban area and setting a collection height threshold, taking the urban area as the bottom surface and the collection height threshold as the height to obtain a three-dimensional area; dividing the three-dimensional area into grids to obtain a number of rectangular grids, obtaining the center point of each rectangular grid, and recording it as a collection point;

[0039] Set a collection time period, select a time node in the collection time period, obtain the temperature at the collection point at each time node, and record it as the temperature of the corresponding rectangular grid;

[0040] It is understandable that the determination of the urban area can be based on administrative divisions, and the urban area can be defined by referring to existing administrative divisions. This includes the boundaries of the city, the division of urban areas and suburbs; this information can usually be obtained from the government or related geographic information systems (GIS); the three-dimensional area obtained in the above steps is the spatial scope of data collection; the temperature of the rectangular grid is obtained based on meteorological instruments or remote sensing technology;

[0041] As a preferred embodiment of the present invention, the setting range of the acquisition height threshold is [150m, 500m];

[0042] It can be understood that the setting range of the acquisition height threshold in the present invention is 150 meters to 500 meters; this means that when collecting meteorological data, the height of the three-dimensional area of ​​interest is between 150 meters and 500 meters above the ground, and this height range is selected as the vertical interval for collecting temperature and other related meteorological data;

[0043] As a preferred embodiment of the present invention, the process of meshing the three-dimensional region includes:

[0044] Setting an interval threshold, wherein the setting range of the interval threshold is [5km, 10km], and dividing the three-dimensional area into grids according to the interval threshold;

[0045] It is understood that in the preferred embodiment of the present invention, the process of gridding the three-dimensional area includes setting an interval threshold, and the setting range of this interval threshold is 5 kilometers to 10 kilometers; this means that when the three-dimensional area is gridded, the size of each grid unit will vary between 5 kilometers and 10 kilometers; this setting may be used to ensure a balance between the accuracy and efficiency of data collection, so that a larger geographical area can be covered while maintaining sufficient spatial resolution to capture important geographical features or meteorological data changes;

[0046] As a preferred embodiment of the present invention, the process of selecting the time node includes:

[0047] The setting range of the collection time period is [3months, 12months], and the selection time interval threshold is set. A node is selected every time interval threshold in the collection time period, which is recorded as a time node. The selection of the time node includes the start and end points of the collection time period;

[0048] It is understandable that the collection time period may include the temperature change of the urban area in one quarter at the shortest and the temperature change of the urban area in one year at the longest;

[0049] Step S2: Obtain all rectangular grids at the bottom layer of the three-dimensional area, recorded as bottom rectangular grids; obtain the population of the bottom rectangular grid at each time node, if the temperature of the bottom rectangular grid at each time node is positively correlated with the population, then obtain the bottom rectangular grid and all rectangular grids directly above it to obtain a rectangular column;

[0050] Number the rectangular squares on the rectangular column from bottom to top, obtain the temperature of each numbered rectangular square, and if the temperature of the rectangular square at each time node is negatively correlated with the number, record the rectangular column as a heat island column; obtain all heat island columns in the three-dimensional area to obtain a heat island area, and record the area other than the heat island area in the three-dimensional area as a non-heat island area;

[0051] It should be noted that densely populated areas are usually accompanied by a large number of human activities, such as industrial production, transportation, and residents' lives, which generate a lot of heat. For example, factory operations, automobile exhaust emissions, and the use of air conditioning and heating directly increase the heat in the air. Areas with large populations often have dense buildings and tall buildings. These buildings absorb solar radiation during the day and release it slowly at night. At the same time, the gaps between buildings are small, which is not conducive to the diffusion of heat, resulting in local temperature increases. Therefore, in the heat island area, the temperature of the rectangular grid is positively correlated with the population.

[0052] It should be noted that according to the basic principles of thermodynamics, hot air is lighter than cold air, so it will rise; in urban heat island areas, because the ground absorbs and releases more heat, the air near the ground will rise after being heated, while the colder air will sink to replenish, forming a natural convection cycle; as the height increases, this heated air gradually mixes with the surrounding colder air, causing the temperature to gradually decrease; in urban heat island areas, high-rise buildings and other buildings are densely packed, these buildings not only absorb and store heat, but also block and guide wind flow; the narrow space between buildings forms a "street canyon" effect, which reduces wind speed and makes it difficult for heat to dissipate. However, as the height increases, the impact of buildings gradually weakens, and air mobility increases, which is conducive to the diffusion and dissipation of heat, resulting in a drop in temperature; therefore, in the heat island area, the temperature of each rectangular grid in the vertical direction is negatively correlated with the height; here, the higher the height of the rectangular grid, the larger its number;

[0053] As a preferred embodiment of the present invention, the process of determining whether the temperature of the bottom rectangular grid at each time node is positively correlated with the population includes:

[0054] According to the temperature of the bottom rectangular grid at each time node, the average temperature t is obtained. ave , and according to the population of the bottom rectangular grid at each time node, the average number of people N is obtained ave; According to the average temperature and the average number of people, the covariance is obtained Where m is the total number of time nodes, t x Represents the temperature of the bottom rectangular grid at the xth time node, N x Indicates the population of the bottom rectangular grid at the xth time node;

[0055] And according to the temperature of the bottom rectangular grid at each time node, the standard deviation of the temperature is obtained According to the population of the bottom rectangular grid at each time node, the standard deviation of the population is obtained. According to the covariance, temperature standard deviation and number standard deviation, the correlation coefficient is obtained. At this time, if r>0, the temperature of the bottom rectangular grid at each time node is positively correlated with the population; if r≤0, the temperature of the bottom rectangular grid at each time node is not positively correlated with the population;

[0056] The process of judging whether the temperature and number of the rectangular grid at each time node are negatively correlated includes:

[0057] According to the above steps, the second correlation coefficient r′ is obtained according to the temperature and number of the rectangular grid at each time node. If r′<0, the temperature and number of the rectangular grid at each time node are negatively correlated; if r′≥0, the temperature and number of the rectangular grid at each time node are not negatively correlated;

[0058] It can be understood that by comparing the correlation coefficient and the second correlation coefficient with 0, it is possible to determine whether the temperature of the bottom rectangular grid is positively correlated with the population, and whether the temperature of the rectangular grid is negatively correlated with the number; if r>0, it indicates that the temperature is positively correlated with the population; if r<0, it indicates that the two are negatively correlated; if r=0, there is no linear relationship between the two;

[0059] Step S3: several stations are set in the non-heat island area, and the stations are used to obtain the temperature; when obtaining the temperature at any location in the city as a monitoring point, if the monitoring point belongs to the heat island area, the station closest to the monitoring point in each direction is obtained and recorded as the influencing station;

[0060] The temperature at the monitoring point is obtained according to the temperature at the influencing site: Where T i represents the temperature at the i-th impact site, P is the location of the monitoring point, and P i represents the location of the i-th impact site, |PP i | for P and P i The distance between them, n is the total number of affected sites, k is the preset correction coefficient and k>0;

[0061] It should be noted that the process of setting up several stations in the non-heat island area includes:

[0062] Gridding the non-heat island area according to the interval threshold to obtain a plurality of rectangular grids, setting a station at the exact center of each rectangular grid to obtain a plurality of stations; for a non-heat island area whose mean radius is less than the interval threshold, only one station is set;

[0063] The process of obtaining the mean radius is:

[0064] Obtain an edge line of the non-heat island area, select a number of reference points equally divided on the edge line, obtain a center point of the non-heat island area, obtain lines connecting the center point and each reference point, and obtain an average value of the lengths of each connecting line, which is recorded as a mean radius;

[0065] It is worth noting that if the monitoring point is not in a heat island area, the temperature of the station closest to the monitoring point is directly obtained as the temperature of the monitoring point;

[0066] As a preferred embodiment of the present invention, the process of obtaining the station closest to the monitoring point in each direction includes:

[0067] An angle interval threshold is set, and any direction of the monitoring point is selected as the initial direction, and the angle of the initial direction is recorded as 0°; and the clockwise direction is taken as positive, then the angle interval [0°, 360°] is obtained, and every other angle interval threshold in the angle interval is recorded as a sub-node, and the sub-nodes include angles of 0° and 360°; an angle sub-interval is obtained from every two adjacent sub-nodes, and a station closest to the monitoring point is selected in each angle sub-interval, then the station closest to the monitoring point in each direction is obtained, and a number of influencing stations are obtained;

[0068] It should be noted that the farther the influencing site is from the monitoring point, the smaller its impact on the temperature at the monitoring point; conversely, the closer the influencing site is to the monitoring point, the greater its impact on the temperature at the monitoring point.

[0069] A meteorological data intelligent collection system, comprising:

[0070] Area division preprocessing module: obtain the urban area, set the acquisition height threshold, take the urban area as the bottom surface, take the acquisition height threshold as the height, and obtain the three-dimensional area; divide the three-dimensional area into grids to obtain a number of rectangular grids, obtain the center point of each rectangular grid, and record it as the acquisition point;

[0071] Set a collection time period, select a time node in the collection time period, obtain the temperature at the collection point at each time node, and record it as the temperature of the corresponding rectangular grid;

[0072] Area division module: obtain all rectangular grids at the bottom layer of the three-dimensional area, record them as bottom rectangular grids; obtain the population of the bottom rectangular grid at each time node, if the temperature of the bottom rectangular grid at each time node is positively correlated with the population, then obtain the bottom rectangular grid and all rectangular grids directly above it to obtain a rectangular column;

[0073] Number the rectangular squares on the rectangular column from bottom to top, obtain the temperature of each numbered rectangular square, and if the temperature of the rectangular square at each time node is negatively correlated with the number, record the rectangular column as a heat island column; obtain all heat island columns in the three-dimensional area to obtain a heat island area, and record the area other than the heat island area in the three-dimensional area as a non-heat island area;

[0074] Temperature estimation module: several stations are set in the non-heat island area, and the stations are used to obtain the temperature; when obtaining the temperature at any location in the city as a monitoring point, if the monitoring point belongs to the heat island area, the station closest to the monitoring point in each direction is obtained and recorded as the influencing station;

[0075] The temperature at the monitoring point is obtained according to the temperature at the influencing site: Where T i represents the temperature at the i-th impact site, P is the location of the monitoring point, and P i represents the location of the i-th impact site, |PP i | for P and P i , n is the total number of affected sites, k is the preset correction coefficient and k>0.

[0076] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for intelligent collection of meteorological data, characterized in that: The following steps are involved: Step S1: obtaining an urban area and setting a collection height threshold, taking the urban area as the bottom surface and the collection height threshold as the height to obtain a three-dimensional area; dividing the three-dimensional area into grids to obtain a number of rectangular grids, obtaining the center point of each rectangular grid, and recording it as a collection point; Set a collection time period, select a time node in the collection time period, obtain the temperature at the collection point at each time node, and record it as the temperature of the corresponding rectangular grid; Step S2: Obtain all rectangular grids at the bottom layer of the three-dimensional area, recorded as bottom rectangular grids; obtain the population of the bottom rectangular grid at each time node, if the temperature of the bottom rectangular grid at each time node is positively correlated with the population, then obtain the bottom rectangular grid and all rectangular grids directly above it to obtain a rectangular column; Number the rectangular squares on the rectangular column from bottom to top, obtain the temperature of each numbered rectangular square, and if the temperature of the rectangular square at each time node is negatively correlated with the number, record the rectangular column as a heat island column; obtain all heat island columns in the three-dimensional area to obtain a heat island area, and record the area other than the heat island area in the three-dimensional area as a non-heat island area; Step S3: several stations are set in the non-heat island area, and the stations are used to obtain the temperature; when obtaining the temperature at any location in the city as a monitoring point, if the monitoring point belongs to the heat island area, the station closest to the monitoring point in each direction is obtained and recorded as the influencing station; The temperature at the monitoring point is obtained according to the temperature at the influencing site: Where T i represents the temperature at the i-th impact site, P is the location of the monitoring point, and P i represents the location of the i-th impact site, |PP i | for P and P i , n is the total number of affected sites, k is the preset correction coefficient and k>0.

2. The method for intelligently collecting meteorological data according to claim 1, characterized in that: In step S1, the setting range of the acquisition height threshold is [150m, 500m].

3. The method for intelligently collecting meteorological data according to claim 1, characterized in that: In step S1, the process of meshing the three-dimensional area includes: An interval threshold is set, the setting range of the interval threshold is [5km, 10km], and the three-dimensional area is grid-divided according to the interval threshold.

4. The method for intelligently collecting meteorological data according to claim 1, characterized in that: In step S1, the time node selection process includes: The setting range of the collection time period is [3 months, 12 months], and a selection time interval threshold is set. A node is selected every time interval threshold in the collection time period and recorded as a time node. The selection of the time node includes the start and end points of the collection time period.

5. The method for intelligently collecting meteorological data according to claim 1, characterized in that: In step S2, the process of determining whether the temperature of the bottom rectangular grid at each time node is positively correlated with the population includes: According to the temperature of the bottom rectangular grid at each time node, the average temperature t is obtained. ave , and according to the population of the bottom rectangular grid at each time node, the average number of people N is obtained ave ; According to the average temperature and the average number of people, the covariance is obtained Where m is the total number of time nodes, t x Represents the temperature of the bottom rectangular grid at the xth time node, N x Indicates the population of the bottom rectangular grid at the xth time node; And according to the temperature of the bottom rectangular grid at each time node, the standard deviation of the temperature is obtained According to the population of the bottom rectangular grid at each time node, the standard deviation of the population is obtained. According to the covariance, temperature standard deviation and number standard deviation, the correlation coefficient is obtained. At this time, if r>0, the temperature of the bottom rectangular grid at each time node is positively correlated with the population; if r≤0, the temperature of the bottom rectangular grid at each time node is not positively correlated with the population.

6. A method for intelligent collection of meteorological data according to claim 5, characterized in that: In step S2, the process of determining whether the temperature and the number of the rectangular grid at each time node are negatively correlated includes: According to the above steps, the second correlation coefficient r′ is obtained according to the temperature and number of the rectangular grid at each time node. If r′<0, the temperature and number of the rectangular grid at each time node are negatively correlated; if r′≥0, the temperature and number of the rectangular grid at each time node are not negatively correlated.

7. The method for intelligently collecting meteorological data according to claim 1, characterized in that: In step S3, the process of obtaining the station closest to the monitoring point in each direction includes: An angle interval threshold is set, and any direction of the monitoring point is selected as the initial direction, and the angle of the initial direction is recorded as 0°; and the clockwise direction is taken as positive, then the angle interval [0°, 360°] is obtained, and every other angle interval threshold in the angle interval is recorded as a sub-node, and the sub-nodes include angles of 0° and 360°; an angle sub-interval is obtained from every two adjacent sub-nodes, and a site closest to the monitoring point is selected in each angle sub-interval, then the site closest to the monitoring point in each direction is obtained, and a number of influencing sites are obtained.

8. A meteorological data intelligent collection system, characterized in that: include: Area division preprocessing module: obtain the urban area, set the acquisition height threshold, take the urban area as the bottom surface, take the acquisition height threshold as the height, and obtain the three-dimensional area; divide the three-dimensional area into grids to obtain a number of rectangular grids, obtain the center point of each rectangular grid, and record it as the acquisition point; Set a collection time period, select a time node in the collection time period, obtain the temperature at the collection point at each time node, and record it as the temperature of the corresponding rectangular grid; Area division module: obtain all rectangular grids at the bottom layer of the three-dimensional area, record them as bottom rectangular grids; obtain the population of the bottom rectangular grid at each time node, if the temperature of the bottom rectangular grid at each time node is positively correlated with the population, then obtain the bottom rectangular grid and all rectangular grids directly above it to obtain a rectangular column; Number the rectangular squares on the rectangular column from bottom to top, obtain the temperature of each numbered rectangular square, and if the temperature of the rectangular square at each time node is negatively correlated with the number, record the rectangular column as a heat island column; obtain all heat island columns in the three-dimensional area to obtain a heat island area, and record the area other than the heat island area in the three-dimensional area as a non-heat island area; Temperature estimation module: several stations are set in the non-heat island area, and the stations are used to obtain the temperature; when obtaining the temperature at any location in the city as a monitoring point, if the monitoring point belongs to the heat island area, the station closest to the monitoring point in each direction is obtained and recorded as the influencing station; The temperature at the monitoring point is obtained according to the temperature at the influencing site: Where T i represents the temperature at the i-th impact site, P is the location of the monitoring point, and P i represents the location of the i-th impact site, |PP i | for P and P i , n is the total number of affected sites, k is the preset correction coefficient and k>0.

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