Method, system, device and medium for spatial grid division based on air pollution data

Abstract

The present invention provides a method, system, device and medium for spatial grid division based on air pollution data, and relates to the field of atmospheric environment governance technology. The method includes: obtaining historical monitoring data of all monitoring points in the target grid, constructing an air pollutant data prediction model, and obtaining air pollutant prediction data; obtaining the attention value of the target grid according to the prediction data and the actual monitoring data; obtaining the weight value of the target grid according to the attention value and the area of ​​the target grid; comparing the weight value with a preset threshold, if the weight value is less than the preset threshold, adding a set number of monitoring points around the target grid, expanding the target grid area, iterating the calculation until the weight value is not less than the threshold, and obtaining an adaptive grid; calculating the attention value density of the air pollutant data of the adaptive grid, establishing an attention value density index, querying the index, and obtaining air pollutant data. The present invention can improve the monitoring accuracy of regional air pollution and the efficiency of data analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of atmospheric environment governance, and in particular to a method, system, device and medium for spatial grid division based on atmospheric pollution data. Background Art

[0002] With the development of industrialization and urbanization, the problem of air pollution is becoming increasingly serious. In order to effectively manage and reduce air pollution, it is necessary to quickly and accurately analyze regional air pollution events. However, there are some problems with traditional air quality monitoring methods, such as sparse data, insufficient monitoring points, and uneven distribution of monitoring equipment, which limit the accurate analysis of regional air pollution events. In addition, current air quality monitoring methods often rely on time series databases or geographic information databases to store data, rely on demarcating fixed-size regional grids, and use some external programming algorithms to implement some data statistics. The performance is limited by the data scale, which affects the monitoring effect of air pollution and the efficiency of data analysis. Summary of the invention

[0003] In view of this, the embodiments of the present application provide a method, system, device and medium for spatial grid division based on air pollution data, so as to achieve the purpose of improving the monitoring accuracy of regional air pollution and the efficiency of data analysis.

[0004] The embodiment of the present application provides the following technical solution: a method for spatial grid division based on air pollution data, comprising:

[0005] Obtain historical monitoring data of all monitoring points in the target grid, construct an atmospheric pollutant data prediction model based on the historical monitoring data, and obtain atmospheric pollutant prediction data of the target grid within a set time through the atmospheric pollutant data prediction model;

[0006] According to the difference relationship between the atmospheric pollutant prediction data and the actual monitoring data, the overall attention value of all monitoring points in the target grid is obtained; according to the attention value and the area of ​​the target grid, the overall weight value of all monitoring points in the target grid is calculated;

[0007] The weight value is compared with a preset threshold value. If the weight value is less than the preset threshold value, a set number of monitoring points are added around the target grid to expand the area of ​​the target grid to obtain a new target grid. The overall weight value of all monitoring points in the new target grid is recalculated, and the weight value of the new target grid is compared with the preset threshold value. The process is repeated until the weight value is not less than the preset threshold value to obtain an adaptive grid.

[0008] Calculating the ratio of the overall attention value of all monitoring points in the adaptive grid to the area of ​​the adaptive grid to obtain the attention value density of the atmospheric pollutant data of the adaptive grid;

[0009] An index of the density of the atmospheric pollutant data attention values ​​of all adaptive grids in the target area is established, and the index is queried according to a set query condition to obtain the atmospheric pollutant data under the query condition.

[0010] According to an embodiment of the present application, the overall attention value of all monitoring points in the target grid is obtained according to the difference relationship between the atmospheric pollutant prediction data and the actual monitoring data, including:

[0011] The difference between the predicted data of atmospheric pollutants and the actual monitoring data of the time series data of each monitoring point in the target grid at time point t is calculated respectively, the average of the differences is calculated, and the average is used as the overall attention value of all monitoring points in the target grid.

[0012] According to an embodiment of the present application, the overall weight value of all monitoring points in the target grid is calculated according to the attention value and the area of ​​the target grid, including:

[0013] According to the attention value and the area of ​​the target grid, the function of obtaining the weight value is as follows:

[0014]

[0015] Where Y weight is the overall weight value of all monitoring points in the target grid, is the overall attention value of all monitoring points in the target grid, and Area is the area of ​​the target grid.

[0016] According to an embodiment of the present application, the method further includes: obtaining the preset threshold value according to the attention values ​​of all adaptive grids in the target area and the total area of ​​the target area through the following function:

[0017]

[0018] In the formula, is the threshold value, is the overall attention value of all monitoring points in a target grid, AllArea is the total area of ​​the target area, N is the number of all adaptive grids in the target area, and N=1...n.

[0019] According to an embodiment of the present application, obtaining historical monitoring data of all monitoring points in a target grid also includes:

[0020] The historical monitoring data is cleaned and preprocessed, and the cleaning and preprocessing include removing duplicate data, filling missing data and data standardization.

[0021] According to an embodiment of the present application, the historical monitoring data includes atmospheric pollutant data and meteorological data.

[0022] According to an embodiment of the present application, the atmospheric pollutant data prediction model is an ARMA model.

[0023] The present application also provides a spatial grid partitioning system based on air pollution data, comprising:

[0024] A prediction module is used to obtain the historical monitoring data of air pollutants at all monitoring points in the target grid, build an air pollutant data prediction model based on the historical monitoring data, and obtain the air pollutant prediction data of the target grid within a set time through the air pollutant data prediction model;

[0025] The first calculation module is used to obtain the overall attention value of all monitoring points in the target grid according to the difference relationship between the atmospheric pollutant prediction data and the actual monitoring data; and calculate the overall weight value of all monitoring points in the target grid according to the attention value and the area of ​​the target grid;

[0026] A comparison module is used to compare the weight value with a preset threshold value. If the weight value is less than the preset threshold value, a set number of monitoring points are added around the target grid to expand the area of ​​the target grid to obtain a new target grid, and the overall weight value of all monitoring points in the new target grid is recalculated. The weight value of the new target grid is compared with the preset threshold value, and it is iterated until the weight value is not less than the preset threshold value to obtain an adaptive grid;

[0027] The second calculation module is used to calculate the ratio of the overall attention value of all monitoring points in the adaptive grid to the area of ​​the adaptive grid, and obtain the attention value density of the atmospheric pollutant data of the adaptive grid;

[0028] The query module is used to establish an index of the density of the atmospheric pollutant data attention values ​​of all adaptive grids in the target area, query the index according to the set query conditions, and obtain the atmospheric pollutant data under the query conditions.

[0029] The present application also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned method for spatial grid division based on atmospheric pollution data when executing the computer program.

[0030] The present application also provides a computer-readable storage medium, which stores a computer program for executing the above-mentioned method for spatial grid division based on atmospheric pollution data.

[0031] Compared with the prior art, the beneficial effects that can be achieved by at least one of the above-mentioned technical solutions adopted in the embodiments of this specification include at least the following: The embodiment of the present invention is a spatial grid division method based on air pollution data, which divides the target area into multiple grids by deploying monitoring equipment, and each grid is provided with multiple monitoring points. According to the monitoring data and location information of the monitoring points, the grid size is adaptively adjusted, and the attention value of each grid is calculated. By comparing the density of attention values ​​of different grids, the location and time period of key air pollution can be quickly located.

[0032] In the data sparse state, the embodiment of the present invention can still adaptively adjust the grid division method according to the actual situation, and mine effective data by adaptively adjusting the grid size, thereby improving the monitoring effect.

[0033] The adaptive grid division based on the data attention value form in the embodiment of the present invention can improve the analysis efficiency. By calculating the attention value density of each grid, the occurrence location and time period of key air pollution can be quickly located, thereby improving the analysis efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0035] Figure 1 is a schematic diagram of a flow chart of a spatial grid partitioning method according to an embodiment of the present invention;

[0036] Figure 2 is a structural block diagram of a spatial grid partitioning system according to an embodiment of the present invention;

[0037] Figure 3 It is a structural schematic diagram of the computer device of the present invention. DETAILED DESCRIPTION

[0038] The embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0039] The following describes the implementation methods of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. The present application can also be implemented or applied through other different specific implementation methods, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that, in the absence of conflict, the following embodiments and the features in the embodiments can be combined with each other. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work belong to the scope of protection of the present application.

[0040] like Figure 1 As shown, an embodiment of the present invention provides a method for spatial grid division based on air pollution data, comprising:

[0041] S101. Obtain historical monitoring data of all monitoring points in a target grid, construct an atmospheric pollutant data prediction model based on the historical monitoring data, and obtain atmospheric pollutant prediction data of the target grid within a set time through the atmospheric pollutant data prediction model.

[0042] In this embodiment, the target area is divided into multiple grids, each grid is provided with multiple monitoring points. First, the historical monitoring data of all monitoring points in the target grid is obtained. In specific implementation, the public data of environmental protection organizations, meteorological bureaus, environmental protection industries, etc. can be obtained through monitoring stations of different monitoring objects as the above-mentioned historical monitoring data.

[0043] In one embodiment, obtaining the historical monitoring data of all monitoring points in the target grid also includes: cleaning and preprocessing the historical monitoring data, wherein the cleaning and preprocessing include removing duplicate data, filling missing data, and data standardization. The historical monitoring data in this embodiment includes air pollutant data and meteorological data. Among them, the air pollutant data includes daily monitoring data of air pollutants such as PM2.5, PM10, O3, CO, etc.; the meteorological data includes temperature, humidity, wind force, wind direction, air pressure, light, etc.

[0044] Next, an atmospheric pollutant data prediction model is constructed based on historical monitoring data. The atmospheric pollutant data prediction model used in this embodiment is the ARMA model. In specific implementation, the process of constructing an atmospheric pollutant data prediction model includes establishing training set data, establishing test set data, model training, model verification and optimization, and finally model application. The model construction process can be implemented using conventional technology, and the specific implementation process will not be repeated here. The atmospheric pollutant data prediction model formula in this embodiment is as follows:

[0045]

[0046] Where Y t represents the predicted value of a monitoring device’s time series data at time point t, c is a constant term, φ i represents the autoregressive coefficient, θ i is the moving average coefficient, Y t-i represents the observed value at time i before time point t, ε t-i is the random error term at time i before time point t, ε t is the random error term at time point t.

[0047] After the atmospheric pollutant data prediction model is constructed, the atmospheric pollutant prediction data of the target grid within the set time is obtained through model calculation.

[0048] S102. According to the difference relationship between the atmospheric pollutant prediction data and the actual monitoring data, the overall attention value of all monitoring points in the target grid is obtained; according to the attention value and the area of ​​the target grid, the overall weight value of all monitoring points in the target grid is calculated.

[0049] In this embodiment, according to the difference relationship between the atmospheric pollutant prediction data and the actual monitoring data, the overall attention value of all monitoring points in the target grid is obtained, including: respectively calculating the difference between the atmospheric pollutant prediction data and the actual monitoring data of each monitoring point time series data in the target grid at time point t, averaging the difference, and obtaining the overall attention value of all monitoring points in the target grid. The specific calculation formula is as follows:

[0050]

[0051] In the formula, is the attention value; Y i is the actual monitoring value of the i-th monitoring point, Y ti is the predicted value of the ith monitoring point at time t, N1 is the number of monitoring points in the target grid, N1 = 1...n1.

[0052] For each monitoring point, this embodiment uses the data within a period of time t to calculate the environmental data attention value. The greater the difference between the actual value and the predicted value, the greater the attention value, which means that this batch of data is more worthy of attention; the smaller the attention value, the more stable and easy to predict this batch of data.

[0053] Next, according to the attention value and the area of ​​the target grid, the overall weight value of all monitoring points in the target grid is calculated, including: according to the attention value and the area of ​​the target grid, the function of obtaining the weight value is as follows:

[0054]

[0055] Where Y weight is the overall weight value of all monitoring points in the target grid, is the overall attention value of all monitoring points in the target grid, and Area is the area of ​​the target grid.

[0056] S103. Compare the weight value with a preset threshold value. If the weight value is less than the preset threshold value, add a set number of monitoring points around the target grid to expand the area of ​​the target grid, obtain a new target grid, recalculate the overall weight value of all monitoring points in the new target grid, compare the weight value of the new target grid with the preset threshold value, iterate until the weight value is not less than the preset threshold value, and obtain an adaptive grid.

[0057] In this embodiment, the preset threshold is obtained according to the attention values ​​of all adaptive grids in the target area and the total area of ​​the target area through the following function:

[0058]

[0059] In the formula, is the threshold value, is the overall attention value of all monitoring points in a target grid, AllArea is the total area of ​​the target area, N is the number of all adaptive grids in the target area, N = 1...n.

[0060] In this embodiment, the weight of each grid is calculated based on the attention value of all grids in the target area. If it is true, then add nearby points to expand the area of ​​the grid and recalculate until it is no longer true. In this way, we can get a data point in time period T. The more active the data is, the smaller the area allocated is, which is easier to fine-tune the positioning; and the more stable the data is, the larger the area allocated is, which is easier to control as a whole.

[0061] In the data sparse state, the embodiment of the present invention can still adaptively adjust the grid division method according to the actual situation, and mine effective data by adaptively adjusting the grid size, thereby improving the monitoring effect.

[0062] S104. Calculate the ratio of the overall attention value of all monitoring points in the adaptive grid to the area of ​​the adaptive grid to obtain the attention value density of the atmospheric pollutant data of the adaptive grid, as shown in the following formula:

[0063]

[0064] In the formula, ρ is the density of the atmospheric pollutant data attention value, is the overall attention value of all monitoring points in a target grid, and Area is the area of ​​the target grid.

[0065] The adaptive grid division based on the data attention value form in the embodiment of the present invention can improve the analysis efficiency. By calculating the attention value density of each grid, the occurrence location and time period of key air pollution can be quickly located, thereby improving the analysis efficiency.

[0066] S105. Establish an index of the density of the atmospheric pollutant data attention values ​​of all adaptive grids in the target area, query the index according to the set query condition, and obtain the atmospheric pollutant data under the query condition.

[0067] Establishing a query index based on the obtained attention value density is conducive to quickly obtaining data results with high information content during query.

[0068] In this embodiment, the query results are obtained based on the data indexed by the attention value density. The attention value density index needs to be used to index the data. It involves calculating the attention value of each grid data point and dividing the data space into multiple grids according to the attention value. The attention value density of each grid can be used as the index value of the grid. Therefore, when querying for a period of time, the most valuable data results can be obtained first.

[0069] Next, the daily monitoring data of all national atmospheric monitoring standard stations is taken as an example to further illustrate the embodiment of the present invention. The data includes the following fields:

[0070] Monitoring site information: including site name, site number, city, longitude and latitude of the location, etc.

[0071] Monitoring data: including daily monitoring data of air pollutants such as PM2.5, PM10, O3, CO, as well as meteorological data such as temperature, humidity, wind speed, wind direction, air pressure, and light.

[0072] Time information: including the collection time and date of each monitoring data.

[0073] Other related information: equipment operation status and other information

[0074] Use the following steps to quickly locate data showing active pollution:

[0075] 1. Data preprocessing: Clean and preprocess daily monitoring data, including removing duplicate data, filling missing values, data standardization, etc. The processed data can be used for subsequent calculations.

[0076] 2. Calculate the attention value: For each monitoring site, calculate the attention value based on its daily monitoring data. The attention value is an indicator used to measure data uncertainty and can be used to evaluate the complexity and degree of change of monitoring data.

[0077] 3. Calculate the weight value Y: Calculate the weight value Y based on the attention value of each monitoring station and the area of ​​the area where it is located. The weight value Y is proportional to the attention value and the area of ​​the area where it is located.

[0078] 4. Divide the adaptive grid: according to the weight value Y and the preset threshold The monitoring area is divided into multiple adaptive grids. When the weight value Y of a monitoring station is less than the threshold When a site is found, it is merged with surrounding sites to form a new grid. This process is continued until all sites are divided into a grid.

[0079] 5. Calculate the grid attention value density: For each grid, calculate its attention value density. The attention value density is the ratio of the attention values ​​of all stations in the grid to the grid area. The attention value density can be used to measure the complexity and degree of change of air pollution in the grid.

[0080] 6. Query tool design: Based on the above calculation results, a query tool based on the density of grid attention values ​​is designed. Users can query grids that meet the conditions and their related information by entering query conditions (such as time range, geographical location, etc.).

[0081] 7. Query processing: When a user submits a query request, the query tool filters out grids that meet the conditions based on the user's query conditions and returns the corresponding data.

[0082] 8. Result display: Display the query results to users in an easy-to-understand manner, such as tables, charts or maps. Users can view the results to understand the air pollution situation and its changing trend in a specific area during a specific period of time.

[0083] like Figure 2 As shown, the present application also provides a spatial grid division system 200 based on air pollution data, comprising:

[0084] The prediction module 201 is used to obtain the historical monitoring data of air pollutants at all monitoring points in the target grid, build an air pollutant data prediction model based on the historical monitoring data, and obtain the air pollutant prediction data of the target grid within a set time through the air pollutant data prediction model;

[0085] The first calculation module 202 is used to obtain the overall attention value of all monitoring points in the target grid according to the difference relationship between the atmospheric pollutant prediction data and the actual monitoring data; and calculate the overall weight value of all monitoring points in the target grid according to the attention value and the area of ​​the target grid;

[0086] The comparison module 203 is used to compare the weight value with a preset threshold value. If the weight value is less than the preset threshold value, a set number of monitoring points are added around the target grid to expand the area of ​​the target grid to obtain a new target grid, and the overall weight value of all monitoring points in the new target grid is recalculated. The weight value of the new target grid is compared with the preset threshold value, and it is iterated until the weight value is not less than the preset threshold value to obtain an adaptive grid.

[0087] The second calculation module 204 is used to calculate the ratio of the overall attention value of all monitoring points in the adaptive grid to the area of ​​the adaptive grid to obtain the attention value density of the atmospheric pollutant data of the adaptive grid;

[0088] The query module 205 is used to establish an index of the density of the atmospheric pollutant data attention values ​​of all adaptive grids in the target area, query the index according to the set query condition, and obtain the atmospheric pollutant data under the query condition.

[0089] In specific implementation, the first calculation module 202 calculates the difference between the predicted data of atmospheric pollutants and the actual monitoring data of each monitoring point time series data in the target grid at time point t, averages the differences, and obtains the overall attention value of all monitoring points in the target grid.

[0090] The prediction module 201 is also used to clean and preprocess the historical monitoring data, and the cleaning and preprocessing include removing duplicate data, filling missing data and data standardization.

[0091] In one embodiment, a computer device is provided, such as Figure 3 As shown, it includes a memory 301, a processor 302 and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, any of the above-mentioned methods for spatial grid division based on atmospheric pollution data is implemented.

[0092] Specifically, the computer device may be a computer terminal, a server or a similar computing device.

[0093] In this embodiment, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program for executing any of the above-mentioned methods for spatial grid division based on air pollution data.

[0094] Specifically, computer-readable storage media include permanent and non-permanent, removable and non-removable media that can be used to store information by any method or technology. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer-readable storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable storage media does not include temporary computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0095] Obviously, those skilled in the art should understand that the modules or steps of the above-mentioned embodiments of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, and optionally, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order from that here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. In this way, the embodiments of the present invention are not limited to any specific combination of hardware and software.

[0096] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed in the present application should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for spatial grid division based on air pollution data, characterized in that: include: Obtaining historical monitoring data of all monitoring points in the target grid, constructing an atmospheric pollutant data prediction model based on the historical monitoring data, and obtaining atmospheric pollutant prediction data of the target grid within a set time through the atmospheric pollutant data prediction model; According to the difference relationship between the atmospheric pollutant prediction data and the actual monitoring data, the overall attention value of all monitoring points in the target grid is obtained; Calculate the overall weight value of all monitoring points in the target grid according to the attention value and the area of ​​the target grid; The weight value is compared with a preset threshold value. If the weight value is less than the preset threshold value, a set number of monitoring points are added around the target grid to expand the area of ​​the target grid to obtain a new target grid. The overall weight value of all monitoring points in the new target grid is recalculated, and the weight value of the new target grid is compared with the preset threshold value. The process is repeated until the weight value is not less than the preset threshold value to obtain an adaptive grid. Calculating the ratio of the overall attention value of all monitoring points in the adaptive grid to the area of ​​the adaptive grid to obtain the attention value density of the atmospheric pollutant data of the adaptive grid; Establishing an index of the density of the atmospheric pollutant data attention values ​​of all adaptive grids in the target area, querying the index according to the set query condition, and obtaining the atmospheric pollutant data under the query condition; According to the attention value and the area of ​​the target grid, the overall weight value of all monitoring points in the target grid is calculated, including: According to the attention value and the area of ​​the target grid, the function of obtaining the weight value is as follows: Where Y weight is the overall weight value of all monitoring points in the target grid, is the overall attention value of all monitoring points in the target grid, and Area is the area of ​​the target grid; The method further comprises: The preset threshold is obtained according to the attention values ​​of all adaptive grids in the target area and the total area of ​​the target area through the following function: In the formula, is the threshold value, is the overall attention value of all monitoring points in a target grid, AllArea is the total area of ​​the target area, N is the number of all adaptive grids in the target area, N = 1...n.

2. The method for spatial grid division based on air pollution data according to claim 1, characterized in that: According to the difference relationship between the predicted data of atmospheric pollutants and the actual monitoring data, the overall attention value of all monitoring points in the target grid is obtained, including: The difference between the predicted data of atmospheric pollutants and the actual monitoring data of the time series data of each monitoring point in the target grid at time point t is calculated respectively, the average of the differences is calculated, and the average is used as the overall attention value of all monitoring points in the target grid.

3. The method for spatial grid division based on air pollution data according to claim 1, characterized in that: Obtain historical monitoring data for all monitoring points in the target grid, including: The historical monitoring data is cleaned and preprocessed, and the cleaning and preprocessing include removing duplicate data, filling missing data and data standardization.

4. The method for spatial grid division based on air pollution data according to claim 1, characterized in that: The historical monitoring data includes air pollutant data and meteorological data.

5. The method for spatial grid division based on air pollution data according to claim 1, characterized in that: The air pollutant data prediction model is an ARMA model.

6. A spatial grid division system based on air pollution data, using the spatial grid division method based on air pollution data as claimed in any one of claims 1 to 5, characterized in that: include: A prediction module is used to obtain the historical monitoring data of air pollutants at all monitoring points in the target grid, build an air pollutant data prediction model based on the historical monitoring data, and obtain the air pollutant prediction data of the target grid within a set time through the air pollutant data prediction model; A first calculation module is used to obtain the overall attention value of all monitoring points in the target grid according to the difference relationship between the atmospheric pollutant prediction data and the actual monitoring data; Calculate the overall weight value of all monitoring points in the target grid according to the attention value and the area of ​​the target grid; A comparison module is used to compare the weight value with a preset threshold value. If the weight value is less than the preset threshold value, a set number of monitoring points are added around the target grid to expand the area of ​​the target grid to obtain a new target grid, and the overall weight value of all monitoring points in the new target grid is recalculated. The weight value of the new target grid is compared with the preset threshold value, and it is iterated until the weight value is not less than the preset threshold value to obtain an adaptive grid; The second calculation module is used to calculate the ratio of the overall attention value of all monitoring points in the adaptive grid to the area of ​​the adaptive grid, and obtain the attention value density of the atmospheric pollutant data of the adaptive grid; The query module is used to establish an index of the density of the atmospheric pollutant data attention values ​​of all adaptive grids in the target area, query the index according to the set query conditions, and obtain the atmospheric pollutant data under the query conditions.

7. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method for spatial grid division based on air pollution data described in any one of claims 1 to 5 is implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program for executing the method for spatial grid division based on air pollution data according to any one of claims 1 to 5.

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