A method and device for predicting regional PM2.5 concentration

Abstract

The present invention discloses a method and device for predicting regional PM2.5 concentration, and relates to the technical field of PM2.5 concentration prediction. The method comprises: obtaining meteorological data and PM2.5 concentration observation values ​​of a region to be predicted at a first time point, and obtaining several concentration dissipation index data of each sub-region; for each sub-region, determining the first concentration dissipation parameter corresponding to the sub-region according to the PM2.5 concentration observation value and all concentration dissipation index data of the sub-region; determining the adjustment coefficient of the first concentration dissipation parameter corresponding to each sub-region according to the meteorological data; for each sub-region, determining the second concentration dissipation parameter corresponding to the sub-region according to the first concentration dissipation parameter corresponding to the sub-region and the adjustment coefficient; determining the PM2.5 concentration prediction value of each sub-region at the second time point according to the PM2.5 concentration observation value and the second concentration dissipation parameter corresponding to each sub-region. It can be seen that the present invention can improve the accuracy of the PM2.5 concentration prediction result of a small area.

Description

Technical Field

[0001] The present invention relates to the field of PM2.5 concentration prediction, and in particular to a method and device for predicting regional PM2.5 concentration. Background Art

[0002] Particles are one of the important factors affecting air quality. Fine particles (PM2.5) have a small particle size, so they will stay and spread in the atmosphere. They also contain harmful substances, which have a greater impact on the atmospheric environment and human health. Therefore, predicting the PM2.5 concentration in the atmosphere and providing prediction results is conducive to making preventive measures in a timely manner based on the prediction results, reducing the impact of PM2.5 on the atmospheric environment and the harm to the human body.

[0003] Most of the PM2.5 prediction methods in the prior art are based on collecting a large amount of PM2.5 concentration data, and then obtaining the prediction results based on the spatiotemporal distribution law of PM2.5 concentration. Practice has shown that the methods in the prior art that predict the PM2.5 concentration in the atmosphere based on the spatiotemporal distribution law of PM2.5 concentration are accurate in predicting PM2.5 concentration in a large area, but not accurate enough for predicting PM2.5 concentration in a small area, such as a local area within a large area. Therefore, it is necessary to propose a new regional PM2.5 concentration prediction method to improve the accuracy of PM2.5 concentration prediction results in a small area. Summary of the invention

[0004] The present invention provides a method and device for predicting regional PM2.5 concentration, which improves the accuracy of PM2.5 concentration prediction results in a small area.

[0005] In order to solve the above technical problems, the first aspect of the present invention discloses a method for predicting regional PM2.5 concentration, the method comprising:

[0006] Acquire meteorological data and PM2.5 concentration observation values ​​of a region to be predicted at a first time point, wherein the region to be predicted includes multiple sub-regions;

[0007] Acquire a number of concentration dissipation index data for each of the sub-areas, where the concentration dissipation index data is environmental information data that affects the change in PM2.5 concentration in the sub-area;

[0008] For each of the sub-areas, determining a first concentration dissipation parameter corresponding to the sub-area according to the PM2.5 concentration observation value and all the concentration dissipation index data of the sub-area;

[0009] Determining, according to the meteorological data, an adjustment coefficient of the first concentration dissipation parameter corresponding to each of the sub-areas;

[0010] For each of the sub-regions, determining a second concentration dissipation parameter corresponding to the sub-region according to the first concentration dissipation parameter corresponding to the sub-region and the adjustment coefficient;

[0011] The predicted PM2.5 concentration value of each sub-area at a second time point is determined based on the PM2.5 concentration observation value and the second concentration dissipation parameter corresponding to each sub-area, wherein the second time point is any time point at which the predicted PM2.5 concentration value needs to be obtained.

[0012] The second aspect of the present invention discloses a regional PM2.5 concentration prediction device, the device comprising:

[0013] A first acquisition module is used to acquire meteorological data and PM2.5 concentration observation values ​​of a region to be predicted at a first time point, wherein the region to be predicted includes multiple sub-regions;

[0014] A second acquisition module is used to acquire a number of concentration dissipation index data for each of the sub-areas, wherein the concentration dissipation index data is environmental information data that affects the PM2.5 concentration change in the sub-area;

[0015] A determination module, for determining, for each of the sub-areas, a first concentration dissipation parameter corresponding to the sub-area according to the PM2.5 concentration observation value and all the concentration dissipation index data of the sub-area; and, for each of the sub-areas, determining an adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-area according to the meteorological data; and, for each of the sub-areas, determining a second concentration dissipation parameter corresponding to the sub-area according to the first concentration dissipation parameter corresponding to the sub-area and the adjustment coefficient;

[0016] A prediction module is used to determine the predicted PM2.5 concentration value of each sub-area at a second time point based on the PM2.5 concentration observation value and the second concentration dissipation parameter corresponding to each sub-area, wherein the second time point is any time point at which the predicted PM2.5 concentration value needs to be obtained.

[0017] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines, for each of the sub-areas, the first concentration dissipation parameter corresponding to the sub-area according to the PM2.5 concentration observation value and all the concentration dissipation index data of the sub-area includes:

[0018] For each of the sub-areas, according to the PM2.5 concentration observation value and each of the concentration dissipation index data of the sub-area, determine the reduction factor of each of the concentration dissipation index data, the reduction factor is used to represent the influence of the corresponding concentration dissipation index data on the change of PM2.5 concentration; extract the characteristic value of each of the concentration dissipation index data, and determine the weight value of each of the concentration dissipation index data according to the characteristic values ​​of all the concentration dissipation index data; determine the reduction parameter corresponding to the sub-area according to the reduction factors and the weight values ​​of all the concentration dissipation index data;

[0019] For each of the sub-regions, determining the regional characteristics corresponding to the sub-region according to the geographical environment information of the sub-region, wherein the regional characteristics are the geographical environment characteristics in the sub-region that affect the diffusion of PM2.5 to the surrounding areas; determining the diffusion parameters corresponding to the sub-region according to the PM2.5 concentration observation value and the regional characteristics of the sub-region;

[0020] For each of the sub-regions, a first concentration dissipation parameter corresponding to the sub-region is determined according to the attenuation parameter and the diffusion parameter corresponding to the sub-region.

[0021] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines, for each of the sub-regions, the regional features corresponding to the sub-region according to the geographical environment information of the sub-region includes:

[0022] For each of the sub-regions, determine the regional characteristics corresponding to the sub-region according to the geographical environment information of the sub-region; determine the surrounding regional characteristics corresponding to the sub-region according to the geographical environment information of all other sub-regions adjacent to the sub-region;

[0023] The regional features include the own regional features and the surrounding regional features;

[0024] And, the specific manner in which the determination module determines, for each of the sub-regions, the diffusion parameter corresponding to the sub-region according to the PM2.5 concentration observation value and the regional characteristics of the sub-region includes:

[0025] For each of the sub-regions, determining an independent diffusion parameter of the sub-region according to the PM2.5 concentration observation value and the regional characteristics of the sub-region itself;

[0026] For each of the sub-regions, a diffusion parameter corresponding to the sub-region is determined according to the independent diffusion parameter of the sub-region, the surrounding region feature and the independent diffusion parameters of all other sub-regions adjacent to the sub-region.

[0027] As an optional implementation, in the second aspect of the present invention, the specific manner in which the determination module determines the adjustment coefficient of the first concentration dissipation parameter corresponding to each of the sub-areas according to the meteorological data includes:

[0028] For each of the sub-regions, based on the meteorological data, the abatement meteorological data of the sub-region is determined, and the abatement meteorological data is the meteorological data that affects the change of PM2.5 concentration in the sub-region; based on the meteorological data and the regional characteristics of the sub-region, the diffusion meteorological data of the sub-region is determined, and the diffusion meteorological data is the meteorological data that affects the diffusion of PM2.5 in the sub-region to the surrounding areas;

[0029] For each of the sub-areas, the abatement parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-area are respectively determined according to the abatement meteorological data and the diffusion meteorological data of the sub-area, and the adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-area is determined according to the abatement parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-area.

[0030] As an optional implementation, in the second aspect of the present invention, the prediction module determines the predicted value of PM2.5 concentration of each sub-region at the second time point according to the PM2.5 concentration observation value and the second concentration dissipation parameter corresponding to each sub-region in a specific manner including:

[0031] Determine whether the time difference between the first time point and the second time point is greater than a preset time difference threshold. If the time difference between the first time point and the second time point is less than or equal to the time difference threshold, then for each of the sub-areas, determine the concentration change of the sub-area according to the time difference between the first time point and the second time point and the second concentration dissipation parameter corresponding to the sub-area; determine the predicted PM2.5 concentration value of the sub-area at the second time point according to the concentration change of the sub-area and the PM2.5 concentration observation value.

[0032] As an optional implementation, in the second aspect of the present invention, the prediction module determines the PM2.5 concentration prediction value of each sub-area at the second time point according to the PM2.5 concentration observation value and the second concentration dissipation parameter corresponding to each sub-area. The specific method also includes:

[0033] If the time difference between the first time point and the second time point is greater than the time difference threshold, obtaining target meteorological information of the area to be predicted between the first time point and the second time point;

[0034] Determining, according to the target meteorological information, an intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to each of the sub-areas;

[0035] For each of the sub-regions, determining a third concentration dissipation parameter corresponding to the sub-region according to the first concentration dissipation parameter and the intermediate adjustment coefficient of the sub-region;

[0036] The predicted PM2.5 concentration value of each sub-area at the second time point is determined based on the PM2.5 concentration observation value and the third concentration dissipation parameter corresponding to each sub-area.

[0037] As an optional implementation, in the second aspect of the present invention, the specific manner in which the prediction module obtains the target meteorological information of the to-be-predicted area between the first time point and the second time point includes:

[0038] Obtaining comprehensive meteorological data of the to-be-predicted area at each intermediate time point, wherein all the intermediate time points are multiple time points between the first time point and the second time point, and the time difference between any two adjacent intermediate time points is less than or equal to the time difference threshold, the time difference between the earliest time point among all the intermediate time points and the earliest time point among the first time point and the second time point is less than or equal to the time difference threshold, and the time difference between the latest time point among all the intermediate time points and the latest time point among the first time point and the second time point is less than or equal to the time difference threshold;

[0039] The target meteorological information includes the comprehensive meteorological data of the to-be-predicted area at each intermediate time point;

[0040] And, the prediction module determines the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to each of the sub-areas according to the target meteorological information in a specific manner including:

[0041] For each of the intermediate time points, based on the comprehensive meteorological data at the intermediate time point, the reduction meteorological data of each of the sub-regions at the intermediate time point is determined; based on the comprehensive meteorological data at the intermediate time point and the regional characteristics of the sub-region, the diffusion meteorological data of each of the sub-regions at the intermediate time point is determined;

[0042] For each of the sub-regions and each of the intermediate time points, the reduction parameter adjustment coefficient of the sub-region at the intermediate time point is determined according to the reduction meteorological data of the sub-region at the intermediate time point, the diffusion parameter adjustment coefficient of the sub-region at the intermediate time point is determined according to the diffusion meteorological data of the sub-region at the intermediate time point, and the adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-region at the intermediate time point is determined according to the reduction parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-region at the intermediate time point;

[0043] For each of the sub-regions, the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-region is determined according to the adjustment coefficients corresponding to the sub-region at all the intermediate time points.

[0044] As an optional implementation, in the second aspect of the present invention, the specific manner in which the prediction module obtains the target meteorological information of the to-be-predicted area between the first time point and the second time point includes:

[0045] Obtaining comprehensive meteorological change data of the area to be predicted between the first time point and the second time point, the comprehensive meteorological change data including multiple types of sub-meteorological change data; for each type of the sub-meteorological change data, determining a change period of the sub-meteorological change data of that type, and determining sub-meteorological data of the sub-meteorological change data of that type within each change period according to the sub-meteorological change data of that type and the change period;

[0046] The target meteorological information includes the sub-meteorological data of each type of the sub-meteorological change data in each change period;

[0047] And, the prediction module determines the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to each of the sub-areas according to the target meteorological information in a specific manner including:

[0048] For each of the sub-regions, the abatement sub-meteorological change data of the sub-region is determined based on the comprehensive meteorological change data; the diffusion sub-meteorological change data of the sub-region is determined based on the comprehensive meteorological change data and the regional characteristics of the sub-region; for each of the abatement sub-meteorological change data, the abatement parameter adjustment coefficient of the sub-region in each of the change periods is determined based on the sub-meteorological data of the abatement sub-meteorological change data in each of the change periods; for each of the diffusion sub-meteorological change data, the diffusion parameter adjustment coefficient of the sub-region in each of the change periods is determined based on the sub-meteorological data of the diffusion sub-meteorological change data in each of the change periods;

[0049] For each type of the sub-meteorological change data of each of the sub-regions, determining the adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-meteorological change data of the type in the sub-region according to the abatement parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-meteorological change data of the type in the sub-region in all the change periods;

[0050] For each of the sub-regions, the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-region is determined according to the adjustment coefficients corresponding to all types of sub-meteorological change data of the sub-region.

[0051] The third aspect of the present invention discloses another regional PM2.5 concentration prediction device, the device comprising:

[0052] A memory storing executable program code;

[0053] a processor coupled to the memory;

[0054] The processor calls the executable program code stored in the memory to execute the regional PM2.5 concentration prediction method disclosed in the first aspect of the present invention.

[0055] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0056] In an embodiment of the present invention, meteorological data and PM2.5 concentration observation values ​​of the area to be predicted at a first time point are obtained, and several concentration dissipation index data of each sub-area are obtained; for each sub-area, the first concentration dissipation parameter corresponding to the sub-area is determined according to the PM2.5 concentration observation value and all concentration dissipation index data of the sub-area; the adjustment coefficient of the first concentration dissipation parameter corresponding to each sub-area is determined according to the meteorological data; for each sub-area, the second concentration dissipation parameter corresponding to the sub-area is determined according to the first concentration dissipation parameter and the adjustment coefficient corresponding to the sub-area; the PM2.5 concentration prediction value of each sub-area at the second time point is determined according to the PM2.5 concentration observation value and the second concentration dissipation parameter corresponding to each sub-area. It can be seen that the implementation of the present invention can comprehensively consider the PM2.5 concentration prediction value, meteorological data and the concentration dissipation index data of each sub-area, and combine the relationship among the PM2.5 concentration prediction value, meteorological data and the concentration dissipation index data of each sub-area to predict the PM2.5 concentration prediction value of each sub-area, thereby improving the accuracy of the PM2.5 concentration prediction result of a small area. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0058] Figure 1 It is a flow chart of a method for predicting regional PM2.5 concentration disclosed in an embodiment of the present invention;

[0059] Figure 2 It is a flow chart of another method for predicting regional PM2.5 concentration disclosed in an embodiment of the present invention;

[0060] Figure 3 It is a structural schematic diagram of a regional PM2.5 concentration prediction device disclosed in an embodiment of the present invention;

[0061] Figure 4 It is a structural schematic diagram of another regional PM2.5 concentration prediction device disclosed in an embodiment of the present invention. DETAILED DESCRIPTION

[0062] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings 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.

[0063] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish different objects rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, device, product or end including a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units that are not listed, or may optionally include other steps or units inherent to these processes, methods, products or ends.

[0064] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0065] The present invention discloses a method and device for predicting regional PM2.5 concentration, which can comprehensively consider the predicted PM2.5 concentration value, meteorological data and factors affecting the PM2.5 concentration change in each sub-region, thereby improving the accuracy of the PM2.5 concentration prediction result in a small area. The following are detailed descriptions.

[0066] Embodiment 1

[0067] See also Figure 1 , Figure 1 : is a flow chart of a method for predicting regional PM2.5 concentration disclosed in an embodiment of the present invention. Figure 1 The described regional PM2.5 concentration prediction method can be applied to a regional PM2.5 concentration prediction device, which can be integrated in a cloud server or a local server, and the embodiments of the present invention are not limited thereto. Figure 1 As shown, the method for predicting PM2.5 concentration in the region may include the following operations:

[0068] 101. Obtain the meteorological data and PM2.5 concentration observation value of the area to be predicted at the first time point.

[0069] In an embodiment of the present invention, specifically, the area to be predicted includes multiple sub-areas. For example, if the area to be predicted is a city, the sub-areas may be multiple administrative districts based on administrative divisions, or multiple topographic areas based on topographic divisions, or areas with different air quality levels divided according to historical PM2.5 observation values.

[0070] In the embodiment of the present invention, specifically, the meteorological data may include one or more of temperature data, humidity data, wind speed data, solar radiation data, and atmospheric pressure data. The PM2.5 concentration observation value is the PM2.5 observation value of the entire area to be predicted obtained using various observation methods. Generally, the observation data of multiple fixed observation stations in the area to be predicted can be obtained, and finally the average value of the observation data of each observation station is calculated as the PM2.5 observation value of the area to be predicted.

[0071] 102. Obtain several concentration dissipation index data for each sub-area.

[0072] In the embodiment of the present invention, specifically, the concentration dissipation index data is the environmental information data that affects the change of PM2.5 concentration in the sub-region, and the concentration dissipation index data may include one or more of green space data, water body data, building data, population data, traffic data, and pollution source data. It can be seen that the embodiment of the present invention can comprehensively consider multiple environmental information that affects the change of PM2.5 concentration in each sub-region, thereby providing more comprehensive and reasonable data for the subsequent PM2.5 concentration prediction step.

[0073] 103. For each sub-area, determine a first concentration dissipation parameter corresponding to the sub-area based on the PM2.5 concentration observation value and all concentration dissipation index data of the sub-area.

[0074] In the embodiment of the present invention, specifically, the first concentration dissipation parameter is used to measure the influence of all concentration dissipation index data in each sub-region on the change of PM2.5 concentration in the sub-region. In actual situations, within a certain PM2.5 concentration range, more vegetation can effectively absorb PM2.5, reducing the PM2.5 concentration. However, when the concentration of atmospheric particulate matter reaches a certain threshold, the reduction effect of plants on particulate matter is reduced. At this time, the relatively closed space in the green space is not conducive to the diffusion of particulate matter. On the other hand, some types of plants may also be unfavorable for the reduction of PM2.5 concentration. For example, Cotinus coggygria and Pinus tabulaeformis have no effect on the reduction of PM2.5 concentration, but will increase the PM2.5 concentration. This is because plants can not only settle, block and adsorb particulate matter, but also emit plant-derived volatile organic compounds, regenerate PM2.5, and have a negative effect on the reduction of PM2.5 concentration. Similarly, the influence of concentration dissipation index data such as water body data, building data, population data, and traffic data on the change of PM2.5 concentration is also related to the PM2.5 concentration itself. Therefore, for each type of concentration dissipation index data, it is necessary to combine it with the PM2.5 concentration observation value to determine its impact on the change of PM2.5 concentration in the sub-area.

[0075] In the embodiment of the present invention, the PM2.5 concentration range can be optionally set to different concentration levels. In different concentration levels, the concentration dissipation index data has different effects on the change of PM2.5 concentration. The PM2.5 concentration level is first determined by the PM2.5 concentration observation value, and then the first concentration dissipation parameter is determined according to the concentration dissipation index data. Optionally, the first concentration dissipation parameter can be a rate used to represent the dissipation rate of PM2.5 concentration over time. For example, the first concentration dissipation parameter is -0.6 , indicating that the concentration of PM2.5 decreases by 0.6 micrograms per cubic meter per minute; further optionally, the first concentration dissipation parameter can be a vector. Since the PM2.5 in the sub-area can both slowly dissipate over time and diffuse to the surrounding area, if the reduction and diffusion are to be considered comprehensively, a vector can be used to represent the first concentration dissipation parameter, which can represent the reduction rate, diffusion direction and diffusion rate of PM2.5.

[0076] In the embodiment of the present invention, optionally, according to the PM2.5 concentration observation value and all concentration dissipation index data of the sub-area, a first concentration dissipation parameter corresponding to the sub-area is determined, and an example is given as follows:

[0077] The observed value of PM2.5 concentration is 115 micrograms per cubic meter, so the concentration level is determined to be level three. For sub-area A, when the concentration level is level three, the influence of green area ratio, water area ratio, building density, population density, traffic activity, pollution sources and other data in the comprehensive concentration dissipation index data on the change of PM2.5 concentration is determined to be -0.6 for the first concentration dissipation parameter corresponding to sub-area A. .

[0078] It can be seen that the embodiment of the present invention takes into account the impact of PM2.5 concentration itself on various concentration dissipation index data, comprehensively considers the impact of multiple concentration dissipation indicators on the change of PM2.5 concentration, and quantifies this impact through the first concentration dissipation parameter, thereby more accurately predicting the PM2.5 concentration change results.

[0079] In an optional embodiment, for each sub-region, determining the first concentration dissipation parameter corresponding to the sub-region according to the PM2.5 concentration observation value and all concentration dissipation index data of the sub-region may include the following operations:

[0080] For each sub-region, based on the PM2.5 concentration observation value and each concentration dissipation index data of the sub-region, determine the reduction factor of each concentration dissipation index data, and the reduction factor is used to represent the influence of the corresponding concentration dissipation index data on the change of PM2.5 concentration; extract the characteristic value of each concentration dissipation index data, and determine the weight value of each concentration dissipation index data based on the characteristic values ​​of all concentration dissipation index data; determine the reduction parameter corresponding to the sub-region based on the reduction factor and weight value of all concentration dissipation index data;

[0081] For each sub-region, the regional characteristics corresponding to the sub-region are determined according to the geographical environment information of the sub-region, and the regional characteristics are the geographical environment characteristics in the sub-region that affect the diffusion of PM2.5 to the surrounding areas; the diffusion parameters corresponding to the sub-region are determined according to the PM2.5 concentration observation value and the regional characteristics of the sub-region;

[0082] For each sub-region, a first concentration dissipation parameter corresponding to the sub-region is determined according to the attenuation parameter and the diffusion parameter corresponding to the sub-region.

[0083] In this optional embodiment, the first concentration dissipation parameter is finally determined based on the reduction parameter and the diffusion parameter. In actual situations, the PM2.5 in an area is reduced by absorption, attachment, decomposition, etc. on the one hand, and diffused to other areas by natural diffusion, air flow, etc. on the other hand. Therefore, it is necessary to comprehensively consider the impact of the above two factors on the PM2.5 concentration of the sub-area. In this optional embodiment, the reduction parameter is used to represent the process of PM2.5 being reduced by absorption, attachment, decomposition, etc., and the diffusion parameter is used to represent the process of PM2.5 being diffused to other areas by natural diffusion, air flow, etc.

[0084] In this optional embodiment, specifically, the reduction factor is used to represent the influence of the corresponding concentration dissipation index data on the change of PM2.5 concentration through absorption, attachment, decomposition, etc. For example, for green space data, the reduction factor can be the change in PM2.5 concentration affected by a unit area of ​​green space per unit time. For population data, the reduction factor can be the change in PM2.5 concentration affected by a unit population density per unit time. For pollution sources, the reduction factor can be the change in PM2.5 concentration affected by a unit pollution source per unit time. The above-mentioned reduction factors are also related to the PM2.5 concentration observation value. For example, when the concentration is large, the change in PM2.5 concentration affected by a unit area of ​​green space per unit time is large; therefore, the preset reduction factors corresponding to different concentration dissipation index data within different PM2.5 concentrations can be determined in advance, and then the final reduction factors are determined by the PM2.5 concentration observation value.

[0085] In this optional embodiment, specifically, the characteristic value is a specific quantitative form of the concentration dissipation index data. For example, for green space data, its characteristic value can be green space area, green space proportion, green space type, etc. For building data, its characteristic value can be average building height, building area proportion, building dispersion, etc. The above-mentioned reduction factor is used to represent the influence of the corresponding concentration dissipation index data on the change of PM2.5 concentration through absorption, attachment, decomposition, etc., and the role of the characteristic value is to combine the specific quantitative indicators of the concentration dissipation index data to measure the influence of the corresponding concentration dissipation index data on the change of PM2.5 concentration through absorption, attachment, decomposition, etc. The influence is reflected in the present embodiment through the weight value; therefore, all the characteristic values ​​of the concentration dissipation index data can be combined to determine the weight value corresponding to the concentration dissipation index data. Finally, the reduction parameter is determined by the reduction factor and the weight value.

[0086] In this optional embodiment, the method for determining the reduction parameter is illustrated as follows:

[0087] For sub-area A, based on the PM2.5 concentration observation value of 115 micrograms per cubic meter and the concentration dissipation index data of sub-area A: green space data, water body data, building data, population data, traffic data, and pollution source data, the reduction factor of each concentration dissipation index data is determined as -0.008 , -0.009 , -0.001 , -0.004 , 0.02 , 0.1 ; The units of the above reduction factors are normalized units. For example, the reduction factor of green space data is 0.008 , indicating that the PM2.5 concentration per unit area of ​​green space per unit time decreased by 0.008 micrograms per cubic meter.

[0088] Extract the characteristic values ​​of each concentration dissipation index data: green space area, green space proportion, green space type, water body area, water body proportion, water body dispersion, building density, building proportion, average building height, population density, traffic activity, pollution source level, and number of pollution sources; According to the characteristic values ​​of all concentration dissipation index data, determine the weight values ​​of green space data, water body data, building data, population data, traffic data, and pollution source data as follows: 120, 100, 20, 10, 20, 10; According to -0.008∙120+(-0.009∙100)+(-0.001∙20)+(-0.004∙10)+(0.02∙20)+(0.1∙10)=0.52; the final reduction parameter is determined to be -0.52 .

[0089] In this optional embodiment, the regional characteristics are the geographical environment characteristics that affect the diffusion of PM2.5 to the surrounding areas in the sub-region, such as the PM2.5 in the sub-region surrounded by mountains diffuses slowly to the surrounding areas, the PM2.5 in the sub-region with lower average building height diffuses quickly to the surrounding areas, and the PM2.5 in the sub-region with higher forest coverage diffuses slowly to the surrounding areas, etc., and the influence of the above regional characteristics on the diffusion of PM2.5 to the surrounding areas is also related to the PM2.5 concentration observation value. For example, when the concentration of atmospheric particulate matter reaches a certain threshold, the relatively closed space in the green space is not conducive to the diffusion of particulate matter. Taking into account the PM2.5 concentration observation value and the regional characteristics of the sub-region, the diffusion parameter corresponding to the sub-region is determined. The diffusion parameter can be a specific numerical parameter like the abatement parameter, or it can be in the form of a vector, thereby indicating the diffusion of PM2.5 in the sub-region in all directions. For example, for a sub-region with an open area in the east and a mountainous area in the west, the PM2.5 diffuses more to the east, and the above situation can be reflected in more detail through the vector.

[0090] It can be seen that when implementing the regional PM2.5 concentration prediction method in this optional embodiment, two ways of PM2.5 concentration dissipation are considered when determining the first concentration dissipation parameter: elimination through absorption, attachment, decomposition, etc., and diffusion to other areas through natural diffusion, air flow, etc.; thereby making the method of determining the first concentration dissipation vector more reasonable and improving the accuracy of the PM2.5 concentration prediction results in a small area.

[0091] 104. Determine an adjustment coefficient of a first concentration dissipation parameter corresponding to each sub-area according to the meteorological data.

[0092] In the embodiment of the present invention, meteorological data such as temperature data, humidity data, wind speed data, solar radiation data, and atmospheric pressure data will also directly or indirectly affect the change in PM2.5 concentration in the sub-region. For example, the higher the temperature, the faster the PM2.5 concentration dissipates. The higher the temperature, the more likely the vegetation will absorb PM2.5. At the same time, northeast winds of a certain speed will blow PM2.5 from the northeast to the southwest, causing PM2.5 to spread to the southwest. Therefore, when predicting the concentration of PM2.5, meteorological data is an indispensable influencing factor that needs to be considered.

[0093] In the embodiment of the present invention, specifically, the adjustment coefficient is used to measure the impact of meteorological data on the change of PM2.5 concentration in the sub-region. Since meteorological data will directly or indirectly affect the change of PM2.5 concentration in the sub-region, the indirect impact is to affect the change of PM2.5 concentration in the sub-region by affecting the impact of concentration dissipation index data on the change of PM2.5 concentration. The above direct and indirect impacts can be reflected by the adjustment coefficient. For example, if the adjustment coefficient is 1.25, it means that meteorological data can speed up the rate of change of PM2.5 concentration by 1.25 times; in addition, when the first concentration dissipation parameter is in the form of a vector, the adjustment coefficient can also be in the form of a vector.

[0094] It can be seen that in the embodiment of the present invention, the direct or indirect impact of meteorological data on the PM2.5 concentration change in the sub-area is comprehensively considered, so as to more accurately predict the PM2.5 concentration change result.

[0095] In an optional embodiment, determining the adjustment coefficient of the first concentration dissipation parameter corresponding to each sub-area according to meteorological data includes:

[0096] For each sub-region, based on the meteorological data, the abatement meteorological data of the sub-region is determined, and the abatement meteorological data is the meteorological data that affects the change of PM2.5 concentration in the sub-region; based on the meteorological data and the regional characteristics of the sub-region, the diffusion meteorological data of the sub-region is determined, and the diffusion meteorological data is the meteorological data that affects the diffusion of PM2.5 in the sub-region to the surrounding areas;

[0097] For each sub-region, the abatement parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-region are respectively determined according to the abatement meteorological data and the diffusion meteorological data of the sub-region, and the adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-region is determined according to the abatement parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-region.

[0098] In this optional embodiment, the change of PM2.5 concentration in the sub-region by meteorological data is also achieved by affecting PM2.5 reduction and PM2.5 diffusion. For example, temperature, solar radiation, humidity, etc. will affect PM2.5 reduction, so temperature, solar radiation, humidity, etc. can be determined as reduction meteorological data; wind speed, wind direction, atmospheric pressure, etc. will affect PM2.5 diffusion, so wind speed, wind direction, atmospheric pressure, etc. can be determined as diffusion meteorological data. The reduction parameter adjustment coefficient and the diffusion parameter adjustment coefficient are determined respectively according to the reduction parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-region, and finally the adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-region is determined according to the reduction parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-region.

[0099] It can be seen that the implementation of the regional PM2.5 concentration prediction method in this optional embodiment can classify meteorological data into abatement meteorological data and diffusion meteorological data according to the way in which meteorological data affects the change in PM2.5 concentration, and then determine the abatement parameter adjustment coefficient and the diffusion parameter adjustment coefficient according to the respective effects of the abatement meteorological data and the diffusion meteorological data on the change in PM2.5 concentration, and finally determine the adjustment coefficient of the first concentration dissipation parameter. The process of determining the first concentration dissipation parameter adjustment coefficient is more reasonable and conforms to the principle of the influence of meteorological data on the change in PM2.5 concentration, thereby improving the accuracy of the PM2.5 concentration prediction results in a small area.

[0100] 105. For each sub-region, determine a second concentration dissipation parameter corresponding to the sub-region according to the first concentration dissipation parameter corresponding to the sub-region and the adjustment coefficient.

[0101] In the embodiment of the present invention, specifically, the second concentration dissipation parameter is used to measure the impact of the PM2.5 concentration observation value, concentration dissipation index data and meteorological data on the PM2.5 concentration change in the sub-area. For example, the first concentration dissipation parameter is -0.6 , the adjustment coefficient is 1.25, then the second concentration dissipation parameter is -0.75 , the second concentration dissipation parameter indicates that the concentration of PM2.5 decreases by 0.75 micrograms per cubic meter per minute. In addition, when the first concentration dissipation parameter and the adjustment coefficient are in the form of vectors, the second concentration dissipation parameter can also be in the form of a vector.

[0102] 106. Determine a predicted PM2.5 concentration value for each sub-region at a second time point based on the PM2.5 concentration observation value and the second concentration dissipation parameter corresponding to each sub-region.

[0103] In an embodiment of the present invention, specifically, the second time point is any time point at which the predicted PM2.5 concentration value needs to be obtained. Optionally, the second time point may be after the first time point or before the second time point. For example, the first time point is 8:00 on January 19, 2024, and the second time point is 9:00 on January 19, 2024. At this time, the predicted PM2.5 concentration value of each sub-area at 9:00 on January 19, 2024 can be predicted by the observed PM2.5 concentration value of the area to be predicted at 8:00 on January 19, 2024; the second time point may also be 7:00 on January 19, 2024. At this time, the predicted PM2.5 concentration value of each sub-area at 7:00 on January 19, 2024 can be inferred by the observed PM2.5 concentration value of the area to be predicted at 8:00 on January 19, 2024.

[0104] It can be seen that the implementation of the regional PM2.5 concentration prediction method in the embodiment of the present invention can comprehensively consider the PM2.5 concentration prediction value, meteorological data and the concentration dissipation index data of each sub-region, and combine the PM2.5 concentration prediction value, meteorological data and the concentration dissipation index data of each sub-region to predict the PM2.5 concentration prediction value of each sub-region, thereby improving the accuracy of the PM2.5 concentration prediction results in a small area.

[0105] In an optional embodiment, determining the predicted PM2.5 concentration value of each sub-region at the second time point according to the PM2.5 concentration observation value and the second concentration dissipation parameter corresponding to each sub-region may include:

[0106] Determine whether the time difference between the first time point and the second time point is greater than a preset time difference threshold. If the time difference between the first time point and the second time point is less than or equal to the time difference threshold, then for each sub-area, determine the concentration change of the sub-area according to the time difference between the first time point and the second time point and the second concentration dissipation parameter corresponding to the sub-area; determine the predicted PM2.5 concentration value of the sub-area at the second time point according to the concentration change and the PM2.5 concentration observation value of the sub-area.

[0107] In an embodiment of the present invention, the meteorological data of the area to be predicted, the PM2.5 concentration observation values ​​and the concentration dissipation index data of each sub-area are combined to realize the prediction of PM2.5 concentration, wherein the meteorological data of the area to be predicted and the PM2.5 concentration observation values ​​will change with time. For example, the temperature data of the area to be measured is different in different time periods of a day. Therefore, if the time difference between the first time point and the second time point in the embodiment of the present invention is too large, the accuracy of the PM2.5 concentration prediction will be reduced. In an embodiment of the present invention, optionally, the time difference threshold can be any time length within one hour.

[0108] In the embodiment of the present invention, the steps for determining the predicted value of PM2.5 concentration of any sub-region at the second time point are illustrated as follows:

[0109] The first time point is 8:00 on January 19, 2024, and the second time point is 9:00 on January 19, 2024. The PM2.5 concentration observation value of the test area at 8:00 on January 19, 2024 is 115 , the second concentration dissipation parameter corresponding to sub-area A is -0.8 , indicating that the concentration of PM2.5 decreases by 0.8 per minute , the time difference between the first time point and the second time point is 60 minutes, then the concentration change of this sub-area is -48 The predicted PM2.5 concentration in sub-area A at 9:00 on January 19, 2024 is 67 .

[0110] It can be seen that the implementation of the regional PM2.5 concentration prediction method in the embodiment of the present invention can comprehensively consider the PM2.5 concentration prediction value, meteorological data, and concentration dissipation index data of each sub-region, and combine the relationship between the PM2.5 concentration prediction value, meteorological data, and concentration dissipation index data of each sub-region to predict the PM2.5 concentration prediction value of each sub-region, thereby improving the accuracy of the PM2.5 concentration prediction result of a small area. And the time between the first time point and the second time point can be a smaller time point, which can predict the change of PM2.5 concentration in a shorter time.

[0111] In another optional embodiment, for each sub-region, determining the region feature corresponding to the sub-region according to the geographical environment information of the sub-region may include:

[0112] For each sub-region, the regional characteristics corresponding to the sub-region are determined according to the geographical environment information of the sub-region; the surrounding regional characteristics corresponding to the sub-region are determined according to the geographical environment information of all other sub-regions adjacent to the sub-region;

[0113] Regional characteristics include own regional characteristics and surrounding regional characteristics;

[0114] And, for each sub-region, according to the PM2.5 concentration observation value and the regional characteristics of the sub-region, determining the diffusion parameter corresponding to the sub-region may include:

[0115] For each sub-region, the independent diffusion parameter of the sub-region is determined based on the PM2.5 concentration observation value and the regional characteristics of the sub-region.

[0116] For each sub-region, a diffusion parameter corresponding to the sub-region is determined according to the independent diffusion parameter of the sub-region, the characteristics of the surrounding region and the independent diffusion parameters of all other sub-regions adjacent to the sub-region.

[0117] In this optional embodiment, the regional characteristics corresponding to each sub-region include its own regional characteristics and surrounding regional characteristics. The own regional characteristics are the geographical environment characteristics within the sub-region that affect the diffusion of PM2.5 to the surrounding areas, such as mountains, average building height, forest coverage, etc.; the surrounding regional characteristics are the geographical environment characteristics around the sub-region that affect the diffusion of PM2.5 to the surrounding areas, such as mountains, forests, and buildings within a preset range around the sub-region. In this optional embodiment, the independent diffusion parameter of the sub-region is first determined by the PM2.5 concentration observation value and the regional characteristics of the sub-region itself. It should be noted that the independent diffusion parameter is a theoretical diffusion parameter obtained without considering the PM2.5 diffusion conditions of other sub-regions surrounding the sub-region. For example, for adjacent sub-regions A and B, theoretically, sub-region A will diffuse a certain amount of PM2.5 to sub-region B, but sub-region B will also diffuse a certain amount of PM2.5 to sub-region A at the same time. After the two offset each other, it is necessary to use the independent diffusion parameters of sub-region A and sub-region B to finally determine their respective final diffusion parameters. At the same time, the surrounding regional characteristics of each sub-region will also affect the overall diffusion parameters of the sub-region. Therefore, after determining the independent diffusion parameter of each sub-region, it is also necessary to determine the diffusion parameter corresponding to the sub-region based on the independent diffusion parameter of the sub-region, the surrounding regional characteristics and the independent diffusion parameters of all other sub-regions adjacent to the sub-region.

[0118] It can be seen that this optional embodiment first determines the independent diffusion parameter of the sub-region through the sub-region's own regional characteristics, and then finally determines the diffusion parameter corresponding to the sub-region based on the surrounding regional characteristics of the sub-region and the independent diffusion parameters of other adjacent sub-regions. This can more reasonably determine the diffusion parameters of each sub-region, thereby improving the accuracy of the PM2.5 concentration prediction results in a small area.

[0119] Embodiment 2

[0120] See also Figure 2, Figure 2 : is a flow chart of another method for predicting regional PM2.5 concentration disclosed in an embodiment of the present invention. Figure 2 The described regional PM2.5 concentration prediction method can be applied to a regional PM2.5 concentration prediction device, which can be integrated in a cloud server or a local server, and the embodiments of the present invention are not limited thereto. Figure 2 As shown, the method for predicting PM2.5 concentration in the region may include the following operations:

[0121] 201. Obtain meteorological data and PM2.5 concentration observation values ​​of the area to be predicted at the first time point.

[0122] 202. Obtain some concentration dissipation index data for each sub-area.

[0123] 203. For each sub-area, determine a first concentration dissipation parameter corresponding to the sub-area according to the PM2.5 concentration observation value and all concentration dissipation index data of the sub-area.

[0124] 204. Determine an adjustment coefficient of a first concentration dissipation parameter corresponding to each sub-area according to the meteorological data.

[0125] 305. For each sub-region, determine a second concentration dissipation parameter corresponding to the sub-region according to the first concentration dissipation parameter corresponding to the sub-region and the adjustment coefficient.

[0126] In the embodiment of the present invention, for other descriptions of steps 201-205, please refer to the detailed description of steps 101-105 in the first embodiment, and the embodiment of the present invention will not be repeated here.

[0127] 206. If the time difference between the first time point and the second time point is greater than the time difference threshold, obtain target meteorological information of the area to be predicted between the first time point and the second time point.

[0128] In the embodiment of the present invention, as described above, the meteorological data and PM2.5 concentration observation values ​​of the area to be predicted will change with the change of time. For example, the temperature data of the area to be measured is different in different time periods of a day. Therefore, if the time difference between the first time point and the second time point in the embodiment of the present invention is too large, the accuracy of PM2.5 concentration prediction will be reduced. In order to solve the above problem, when the time difference between the first time point and the second time point is greater than the time difference threshold, it is necessary to obtain the specific data of the meteorological data of the area to be predicted that changes with the change of time, that is, the target meteorological information. The target meteorological information can be the meteorological data of several intermediate time points between the first time point and the second time point, or the comprehensive meteorological change data between the first time point and the second time point can be directly obtained. The target meteorological data is used to provide data support for the calculation of the PM2.5 concentration prediction value at the second time point.

[0129] 207. Determine an intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to each sub-area according to the target meteorological information.

[0130] In the embodiment of the present invention, specifically, the intermediate adjustment coefficient is used to measure the impact of the target meteorological information on the change of PM2.5 concentration in the sub-area.

[0131] 208. For each sub-region, determine a third concentration dissipation parameter corresponding to the sub-region according to the first concentration dissipation parameter and the intermediate adjustment coefficient of the sub-region.

[0132] In the embodiment of the present invention, specifically, the third concentration dissipation parameter is used to measure the impact of the PM2.5 concentration observation value, concentration dissipation index data and target meteorological information on the PM2.5 concentration change in the sub-area.

[0133] 209. Determine a predicted PM2.5 concentration value for each sub-region at a second time point based on the PM2.5 concentration observation value and the third concentration dissipation parameter corresponding to each sub-region.

[0134] It can be seen that the implementation of the regional PM2.5 concentration prediction method in the embodiment of the present invention can, when the time difference between the first time point and the second time point is greater than the time difference threshold, obtain the target meteorological information that changes with time in the meteorological data of the area to be predicted, comprehensively consider the PM2.5 concentration prediction value, the target meteorological information and the concentration dissipation index data of each sub-area, and combine the relationship between the PM2.5 concentration prediction value, the target meteorological information and the concentration dissipation index data of each sub-area to predict the PM2.5 concentration prediction value of each sub-area, so that the PM2.5 concentration prediction result of a small area can be accurately obtained even when the time difference between the first time point and the second time point is large.

[0135] In an optional embodiment, obtaining target meteorological information of a to-be-predicted area between a first time point and a second time point may include:

[0136] Obtain comprehensive meteorological data of the area to be predicted at each intermediate time point;

[0137] The target meteorological information includes the comprehensive meteorological data of the area to be predicted at each intermediate time point;

[0138] And, determining the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to each sub-area according to the target meteorological information may include:

[0139] For each intermediate time point, based on the comprehensive meteorological data at the intermediate time point, the reduction meteorological data of each sub-region at the intermediate time point is determined; based on the comprehensive meteorological data at the intermediate time point and the regional characteristics of the sub-region, the diffusion meteorological data of each sub-region at the intermediate time point is determined;

[0140] For each sub-region and each intermediate time point, the reduction parameter adjustment coefficient of the sub-region at the intermediate time point is determined according to the reduction meteorological data of the sub-region at the intermediate time point, the diffusion parameter adjustment coefficient of the sub-region at the intermediate time point is determined according to the diffusion meteorological data of the sub-region at the intermediate time point, and the adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-region at the intermediate time point is determined according to the reduction parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-region at the intermediate time point;

[0141] For each sub-region, the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-region is determined according to the adjustment coefficients corresponding to the sub-region at all intermediate time points.

[0142] In this optional embodiment, specifically, all intermediate time points are multiple time points between the first time point and the second time point, and the time difference between any two adjacent intermediate time points is less than or equal to the time difference threshold, the time difference between the earliest time point among all intermediate time points and the earliest time point among the first time point and the second time point is less than or equal to the time difference threshold, and the time difference between the latest time point among all intermediate time points and the latest time point among the first time point and the second time point is less than or equal to the time difference threshold.

[0143] It can be seen that when implementing the regional PM2.5 concentration prediction method in this optional embodiment, when the time difference between the first time point and the second time point is large, by setting several intermediate time points between the first time point and the second time point and obtaining comprehensive meteorological data at each intermediate time point, it is possible to take into account the factors that the meteorological data of the area to be predicted and the PM2.5 concentration observation values ​​change over time, thereby being able to accurately obtain the PM2.5 concentration prediction value at the second time point even when the time difference between the first time point and the second time point is large.

[0144] In another optional embodiment, obtaining target meteorological information of the area to be predicted between the first time point and the second time point may include:

[0145] Obtain comprehensive meteorological change data of a region to be predicted between a first time point and a second time point, the comprehensive meteorological change data including multiple types of sub-meteorological change data; for each type of sub-meteorological change data, determine a change period of the sub-meteorological change data of the type, and determine sub-meteorological data of the sub-meteorological change data of the type within each change period according to the sub-meteorological change data of the type and the change period;

[0146] The target meteorological information includes the sub-meteorological data of each type of sub-meteorological change data in each change period;

[0147] And, determining the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to each sub-area according to the target meteorological information may include:

[0148] For each sub-region, the reduction sub-meteorological change data of the sub-region is determined based on the comprehensive meteorological change data; the diffusion sub-meteorological change data of the sub-region is determined based on the comprehensive meteorological change data and the regional characteristics of the sub-region; for each reduction sub-meteorological change data, the reduction parameter adjustment coefficient of the sub-region in each change period is determined based on the sub-meteorological data of the reduction sub-meteorological change data in each change period; for each diffusion sub-meteorological change data, the diffusion parameter adjustment coefficient of the sub-region in each change period is determined based on the sub-meteorological data of the diffusion sub-meteorological change data in each change period;

[0149] For each type of sub-meteorological change data of each sub-region, determine the adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-meteorological change data of the type in the sub-region according to the abatement parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-meteorological change data of the type in the sub-region in all change periods;

[0150] For each sub-region, the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-region is determined according to the adjustment coefficients corresponding to all types of sub-meteorological change data of the sub-region.

[0151] In the embodiment of the present invention, when the time difference between the first time point and the second time point is large, the change of each sub-meteorological change data is periodic. For example, for the sub-meteorological data of temperature, the temperature of the area to be measured is different in different time periods of a day. For example, the average temperature from 0 to 5 o'clock is 10 degrees Celsius, the average temperature from 5 to 8 o'clock is 15 degrees Celsius, the average temperature from 8 to 12 o'clock is 20 degrees Celsius, the average temperature from 12 to 14 o'clock is 25 degrees Celsius, the average temperature from 14 to 18 o'clock is 15 degrees Celsius, and the average temperature from 18 to 18 o'clock is 18 degrees Celsius. The average temperature from 0 o'clock to 0 o'clock on the next day is 11 degrees Celsius; for another example, for the sub-meteorological change data of wind direction: 0 o'clock to 5 o'clock is northeast wind, 5 o'clock to 12 o'clock is east wind, 12 o'clock to 18 o'clock is northeast wind, and 18 o'clock to 0 o'clock on the next day is north wind; for the sub-meteorological change data of wind speed, 0 o'clock to 5 o'clock, 5 o'clock to 12 o'clock, 12 o'clock to 18 o'clock, and 18 o'clock to 0 o'clock on the next day are the change periods of this sub-meteorological change data, and the sub-meteorological data of this sub-meteorological change data in each change period are: northeast wind, east wind, northeast wind, and north wind.

[0152] It can be seen that by implementing the regional PM2.5 concentration prediction method in this optional embodiment, when the time difference between the first time point and the second time point is large, by determining the change period of each sub-meteorological change data between the first time point and the second time point, and obtaining the sub-meteorological data within each change period, it is possible to take into account the factors that the meteorological data and PM2.5 concentration observation values ​​of the area to be predicted change over time, thereby being able to accurately obtain the PM2.5 concentration prediction value at the second time point even when the time difference between the first time point and the second time point is large.

[0153] Embodiment 3

[0154] See also Figure 3 , Figure 3 Schematic diagram of the structure of a regional PM2.5 concentration prediction device disclosed in an embodiment of the present invention. Figure 3 As shown, the PM2.5 concentration prediction device in the area may include:

[0155] The first acquisition module 301 is used to obtain meteorological data and PM2.5 concentration observation values ​​of the area to be predicted at a first time point, where the area to be predicted includes multiple sub-areas;

[0156] The second acquisition module 302 is used to acquire a number of concentration dissipation index data for each sub-area, where the concentration dissipation index data is environmental information data that affects the PM2.5 concentration change in the sub-area;

[0157] The determination module 303 is used to determine, for each sub-region, a first concentration dissipation parameter corresponding to the sub-region according to the PM2.5 concentration observation value and all concentration dissipation index data of the sub-region; and, according to the meteorological data, determine an adjustment coefficient of the first concentration dissipation parameter corresponding to each sub-region; and, for each sub-region, determine a second concentration dissipation parameter corresponding to the sub-region according to the first concentration dissipation parameter corresponding to the sub-region and the adjustment coefficient;

[0158] The prediction module 304 is used to determine the predicted PM2.5 concentration value of each sub-area at a second time point based on the PM2.5 concentration observation value and the second concentration dissipation parameter corresponding to each sub-area, wherein the second time point is any time point at which the predicted PM2.5 concentration value needs to be obtained.

[0159] It can be seen that implementation Figure 3 The described device can comprehensively consider the PM2.5 concentration prediction value, meteorological data and the concentration dissipation index data of each sub-region, and combine the relationship among the PM2.5 concentration prediction value, meteorological data and the concentration dissipation index data of each sub-region to predict the PM2.5 concentration prediction value of each sub-region, thereby improving the accuracy of the PM2.5 concentration prediction results in a small area.

[0160] In an optional embodiment, the specific manner in which the determination module 303 determines the first concentration dissipation parameter corresponding to each sub-region according to the PM2.5 concentration observation value and all concentration dissipation index data of the sub-region may include:

[0161] For each sub-region, based on the PM2.5 concentration observation value and each concentration dissipation index data of the sub-region, determine the reduction factor of each concentration dissipation index data, and the reduction factor is used to represent the influence of the corresponding concentration dissipation index data on the change of PM2.5 concentration; extract the characteristic value of each concentration dissipation index data, and determine the weight value of each concentration dissipation index data based on the characteristic values ​​of all concentration dissipation index data; determine the reduction parameter corresponding to the sub-region based on the reduction factor and weight value of all concentration dissipation index data;

[0162] For each sub-region, the regional characteristics corresponding to the sub-region are determined according to the geographical environment information of the sub-region, and the regional characteristics are the geographical environment characteristics in the sub-region that affect the diffusion of PM2.5 to the surrounding areas; the diffusion parameters corresponding to the sub-region are determined according to the PM2.5 concentration observation value and the regional characteristics of the sub-region;

[0163] For each sub-region, a first concentration dissipation parameter corresponding to the sub-region is determined according to the attenuation parameter and the diffusion parameter corresponding to the sub-region.

[0164] It can be seen that when implementing the device described in this optional embodiment, two ways of PM2.5 concentration dissipation are considered when determining the first concentration dissipation parameter: elimination through absorption, attachment, decomposition, etc., and diffusion to other areas through natural diffusion, air flow, etc.; thereby making the method of determining the first concentration dissipation vector more reasonable and improving the accuracy of PM2.5 concentration prediction results in a small area.

[0165] In another optional embodiment, the specific manner in which the determination module 303 determines, for each sub-region, the regional feature corresponding to the sub-region according to the geographical environment information of the sub-region may include:

[0166] For each sub-region, the regional characteristics corresponding to the sub-region are determined according to the geographical environment information of the sub-region; the surrounding regional characteristics corresponding to the sub-region are determined according to the geographical environment information of all other sub-regions adjacent to the sub-region;

[0167] Regional characteristics include own regional characteristics and surrounding regional characteristics;

[0168] Furthermore, the specific manner in which the determination module 303 determines the diffusion parameter corresponding to each sub-region according to the PM2.5 concentration observation value and the regional characteristics of the sub-region may include:

[0169] For each sub-region, the independent diffusion parameter of the sub-region is determined based on the PM2.5 concentration observation value and the regional characteristics of the sub-region.

[0170] For each sub-region, a diffusion parameter corresponding to the sub-region is determined according to the independent diffusion parameter of the sub-region, the characteristics of the surrounding region and the independent diffusion parameters of all other sub-regions adjacent to the sub-region.

[0171] It can be seen that the device described in the optional embodiment first determines the independent diffusion parameters of the sub-region through the sub-region's own regional characteristics, and then finally determines the diffusion parameters corresponding to the sub-region based on the surrounding regional characteristics of the sub-region and the independent diffusion parameters of other sub-regions adjacent to it. The diffusion parameters of each sub-region can be determined more reasonably, thereby improving the accuracy of the PM2.5 concentration prediction results in a small area.

[0172] In yet another optional embodiment, the specific manner in which the determination module 303 determines the adjustment coefficient of the first concentration dissipation parameter corresponding to each sub-region according to the meteorological data may include:

[0173] For each sub-region, based on the meteorological data, the abatement meteorological data of the sub-region is determined, and the abatement meteorological data is the meteorological data that affects the change of PM2.5 concentration in the sub-region; based on the meteorological data and the regional characteristics of the sub-region, the diffusion meteorological data of the sub-region is determined, and the diffusion meteorological data is the meteorological data that affects the diffusion of PM2.5 in the sub-region to the surrounding areas;

[0174] For each sub-region, the abatement parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-region are respectively determined according to the abatement meteorological data and the diffusion meteorological data of the sub-region, and the adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-region is determined according to the abatement parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-region.

[0175] It can be seen that the device described in the optional embodiment first determines the independent diffusion parameters of the sub-region through the sub-region's own regional characteristics, and then finally determines the diffusion parameters corresponding to the sub-region based on the surrounding regional characteristics of the sub-region and the independent diffusion parameters of other sub-regions adjacent to it. The diffusion parameters of each sub-region can be determined more reasonably, thereby improving the accuracy of the PM2.5 concentration prediction results in a small area.

[0176] In another optional embodiment, the prediction module 304 determines the predicted value of PM2.5 concentration of each sub-region at the second time point according to the PM2.5 concentration observation value and the second concentration dissipation parameter corresponding to each sub-region in a specific manner that may include:

[0177] Determine whether the time difference between the first time point and the second time point is greater than a preset time difference threshold. If the time difference between the first time point and the second time point is less than or equal to the time difference threshold, then for each sub-area, determine the concentration change of the sub-area according to the time difference between the first time point and the second time point and the second concentration dissipation parameter corresponding to the sub-area; determine the predicted PM2.5 concentration value of the sub-area at the second time point according to the concentration change and the PM2.5 concentration observation value of the sub-area.

[0178] It can be seen that the device described in the implementation of this optional embodiment can comprehensively consider the PM2.5 concentration prediction value, meteorological data, and concentration dissipation index data of each sub-region, and combine the relationship between the PM2.5 concentration prediction value, meteorological data, and concentration dissipation index data of each sub-region to predict the PM2.5 concentration prediction value of each sub-region, thereby improving the accuracy of the PM2.5 concentration prediction result of a small area. And the time between the first time point and the second time point can be a smaller time point, which can predict the change of PM2.5 concentration in a shorter time.

[0179] In another optional embodiment, the prediction module 304 determines the predicted value of PM2.5 concentration of each sub-region at the second time point according to the PM2.5 concentration observation value and the second concentration dissipation parameter corresponding to each sub-region in a specific manner, which may also include:

[0180] If the time difference between the first time point and the second time point is greater than the time difference threshold, obtaining target meteorological information of the area to be predicted between the first time point and the second time point;

[0181] Determining an intermediate adjustment coefficient of a first concentration dissipation parameter corresponding to each sub-area according to target meteorological information;

[0182] For each sub-region, determining a third concentration dissipation parameter corresponding to the sub-region according to the first concentration dissipation parameter and the intermediate adjustment coefficient of the sub-region;

[0183] According to the PM2.5 concentration observation value and the third concentration dissipation parameter corresponding to each sub-area, the PM2.5 concentration prediction value of each sub-area at the second time point is determined.

[0184] It can be seen that the device described in the implementation of this optional embodiment can, when the time difference between the first time point and the second time point is greater than the time difference threshold, obtain target meteorological information that changes with time in the meteorological data of the area to be predicted, comprehensively consider the PM2.5 concentration prediction value, target meteorological information, and the concentration dissipation index data of each sub-area, and combine the relationship between the PM2.5 concentration prediction value, the target meteorological information, and the concentration dissipation index data of each sub-area to predict the PM2.5 concentration prediction value of each sub-area, so that the PM2.5 concentration prediction result of a small area can be accurately obtained even when the time difference between the first time point and the second time point is large.

[0185] In yet another optional embodiment, the specific manner in which the prediction module 304 obtains the target meteorological information of the area to be predicted between the first time point and the second time point may include:

[0186] Obtain comprehensive meteorological data of the area to be predicted at each intermediate time point, wherein all the intermediate time points are multiple time points between the first time point and the second time point, and the time difference between any two adjacent intermediate time points is less than or equal to the time difference threshold, the time difference between the earliest time point among all the intermediate time points and the earliest time point among the first time point and the second time point is less than or equal to the time difference threshold, and the time difference between the latest time point among all the intermediate time points and the latest time point among the first time point and the second time point is less than or equal to the time difference threshold;

[0187] The target meteorological information includes the comprehensive meteorological data of the area to be predicted at each intermediate time point;

[0188] Furthermore, the specific manner in which the prediction module 304 determines the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to each sub-region according to the target meteorological information may include:

[0189] For each intermediate time point, based on the comprehensive meteorological data at the intermediate time point, the reduction meteorological data of each sub-region at the intermediate time point is determined; based on the comprehensive meteorological data at the intermediate time point and the regional characteristics of the sub-region, the diffusion meteorological data of each sub-region at the intermediate time point is determined;

[0190] For each sub-region and each intermediate time point, the reduction parameter adjustment coefficient of the sub-region at the intermediate time point is determined according to the reduction meteorological data of the sub-region at the intermediate time point, the diffusion parameter adjustment coefficient of the sub-region at the intermediate time point is determined according to the diffusion meteorological data of the sub-region at the intermediate time point, and the adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-region at the intermediate time point is determined according to the reduction parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-region at the intermediate time point;

[0191] For each sub-region, the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-region is determined according to the adjustment coefficients corresponding to the sub-region at all intermediate time points.

[0192] It can be seen that when the device described in this optional embodiment is implemented, when the time difference between the first time point and the second time point is large, by setting several intermediate time points between the first time point and the second time point and obtaining comprehensive meteorological data for each intermediate time point, it is possible to take into account the factors that the meteorological data of the area to be predicted and the PM2.5 concentration observation value change over time, thereby being able to accurately obtain the PM2.5 concentration prediction value at the second time point even when the time difference between the first time point and the second time point is large.

[0193] In yet another optional embodiment, the specific manner in which the prediction module 304 obtains the target meteorological information of the area to be predicted between the first time point and the second time point may include:

[0194] Obtain comprehensive meteorological change data of a region to be predicted between a first time point and a second time point, the comprehensive meteorological change data including multiple types of sub-meteorological change data; for each type of sub-meteorological change data, determine a change period of the sub-meteorological change data of the type, and determine sub-meteorological data of the sub-meteorological change data of the type within each change period according to the sub-meteorological change data of the type and the change period;

[0195] The target meteorological information includes the sub-meteorological data of each type of sub-meteorological change data in each change period;

[0196] Furthermore, the specific manner in which the prediction module 304 determines the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to each sub-region according to the target meteorological information may include:

[0197] For each sub-region, the reduction sub-meteorological change data of the sub-region is determined based on the comprehensive meteorological change data; the diffusion sub-meteorological change data of the sub-region is determined based on the comprehensive meteorological change data and the regional characteristics of the sub-region; for each reduction sub-meteorological change data, the reduction parameter adjustment coefficient of the sub-region in each change period is determined based on the sub-meteorological data of the reduction sub-meteorological change data in each change period; for each diffusion sub-meteorological change data, the diffusion parameter adjustment coefficient of the sub-region in each change period is determined based on the sub-meteorological data of the diffusion sub-meteorological change data in each change period;

[0198] For each type of sub-meteorological change data of each sub-region, determine the adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-meteorological change data of the type in the sub-region according to the abatement parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-meteorological change data of the type in the sub-region in all change periods;

[0199] For each sub-region, the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-region is determined according to the adjustment coefficients corresponding to all types of sub-meteorological change data of the sub-region.

[0200] It can be seen that when the device described in this optional embodiment is implemented, when the time difference between the first time point and the second time point is large, by determining the change period of each sub-meteorological change data between the first time point and the second time point, and obtaining the sub-meteorological data within each change period, it is possible to take into account the factors that the meteorological data of the area to be predicted and the PM2.5 concentration observation values ​​change over time, thereby being able to accurately obtain the PM2.5 concentration prediction value at the second time point even when the time difference between the first time point and the second time point is large.

[0201] Embodiment 4

[0202] See also Figure 4 , Figure 4 FIG. 1 is a schematic diagram of the structure of another regional PM2.5 concentration prediction device disclosed in an embodiment of the present invention. Figure 4 As shown, the PM2.5 concentration prediction device in the area may include:

[0203] A memory 401 storing executable program codes;

[0204] a processor 402 coupled to the memory 401;

[0205] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the regional PM2.5 concentration prediction method described in the first embodiment of the present invention or the second embodiment of the present invention.

[0206] Embodiment 5

[0207] An embodiment of the present invention discloses a computer storable medium, which stores computer instructions. When the computer instructions are called, they are used to execute the steps in the regional PM2.5 concentration prediction method described in Embodiment 1 or Embodiment 2 of the present invention.

[0208] Embodiment 6

[0209] An embodiment of the present invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute the steps in the regional PM2.5 concentration prediction method described in Example 1 or Example 2.

[0210] The device embodiments described above are only illustrative, wherein the modules described as separate components may or may not be physically separated, and the components displayed as modules may or may not be physical modules, i.e., they may be located in one place, or they may be distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative work.

[0211] Through the specific description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on such an understanding, the above technical solution can be essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0212] Finally, it should be noted that the method and device for predicting regional PM2.5 concentration disclosed in the embodiments of the present invention disclose only the preferred embodiments of the present invention, which are only used to illustrate the technical scheme of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical schemes described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical schemes from the spirit and scope of the technical schemes of the embodiments of the present invention.

Claims

1. A method for predicting regional PM2.5 concentration, characterized in that: The method comprises: Acquire meteorological data and PM2.5 concentration observation values ​​of a region to be predicted at a first time point, wherein the region to be predicted includes multiple sub-regions; Acquire a number of concentration dissipation index data for each of the sub-areas, where the concentration dissipation index data is environmental information data that affects the change in PM2.5 concentration in the sub-area; For each of the sub-areas, determining a first concentration dissipation parameter corresponding to the sub-area according to the PM2.5 concentration observation value and all the concentration dissipation index data of the sub-area; Determining, according to the meteorological data, an adjustment coefficient of the first concentration dissipation parameter corresponding to each of the sub-areas; For each of the sub-regions, determining a second concentration dissipation parameter corresponding to the sub-region according to the first concentration dissipation parameter corresponding to the sub-region and the adjustment coefficient; Determine, according to the PM2.5 concentration observation value and the second concentration dissipation parameter corresponding to each of the sub-areas, a PM2.5 concentration prediction value for each of the sub-areas at a second time point, wherein the second time point is any time point at which a PM2.5 concentration prediction value needs to be obtained; Wherein, for each of the sub-areas, determining the first concentration dissipation parameter corresponding to the sub-area according to the PM2.5 concentration observation value and all the concentration dissipation index data of the sub-area includes: For each of the sub-areas, according to the PM2.5 concentration observation value and each of the concentration dissipation index data of the sub-area, determine the reduction factor of each of the concentration dissipation index data, the reduction factor is used to represent the influence of the corresponding concentration dissipation index data on the change of PM2.5 concentration; extract the characteristic value of each of the concentration dissipation index data, and determine the weight value of each of the concentration dissipation index data according to the characteristic values ​​of all the concentration dissipation index data; determine the reduction parameter corresponding to the sub-area according to the reduction factors and the weight values ​​of all the concentration dissipation index data; For each of the sub-regions, determining the regional characteristics corresponding to the sub-region according to the geographical environment information of the sub-region, wherein the regional characteristics are the geographical environment characteristics in the sub-region that affect the diffusion of PM2.5 to the surrounding areas; determining the diffusion parameter corresponding to the sub-region according to the PM2.5 concentration observation value and the regional characteristics of the sub-region; For each of the sub-regions, a first concentration dissipation parameter corresponding to the sub-region is determined according to the attenuation parameter and the diffusion parameter corresponding to the sub-region.

2. The method for predicting regional PM2.5 concentration according to claim 1, characterized in that: For each of the sub-regions, determining the regional features corresponding to the sub-region according to the geographical environment information of the sub-region includes: For each of the sub-regions, determine the corresponding regional features of the sub-region according to the geographical environment information of the sub-region; determine the corresponding surrounding regional features of the sub-region according to the geographical environment information of all other sub-regions adjacent to the sub-region; The regional features include the own regional features and the surrounding regional features; And, for each of the sub-regions, determining the diffusion parameter corresponding to the sub-region according to the PM2.5 concentration observation value and the regional characteristics of the sub-region, including: For each of the sub-regions, determining an independent diffusion parameter of the sub-region according to the PM2.5 concentration observation value and the regional characteristics of the sub-region itself; For each of the sub-regions, a diffusion parameter corresponding to the sub-region is determined according to the independent diffusion parameter of the sub-region, the surrounding region feature and the independent diffusion parameters of all other sub-regions adjacent to the sub-region.

3. The method for predicting regional PM2.5 concentration according to claim 1, characterized in that: The step of determining the adjustment coefficient of the first concentration dissipation parameter corresponding to each of the sub-areas according to the meteorological data includes: For each of the sub-regions, based on the meteorological data, the abatement meteorological data of the sub-region is determined, and the abatement meteorological data is the meteorological data that affects the change of PM2.5 concentration in the sub-region; based on the meteorological data and the regional characteristics of the sub-region, the diffusion meteorological data of the sub-region is determined, and the diffusion meteorological data is the meteorological data that affects the diffusion of PM2.5 in the sub-region to the surrounding areas; For each of the sub-areas, the abatement parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-area are respectively determined according to the abatement meteorological data and the diffusion meteorological data of the sub-area, and the adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-area is determined according to the abatement parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-area.

4. The method for predicting regional PM2.5 concentration according to claim 1, characterized in that: Determining the predicted PM2.5 concentration value of each sub-region at the second time point according to the PM2.5 concentration observation value and the second concentration dissipation parameter corresponding to each sub-region includes: Determine whether the time difference between the first time point and the second time point is greater than a preset time difference threshold. If the time difference between the first time point and the second time point is less than or equal to the time difference threshold, then for each of the sub-areas, determine the concentration change of the sub-area according to the time difference between the first time point and the second time point and the second concentration dissipation parameter corresponding to the sub-area; determine the predicted PM2.5 concentration value of the sub-area at the second time point according to the concentration change of the sub-area and the PM2.5 concentration observation value.

5. The method for predicting regional PM2.5 concentration according to claim 4, characterized in that: The step of determining the predicted PM2.5 concentration value of each sub-region at a second time point according to the PM2.5 concentration observation value and the second concentration dissipation parameter corresponding to each sub-region further includes: If the time difference between the first time point and the second time point is greater than the time difference threshold, obtaining target meteorological information of the area to be predicted between the first time point and the second time point; Determining, according to the target meteorological information, an intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to each of the sub-areas; For each of the sub-regions, determining a third concentration dissipation parameter corresponding to the sub-region according to the first concentration dissipation parameter and the intermediate adjustment coefficient of the sub-region; The predicted PM2.5 concentration value of each sub-area at the second time point is determined based on the PM2.5 concentration observation value and the third concentration dissipation parameter corresponding to each sub-area.

6. The method for predicting regional PM2.5 concentration according to claim 5, characterized in that: The acquiring target meteorological information of the to-be-predicted area between the first time point and the second time point includes: Obtaining comprehensive meteorological data of the to-be-predicted area at each intermediate time point, wherein all the intermediate time points are multiple time points between the first time point and the second time point, and the time difference between any two adjacent intermediate time points is less than or equal to the time difference threshold, the time difference between the earliest time point among all the intermediate time points and the earliest time point among the first time point and the second time point is less than or equal to the time difference threshold, and the time difference between the latest time point among all the intermediate time points and the latest time point among the first time point and the second time point is less than or equal to the time difference threshold; The target meteorological information includes the comprehensive meteorological data of the to-be-predicted area at each intermediate time point; And, determining the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to each of the sub-areas according to the target meteorological information includes: For each of the intermediate time points, based on the comprehensive meteorological data at the intermediate time point, the reduction meteorological data of each of the sub-regions at the intermediate time point is determined; based on the comprehensive meteorological data at the intermediate time point and the regional characteristics of the sub-region, the diffusion meteorological data of each of the sub-regions at the intermediate time point is determined; For each of the sub-regions and each of the intermediate time points, the reduction parameter adjustment coefficient of the sub-region at the intermediate time point is determined according to the reduction meteorological data of the sub-region at the intermediate time point, the diffusion parameter adjustment coefficient of the sub-region at the intermediate time point is determined according to the diffusion meteorological data of the sub-region at the intermediate time point, and the adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-region at the intermediate time point is determined according to the reduction parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-region at the intermediate time point; For each of the sub-regions, the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-region is determined according to the adjustment coefficients corresponding to the sub-region at all the intermediate time points.

7. The method for predicting regional PM2.5 concentration according to claim 5, characterized in that: The acquiring target meteorological information of the to-be-predicted area between the first time point and the second time point includes: Obtaining comprehensive meteorological change data of the area to be predicted between the first time point and the second time point, the comprehensive meteorological change data including multiple types of sub-meteorological change data; for each type of the sub-meteorological change data, determining a change period of the sub-meteorological change data of that type, and determining sub-meteorological data of the sub-meteorological change data of that type within each change period according to the sub-meteorological change data of that type and the change period; The target meteorological information includes the sub-meteorological data of each type of the sub-meteorological change data in each change period; And, determining the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to each of the sub-areas according to the target meteorological information includes: For each of the sub-regions, the abatement sub-meteorological change data of the sub-region is determined based on the comprehensive meteorological change data; the diffusion sub-meteorological change data of the sub-region is determined based on the comprehensive meteorological change data and the regional characteristics of the sub-region; for each of the abatement sub-meteorological change data, the abatement parameter adjustment coefficient of the sub-region in each of the change periods is determined based on the sub-meteorological data of the abatement sub-meteorological change data in each of the change periods; for each of the diffusion sub-meteorological change data, the diffusion parameter adjustment coefficient of the sub-region in each of the change periods is determined based on the sub-meteorological data of the diffusion sub-meteorological change data in each of the change periods; For each type of the sub-meteorological change data of each of the sub-regions, determining the adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-meteorological change data of the type in the sub-region according to the abatement parameter adjustment coefficient and the diffusion parameter adjustment coefficient of the sub-meteorological change data of the type in the sub-region in all the change periods; For each of the sub-regions, the intermediate adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-region is determined according to the adjustment coefficients corresponding to all types of sub-meteorological change data of the sub-region.

8. A regional PM2.5 concentration prediction device, characterized in that: The device comprises: A first acquisition module is used to acquire meteorological data and PM2.5 concentration observation values ​​of a region to be predicted at a first time point, wherein the region to be predicted includes multiple sub-regions; A second acquisition module is used to acquire a number of concentration dissipation index data for each of the sub-areas, wherein the concentration dissipation index data is environmental information data that affects the PM2.5 concentration change in the sub-area; A determination module, for determining, for each of the sub-areas, a first concentration dissipation parameter corresponding to the sub-area according to the PM2.5 concentration observation value and all the concentration dissipation index data of the sub-area; and, for each of the sub-areas, determining an adjustment coefficient of the first concentration dissipation parameter corresponding to the sub-area according to the meteorological data; and, for each of the sub-areas, determining a second concentration dissipation parameter corresponding to the sub-area according to the first concentration dissipation parameter corresponding to the sub-area and the adjustment coefficient; A prediction module, used to determine a predicted PM2.5 concentration value for each of the sub-areas at a second time point according to the PM2.5 concentration observation value and the second concentration dissipation parameter corresponding to each of the sub-areas, wherein the second time point is any time point at which a predicted PM2.5 concentration value needs to be obtained; The specific manner in which the determination module determines, for each of the sub-areas, the first concentration dissipation parameter corresponding to the sub-area according to the PM2.5 concentration observation value and all the concentration dissipation index data of the sub-area includes: For each of the sub-areas, according to the PM2.5 concentration observation value and each of the concentration dissipation index data of the sub-area, determine the reduction factor of each of the concentration dissipation index data, the reduction factor is used to represent the influence of the corresponding concentration dissipation index data on the change of PM2.5 concentration; extract the characteristic value of each of the concentration dissipation index data, and determine the weight value of each of the concentration dissipation index data according to the characteristic values ​​of all the concentration dissipation index data; determine the reduction parameter corresponding to the sub-area according to the reduction factors and the weight values ​​of all the concentration dissipation index data; For each of the sub-regions, determining the regional characteristics corresponding to the sub-region according to the geographical environment information of the sub-region, wherein the regional characteristics are the geographical environment characteristics in the sub-region that affect the diffusion of PM2.5 to the surrounding areas; determining the diffusion parameter corresponding to the sub-region according to the PM2.5 concentration observation value and the regional characteristics of the sub-region; For each of the sub-regions, a first concentration dissipation parameter corresponding to the sub-region is determined according to the attenuation parameter and the diffusion parameter corresponding to the sub-region.

9. A regional PM2.5 concentration prediction device, characterized in that: The device includes: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the regional PM2.5 concentration prediction method as described in any one of claims 1-7.

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