Ozone pollution prediction method and device and electronic equipment

Abstract

The invention provides an ozone pollution prediction method and apparatus, and an electronic device. The method comprises the steps of obtaining initial prediction preparation data of a target area under each time index in at least one time index; determining target prediction preparation data under each time index from the initial prediction preparation data of the target area under each time index; constructing a pollution prediction regression analysis model under each time index based on the target prediction preparation data under each time index; based on the pollution prediction regression analysis model under each time index, a target reference data set is determined, the target reference data set comprises target reference data under each time index, and the target reference data set supports an ozone pollution prediction result used for predicting the target area within the target prediction time. According to the embodiment of the invention, ozone pollution prediction can be conveniently carried out, and prediction cost and prediction time consumption can be reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of air quality prediction, and in particular to an ozone pollution prediction method, device and electronic equipment. Background Art

[0002] At present, air quality prediction (i.e., prediction of pollutants) has received widespread attention; among them, ozone (O 3 ) is an important trace gas in the earth's atmosphere, which has a significant impact on atmospheric chemical cycles, crops, human health and life. However, related technologies usually rely on air quality numerical models combined with atmospheric pollution source emission inventories to achieve ozone pollution prediction, that is, the air quality (including ozone concentration) within a period of time is usually predicted through air quality numerical models, and air quality numerical models rely on high-performance computers to work. The speed and amount of calculations depend on the performance of the computer, resulting in high costs and long time consumption. Based on this, there is currently no good solution for how to conveniently predict ozone pollution to reduce prediction costs and reduce prediction time. Summary of the invention

[0003] In view of this, the embodiments of the present invention provide an ozone pollution prediction method, device and electronic device to solve the problems of high cost and long time consumption of related technologies; that is, the embodiments of the present invention can conveniently perform ozone pollution prediction without the need to implement ozone pollution prediction through air quality numerical models, which can effectively reduce prediction costs and reduce prediction time.

[0004] According to one aspect of an embodiment of the present invention, a method for predicting ozone pollution is provided, the method comprising: Acquire initial forecast preparation data for each time index in at least one time index of the target area, wherein one initial forecast preparation data includes an indicator data set for each pollution impact index in multiple pollution impact indexes under the corresponding time index, and one indicator data set for a pollution impact index includes multiple indicator data for the corresponding pollution impact index within a historical time range corresponding to the corresponding time index; Determining target prediction preparation data under each time index from the initial prediction preparation data of the target area under each time index, wherein one target prediction preparation data includes an indicator data set of each benchmark pollution impact indicator in at least one benchmark pollution impact indicator under the corresponding time index; Based on the target prediction preparation data under each time index, a pollution prediction regression analysis model under each time index is constructed; Based on the pollution prediction regression analysis model under the various time indicators, a target benchmark data set is determined, wherein the target benchmark data set includes the target benchmark data under the various time indicators, and the target benchmark data set supports the prediction of ozone pollution results in the target area within the target prediction time.

[0005] According to another aspect of an embodiment of the present invention, there is provided an ozone pollution prediction device, the device comprising: an acquisition unit, used to acquire initial forecast preparation data for a target area under each time index in at least one time index, wherein one initial forecast preparation data includes an indicator data set for each pollution impact index among multiple pollution impact indexes under the corresponding time index, and one indicator data set for a pollution impact index includes multiple indicator data for the corresponding pollution impact index within a historical time range corresponding to the corresponding time index; A processing unit, configured to determine target prediction preparation data under each time index from the initial prediction preparation data of the target area under each time index, wherein one target prediction preparation data includes an indicator data set of each benchmark pollution impact indicator in at least one benchmark pollution impact indicator under the corresponding time index; The processing unit is further used to construct a pollution prediction regression analysis model under each time index based on the target prediction preparation data under each time index; The processing unit is also used to determine a target benchmark data set based on the pollution prediction regression analysis model under the various time indicators, wherein the target benchmark data set includes the target benchmark data under the various time indicators, and the target benchmark data set supports predicting ozone pollution prediction results in the target area within the target prediction time.

[0006] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising a processor and a memory storing a program, wherein the program comprises instructions, which, when executed by the processor, enable the processor to perform the above-mentioned method.

[0007] The embodiment of the present invention can obtain the initial prediction preparation data of the target area under each time index in at least one time index, and then determine the target prediction preparation data under each time index from the initial prediction preparation data of the target area under each time index, one initial prediction preparation data includes the index data set of each pollution impact index among multiple pollution impact indicators under the corresponding time index, one index data set of pollution impact indicators includes multiple index data of the corresponding pollution impact index within the historical time range corresponding to the corresponding time index, and one target prediction preparation data includes the index data set of each benchmark pollution impact index in at least one benchmark pollution impact indicator under the corresponding time index. Based on this, the pollution prediction regression analysis model under each time index can be constructed based on the target prediction preparation data under each time index respectively; and based on the pollution prediction regression analysis model under each time index, the target benchmark data set is determined, the target benchmark data set includes the target benchmark data under each time index, and the target benchmark data set supports the prediction result of ozone pollution in the target area within the target prediction time. It can be seen that the embodiments of the present invention can conveniently predict ozone pollution without the need to implement ozone pollution prediction through air quality numerical models, which can effectively reduce prediction costs and reduce prediction time; that is, the ozone pollution prediction method provided by the embodiments of the present invention has the characteristics of simple modeling, low computational cost and high timeliness. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Further details, features and advantages of the invention are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 A schematic flow chart of an ozone pollution prediction method according to an exemplary embodiment of the present invention is shown; Figure 2 A schematic flow chart of another ozone pollution prediction method according to an exemplary embodiment of the present invention is shown; Figure 3 A schematic diagram of a nomogram according to an exemplary embodiment of the present invention is shown; Figure 4 A schematic flow chart of another ozone pollution prediction method according to an exemplary embodiment of the present invention is shown; Figure 5 A schematic block diagram of an ozone pollution prediction device according to an exemplary embodiment of the present invention is shown; Figure 6 A block diagram of an exemplary electronic device that can be used to implement an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0009] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein, which are instead provided for a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0010] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0011] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0012] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0013] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0014] It should be noted that the execution subject of the ozone pollution prediction method provided in the embodiment of the present invention may be one or more electronic devices, and the present invention does not limit this; wherein, the electronic device may be a terminal (i.e., a client) or a server, then when the execution subject includes multiple electronic devices, and the multiple electronic devices include at least one terminal and at least one server, the ozone pollution prediction method provided in the embodiment of the present invention may be jointly executed by the terminal and the server. Accordingly, the terminals mentioned here may include but are not limited to: smart phones, tablet computers, laptop computers, desktop computers, etc.; the servers mentioned here may be independent physical servers, or server clusters or distributed systems composed of multiple physical servers, or cloud servers that provide cloud services, cloud databases, cloud computing (cloud computing), cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and basic cloud computing services such as big data and artificial intelligence platforms, etc.

[0015] Based on the above description, an embodiment of the present invention proposes an ozone pollution prediction method, which can be executed by the electronic device (terminal or server) mentioned above; or, the ozone pollution prediction method can be executed by the terminal and the server together. For the sake of convenience, the following description will be given by taking the electronic device executing the ozone pollution prediction method as an example; Figure 1 As shown, the ozone pollution prediction method may include the following steps S101-S104: S101, obtaining initial forecast preparation data for a target area under each time indicator in at least one time indicator, wherein one initial forecast preparation data includes an indicator data set for each pollution impact indicator in multiple pollution impact indicators under a corresponding time indicator, and one indicator data set for a pollution impact indicator includes multiple indicator data for a corresponding pollution impact indicator within a historical time range corresponding to the corresponding time indicator.

[0016] Optionally, the target area may be any area, which is not limited in the embodiment of the present invention; for example, the target area may be a province or a city.

[0017] Optionally, a time indicator may be used to indicate a year, in which case the number of time indicators in at least one time indicator may be 1, i.e., at least one time indicator may include the time indicator "year"; or, a time indicator may be used to indicate a quarter, in which case the number of time indicators in at least one time indicator may be 4, i.e., at least one time indicator may include 4 quarters; or, a time indicator may be used to indicate a month, in which case the number of time indicators in at least one time indicator may be 12, i.e., at least one time indicator may include 12 months, and so on; this is not limited to the embodiments of the present invention.

[0018] Optionally, the historical time range corresponding to any time indicator may include the time range within the specified historical time range; optionally, the specified historical time range may be set according to experience or according to actual needs, and the embodiment of the present invention does not limit this. Optionally, when any time indicator is a year, the historical time range corresponding to any time indicator may include the specified months of each year in the specified historical time range (such as April-October). For example, assuming that the specified historical time range is 2021-2023, the historical time range corresponding to any time indicator may include the specified months of 2021, the specified months of 2022, and the specified months of 2023; optionally, the specified months may be set according to experience or according to actual needs, and the embodiment of the present invention does not limit this; it should be noted that when the specified months are April-October, it is possible to more accurately predict whether ozone pollution occurs, because ozone pollution in various regions mainly occurs in April-October each year. Optionally, when any time indicator is any month, the historical time range corresponding to any time indicator may include any month of each year in the specified historical time range. For example, when the specified historical time range is 2021-2023 and any month is May, the historical time range corresponding to any time indicator may include May 2021, May 2022 and May 2023; when any time indicator is any quarter, the historical time range corresponding to any time indicator may include any quarter of each year in the specified historical time range. For example, when the specified historical time range is 2021-2023 and any quarter is the first quarter, the historical time range corresponding to any time indicator may include the first quarter of 2021, the first quarter of 2022 and the first quarter of 2023, and so on.

[0019] Optionally, one indicator data may be an indicator data within a time sub-range (such as one day), that is, one indicator data may be used to indicate the corresponding data of one day. Optionally, multiple pollution impact indicators may include but are not limited to: temperature, humidity, wind speed, wind direction, precipitation, air pressure, nitrogen dioxide (NO 2 ) and volatile organic compounds (VOCs), etc.; the embodiment of the present invention does not limit this. Optionally, multiple pollution impact indicators under different time indicators can be the same or different, and the embodiment of the present invention does not limit this.

[0020] Among them, nitrogen dioxide and volatile organic compounds are precursors of ozone, that is, the nitrogen dioxide concentration and volatile organic compound concentration at night corresponding to any day can provide sufficient precursor conditions for the formation of ozone during the daytime of any day; for example, a higher NO at night (such as from 20:00 yesterday to 7:00 today, that is, the corresponding night of any day can be from 20:00 yesterday to 7:00 today) 2 The concentration can be daytime O 3 Providing sufficient precursor conditions for the generation of O 3 Generate, to some extent, help to increase daytime O 3 concentration; Based on this, in order to predict the day's O 3 Pollution situation, the embodiment of the present invention can use the NO corresponding to the night 2 And / or the average VOCs concentration as the corresponding indicator data.

[0021] Based on this, an initial forecast preparation data may include historical meteorological monitoring data and historical precursor monitoring data, that is, the initial forecast preparation data under any time index may include historical meteorological monitoring data and historical precursor monitoring data under any time index, the historical meteorological monitoring data under any time index may include an indicator data set of each meteorological indicator in at least one meteorological indicator among multiple pollution impact indicators under any time index, and the historical precursor monitoring data under any time index may include an indicator data set of each precursor indicator in at least one precursor indicator among multiple pollution impact indicators under any time index; optionally, at least one meteorological indicator may include but is not limited to at least one of the following: temperature, humidity, wind speed, wind direction, precipitation, and air pressure, etc., and at least one precursor indicator may include but is not limited to at least one of the following: nitrogen dioxide and volatile organic compounds, etc., and the embodiments of the present invention are not limited to this. Optionally, a pollution impact index may correspond to an impact interval index, and an impact interval index may be used to indicate the time range of the target prediction time under the corresponding impact interval index, that is, the index data of different pollution impact indexes may be determined based on the time range indicated by the impact interval index corresponding to different pollution impact indexes; for example, assuming that the impact interval index corresponding to any pollution impact index is from 20:00 yesterday to 7:00 today, then when the target prediction time is October 6, 2024, the time range of the target prediction time under the impact interval index corresponding to any pollution impact index may be from 20:00 on October 5, 2024 to 7:00 on October 6, 2024; or, assuming that the impact interval index corresponding to any pollution impact index is the day (i.e., 0-24:00 today), then when the target prediction time is October 6, 2024, the time range of the target prediction time under the impact interval index corresponding to any pollution impact index may be October 6, 2024, and so on. Optionally, the impact interval index corresponding to a pollution impact index may be set according to experience or according to actual needs, and the embodiment of the present invention does not limit this.

[0022] Optionally, when a pollution impact index is a meteorological index, the impact interval index corresponding to the pollution impact index may be the current day, and at this time, one index data of the pollution impact index may be the daily monitoring data of a certain day within the corresponding historical time range, that is, the index data set of a meteorological index may include all daily monitoring data of the corresponding meteorological index within the historical time range corresponding to the corresponding time index (i.e., the monitoring data of the corresponding meteorological index within each time sub-range of the historical time range corresponding to the corresponding time index). Exemplarily, when any meteorological index is temperature, one index data of any meteorological index may be the highest monitored temperature of a day; when any meteorological index is humidity, one index data of any meteorological index may be the average monitored humidity of a day; when any meteorological index is wind speed, one index data of any meteorological index may be the average monitored wind speed of a day; when any meteorological index is wind direction, one index data of any meteorological index may be the dominant wind direction of a day; when any meteorological index is precipitation, one index data of any meteorological index may be the total precipitation of a day; when any meteorological index is air pressure, one index data of any meteorological index may be the average monitored air pressure of a day, and so on.

[0023] Optionally, when a pollution impact index is a precursor index, the impact interval index corresponding to the pollution impact index may be from 20:00 yesterday to 7:00 today. Exemplarily, when any precursor index is nitrogen dioxide, an index data of any precursor index may be the average nitrogen dioxide concentration from 20:00 yesterday to 7:00 today on any day; when any precursor index is volatile organic compounds, an index data of any precursor index may be the average volatile organic compound concentration from 20:00 yesterday to 7:00 today on any day. Based on this, the index data of any precursor index on any day may be the average concentration of any precursor from 20:00 yesterday to 7:00 today on any day.

[0024] Optionally, for any time indicator of the at least one time indicator, the method for acquiring the initial forecast preparation data of the target area under any time indicator may include but is not limited to the following: The first acquisition method: the electronic device's own storage space can store indicator data of various pollution impact indicators in the target area and within a specified historical time range. In this case, the electronic device can obtain the indicator data set of each pollution impact indicator under any time indicator from its own storage space to obtain the initial prediction preparation data of the target area under any time indicator.

[0025] The second acquisition method: the electronic device can obtain the initial data download link under any time index, and use the data downloaded based on the initial data download link under any time index as the initial forecast preparation data of the target area under any time index, etc. Optionally, the initial data download link under any time index can also include an initial meteorological monitoring data download link and an initial precursor monitoring data download link. Based on this, the initial meteorological monitoring data download link can be used to download the historical meteorological monitoring data of the target area under any time index, and the initial precursor monitoring data download link can be used to download the historical precursor monitoring data of the target area under any time index, so as to achieve the acquisition of the initial forecast preparation data of the target area under any time index, etc.

[0026] S102, determining target prediction preparation data under each time index from the initial prediction preparation data of the target area under each time index, wherein one target prediction preparation data includes an indicator data set of each benchmark pollution impact indicator in at least one benchmark pollution impact indicator under the corresponding time index.

[0027] In one embodiment, for any time index under at least one time index, the electronic device can determine the influence relationship indication information of each pollution impact index under any time index based on the initial prediction preparation data of the target area under any time index, and one influence relationship indication information can be used to indicate the influence relationship between the corresponding pollution impact index and ozone; then, based on the influence relationship indication information of each pollution impact index under any time index, at least one baseline pollution impact index can be selected from multiple pollution impact indicators under any time index, and the selected at least one baseline pollution impact indicator can be used as at least one baseline pollution impact indicator under any time index; further, from the initial prediction preparation data of the target area under any time index, an indicator data set of each baseline pollution impact index under any time index can be determined to achieve the determination of the target prediction preparation data under any time index.

[0028] In a specific implementation, the influence relationship indication information of a pollution impact indicator may be the significance test result of the corresponding pollution impact indicator. Based on this, the electronic device may use the initial prediction preparation data of the target area under any time indicator and the set of ozone pollution occurrence probabilities of the target area under any time indicator (including each ozone pollution occurrence probability within the historical time range corresponding to any time indicator (i.e., the ozone pollution occurrence probability within each time sub-range), such as y indicating whether ozone pollution occurs, when y=1, it indicates that ozone pollution occurs, and when y=0, it indicates that ozone pollution does not occur), to construct an initial pollution prediction regression analysis model under any time indicator, so as to determine the significance test results of each pollution impact indicator under any time indicator, and use the significance test results of each pollution impact indicator under any time indicator as the influence relationship indication information of the corresponding pollution impact indicator, so as to achieve the determination of the influence relationship indication information of each pollution impact indicator under any time indicator; in this case, the influence relationship between the corresponding pollution impact indicator and ozone indicated by an influence relationship indication information may be the significance of the influence of the corresponding pollution impact indicator on ozone. Then, accordingly, the pollution impact indicators whose significance test results under any time index are less than the preset significance level can be used as the benchmark pollution impact indicators, so as to select at least one benchmark pollution impact indicator from multiple pollution impact indicators under any time index, and the pollution impact indicators whose significance test results are less than the preset significance level refer to the pollution impact indicators that have a significant impact on ozone; wherein, when the significance test result is less than the preset significance level, it means that the corresponding pollution impact indicator will have a significant impact on ozone, and when the significance test result is greater than or equal to the preset significance level, it means that the corresponding pollution impact indicator will not have a significant impact on ozone. Optionally, the preset significance level can be set according to experience or according to actual needs, and the embodiment of the present invention is not limited to this; illustratively, the preset significance level can be 0.05.

[0029] Optionally, when determining the significance test results of each pollution impact indicator under any time index, the Box-Tidwell method (a method for testing whether there is a linear relationship between the logit transformed (logarithmic unit transformed) values ​​of a continuous independent variable and a dependent variable) can be used to test each pollution impact indicator under any time index, and the likelihood ratio test can also be performed on each pollution impact indicator under any time index, etc.; the embodiment of the present invention is not limited to this. Optionally, the electronic device can also perform a significance test on the initial pollution prediction regression analysis model under any time index to obtain the significance test result of the initial pollution prediction regression analysis model under any time index, so as to indicate whether the initial pollution prediction regression analysis model under any time index is effective based on the significance test result of the initial pollution prediction regression analysis model under any time index; optionally, when performing a significance test on the initial pollution prediction regression analysis model under any time index, a likelihood ratio test can be performed on the initial pollution prediction regression analysis model under any time index (at this time, the significance test result of the initial pollution prediction regression analysis model under any time index is a likelihood ratio test result, and when the likelihood ratio test result is less than the preset significance level, it indicates that the model is effective. The model is valid, otherwise the model is not valid), and the initial pollution prediction regression analysis model under any time index can also be subjected to the Hosmer-Lemesho goodness-of-fit test (Hosmer-Lemesho, HL test, which can be used to evaluate whether the model makes full use of the existing information, maximizes the fit of the model, and explains the variation of the model) (at this time, the significance test result of the initial pollution prediction regression analysis model under any time index is a Hosmer-Lemesho goodness-of-fit test result. When the Hosmer-Lemesho goodness-of-fit test result is greater than the preset significance level, it indicates that the model goodness-of-fit effect is good. When the Hosmer-Lemesho goodness-of-fit test result is less than or equal to the preset significance level, it indicates that the model construction effect is poor), etc.; the embodiment of the present invention is not limited to this.

[0030] Optionally, the electronic device can construct an initial pollution prediction regression analysis model under any time index through R software (RMS software package, a complete data processing, calculation and mapping software system) or SPSS (Statistical Product and Service Solutions, statistical analysis software); optionally, the significance test results of each pollution impact indicator under any time index can be determined through SPSS, etc., as well as the significance test results of the initial pollution prediction regression analysis model under any time index, etc.

[0031] It should be understood that the electronic device can also obtain a set of ozone pollution occurrence probabilities for the target area under any time index (including the ozone pollution occurrence probability of the target area in each time sub-range in the historical time range corresponding to any time index, and any time sub-range can be any day), and the ozone pollution occurrence probability in a time sub-range can be used to indicate whether ozone pollution occurs in the corresponding time sub-range. Optionally, the electronic device stores a set of ozone pollution occurrence probabilities for the target area under any time index in its own storage space, and then obtains the set of ozone pollution occurrence probabilities for the target area under any time index from its own storage space; or, obtains an ozone data download link corresponding to any time index, and downloads the set of ozone pollution occurrence probabilities for the target area under any time index based on the ozone data download link corresponding to any time index, and so on; the embodiments of the present invention are not limited to this. It should be understood that the indicator data group within any time sub-range of the historical time range corresponding to any time indicator for the target area (including the indicator data of each pollution impact indicator within any time sub-range under any time indicator or including the indicator data of each baseline pollution impact indicator within any time sub-range under any time indicator) may correspond to the probability of occurrence of ozone pollution in the target area within any time sub-range of the historical time range corresponding to any time indicator.

[0032] In another specific implementation, the influence relationship indication information of a pollution impact indicator may be the correlation analysis result of the corresponding pollution impact indicator. Based on this, the electronic device may respectively perform correlation analysis on the indicator data set of each pollution impact indicator under any time indicator and the ozone concentration set of the target area under any time indicator (including multiple ozone concentrations within the historical time range corresponding to any time indicator, and one ozone concentration may be the mean of ozone observations within a time sub-range), and obtain the correlation analysis results of each pollution impact indicator under any time indicator, so as to use the correlation analysis results of each pollution impact indicator under any time indicator as the influence relationship indication information of the corresponding pollution impact indicator. Then, accordingly, the pollution impact indicators whose correlation analysis results under any time indicator are greater than the preset correlation threshold value may be used as the benchmark pollution impact indicators; or, the top M pollution impact indicators with the largest correlation analysis results under any time indicator may be used as the benchmark pollution impact indicators, so as to select at least one benchmark pollution impact indicator from multiple pollution impact indicators under any time indicator, M is a positive integer, and so on; the embodiment of the present invention does not limit this. Optionally, the preset correlation threshold value may be set according to experience or according to actual needs, and the embodiment of the present invention does not limit this.

[0033] Optionally, the electronic device may also obtain a set of ozone concentrations of the target area under any time index (including the ozone concentration of the target area in each time sub-range in the historical time range corresponding to any time index, i.e., the mean ozone concentration). Optionally, the electronic device's own storage space stores a set of ozone concentrations of the target area under any time index, and the set of ozone concentrations of the target area under any time index may be obtained from its own storage space; or, an ozone concentration download link corresponding to any time index may be obtained, and the set of ozone concentrations of the target area under any time index may be downloaded based on the ozone concentration download link corresponding to any time index, and so on; the embodiments of the present invention are not limited to this. Among them, the indicator data group of the target area in any time sub-range in the historical time range corresponding to any time index may correspond to the ozone concentration of the target area in any time sub-range in the historical time range corresponding to any time index.

[0034] In another implementation, each pollution impact indicator under any time index can be used as a baseline pollution impact indicator under any time index, so that at least one baseline pollution impact indicator under any time index includes each pollution impact indicator under any time index; that is, the initial prediction preparation data under any time index can be used as the target prediction preparation data under any time index, and so on.

[0035] S103, constructing a pollution prediction regression analysis model under each time index based on the target prediction preparation data under each time index.

[0036] In an embodiment of the present invention, for any time index under at least one time index, the electronic device may use the target prediction preparation data under any time index and the set of ozone pollution occurrence probabilities under any time index (an ozone pollution occurrence probability can be used to indicate whether ozone pollution occurs on a certain day, and an ozone pollution occurrence probability can be expressed as y) to construct a pollution prediction regression analysis model under any time index. Among them, a pollution prediction regression analysis model can be a Logistic regression model (logistic regression model); then correspondingly, the probability of ozone pollution occurring is p, and the probability of ozone pollution not occurring is 1-p, then the pollution prediction regression analysis model under any time index can be as shown in Formula 1.1: Formula 1.1 Among them, x 1 ,x 2 ,…,x n represents the n baseline pollution impact indicators (i.e., independent variables) on the result y, n is the number of baseline pollution impact indicators under any time index, α is a constant term, β 1 ,β 2 ,…β nis the regression coefficient, and β 1 ,β 2 ,…β n It can be expressed as β; wherein α and β can be obtained by maximum likelihood estimation, that is, the electronic device can use the target prediction preparation data under any time index and the ozone pollution occurrence probability set under any time index to construct a log-likelihood function, so as to estimate α and β using the log-likelihood function, thereby realizing the construction of a pollution prediction regression analysis model under any time index.

[0037] Then, correspondingly, Formula 1.2 can be used to calculate the probability p of ozone pollution, that is, the pollution prediction regression analysis model under any time index can also be shown as Formula 1.2: Formula 1.2 The probability of ozone pollution is a variable represented by the explanatory variable x. n It should be understood that the daily ozone observations can be labeled as y 1 ,y 2 ,…,y k (1 if ozone pollution occurs, 0 if ozone pollution does not occur), k is the number of days of historical ozone observations, that is, the number of time sub-ranges in the historical time range corresponding to any time indicator, and the indicator data group x under any time indicator i Can be x 1 ,x 2 ,…,x k The daily monitoring data of the i-th day in (may include the indicator data of each benchmark pollution impact indicator under any time indicator within the i-th time sub-range in the historical time range corresponding to any time indicator). Accordingly, formula 1.3 can be used to express the above log-likelihood function: Formula 1.3 Among them, y i The label indicating whether ozone pollution occurs on the i-th day can be the probability of ozone pollution occurring on the i-th day. It can be seen that the electronic device can estimate α and β using the maximum likelihood estimation method through formula 1.3. 1 ,β 2 ,…,β n , thereby realizing the construction of a pollution prediction regression analysis model under any time index.

[0038] S104, based on the pollution prediction regression analysis model under each time index, determine the target benchmark data set, the target benchmark data set includes the target benchmark data under each time index, and the target benchmark data set supports the prediction of ozone pollution results in the target area within the target prediction time.

[0039] In an embodiment of the present invention, for any time indicator among at least one time indicator, the electronic device can determine the target baseline data under any time indicator based on the pollution prediction regression analysis model under any time indicator. That is to say, the regression coefficients of each benchmark pollution impact indicator in the pollution prediction regression analysis model under any time indicator can be used to determine the target baseline data under any time indicator.

[0040] The embodiment of the present invention can obtain the initial prediction preparation data of the target area under each time index in at least one time index, and then determine the target prediction preparation data under each time index from the initial prediction preparation data of the target area under each time index, one initial prediction preparation data includes the index data set of each pollution impact index among multiple pollution impact indicators under the corresponding time index, one index data set of pollution impact indicators includes multiple index data of the corresponding pollution impact index within the historical time range corresponding to the corresponding time index, and one target prediction preparation data includes the index data set of each benchmark pollution impact index in at least one benchmark pollution impact indicator under the corresponding time index. Based on this, the pollution prediction regression analysis model under each time index can be constructed based on the target prediction preparation data under each time index respectively; and based on the pollution prediction regression analysis model under each time index, the target benchmark data set is determined, the target benchmark data set includes the target benchmark data under each time index, and the target benchmark data set supports the prediction result of ozone pollution in the target area within the target prediction time. It can be seen that the embodiments of the present invention can conveniently predict ozone pollution without the need to implement ozone pollution prediction through air quality numerical models, which can effectively reduce prediction costs and reduce prediction time; that is, the ozone pollution prediction method provided by the embodiments of the present invention has the characteristics of simple modeling, low computational cost and high timeliness.

[0041] Based on the above description, the embodiment of the present invention also proposes a more specific ozone pollution prediction method. Accordingly, the ozone pollution prediction method can be executed by the electronic device (terminal or server) mentioned above; or, the ozone pollution prediction method can be executed by the terminal and the server together. For the convenience of explanation, the following description will be based on the example of an electronic device executing the ozone pollution prediction method; please refer to Figure 2 The ozone pollution prediction method may include the following steps S201-S207: S201, obtaining initial forecast preparation data for the target area under each time indicator in at least one time indicator, one initial forecast preparation data includes an indicator data set for each pollution impact indicator in multiple pollution impact indicators under the corresponding time indicator, and one indicator data set for a pollution impact indicator includes multiple indicator data of the corresponding pollution impact indicator within a historical time range corresponding to the corresponding time indicator.

[0042] S202, determining target prediction preparation data under each time index from the initial prediction preparation data of the target area under each time index, wherein one target prediction preparation data includes an indicator data set of each benchmark pollution impact indicator in at least one benchmark pollution impact indicator under the corresponding time index.

[0043] S203, respectively building a pollution prediction regression analysis model under each time index based on the target prediction preparation data under each time index.

[0044] S204: traverse each time index in at least one time index, and use the currently traversed time index as the current time index.

[0045] S205, determining model indication data under the current time index from the pollution prediction regression analysis model under the current time index, the model indication data including the regression coefficients of each benchmark pollution impact index under the current time index.

[0046] S206: Determine target benchmark data under the current time index based on the model indication data under the current time index.

[0047] In one embodiment, the electronic device may determine the correspondence between the benchmark indicator range and the scoring standard range of each benchmark pollution impact indicator under the current time indicator based on the regression coefficient of each benchmark pollution impact indicator under the current time indicator, and the correspondence between the benchmark indicator range of a benchmark pollution impact indicator and the scoring standard range is used to indicate the scoring value corresponding to any indicator value of the corresponding benchmark pollution impact indicator within the scoring standard range; and based on the correspondence between the benchmark indicator range and the scoring standard range of each benchmark pollution impact indicator under the current time indicator, determine the target benchmark data under the current time indicator. Optionally, the scoring standard range can be set according to experience or according to actual needs, and the embodiment of the present invention is not limited to this; illustratively, the scoring standard range can be 0-100. Accordingly, a benchmark indicator range can be set according to experience or according to actual needs, or can be obtained based on the statistics of the indicator data set of the corresponding benchmark pollution impact indicator under the corresponding time indicator, and so on; the embodiment of the present invention is not limited to this.

[0048] Optionally, when determining the correspondence between the benchmark indicator range and the scoring standard range of each benchmark pollution impact indicator under the current time indicator based on the regression coefficient of each benchmark pollution impact indicator under the current time indicator, for any benchmark pollution impact indicator under the current time indicator, the electronic device may calculate the coefficient conversion result of any benchmark pollution impact indicator under the current time indicator based on the regression coefficient and the benchmark indicator range of any benchmark pollution impact indicator under the current time indicator; illustratively, formula 2.1 may be used to calculate the coefficient conversion result of any benchmark pollution impact indicator under the current time indicator: Formula 2.1 Among them, N m represents the coefficient conversion result of any benchmark pollution impact index under the current time index, m∈[1,n], m is a positive integer, that is, any benchmark pollution impact index under the current time index can be the mth benchmark pollution impact index under the current time index, and the number of benchmark pollution impact indicators under the current time index can be n; correspondingly, β m It can represent the regression coefficient of any benchmark pollution impact index under the current time index, x m(min) ~x m(max) It can represent the benchmark index range of any benchmark pollution impact index under the current time index (i.e. x m(min) It can represent the minimum value in the benchmark index range of any benchmark pollution impact index under the current time index, x m(max) It can represent the maximum value in the benchmark indicator range of any benchmark pollution impact indicator under the current time indicator).

[0049] Based on this, after obtaining the coefficient conversion results of each benchmark pollution impact index under the current time index, the maximum coefficient conversion result (N max), and use the maximum coefficient conversion result and the coefficient conversion result of any baseline pollution impact indicator under the current time indicator to calculate the estimated maximum score of any baseline pollution impact indicator under the current time indicator, so as to determine the correspondence between the baseline indicator range and the scoring standard range of any baseline pollution impact indicator under the current time indicator based on the estimated maximum score of any baseline pollution impact indicator under the current time indicator. It should be understood that the baseline pollution impact indicator corresponding to the maximum coefficient conversion result has the greatest impact on the ozone pollution prediction result, and the scoring standard range can be used as the estimated score range of the baseline pollution impact indicator corresponding to the maximum coefficient conversion result to determine the correspondence between the baseline indicator range of the baseline pollution impact indicator corresponding to the maximum coefficient conversion result and the scoring standard range, such as the maximum value in the baseline indicator range of the baseline pollution impact indicator corresponding to the maximum coefficient conversion result can correspond to the maximum value in the scoring standard range. Among them, the correspondence between the benchmark indicator range and the scoring standard range of a benchmark pollution impact indicator can be used to indicate the correspondence between the benchmark indicator range of the corresponding benchmark pollution impact indicator and the scoring estimation range of the corresponding benchmark pollution impact indicator. The scoring estimation range of a benchmark pollution impact indicator is the scoring range within the scoring standard range; that is, based on the maximum value of the score estimation of any benchmark pollution impact indicator under the current time indicator, the scoring estimation range of any benchmark pollution impact indicator under the current time indicator can be determined to achieve the determination of the correspondence between the benchmark indicator range and the scoring standard range of any benchmark pollution impact indicator under the current time indicator.

[0050] Optionally, the electronic device may use Formula 2.2 to calculate the estimated maximum score of any baseline pollution impact indicator under the current time indicator: Formula 2.2 Among them, Point m It can represent the maximum value of the score estimate of any benchmark pollution impact indicator under the current time indicator. Optionally, when determining the correspondence between the benchmark indicator range and the scoring standard range of any benchmark pollution impact indicator under the current time indicator based on the maximum value of the score estimate of any benchmark pollution impact indicator under the current time indicator, if the regression coefficient of any benchmark pollution impact indicator under the current time indicator is greater than 0, then the score estimate range of any benchmark pollution impact indicator under the current time indicator (i.e., 0 to the maximum value of the score estimate) is determined to be the benchmark indicator range of any benchmark pollution impact indicator under the current time indicator (i.e., x m(min) ~x m(max)) score, so that the maximum value in the benchmark indicator range of any benchmark pollution impact indicator under the current time indicator corresponds to the estimated maximum score value of any benchmark pollution impact indicator under the current time indicator, that is, the maximum value in the benchmark indicator range of any benchmark pollution impact indicator under the current time indicator within the scoring standard range is the estimated maximum score value of any benchmark pollution impact indicator under the current time indicator; if the regression coefficient of any benchmark pollution impact indicator under the current time indicator is less than 0, then the estimated score range of any benchmark pollution impact indicator under the current time indicator is determined to be the inverse benchmark indicator range of any benchmark pollution impact indicator under the current time indicator (i.e. x m(max) ~x m(min) ) score, so that the minimum value in the benchmark indicator range of any benchmark pollution impact indicator under the current time indicator corresponds to the estimated maximum score of any benchmark pollution impact indicator under the current time indicator, that is, the score value of the minimum value in the benchmark indicator range of any benchmark pollution impact indicator under the current time indicator within the scoring standard range is the estimated maximum score of any benchmark pollution impact indicator under the current time indicator, so as to realize the corresponding relationship between the benchmark indicator range and the scoring standard range of any benchmark pollution impact indicator under the current time indicator, thereby realizing the determination of the score value corresponding to any indicator value within the benchmark indicator range of any benchmark pollution impact indicator under the current time indicator within the scoring standard range. Based on this, the score of each unit independent variable can be equal to the total score of each variable (such as the estimated maximum score of any benchmark pollution impact indicator under the current time indicator) / (x m(max) ~x m(min) ).

[0051] In a specific implementation, when determining the target benchmark data under the current time index based on the correspondence between the benchmark index range of each benchmark pollution impact index under the current time index and the scoring standard range, the electronic device can also determine the correspondence between the total index score range (the range of the sum of the scores of each variable) under the current time index and the ozone pollution probability range (which can also be called the correspondence between the total score of the benchmark pollution impact index under the current time index and the ozone pollution probability, which can be used to indicate the correspondence between the total score of the benchmark pollution impact index under the current time index and the ozone pollution probability), and add the correspondence between the benchmark index range of each benchmark pollution impact index under the current time index and the scoring standard range, as well as the correspondence between the total index score range under the current time index and the ozone pollution probability range to the target benchmark data under the current time index, so as to determine the target benchmark data under the current time index.

[0052] Optionally, when determining the correspondence between the total range of indicator scores under the current time indicator and the range of ozone pollution probability, the electronic device may call the pollution prediction regression analysis model under the current time indicator, and based on the target prediction preparation data under the current time indicator (including the indicator data of each benchmark pollution impact indicator under the current time indicator in each time sub-range in the historical time range corresponding to the current time indicator), respectively determine the ozone pollution probability in each time sub-range in the historical time range corresponding to the current time indicator (i.e., the ozone pollution probability for each day in the historical time range corresponding to the current time indicator); and based on the indicator data of each benchmark pollution impact indicator under the current time indicator in any time sub-range in the historical time range corresponding to the current time range, and the correspondence between the benchmark indicator range and the scoring standard range of each benchmark pollution impact indicator under the current time indicator, determine the scoring value of each benchmark pollution impact indicator under the current time indicator in any time sub-range, and perform weighted summation (such as summation operation, etc.) on the scoring value of each benchmark pollution impact indicator under the current time indicator in any time sub-range, and obtain the total score value in any time sub-range within the historical time range corresponding to the current time indicator, thereby obtaining the total score value in each time sub-range in the historical time range corresponding to the current time indicator; optionally, the weight of each benchmark pollution impact indicator under the current time indicator can be set according to experience or according to actual needs, and the embodiment of the present invention is not limited to this. Furthermore, the correspondence between the total score range of the indicator under the current time indicator and the range of the ozone pollution probability can be determined through the ozone pollution probability and the total score in each time sub-range in the historical time range corresponding to the current time indicator; illustratively, the total score and the ozone pollution probability in each time sub-range in the historical time range corresponding to the current time indicator can be sorted in order from small to large according to the total score in each time sub-range in the historical time range corresponding to the current time indicator, so as to obtain the total score sorting result and the ozone pollution probability sorting result under the current time indicator, so as to determine the correspondence between the total score range of the indicator under the current time indicator and the range of the ozone pollution probability based on the total score sorting result and the ozone pollution probability sorting result under the current time indicator; it should be understood that since the higher the total score, the greater the ozone pollution probability, the ozone pollution probability after sorting is also arranged from small to large.Optionally, the correspondence between the total range of indicator scores under the current time indicator and the range of ozone pollution probability can be represented by the ranking results of the total scores under the current time indicator and the ranking results of the ozone pollution probability; or, based on the ranking results of the total scores under the current time indicator and the ranking results of the ozone pollution probability, the total scores corresponding to each of the multiple specified ozone pollution probabilities can be determined, so that the total scores corresponding to each specified ozone pollution probability can be used to represent the correspondence between the total range of indicator scores under the current time indicator and the range of ozone pollution probability, that is, the correspondence between the total range of indicator scores under the current time indicator and the range of ozone pollution probability can be represented by the total scores corresponding to each specified ozone pollution probability, and so on; the embodiment of the present invention does not limit this. Optionally, multiple specified ozone pollution probabilities can be set according to experience or according to actual needs, and the embodiment of the present invention does not limit this; illustratively, multiple specified ozone pollution probabilities can include 0.1, 0.2, ..., 0.9, and so on.

[0053] In another specific implementation, a target benchmark data may be a nomogram (i.e., a nomogram, also known as an ozone pollution probability nomogram). In this case, the electronic device may use the correspondence between the benchmark indicator range and the scoring standard range of each benchmark pollution impact indicator under the current time indicator, and the correspondence between the total indicator score range under the current time indicator and the ozone pollution probability range, to draw the nomogram under the current time indicator, so that the nomogram under the current time indicator can be used to indicate the correspondence between the benchmark indicator range and the scoring standard range of each benchmark pollution impact indicator under the current time indicator, and the correspondence between the total indicator score range under the current time indicator and the ozone pollution probability range. Among them, the nomogram uses scaled line segments to draw complex regression equations on the same plane according to a certain proportion, which can be used to express the relationship between the variables in the pollution prediction regression analysis model, so that the results of the pollution prediction regression analysis model are readable. For example, assuming that at least one baseline pollution impact indicator under the current time index includes nitrogen dioxide, wind direction, wind speed, temperature and humidity, and the estimated maximum scores of nitrogen dioxide, wind direction, wind speed, temperature and humidity are 34 points, 21 points, 100 points, 72 points and 36 points respectively, then the nomogram under the current time index can be as follows: Figure 3 shown.

[0054] In another specific implementation, the correspondence between the benchmark indicator range and the scoring standard range of each benchmark pollution impact indicator under the current time indicator can be added to the target benchmark data under the current time indicator to determine the target benchmark data under the current time indicator, and so on.

[0055] In another embodiment, the electronic device may also add the regression coefficients of each benchmark pollution impact index under the current time index and the constant term in the pollution prediction regression analysis model under the current time index to the target benchmark data under the current time index, so that the target benchmark data under the current time index can be used to indicate the pollution prediction regression analysis model under the current time index, and so on.

[0056] S207, after traversing each time index in at least one time index, a target benchmark data set is obtained, the target benchmark data set includes target benchmark data under each time index, and the target benchmark data set supports the prediction of ozone pollution results in the target area within the target prediction time.

[0057] In an embodiment of the present invention, the electronic device may also obtain target prediction data, the target prediction data including impact indication data of each target pollution impact indicator in at least one target pollution impact indicator of the target area at the target prediction time, the at least one target pollution impact indicator being at least one benchmark pollution impact indicator under the time indicator to which the target prediction time belongs, and the impact indication data of any target pollution impact indicator being data of any target pollution impact indicator within a specified time range corresponding to the target prediction time, the specified time range being the time range of the target prediction time under the impact interval indicator corresponding to any target pollution impact indicator; and based on the impact indication data of each target pollution impact indicator, respectively determining the index data of each target pollution impact indicator, thereby predicting the target ozone pollution indication data of the target area at the target prediction time based on the target benchmark data set and the index data of each target pollution impact indicator, the target ozone pollution indication data being determined based on the target benchmark data under the time indicator to which the target prediction time belongs in the target benchmark data set, so as to determine the ozone pollution prediction result of the target area at the target prediction time by using the target ozone pollution indication data.

[0058] Optionally, the target prediction data may be stored in the storage space of the electronic device itself. In this case, the target prediction data may be obtained from the storage space itself; or, a target prediction data download link may be obtained, and the target prediction data may be downloaded through the target prediction data download link to obtain the target prediction data, etc. Optionally, the target prediction time may be any time range, such as a certain day in the future; it should be understood that when a time indicator is a month, the time indicator to which the target prediction time belongs is the time indicator corresponding to the month to which the target prediction time belongs (such as when the target prediction time is October 6, 2024, the time indicator to which the target prediction time belongs is the time indicator "October"); when a time indicator is a "year", the time indicator to which the target prediction time belongs is the year. Exemplarily, assuming that the target prediction time is October 6, 2024, then when the impact interval indicator corresponding to any target pollution impact indicator is from 20:00 yesterday to 7:00 today, the time range of the target prediction time under the impact interval indicator corresponding to any target pollution impact indicator is from 20:00 on October 5, 2024 to 7:00 on October 6, 2024, that is, the specified time range at this time is from 20:00 on October 5, 2024 to 7:00 on October 6, 2024, and the impact indication data of any target pollution impact indicator may be the data of any target pollution impact indicator within 20:00 on October 5, 2024 to 7:00 on October 6, 2024; when the impact interval indicator corresponding to any target pollution impact indicator is today, the time range of the target prediction time under the impact interval indicator corresponding to any target pollution impact indicator is October 6, 2024, that is, the specified time range at this time is October 6, 2024, and so on.

[0059] Optionally, for any target pollution impact indicator of at least one target pollution impact indicator, if the impact indication data of any target pollution impact indicator includes multiple indicator values ​​(such as hourly data) of any target pollution impact indicator in a specified time range corresponding to the target prediction time, the indicator data of any target pollution impact indicator (that is, the indicator data of any target pollution impact indicator at the target prediction time) can be determined based on the multiple indicator values ​​of any target pollution impact indicator in the specified time range corresponding to the target prediction time. For example, when any target pollution impact indicator is temperature, the highest temperature in the impact indication data of any target pollution impact indicator data can be used as the indicator data of any target pollution impact indicator; or, if the impact indication data of any target pollution impact indicator is the indicator data of any target pollution impact indicator within the specified time range corresponding to the target prediction time (such as mean data or maximum temperature, etc.), the impact indication data of any target pollution impact indicator is used as the indicator data of any target pollution impact indicator. Exemplarily, when the target prediction time is October 6, 2024, and the impact interval index corresponding to any target pollution impact index is from 20:00 yesterday to 7:00 today, the index data of any target pollution impact index may be the average data (such as the average nitrogen dioxide concentration) from 20:00 on October 5, 2024 to 7:00 on October 6, 2024, etc. Optionally, when any target pollution impact index is a meteorological index, the impact indicator data of any target pollution impact index may be obtained through meteorological forecast (that is, it may be meteorological forecast data); when any target pollution impact index is a precursor indicator, the impact indicator data of any target pollution impact index may be obtained through observation (that is, it may be precursor observation data, which may also be called precursor monitoring data, and because the precursor is observed at night, it may also be called nighttime precursor monitoring data).

[0060] In one embodiment, a target benchmark data supports the correspondence between the benchmark indicator range and the scoring standard range for indicating each benchmark pollution impact indicator under the corresponding time indicator, such as a target benchmark data includes the correspondence between the benchmark indicator range and the scoring standard range for each benchmark pollution impact indicator under the corresponding time indicator. Based on this, when predicting the target ozone pollution indication data of the target area at the target prediction time based on the target benchmark data set and the indicator data of each target pollution impact indicator, the electronic device can determine the target benchmark data under the time indicator to which the target prediction time belongs from the target benchmark data set, and determine the pollution impact indicator score value corresponding to the indicator data of each target pollution impact indicator based on the target benchmark data under the time indicator to which the target prediction time belongs, that is, based on the correspondence between the benchmark indicator range and the scoring standard range for each benchmark pollution impact indicator indicated by the target benchmark data under the time indicator to which the target prediction time belongs, the score value corresponding to the indicator data of each target pollution impact indicator within the scoring standard range can be determined to achieve the determination of the pollution impact indicator score value corresponding to the indicator data of each target pollution impact indicator (the score value corresponding to the indicator data of a target pollution impact indicator within the scoring standard range can also be referred to as the pollution impact indicator score value corresponding to the indicator data of the corresponding target pollution impact indicator). Based on this, the pollution impact index score values ​​corresponding to the index data of each target pollution impact indicator can be weightedly summed (such as a summation operation) to obtain the target score value of the target area at the target prediction time; and based on the target score value, the target ozone pollution indication data of the target area at the target prediction time can be determined.

[0061] Optionally, the electronic device may use the target score value as the target ozone pollution indication data. In this case, if the target ozone pollution indication data is used to indicate that the ozone pollution score value (i.e., the target score value) of the target area within the target prediction time is greater than the preset ozone pollution score threshold (i.e., the target ozone pollution indication data is greater than the preset ozone pollution score threshold), then the ozone pollution risk indication information is used as the ozone pollution prediction result of the target area within the target prediction time; if the target ozone pollution indication data is used to indicate that the ozone pollution score value of the target area within the target prediction time is less than or equal to the preset ozone pollution score threshold (i.e., the target ozone pollution indication data is less than or equal to the preset ozone pollution score threshold), then the ozone pollution risk indication information is used as the ozone pollution prediction result.

[0062] In another embodiment, a target benchmark data also supports indicating the correspondence between the total range of indicator scores under the corresponding time index and the range of ozone pollution probability, such as a target benchmark data may also include the correspondence between the total range of indicator scores under the corresponding time index and the range of ozone pollution probability. Based on this, when determining the target ozone pollution indication data of the target area at the target prediction time based on the target score value, the target probability indication data corresponding to the target score value may be determined based on the target benchmark data under the time index to which the target prediction time belongs, that is, the target probability indication data corresponding to the target score value may be determined based on the correspondence between the total range of indicator scores indicated by the target benchmark data under the time index to which the target prediction time belongs and the range of ozone pollution probability; and the target probability indication data is used as the target ozone pollution indication data of the target area at the target prediction time.

[0063] Based on this, when using the target ozone pollution indication data to determine the ozone pollution prediction result of the target area within the target prediction time, if the target ozone pollution indication data is used to indicate that the probability of ozone pollution in the target area within the target prediction time is greater than the preset ozone pollution probability threshold (that is, the target ozone pollution indication data is greater than the preset ozone pollution probability threshold), then the ozone pollution risk indication information is used as the ozone pollution prediction result for the target area within the target prediction time, and the ozone pollution risk indication information can be used to indicate that the target area has a risk of ozone pollution within the target prediction time; if the target ozone pollution indication data is used to indicate that the probability of ozone pollution in the target area within the target prediction time is less than or equal to the preset ozone pollution probability threshold (that is, the target ozone pollution indication data is less than or equal to the preset ozone pollution probability threshold), then the no ozone pollution risk indication information is used as the ozone pollution prediction result, and the no ozone pollution risk indication information can be used to indicate that the target area has no risk of ozone pollution within the target prediction time.

[0064] Optionally, the preset ozone pollution score threshold, the preset ozone pollution probability threshold, the ozone pollution risk indication information, and the ozone pollution risk non-indication information can all be set according to experience or according to actual needs, and the embodiments of the present invention are not limited to this; illustratively, the preset ozone pollution probability threshold can be 0.5, the ozone pollution risk non-indication information can be "no ozone pollution risk" or 0, and so on.

[0065] Optionally, when a target reference data is a nomogram, the electronic device may further output a target reference data set so that the target reference data set is used to guide the target object to perform ozone pollution prediction, thereby obtaining the ozone pollution probability of the target area within the target prediction time range (i.e., target probability indication data), such as Figure 4As shown; optionally, the target object can be any user, that is, the target object can use the nomogram under each time index to predict the ozone pollution prediction result of the target area within the target prediction time.

[0066] Exemplarily, taking at least one time indicator including the time indicator "year" as an example, it is assumed that historical meteorological data, daily ozone monitoring data (such as ozone pollution occurrence probability set and / or ozone concentration set) and historical precursor monitoring data (such as nighttime nitrogen dioxide concentration data) of the target area from April to October 2021 to 2023 are collected. Since there are many VOCs missing in the above period, VOCs data may not be used in this embodiment; it is also assumed that a pollution prediction regression analysis model (i.e., a Logistic regression model) is constructed based on the above data using R language, in which the independent variables that pass the significance test (significance P value <0.05) are NO 2 , wind direction, wind speed, temperature and humidity, and based on the historical data of the above independent variables (i.e., the target prediction preparation data), a Logistic regression equation is constructed (e.g., the constant term can be -1.5899, NO 2 , wind direction, wind speed, temperature and humidity can be 0.0491, -0.0059, -1.7622, ​​0.2356 and -0.0594 respectively. At this time, the P value of the likelihood ratio test of the model can be 0, that is, P<0.05, indicating that among the variables included in the model fitted this time, at least one independent variable has a statistically significant effect on the dependent variable, that is, the model is meaningful overall; the Hosmer-Lemmeshow goodness of fit test of the model goodness of fit shows that the model P=0.858, that is, P>0.05, indicating that the model goodness of fit effect is good; the Logistic regression equation predicts ozone pollution weather in the target area with an accuracy of 83.6%, indicating that the Logistic regression equation has a good predictive ability for ozone pollution weather in the target area). Based on this, the scoring system for the occurrence of ozone pollution weather is established by drawing the nomogram, NO 2 The value is 34 points (i.e. the estimated maximum score), wind direction is 21 points, wind speed is 100 points, temperature is 72 points, and humidity is 36 points. The sum of the scores of each factor is the Nomo score for the occurrence of ozone pollution weather. Then, based on the target prediction data, the score corresponding to each independent variable value can be obtained according to the Nomo diagram. The probability corresponding to the sum of the scores is the probability of ozone pollution weather (i.e. the probability of ozone pollution). Furthermore, at night during the target prediction time (e.g., 20:00 on October 5, 2024 to 7:00 on October 6, 2024) NO 2Taking the daytime ozone level as an example, the atmospheric ozone risk is 39 (μg / m³, micrograms per cubic meter), the daytime wind direction is 98 (degrees), the wind speed is 1.4 (m / s, meters per second), the temperature is 33.3 (℃, degrees Celsius), and the humidity is 64 (%, percentage), the corresponding score values ​​(Points) are 17, 16, 86, 48, and 19, respectively. The target score value is 186, and the corresponding ozone pollution probability is 0.8, which is greater than 0.5, indicating that there is a risk of ozone pollution on that day, and corresponding control arrangements should be made in advance, etc.

[0067] In other embodiments, the electronic device may also determine the pollution prediction regression analysis model under the time index to which the target prediction time belongs based on the target benchmark data under the time index to which the target prediction time belongs, and call the pollution prediction regression analysis model under the time index to which the target prediction time belongs, and determine the ozone pollution prediction result of the target area within the target prediction time based on the target prediction data, and so on; the present invention is not limited to this.

[0068] The embodiment of the present invention can obtain the initial prediction preparation data of the target area under each time index in at least one time index, and then determine the target prediction preparation data under each time index from the initial prediction preparation data of the target area under each time index, and one target prediction preparation data includes the index data set of each benchmark pollution impact index in at least one benchmark pollution impact index under the corresponding time index; and build the pollution prediction regression analysis model under each time index based on the target prediction preparation data under each time index. Based on this, each time index in at least one time index can be traversed, and the currently traversed time index can be used as the current time index; and from the pollution prediction regression analysis model under the current time index, the model indication data under the current time index can be determined, and the model indication data includes the regression coefficients of each benchmark pollution impact index under the current time index, so as to determine the target benchmark data under the current time index based on the model indication data under the current time index; after traversing each time index in at least one time index, a target benchmark data set is obtained, and the target benchmark data set includes the target benchmark data under each time index, and the target benchmark data set supports the prediction result of ozone pollution in the target prediction time for the target area. It can be seen that the embodiments of the present invention can combine historical meteorological monitoring data, atmospheric environment monitoring data (such as historical precursor monitoring data, etc.) with the Logistic regression analysis method to construct a pollution prediction regression analysis model to obtain a target benchmark data set for predicting the ozone pollution prediction results of the target area within the target prediction time, so that the meteorological forecast data of a future day and the ozone precursor monitoring data of the previous night (i.e., precursor monitoring data) can be used to quickly predict the ozone pollution prediction results of the target area within the target prediction time in the morning through the target benchmark data set, which can provide a reference for whether to take ozone pollution control measures on that day; in addition, a target benchmark data can be a Nomograph, at this time the pollution prediction regression analysis model can be visualized as a Nomograph, so that the ozone pollution prediction results of the target area within the target prediction time can be quickly predicted in the morning through the Nomograph, and the rapid prediction of ozone pollution weather can be achieved.

[0069] Based on the description of the above-mentioned related embodiments of the ozone pollution prediction method, the embodiment of the present invention further proposes an ozone pollution prediction device, which can be a computer program (including program code) running in an electronic device; Figure 5 As shown, the ozone pollution prediction device may include an acquisition unit 501 and a processing unit 502. The ozone pollution prediction device may execute Figure 1 or Figure 2 The ozone pollution prediction method shown, that is, the ozone pollution prediction device can run the above units: An acquisition unit 501 is used to acquire initial prediction preparation data for a target area under each time index in at least one time index, wherein one initial prediction preparation data includes an indicator data set for each pollution impact index among multiple pollution impact indexes under the corresponding time index, and one indicator data set for a pollution impact index includes multiple indicator data of the corresponding pollution impact index within a historical time range corresponding to the corresponding time index; The processing unit 502 is used to determine the target prediction preparation data under each time index from the initial prediction preparation data of the target area under each time index, wherein one target prediction preparation data includes an indicator data set of each benchmark pollution impact indicator in at least one benchmark pollution impact indicator under the corresponding time index; The processing unit 502 is further configured to construct a pollution prediction regression analysis model under each time index based on the target prediction preparation data under each time index; The processing unit 502 is also used to determine a target benchmark data set based on the pollution prediction regression analysis model under the various time indicators, and the target benchmark data set includes target benchmark data under the various time indicators, and the target benchmark data set supports predicting ozone pollution prediction results in the target area within the target prediction time.

[0070] In one embodiment, when the processing unit 502 determines the target benchmark data set based on the pollution prediction regression analysis model under the various time indicators, it can be specifically used to: traverse each time indicator in the at least one time indicator, and use the currently traversed time indicator as the current time indicator; determine the model indication data under the current time indicator from the pollution prediction regression analysis model under the current time indicator, and the model indication data includes the regression coefficients of each benchmark pollution impact indicator under the current time indicator; determine the target benchmark data under the current time indicator based on the model indication data under the current time indicator; after traversing each time indicator in the at least one time indicator, obtain the target benchmark data set.

[0071] In another embodiment, when the processing unit 502 determines the target benchmark data under the current time index based on the model indication data under the current time index, it can be specifically used to: determine the correspondence between the benchmark indicator range and the scoring standard range of each benchmark pollution impact indicator under the current time index based on the regression coefficient of each benchmark pollution impact indicator under the current time index, the correspondence between the benchmark indicator range of a benchmark pollution impact indicator and the scoring standard range is used to indicate the scoring value corresponding to any indicator value of the corresponding benchmark pollution impact indicator within the scoring standard range; determine the target benchmark data under the current time index based on the correspondence between the benchmark indicator range of each benchmark pollution impact indicator under the current time index and the scoring standard range.

[0072] In another embodiment, a target benchmark data is a nomogram; the processing unit 502 can also be used to: output the target benchmark data set, so that the target benchmark data set is used to guide the target object to predict ozone pollution.

[0073] In another embodiment, when the processing unit 502 determines the target prediction preparation data under each time index from the initial prediction preparation data of the target area under each time index, it can be specifically used to: for any time index under the at least one time index, based on the initial prediction preparation data of the target area under the any time index, respectively determine the influence relationship indication information of each pollution impact index under the any time index, one influence relationship indication information is used to indicate the influence relationship between the corresponding pollution impact index and ozone; based on the influence relationship indication information of each pollution impact index under the any time index, select at least one baseline pollution impact index from multiple pollution impact indicators under any time index, and use the selected at least one baseline pollution impact indicator as at least one baseline pollution impact indicator under the any time index; from the initial prediction preparation data of the target area under the any time index, determine the indicator data set of each baseline pollution impact index under the any time index, so as to determine the target prediction preparation data under the any time index.

[0074] In another embodiment, the acquisition unit 501 can also be used to: acquire target prediction data, the target prediction data including impact indication data of each target pollution impact indicator of the target area at least one target pollution impact indicator at the target prediction time, the at least one target pollution impact indicator is at least one benchmark pollution impact indicator under the time indicator to which the target prediction time belongs, and the impact indication data of any target pollution impact indicator is data of any target pollution impact indicator within a specified time range corresponding to the target prediction time, and the specified time range is the time range of the target prediction time under the impact interval indicator corresponding to any target pollution impact indicator; The processing unit 502 can also be used to: determine the index data of each target pollution impact indicator respectively based on the impact indication data of each target pollution impact indicator; predict the target ozone pollution indication data of the target area at the target prediction time based on the target benchmark data set and the index data of each target pollution impact indicator, the target ozone pollution indication data being determined based on the target benchmark data under the time indicator to which the target prediction time belongs in the target benchmark data set; and determine the ozone pollution prediction result of the target area within the target prediction time using the target ozone pollution indication data.

[0075] In another embodiment, a target benchmark data supports the correspondence between the benchmark indicator range and the scoring standard range for each benchmark pollution impact indicator under the corresponding time indicator. When the processing unit 502 predicts the target ozone pollution indication data of the target area at the target prediction time based on the target benchmark data set and the indicator data of each target pollution impact indicator, it can be specifically used to: determine the target benchmark data under the time indicator to which the target prediction time belongs from the target benchmark data set, and determine the pollution impact indicator scoring values ​​corresponding to the indicator data of each target pollution impact indicator based on the target benchmark data under the time indicator to which the target prediction time belongs; perform weighted summation of the pollution impact indicator scoring values ​​corresponding to the indicator data of each target pollution impact indicator to obtain the target scoring value of the target area at the target prediction time; and determine the target ozone pollution indication data of the target area at the target prediction time based on the target scoring value.

[0076] In another embodiment, a target benchmark data also supports the corresponding relationship between the total range of the index score under the corresponding time index and the ozone pollution probability range; when the processing unit 502 determines the target ozone pollution indication data of the target area at the target prediction time based on the target score value, it can be specifically used to: determine the target probability indication data corresponding to the target score value based on the target benchmark data under the time index to which the target prediction time belongs, and use the target probability indication data as the target ozone pollution indication data of the target area at the target prediction time; When the processing unit 502 uses the target ozone pollution indication data to determine the ozone pollution prediction result of the target area within the target prediction time, it can be specifically used to: if the target ozone pollution indication data is used to indicate that the probability of ozone pollution in the target area within the target prediction time is greater than a preset ozone pollution probability threshold, then use ozone pollution risk indication information as the ozone pollution prediction result of the target area within the target prediction time, and the ozone pollution risk indication information is used to indicate that the target area has a risk of ozone pollution within the target prediction time; if the target ozone pollution indication data is used to indicate that the probability of ozone pollution in the target area within the target prediction time is less than or equal to the preset ozone pollution probability threshold, then use no ozone pollution risk indication information as the ozone pollution prediction result, and the no ozone pollution risk indication information is used to indicate that the target area has no risk of ozone pollution within the target prediction time.

[0077] According to one embodiment of the present invention, Figure 5 Each unit in the ozone pollution prediction device shown can be separately or completely combined into one or several other units to form a unit, or one (or some) of the units can be further divided into multiple functionally smaller units to form a unit, which can achieve the same operation without affecting the realization of the technical effects of the embodiments of the present invention. The above-mentioned units are divided based on logical functions. In practical applications, the function of one unit can also be implemented by multiple units, or the functions of multiple units can be implemented by one unit. In other embodiments of the present invention, any ozone pollution prediction device may also include other units. In practical applications, these functions can also be implemented with the assistance of other units, and can be implemented by the collaboration of multiple units.

[0078] According to another embodiment of the present invention, the program can be executed by running a program on a general electronic device such as a computer including a central processing unit (CPU), a random access memory medium (RAM), a read-only memory medium (ROM), and other processing elements and storage elements. Figure 1 or Figure 2 A computer program (including program code) for each step involved in the corresponding method shown in Figure 5 The ozone pollution prediction device shown in and the ozone pollution prediction method of the embodiment of the present invention are implemented. The computer program can be recorded on, for example, a computer storage medium, and loaded into the above-mentioned electronic device through the computer storage medium and run therein.

[0079] Based on the description of the above method embodiment and device embodiment, the exemplary embodiment of the present invention further provides an electronic device, including: at least one processor; and a memory connected to the at least one processor in communication. The memory stores a computer program that can be executed by the at least one processor, and the computer program is used to enable the electronic device to perform the method according to the embodiment of the present invention when executed by the at least one processor.

[0080] An exemplary embodiment of the present invention further provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor of a computer, the computer is used to enable the computer to perform a method according to an embodiment of the present invention.

[0081] refer to Figure 6 , a block diagram of an electronic device 600 that can be used as a server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0082] like Figure 6 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the electronic device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0083] A plurality of components in the electronic device 600 are connected to the I / O interface 605, including: an input unit 606, an output unit 607, a storage unit 608, and a communication unit 609. The input unit 606 may be any type of device capable of inputting information to the electronic device 600, and the input unit 606 may receive input digital or character information, and generate key signal inputs related to user settings and / or function control of the electronic device. The output unit 607 may be any type of device capable of presenting information, and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 608 may include, but is not limited to, a disk, an optical disk. The communication unit 609 allows the electronic device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0084] The computing unit 601 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 601 performs the various methods and processes described above. For example, in some embodiments, the ozone pollution prediction method may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 600 via the ROM 602 and / or the communication unit 609. In some embodiments, the computing unit 601 may be configured to perform the ozone pollution prediction method in any other appropriate manner (e.g., by means of firmware).

[0085] The program code for implementing the method of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as a stand-alone software package and partially on a remote machine, or entirely on a remote machine or server.

[0086] In the context of the present invention, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0087] As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and / or device (e.g., disk, optical disk, memory, programmable logic device (PLD)) for providing machine instructions and / or data to a programmable processor, including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal for providing machine instructions and / or data to a programmable processor.

[0088] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0089] Furthermore, it should be understood that what is disclosed above is only a preferred embodiment of the present invention, and certainly cannot be used to limit the scope of rights of the present invention. Therefore, equivalent changes made according to the claims of the present invention are still within the scope of the present invention.

Claims

1. A method for predicting ozone pollution, characterized in that: include: Acquire initial forecast preparation data for each time index in at least one time index of the target area, wherein one initial forecast preparation data includes an indicator data set for each pollution impact index in multiple pollution impact indexes under the corresponding time index, and one indicator data set for a pollution impact index includes multiple indicator data for the corresponding pollution impact index within a historical time range corresponding to the corresponding time index; Determining target prediction preparation data under each time index from the initial prediction preparation data of the target area under each time index, wherein one target prediction preparation data includes an indicator data set of each benchmark pollution impact indicator in at least one benchmark pollution impact indicator under the corresponding time index; Based on the target prediction preparation data under each time index, a pollution prediction regression analysis model under each time index is constructed; Based on the pollution prediction regression analysis model under the various time indicators, a target benchmark data set is determined, wherein the target benchmark data set includes the target benchmark data under the various time indicators, and the target benchmark data set supports the prediction of ozone pollution results in the target area within the target prediction time.

2. The method according to claim 1, characterized in that: The pollution prediction regression analysis model based on each time index determines the target benchmark data set, including: Traversing each time index of the at least one time index, and taking the currently traversed time index as the current time index; Determining, from the pollution prediction regression analysis model under the current time index, model indication data under the current time index, the model indication data including regression coefficients of various benchmark pollution impact indicators under the current time index; Determine the target reference data under the current time index based on the model indication data under the current time index; After traversing each time indicator in the at least one time indicator, the target reference data set is obtained.

3. The method according to claim 2, characterized in that The determining, based on the model indication data under the current time indicator, the target reference data under the current time indicator comprises: Based on the regression coefficients of the respective benchmark pollution impact indicators under the current time indicator, determining the correspondence between the benchmark indicator range and the scoring standard range of the respective benchmark pollution impact indicators under the current time indicator, the correspondence between the benchmark indicator range of a benchmark pollution impact indicator and the scoring standard range is used to indicate the scoring value corresponding to any indicator value of the corresponding benchmark pollution impact indicator within the scoring standard range; Based on the corresponding relationship between the benchmark indicator range of each benchmark pollution impact indicator under the current time indicator and the scoring standard range, the target benchmark data under the current time indicator is determined.

4. The method according to claim 3, characterized in that A target reference data is a nomogram; the method further comprises: The target benchmark data set is outputted so that the target benchmark data set is used to guide the target object to perform ozone pollution prediction.

5. The method according to any one of claims 1 to 4, characterized in that: The step of determining target forecast preparation data under each time index from the initial forecast preparation data of the target area under each time index comprises: For any time index under the at least one time index, based on the initial prediction preparation data of the target area under the any time index, respectively determine the influence relationship indication information of each pollution impact index under the any time index, one influence relationship indication information is used to indicate the influence relationship between the corresponding pollution impact index and ozone; Based on the influence relationship indication information of each pollution impact indicator under the any time indicator, at least one baseline pollution impact indicator is selected from multiple pollution impact indicators under any time indicator, and the selected at least one baseline pollution impact indicator is used as the at least one baseline pollution impact indicator under the any time indicator; From the initial forecast preparation data of the target area under the any time index, an index data set of each benchmark pollution impact index under the any time index is determined to achieve the determination of the target forecast preparation data under the any time index.

6. The method according to any one of claims 1 to 4, characterized in that: The method further comprises: Acquire target prediction data, the target prediction data including impact indication data of each target pollution impact indicator of the target area at least one target pollution impact indicator at the target prediction time, the at least one target pollution impact indicator being at least one baseline pollution impact indicator under the time indicator to which the target prediction time belongs, and the impact indication data of any target pollution impact indicator being data of any target pollution impact indicator within a specified time range corresponding to the target prediction time, the specified time range being the time range of the target prediction time under the impact interval indicator corresponding to any target pollution impact indicator; Based on the impact indication data of each target pollution impact indicator, respectively determine the indicator data of each target pollution impact indicator; Based on the target benchmark data set and the index data of each target pollution impact index, predicting target ozone pollution indication data of the target area at the target prediction time, wherein the target ozone pollution indication data is determined based on the target benchmark data under the time index to which the target prediction time belongs in the target benchmark data set; The target ozone pollution indication data is used to determine an ozone pollution prediction result for the target area within the target prediction time.

7. The method according to claim 6, characterized in that A target benchmark data support is used to indicate the corresponding relationship between the benchmark indicator range and the scoring standard range of each benchmark pollution impact indicator under the corresponding time indicator, and the target ozone pollution indicator data of the target area at the target prediction time is predicted based on the target benchmark data set and the indicator data of each target pollution impact indicator, including: Determine the target benchmark data under the time index to which the target prediction time belongs from the target benchmark data set, and determine the pollution impact index score values ​​corresponding to the index data of each target pollution impact index based on the target benchmark data under the time index to which the target prediction time belongs; Performing a weighted summation on the pollution impact index score values ​​corresponding to the index data of each target pollution impact index to obtain a target score value of the target area at the target prediction time; Based on the target score value, target ozone pollution indication data for the target area at the target prediction time is determined.

8. The method according to claim 7, characterized in that A target benchmark data also supports the corresponding relationship between the total range of the indicator score under the corresponding time indicator and the ozone pollution probability range; the target ozone pollution indication data of the target area at the target prediction time is determined based on the target score value, including: Based on the target benchmark data under the time index to which the target prediction time belongs, determine the target probability indication data corresponding to the target score value, and use the target probability indication data as the target ozone pollution indication data of the target area at the target prediction time; The step of using the target ozone pollution indication data to determine the ozone pollution prediction result of the target area within the target prediction time includes: If the target ozone pollution indication data is used to indicate that the probability of ozone pollution in the target area within the target prediction time is greater than a preset ozone pollution probability threshold, the ozone pollution risk indication information is used as the ozone pollution prediction result of the target area within the target prediction time, and the ozone pollution risk indication information is used to indicate that the target area has a risk of ozone pollution within the target prediction time; If the target ozone pollution indication data is used to indicate that the ozone pollution probability of the target area within the target prediction time is less than or equal to the preset ozone pollution probability threshold, then the no ozone pollution risk indication information is used as the ozone pollution prediction result, and the no ozone pollution risk indication information is used to indicate that the target area has no risk of ozone pollution within the target prediction time.

9. An ozone pollution prediction device, characterized in that: The device comprises: an acquisition unit, used to acquire initial forecast preparation data for a target area under each time index in at least one time index, wherein one initial forecast preparation data includes an indicator data set for each pollution impact index among multiple pollution impact indexes under the corresponding time index, and one indicator data set for a pollution impact index includes multiple indicator data for the corresponding pollution impact index within a historical time range corresponding to the corresponding time index; A processing unit, configured to determine target prediction preparation data under each time index from the initial prediction preparation data of the target area under each time index, wherein one target prediction preparation data includes an indicator data set of each benchmark pollution impact indicator in at least one benchmark pollution impact indicator under the corresponding time index; The processing unit is further used to construct a pollution prediction regression analysis model under each time index based on the target prediction preparation data under each time index; The processing unit is also used to determine a target benchmark data set based on the pollution prediction regression analysis model under the various time indicators, wherein the target benchmark data set includes the target benchmark data under the various time indicators, and the target benchmark data set supports predicting ozone pollution prediction results in the target area within the target prediction time.

10. An electronic device, characterized in that: include: processor; as well as Memory for storing programs, The program includes instructions, which, when executed by the processor, cause the processor to perform the method according to any one of claims 1 to 8.

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