A method and system for identifying high-value areas of ozone generation

Abstract

The present invention discloses a method and system for identifying high-value areas of ozone generation, which relates to the technical field of environmental monitoring. By obtaining the historical ozone content detection and statistical data sequence within the area to be identified, dividing the ozone content interval state, defining the temperature state, relative humidity state, wind speed state, and boundary layer height state, classifying the obtained ozone content detection and statistical data sequence according to various states corresponding to the data measurement, calculating the state transition probability and state transition probability matrix of the ozone content within the area to be identified based on the classified ozone content data sequence, using the generated state transition probability matrix to obtain the predicted value of the ozone content at the next moment according to the obtained data, and identifying the high-value areas of ozone generation within the area to be identified according to the obtained predicted value, it is possible to monitor the change trend of the ozone content in the detection area using conventional state parameters, and improve the timeliness of identifying high-value areas of ozone generation.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental monitoring, and particularly relates to a method and system for identifying high-value areas of ozone generation. Background Art

[0002] The generation of ozone pollution is closely related to meteorological factors such as sunlight and temperature, and mainly occurs in the summer and autumn seasons with strong sunlight. Ozone precursors such as nitrogen oxides (NOx) and volatile organic compounds (VOCs) emitted by vehicle exhausts and industrial enterprises, under the action of high temperature and strong light radiation, undergo a series of complex photochemical reactions to produce ozone pollutants. As the primary pollutant in many regions of China in summer and autumn in recent years, its concentration is mainly affected by its two major precursors, VOCs and NOx. Ozone pollution is currently the most important pollution factor affecting the excellent air quality days in cities. Therefore, how to obtain the near-surface ozone concentration and promptly and accurately discover ozone pollution sources is an important problem faced by the current governance of atmospheric ozone pollution.

[0003] In the patent document with the application publication number CN113176216A, a method is proposed to comprehensively determine which precursor mainly controls ozone generation through satellite remote sensing monitoring results, and then control ozone generation through targeted emission reduction; in the patent document with the application publication number CN112990111A, to solve the technical problems that the spatial resolution of satellite data is relatively low, and a large amount of volatile organic compounds released by vegetation will affect the volatile organic compound data retrieved using satellite data, resulting in the inability to completely and accurately identify high-value areas of ozone generation, a method for identifying high-value areas of ozone generation is proposed. By using satellite remote sensing data and normalized difference vegetation index data, it is possible to quickly lock potential high-value areas of ozone generation on a large scale, and then comprehensively judge by combining multi-source data such as enterprise-related data, and accurately identify high-value areas of ozone generation; in the patent document with the application publication number CN110942049A, the tropospheric ozone profile is retrieved by using TropOMI ultraviolet hyperspectral data; by using TropOMI tropospheric NO2 and HCHO column concentration products, combined with the atmospheric chemistry model Geos-Chem, the near-surface NO2 and HCHO concentrations are obtained, the concentration ratio of the two is calculated, and an indicator value of near-surface ozone pollution source is obtained; by comprehensively considering NO2, HCHO and the ozone monitoring values of ground national control stations, etc., the near-surface ozone concentration is obtained through a multiple regression model; by using the near-surface ozone concentration result, ozone heavy pollution areas are selected, and combined with the near-surface ozone pollution source indicator value and sub-meter high-resolution images, ozone pollution sources are identified.

[0004] In the above-mentioned publicly available prior arts, the identification and monitoring of ground ozone high-value areas are all based on satellite remote sensing data. The research by Wang Wenpeng et al. on the sources of ozone in the surrounding areas of Lanzhou shows that the meteorological elements affecting the ozone concentration in various regions of Lanzhou include temperature, relative humidity, wind speed, and boundary layer height, etc. As a secondary pollutant with strong fluidity, the distribution of ozone on the ground is affected by various factors and has a fast change rate. However, the acquisition period of remote sensing data is long. Therefore, the ground ozone content data obtained by satellite remote sensing technology can only reflect the ozone content level in the area at the moment when the remote sensing image is collected, and it is difficult to monitor the change trend of the ozone content in the detection area. For this reason, we propose a method and system for identifying ozone generation high-value areas. Summary of the Invention

[0005] The main purpose of the present invention is to provide a method and system for identifying ozone generation high-value areas, which can effectively solve the problems in the background technology.

[0006] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0007] A method for identifying ozone generation high-value areas includes:

[0008] Step 1: Obtain the historical ozone content detection and statistical data sequence ρ in the area to be identified, divide the ozone content interval of the area to be identified into S states, and define the temperature state T = {T1, T2,..., T U}, relative humidity state H = {H1, H2,..., H V}, wind speed state W = {W1, W2,..., W W}, and boundary layer height state B = {B1, B2,..., B R} in the area to be identified; where, T U represents the U-th temperature state; H V represents the V-th relative humidity state; W W represents the W-th wind speed state; B R represents the R-th boundary layer height state;

[0009] The division methods of ozone content interval state, temperature state, relative humidity state, wind speed state, and boundary layer height state include the following steps:

[0010] Step S11: Set the sampling value of any one of the above-mentioned states as Sv;

[0011] Step S12: Create a sample set using the sampling value Sv of the state, denoted as {Sv1, Sv2,..., Sv n}, where n is the total number of sampling values;

[0012] Step S13: Obtain the mean and standard deviation of the sample set, and standardize the data using the mean and standard deviation. The standardization formula is In this formula, z is the standard parameter, σ is the variance of the sample data, and μ is the mean of the sample data;

[0013] After completing the standardization, use to adjust the numerical range to between [0, 1], and classify the sampling value Sv using the function value of f(k). The classification mechanism is as follows:

[0014] When f(k) min ≤ f(k) < f(k)1, the sampling value Sv is classified as the first level;

[0015] When f(k)1 ≤ f(k) < f(k)2, the sampling value Sv is classified as the second level;

[0016] When f(k)2 ≤ f(k) < f(k)3, the sampling value Sv is classified as the third level;

[0017] And so on. When f(k) q ≤ f(k) < f(k) max , the sampling value Sv is classified as the t-th level;

[0018] Among them, f(k) min , f(k) max are respectively the minimum and maximum values of the function value of f(k), f(k)1, f(k)2,..., f(k) t are respectively the intermediate values of the function value of f(k), and f(k) min < f(k)1 < f(k)2 <,..., < f(k) q < f(k) max , where t is a positive integer;

[0019] Step S15: According to the classification of the sampling value Sv, determine its corresponding status level. The determination principle is as follows:

[0020] When the sampling value Sv is classified as the first level, the corresponding status level is the first level;

[0021] When the sampling value Sv is classified as the second level, the corresponding status level is the second level;

[0022] When the sampling value Sv is classified as the third level, the corresponding status level is the third level;

[0023] And so on,

[0024] When the sampling value Sv is classified as the q-th level, the corresponding status level is the q-th level;

[0025] When the sampling value Sv is the ozone content, the corresponding state is the ozone content interval state, then q = S;

[0026] When the sampling value Sv is the temperature, the corresponding state is the temperature state, then q = U;

[0027] When the sampling value Sv is the relative humidity, the corresponding state is the relative humidity state, then q = V;

[0028] When the sampling value Sv is the wind speed, the corresponding state is the wind speed state, then q = W;

[0029] When the sampling value Sv is the boundary layer height, the corresponding state is the boundary layer height state, then q = R.

[0030] Step 2: Classify the obtained ozone content detection and statistical data sequence ρ according to various states corresponding to data measurement, and obtain the ozone content data sequence ρ under the u-th temperature state, the v-th relative humidity state, the w-th wind speed state, and the r-th boundary layer height state, u,v,w,r where u ∈ U; v ∈ V; w ∈ W; r ∈ R;

[0031] Step 3: According to the classified ozone content data sequence ρ u,v,w,r Calculate the state transition probability that the ozone content in the area to be recognized transfers from state i to state j in one step From the state transition probability Further generate a state transition probability matrix i, j ∈ S;

[0032] The calculation formula of the state transition probability is:

[0033]

[0034] In the formula, is the frequency that the ozone content in the data sequence ρ u,v,w,r transfers from state i to state j in one step.

[0035] Step 4: Obtain the real-time data of each state at the current moment in the area to be recognized, use the generated state transition probability matrix according to the obtained data to obtain the predicted value of the ozone content at the next moment, and identify the high-value area of ozone generation in the area to be recognized according to the obtained predicted value. The specific process includes the following steps:

[0036] Step S41: Uniformly divide the area to be recognized into λ × λ grids, and number the grids in a certain order;

[0037] Step S42: Set the current time as t. The ozone content interval of any grid area within the area to be recognized is in the state m at the current time t t , where m t ∈S, the ozone content is C t , the current temperature state is T t , the relative humidity state is H t , the wind speed state is W t , the boundary layer height state is B t ;

[0038] Step S43: Generate a random number ε that follows a uniform distribution according to the state of the grid area t , where ε t ∈[0,1];

[0039] Step S44: Determine the corresponding state transition probability matrix according to the current state parameters T t , H t , W t , B t of the grid area , where m t+1 is the ozone content interval state of the grid area at time t+1;

[0040] Step S45: Determine the ozone content interval state m of the grid area at time t+1 according to the obtained state transition probability matrix , and generate a random number ε that follows a uniform distribution according to the obtained ozone content interval state m t+1 , where ε t+1 ∈[0,1], and ε t+1 is independent of ε t+1 ; t+1 and ε t are independent of each other;

[0041] Step S46: If the ozone content interval corresponding to the state m t+1 is [C l , C h , then the calculation formula for the ozone content C t+1 of the grid area at time t+1 is: C t+1 = C t + ε t+1 (C h - C l );

[0042] Step S47: Repeat Step S42 - Step S46 to obtain the ozone content of all grid areas within the area to be recognized. Set an ozone content threshold, and screen the grid areas with ozone content greater than the set threshold as high ozone generation areas.

[0043] An ozone generation high-value area identification system, comprising:

[0044] A data acquisition module for acquiring ozone content and status data within the area to be identified;

[0045] A status definition module for the ozone content, temperature, relative humidity, wind speed, and boundary layer height status in the area to be identified;

[0046] For classifying the acquired ozone content detection statistical data sequence ρ according to various statuses corresponding to the data measurement, and obtaining the ozone content data sequence ρ under the u-th temperature status, the v-th relative humidity status, the w-th wind speed status, and the r-th boundary layer height status u,v,w,r of the data classification module;

[0047] For calculating the state transition probability that the ozone content in the area to be identified transfers from state i to state j in one step according to the classified ozone content data sequence ρ u,v,w,r and further generating a state transition probability matrix by the state transition probability of the state chain construction module; For obtaining the predicted value of the ozone content at the next moment according to the acquired data by using the generated state transition probability matrix, and identifying the high-value area of ozone generation in the area to be identified according to the acquired predicted value of the high-value area identification module.

[0048] The system further includes a memory, a processor, and a computer program stored on the memory and executable on the processor.

[0049] The present invention has the following beneficial effects.

[0050] Compared with the prior art, by acquiring the historical ozone content detection statistical data sequence ρ in the area to be identified, dividing the ozone content interval of the area to be identified into S states, defining the temperature state T, relative humidity state H, wind speed state W, and boundary layer height state B in the area to be identified, classifying the acquired ozone content detection statistical data sequence ρ according to various statuses corresponding to the data measurement, and obtaining the ozone content data sequence ρ under the u-th temperature status, the v-th relative humidity status, the w-th wind speed status, and the r-th boundary layer height status

[0051] u,v,w,r u,v,w,r According to the classified ozone content data sequence ρ u,v,w,r calculate the state transition probability that the ozone content in the area to be identified transfers from state i to state j in one step and further generate a state transition probability matrix by the state transition probability ​Obtain the real-time data of each state at the current moment within the area to be recognized. Based on the obtained data and using the generated state transition probability matrix, obtain the predicted value of the ozone content at the next moment. Identify the high-value area of ozone generation within the area to be recognized according to the obtained predicted value. It is possible to monitor the change trend of the ozone content in the detection area by using conventional state parameters such as temperature, relative humidity, wind speed, and boundary layer height, and improve the timeliness of identifying the high-value area of ozone generation. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic flowchart of a method for identifying a high-value area of ozone generation according to the present invention;

[0053] Figure 2 It is a schematic structural diagram of a system for identifying a high-value area of ozone generation according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0054] The present invention will be further described below in conjunction with the specific embodiments. Among them, the drawings are only for illustrative purposes, showing only schematic diagrams, not physical diagrams, and should not be construed as a limitation of the present invention. In order to better illustrate the specific embodiments of the present invention, some components in the drawings will be omitted, enlarged or reduced, and do not represent the dimensions of the actual product.

[0055] The specific implementation process of the technical solution of the present invention includes the following steps:

[0056] Step 1: Obtain the historical ozone content detection and statistical data sequence ρ within the area to be recognized.

[0057] Step 2: Divide the ozone content interval of the area to be recognized into S states, and define the temperature state T = {T1, T2,..., T U}, relative humidity state H = {H1, H2,..., H V}, wind speed state W = {W1, W2,..., W W}, and boundary layer height state B = {B1, B2,..., B R} within the area to be recognized; where T U represents the U-th temperature state; H V represents the V-th relative humidity state; W W represents the W-th wind speed state; B R represents the R-th boundary layer height state;

[0058] Specifically, the method for dividing the ozone content interval state, temperature state, relative humidity state, wind speed state, and boundary layer height state includes the following steps:

[0059] Step S21: Set the sampling value of any state as Sv;

[0060] Step S22: Create a sample set using the sampled value Sv of the state, denoted as {Sv1, Sv2,..., Sv n}, where n is the total number of sampled values;

[0061] Step S23: Obtain the mean and standard deviation in the sample set, and standardize the data using the mean and standard deviation. The standardization formula is In this formula, z is the standard parameter, σ is the variance of the sample data, and μ is the mean of the sample data;

[0062] Step S24: After completing the standardization, adjust the standard parameter using Adjust the numerical interval to between [0, 1], and classify the sampled value Sv using the function value of f(k). The classification mechanism is:

[0063] When f(k) min ≤ f(k) < f(k)1, the sampled value Sv is classified as the first level;

[0064] When f(k)1 ≤ f(k) < f(k)2, the sampled value Sv is classified as the second level;

[0065] When f(k)2 ≤ f(k) < f(k)3, the sampled value Sv is classified as the third level;

[0066] And so on. When f(k) q ≤ f(k) < f(k) max , the sampled value Sv is classified as the t-th level;

[0067] Among them, f(k) min , f(k) max are the minimum and maximum values of the function value of f(k) respectively, and f(k)1, f(k)2,..., f(k) t are the intermediate values of the function value of f(k) respectively, and f(k) min < f(k)1 < f(k)2 <,..., < f(k) q < f(k) max , where t is a positive integer;

[0068] Step S25: According to the classification situation of the sampled value Sv, determine the corresponding state level. The determination principle is:

[0069] When the sampled value Sv is classified as the first level, the corresponding state level is the first level;

[0070] When the sampled value Sv is classified as the second level, the corresponding state level is the second level;

[0071] When the sampled value Sv is classified as the third level, the corresponding state level is the third level;

[0072] And so on,

[0073] When the sampling value Sv is classified into the q-th level, the corresponding status level is the q-th level;

[0074] When the sampling value Sv is the ozone content, the corresponding status is the ozone content interval status, then q = S;

[0075] When the sampling value Sv is the temperature, the corresponding status is the temperature status, then q = U;

[0076] When the sampling value Sv is the relative humidity, the corresponding status is the relative humidity status, then q = V;

[0077] When the sampling value Sv is the wind speed, the corresponding status is the wind speed status, then q = W;

[0078] When the sampling value Sv is the boundary layer height, the corresponding status is the boundary layer height status, then q = R.

[0079] Step 3: Classify the obtained ozone content detection and statistical data sequence ρ according to various corresponding statuses during data measurement, and obtain the ozone content data sequence ρ under the u-th temperature status, the v-th relative humidity status, the w-th wind speed status, and the r-th boundary layer height status, u,v,w,r , where u ∈ U; v ∈ V; w ∈ W; r ∈ R.

[0080] Step 4: According to the classified ozone content data sequence ρ u,v,w,r Calculate the state transition probability that the ozone content in the area to be recognized transfers from state i to state j in one step From the state transition probability Further generate the state transition probability matrix i, j ∈ S; The calculation formula for the state transition probability is:

[0081]

[0082] In the formula, is the frequency that the ozone content in the data sequence ρ u,v,w,r transfers from state i to state j in one step.

[0083] Step 5: Obtain the real-time data of each status at the current moment in the area to be recognized, use the generated state transition probability matrix according to the obtained data to obtain the predicted value of the ozone content at the next moment, and identify the high-value area of ozone generation in the area to be recognized according to the obtained predicted value.

[0084] The specific process includes the following steps:

[0085] Step S51: Evenly divide the area to be recognized into a λ×λ grid, and number the grids in a certain order;

[0086] Step S52: Set the current time as t, and the ozone content interval of any grid area within the area to be recognized is in the state m at the current time t t , where m t ∈S, the ozone content is C t , the current temperature state is T t , the relative humidity state is H t , the wind speed state is W t , the boundary layer height state is B t ;

[0087] Step S53: Generate a random number ε that follows a uniform distribution according to the state of this grid area t , where ε t ∈[0,1];

[0088] Step S54: Determine the corresponding state transition probability matrix according to the current state parameters T t , H t , W t , B t of this grid area where m t+1 is the ozone content interval state of this grid area at time t+1;

[0089] Step S55: Determine the ozone content interval state m of this grid area at time t+1 according to the obtained state transition probability matrix , and generate a random number ε that follows a uniform distribution according to the obtained ozone content interval state m t+1 , where ε t+1 ∈[0,1], and ε t+1 is independent of ε t+1 ; t+1 and ε t are independent of each other;

[0090] Step S56: If the ozone content interval corresponding to the state m t+1 is [C l , C h , then the calculation formula for the ozone content C t+1 of this grid area at time t+1 is: C t+1 = C t + ε t+1 (C h - C l );

[0091] Step S57: Repeat steps S42 - S44 to obtain the ozone content of all grid regions within the region to be recognized, set an ozone content threshold, and screen the grid regions with ozone content greater than the set ozone content threshold as high ozone generation regions.

[0092] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification only illustrate the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. A method for identifying high-value areas of ozone generation, characterized in that, Including: Step 1: Obtain the historical ozone content detection and statistical data sequence ρ within the area to be recognized. Divide the ozone content interval of the area to be recognized into s states, and define the temperature state within the area to be recognized , relative humidity state , wind speed state , boundary layer height state ; where represents the U-th temperature state; represents the V-th relative humidity state; represents the W-th wind speed state; represents the R-th boundary layer height state; Step 2: Classify the obtained ozone content detection and statistical data sequence ρ according to various states corresponding to data measurement, and obtain the ozone content data sequence under the u-th temperature state, the v-th relative humidity state, the w-th wind speed state, and the r-th boundary layer height state , where ; Step 3: According to the classified ozone content data sequence Calculate the state transition probability that the ozone content in the area to be recognized transfers from state to state j in one step , and further generate a state transition probability matrix from the state transition probability ; Step 4: Obtain the real-time data of each state at the current moment within the area to be recognized. Use the generated state transition probability matrix to obtain the predicted value of the ozone content at the next moment based on the obtained data, and identify the high-value area of ozone generation within the area to be recognized according to the obtained predicted value. The specific process of Step 4 includes the following steps: Step S41: Uniformly divide the area to be recognized into λ×λ grids, and number the grids in a certain order. Step S42: Set the current moment as t. The state of the ozone content interval in any grid area within the area to be recognized at the current moment t is , where , the ozone content is , the current temperature state , the relative humidity state is , the wind speed state is , the boundary layer height state is ; Step S43: Generate a random number that follows a uniform distribution according to the state of the grid region wherein ; Step S44: Determine the corresponding state transition probability matrix according to the state parameters of the grid region at present wherein, is the ozone content interval state of the grid region at time ​​ Step S45: According to the obtained state transition probability matrix , determine the ozone content interval state of the grid area at time , and according to the obtained ozone content interval state , generate a random number that follows a uniform distribution , where , and , and and are independent of each other; Step S46: If the ozone content range corresponding to the state is , then the ozone content in the grid area at time is calculated by the formula: ; Step S47: Repeat Step S42 - Step S46 to obtain the ozone content of all grid areas within the area to be recognized. Set an ozone content threshold, and screen the grid areas with ozone content greater than the set threshold as the high-value areas of ozone generation.

2. The identification method of a high-value area for ozone generation according to claim 1, wherein The calculation formula for the state transition probability is as follows: ; In the formula, is the data sequence The frequency of the ozone content in which the state transfers to state j in one step.

3. The method for identifying a high-value area of ozone generation according to claim 1, characterized in that, In Step 1, the division methods of the ozone content interval state, temperature state, relative humidity state, wind speed state, and boundary layer height state include the following steps: Step S11: Set the sampling value of any one of the stated states to be ; Step S12: Use the sampled values of the state to create a sample set, denoted as , where n is the total number of sampled values; Step S13: Obtain the mean and standard deviation in the sample set, and standardize the data by using the mean and standard deviation. The standardization formula is , where z is the standard parameter, σ is the variance of the sample data, and μ is the mean of the sample data; Step S14: After completing the standardization, use the standard parameters to adjust the numerical range to between [0, 1], and use the function value of to classify the sampled values The classification mechanism is as follows: When the sampled value is classified as the first level; When the sampled value is classified as the second level; When the sampled value is classified as the third level; And so on, when the sampled value is classified as the t-th level; Among them, , are respectively the minimum and maximum values of the function value, are respectively the intermediate value of the function value, and , where t is a positive integer; Step S15: According to the classification of the sampling value , determine the corresponding status level. The determination principle is as follows: When the sampled value is classified as the first level, the corresponding status level is level one; When the sampled value is classified as the second level, the corresponding status level is the second level; When the sampled value is classified as the third level, the corresponding status level is level three; And so on. When the sampled value is classified as the q-th level, the corresponding status level is level q.

4. According to the method for identifying a high-value area of ozone generation as claimed in claim 3, wherein When the sampled value is the ozone content, the corresponding state is the ozone content interval state, then q = s; When the sampled value is temperature, the corresponding state is the temperature state, then q = u; When the sampled value is relative humidity, the corresponding state is the relative humidity state, then q = v; When the sampled value is the wind speed, the corresponding state is the wind speed state, then q = w; When the sampling value is the boundary layer height, the corresponding state is the boundary layer height state, then q = R.

5. An identification system for high-value areas of ozone generation, characterized in that, The system is used to implement the steps of the method for identifying a high-value area of ozone generation as claimed in any one of claims 1 - 4, including: A data acquisition module for obtaining the ozone content and state data within the area to be recognized; A state definition module for the ozone content, temperature, relative humidity, wind speed, and boundary layer height state of the area to be recognized; A data classification module for classifying the obtained ozone content detection statistical data sequence ρ according to various states corresponding to data measurement, and obtaining the ozone content data sequence under the th temperature state, the th relative humidity state, the th wind speed state, and the th boundary layer height state ; For calculating, according to the classified ozone content data sequence the state transition probability that the ozone content in the area to be recognized transfers from one state to in one step, and further generating a state chain construction module of a state transition probability matrix from the state transition probability ; ​ A high-value area identification module for using the generated state transition probability matrix to obtain the predicted value of the ozone content at the next moment based on the obtained data, and identifying the high-value area of ozone generation within the area to be recognized according to the obtained predicted value.

6. The identification system for high-value ozone generation areas according to claim 5, characterized in that, The system further includes a memory, a processor, and a computer program stored on the memory and executable on the processor.

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