Method and system for identifying ozone generation high-value region

Abstract

The invention discloses a method and a system for identifying an ozone generation high-value region, and relates to the technical field of environment monitoring. The method comprises the following steps: acquiring a historical ozone content detection statistical data sequence in an area to be identified, dividing ozone content interval states, defining a temperature state, a relative humidity state, a wind speed state and a boundary layer height state, and classifying the acquired ozone content detection statistical data sequence according to various states corresponding to data measurement; and according to the classified ozone content data sequence, calculating the state transition probability and the state transition probability matrix of the ozone content in the to-be-identified region, and obtaining an ozone content predicted value at the next moment by utilizing the generated state transition probability matrix according to the obtained data. According to the method, the ozone generation high-value region in the to-be-recognized region is recognized according to the obtained predicted value, the change trend of the ozone content in the detection region can be monitored by using the conventional state parameters, and the timeliness of recognition of the ozone generation high-value region is improved.

Description

Technical Field

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

[0002] The formation of ozone pollution is closely related to meteorological factors such as light and temperature, and mainly occurs in the summer and autumn when the sun is strong. Ozone precursors such as nitrogen oxides (NOx) and volatile organic compounds (VOCs) emitted by automobile exhaust and industrial enterprises undergo a series of complex photochemical reactions under the action of high temperature and strong light radiation to produce ozone pollutants. As the primary pollutant in summer and autumn in many regions of my country 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 number of good days in cities. Therefore, how to obtain near-ground ozone concentration and promptly and accurately discover ozone pollution sources are important issues currently facing the control of atmospheric ozone pollution.

[0003] In the patent document with application publication number CN113176216A, a method is proposed to comprehensively determine which precursor controls ozone generation through satellite remote sensing monitoring results, and then control ozone generation through targeted emission reduction; in the patent document with application publication number CN112990111A, in order to solve the technical problem that the spatial resolution of satellite data is low and the large amount of volatile organic matter released by vegetation will affect the volatile organic matter data inverted by satellite data, resulting in the inability to fully and accurately identify the high-value area of ​​ozone generation, a method is proposed. A method for identifying high-value ozone generation areas is proposed. By using satellite remote sensing data and normalized vegetation index data, potential high-value ozone generation areas can be quickly locked in a large range, and then combined with multi-source data such as enterprise-related data for comprehensive judgment, high-value ozone generation areas can be accurately identified. In the patent document with application publication number CN110942049A, the tropospheric ozone profile is inverted by using TropOMI ultraviolet hyperspectral data; the near-ground NO2 and HCHO concentrations are obtained by using TropOMI tropospheric NO2 and HCHO column concentration products, combined with the atmospheric chemistry model Geos-Chem, and the concentration ratio of the two is calculated to obtain the near-ground ozone pollution source indicator value; the near-ground ozone concentration is obtained by a multivariate regression model based on NO2, HCHO and ozone monitoring values ​​of ground national control stations; the near-ground ozone concentration result is used to select the ozone heavy pollution area, and the ozone pollution source is identified by combining the near-ground ozone pollution source indicator value and sub-meter high-resolution imagery.

[0004] In the above-disclosed prior art, the identification and monitoring of high-value areas of ground ozone are all based on satellite remote sensing data. Wang Wenpeng et al. conducted a study on the sources of ozone in the surrounding areas of Lanzhou City and found that the meteorological factors that affect the ozone concentration in various regions of Lanzhou City include temperature, relative humidity, wind speed, and boundary layer height. Ozone is a highly mobile secondary pollutant. Its distribution on the surface is affected by many factors and changes rapidly, while the remote sensing data acquisition cycle 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 time of remote sensing image acquisition, and it is difficult to monitor the changing trend of ozone content in the detection area. To this end, we propose a method and system for identifying high-value areas of ozone generation. Summary of the invention

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

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A method for identifying a high-value ozone generation area, comprising:

[0008] Step 1: Obtain the historical ozone content detection 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 in the area to be identified = {T1, T2, ..., T U}, relative humidity state H = {H1, H2, ..., H V}, wind speed state W = {W1, W2, ..., W W}, boundary layer height state B = {B1, B2, ..., B R}; where T U Indicated as the Uth temperature state; H V Expressed as the Vth relative humidity state; W W Indicates the Wth wind speed state; B R It is expressed as the Rth boundary layer height state;

[0009] 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:

[0010] Step S11: setting the sampling value of any state as Sv;

[0011] Step S12: 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 sample values;

[0012] Step S13: Obtain the mean and standard deviation of the sample set, and use the mean and standard deviation to standardize the data. 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] Step S14: After completing the standardization, the standard parameters are used Adjust the numerical interval to [0,1], and use the function value of f(k) to classify the sample value Sv. The classification mechanism is:

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

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

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

[0017] Similarly, when f(k) q ≤f(k)<f(k) max When , the sample value Sv is classified into the tth level;

[0018] Among them, f(k) min ,f(k) max are the minimum and maximum values ​​of the f(k) function, f(k)1, f(k)2, ..., f(k) t are the middle values ​​of the f(k) function, 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: Determine the corresponding state level according to the classification of the sample value Sv, and the determination principle is:

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

[0021] When the sampling value Sv is classified as the second level, the corresponding state level is level 2;

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

[0023] And so on.

[0024] When the sampling value Sv is classified as the qth level, the corresponding state level is level q;

[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 temperature, the corresponding state is the temperature state, then q = U;

[0027] When the sampling value Sv is relative humidity, the corresponding state is 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, and q=R.

[0030] Step 2: Classify the obtained ozone content detection statistical data sequence ρ according to the various states corresponding to the data measurement, and obtain the ozone content data sequence ρ under the uth temperature state, the vth relative humidity state, the wth wind speed state and the rth 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 of the ozone content in the area to be identified from state i to state j in one step By state transition probability Further generate the state transition probability matrix i, j∈S;

[0032] The calculation formula for state transition probability is:

[0033]

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

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

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

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

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

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

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

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

[0042] Step S47: Repeat steps S42 to S46 to obtain the ozone content of all grid areas in the area to be identified, set an ozone content threshold, and select grid areas with an ozone content greater than the set ozone content threshold as high-value ozone generation areas.

[0043] A system for identifying high-value ozone generation areas, comprising:

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

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

[0046] It is used to classify the obtained ozone content detection statistical data sequence ρ according to the various states corresponding to the data measurement, and obtain the ozone content data sequence ρ under the uth temperature state, the vth relative humidity state, the wth wind speed state and the rth boundary layer height state. u,v,w,r Data classification module;

[0047] Used to classify the ozone content data series ρ u,v,w,r Calculate the state transition probability of the ozone content in the area to be identified from state i to state j in one step By state transition probability A state chain building module that further generates a state transition probability matrix;

[0048] A high-value area identification module is used to obtain the predicted value of ozone content at the next moment according to the acquired data using the generated state transition probability matrix, and to identify the high-value ozone generation area in the area to be identified according to the acquired predicted value.

[0049] The system also includes a memory, a processor, and a computer program stored on the memory and executable on the processor.

[0050] The present invention has the following beneficial effects:

[0051] Compared with the prior art, by obtaining a historical ozone content detection statistical data sequence ρ in the area to be identified, the ozone content interval of the area to be identified is divided into S states, the temperature state T, the relative humidity state H, the wind speed state W, and the boundary layer height state B in the area to be identified are defined, and the obtained ozone content detection statistical data sequence ρ is classified according to the various states corresponding to the data measurement, and the ozone content data sequence ρ in the uth temperature state, the vth relative humidity state, the wth wind speed state, and the rth boundary layer height state is obtained. u,v,w,r , according to the classified ozone content data sequence ρ u,v,w,r Calculate the state transition probability of the ozone content in the area to be identified from state i to state j in one step By state transition probability Further generate the state transition probability matrix Acquire real-time data of various states at the current moment in the area to be identified, use the generated state transition probability matrix to obtain the predicted value of ozone content at the next moment based on the acquired data, and identify the high-value ozone generation area in the area to be identified based on the obtained predicted value. It can use conventional state parameters such as temperature, relative humidity, wind speed, boundary layer height, etc. to monitor the changing trend of ozone content in the detection area, thereby improving the timeliness of identifying high-value ozone generation areas. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic flow chart of a method for identifying a high-value ozone generation area according to the present invention;

[0053] Figure 2 The present invention is a schematic structural diagram of a system for identifying high-value ozone generation areas. DETAILED DESCRIPTION

[0054] The present invention will be further described below in conjunction with specific implementation methods, wherein the accompanying drawings are only used for exemplary descriptions and represent only schematic diagrams rather than actual drawings, and should not be understood as limiting the present invention. In order to better illustrate the specific implementation methods of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the size 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 statistical data sequence ρ in the area to be identified.

[0057] Step 2: Divide the ozone content range of the area to be identified into S states, and define the temperature state T in the area to be identified as {T1, T2, ..., T U}, relative humidity state H = {H1, H2, ..., H V}, wind speed state W = {W1, W2, ..., W W}, boundary layer height state B = {B1, B2, ..., B R}; where T U Indicated as the Uth temperature state; H V Expressed as the Vth relative humidity state; W W Indicates the Wth wind speed state; B R It is expressed as the Rth 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: setting the sampling value of any state to 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 sample values;

[0061] Step S23: Obtain the mean and standard deviation of the sample set, and use the mean and standard deviation to standardize the data. 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, the standard parameters are used Adjust the numerical interval to [0,1], and use the function value of f(k) to classify the sample value Sv. The classification mechanism is:

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

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

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

[0066] Similarly, when f(k) q ≤f(k)<f(k) max When , the sample value Sv is classified into the tth level;

[0067] Among them, f(k) min ,f(k) max are the minimum and maximum values ​​of the f(k) function, f(k)1, f(k)2, ..., f(k) t are the middle values ​​of the f(k) function, 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: Determine the corresponding state level according to the classification of the sample value Sv, and the determination principle is:

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

[0070] When the sampling value Sv is classified as the second level, the corresponding state level is level 2;

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

[0072] And so on.

[0073] When the sampling value Sv is classified as the qth level, the corresponding state level is level q;

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

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

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

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

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

[0079] Step 3: Classify the obtained ozone content detection statistical data sequence ρ according to the various states corresponding to the data measurement, and obtain the ozone content data sequence ρ under the uth temperature state, the vth relative humidity state, the wth wind speed state and the rth boundary layer height state. 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 of the ozone content in the area to be identified from state i to state j in one step By 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 data sequence ρ u,v,w,r The frequency of ozone content transferring from state i to state j in one step.

[0083] Step 5: Obtain real-time data of various states at the current moment in the area to be identified, use the generated state transition probability matrix to obtain the predicted value of ozone content at the next moment based on the acquired data, and identify the high-value ozone generation area in the area to be identified based on the acquired predicted value.

[0084] The specific process includes the following steps:

[0085] Step S51: evenly divide the area to be identified into λ×λ grids, 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 in the area to be identified, and the state at the current time t is m t , where m t ∈S, ozone content is C t , current temperature state T t , relative humidity state is H t , wind speed state is W t , the boundary layer height state is B t ;

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

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

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

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

[0091] Step S57: Repeat steps S42 to S44 to obtain the ozone content of all grid areas in the area to be identified, set an ozone content threshold, and select grid areas with an ozone content greater than the set ozone content threshold as high-value ozone generation areas.

[0092] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for identifying high-value ozone generation areas, characterized in that: include: Step 1: Obtain the historical ozone content detection 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 in the area to be identified = {T1, T2, ..., T U }, relative humidity state H = {H1, H2, ..., H V }, wind speed state W = {W1, W2, ..., W W }, boundary layer height state B = {B1, B2, ..., B R }; where T U Indicated as the Uth temperature state; H V Expressed as the Vth relative humidity state; W W Indicates the Wth wind speed state; B R It is expressed as the Rth boundary layer height state; Step 2: Classify the obtained ozone content detection statistical data sequence ρ according to the various states corresponding to the data measurement, and obtain the ozone content data sequence ρ under the uth temperature state, the vth relative humidity state, the wth wind speed state and the rth boundary layer height state u,v,w,r , where u∈U; v∈V; w∈W; r∈R; Step 3: According to the classified ozone content data sequence ρ u,v,w,r Calculate the state transition probability of the ozone content in the area to be identified from state i to state j in one step By state transition probability Further generate the state transition probability matrix i, j∈S; Step 4: Obtain real-time data of various states in the area to be identified at the current moment, use the generated state transition probability matrix to obtain the predicted value of ozone content at the next moment based on the acquired data, and identify the high-value ozone generation area in the area to be identified based on the acquired predicted value.

2. The method for identifying a high-value ozone generation area according to claim 1, characterized in that: The calculation formula for state transition probability is: In the formula, is the data sequence ρ u,v,w,r The frequency of ozone content transferring from state i to state j in one step.

3. The method for identifying a high-value ozone generation area according to claim 1, characterized in that: In step 1, 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: Step S11: setting the sampling value of any state as Sv; Step S12: 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 sample values; Step S13: Obtain the mean and standard deviation of the sample set, and use the mean and standard deviation to standardize the data. 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; Step S14: After completing the standardization, the standard parameters are used Adjust the numerical interval to [0,1], and use the function value of f(k) to classify the sample value Sv. The classification mechanism is: When f(k) min When ≤f(k)<f(k)1, the sample value Sv is classified as the first level; When f(k)1≤f(k)<f(k)2, the sample value Sv is classified as the second level; When f(k)2≤f(k)<f(k)3, the sample value Sv is classified as the third level; Similarly, when f(k) q ≤f(k)<f(k) max When , the sample value Sv is classified into the tth level; Among them, f(k) min ,f(k) max are the minimum and maximum values ​​of the f(k) function, f(k)1, f(k)2, ..., f(k) t are the middle values ​​of the f(k) function, and f(k) min <f(k)1<f(k)2<,...,<f(k) q <f(k) max , where t is a positive integer; Step S15: Determine the corresponding state level according to the classification of the sample value Sv, and the determination principle is: When the sampling value Sv is classified as the first level, the corresponding state level is level one; When the sampling value Sv is classified as the second level, the corresponding state level is level 2; When the sampling value Sv is classified as the third level, the corresponding state level is level three; And so on. When the sampling value Sv is classified into the qth level, the corresponding state level is level q.

4. The method for identifying a high-value ozone generation area according to claim 3, characterized in that: When the sampling value Sv is the ozone content, the corresponding state is the ozone content interval state, then q = S; When the sampling value Sv is temperature, the corresponding state is the temperature state, then q = U; When the sampling value Sv is relative humidity, the corresponding state is relative humidity state, then q = V; When the sampling value Sv is the wind speed, the corresponding state is the wind speed state, then q = W; When the sampling value Sv is the boundary layer height, the corresponding state is the boundary layer height state, and q=R.

5. The method for identifying a high-value ozone generation area according to claim 1, characterized in that: The specific process of step 4 includes the following steps: Step S41: evenly divide the area to be identified into λ×λ grids, and number the grids in a certain order; Step S42: Set the current time as t, and the ozone content interval of any grid area in the area to be identified, and the state at the current time t is m t , where m t ∈S, ozone content is C t , current temperature state T t , relative humidity state is H t , wind speed state is W t , the boundary layer height state is B t ; Step S43: Generate a random number ε that obeys uniform distribution according to the state of the grid area t , where ε t ∈[0,1]; Step S44: according to the current state parameter T of the grid area t , H t , W t , B t Determine the corresponding state transition probability matrix Among them, m t+1 is the ozone content interval state of the grid area at time t+1; Step S45: According to the acquired state transition probability matrix Determine the ozone content interval state m of the grid area at time t+1 t+1 , according to the obtained ozone content interval state m t+1 Generate a random number ε that follows a uniform distribution t+1 , where ε t+1 ∈[0,1], and ε t+1 With ε t Independent of each other; Step S46: If the state m t+1 The corresponding ozone content range is [C l , C h ], then the ozone content C in the grid area at time t+1 t+1 The calculation formula is: C t+1 =C t +ε t+1 (C h -C l ); Step S47: Repeat steps S42 to S46 to obtain the ozone content of all grid areas in the area to be identified, set an ozone content threshold, and select grid areas with an ozone content greater than the set ozone content threshold as high-value ozone generation areas.

6. A system for identifying high-value ozone generation areas, characterized in that: The system is used to implement the steps of a method for identifying a high-value ozone generation area according to any one of claims 1 to 5, comprising: A data acquisition module for acquiring ozone content and status data in the area to be identified; A state definition module for the ozone content, temperature, relative humidity, wind speed, and boundary layer height state of the area to be identified; It is used to classify the obtained ozone content detection statistical data sequence ρ according to the various states corresponding to the data measurement, and obtain the ozone content data sequence ρ under the uth temperature state, the vth relative humidity state, the wth wind speed state and the rth boundary layer height state. u,v,w,r Data classification module; Used to classify the ozone content data series ρ u,v,w,r Calculate the state transition probability of the ozone content in the area to be identified from state i to state j in one step By state transition probability A state chain building module that further generates a state transition probability matrix; A high-value area identification module is used to obtain the predicted value of ozone content at the next moment according to the acquired data using the generated state transition probability matrix, and to identify the high-value ozone generation area in the area to be identified according to the acquired predicted value.

7. A system for identifying high-value ozone generation areas according to claim 6, characterized in that: The system also includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can implement the steps of a method for identifying a high-value ozone generation area as described in any one of claims 1-5 when executing the program.

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