Gas concentration space-time diffusion prediction method fusing meteorological factors and historical data

Abstract

The invention discloses a gas concentration space-time diffusion prediction method fusing meteorological factors and historical data, and relates to the technical field of gas concentration diffusion prediction, and the method comprises the following steps: constructing a meteorological monitoring array, and monitoring the gas concentration of pollution gas; carrying out range circling on the pollution gas to obtain a pollution range, and analyzing the range center of the pollution gas; acquiring historical diffusion data, and analyzing a center diffusion rule during range center diffusion of the pollution gas; range features of the pollution range are extracted, and a feature diffusion rule of the range features of the pollution gas is analyzed based on historical diffusion data and meteorological factors; performing space-time diffusion prediction on the gas concentration of the pollution gas based on a center diffusion rule and a characteristic diffusion rule; the method is used for solving the problems that according to an existing gas concentration diffusion prediction technology, the prediction result deviation of gas concentration diffusion is too large, the prediction mode is too traditional, the prediction result does not have reference value, and applicability is poor.

Description

Technical Field

[0001] This invention relates to the field of gas concentration diffusion prediction technology, specifically a method for predicting the spatiotemporal diffusion of gas concentration by integrating meteorological factors and historical data. Background Technology

[0002] Gas concentration diffusion prediction technology refers to a comprehensive technology that uses mathematical models, physical laws, and data analysis to simulate and predict the concentration distribution and changing trends of specific pollutants in three-dimensional space (longitude, latitude, and altitude) over a future period of time.

[0003] Gas concentration diffusion prediction technologies typically cannot accurately predict outdoor gas concentration diffusion because too many factors influence it. Therefore, only fuzzy predictions are possible. Even with fuzzy predictions, some reference factors still influence each other. Existing gas concentration diffusion prediction technologies do not consider these relationships when making fuzzy predictions, making it impossible to calibrate the results. This results in excessively large deviations and lack of reference value. For example, patent application CN115705457A discloses a "Gas Diffusion Situation Prediction Method and System, Storage Medium and Terminal." This scheme only predicts gas concentration diffusion using a spatiotemporal distribution map database of gas concentration values, essentially searching for identical cases from historical data. However, historical data does not encompass all situations. Without calculation and correction, the prediction results lack reference value and have poor applicability. Existing gas concentration diffusion prediction technologies also suffer from excessively large deviations in prediction results and overly traditional prediction methods, leading to unreliable and poorly applicable predictions. Summary of the Invention

[0004] This invention aims to at least partially solve one of the technical problems in the prior art. By constructing a meteorological monitoring array, it acquires and records the gas concentration of pollutants monitored by different micro-meteorological stations. Then, based on the relative positions between different installation points and the gas concentration, it delineates the range of pollutants to obtain the pollution range. Based on the pollution range, it analyzes the center of the pollution range, acquires historical diffusion data, and combines meteorological factors to analyze the center diffusion law of the pollution range. Then, it extracts features of the pollution range to obtain the range characteristics of the pollution range. Based on historical diffusion data and meteorological factors, it analyzes the characteristic diffusion law of the range characteristics of the pollution gas. Finally, based on the center diffusion law and characteristic diffusion law, it predicts the spatiotemporal diffusion of the pollutant concentration. This invention solves the problems of existing gas concentration diffusion prediction technologies having excessively large prediction deviations and overly traditional prediction methods, resulting in prediction results that lack reference value and have poor applicability.

[0005] To achieve the above objectives, in a first aspect, this application provides a method for predicting the spatiotemporal diffusion of gas concentration by integrating meteorological factors and historical data, comprising the following steps: Construct a meteorological monitoring array to acquire and record the gas concentrations of pollutants monitored by different micro-meteorological stations; Based on the locational relationship between micro weather stations, the range of polluting gases is delineated by combining gas concentration to obtain the pollution range, and the center of the pollution range is analyzed based on the pollution range. Acquire historical diffusion data and combine it with meteorological factors to analyze the central diffusion pattern of pollutants during the diffusion of the range center. Extract the extent characteristics of pollution range, and analyze the characteristic diffusion patterns of pollutant gases based on historical diffusion data and meteorological factors. Spatiotemporal diffusion prediction of pollutant gas concentration is performed based on the central diffusion law and characteristic diffusion law.

[0006] Furthermore, constructing a meteorological monitoring array to acquire and record the gas concentrations of pollutants monitored by different micro-meteorological stations includes the following sub-steps: Obtain the number of micro weather stations to be pre-installed, name it the number of installations, obtain the monitoring area, select the monitoring area with a rectangle to obtain the installation area; The installation area is evenly divided into a number of rectangles, which are named installation zones. The geometric center of each installation zone is obtained and named the installation point. A miniature weather station is installed at each installation point. The concentration of polluting gases is monitored and recorded by a micro weather station, and meteorological factors, including latitude and longitude, wind speed and wind direction, are also monitored and recorded. When recording gas concentration and meteorological factors, timestamps of gas concentration and meteorological factors are also recorded. The timestamps, gas concentration and meteorological factors are combined to form a monitoring data.

[0007] Furthermore, based on the locational relationship between micro-weather stations and combined with gas concentration, the range of pollutants is delineated to obtain the pollution range. Simultaneously, the analysis of the center of the pollutant range based on the pollution range includes the following sub-steps: Based on the relative positions of different installation points and the gas concentration, the range of pollutants is delineated to obtain the pollution range. The range center of polluting gases is analyzed based on the pollution range.

[0008] Furthermore, based on the relative positions of different installation points and the gas concentration, the pollution range is delineated to obtain the pollution range, which includes the following sub-steps: Obtain a top view of the installation area, number the installation partitions within the installation area in the top view, and label the installation partition located in the nth row and mth column as IP(n,m), where n and m are both positive integers and (n,m) is the sequence number of IP. Label the micro weather station in IP(n,m) as MS(n,m). If MS(n,m) detects a gas concentration that is not zero, meaning that there is contaminated gas in the installation partition, then the installation partition is marked as a contaminated partition, and all contaminated partitions are counted. Analyze any two contaminated zones and name them as Zone 1 and Zone 2 respectively. Label n and m of Zone 1 as N1 and M1 respectively, and label n and m of Zone 2 as N2 and M2 respectively. Calculate |N1-N2| and |M1-M2|, and label the calculation results as Q1 and Q2 respectively. If Q1≤1 and Q2≤1, then the first partition and the second partition are considered to be adjacent. All contaminated partitions are analyzed, and a contaminated range is formed by consecutively adjacent contaminated partitions.

[0009] Furthermore, based on the analysis of the pollution range, the center of the pollution gas range is specifically marked as the installation point with the highest gas concentration within the pollution range.

[0010] Furthermore, obtaining historical diffusion data and combining it with meteorological factors to analyze the central diffusion pattern of pollutants during the diffusion of the pollution center includes the following sub-steps: The historical diffusion data refers to historical monitoring data. The timestamp, gas concentration, and meteorological factors in the historical diffusion data are named historical time, historical concentration, and historical factor, respectively. The latitude and longitude, wind speed, and wind direction in the historical factors are named historical latitude and longitude, historical wind speed, and historical wind direction, respectively. To obtain the expected forecast time span, historical diffusion data with unchanged historical wind direction and continuous expected forecast time span are integrated into a set of diffusion analysis data. The average historical wind speed in the diffusion analysis data is calculated and named as the average wind speed. When analyzing any set of diffusion analysis data, name it the "analysis data" and name the earliest and latest historical times in the analysis data the "start time" and "end time," respectively. Analyze the centers of the ranges for the start and end times, and name them the start center and end center, respectively. Obtain the distance between the starting center and the ending center, and name it the diffusion distance. Establish a two-dimensional coordinate system with the average wind speed as the X-axis and the diffusion distance as the Y-axis, and name it the distance diffusion analysis chart. Enter the diffusion distance from different diffusion analysis data into the distance diffusion analysis chart according to the average wind speed. A fitting analysis was performed on the distance diffusion analysis plot, and the fitting function was named the distance diffusion relationship function. Calculate the distance between each micro-weather station and the center of the range, and name it the relative distance. Calculate the ratio of the historical concentration of each micro-weather station to the historical concentration of the center of the range, and name it the concentration ratio. Establish a two-dimensional coordinate system with the relative distance as the horizontal axis and the concentration ratio as the vertical axis, and name it the concentration change analysis chart. Enter the concentration ratio into the concentration change analysis chart according to the relative distance. A fitting analysis was performed on the concentration change analysis graph, and the fitting function was named the concentration change relationship function. The distance diffusion relationship function and the concentration change relationship function together constitute the central diffusion law.

[0011] Furthermore, the extent characteristics of the pollution zone are extracted. Based on historical diffusion data and meteorological factors, the characteristic diffusion patterns of the pollutant gas extent characteristics are analyzed, including the following sub-steps: Feature extraction is performed on the pollution range to obtain the range features of the pollution range; Based on historical diffusion data and meteorological factors, this study analyzes the characteristic diffusion patterns of pollutant gases, focusing on their range and characteristics.

[0012] Furthermore, feature extraction is performed on the contamination area to obtain its range features, including the following sub-steps: Starting from the center of the area, draw rays along the positive direction of the historical wind direction, and name them feature auxiliary lines; The outline of the contaminated area is named the range outline. The intersection of the feature auxiliary line and the range outline is named the forward boundary point. The distance between the forward boundary point and the center of the range is named the diffusion span. The diffusion span and the range outline are the range features.

[0013] Furthermore, the characteristic diffusion patterns of pollutant gases, based on historical diffusion data and meteorological factors, include the following sub-steps: The range profiles of polluting gases are integrated to obtain a profile map set. Different polluting gases have independent profile map sets. The diffusion span of each range profile in the profile map set is named the sample span. Analyze the diffusion span at the start and end times, and name them the start span and end span respectively. Calculate the difference between the end span and the start span, and name it the span difference. A two-dimensional coordinate system is established with average wind speed as the X-axis and span difference as the Y-axis, named the Span Variation Analysis Chart. The span difference is entered into the Span Variation Analysis Chart according to the average wind speed. A two-dimensional coordinate system is established with historical concentration as the horizontal axis and span difference as the vertical axis, named the Span Correction Analysis Chart. The span difference is entered into the Span Correction Analysis Chart according to the historical concentration. Fitting analysis was performed on the span variation analysis chart and the span correction analysis chart respectively, and the resulting fitted curves were named the span variation curve and the span correction curve respectively. The span variation curve, the span correction curve and the contour map set together constitute the feature diffusion law.

[0014] Furthermore, the spatiotemporal diffusion prediction of pollutant gas concentration based on central diffusion law and characteristic diffusion law includes the following sub-steps: Obtain the average wind speed and wind direction within the expected prediction time span, and name them as predicted wind speed and predicted wind direction, respectively. Analyze the current range center and name it as real-time center. Obtain the gas concentration at the real-time center and name it as center concentration. Obtain the current diffusion span and name it as real-time span, denoted by the symbol F. Find the coordinate point in the span variation curve where the X-axis equals the predicted wind speed, and name it the basic prediction point. Obtain the Y-axis value of the basic prediction point and name it the basic prediction span, represented by the symbol K. Find the coordinate point in the span correction curve where the horizontal axis equals the center concentration, and name it the correction prediction point. Obtain the vertical axis value of the correction prediction point and name it the correction prediction span, represented by the symbol Z. Obtain the minimum and maximum values ​​of historical concentrations on the span correction curve, and name them minimum concentration and maximum concentration, respectively. At the same time, obtain the average value of the minimum and maximum concentrations, and name it median concentration. Obtain the values ​​of the vertical axis corresponding to the minimum concentration, median concentration, and maximum concentration, and label them as S1, S2, and S3, respectively. Name the coordinate points in the span variation analysis graph as span variation analysis points. Obtain the span variation analysis points in the span variation analysis graph where the X-axis is equal to the predicted wind speed and name them as correction auxiliary points. Mark the minimum and maximum values ​​of the Y-axis in the correction auxiliary points as W1 and W2, respectively. Through formula Calculate the predicted value of the diffusion span, named the final predicted span, where G is the final predicted span; Substitute the predicted wind speed into the distance diffusion relationship function to solve for the diffusion distance of the real-time center along the predicted wind direction, which is named the center displacement distance. With the real-time center as the endpoint, draw a ray along the positive direction of the predicted wind direction, which is named the wind direction ray. The point on the wind direction ray that is a distance from the real-time center from the center displacement distance is named the prediction center. The point on the positive direction of the wind direction ray that is a distance from the prediction center from the final predicted span is named the prediction boundary point. Find the range of the pollutant gas whose diffusion span differs from the final predicted span in the set of contour maps, and name it the predicted contour. Overlay the feature auxiliary lines in the predicted contour with the wind direction ray, and overlap the center of the range with the center of the prediction to obtain the predicted diffusion range. The micro-weather stations within the predicted diffusion range are identified and named the predicted pollution points. The distance between the predicted pollution points and the predicted center is obtained and named the predicted spacing. The predicted spacing is substituted into the concentration change relationship function to solve for the gas concentration at the predicted pollution points and named the predicted concentration.

[0015] The beneficial effects of this invention are as follows: This invention constructs a meteorological monitoring array to acquire and record the gas concentrations of pollutants monitored by different micro-meteorological stations. Then, based on the relative positions between different installation points and the gas concentrations, the range of pollutants is delineated to obtain the pollution range. Based on the pollution range, the center of the pollution range is analyzed to obtain historical diffusion data. Combined with meteorological factors, the center diffusion pattern of the pollution gas is analyzed. The advantage is that it is impossible to accurately delineate the pollution range of pollutants in real life. Therefore, a grid division method is used to delineate the pollution range. There must be a source within a pollution range. Since high concentrations diffuse to low concentrations, the point with the highest concentration within the pollution range is its center. The center of the range will diffuse outwards, and the center diffusion pattern is the pattern of the center of the range moving with changes in wind speed. At the same time, it also reveals the proportional relationship of gas concentrations in different areas within the pollution range, improving the accuracy and effectiveness of gas concentration diffusion prediction. This invention extracts features from the pollution range to obtain its range characteristics. Then, based on historical diffusion data and meteorological factors, it analyzes the characteristic diffusion patterns of the polluted gas range. Finally, based on the center diffusion pattern and the characteristic diffusion pattern, it predicts the spatiotemporal diffusion of the polluted gas concentration. The advantage lies in the existence of a span variation curve and a span correction curve in the characteristic diffusion pattern. The span variation curve reveals the change in diffusion span with wind speed, because the higher the wind speed, the farther the gas diffuses, i.e., the larger the diffusion span. However, another influencing factor is the gas concentration. If the gas concentration is high, it can diffuse further at the same wind speed. This is because the farther away from the center of the range, the lower the gas concentration, eventually becoming undetectable. Conversely, the higher the gas concentration, the farther the distance required to reach undetectability. Therefore, the span variation curve can be corrected by the gas concentration to obtain more accurate prediction results, improving the accuracy and rationality of gas concentration diffusion prediction. Attached Figure Description

[0016] Figure 1 This is a flowchart of the steps of the method of the present invention; Figure 2 This is a schematic diagram of the installation area of ​​the present invention; Figure 3 This is a schematic diagram of the mounting point of the present invention; Figure 4 This is a schematic diagram of the distance diffusion analysis diagram of the present invention; Figure 5 This is a schematic diagram of the concentration change analysis graph of the present invention; Figure 6 This is a schematic diagram of the feature auxiliary lines of the present invention; Figure 7 This is a schematic diagram of the span variation curve of the present invention; Figure 8 This is a schematic diagram of the span correction curve of the present invention; Figure 9 This is a schematic diagram of the correction auxiliary points of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Example 1, please refer to Figure 1 As shown, this application provides a method for predicting the spatiotemporal diffusion of gas concentration by fusing meteorological factors and historical data, including the following steps: Step S1 involves constructing a meteorological monitoring array to acquire and record the gas concentrations of pollutants monitored by different micro-meteorological stations. Step S1 includes the following steps: Please see Figures 2 to 3 As shown, in step S101, the number of micro weather stations to be pre-installed is obtained and named as the number of installations; the monitoring area is obtained and a rectangular box is selected in the monitoring area to obtain the installation area. Step S102: Divide the installation area evenly into a number of rectangles, named installation partitions, obtain the geometric center of the installation partitions, named installation points, and install a miniature weather station at each installation point; Step S103: The micro weather station monitors and records the gas concentration of polluting gas, and at the same time monitors and records meteorological factors, including latitude and longitude, wind speed and wind direction. When recording the gas concentration and meteorological factors, the timestamps of the gas concentration and meteorological factors are also recorded. The timestamps, gas concentration and meteorological factors are combined to form a monitoring data. In practice, the required number of installations is 50, meaning 50 mini weather stations need to be installed within the monitoring area. A rectangular selection is made within the monitoring area to obtain the installation area as shown below. Figure 2 As shown, the geometric center is the intersection of the diagonals of the rectangle, resulting in the installation point as follows. Figure 3 As shown, a miniature weather station is installed at the installation point.

[0019] Step S2 involves delineating the pollution range based on the locational relationship between micro-weather stations and gas concentration, and simultaneously analyzing the center of the pollution range based on the pollution range. Step S2 includes the following steps: Step S201: Based on the relative positions between different installation points and the gas concentration, the range of pollutant gas is delineated to obtain the pollution range; Step S201 includes the following steps: Step S2011: Obtain a top view of the installation area, number the installation partitions within the installation area in the top view, and mark the installation partition located in the nth row and mth column as IP(n,m), where n and m are both positive integers and (n,m) is the sequence number of IP, and mark the micro weather station in IP(n,m) as MS(n,m); Step S2012: If MS(n,m) detects that the gas concentration is not zero, that is, there is polluting gas in the installation partition, then the installation partition is marked as a polluted partition, and all polluted partitions are counted. Step S2013: Analyze any two contaminated zones and name them as the first zone and the second zone respectively. Label n and m of the first zone as N1 and M1 respectively, and label n and m of the second zone as N2 and M2 respectively. Calculate |N1-N2| and |M1-M2|, and label the calculation results as Q1 and Q2 respectively. Step S2014: If Q1≤1 and Q2≤1, the first partition and the second partition are considered to be adjacent. Analyze all contaminated partitions and form a contaminated range by continuously adjacent contaminated partitions. In practice Figure 2 and Figure 3 All are top views. IP(n,m) and MS(n,m) are obtained by numbering, where 1≤n≤5 and 1≤m≤10. In this embodiment, the gas concentration specifically refers to the concentration of polluting gas. For example, MS(2,5), MS(2,6), MS(3,4), MS(3,5), and MS(5,10) are monitored as pollution zones. Taking MS(2,4) and MS(2,5) as examples, Q1 and Q2 are calculated to be 0 and 1 respectively, satisfying the conditions Q1≤1 and Q2≤1, that is, MS(2,4) and MS(2,5) are respectively. 5) Adjacency: Similarly, the adjacency conditions of MS(3,4), MS(3,5) and MS(5,10) are analyzed. It is found that MS(2,5), MS(2,6), MS(3,4) and MS(3,5) are consecutively adjacent. Therefore, MS(2,5), MS(2,6), MS(3,4) and MS(3,5) form a contaminated range, while MS(5,10) is not adjacent to any contaminated zone and forms a contaminated range independently. In the subsequent analysis, the analysis process of each contaminated range is independent.

[0020] Step S202: Analyze the center of the polluted gas range based on the pollution range; specifically, mark the installation point with the highest gas concentration within the pollution range as the center of the range. In practice, since the gas concentration diffuses from high to low, the installation point with the highest gas concentration is the source of the pollutant gas. Using this as the center of the range allows for a more accurate analysis of the diffusion of the pollutant gas. In this embodiment, the pollution range consisting of MS(2,5), MS(2,6), MS(3,4), and MS(3,5) has MS(3,4) as its center.

[0021] Step S3: Obtain historical diffusion data and analyze the central diffusion pattern of pollutant gases during the diffusion of the pollution center, combined with meteorological factors; Step S3 includes the following steps: Step S301: Historical diffusion data refers to historical monitoring data. The timestamp, gas concentration, and meteorological factors in the historical diffusion data are named historical time, historical concentration, and historical factor, respectively. The latitude and longitude, wind speed, and wind direction in the historical factors are named historical latitude and longitude, historical wind speed, and historical wind direction, respectively. Step S302: Obtain the expected prediction time span, integrate the historical diffusion data with the historical wind direction unchanged and the expected prediction time span into a set of diffusion analysis data, calculate the average value of the historical wind speed in the diffusion analysis data, and name it the average wind speed. In practice, latitude and longitude are only used to determine the location and distance of the micro weather station and have no other purpose, so they are omitted in this embodiment. Distance information can be obtained directly. The expected prediction time span is 1 hour, which means that the distribution of gas concentration needs to be predicted after 1 hour. The expected prediction time span can be modified according to the prediction requirements. After each modification, the central diffusion law and characteristic diffusion law can be re-analyzed. For example, historical diffusion data with wind direction of 31° east of north for 1 hour is a set of diffusion analysis data. The average historical wind speed within this 1 hour is calculated, and the average wind speed is 5.6 m / s.

[0022] Step S303: When analyzing any set of diffusion analysis data, name it as the called analysis data, and name the earliest historical time and the latest historical time in the called analysis data as the start time and end time, respectively. Step S304: Analyze the range centers of the start time and the end time, and name them as the start center and the end center, respectively; Please see Figure 4As shown, in step S305, the distance between the starting center and the ending center is obtained and named the diffusion distance. A two-dimensional coordinate system is established with the average wind speed as the X-axis and the diffusion distance as the Y-axis, named the distance diffusion analysis chart. The diffusion distances in different diffusion analysis data are entered into the distance diffusion analysis chart according to the average wind speed. Step S306: Perform a fitting analysis on the distance diffusion analysis diagram and name the fitting function the distance diffusion relationship function; In the specific implementation, the start time was obtained as 13:25:47 on June 5, 2025, and the end time was obtained as 14:25:47 on June 5, 2025. The center of the pollution range at 13:25:47 and 14:25:47 on June 5, 2025, was extracted from the analysis data to obtain the start center and end center. The diffusion distance was obtained as 21.64 km, which means that the point with the highest concentration of pollutant gas moved 21.64 km along the wind direction. The distance diffusion analysis map is constructed as follows. Figure 4 As shown, the distance diffusion relationship function obtained through fitting analysis is YA=3.6971×XA+0.2992, where YA is the diffusion distance and XA is the average wind speed.

[0023] Please see Figure 5 As shown, in step S307, calculate the distance between each micro weather station and the center of the range, named the relative distance, calculate the ratio of the historical concentration of each micro weather station to the historical concentration of the center of the range, named the concentration ratio, establish a two-dimensional coordinate system with the relative distance as the horizontal axis and the concentration ratio as the vertical axis, named the concentration change analysis chart, and enter the concentration ratio into the concentration change analysis chart according to the relative distance. Step S308: Perform a fitting analysis on the concentration change analysis graph, and name the fitting function the concentration change relationship function. The distance diffusion relationship function and the concentration change relationship function together constitute the central diffusion law. In practical implementation, for example, a micro-weather station monitors a historical concentration of 11.76 mg / m³, with a relative distance of 5 km from the center of the range. The historical concentration at the center of the range is 15.6 mg / m³. The calculated concentration ratio is approximately 0.7538. The result is rounded to four decimal places. Based on this, a concentration change analysis chart is constructed as follows: Figure 5 As shown, the concentration change relationship function obtained through fitting analysis is YB = -0.0079 × XB 2 -0.0098×XB+1, where YB is the concentration ratio and YX is the relative distance.

[0024] Step S4 involves extracting the extent characteristics of the pollution zone and analyzing the characteristic diffusion patterns of the pollutant gases based on historical diffusion data and meteorological factors. Step S4 includes the following steps: Step S401: Extract features from the contaminated area to obtain the range features of the contaminated area; Step S401 includes the following steps: Please see Figure 6 As shown, in step S4011, starting from the center of the range, draw a ray along the positive direction of the historical wind direction, and name it the feature auxiliary line; Step S4012: Name the outline of the contaminated area as the range outline, obtain the intersection of the feature auxiliary line and the range outline, name it as the forward boundary point, obtain the distance between the forward boundary point and the center of the range, name it as the diffusion span, and the diffusion span and the range outline are the range features. In practice, the feature auxiliary lines are drawn as follows: Figure 6 As shown, in Figure 6 In the diagram, the gray area represents the contaminated area, the black dot indicates the center of the area, the dashed line is the feature auxiliary line, and the light gray dot indicates the forward boundary point. The obtained diffusion span is 5.6 km, and the area outline is... Figure 6 The boundary of the gray area.

[0025] Step S402: Analyze the characteristic diffusion patterns of pollutant gases based on historical diffusion data and meteorological factors to understand their range characteristics. Step S402 includes the following steps: Step S4021: Integrate the range contours of the polluting gases to obtain a contour map set. Different polluting gases have independent contour map sets. Name the diffusion span of each range contour in the contour map set as the sample span. Step S4022: Analyze the diffusion span at the start time and the end time, and name them the start span and the end span respectively. Calculate the difference between the end span and the start span, and name it the span difference. Please see Figures 7 to 8 As shown, in step S4023, a two-dimensional coordinate system is established with the average wind speed as the X-axis and the span difference as the Y-axis, named the span variation analysis chart. The span difference is entered into the span variation analysis chart according to the average wind speed. A two-dimensional coordinate system is established with the historical concentration as the horizontal axis and the span difference as the vertical axis, named the span correction analysis chart. The span difference is entered into the span correction analysis chart according to the historical concentration. Step S4024: Perform fitting analysis on the span change analysis chart and the span correction analysis chart respectively, and name the obtained fitting curves as the span change curve and the span correction curve respectively. The span change curve, the span correction curve and the contour map set together constitute the feature diffusion law. In practice, the pollution range of sulfur dioxide is integrated into a dedicated sulfur dioxide profile map set, and the pollution range of nitric oxide is integrated into a dedicated nitric oxide profile map set, and so on. Each pollutant has its own independent profile map set. The span difference represents the difference between the current diffusion span of the pollutant and the predicted diffusion span after 1 hour. As time goes on, the pollution range of the pollutant will gradually expand, and the diffusion span will also increase accordingly. The resulting span change analysis map and span correction analysis map are shown below. Figure 7 As shown, the factors affecting gas diffusion mainly include concentration, wind speed, wind direction, temperature, terrain, buildings, and the properties of the pollutants themselves. This embodiment analyzes different pollutants independently, so the properties of the pollutants themselves remain unchanged. Within the same monitoring area, the terrain and buildings are fixed. Therefore, the main factors affecting gas diffusion are concentration, wind direction, wind speed, and temperature. Temperature has a weaker impact on gas diffusion under the influence of wind speed. Furthermore, this embodiment analyzes whether the diffusion of pollutants will affect residential areas, and the altitude of action is low, where there are no significant changes in temperature layers; therefore, the temperature factor can be ignored. Wind direction only affects the direction of diffusion. The only factors to consider are concentration and wind speed, with wind speed being the dominant factor. Therefore, the span variation curve is analyzed and used as a basis. However, the span variation curve is still affected by gas concentration, so a span correction curve is derived. Although the span correction curve is also affected by wind speed, this embodiment only needs to obtain the approximate trend of the span difference with temperature, not the precise value. Furthermore, the span variation analysis graph already contains the influence of wind speed on the span difference, so the influence of wind speed on the span correction analysis graph can be ignored. Since the span variation curve requires precise values, a span correction analysis graph is needed to correct it. The resulting span variation curve is shown below. Figure 7 As shown in the curve, the span correction curve is as follows: Figure 8 As shown by the curve in the figure, due to the large amount of data, Figure 7 and Figure 8 The image only shows a portion of the coordinates. Figure 7 and Figure 8 It can be seen that, Figure 7 The regression effect was significantly better than Figure 8 good, Figure 8 There are some coordinate points that differ significantly from the span correction curve. This is due to the different wind speeds. However, based on the density of the coordinate points, we can still obtain a general pattern of how the span difference changes as the gas concentration increases.

[0026] Step S5 involves predicting the spatiotemporal diffusion of pollutant gas concentrations based on central diffusion patterns and characteristic diffusion patterns. Step S5 includes the following steps: Step S501: Obtain the average wind speed and wind direction within the expected prediction time span, and name them as predicted wind speed and predicted wind direction respectively. Analyze the current range center and name it as real-time center. Obtain the gas concentration at the real-time center and name it as center concentration. Obtain the current diffusion span and name it as real-time span, represented by the symbol F. Step S502: Find the coordinate point in the span change curve where the X-axis is equal to the predicted wind speed, name it the basic prediction point, obtain the Y-axis value of the basic prediction point, name it the basic prediction span, and represent it with the symbol K; find the coordinate point in the span correction curve where the horizontal axis is equal to the center concentration, name it the correction prediction point, obtain the vertical axis value of the correction prediction point, name it the correction prediction span, and represent it with the symbol Z. Step S503: Obtain the minimum and maximum values ​​of historical concentrations on the span correction curve, and name them as minimum concentration and maximum concentration, respectively. At the same time, obtain the average value of the minimum and maximum concentrations, and name it as median concentration. Obtain the values ​​of the vertical axis corresponding to the minimum concentration, median concentration and maximum concentration, and label them as S1, S2 and S3, respectively. Please see Figure 9 As shown, in step S504, the coordinate points in the span change analysis diagram are named span change analysis points, the span change analysis points in the span change analysis diagram where the X-axis is equal to the predicted wind speed are obtained and named correction auxiliary points, and the minimum and maximum values ​​of the Y-axis in the correction auxiliary points are marked as W1 and W2 respectively. In practice, wind direction may change within one hour, but usually not significantly. Therefore, wind direction frequency is used as the standard, which is the percentage of times a certain wind direction occurs within one hour out of the total number of observations. For example, if the predicted wind direction frequency for the next hour is 52% for 31° east of north, 38% for 28° east of north, and 10% for 33° east of north, then the analysis is based on the wind direction of 31° east of north, resulting in a predicted wind speed of 6.8 m / s and a predicted wind direction of 31° east of north. With a central concentration of 30 mg / m³ and a real-time span F of 1 km, the coordinate point on the span variation curve where the X-axis equals 6.8 m / s is found, yielding a basic predicted span K of 2.52 km. The coordinate point on the span correction curve where the horizontal axis equals 30 mg / m³ is found, yielding a corrected predicted span Z of 1.86 km. Simultaneously, S1, S2, and S3 are obtained as 0.6 km, 1.9 km, and 4.91 km, respectively. The correction auxiliary point on the span variation analysis graph where the X-axis equals 6.8 m / s is obtained as follows: Figure 9 As shown, W1 and W2 were extracted to be 1.92 km and 3.09 km respectively.

[0027] Step S505, using the formula Calculate the predicted value of the diffusion span, named the final predicted span, where G is the final predicted span; In practical implementation, during the fitting analysis, points on the span variation curve approach the average level when X is equal. That is, the basic predicted span in the span correction analysis chart approaches the median of historical concentrations, i.e., K approaches S2. The highest and lowest points in the correction auxiliary points approach the highest and lowest historical concentrations in the span correction analysis chart, i.e., S1 approaches W1, and S3 approaches W2. If we assume the actual span difference in the span variation analysis chart is β, then... , and Similarly, the three calculated β values ​​are not equal because their actual relationship is only approximately equal. However, by calculating the average of the three approximate relationships, a very close prediction value can be obtained. Adding this to F gives the predicted diffusion span, i.e., solving for the final predicted span G. In this embodiment, G is calculated to be 4.20 km. The calculation result is rounded to two decimal places. That is, the diffusion span predicted by the traditional prediction method is 2.52 km + F = 3.52 km, but the diffusion span predicted after correction is 4.2 km. Finally, after 1 hour, the actual diffusion span was monitored to be 4.12 km. It can be seen that the prediction result after correction is much more accurate than the traditional prediction result.

[0028] Step S506: Substitute the predicted wind speed into the distance diffusion relationship function to solve for the diffusion distance of the real-time center along the predicted wind direction, which is named the center displacement distance. With the real-time center as the endpoint, draw a ray along the positive direction of the predicted wind direction, which is named the wind direction ray. Name the point on the wind direction ray that is a distance from the real-time center to the center displacement distance as the prediction center. Name the point on the positive direction of the wind direction ray that is a distance from the prediction center to the final predicted span as the prediction boundary point. Step S507: Find the range of the contour map corresponding to the polluted gas with the smallest difference between the diffusion span and the final predicted span, name it the predicted contour, overlap the feature auxiliary lines in the predicted contour with the wind direction ray, and overlap the center of the range with the predicted center to obtain the predicted diffusion range. In practice, substituting XA = 6.8 m / s into YA = 3.6971 × XA + 0.2992, the center displacement distance is calculated to be 25.43948 km. This means the real-time center will move 25.43948 km towards the real-time wind direction after 1 hour. A wind direction ray is plotted, and the point 25.43948 km away from the real-time center on the positive direction of the wind direction ray is the predicted center, representing the center of the pollution range after 1 hour. The point 4.2 km away from the predicted center on the positive direction of the wind direction ray is the predicted boundary point, representing the farthest distance the pollution range can spread relative to the predicted center after 1 hour. Since the buildings and terrain within a monitoring area are fixed, and different pollutants are analyzed independently, the overhead profiles of pollutants during diffusion usually have a certain similarity. Therefore, the range profiles in the profile set have certain reference value. Because the profiles after gas diffusion cannot be accurately predicted, only this approximate state can be selected for representation. The feature auxiliary lines in the predicted profile are overlapped with the wind direction rays, and the range center is overlapped with the prediction center to finally obtain the predicted diffusion range, which represents the pollutants that may exist in this range after 1 hour. This can provide early warning for environmental safety behaviors such as environmental protection and personnel evacuation.

[0029] Step S508: Obtain the micro-weather station within the predicted diffusion range, name it the predicted pollution point, obtain the distance between the predicted pollution point and the predicted center, name it the predicted spacing, substitute the predicted spacing into the concentration change relationship function, solve for the gas concentration at the predicted pollution point, name it the predicted concentration. In practice, since the concentration of pollutant gas follows the law of conservation of energy, the total concentration of pollutant gas usually does not change significantly. The concentration change relationship function reveals the attenuation of gas concentration with increasing distance. Combined with the area of ​​the predicted diffusion range, the gas concentration in different areas can be estimated. This embodiment focuses on the prediction of the pollution range. The specific gas concentration distribution can also be obtained based on the concentration change relationship function, the area of ​​the predicted diffusion range, and the predicted spacing. This embodiment will not be described in detail.

[0030] Example 2: This application provides an electronic device, which may include a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory communicate with each other via the communication bus. The memory stores computer-readable instructions, and the processor can call these instructions. When the processor executes a computer-readable instruction, it performs steps such as those in a gas concentration spatiotemporal diffusion prediction method that integrates meteorological factors and historical data to achieve the following functions: constructing a meteorological monitoring array to monitor the gas concentration of pollutants; delineating the range of pollutants to obtain the pollution range and analyzing the center of the pollution range; acquiring historical diffusion data and analyzing the center diffusion pattern of the pollution range; extracting the range characteristics of the pollution range and analyzing the characteristic diffusion pattern of the pollution range characteristics based on historical diffusion data and meteorological factors; and predicting the spatiotemporal diffusion of the pollutant concentration based on the center diffusion pattern and the characteristic diffusion pattern.

[0031] Furthermore, when the logical instructions in the aforementioned memory can be implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0032] Example 3: This application also provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the gas concentration spatiotemporal diffusion prediction method that integrates meteorological factors and historical data provided by the above methods. The method includes: constructing a meteorological monitoring array to monitor the gas concentration of pollutants; delineating the range of pollutants to obtain the pollution range, and simultaneously analyzing the center of the pollution range; acquiring historical diffusion data and analyzing the center diffusion pattern of the pollution range; extracting the range characteristics of the pollution range, and analyzing the characteristic diffusion pattern of the pollution range characteristics based on historical diffusion data and meteorological factors; and performing spatiotemporal diffusion prediction of the gas concentration of pollutants based on the center diffusion pattern and the characteristic diffusion pattern.

[0033] Example 4: This application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it performs the steps in the above-described method for predicting the spatiotemporal diffusion of gas concentration by fusing meteorological factors and historical data, to achieve the following functions: constructing a meteorological monitoring array to monitor the gas concentration of pollutants; delineating the range of pollutants to obtain the pollution range, and simultaneously analyzing the center of the pollution range; acquiring historical diffusion data and analyzing the center diffusion pattern of the pollution range; extracting the range characteristics of the pollution range, and analyzing the characteristic diffusion pattern of the pollution range characteristics based on historical diffusion data and meteorological factors; and predicting the spatiotemporal diffusion of the pollutant concentration based on the center diffusion pattern and the characteristic diffusion pattern.

[0034] Based on the above description of the embodiments, the embodiments of the present invention can be provided as methods, systems, or computer program products. Based on this understanding, the above technical solutions, in essence or in terms of their contribution to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or certain parts of the embodiments.

[0035] In the embodiments provided in this application, it should be understood that the disclosed system or method can be implemented in other ways. The embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division, and there may be other division methods in actual implementation. Furthermore, multiple modules or units may be combined or integrated into another system, or some features may be ignored or not executed. Additionally, the coupling or direct coupling or communication connection shown or discussed may be through some communication interfaces. The indirect coupling or communication connection between systems, modules, and units may be electrical, mechanical, or other forms.

[0036] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A method for predicting the spatiotemporal diffusion of gas concentration by integrating meteorological factors and historical data, characterized in that: Includes the following steps: Construct a meteorological monitoring array to acquire and record the gas concentrations of pollutants monitored by different micro-meteorological stations; Based on the locational relationship between micro weather stations, the range of polluting gases is delineated by combining gas concentration to obtain the pollution range, and the center of the pollution range is analyzed based on the pollution range. Acquire historical diffusion data and combine it with meteorological factors to analyze the central diffusion pattern of pollutants during the diffusion of the range center. Extract the extent characteristics of pollution range, and analyze the characteristic diffusion patterns of pollutant gases based on historical diffusion data and meteorological factors. Spatiotemporal diffusion prediction of pollutant gas concentration is performed based on the central diffusion law and characteristic diffusion law.

2. The method for predicting the spatiotemporal diffusion of gas concentration by integrating meteorological factors and historical data according to claim 1, characterized in that, Constructing a meteorological monitoring array to acquire and record the gas concentrations of pollutants monitored by different micro-meteorological stations includes the following sub-steps: Obtain the number of micro weather stations to be pre-installed, name it the number of installations, obtain the monitoring area, select the monitoring area with a rectangle to obtain the installation area; The installation area is evenly divided into a number of rectangles, which are named installation zones. The geometric center of each installation zone is obtained and named the installation point. A miniature weather station is installed at each installation point. The concentration of polluting gases is monitored and recorded by a micro weather station, and meteorological factors, including latitude and longitude, wind speed and wind direction, are also monitored and recorded. When recording gas concentration and meteorological factors, timestamps of gas concentration and meteorological factors are also recorded. The timestamps, gas concentration and meteorological factors are combined to form a monitoring data.

3. The method for predicting the spatiotemporal diffusion of gas concentration by integrating meteorological factors and historical data according to claim 2, characterized in that, Based on the locational relationships between micro-weather stations and combined with gas concentration, the range of pollutants is delineated to obtain the pollution range. Simultaneously, the analysis of the center of the pollutant range based on the pollution range includes the following sub-steps: Based on the relative positions of different installation points and the gas concentration, the range of pollutants is delineated to obtain the pollution range. The range center of polluting gases is analyzed based on the pollution range.

4. The method for predicting the spatiotemporal diffusion of gas concentration by integrating meteorological factors and historical data according to claim 3, characterized in that, Based on the relative positions of different installation points and combined with gas concentration, the pollution range is delineated, and the pollution range is obtained through the following sub-steps: Obtain a top view of the installation area, number the installation partitions within the installation area in the top view, and label the installation partition located in the nth row and mth column as IP(n,m), where n and m are both positive integers and (n,m) is the sequence number of IP. Label the micro weather station in IP(n,m) as MS(n,m). If MS(n,m) detects a gas concentration that is not zero, meaning that there is contaminated gas in the installation partition, then the installation partition is marked as a contaminated partition, and all contaminated partitions are counted. Analyze any two contaminated zones and name them as Zone 1 and Zone 2 respectively. Label n and m of Zone 1 as N1 and M1 respectively, and label n and m of Zone 2 as N2 and M2 respectively. Calculate |N1-N2| and |M1-M2|, and label the calculation results as Q1 and Q2 respectively. If Q1≤1 and Q2≤1, then the first partition and the second partition are considered to be adjacent. All contaminated partitions are analyzed, and a contaminated range is formed by consecutively adjacent contaminated partitions.

5. The method for predicting the spatiotemporal diffusion of gas concentration by integrating meteorological factors and historical data according to claim 4, characterized in that, Based on the analysis of the pollution range, the center of the pollution gas range is specifically marked as the installation point with the highest gas concentration within the pollution range.

6. The method for predicting the spatiotemporal diffusion of gas concentration by fusing meteorological factors and historical data according to claim 5, characterized in that, Obtaining historical diffusion data and analyzing the central diffusion pattern of pollutants during the range-center diffusion of pollutants in conjunction with meteorological factors includes the following sub-steps: The historical diffusion data refers to historical monitoring data. The timestamp, gas concentration, and meteorological factors in the historical diffusion data are named historical time, historical concentration, and historical factor, respectively. The latitude and longitude, wind speed, and wind direction in the historical factors are named historical latitude and longitude, historical wind speed, and historical wind direction, respectively. To obtain the expected forecast time span, historical diffusion data with unchanged historical wind direction and continuous expected forecast time span are integrated into a set of diffusion analysis data. The average historical wind speed in the diffusion analysis data is calculated and named as the average wind speed. When analyzing any set of diffusion analysis data, name it the "analysis data" and name the earliest and latest historical times in the analysis data the "start time" and "end time," respectively. Analyze the centers of the ranges for the start and end times, and name them the start center and end center, respectively. Obtain the distance between the starting center and the ending center, and name it the diffusion distance. Establish a two-dimensional coordinate system with the average wind speed as the X-axis and the diffusion distance as the Y-axis, and name it the distance diffusion analysis chart. Enter the diffusion distance from different diffusion analysis data into the distance diffusion analysis chart according to the average wind speed. A fitting analysis was performed on the distance diffusion analysis plot, and the fitting function was named the distance diffusion relationship function. Calculate the distance between each micro-weather station and the center of the range, and name it the relative distance. Calculate the ratio of the historical concentration of each micro-weather station to the historical concentration of the center of the range, and name it the concentration ratio. Establish a two-dimensional coordinate system with the relative distance as the horizontal axis and the concentration ratio as the vertical axis, and name it the concentration change analysis chart. Enter the concentration ratio into the concentration change analysis chart according to the relative distance. A fitting analysis was performed on the concentration change analysis graph, and the fitting function was named the concentration change relationship function. The distance diffusion relationship function and the concentration change relationship function together constitute the central diffusion law.

7. The method for predicting the spatiotemporal diffusion of gas concentration by integrating meteorological factors and historical data according to claim 6, characterized in that, Extracting the extent characteristics of pollution zones, and analyzing the characteristic diffusion patterns of pollutant gases based on historical diffusion data and meteorological factors, includes the following sub-steps: Feature extraction is performed on the pollution range to obtain the range features of the pollution range; Based on historical diffusion data and meteorological factors, this study analyzes the characteristic diffusion patterns of pollutant gases, focusing on their range and characteristics.

8. The method for predicting the spatiotemporal diffusion of gas concentration by integrating meteorological factors and historical data according to claim 7, characterized in that, Extracting features from the contaminated area to obtain its range features includes the following sub-steps: Starting from the center of the area, draw rays along the positive direction of the historical wind direction, and name them feature auxiliary lines; The outline of the contaminated area is named the range outline. The intersection of the feature auxiliary line and the range outline is named the forward boundary point. The distance between the forward boundary point and the center of the range is named the diffusion span. The diffusion span and the range outline are the range features.

9. The method for predicting the spatiotemporal diffusion of gas concentration by integrating meteorological factors and historical data according to claim 8, characterized in that, The characteristic diffusion patterns of pollutant gases, based on historical diffusion data and meteorological factors, include the following sub-steps: The range profiles of polluting gases are integrated to obtain a profile map set. Different polluting gases have independent profile map sets. The diffusion span of each range profile in the profile map set is named the sample span. Analyze the diffusion span at the start and end times, and name them the start span and end span respectively. Calculate the difference between the end span and the start span, and name it the span difference. A two-dimensional coordinate system is established with average wind speed as the X-axis and span difference as the Y-axis, named the Span Variation Analysis Chart. The span difference is entered into the Span Variation Analysis Chart according to the average wind speed. A two-dimensional coordinate system is established with historical concentration as the horizontal axis and span difference as the vertical axis, named the Span Correction Analysis Chart. The span difference is entered into the Span Correction Analysis Chart according to the historical concentration. Fitting analysis was performed on the span variation analysis chart and the span correction analysis chart respectively, and the resulting fitted curves were named the span variation curve and the span correction curve respectively. The span variation curve, the span correction curve and the contour map set together constitute the feature diffusion law.

10. The method for predicting the spatiotemporal diffusion of gas concentration by fusing meteorological factors and historical data according to claim 9, characterized in that, Predicting the spatiotemporal diffusion of pollutant gas concentrations based on central diffusion and characteristic diffusion patterns includes the following sub-steps: Obtain the average wind speed and wind direction within the expected prediction time span, and name them as predicted wind speed and predicted wind direction, respectively. Analyze the current range center and name it as real-time center. Obtain the gas concentration at the real-time center and name it as center concentration. Obtain the current diffusion span and name it as real-time span, denoted by the symbol F. Find the coordinate point in the span variation curve where the X-axis equals the predicted wind speed, and name it the basic prediction point. Obtain the Y-axis value of the basic prediction point and name it the basic prediction span, represented by the symbol K. Find the coordinate point in the span correction curve where the horizontal axis equals the center concentration, and name it the correction prediction point. Obtain the vertical axis value of the correction prediction point and name it the correction prediction span, represented by the symbol Z. Obtain the minimum and maximum values ​​of historical concentrations on the span correction curve, and name them minimum concentration and maximum concentration, respectively. At the same time, obtain the average value of the minimum and maximum concentrations, and name it median concentration. Obtain the values ​​of the vertical axis corresponding to the minimum concentration, median concentration, and maximum concentration, and label them as S1, S2, and S3, respectively. Name the coordinate points in the span variation analysis graph as span variation analysis points. Obtain the span variation analysis points in the span variation analysis graph where the X-axis is equal to the predicted wind speed and name them as correction auxiliary points. Mark the minimum and maximum values ​​of the Y-axis in the correction auxiliary points as W1 and W2, respectively. Through formula Calculate the predicted value of the diffusion span, named the final predicted span, where G is the final predicted span; Substitute the predicted wind speed into the distance diffusion relationship function to solve for the diffusion distance of the real-time center along the predicted wind direction, which is named the center displacement distance. With the real-time center as the endpoint, draw a ray along the positive direction of the predicted wind direction, which is named the wind direction ray. The point on the wind direction ray that is a distance from the real-time center from the center displacement distance is named the prediction center. The point on the positive direction of the wind direction ray that is a distance from the prediction center from the final predicted span is named the prediction boundary point. Find the range of the pollutant gas whose diffusion span differs from the final predicted span in the set of contour maps, and name it the predicted contour. Overlay the feature auxiliary lines in the predicted contour with the wind direction ray, and overlap the center of the range with the center of the prediction to obtain the predicted diffusion range. The micro-weather stations within the predicted diffusion range are identified and named the predicted pollution points. The distance between the predicted pollution points and the predicted center is obtained and named the predicted spacing. The predicted spacing is substituted into the concentration change relationship function to solve for the gas concentration at the predicted pollution points and named the predicted concentration.

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