Petrochemical data large model analysis and management method based on AI

Abstract

The invention relates to the technical field of petrochemical data analysis, in particular to an AI-based petrochemical data large model analysis management method, which comprises the following steps: acquiring a petrochemical production area land, dividing the petrochemical production area land into a plurality of areas, collecting soil data of each area, and determining whether a polluted area exists or not; acquiring position data of the polluted area according to the determined polluted area, and outputting a pollution evaluation value of the petrochemical production area land through a pollution analysis model of the petrochemical production area land; if the pollution evaluation value of the petrochemical production area land is greater than or equal to the pollution evaluation threshold value of the petrochemical production area land, generating a petrochemical production area land pollution severity signal; determining whether external pollution and detection points of the external pollution exist or not based on the severe land pollution signal of the petrochemical production area, and planning a detection route; according to the invention, the external pollution source can be detected, and the detection sequence can be reasonably planned.

Description

Technical Field

[0001] The present invention relates to the technical field of petrochemical data analysis, and in particular to an AI-based petrochemical data large model analysis and management method. Background Art

[0002] As an important provider of energy and chemical raw materials, the petrochemical industry's production activities often require a large amount of land for the construction of factories, storage facilities, etc. The petrochemical industry produces a large amount of pollutants during the production process, including wastewater, waste gas, waste residue, etc. If these pollutants are not properly treated, they may cause pollution to environmental elements such as soil and water sources.

[0003] In existing technologies, combining petrochemical data with land pollution monitoring data can more accurately identify pollution sources, pollution scope and pollution degree, provide a scientific basis for formulating effective pollution prevention and control measures, and help to promptly discover and deal with environmental pollution problems, prevent the expansion of pollution scope and the aggravation of pollution degree.

[0004] Based on this, this application proposes an AI-based petrochemical data big model analysis and management method. Summary of the invention

[0005] The purpose of the present invention is to provide an AI-based petrochemical data large model analysis and management method. The technical problem solved by the present invention is: it is difficult to accurately evaluate the pollution level of petrochemical production area land based on limited land survey data; it is even more impossible to analyze and judge the pollution sources and pollution detection points of petrochemical production area land based on the evaluation results, and ultimately it is impossible to reasonably plan the pollution level of petrochemical production area land to solve the problem of efficient detection of pollution in petrochemical production area land.

[0006] The purpose of the present invention can be achieved through the following technical solutions: A petrochemical data big model analysis and management method based on AI, comprising the following steps: Step 1: Obtain the land in the petrochemical production area, divide the land in the petrochemical production area into multiple areas, collect soil data in each area, and determine whether there is a contaminated area; Step 2: According to the determined pollution area, the location data of the pollution area is obtained, and the pollution assessment value of the petrochemical production area land is output through the pollution analysis model of the petrochemical production area land; Step 3: If the pollution assessment value of the petrochemical production area land is greater than or equal to the pollution assessment threshold of the petrochemical production area land, a serious pollution signal of the petrochemical production area land is generated; Step 4: Based on the serious pollution signal of the land used in the petrochemical production area, determine whether there is external pollution and the detection points of external pollution, and plan the detection route.

[0007] As a further solution of the present invention: in step 1, the contaminated area determination process is: Divide the reserved land into multiple areas in equal areas, sample and analyze the soil in each area, and obtain soil data for each area; If the soil data is greater than or equal to the corresponding standard soil data, it means that the soil quality in the area is relatively low and does not meet the land use standards, and the area is marked as a polluted area.

[0008] As a further solution of the present invention: in step 2, the output process of the pollution assessment value of the petrochemical production area land is: Through the pollution analysis model of petrochemical production area land , the pollution assessment value ZRT of the petrochemical production area is calculated; among them, BF represents the pollution area dispersion ratio, SWi represents the pollution excess value of the i-th pollution area, and i represents the number of pollution areas.

[0009] As a further solution of the present invention: the process of obtaining the pollution area dispersion ratio is: Obtain the determined contaminated area, connect the contaminated area in a clockwise direction and from inside to outside, and construct a closed curve of the contaminated area; The length of the closed curve of the polluted area and the length of the edge of the petrochemical production area are obtained, and the ratio of the length of the closed curve of the polluted area to the length of the edge of the petrochemical production area is calculated to obtain the dispersion ratio of the polluted area.

[0010] As a further solution of the present invention: the process of obtaining the pollution exceeding standard value of the i-th pollution area is: If the soil data is greater than or equal to the soil data of the corresponding standard, the area is marked as a contaminated area; The soil data in the polluted area that is greater than or equal to the corresponding standard is obtained and marked as excessive soil data. , calculate the soil data exceeding standard value STi; among them, BTi represents the standard soil data, and CTi represents the exceeding standard soil data; The pollution excess value SWi of the i-th polluted area is obtained by summing up all the soil data excess values ​​STi.

[0011] As a further solution of the present invention: in step 4, the process of determining the suspected external pollution area is: Obtain whether the contaminated area is within the land edge area. If so, mark the contaminated area as a suspected external contamination area.

[0012] As a further solution of the present invention: the process of determining the detection point of external contamination is: If all suspected external pollution areas have discontinuous distribution, multiple external pollution analysis areas are obtained; According to multiple external pollution analysis areas, the external pollution analysis area with the maximum pollution exceeding standard value is obtained, and the position where the external pollution analysis area with the maximum pollution exceeding standard value borders the land edge area is marked as an external pollution detection point.

[0013] As a further solution of the present invention: the planning process of the detection sequence is: Based on the suspected external pollution comparison value of each external pollution analysis area, the external pollution detection points are arranged from large to small according to the suspected external pollution comparison value to obtain the arrangement order of the external pollution detection points.

[0014] As a further solution of the present invention: the process of obtaining the suspected comparison value of external pollution is: The regional numerical diffusion coefficient and the excess numerical diffusion coefficient are marked as XSq and XSc, respectively, and the formula , the external pollution suspected comparison value ZBW is calculated; where a1 and a2 are weight proportion coefficients.

[0015] As a further solution of the present invention: the process of obtaining the numerical diffusion coefficient is: Obtain the number of suspected external pollution areas and the number of polluted areas adjacent to the suspected external pollution areas, and calculate the sum of the values ​​to obtain the regional numerical diffusion value, and then calculate the ratio of the regional numerical diffusion value to the number of areas divided into land use areas in the petrochemical production area to obtain the numerical diffusion coefficient; The process of obtaining the excessive numerical diffusion coefficient is as follows: Obtain the pollution excess value of the suspected external pollution area and the pollution excess value of the polluted area adjacent to the suspected external pollution area, and sum them up to obtain the excess numerical diffusion value, and calculate the ratio of the excess numerical diffusion value to the total pollution excess value of all polluted areas to obtain the excess numerical diffusion coefficient.

[0016] Beneficial effects of the present invention: (1) The present invention obtains the land of the petrochemical production area, divides the land of the petrochemical production area into multiple areas, collects soil data of each area, and determines whether there is a polluted area; according to the determined polluted area, obtains the location data of the polluted area, and outputs the pollution assessment value of the land of the petrochemical production area through the pollution analysis model of the land of the petrochemical production area; compares the pollution assessment value of the land of the petrochemical production area with the corresponding threshold value, and generates a signal whether the pollution of the land of the petrochemical production area is serious; the present invention detects and divides the polluted area of ​​the land of the petrochemical production area, and according to the location and pollution degree of the divided polluted area, the quality of the land of the petrochemical production area can be more accurately monitored, and the subsequent planning work can be completed more reasonably based on the data and signals obtained by monitoring; (2) The present invention determines whether there is external pollution and the detection points of external pollution, and plans the detection route based on the serious pollution signal of the land used in the petrochemical production area. The present invention makes a preliminary judgment on whether the pollution of the land used in the petrochemical production area is caused by the external pollution source, and then determines the location of the detection point based on the result of the preliminary judgment, and the arrangement order of the external pollution detection points obtained according to the suspected comparison value of the external pollution, so as to rationally plan the priority of the external pollution, thereby not only realizing the detection of the external pollution source, but also realizing the reasonable planning of the detection order. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below in conjunction with the accompanying drawings.

[0018] Figure 1 is a flowchart of Embodiment 1 of the present invention; Figure 2 is a flowchart of Embodiment 2 of the present invention; Figure 3 It is a system block diagram of embodiment 3 of the present invention. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Example 1 See also Figure 1 As shown, the present invention is a petrochemical data big model analysis and management method based on AI, comprising the following steps: Step 1: Obtain the land in the petrochemical production area, divide the land in the petrochemical production area into multiple areas, collect soil data in each area, and determine whether there is a contaminated area; In some embodiments, the scope of the petrochemical production area is obtained, the petrochemical production area is divided into multiple areas in an equal area manner (or the functional role of the area), and the soil of each area is sampled and analyzed to obtain soil data of each area; Among them, soil data for each area include heavy metal pollutant content, organic pollutant content, and biological (petroleum) pollutant content; Compare the soil data of each area with the soil data of the corresponding standard; If the soil data is smaller than the corresponding standard soil data, it means that the soil pollution level in the area is low, and the area is marked as a non-polluted area; If the soil data is greater than or equal to the corresponding standard soil data, it means that the soil pollution level in the area is high, and the area is marked as a polluted area; An example is: soil data for each area is compared with soil data for the corresponding standard; If any one or more of the heavy metal pollutant content, organic pollutant content, and biological pollutant content in each area is greater than or equal to the corresponding standard heavy metal pollutant content, organic pollutant content, and biological pollutant content, the area will be marked as a non-polluted area; If the heavy metal pollutant content, organic pollutant content, and biological pollutant content of each area are all less than the corresponding standard heavy metal pollutant content, organic pollutant content, and biological pollutant content, the area will be marked as a polluted area; Step 2: According to the determined pollution area, the location data of the pollution area is obtained, and the pollution assessment value of the petrochemical production area land is output through the pollution analysis model of the petrochemical production area land; In some embodiments, the determined contaminated area is obtained, and the contaminated area is connected in a clockwise direction and from the inside to the outside to construct a closed curve of the contaminated area; It should be noted that: if the polluted areas are in the same horizontal or vertical direction, the area close to the center of the land is used as the starting point of the line segment, and the area away from the center of the land is used as the end point of the curve, and multiple polluted areas in the same horizontal or vertical direction are connected. If the polluted areas are not in the same horizontal or vertical direction, the polluted areas are connected in a clockwise direction; The length of the closed curve of the pollution area and the length of the edge of the petrochemical production area land are obtained, and the ratio of the length of the closed curve of the pollution area to the length of the edge of the petrochemical production area land is calculated to obtain the dispersion ratio of the pollution area, which indicates the dispersion degree of the pollution area in the petrochemical production area land. The larger the dispersion ratio of the pollution area, the greater the dispersion degree of multiple pollution areas in the petrochemical production area land, which will make it have a greater pollution diffusion capacity based on the current situation, and the petrochemical production area land has a greater pollution risk. The smaller the dispersion ratio of the pollution area, the smaller the dispersion degree of multiple pollution areas in the petrochemical production area land, which will make it have a smaller pollution diffusion capacity based on the current situation, and the petrochemical production area land has a smaller pollution risk; Through the pollution analysis model of petrochemical production area land , the pollution assessment value ZRT of the petrochemical production area is calculated; where BF represents the pollution area dispersion ratio, SWi represents the pollution excess value of the i-th pollution area, and i represents the number of pollution areas; The process of obtaining SWi is as follows: If the soil data is greater than or equal to the corresponding standard soil data, it means that the soil quality in the area is relatively low and does not meet the land use standards, and the area is marked as a polluted area; The soil data in the polluted area that is greater than or equal to the corresponding standard is obtained and marked as excessive soil data. , calculate the soil data exceeding standard value STi; among them, BTi represents the standard soil data, and CTi represents the exceeding standard soil data; All soil data exceeding standard values ​​STi are summed up to obtain the pollution exceeding standard value SWi of the i-th polluted area; Step 3: Compare the pollution assessment value of the land in the petrochemical production area with the corresponding threshold value to generate a signal of whether the land pollution in the petrochemical production area is serious; In some embodiments, a pollution assessment value of land in a petrochemical production area is obtained, and the pollution assessment value of land in a petrochemical production area is compared with a corresponding threshold value; If the pollution assessment value of the land in the petrochemical production area is greater than or equal to the pollution assessment threshold of the land in the petrochemical production area, a serious pollution signal of the land in the petrochemical production area is generated; If the pollution assessment value of the land in the petrochemical production area is less than the pollution assessment threshold of the land in the petrochemical production area, a signal indicating that the land in the petrochemical production area is not seriously polluted is generated; It needs to be explained that the pollution assessment value of the petrochemical production area reflects the dispersion degree of the polluted area and the degree of exceeding the standard in the petrochemical production area. That is, the larger the pollution assessment value of the petrochemical production area, the more serious the pollution of the petrochemical production area, and the smaller the pollution assessment value of the petrochemical production area, the less serious the pollution of the petrochemical production area. The technical solution of the embodiment of the present invention is as follows: obtaining the land of the petrochemical production area, dividing the land of the petrochemical production area into multiple areas, collecting soil data of each area, and determining whether there is a polluted area; obtaining the location data of the polluted area according to the determined polluted area, and outputting the pollution assessment value of the land of the petrochemical production area through the pollution analysis model of the land of the petrochemical production area; comparing the pollution assessment value of the land of the petrochemical production area with the corresponding threshold value, and generating a signal whether the pollution of the land of the petrochemical production area is serious; the present invention detects and divides the polluted areas of the land of the petrochemical production area, and according to the location and pollution degree of the divided polluted areas, the quality of the land of the petrochemical production area can be more accurately monitored, and the subsequent planning work can be completed more reasonably based on the data and signals obtained by monitoring.

[0021] Example 2 See also Figure 2 As shown, the present invention is a petrochemical data big model analysis and management method based on AI, which also includes the following steps: Step 4: Based on the serious pollution signal of the land used in the petrochemical production area, determine whether there are suspected areas of external pollution and detection points of external pollution, and plan the detection sequence; In some implementation schemes, when a serious pollution signal of petrochemical production area land is obtained, it is determined whether the polluted area is located in the edge area of ​​the petrochemical production area land. If so, the polluted area is marked as an external pollution suspected area, and if not, the polluted area is marked as an internal pollution area. The external pollution suspected area indicates that the pollution of the land area may be caused by pollution sources outside the petrochemical production area land with a high probability, and the internal pollution area indicates that the pollution of the land area may be caused by pollution sources outside the petrochemical production area land with a low probability. If all the suspected external pollution areas do not have discontinuous distribution (for example, if there are 5 suspected external pollution areas, and these 5 suspected external pollution areas are arranged in sequence, and there is no non-polluted area in between, this is the case of no discontinuous distribution), obtain the suspected external pollution area with the maximum pollution exceeding standard value, and mark the location where the suspected external pollution area with the maximum pollution exceeding standard value borders with the land edge area as the external pollution detection point; On the contrary, if all the suspected external pollution areas have a discontinuous distribution phenomenon (for example, if there are 5 suspected external pollution areas, and the 1st and 2nd suspected external pollution areas are arranged in sequence, the 3rd in the middle is a non-polluted area, and the 4th, 5th, and 6th suspected external pollution areas are arranged in sequence, this is a discontinuous distribution phenomenon), multiple external pollution analysis areas are divided (wherein, the division method is determined according to the non-polluted area in the middle, and both sides of the non-polluted area in the middle are suspected external pollution areas, marked as external pollution analysis areas); According to multiple external pollution analysis areas, the external pollution analysis area with the maximum pollution exceeding standard value is obtained, and the location where the external pollution analysis area with the maximum pollution exceeding standard value borders the land edge area is marked as an external pollution detection point; at the same time, the external pollution suspected comparison value of each external pollution analysis area is output, and the external pollution suspected comparison value is arranged from large to small to obtain the arrangement order of the external pollution detection points; When the external pollution detection points of the petrochemical production area are obtained, personnel are arranged to conduct on-site inspections of each external pollution detection point to further determine whether the petrochemical production area has external pollution; and when there are multiple external pollution detection points, it indicates that the current petrochemical production area may have multiple external pollution sources, as well as the pollution level of each external pollution analysis area. Further, according to the arrangement order of the external pollution detection points obtained by the suspected comparison value of external pollution, the priority of external pollution is rationally planned; Specifically, the process of obtaining the external pollution suspected comparison value is as follows: Obtain the number of suspected external pollution areas and the number of polluted areas adjacent to the suspected external pollution areas, respectively marking them as the suspected external pollution area value and the suspected external pollution adjacent area value, summing up the suspected external pollution area value and the suspected external pollution adjacent area value to obtain the regional numerical diffusion value, and then performing a ratio calculation between the regional numerical diffusion value and the number of areas divided into land use areas of the petrochemical production area to obtain the numerical diffusion coefficient; Obtain the pollution exceeding standard value of the suspected external pollution area and the pollution exceeding standard value of the polluted area adjacent to the suspected external pollution area, mark them as the suspected external pollution area exceeding standard value and the suspected external pollution adjacent area exceeding standard value, sum them up and calculate to obtain the exceeding standard numerical diffusion value, calculate the ratio of the exceeding standard numerical diffusion value to the total pollution exceeding standard value of all polluted areas (obtained by summing the pollution exceeding standard values ​​of all polluted areas), and obtain the exceeding standard numerical diffusion coefficient; The regional numerical diffusion coefficient and the excess numerical diffusion coefficient are marked as XSq and XSc, respectively, and the formula , the suspected comparison value ZBW of external pollution is calculated; among them, a1 and a2 are weight ratio coefficients, a1+a2=1, a1 is 0.33, a2 is 0.67, and the values ​​of a1 and a2 represent the influence of the regional numerical diffusion coefficient and the excessive numerical diffusion coefficient on the suspected comparison value of external pollution; It should be explained that the external pollution suspected comparison value is obtained by comprehensive analysis of the number of external pollution suspected areas and pollution values. It can not only detect the diffusion capacity of pollution in each edge area, but also rationally plan the priority of external pollution based on the arrangement order of external pollution detection points obtained by the external pollution suspected comparison value. The technical solution of the embodiment of the present invention is as follows: based on the serious pollution signal of the land used in the petrochemical production area, it is determined whether there is external pollution and the detection points of external pollution, and the detection route is planned; the present invention makes a preliminary judgment on whether the pollution of the land used in the petrochemical production area is caused by the external pollution source, and then determines the location of the detection point according to the result of the preliminary judgment, and the arrangement order of the external pollution detection points obtained according to the suspected comparison value of the external pollution, so as to rationally plan the priority of the external pollution, thereby not only realizing the detection of the external pollution source, but also realizing the reasonable planning of the detection order.

[0022] It should be noted that when the petrochemical production area land is located in a petrochemical park, the petrochemical production area land can be the production area of ​​any petrochemical production enterprise, while external pollution may be caused by other adjacent production enterprises.

[0023] Example 3 See also Figure 3 As shown, the present invention is an AI-based petrochemical data large model analysis and management system, comprising: Detection module: obtain the land in the petrochemical production area, divide the land in the petrochemical production area into multiple areas, collect soil data in each area, and determine whether there is a polluted area; Analysis module: According to the determined pollution area, the location data of the pollution area is obtained, and the pollution assessment value of the petrochemical production area land is output through the pollution analysis model of the petrochemical production area land; Evaluation module: compares the pollution assessment value of the land used in the petrochemical production area with the corresponding threshold value to generate a signal of whether the land used in the petrochemical production area is seriously polluted; Planning module: Based on the serious pollution signals of the land used in the petrochemical production area, determine whether there are suspected areas of external pollution and detection points of external pollution, and plan the detection sequence.

[0024] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0025] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A petrochemical data big model analysis and management method based on AI, characterized in that: The following steps are involved: Step 1: Obtain the land in the petrochemical production area, divide the land in the petrochemical production area into multiple areas, collect soil data in each area, and determine whether there is a contaminated area; Step 2: According to the determined pollution area, the location data of the pollution area is obtained, and the pollution assessment value of the petrochemical production area land is output through the pollution analysis model of the petrochemical production area land; Step 3: If the pollution assessment value of the petrochemical production area land is greater than or equal to the pollution assessment threshold of the petrochemical production area land, a serious pollution signal of the petrochemical production area land is generated; Step 4: Based on the serious pollution signal of the land used in the petrochemical production area, determine whether there is external pollution and the detection points of external pollution, and plan the detection route.

2. According to the AI-based petrochemical data big model analysis and management method of claim 1, it is characterized in that: In step 1, the contaminated area is determined as follows: Divide the reserved land into multiple areas in equal areas, sample and analyze the soil in each area, and obtain soil data for each area; If the soil data is greater than or equal to the corresponding standard soil data, the area is marked as a contaminated area.

3. According to the AI-based petrochemical data big model analysis and management method of claim 1, it is characterized in that: In step 2, the output process of the pollution assessment value of the petrochemical production area land is: Through the pollution analysis model of petrochemical production area land , the pollution assessment value ZRT of the petrochemical production area is calculated; among them, BF represents the pollution area dispersion ratio, SWi represents the pollution excess value of the i-th pollution area, and i represents the number of pollution areas.

4. According to the AI-based petrochemical data big model analysis and management method of claim 3, it is characterized in that: The process of obtaining the pollution area dispersion ratio is as follows: Obtain the determined contaminated area, connect the contaminated area in a clockwise direction and from inside to outside, and construct a closed curve of the contaminated area; The length of the closed curve of the polluted area and the length of the edge of the petrochemical production area are obtained, and the ratio of the length of the closed curve of the polluted area to the length of the edge of the petrochemical production area is calculated to obtain the dispersion ratio of the polluted area.

5. According to the AI-based petrochemical data big model analysis and management method of claim 4, it is characterized in that: The process of obtaining the pollution excess value of the i-th pollution area is as follows: If the soil data is greater than or equal to the soil data of the corresponding standard, the area is marked as a contaminated area; The soil data in the polluted area that is greater than or equal to the corresponding standard is obtained and marked as excessive soil data. , calculate the soil data exceeding standard value STi; among them, BTi represents the standard soil data, and CTi represents the exceeding standard soil data; The pollution excess value SWi of the i-th polluted area is obtained by summing up all the soil data excess values ​​STi.

6. The AI-based petrochemical data big model analysis and management method according to claim 1 is characterized in that: In step 4, the process of determining the suspected external contamination area is as follows: Obtain whether the contaminated area is within the land edge area. If so, mark the contaminated area as a suspected external contamination area.

7. The AI-based petrochemical data big model analysis and management method according to claim 6 is characterized in that: The process of determining the detection point of external contamination is as follows: If all suspected external pollution areas have discontinuous distribution, multiple external pollution analysis areas are obtained; According to multiple external pollution analysis areas, the external pollution analysis area with the maximum pollution exceeding standard value is obtained, and the position where the external pollution analysis area with the maximum pollution exceeding standard value borders the land edge area is marked as an external pollution detection point.

8. The AI-based petrochemical data big model analysis and management method according to claim 7 is characterized in that: The planning process of the detection sequence is: Based on the suspected external pollution comparison value of each external pollution analysis area, the external pollution detection points are arranged from large to small according to the suspected external pollution comparison value to obtain the arrangement order of the external pollution detection points.

9. The AI-based petrochemical data big model analysis and management method according to claim 8 is characterized in that: The process of obtaining the suspected comparison value of external pollution is as follows: The regional numerical diffusion coefficient and the excess numerical diffusion coefficient are marked as XSq and XSc, respectively, and the formula , the external pollution suspected comparison value ZBW is calculated; where a1 and a2 are weight proportion coefficients.

10. The AI-based petrochemical data big model analysis and management method according to claim 9 is characterized in that: The process of obtaining the numerical diffusion coefficient is: Obtain the number of suspected external pollution areas and the number of polluted areas adjacent to the suspected external pollution areas, and calculate the sum of the values ​​to obtain the regional numerical diffusion value, and then calculate the ratio of the regional numerical diffusion value to the number of areas divided into land use areas in the petrochemical production area to obtain the numerical diffusion coefficient; The process of obtaining the excessive numerical diffusion coefficient is as follows: Obtain the pollution excess value of the suspected external pollution area and the pollution excess value of the polluted area adjacent to the suspected external pollution area, and sum them up to obtain the excess numerical diffusion value, and calculate the ratio of the excess numerical diffusion value to the total pollution excess value of all polluted areas to obtain the excess numerical diffusion coefficient.

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