Risk management and control partitioning method and system based on multi-medium interaction data
By employing random detection, vector line analysis, and multi-media comparison models, the pollution range can be accurately determined, solving the problem of ambiguous pollutant migration in traditional methods and achieving precise risk control and real-time response to multi-media pollution.
Patent Information
- Application Number
- CN202511388584.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-26
- Publication Date
- 2026-01-13
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional risk management zoning methods cannot accurately capture the complex migration and transformation of pollutants between different media, resulting in blurred zoning boundaries, significant prediction biases, and an inability to track pollution diffusion paths in real time.
Initial risk zones are delineated by detecting random contamination points, a concentration decrease vector line is constructed, multi-directional edge regions are defined, differences in vector lines are analyzed, the actual contamination range is accurately determined, a multi-media interactive contamination comparison model is established, and real-time data is analyzed.
It improves the accuracy, dynamic adaptability and real-time response efficiency of multi-media pollution risk management, and supports efficient zoning and early warning in complex terrain scenarios.
Smart Images
Figure CN121328908A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of pollution control, in particular to a risk control partitioning method and system based on multi-medium interaction data. BACKGROUND
[0002] In the field of environmental pollution control, multi-medium interaction pollution has become an increasingly prominent challenge. Due to the complex migration and transformation of pollutants between different media such as soil, water, and air through penetration, volatilization, runoff, and other pathways, the definition of pollution range faces temporal and spatial dynamics and interaction uncertainty. Traditional risk control partitioning methods mainly rely on static sampling and empirical models, ignoring the heterogeneous distribution of pollutant concentration gradients and the coupling effects between media, resulting in fuzzy partition boundaries, significant prediction bias, and the inability to accurately capture real-time diffusion paths. SUMMARY
[0003] The purpose of the present application is to provide a risk control partitioning method and system that can relatively accurately determine the pollution diffusion range.
[0004] The present application discloses a risk control partitioning method based on multi-medium interaction pollution data, comprising:
[0005] Step S100, randomly detecting pollution points in the area to be detected, and defining an initial risk pollution zone for each random pollution point. Determine the main pollutants of each medium in the initial risk pollution zone, and uniformly set pollution detection points in the initial risk pollution zone. Determine the content of the main pollutants of each medium at each pollution detection point;
[0006] Step S200, analyze the content variation gradient of the main pollutants of the pollution detection points in the initial risk pollution zone, and based on the analysis results, construct a content decline vector line and mark it in the initial risk pollution zone;
[0007] Step S300, set a multi-directional edge area for the initial risk pollution zone, and analyze the difference performance of the content decline vector line in the edge area relative to the content decline vector line in other areas. If the difference performance does not reach the preset difference standard, the initial risk pollution zone is expanded until the difference performance in the edge area reaches the preset difference performance, or a cross-region appears between the initial risk pollution zones;
[0008] Step S400, accurately determine the actual pollution range of the initial risk pollution zone, and define the actual pollution risk zone. Determine the geographical features of the initial risk pollution zone, and associate the distribution characteristics of the content decline vector line, the geographical features of the initial risk pollution zone, and the main pollutants with the actual pollution zone respectively to form a single risk pollution zone association group. Associate the single risk pollution zone association groups of the same detection area.
[0009] Step S500, the following descending vector line distribution characteristics, geographical characteristics of the initial risk pollution area and the main pollutants are compared with the elements, and the multi-medium interactive pollution comparison model is constructed, and the real-time multi-medium interactive pollution data is analyzed by using the multi-medium interactive comparison model, and the real-time actual pollution risk area is determined.
[0010] In some embodiments disclosed in the present application, the method for determining the content of the main pollutants of each medium of each pollution detection point comprises:
[0011] Step S101, a content-diffusion diameter comparison table is set for each pollutant, the content-diffusion diameter comparison table comprises a plurality of content ranges, and each content range corresponds to a diffusion diameter, wherein the method for determining the corresponding relationship between the content and the diffusion diameter comprises analyzing historical pollution diffusion data or testing the content-diffusion diameter of the pollutants;
[0012] Step S102, the pollutants and their respective contents of each pollution detection point are detected, and are substituted into the respective content-diffusion diameter comparison table, the diffusion diameter of each pollutant is determined, and the pollutants with a diffusion diameter greater than or equal to a preset value are determined as the main pollutants.
[0013] In some embodiments disclosed in the present application, the method for constructing the content descending vector line comprises:
[0014] Step S201, the content of the main pollutants of each pollution detection point is determined, the content of the main pollutants between adjacent pollution detection points is compared, and based on the content of the main pollutants, the content descending vector line with a vector direction is constructed, and a content descending vector network for each main pollutant is formed.
[0015] In some embodiments disclosed in the present application, the method for analyzing the difference between the content descending vector line in the edge area and the content descending vector line in other areas comprises:
[0016] Step S301, when the initial risk pollution area is delimited, the initial risk pollution area is a square, a plurality of reference directions are constructed by referring to the geographical direction, including the south direction, the north direction, the west direction and the east direction, each edge of the initial risk pollution area is at least flush with the south direction and the north direction, or flush with the east direction and the west direction, the edge of the initial risk pollution area is determined, and a preset distance within the edge is taken as the delimitation width of the edge area;
[0017] Step S302, the content reduction vector lines in the edge region and other regions are determined, and the sum of the north component of the content reduction vector lines in the region relative to the true south direction, the sum of the north component of the true north direction, the sum of the west component of the true west direction, and the sum of the east component of the true east direction are determined to obtain a true direction component sum group;
[0018] Step S303, the component sum difference of each direction component sum between the true direction component sum group corresponding to the edge region and the true direction component sum group corresponding to the other region is determined as a difference performance.
[0019] Wherein, if the direction component sum difference is greater than or equal to a preset value, it is determined that the difference performance reaches a preset difference standard.
[0020] In some embodiments disclosed in the present application, the method for determining the geographical features of the initial risk pollution area comprises:
[0021] Step S401, the elevation information of the initial risk pollution area is obtained, the altitude of each pollution detection point is obtained, and the altitude is marked on each pollution detection point;
[0022] Step S402, the initial risk pollution area is divided into several initial risk pollution sub-areas, the average value of the altitude points of all pollution detection points in the initial risk pollution sub-area is calculated to obtain the average altitude, and the average altitude is associated with the initial risk pollution sub-area. The average altitude of each initial risk pollution sub-area in the initial risk pollution area is the geographical feature.
[0023] In some embodiments disclosed in the present application, the method for constructing a multi-medium interactive pollution comparison model comprises:
[0024] Step S403, the distribution characteristics of the content reduction line in the initial risk pollution area and the geographical features of the initial risk pollution area are searched and extracted, including the true direction wind sum group of the content reduction line distribution characteristics, and the height size change sequence of the average altitude of the geographical features, wherein the determination method of the height size change sequence of the average altitude comprises determining the diagonal line in the initial risk pollution area, and recording the average altitude corresponding to each diagonal line in the initial risk pollution sub-area to obtain the height size change sequence.
[0025] Step S404, based on the search key points corresponding to the initial risk pollution area, a search relationship is established for a plurality of risk pollution area association groups to form a multi-medium interactive pollution comparison model, wherein the search key points further include the main pollutants of the initial risk pollution area and the average content of the main pollutants.
[0026] In some embodiments of the present disclosure, the method for analyzing real-time multi-medium interaction pollution data by using a multi-medium interaction comparison model comprises:
[0027] In step S501, real-time multi-medium interaction pollution data is analyzed to determine real-time detection areas that need to be detected, distribution characteristics of content decline vector lines of real-time risk pollution blocks in the real-time detection areas, geographical characteristics of the real-time risk pollution blocks, and real-time search points of each real-time risk pollution block;
[0028] In step S502, the real-time search points are used to determine corresponding risk pollution area association groups in the multi-medium interaction comparison model, and the risk pollution area association groups are classified based on whether they belong to the same detection area to obtain a plurality of risk pollution area association sets for comparison;
[0029] In step S503, a combination of the distribution characteristics of the content decline vector lines and the geographical characteristics of a plurality of real-time risk pollution areas is recorded as a real-time risk pollution area association set, each real-time risk pollution area association set is compared with different risk pollution area association sets for comparison, the degree of coincidence between them is determined, and based on the degree of coincidence, the most suitable risk pollution block association set is screened out. The actual pollution area corresponding to each initial risk pollution area in the risk pollution area association set is identified as a small-range real-time pollution risk area, and the actual pollution areas corresponding to all other risk pollution area association groups in the same detection area of the risk pollution area association set are identified as a large-range real-time pollution risk area.
[0030] In some embodiments of the present disclosure, the method for determining the degree of coincidence comprises:
[0031] In step S5031, the actual directions of the content decline lines in the risk pollution areas are compared, and the average altitudes of each risk pollution area are compared, and based on the comparison results, a coincidence sub-parameter between the single risk pollution areas is determined.
[0032] In step S5032, it is determined whether the coincidence sub-parameter between the risk pollution areas is greater than or equal to a preset value. If it is greater than or equal to the preset value, it is determined that the risk pollution areas coincide with each other. If they coincide with each other, the coincidence proportion of the real-time risk pollution areas that coincide with each other in all real-time risk pollution areas is analyzed.
[0033] In step S5033, the degree of coincidence between the real-time risk pollution area association set and the risk pollution area association set for comparison is determined in combination with the coincidence sub-parameter and the coincidence proportion.
[0034] The expression for calculating the degree of coincidence is:
[0035]
[0036] wherein, W is the degree of convergence, g i is the actual pointing same quantity of the content reduction line between the risk pollution areas, n is the total number of pollution media, G is all quantities of the content reduction line between the risk pollution areas, R1 is the pointing convergence influence adjustment coefficient, m1 is the number of risk pollution sub-areas between the risk pollution areas, the average altitude difference quantity is less than or equal to the preset value, M is the total number of risk pollution sub-areas of the risk pollution area, b is the convergence degree influence adjustment constant, β (h) is the convergence ratio analysis function, used for judging the preset interval to which the convergence ratio belongs, and outputting the corresponding convergence ratio adjustment coefficient, and h is the convergence ratio.
[0037] In some embodiments disclosed in the present application, a risk control partitioning system based on multi-medium interactive pollution data is also disclosed, comprising:
[0038] The first module is used for randomly detecting pollution points in the area to be detected, and delimiting an initial risk pollution area for each random pollution point drop, determining the main pollutants of each medium in the initial risk pollution area, and uniformly setting pollution detection points in the initial risk pollution area, and determining the content of the main pollutants of each medium at each pollution detection point.
[0039] The second module is used for analyzing the content variation gradient of the main pollutants of the pollution detection points in the initial risk pollution area, and based on the analysis result, constructing a content reduction vector line and marking in the initial risk pollution area.
[0040] The third module is used for setting a multi-directional edge area in the initial risk pollution area, and analyzing the difference performance of the content reduction vector line in the edge area relative to the content reduction vector line in other areas, if the difference performance does not reach the preset difference standard, the edge of the initial risk pollution area is expanded until the difference performance in the edge area reaches the preset difference performance, or a cross area appears between the initial risk pollution areas.
[0041] The fourth module is used for accurately determining the actual pollution range of the initial risk pollution area, and delimiting an actual pollution risk area, determining the geographical features of the initial risk pollution area, and correlating the distribution characteristics of the content reduction vector line, the geographical features of the initial risk pollution area and the main pollutants with the actual pollution area respectively, forming a single risk pollution area correlation group, and correlating the single risk pollution area correlation groups belonging to the same detection area.
[0042] The fifth module is configured to: taking the distribution characteristics of the descending vector line, the geographical characteristics of the initial risk pollution area and the main pollutants as comparison elements, constructing a multi-medium interaction pollution comparison model, and using the multi-medium interaction pollution comparison model to analyze real-time multi-medium interaction pollution data to determine a real-time actual pollution risk area.
[0043] The application discloses a risk management and control partition method and system based on multi-medium interaction pollution data, relates to pollution management and control, and comprises the following steps: delimiting an initial risk pollution area through random pollution point detection and determining main pollutants of each medium and contents of the main pollutants; analyzing content change gradients of the pollution points to construct a content descending vector line; setting a multi-directional edge area, analyzing vector line differences, and expanding the edge until a standard is met or crossed if the standard is not met; accurately determining an actual pollution range, extracting geographical characteristics, and establishing a correlation group of pollutants, vector distribution and geographical characteristics; taking the vector distribution, the geographical characteristics and the pollutants as elements to construct a multi-medium interaction pollution comparison model, and analyzing real-time data.
[0044] The technical scheme of the application will be further described in detail below with reference to the drawings and embodiments. BRIEF DESCRIPTION OF DRAWINGS
[0045] Figure 1 A method step diagram of the risk management and control partition method based on multi-medium interaction pollution data disclosed in the embodiments of the application is shown in the figure. DETAILED DESCRIPTION
[0046] The technical scheme of the application will be further described in detail below with reference to the drawings and embodiments.
[0047] The technical scheme of the application will be further described in detail below with reference to the drawings and embodiments.
[0048] Embodiment:
[0049] The application discloses a risk management and control partition method based on multi-medium interaction pollution data, comprising the following steps:
[0050] Step S100, random pollution point detection is performed on the detection area in need of detection, and an initial risk pollution area is demarcated for each random pollution point, the main pollutants of each medium in the initial risk pollution area are determined, and pollution detection points are uniformly set in the initial risk pollution area, and the content of the main pollutants of each medium at each pollution detection point is determined.
[0051] The principle of step S100 is to efficiently cover the detection area through random pollution point detection, avoiding the resource waste and blind area problem of traditional grid scanning. Specifically, first, random point sampling is performed on the detection area, and an initial risk pollution area is demarcated for each random pollution point as a potential source, which is based on the randomness and uncertainty principle of pollution diffusion, ensuring that the initial area can capture the core area of pollution; then, the main pollutants of each medium (such as air, water, and soil) in the initial area are identified, and a preset content-diffusion diameter ratio table (corresponding relationship derived from historical data or experimental verification) is used to quantify the influence range of the pollutants, and the pollutants with a diffusion diameter exceeding a threshold value are defined as the main pollutants, which embodies the principle of pollution diffusion dynamics, i.e. the higher the content, the greater the diffusion potential; at the same time, pollution detection points are uniformly set in the initial area, and the content of the main pollutants of each medium at each point is measured, forming a refined data grid to support subsequent gradient analysis. This step as a whole follows the principles of statistical sampling and diffusion model, ensuring the representativeness and accuracy of data collection, and laying a foundation for preliminary modeling of multi-medium interactive pollution.
[0052] Step S200, the content change gradient of the main pollutants of the pollution detection points in the initial risk pollution area is analyzed, and based on the analysis result, a content decrease vector line is constructed and marked in the initial risk pollution area.
[0053] The principle of step S200 is to construct a content decrease vector line by analyzing the content change gradient of the main pollutants between pollution detection points, so as to quantify the spatial diffusion direction and intensity of the pollutants. Specifically, first, the content of the pollutants at each detection point is determined, then the content between adjacent points is compared to form a vector pointing from high to low, which is based on the gradient descent principle, i.e. the physical and chemical process of natural attenuation of pollutant concentration along the diffusion path; the vector line network constructed for each main pollutant independently reflects the gradient difference between different media (such as migration from soil to water) in multi-medium interaction. This step uses a vector mathematical model to convert discrete data into continuous distribution characteristics, facilitating visualization and subsequent regional difference analysis, ensuring that the dynamic trend of pollution diffusion is captured rather than static concentration values, supporting pollution pattern recognition from local to global in the entire scheme.
[0054] Step S300, set a multi-directional edge region for the initial risk pollution area, and analyze the difference performance of the content decline vector line in the edge region relative to the content decline vector line in other regions. If the difference performance does not reach the preset difference standard, the edge of the initial risk pollution area is expanded until the difference performance in the edge region reaches the preset difference performance, or a cross region appears between the initial risk pollution areas.
[0055] The principle of step S300 is to realize the dynamic expansion of the initial risk pollution area through the vector line difference analysis of the edge region, and to ensure that the boundary accurately reflects the actual boundary of pollution diffusion. Specifically, the initial area is set as a square and the multi-directional edge region is defined with reference to the geographical direction (such as due south, due north, etc.), which is based on the guiding effect of environmental factors such as wind direction, terrain, etc. on pollution diffusion; then the sum of the directional components (such as the sum of the due south components) of the edge and other regional vector lines is calculated, and the difference is evaluated (if the difference is greater than the preset value, it reaches the standard), which embodies the principles of vector decomposition and statistical comparison, and is used to detect abnormal attenuation or enhancement at the boundary; if the difference does not reach the standard, the edge is iteratively expanded until it meets or crosses the region, avoiding too small or too large initial areas. This step follows the boundary optimization and iterative convergence principle, integrates the geographical reference system, improves the adaptability of the partition to complex environments, and prevents the interactive influence between multiple pollution sources from being ignored.
[0056] Step S400, accurately determine the actual pollution range of the initial risk pollution area, and delineate the actual pollution risk area, determine the geographical features of the initial risk pollution area, and associate the distribution characteristics of the content decline vector line, the geographical features of the initial risk pollution area, and the main pollutants with the actual pollution area respectively, form a single risk pollution area association group, and associate the single risk pollution area association groups belonging to the same detection area.
[0057] The principle of step S400 is to accurately determine the actual pollution range and extract geographical features, establish a multi-dimensional association group, and realize a closed loop from initial inference to actual verification. Specifically, first, the initial area is measured and the actual risk area is delineated, which is based on the principle of field verification to correct the uncertainty of random sampling; then the elevation information is obtained and the average altitude of the sub-area is calculated as the geographical feature, which embodies the influence principle of terrain on pollution diffusion (such as fast drainage in high altitude area, easy migration of pollutants); the vector distribution characteristics (such as vector line network), geographical features and main pollutants are associated with the actual area to form a single association group, and further associated with the group in the same region, which follows the data association and clustering principle, and is used to capture the mode of multi-medium interaction (such as cross-medium propagation of pollutants in different terrains). This step overall builds a historical knowledge base to support the retrieval and comparison of subsequent models, ensuring the robustness and scalability of the scheme.
[0058] Step S500, the following descending vector line distribution characteristics, geographical features of the initial risk pollution area and the main pollutants are compared with the elements, and a multi-medium interactive pollution comparison model is constructed, and the real-time multi-medium interactive pollution data is analyzed by using the multi-medium interactive comparison model, and the real-time actual pollution risk area is determined.
[0059] The principle of step S500 is to construct an interactive pollution model by comparing multiple elements, to realize the analysis of real-time data and the determination of risk area, and to improve the prediction ability from history to real time. Specifically, the vector distribution (such as the sum of the positive direction components), the geographical features and the pollutants (including the average content) are taken as the retrieval points to establish a multi-medium interactive comparison model, which is based on the similarity matching and multi-dimensional feature extraction principle and is used to simulate the interactive dynamics of pollutants between media (such as the migration path reflected by the content descending vector); the corresponding points of the real-time data are extracted, the history correlation set is compared in the model, the degree of agreement is calculated (through the formula integration of the same direction, the altitude difference and the agreement ratio), the most suitable set is screened, and the small range (direct matching) and the large range (extended correlation) real-time risk area are divided. This step follows the retrieval and quantitative evaluation principle of machine learning, ensures the accuracy and efficiency of real-time analysis, and the overall technical effect lies in realizing dynamic risk control through multi-medium interaction simulation.
[0060] In some embodiments disclosed in the present application, the method for determining the content of the main pollutants of each medium of each pollution detection point comprises:
[0061] Step S101, a content-diffusion diameter comparison table is set for each pollutant, the content-diffusion diameter comparison table includes a plurality of content ranges, and each content range corresponds to a diffusion diameter, wherein the method for determining the corresponding relationship between the content and the diffusion diameter comprises analyzing the historical pollution diffusion data or testing the content-diffusion diameter of the pollutants.
[0062] The principle of step S101 is to provide a quantitative evaluation basis for the influence range of the pollutants by establishing a content-diffusion diameter comparison table, and to ensure that the screening of the main pollutants has a scientific basis. Specifically, for each pollutant, based on historical pollution diffusion data analysis or experimental testing, a content-diffusion diameter comparison table is constructed, the content of the pollutant is divided into several ranges, and a specific diffusion diameter is corresponded. This is based on the physical and chemical principles of pollution diffusion, that is, the concentration (content) of the pollutant is positively correlated with the diffusion range, and the high concentration of the pollutant has a stronger migration or chemical activity and a larger diffusion range. The historical data analysis uses statistical and regression analysis methods to extract the empirical relationship between the content and the diffusion diameter, and the experimental testing simulates the pollution diffusion behavior by controlling variables (such as medium type, environmental conditions) to verify the corresponding relationship. This step combines data-driven and experimental verification to establish a standardized pollution impact evaluation framework, providing a reliable reference for subsequent screening of main pollutants, and ensuring the objectivity and repeatability of the screening process.
[0063] In step S102, the pollutants at each pollution detection point and their respective contents are detected, and the respective content-diffusion diameter comparison table is substituted to determine the diffusion diameter of each pollutant. The pollutants with a diffusion diameter greater than or equal to the preset value are determined as the main pollutants.
[0064] In some embodiments disclosed in the present application, the method for constructing the content decline vector line includes:
[0065] In step S201, the content of the main pollutant at each pollution detection point is determined, and the content of the main pollutant between adjacent pollution detection points is compared. Based on the high and low of the content of the main pollutant, a content decline vector line with a vector direction is constructed, and a content decline vector network for each main pollutant is formed.
[0066] In some embodiments disclosed in the present application, the method for analyzing the difference performance of the content decline vector line in the edge area relative to the content decline vector line in other areas includes:
[0067] In step S301, when the initial risk pollution area is delimited, the initial risk pollution area is a square, and a plurality of reference directions are constructed by referring to the geographical direction, including the positive south direction, the positive north direction, the positive west direction and the positive east direction. Each edge of the initial risk pollution area is at least flush with the positive south direction and the positive north direction, or flush with the positive east direction and the positive west direction, to determine the edge of the initial risk pollution area, and the preset distance within the edge is the delimitation width of the edge area.
[0068] Step S302, the content reduction vector lines in the edge region and other regions are determined, and the sum of the north component of the content reduction vector lines in the region relative to the true south direction, the sum of the north component of the true north direction, the sum of the west component of the true west direction, and the sum of the east component of the true east direction are determined to obtain a true direction component sum group;
[0069] Step S303, the component sum difference of each direction component sum between the true direction component sum group corresponding to the edge region and the true direction component sum group corresponding to the other region is determined as a difference performance.
[0070] Wherein, if the direction component sum difference is greater than or equal to a preset value, it is determined that the difference performance reaches a preset difference standard.
[0071] In some embodiments disclosed in the present application, the method for determining the geographical features of the initial risk pollution area comprises:
[0072] Step S401, the elevation information of the initial risk pollution area is obtained, the altitude of each pollution detection point is obtained, and the altitude is marked on each pollution detection point;
[0073] Step S402, the initial risk pollution area is divided into several initial risk pollution sub-areas, the average value of the altitude points of all pollution detection points in the initial risk pollution sub-area is calculated to obtain the average altitude, and the average altitude is associated with the initial risk pollution sub-area, and the average altitude of each initial risk pollution sub-area in the initial risk pollution area is the geographical feature.
[0074] In some embodiments disclosed in the present application, the method for constructing a multi-medium interactive pollution comparison model comprises:
[0075] Step S403, the distribution characteristics of the content reduction line in the initial risk pollution area and the geographical features of the initial risk pollution area are searched and extracted, including the true direction wind sum group of the content reduction line distribution characteristics, and the height size change sequence of the average altitude of the geographical features, wherein the determination method of the height size change sequence of the average altitude comprises determining the diagonal line in the initial risk pollution area, and recording the average altitude corresponding to each diagonal line in the initial risk pollution sub-area to obtain the height size change sequence.
[0076] Step S404, based on the search key points corresponding to the initial risk pollution area, a search relationship is established for a plurality of risk pollution area association groups to form a multi-medium interactive pollution comparison model, wherein the search key points further include the main pollutants of the initial risk pollution area and the average content of the main pollutants.
[0077] In some embodiments of the present disclosure, the method for analyzing real-time multi-medium interaction pollution data by using a multi-medium interaction comparison model comprises:
[0078] In step S501, real-time multi-medium interaction pollution data is analyzed to determine real-time detection areas that need to be detected, determine the distribution characteristics of the content decline vector lines of real-time risk pollution blocks in the real-time detection areas and the geographical characteristics of the real-time risk pollution blocks, and determine real-time search points of each real-time risk pollution block.
[0079] In step S502, real-time search points are used to determine corresponding risk pollution area association groups in the multi-medium interaction comparison model, and the risk pollution area association groups are classified based on whether they belong to the same detection area to obtain a plurality of risk pollution area association sets for comparison.
[0080] In step S503, the combination of the distribution characteristics of the content decline vector lines and the geographical characteristics of a plurality of real-time risk pollution areas are recorded as a real-time risk pollution area association set, each real-time risk pollution area association set is compared with different risk pollution area association sets for comparison to determine the degree of coincidence between them, and the most suitable risk pollution block association set is screened out based on the degree of coincidence, the actual pollution areas corresponding to each initial risk pollution area in the risk pollution area association set are identified as small-range real-time pollution risk areas, and the actual pollution areas corresponding to other risk pollution area association groups in the same detection area of the risk pollution area association set are identified as large-range real-time pollution risk areas.
[0081] In some embodiments of the present disclosure, the method for determining the degree of coincidence comprises:
[0082] In step S5031, the actual directions of the content decline lines in the risk pollution areas are compared, and the average altitudes of each risk pollution area in the risk pollution areas are compared, and based on the comparison results, the coincidence sub-parameters between the single risk pollution areas are determined.
[0083] The principle of step S5031 is to quantify the similarity between single risk pollution areas by comparing the direction of the content decrease vector line and the average elevation, providing a basic parameter for subsequent degree of coincidence evaluation. Specifically, the actual direction of the content decrease vector line reflects the diffusion direction of the pollutant in space, which is affected by factors such as medium characteristics, wind direction or terrain, while the average elevation represents the constraint of terrain on pollution diffusion (e.g. pollutants in high altitude areas are easily transported by water flow). By comparing the consistency of the vector line direction and the difference in elevation between the two risk pollution areas, the coincidence sub-parameter is calculated, which is based on the similarity measurement principle. This step uses multi-dimensional features (dynamic diffusion direction and static terrain characteristics) for comparison, follows the logic of environmental data correlation analysis, and aims to capture the similarity of pollution areas in diffusion mode and geographical environment, providing a quantitative basis for subsequent coincidence judgment to ensure the accuracy and environmental adaptability of the evaluation.
[0084] Step S5032, determine whether the coincidence sub-parameter between the risk pollution areas is greater than or equal to the preset value, if greater than or equal to the preset value, determine that the risk pollution areas coincide, if coincide, analyze the coincidence proportion of the real-time risk pollution areas that coincide in all real-time risk pollution areas.
[0085] The principle of step S5032 is to set a threshold for the coincidence sub-parameter to filter out highly similar risk pollution areas and further analyze the coincidence proportion to provide a group-level basis for the overall degree of coincidence. Specifically, first determine whether the coincidence sub-parameter calculated in S5031 reaches the preset threshold, which is based on the threshold classification principle and used to determine the matching degree of the two pollution areas in vector direction and elevation characteristics; if the sub-parameter is greater than or equal to the threshold, it is considered that the two pollution areas coincide, indicating that they have significant similarity in pollution diffusion mode and geographical characteristics. Then, the proportion of real-time risk pollution areas that coincide in all real-time pollution areas is calculated, which follows the statistical proportion analysis principle and reflects the distribution of similar pollution patterns in real-time data. This step provides multi-level data support for the comprehensive evaluation of the degree of coincidence through the progressive logic of individual matching to group analysis, ensuring the robustness of the results.
[0086] Step S5033, determine the degree of coincidence between the real-time risk pollution area association set and the risk pollution area association set used for comparison based on the coincidence sub-parameter and the coincidence proportion.
[0087] Wherein, the expression for calculating the degree of coincidence is:
[0088]
[0089] Wherein, W is the degree of coincidence, g iThe content reduction line of the i-th medium between the risk pollution areas actually points to the same quantity, n is the total number of pollution media, G is all quantities of the content reduction line between the risk pollution areas, R1 is an adjustment coefficient for pointing to the matching influence, m1 is the number of risk pollution sub-areas between the risk pollution areas, the average altitude difference of which is less than or equal to a preset value, M is the total number of risk pollution sub-areas of the risk pollution area, b is an adjustment constant for the matching degree, and beta (h) is a matching proportion analysis function for judging a preset interval to which a matching proportion belongs and outputting a corresponding matching proportion adjustment coefficient, and h is the matching proportion.
[0090] In some embodiments disclosed in the present application, a risk management and control partitioning system based on multi-medium interactive pollution data is also disclosed, comprising:
[0091] The first module is configured to perform random pollution point detection on a detection area in need of detection, and delimit an initial risk pollution area for each random pollution point drop; determine the main pollutants of each medium in the initial risk pollution area; and determine the content of the main pollutants of each medium at each pollution detection point in the initial risk pollution area.
[0092] The second module is configured to analyze the content variation gradient of the main pollutants at the pollution detection points in the initial risk pollution area, and based on the analysis result, construct a content reduction vector line and mark the content reduction vector line in the initial risk pollution area.
[0093] The third module is configured to set a multi-directional edge area in the initial risk pollution area, and analyze the difference performance of the content reduction vector line in the edge area relative to the content reduction vector line in other areas; if the difference performance does not reach a preset difference standard, the initial risk pollution area is expanded until the difference performance in the edge area reaches a preset difference performance, or a cross area appears between the initial risk pollution areas.
[0094] The fourth module is configured to accurately determine the actual pollution range of the initial risk pollution area, and delimit an actual pollution risk area; determine the geographical features of the initial risk pollution area; and associate the distribution characteristics of the content reduction vector line, the geographical features of the initial risk pollution area, and the main pollutants with the actual pollution area respectively, form a single risk pollution area association group, and associate the single risk pollution area association groups belonging to the same detection area.
[0095] The fifth module is configured to use the distribution characteristics of the content reduction vector line, the geographical features of the initial risk pollution area, and the main pollutants as comparison elements to construct a multi-medium interactive pollution comparison model, and use the multi-medium interactive pollution comparison model to analyze real-time multi-medium interactive pollution data, and determine a real-time actual pollution risk area.
[0096] The application discloses a risk management and control partitioning method and system based on multi-medium interactive pollution data, relates to pollution management and control, and comprises the following steps: determining an initial risk pollution area by detecting random pollution points, determining main pollutants of each medium and the content of the main pollutants; analyzing the content change gradient of the pollution points, and constructing a content drop vector line; setting a multi-directional edge area, analyzing the vector line difference, and expanding the edge until the standard is met or crossed if the standard is not met; accurately determining an actual pollution range, extracting geographical features, and establishing a correlation group of pollutants, vector distribution and geographical features; taking the vector distribution, geographical features and pollutants as elements, constructing a multi-medium interactive pollution comparison model, and analyzing real-time data; and the application improves the accuracy, dynamic adaptability and real-time response efficiency of multi-medium pollution risk management and control, and supports efficient partitioning and early warning in a complex terrain scene.
[0097] Those skilled in the art can clearly understand the application through the above description of the embodiments that the application can be implemented by hardware or by means of software and a necessary general hardware platform. Based on such understanding, the technical solutions of the application can be embodied in the form of a software product, which can be stored in a nonvolatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk or the like) and includes a plurality of instructions for enabling a computer device (which can be a personal computer, a server or a network device) to execute the methods described in the various embodiments of the application.
[0098] Finally, it should be noted that: the above examples are only used to illustrate the technical solutions of the application rather than limit the same, and although the application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that: the technical solutions of the application can still be modified or replaced by equivalents, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the application.
Claims
1. A risk management zoning method based on multi-media interactive pollution data, characterized in that, include: Step S100: Random contamination point detection is performed on the area to be detected, and an initial risk contamination zone is defined for each random contamination point. The main pollutants of each medium in the initial risk contamination zone are determined, and contamination detection points are uniformly set in the initial risk contamination zone. The content of the main pollutants of each medium at each contamination detection point is determined. Step S200: Analyze the content change gradient of the main pollutants at the pollution detection points in the initial risk pollution zone, and based on the analysis results, construct a content decrease vector line and mark it in the initial risk pollution zone; Step S300: Set a multi-directional edge region for the initial risk contamination area, and analyze the difference between the content decrease vector line in the edge region and the content decrease vector line in other regions. If the difference does not reach the preset difference standard, the edge of the initial risk contamination area is expanded until the difference in the edge region reaches the preset difference standard, or an intersection area appears between the initial risk contamination areas. Step S400: Accurately determine the actual pollution range of the initial risk pollution area, delineate the actual pollution risk area, determine the geographical characteristics of the initial risk pollution area, and establish correlations between the distribution characteristics of the content decline vector line, the geographical characteristics of the initial risk pollution area, and the main pollutants and the actual pollution area to form a single risk pollution area association group, and associate the single risk pollution area association groups belonging to the same detection area. Step S500: Using the distribution characteristics of the descent vector line, the geographical features of the initial risk pollution area, and the main pollutants as comparison elements, a multi-media interactive pollution comparison model is constructed. The multi-media interactive pollution comparison model is then used to analyze real-time multi-media interactive pollution data to determine the real-time actual pollution risk area.
2. The risk management zoning method based on multi-media interactive pollution data according to claim 1, characterized in that, Methods for determining the content of the main pollutants in each medium at each pollution detection point include: Step S101: For each pollutant, a content-diffusion diameter comparison table is set. The content-diffusion diameter comparison table includes several content ranges, and each content range corresponds to a diffusion diameter. The method for determining the correspondence between content and diffusion diameter includes analyzing historical pollution diffusion data or conducting experiments on the content-diffusion diameter of pollutants. Step S102: Detect the pollutants and their contents at each pollutant detection point, and substitute them into the content-diffusion diameter comparison table to determine the diffusion diameter of each pollutant. Pollutants with diffusion diameters greater than or equal to preset values are identified as major pollutants.
3. The risk management zoning method based on multi-media interactive pollution data according to claim 1, characterized in that, Methods for constructing content decrease vector lines include: Step S201: Determine the content of the main pollutants at each pollution detection point, compare the content of the main pollutants between adjacent pollution detection points, and construct a content decrease vector line with a vector direction based on the content of the main pollutants, forming a content decrease vector representation network for each main pollutant.
4. The risk management zoning method based on multi-media interactive pollution data according to claim 3, characterized in that, Methods for analyzing the differences in content decrease vector lines within the marginal region relative to those in other regions include: Step S301: When delineating the initial risk pollution zone, the initial risk pollution zone is a square, and several reference directions are constructed with reference to geographical directions, including due south, due north, due west and due east. Each edge of the initial risk pollution zone is at least aligned with due south and due north, or with due east and due west, to determine the edge of the initial risk pollution zone, and the predetermined distance within the edge is the delineation width of the edge area. Step S302: Determine the content decrease vector lines in the edge region and other regions, and determine the sum of the south component, the sum of the north component, the sum of the west component, and the sum of the east component of the content decrease vector lines in the region relative to the south direction, to obtain the positive direction component sum group. Step S303: The difference in the sum of the positive direction components of the edge region and the positive direction component sum of the other region is identified as the difference performance. If the sum of the directional components is greater than or equal to a preset value, then the difference is considered to have reached the preset difference standard.
5. The risk management zoning method based on multi-media interactive pollution data according to claim 4, characterized in that, Methods for determining the geographical features of initial risk contamination zones include: Step S401: Obtain the elevation information of the initial risk pollution area, obtain the altitude of each pollution detection point, and mark the altitude at each pollution detection point. Step S402: The initial risk pollution area is divided into several initial risk pollution sub-areas. The average elevation of all pollution detection points in the initial risk pollution sub-areas is calculated to obtain the average elevation. The average elevation is then associated with the initial risk pollution sub-areas, and the average elevation of each initial risk pollution sub-area in the initial risk pollution area is taken as a geographical feature.
6. The risk management zoning method based on multi-media interactive pollution data according to claim 5, characterized in that, Methods for constructing multi-media interactive contamination comparison models include: Step S403: Extract key points from the distribution characteristics of the content decline line in the initial risk pollution area and the geographical features of the initial risk pollution area, including the sum of the positive wind volume of the content decline line distribution characteristics and the height change sequence of the average altitude of the geographical features. The method for determining the height change sequence of the average altitude includes determining the diagonal in the initial risk pollution area and recording the average altitude corresponding to the initial risk pollution sub-area traversed by each diagonal to obtain the height change sequence. Step S404: Based on the retrieval points corresponding to the initial risk pollution area, establish retrieval relationships for several risk pollution area association groups to form a multi-media interactive pollution comparison model. The retrieval points also include the main pollutants in the initial risk pollution area and the average content of the main pollutants.
7. The risk management zoning method based on multi-media contamination data according to claim 5, characterized in that, Methods for analyzing real-time multi-media interactive pollution data using multi-media interactive comparison models include: Step S501: Analyze the real-time multi-media interactive pollution data, determine the real-time detection area that needs to be detected, determine the distribution characteristics of the content decline vector line of the real-time risk pollution block in the real-time detection area, as well as the geographical characteristics of the real-time risk pollution block, and determine the real-time retrieval points of each real-time risk pollution block. Step S502: Using real-time retrieval key points, the corresponding risk pollution area association group is determined in the multi-media interactive comparison model, and the risk pollution area association group is classified based on whether they belong to the same detection area, so as to obtain several risk pollution area association sets for comparison. Step S503: The distribution characteristics of the content decrease vector lines corresponding to several real-time risk pollution areas and the combination of geographical features are recorded as real-time risk pollution area association sets. Each real-time risk pollution area association set is compared with different risk pollution area association sets used for comparison to determine the degree of consistency between the two. Based on the degree of consistency, the most suitable risk pollution block association set is selected. The risk pollution area association set, and the actual pollution area corresponding to each initial risk pollution area in the risk pollution area association set, are identified as small-scale real-time pollution risk areas. The actual pollution areas corresponding to all other risk pollution area association groups in the same detection area corresponding to the risk pollution area association set are identified as large-scale real-time pollution risk areas.
8. The risk management zoning method based on multi-media contamination data according to claim 7, characterized in that, Methods for determining the degree of fit include: Step S5031: Compare the actual direction of the content decline line in the risk pollution area, and compare the average altitude of each risk pollution sample in the risk pollution area. Based on the comparison results, determine the matching sub-parameters between individual risk pollution areas. Step S5032: Determine whether the matching sub-parameter between risk pollution areas is greater than or equal to the preset value. If it is greater than or equal to the preset value, then determine that the risk pollution areas match. If they match, then analyze the matching ratio of the real-time risk pollution areas to all real-time risk pollution areas. Step S5033: Combine the matching sub-parameters and the matching ratio to determine the degree of matching between the real-time risk contamination area association set and the risk contamination area association set used for comparison. The expression for calculating the degree of similarity is as follows: Where W represents the degree of agreement, g i Let n be the number of media whose content decreases between risk-contaminated areas actually point to the same number of media, n be the total number of media, G be the total number of media whose content decreases between risk-contaminated areas, R1 be the adjustment coefficient for the influence of the direction matching, m1 be the number of risk-contaminated sub-areas where the difference in average altitude between risk-contaminated areas is less than or equal to a preset value, M be the total number of risk-contaminated sub-areas of risk-contaminated areas, b be the adjustment constant for the influence of the degree of matching, and β(h) be the matching ratio analysis function, used to determine the preset interval to which the matching ratio belongs and output the corresponding matching ratio adjustment coefficient.
9. A risk management zoning system based on multi-media interactive pollution data, characterized in that, include: The first module is used to detect random contamination points in the area to be tested, and to delineate an initial risk contamination zone for each random contamination point. The main pollutants of each medium in the initial risk contamination zone are determined, and contamination detection points are uniformly set in the initial risk contamination zone to determine the content of the main pollutants of each medium at each contamination detection point. The second module is used to analyze the content change gradient of the main pollutants at the pollution detection points in the initial risk pollution zone, and based on the analysis results, construct a content decrease vector line and mark it in the initial risk pollution zone. The third module is used to set multi-directional edge regions for the initial risk contamination zone and analyze the difference between the content decrease vector line in the edge region and the content decrease vector line in other regions. If the difference does not reach the preset difference standard, the edge of the initial risk contamination zone is expanded until the difference in the edge region reaches the preset difference standard, or an intersection region appears between the initial risk contamination zones. The fourth module is used to accurately determine the actual pollution range of the initial risk pollution area, delineate the actual pollution risk area, determine the geographical characteristics of the initial risk pollution area, and establish correlations between the distribution characteristics of the content decline vector line, the geographical characteristics of the initial risk pollution area, and the main pollutants and the actual pollution area, forming a single risk pollution area association group, and linking the single risk pollution area association groups belonging to the same detection area. The fifth module is used to construct a multi-media interactive pollution comparison model using the distribution characteristics of the descent vector line, the geographical features of the initial risk pollution area, and the main pollutants as comparison elements. The multi-media interactive comparison model is then used to analyze real-time multi-media interactive pollution data and determine the real-time actual pollution risk area.