A surface water pollution partition prevention dynamic management system and method

Abstract

The application discloses a kind of ground water pollution partition prevention dynamic management system and method, method includes: obtaining the sampling data of monitoring coordinate;According to sampling data analysis, determine the detection value of influence parameter i;The first risk assessment score of detection value is calculated;Based on the influence coefficient change curve, it is judged whether there is mutual influence relationship between influence parameters, the coordinated risk assessment score of influence parameter that exists mutual influence is calculated, the first risk assessment score of corresponding influence parameter is replaced;The risk assessment score of monitoring coordinate is calculated;According to risk assessment score, the pollution prevention and control evaluation score of the water area partition corresponding to monitoring coordinate is calculated, and the pollution prevention and control partition grade of water area partition is determined.The pollution prevention and control partition grade of each water area partition is determined based on the mutual influence relationship between different influence parameters in the application, to improve the accuracy of water pollution assessment.

Description

Technical Field

[0001] This application relates to the field of water pollution prevention and control technology, and more specifically, to a dynamic management system and method for the prevention and control of surface water pollution by zone. Background Technology

[0002] As a vital water source for human life and production, the prevention and control of surface water pollution is of paramount importance. Traditional methods for surface water pollution control often lack specificity and dynamism, making it difficult to effectively address water pollution problems in different regions and at different times.

[0003] In addition, the conventional dynamic management of surface water pollution prevention and control by zone does not take into account the mutual influence between different detected components and different detection areas, resulting in low accuracy of surface water pollution prevention and control zoning.

[0004] Therefore, the existing technology has defects and urgently needs improvement. Summary of the Invention

[0005] In view of the above problems, the purpose of this invention is to provide a dynamic management system and method for the prevention and control of surface water pollution by zone, which can determine the pollution prevention and control zoning level of each water zone according to the mutual influence relationship between different influencing parameters and between different detection areas, thereby improving the accuracy of water pollution assessment.

[0006] The first aspect of this invention provides a dynamic management method for the prevention and control of surface water pollution by zone, comprising:

[0007] Sampling data of the monitoring coordinates are acquired based on a preset acquisition time;

[0008] The sampling data is analyzed to determine the detection value affecting parameter i;

[0009] Calculate the first risk assessment score of the detected value;

[0010] Based on the change curve of the influence coefficient, determine whether there is a mutual influence relationship between the influence parameters, calculate the coordination risk assessment score of the influence parameters that have mutual influence, and replace the first risk assessment score of the corresponding influence parameter.

[0011] The risk assessment score of the monitoring coordinates is determined by the ratio of the sum of the first risk assessment scores and the replaced coordinated risk assessment scores of all influencing parameters to the number of influencing parameters.

[0012] Based on the risk assessment score, the pollution prevention and control assessment score of the water area corresponding to the monitoring coordinates is calculated, and the pollution prevention and control zoning level of the water area is determined.

[0013] In this scheme, the step of analyzing the sampled data to determine the detection value affecting parameter i includes:

[0014] The sampled data is preprocessed to obtain preprocessed data; the data preprocessing includes data cleaning and data transformation.

[0015] Extract the detection value P of the influencing parameter i from the preprocessed data. (i) .

[0016] In this scheme, calculating the first risk assessment score of the detected value includes:

[0017] The detection value P (i) The corresponding preset warning value P (i-max) By comparison, the first risk assessment score Q affecting parameter i is determined. (i) ;

[0018]

[0019] This plan also includes:

[0020] Obtain historical sampling data;

[0021] Based on the analysis of the detection values ​​of the influence parameters in the historical sampling data, an influence parameter is randomly selected as the first influence parameter, and multiple detection value change curves of the first influence parameter under the fluctuation of other influence parameters are plotted.

[0022] The curve of the change in the detected value of the first influencing parameter when it is not affected by other influencing parameters is determined as the first change curve.

[0023] Compare the first change curve with the other detection value change curves of the first influencing parameter, and record the detection values ​​of other influencing parameters when the curve fluctuation value is greater than the preset fluctuation threshold;

[0024] The detection values ​​of each other influencing parameter are analyzed sequentially. When the detection values ​​of other influencing parameters are linearly correlated with the detection value of the first influencing parameter, the other influencing parameter is determined as the second influencing parameter.

[0025] Based on the change curves of the detection values ​​of the first influencing parameter and the fluctuation values ​​of the first change curve under different detection values ​​of the second influencing parameter, the change curves of the influence coefficients of the first and second influencing parameters are plotted.

[0026] In this scheme, the step of determining whether there is a mutual influence relationship between the influencing parameters based on the influence coefficient change curve, calculating the coordination risk assessment score of the influencing parameters that have mutual influence, and replacing the first risk assessment score of the corresponding influencing parameter includes:

[0027] The detection values ​​P of the influencing parameters x and y (x) and P(y) Input the value into the corresponding influence coefficient change curve to determine the first influence coefficient 'a'. (x-y) ;

[0028] When the first influence coefficient a (x-y) When the values ​​are greater than the corresponding preset influence coefficient threshold, it is determined that the influence parameter x and the influence parameter y have a mutual influence.

[0029] According to the first influence coefficient a (x-y) Calculate the synergistic risk assessment score Q for influencing parameters x and y. (x-y) ;

[0030] Q (x-y) =a (x-y) ×(Q (x) +Q (y) );

[0031] Based on the collaborative risk assessment score Q (x-y) The first risk assessment score Q for the influencing parameters x and y (x) and Q (y) Replace it.

[0032] This plan also includes:

[0033] When there is mutual influence among the three influencing parameters, multiple sets of mutually influencing parameters are determined.

[0034] Calculate the first change score for each influencing parameter based on the detection values ​​of the influencing parameters at the current acquisition time and the previous acquisition time;

[0035]

[0036] Among them, G (i) To influence the score of the first change in parameter i, P Tq(i) To influence parameter i at the current acquisition time T q The detection value, P Tq-1(i) To influence parameter i at the previous acquisition time T q-1 The detected value;

[0037] The second change score for each group of mutual influence parameters is determined based on the sum of the influencing parameters among the same mutual influence parameters;

[0038] The second influence coefficient b of each group of mutually influencing parameters is determined based on the ratio of the second change scores of each group of mutually influencing parameters;

[0039] The collaborative risk assessment score Q of the three influencing parameters is determined by calculating the second influence coefficient b and the corresponding first influence coefficient a for each group of mutually influencing parameters. (x-y-z) ;

[0040] Q (x-y-z) =b (x-y) ×a (x-y) ×[Q (x) +Q (y) ]+b (x-z) ×a (x-z) ×[Q (x) +Q (z) ]+b (y-z) ×a (y-z) ×[Q (y) +Q (z) ];

[0041] Among them, a (x-y) The first influence coefficients and a for influencing parameters x and y are... (x-z) The first influence coefficients and a of the influencing parameters x and z are... (y-z) The first influence coefficients and b for influencing parameters y and z are... (x-y) The second influence coefficients for influencing parameters x and y, b (x-z) The second influence coefficients for influencing parameters x and z, b (y-z) The second influence coefficients and Q for influencing parameters y and z are... (x-y-z) The score represents the collaborative risk assessment of the influencing parameters x, y, and z.

[0042] In this scheme, the step of calculating the pollution prevention and control assessment score of the water area corresponding to the monitoring coordinates based on the risk assessment score, and determining the pollution prevention and control zoning level of the water area, includes:

[0043] A gradient curve of risk assessment score change is plotted based on the risk assessment score of each monitoring coordinate and the surface water distribution image.

[0044] The pollution prevention and control assessment score of the water area is determined by the sum of the product of the area proportion of the water area in each gradient region of the risk assessment score gradient change curve and the corresponding gradient score.

[0045] The pollution prevention and control zoning level of the water area is determined based on the pollution prevention and control assessment score and water area type.

[0046] This plan also includes:

[0047] Determine n simulated acquisition times t within the time interval T between the current acquisition time and the next acquisition time. n ;

[0048] The diffusion coefficient K is calculated based on the detected values ​​of the influence parameter i at the current monitoring location A and the adjacent monitoring location B. 1(i) ;

[0049] The permeability coefficient K is determined based on the soil type of the water zone where the current monitoring location is located. 2(i) ;

[0050] According to the diffusion coefficient K 1(i) and the permeability coefficient K 2(i) Calculate the current monitoring location A at the simulated acquisition time t. n The first predicted risk score D of parameter i A(i)n ;

[0051] D A(i)n =(t n -t n-1 )×[K 1(i) ×(P B(i)n-1 -P A(i)n-1 )+K 2(i) ×P A(i)n-1 ];

[0052] Among them, P A(i)n-1 Let i be the influence parameter of the current monitoring location A during the simulated acquisition time t. n-1 The detected or simulated value, P B(i)n-1 The influence parameter i of adjacent monitoring location B during the simulated acquisition time t n-1 The detected or simulated value;

[0053] Based on the first predicted risk score D A(i)n Calculate the current monitoring location at the simulated acquisition time t n Second Predictive Risk Score E An ;

[0054]

[0055] This plan also includes:

[0056] For all simulated acquisition times t n Second Predictive Risk Score E An Perform weighted calculations to determine the pollution fluctuation score F at the current monitoring coordinate A. An ;

[0057] When the pollution fluctuation score F An When the pollution fluctuation score exceeds the preset threshold, the current monitoring coordinate A is marked with a risk indicator.

[0058] Calculate the percentage of monitoring coordinates of risk markers within each water zone;

[0059] When the proportion of the quantity is greater than the preset proportion threshold, the pollution prevention and control zoning level of the corresponding water area is increased by one.

[0060] Conversely, no action is taken.

[0061] A second aspect of the present invention provides a dynamic management system for the prevention and control of surface water pollution by zone, comprising:

[0062] The data acquisition module is used to acquire sampling data of the monitoring coordinates based on a preset acquisition time.

[0063] The data analysis module is used to analyze the sampled data, determine the detection value affecting parameter i, and calculate the first risk assessment score of the detection value.

[0064] The parameter impact assessment module is used to determine whether there is a mutual influence relationship between the impact parameters based on the change curve of the impact coefficient, calculate the coordination risk assessment score of the impact parameters that have mutual influence, and replace the first risk assessment score of the corresponding impact parameter.

[0065] The risk assessment score calculation module is used to calculate the risk assessment score of the monitoring coordinates by the ratio of the sum of the first risk assessment score and the replaced coordinated risk assessment score of all influencing parameters to the number of influencing parameters.

[0066] A pollution prevention and control zoning module is used to calculate the pollution prevention and control assessment score of the water area corresponding to the monitoring coordinates based on the risk assessment score, and to determine the pollution prevention and control zoning level of the water area. A third aspect of the present invention provides a computer-readable storage medium including a dynamic management method program for surface water pollution zoning prevention and control. When executed by a processor, the dynamic management method program for surface water pollution zoning prevention and control implements the steps of the dynamic management method for surface water pollution zoning prevention and control as described above.

[0067] This invention discloses a dynamic management system and method for zoning prevention and control of surface water pollution. The method includes: acquiring sampling data of monitoring coordinates; analyzing the sampling data to determine the detection value of influencing parameter i; calculating the first risk assessment score of the detection value; determining whether there is a mutual influence relationship between influencing parameters based on the influence coefficient change curve, calculating the coordination risk assessment score of the influencing parameters with mutual influence, and replacing the first risk assessment score of the corresponding influencing parameter; calculating the risk assessment score of the monitoring coordinates; and calculating the pollution prevention and control assessment score of the corresponding water area zone based on the risk assessment score, thereby determining the pollution prevention and control zoning level of the water area zone. This invention determines the pollution prevention and control zoning level of each water area zone based on the mutual influence relationship between different influencing parameters, improving the accuracy of water pollution assessment. Attached Figure Description

[0068] Figure 1 A flowchart of a dynamic management method for zoning prevention and control of surface water pollution provided by the present invention is shown;

[0069] Figure 2 A flowchart of the method for determining the influence coefficient variation curve provided by the present invention is shown;

[0070] Figure 3 The flowchart of the method for determining whether there is a mutual influence relationship between influencing parameters provided by the present invention is shown;

[0071] Figure 4 A block diagram of a dynamic management system for zoning prevention and control of surface water pollution provided by the present invention is shown. Detailed Implementation

[0072] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0073] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0074] Figure 1 The flowchart illustrates a dynamic management method for zoning prevention and control of surface water pollution provided by the present invention.

[0075] like Figure 1 As shown, this invention discloses a dynamic management method for zoning prevention and control of surface water pollution, comprising:

[0076] S102, acquire sampling data of monitoring coordinates based on preset acquisition time;

[0077] S104, Analyze the sampled data to determine the detection value affecting parameter i;

[0078] S106, Calculate the first risk assessment score of the detected value;

[0079] S108. Based on the change curve of the influence coefficient, determine whether there is a mutual influence relationship between the influence parameters, calculate the coordination risk assessment score of the influence parameters that have mutual influence, and replace the first risk assessment score of the corresponding influence parameter.

[0080] S110, Calculate the risk assessment score of the monitoring coordinates by the ratio of the sum of the first risk assessment scores and the replaced coordinated risk assessment scores of all influencing parameters to the number of influencing parameters.

[0081] S112. Calculate the pollution prevention and control assessment score of the corresponding water area based on the risk assessment score, and determine the pollution prevention and control zoning level of the water area.

[0082] According to an embodiment of the present invention, the preset collection time is determined by the system based on a pre-set preset data collection interval (e.g., every 24 hours, every 7 days, etc.). Those skilled in the art can adjust the preset collection time and preset data collection interval according to actual needs. During the preset collection time, sampling data of the monitoring coordinates is obtained through methods such as collecting water samples and sensor data detection. Multiple influencing parameters i (including chemical oxygen demand (COD), permanganate index, biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), total phosphorus (TP), etc.) are detected from the collected data through water sample analysis and data preprocessing. (i) The detection value P (i) The corresponding preset warning value P (i-max) By comparison, the first risk assessment score Q affecting parameter i is determined. (i) The risk assessment score for the current monitoring coordinate is determined by averaging the first risk assessment scores of all influencing parameters based on the number of influencing parameters.

[0083] Before calculating the risk assessment score of the monitoring coordinates, it is necessary to determine whether there are any mutual influences between the various influencing parameters. The detection value P of the influencing parameter i is then used. (i) The parameters are input into the corresponding influence coefficient change curves to determine whether there is a mutual influence relationship between them, and the first influence coefficient of the parameters with mutual influence relationships is calculated. The collaborative risk assessment score is calculated based on the first influence coefficient of the parameters with mutual influence relationships. In the process of calculating the risk assessment score of the monitoring coordinates, the collaborative risk assessment score replaces the first risk assessment score of the corresponding influence parameter.

[0084] Based on the risk assessment score for each monitoring coordinate and combined with the surface water distribution image, a gradient curve of the risk assessment score is plotted. The pollution prevention and control assessment score for each water zone is calculated according to the area proportion of each gradient region. The pollution prevention and control zoning level of the water zone is determined by combining the water type of that water zone. The water types of the water zones include drinking water source protection areas, nature reserves, scenic areas, agricultural water use areas, and fishery water use areas.

[0085] In addition, the diffusion coefficient can be calculated based on the detection values ​​of adjacent monitoring locations, the permeability coefficient can be determined based on the soil type of the water area, and the pollution fluctuation score of each monitoring coordinate in each water area during the current collection time and the next collection time can be calculated based on the diffusion coefficient and the permeability coefficient, so as to adjust the pollution prevention and control zoning level of the corresponding water area.

[0086] According to an embodiment of the present invention, the detection value of the influencing parameter i is determined by analyzing the sampled data, including:

[0087] The sampled data is preprocessed to obtain preprocessed data; data preprocessing includes data cleaning and data transformation.

[0088] Extract the detection value P that affects parameter i from the preprocessed data. (i) .

[0089] It should be noted that the influencing parameters include chemical oxygen demand (COD), permanganate index, biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), and total phosphorus (TP). The sampling data includes collected water samples and data obtained directly from the sensors. For the collected water samples, the detection values ​​of each influencing parameter are extracted using a pre-set water sample processing method. The extracted values ​​are then preprocessed with outlier removal and normalization to determine the final detection values ​​of the influencing parameters. For the sensor-acquired detection data, outlier removal and normalization are directly applied to determine the final detection values ​​of the influencing parameters.

[0090] According to an embodiment of the present invention, calculating a first risk assessment score for the detected value includes:

[0091] The detection value P (i) The corresponding preset warning value P (i-max) By comparison, the first risk assessment score Q affecting parameter i is determined. (i) ;

[0092]

[0093] It should be noted that those skilled in the art set corresponding preset warning values ​​based on the values ​​of each influencing parameter when the governance conditions are met, calculate the ratio of the detected value of the influencing parameter to the corresponding preset warning value, and determine the first risk assessment score of the influencing parameter based on the magnitude of the ratio.

[0094] Figure 2 A flowchart of the method for determining the influence coefficient variation curve provided by the present invention is shown.

[0095] like Figure 2 As shown, according to an embodiment of the present invention, it further includes:

[0096] S202, Obtain historical sampling data;

[0097] S204. Analyze the detection values ​​of the influencing parameters based on the historical sampling data, randomly select one influencing parameter as the first influencing parameter, and plot multiple detection value change curves of the first influencing parameter under the fluctuation of other influencing parameters;

[0098] S206, The curve of the change of the detection value of the first influencing parameter when it is not affected by other influencing parameters is determined as the first change curve;

[0099] S208, compare the first change curve with the other detection value change curves of the first influencing parameter, and record the detection values ​​of other influencing parameters when the curve fluctuation value is greater than the preset fluctuation threshold;

[0100] S210, Analyze the detection values ​​of each other influencing parameter in turn. When the detection values ​​of other influencing parameters are linearly correlated with the detection value of the first influencing parameter, the other influencing parameter is determined as the second influencing parameter.

[0101] S212, based on the change curve of the detection value of the first influence parameter under different detection values ​​and the curve fluctuation value of the first change curve, plot the change curve of the influence coefficient of the first influence parameter and the second influence parameter.

[0102] It should be noted that the content and changes of parameters in water bodies are often not isolated, but rather interconnected and mutually influential. Taking Chemical Oxygen Demand (COD) as an example, COD reflects the degree of pollution in water by reducing substances, especially organic matter. When COD is high, it indicates a high content of organic matter in the water. These organic compounds consume a large amount of dissolved oxygen during decomposition, thus affecting the value of Biochemical Oxygen Demand (BOD5). Furthermore, the decomposition of organic matter may also produce nitrogen-containing compounds such as ammonia nitrogen; therefore, there is also a certain correlation between COD and ammonia nitrogen (NH3-N) content.

[0103] After determining the primary influencing parameter, historical sampling data is selected from which all other influencing parameters, except for the primary parameter, fall within the numerical fluctuation range of the optimal water environment (i.e., the primary parameter is unaffected by other influencing parameters). Based on the detected values ​​of the influencing parameter in this portion of historical sampling data, a first variation curve of the primary influencing parameter's value over time is plotted. By analyzing and comparing the first variation curve with the variation curves of other detected values ​​of the primary influencing parameter, a second influencing parameter with an interdependent relationship with the primary parameter is determined. Historical sampling data is then selected from which all other influencing parameters, except for the primary and second influencing parameters, fall within the numerical fluctuation range of the optimal water environment (i.e., the primary parameter is unaffected by other influencing parameters) for analysis. The influence coefficients of the primary and second influencing parameters at different values ​​are determined, and the variation curves of the influence coefficients of the primary and second influencing parameters are plotted. Each influencing parameter is then identified as the primary influencing parameter for analysis, identifying one or more groups of influencing parameters with interdependent relationships, and the variation curves of the influence coefficients of each group of interdependent influencing parameters are plotted.

[0104] Among them, historical sampling data refers to various sampling data collected during the historical acquisition process, as well as the detection values ​​of various influencing parameters obtained from the analysis. The preset fluctuation threshold is set by those skilled in the art according to actual needs.

[0105] Figure 3 The flowchart of the method for determining whether there is a mutual influence relationship between influencing parameters provided by the present invention is shown.

[0106] like Figure 3 As shown in the embodiment of the present invention, based on the influence coefficient change curve, it is determined whether there is a mutual influence relationship between the influence parameters, the coordination risk assessment score of the influence parameters that have mutual influence is calculated, and the first risk assessment score of the corresponding influence parameter is replaced, including:

[0107] S302, the detection values ​​P that affect parameter x and parameter y. (x) and P (y) Input the value into the corresponding influence coefficient change curve to determine the first influence coefficient 'a'. (x-y) ;

[0108] S304, when the first influence coefficient a (x-y) When the values ​​are greater than the corresponding preset influence coefficient threshold, it is determined that the influence parameter x and the influence parameter y have a mutual influence.

[0109] S306, based on the first influence coefficient a (x-y) Calculate the synergistic risk assessment score Q for influencing parameters x and y. (x-y) ;

[0110] Q (x-y) =a (x-y) ×(Q (x) +Q (y) );

[0111] S308, based on the collaborative risk assessment score Q (x-y) The first risk assessment score Q for the influencing parameters x and y (x) and Q (y) Replace it.

[0112] It should be noted that the corresponding influence coefficient change curve is selected based on the influence parameters x and y, and the measured values ​​P of the influence parameters x and y are used as the basis for the selection. (x) and P (y) Output the first influence coefficient a of the influencing parameters x and y. (x-y) When the first influence coefficient a (x-y) When the detected values ​​of the influencing parameters x and y are less than or equal to the corresponding preset influence coefficient threshold, it indicates that the minimum values ​​for mutual influence between the two parameters have not been reached, and there is no mutual influence relationship between them. When the first influence coefficient a... (x-y) When the values ​​exceed the corresponding preset impact coefficient threshold, it is determined that the impact parameter x and the impact parameter y have a mutual influence condition. The collaborative risk assessment score Q of the impact parameter x and the impact parameter y is then calculated using the system's preset collaborative risk assessment score. (x-y) Furthermore, in the process of calculating the risk assessment score of the monitoring coordinates, the collaborative risk assessment score Q of the influencing parameter x and the influencing parameter y is used. (x-y) The first risk assessment score Q for the influencing parameters x and y (x) and Q (y) Replacement is performed. The preset influence coefficient threshold is set by those skilled in the art according to actual needs.

[0113] According to an embodiment of the present invention, it further includes:

[0114] When there is mutual influence among the three influencing parameters, multiple sets of mutually influencing parameters are determined.

[0115] Calculate the first change score for each influencing parameter based on the detection values ​​of the influencing parameters at the current acquisition time and the previous acquisition time;

[0116]

[0117] Among them, G (i) To influence the score of the first change in parameter i, P Tq(i) To influence parameter i at the current acquisition time T q The detection value, P Tq-1(i)To influence parameter i at the previous acquisition time T q-1 The detected value;

[0118] The second change score for each group of mutual influence parameters is determined based on the sum of the influencing parameters among the same mutual influence parameters;

[0119] The second influence coefficient b of each group of mutually influencing parameters is determined based on the ratio of the second change scores of each group of mutually influencing parameters;

[0120] The collaborative risk assessment score Q of the three influencing parameters is determined by calculating the second influence coefficient b and the corresponding first influence coefficient a for each group of mutually influencing parameters. (x-y-z) ;

[0121] Q (x-y-z) =b (x-y) ×a (x-y) ×[Q (x) +Q (y) ]+b (x-z) ×a (x-z) ×[Q (x) +Q (z) ]+b (y-z) ×a (y-z) ×[Q (y) +Q (z) ];

[0122] Among them, a (x-y) The first influence coefficients and a for influencing parameters x and y are... (x-z) The first influence coefficients and a of the influencing parameters x and z are... (y-z) The first influence coefficients and b for influencing parameters y and z are... (x-y) The second influence coefficients for influencing parameters x and y, b (x-z) The second influence coefficients for influencing parameters x and z, b (y-z) The second influence coefficients and Q for influencing parameters y and z are... (x-y-z) The score represents the collaborative risk assessment of the influencing parameters x, y, and z.

[0123] It should be noted that the influence coefficient change curve can determine whether there is mutual influence between any two (or more) influencing parameters. When multiple influencing parameters (taking three influencing parameters x, y, and z as an example) are mutually influential, each group of mutually influential parameters is combined to determine multiple groups of mutually influential parameters. The first change score of the influencing parameter is determined by the ratio of the difference between the detected values ​​at the current acquisition time and the previous acquisition time to the detected value at the current acquisition time. The second change score of each group of mutually influential parameters is determined by calculating the sum of the influencing parameters in each group. The second influence coefficient of each group of mutually influential parameters is determined by the ratio of the second change scores of each group of mutually influential parameters. The sum of the second influence coefficients of all mutually influential parameters is 1.

[0124] In calculating the risk assessment score of the monitoring coordinates, the collaborative risk assessment score Q, which affects parameters x, y, and z, is used. (x-y-z) The first risk assessment score Q for the parameter x (x) The first risk assessment score Q affecting parameter y (y) And the first risk assessment score Q of the parameter z (z) Replace it.

[0125] According to an embodiment of the present invention, the pollution prevention and control assessment score of the water area corresponding to the monitoring coordinates is calculated based on the risk assessment score, and the pollution prevention and control zoning level of the water area is determined, including:

[0126] A gradient curve of risk assessment score change is plotted based on the risk assessment score of each monitoring coordinate and the surface water distribution image.

[0127] The pollution prevention and control assessment score of a water area is determined by the sum of the product of the area proportion of the water area in each gradient region of the risk assessment score gradient change curve and the corresponding gradient score.

[0128] The pollution prevention and control zoning level of a water area is determined based on its pollution prevention and control assessment score and water area type.

[0129] It should be noted that adjacent monitoring coordinates are connected in pairs. Based on the risk assessment scores of adjacent monitoring coordinates, the coordinate positions corresponding to one or more preset risk assessment scores (determined by the system according to preset score intervals, for example, the preset score interval can be set to 1 point) between adjacent monitoring coordinates are determined. If adjacent monitoring coordinates are all within the same water area, the risk assessment scores between adjacent monitoring coordinates change proportionally. If adjacent monitoring coordinates are in two different water areas, the risk assessment score change value of each water area is determined according to the type of the two water areas, so that the risk assessment score changes in each water area according to the corresponding proportion.

[0130] The gradient score of the gradient region is the average of the risk assessment scores corresponding to the two adjacent gradient lines in that gradient region.

[0131] Different types of water area zones correspond to different pollution prevention and control assessment score ranges for their pollution prevention and control zoning levels. Water area zones are typically classified into three levels: Level 1, Level 2, and Level 3. For example, in drinking water source protection areas, Level 1 is [0,2], Level 2 is (2,4], and Level 3 is (4,5]; in agricultural water use functional zones, Level 1 is [0,4], Level 2 is (4,5], and Level 3 is (5,7], etc.

[0132] According to an embodiment of the present invention, it further includes:

[0133] Determine n simulated acquisition times t within the time interval T between the current acquisition time and the next acquisition time. n ;

[0134] The diffusion coefficient K is calculated based on the detected values ​​of the influence parameter i at the current monitoring location A and the adjacent monitoring location B. 1(i) ;

[0135] The permeability coefficient K is determined based on the soil type of the water zone where the current monitoring location is located. 2(i) ;

[0136] According to the diffusion coefficient K 1(i) and permeability coefficient K 2(i) Calculate the current monitoring location A at the simulated acquisition time t. n The first predicted risk score D of parameter i A(i)n ;

[0137] D A(i)n =(t n -t n-1 )×[K 1(i) ×(P B(i)n-1 -P A(i)n-1 )+K 2(i) ×P A(i)n-1 ];

[0138] Among them, P A(i)n-1 Let i be the influence parameter of the current monitoring location A during the simulated acquisition time t. n-1 The detected or simulated value, P B(i)n-1 The influence parameter i of adjacent monitoring location B during the simulated acquisition time t n-1 The detected or simulated value;

[0139] Based on the first predicted risk score D A(i)n Calculate the current monitoring location at the simulated acquisition time t. nSecond Predictive Risk Score E An ;

[0140]

[0141] It should be noted that the simulated acquisition time t n The diffusion coefficient K is determined based on the system's preset time interval. 1(i) The diffusion coefficient is determined by the system based on the detected value of the influence parameter i at the current monitoring location, and the difference between the detected values ​​of the influence parameter i at the current monitoring location A and the adjacent monitoring location B. Different detected values ​​and differences correspond to different diffusion coefficients, and the specific values ​​of the diffusion coefficient are determined by the system through table lookup. Permeability coefficient K 2(i) The system determines the soil type by looking up a table based on the water area zone.

[0142] Calculate the current monitoring location A at the simulated data acquisition time t. n The first predicted risk score D of parameter i A(i)n During the process, when n=1, P A(i)n-1 and P B(i)n-1 The value is the detection value at the current acquisition time; when n > 1, P A(i)n-1 and P B(i)n-1 For simulating the acquisition time t n-1 The simulated value is obtained by considering the difference (or simulated value) of the influence parameter i between the current monitoring location A and the adjacent monitoring location B, the diffusion coefficient, and the simulated acquisition time t. n to t n-1 The time length is calculated. Finally, based on the number of influencing parameters, the average of the first predicted risk scores of all influencing parameters is calculated to determine the current monitoring location at the simulated acquisition time t. n The second predicted risk score.

[0143] According to an embodiment of the present invention, it further includes:

[0144] For all simulated acquisition times t n Second Predictive Risk Score E An Perform weighted calculations to determine the pollution fluctuation score F at the current monitoring coordinate A. An ;

[0145] When the pollution fluctuation score F An When the pollution fluctuation score exceeds the preset threshold, the current monitoring coordinate A is marked as a risk.

[0146] Calculate the percentage of monitoring coordinates of risk markers within each water zone;

[0147] When the proportion of the quantity exceeds the preset threshold, the pollution prevention and control zoning level of the corresponding water area is increased by one.

[0148] Conversely, no action is taken.

[0149] It should be noted that the simulated acquisition time t is first determined based on the time difference between the simulated acquisition time and the current acquisition time. n Second Predictive Risk Score E An The influence weight is determined by the proximity to the current acquisition time; the closer to the current acquisition time, the higher the corresponding influence weight. Then, based on the total simulated acquisition time t... n Second Predictive Risk Score E An The corresponding impact weights are used to calculate the weighted average of all second-predicted risk scores, and the pollution fluctuation score F at the current monitoring coordinate A is determined. An When the pollution fluctuation score F An When the pollution fluctuation score exceeds the preset threshold, it indicates that the pollution at the current monitoring coordinate will increase over time, and a risk marker will be assigned. When the proportion of risk-marked monitoring coordinates within a water area exceeds the preset proportion threshold, it indicates that the pollution prevention and control zoning level of that water area will increase over time, allowing for early warning and incrementing the pollution prevention and control zoning level of that water area by one. Simultaneously, relevant personnel can be notified in advance to carry out pollution prevention and control measures.

[0150] The preset pollution fluctuation score threshold and the preset quantity percentage threshold are both set by those skilled in the art according to actual needs.

[0151] Figure 4 A block diagram of a dynamic management system for zoning prevention and control of surface water pollution provided by the present invention is shown.

[0152] like Figure 4 As shown, a second aspect of the present invention provides a dynamic management system for the prevention and control of surface water pollution by zone, comprising:

[0153] The data acquisition module is used to acquire sampling data of the monitoring coordinates based on a preset acquisition time.

[0154] The data analysis module is used to analyze the sampled data, determine the detection value affecting parameter i, and calculate the first risk assessment score of the detection value.

[0155] The parameter impact assessment module is used to determine whether there is a mutual influence relationship between the impact parameters based on the change curve of the impact coefficient, calculate the coordination risk assessment score of the impact parameters that have mutual influence, and replace the first risk assessment score of the corresponding impact parameter.

[0156] The risk assessment score calculation module is used to calculate the risk assessment score of the monitoring coordinates by the ratio of the sum of the first risk assessment scores of all influencing parameters and the sum of the replaced coordinated risk assessment scores to the number of influencing parameters.

[0157] The pollution prevention and control zoning module is used to calculate the pollution prevention and control assessment score of the corresponding water area based on the risk assessment score, and to determine the pollution prevention and control zoning level of the water area.

[0158] A third aspect of the present invention provides a computer-readable storage medium comprising a program for a dynamic management method for the prevention and control of surface water pollution by zone. When the program is executed by a processor, it implements the steps of the dynamic management method for the prevention and control of surface water pollution by zone as described above.

[0159] All information (including but not limited to user equipment information, user personal information, etc.), data (including but not limited to data used for analysis, stored data, displayed data, etc.), and signals (including but not limited to signals transmitted between user terminals and other devices) involved in this application have been authorized by the user or fully authorized by all parties, and the collection, use, and processing of related data must comply with the relevant laws, regulations, and standards of the relevant countries and regions. For example, the "sampling data of monitoring coordinates" and "historical sampling data" involved in this disclosure were obtained under full authorization.

[0160] This invention discloses a dynamic management system and method for zoning prevention and control of surface water pollution. The method includes: acquiring sampling data of monitoring coordinates; analyzing the sampling data to determine the detection value of influencing parameter i; calculating the first risk assessment score of the detection value; determining whether there is a mutual influence relationship between influencing parameters based on the influence coefficient change curve, calculating the coordination risk assessment score of the influencing parameters with mutual influence, and replacing the first risk assessment score of the corresponding influencing parameter; calculating the risk assessment score of the monitoring coordinates; and calculating the pollution prevention and control assessment score of the corresponding water area zone based on the risk assessment score, thereby determining the pollution prevention and control zoning level of the water area zone. This invention determines the pollution prevention and control zoning level of each water area zone based on the mutual influence relationship between different influencing parameters, improving the accuracy of water pollution assessment.

[0161] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0162] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0163] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0164] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0165] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

Claims

1. A dynamic management method for the prevention and control of surface water pollution by zone, characterized in that, include: Sampling data of the monitoring coordinates are acquired based on a preset acquisition time; The sampling data is analyzed to determine the detection value affecting parameter i; Calculate the first risk assessment score of the detected value; Based on the change curve of the influence coefficient, determine whether there is a mutual influence relationship between the influence parameters, calculate the coordination risk assessment score of the influence parameters that have mutual influence, and replace the first risk assessment score of the corresponding influence parameter. The risk assessment score of the monitoring coordinates is determined by the ratio of the sum of the first risk assessment scores and the replaced coordinated risk assessment scores of all influencing parameters to the number of influencing parameters. Based on the risk assessment score, calculate the pollution prevention and control assessment score of the water area corresponding to the monitoring coordinates, and determine the pollution prevention and control zoning level of the water area. The process of determining whether there is a mutual influence relationship between influencing parameters based on the influence coefficient change curve, calculating the coordination risk assessment score of the influencing parameters that have mutual influence, and replacing the first risk assessment score of the corresponding influencing parameter includes: The detection values ​​P of the influencing parameters x and y (x) and P (y) Input the value into the corresponding influence coefficient change curve to determine the first influence coefficient 'a'. (x-y) ; When the first influence coefficient a (x-y) When the values ​​are greater than the corresponding preset influence coefficient threshold, it is determined that the influence parameter x and the influence parameter y have a mutual influence. According to the first influence coefficient a (x-y) Calculate the synergistic risk assessment score Q for influencing parameters x and y. (x-y) ; ； Based on the collaborative risk assessment score Q (x-y) The first risk assessment score Q for the influencing parameters x and y (x) and Q (y) Replace it.

2. The dynamic management method for zoning prevention and control of surface water pollution according to claim 1, characterized in that, The step of analyzing the sampled data to determine the detection value affecting parameter i includes: The sampled data is preprocessed to obtain preprocessed data; the data preprocessing includes data cleaning and data transformation. Extract the detection value P of the influencing parameter i from the preprocessed data. (i) .

3. The dynamic management method for zoning prevention and control of surface water pollution according to claim 1, characterized in that, The calculation of the first risk assessment score for the detected value includes: The detection value P (i) The corresponding preset warning value P (i-max) By comparison, the first risk assessment score Q affecting parameter i is determined. (i) ; 。 4. The dynamic management method for zoning prevention and control of surface water pollution according to claim 1, characterized in that, Also includes: Obtain historical sampling data; Based on the analysis of the detection values ​​of the influence parameters in the historical sampling data, an influence parameter is randomly selected as the first influence parameter, and multiple detection value change curves of the first influence parameter under the fluctuation of other influence parameters are plotted. The curve of the change in the detected value of the first influencing parameter when it is not affected by other influencing parameters is determined as the first change curve. Compare the first change curve with the other detection value change curves of the first influencing parameter, and record the detection values ​​of other influencing parameters when the curve fluctuation value is greater than the preset fluctuation threshold; The detection values ​​of each other influencing parameter are analyzed sequentially. When the detection values ​​of other influencing parameters are linearly correlated with the detection value of the first influencing parameter, the other influencing parameter is determined as the second influencing parameter. Based on the change curves of the detection values ​​of the first influencing parameter and the fluctuation values ​​of the first change curve under different detection values ​​of the second influencing parameter, the change curves of the influence coefficients of the first and second influencing parameters are plotted.

5. The dynamic management method for zoning prevention and control of surface water pollution according to claim 1, characterized in that, Also includes: When there is mutual influence among the three influencing parameters, multiple sets of mutually influencing parameters are determined. Calculate the first change score for each influencing parameter based on the detection values ​​of the influencing parameters at the current acquisition time and the previous acquisition time; ； Among them, G (i) To influence the score of the first change in parameter i, P Tq(i) To influence parameter i at the current acquisition time T q The detection value, P Tq-1(i) To influence parameter i at the previous acquisition time T q-1 The detected value; The second change score for each group of mutual influence parameters is determined based on the sum of the influencing parameters among the same mutual influence parameters; The second influence coefficient b of each group of mutually influencing parameters is determined based on the ratio of the second change scores of each group of mutually influencing parameters; The collaborative risk assessment score Q of the three influencing parameters is determined by calculating the second influence coefficient b and the corresponding first influence coefficient a for each group of mutually influencing parameters. (x-y-z) ; ； Among them, a (x-y) The first influence coefficients and a for influencing parameters x and y are... (x-z) The first influence coefficients and a of the influencing parameters x and z are... (y-z) The first influence coefficients and b for influencing parameters y and z are... (x-y) The second influence coefficients for influencing parameters x and y, b (x-z) The second influence coefficients for influencing parameters x and z, b (y-z) The second influence coefficients and Q for influencing parameters y and z are... (x-y-z) The score represents the collaborative risk assessment of the influencing parameters x, y, and z.

6. The dynamic management method for zoning prevention and control of surface water pollution according to claim 1, characterized in that, The step of calculating the pollution prevention and control assessment score of the water area corresponding to the monitoring coordinates based on the risk assessment score, and determining the pollution prevention and control zoning level of the water area, includes: A gradient curve of risk assessment score change is plotted based on the risk assessment score of each monitoring coordinate and the surface water distribution image. The pollution prevention and control assessment score of the water area is determined by the sum of the product of the area proportion of the water area in each gradient region of the risk assessment score gradient change curve and the corresponding gradient score. The pollution prevention and control zoning level of the water area is determined based on the pollution prevention and control assessment score and water area type.

7. The dynamic management method for zoning prevention and control of surface water pollution according to claim 1, characterized in that, Also includes: Determine n simulated acquisition times t within the time interval T between the current acquisition time and the next acquisition time. n ; The diffusion coefficient K is calculated based on the detected values ​​of the influence parameter i at the current monitoring location A and the adjacent monitoring location B. 1(i) ; The permeability coefficient K is determined based on the soil type of the water zone where the current monitoring location is located. 2(i) ; According to the diffusion coefficient K 1(i) and the permeability coefficient K 2(i) Calculate the current monitoring location A at the simulated acquisition time t. n The first predicted risk score D of parameter i A(i)n ; ； Among them, P A(i)n-1 Let i be the influence parameter of the current monitoring location A during the simulated acquisition time t. n-1 The detected or simulated value, P B(i)n-1 The influence parameter i of adjacent monitoring location B during the simulated acquisition time t n-1 The detected or simulated value; Based on the first predicted risk score D A(i)n Calculate the current monitoring location at the simulated acquisition time t n Second Predictive Risk Score E An ; 。 8. The dynamic management method for zoning prevention and control of surface water pollution according to claim 7, characterized in that, Also includes: For all simulated acquisition times t n Second Predictive Risk Score E An Perform weighted calculations to determine the pollution fluctuation score F at the current monitoring coordinate A. An ; When the pollution fluctuation score F An When the pollution fluctuation score exceeds the preset threshold, the current monitoring coordinate A is marked with a risk indicator. Calculate the percentage of monitoring coordinates of risk markers within each water zone; When the proportion of the quantity is greater than the preset proportion threshold, the pollution prevention and control zoning level of the corresponding water area is increased by one. Conversely, no action is taken.

9. A dynamic management system for zoning prevention and control of surface water pollution, used to implement the dynamic management method for zoning prevention and control of surface water pollution as described in any one of claims 1-8, characterized in that, include: The data acquisition module is used to acquire sampling data of the monitoring coordinates based on a preset acquisition time. The data analysis module is used to analyze the sampled data and determine the detection value that affects parameter i. Calculate the first risk assessment score of the detected value; The parameter impact assessment module is used to determine whether there is a mutual influence relationship between the impact parameters based on the change curve of the impact coefficient, calculate the coordination risk assessment score of the impact parameters that have mutual influence, and replace the first risk assessment score of the corresponding impact parameter. The risk assessment score calculation module is used to calculate the risk assessment score of the monitoring coordinates by the ratio of the sum of the first risk assessment score and the replaced coordinated risk assessment score of all influencing parameters to the number of influencing parameters. The pollution prevention and control zoning module is used to calculate the pollution prevention and control assessment score of the water area corresponding to the monitoring coordinates based on the risk assessment score, and to determine the pollution prevention and control zoning level of the water area. The process of determining whether there is a mutual influence relationship between influencing parameters based on the influence coefficient change curve, calculating the coordination risk assessment score of the influencing parameters that have mutual influence, and replacing the first risk assessment score of the corresponding influencing parameter includes: The detection values ​​P of the influencing parameters x and y (x) and P (y) Input the value into the corresponding influence coefficient change curve to determine the first influence coefficient 'a'. (x-y) ; When the first influence coefficient a (x-y) When the values ​​are greater than the corresponding preset influence coefficient threshold, it is determined that the influence parameter x and the influence parameter y have a mutual influence. According to the first influence coefficient a (x-y) Calculate the synergistic risk assessment score Q for influencing parameters x and y. (x-y) ; ； Based on the collaborative risk assessment score Q (x-y) The first risk assessment score Q for the influencing parameters x and y (x) and Q (y) Replace it.

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