Method and system for analyzing causes of surface water pollution based on multi-source composite relationship distribution and interpretation

Abstract

The present invention provides a method and system for analyzing the causes of surface water pollution based on the distribution of multi-source composite relationships. The method includes: Step 1, obtaining the time series of hydrological, pollutant concentration, and index factor monitoring data of surface water control sections; Step 2, data interpolation and normalization processing; Step 3, expressing the pollutant concentration as a linear superposition of the first-order terms and second-order partial derivative terms of various influencing factors; the direct relationship between the change of the influencing factor and the change of the pollutant concentration is described by the first-order partial derivative term, and the influence of the multi-factor composite action on the pollutant concentration during the formation process of the pollutant is described by the second-order partial derivative term; Step 4, spreading the change mechanism of the pollutant under the multi-factor composite action into a linear superposition form to determine the influence degree of various factors on the change of the pollutant concentration; Step 5, using multiple linear estimation to determine the linear relationship between the pollutant concentration and the first-order and second-order derivatives of the influencing factors, and realizing the analysis of the causes of surface water pollution according to the linear coefficients of each influencing factor.

Description

Technical Field

[0001] The present invention belongs to the technical field of surface water pollution analysis and simulation, and particularly relates to a method and system for analyzing the causes of surface water pollution based on the distribution of multi-source composite relationships. Background Art

[0002] Pollutants in surface water come from different pollution sources, and after the pollutants enter the surface water, various physical, chemical, and biological processes also greatly affect the change of pollutant concentration. Determining the main factors causing the change of pollutant concentration and their influencing degree is not only an important basis for predicting and simulating surface water pollution, but also an important basis for pollution control decision-making.

[0003] Currently, the methods for analyzing the causes of surface water pollution are mainly divided into two categories. The first category of methods analyzes the causes of pollution by measuring the typical characteristic indicators (such as isotope abundance or DNA, etc.) of pollutants from different sources and comparing them with the characteristic indicators of pollutants in the river channel. The second category of methods, according to the physical model after parameter calibration, uses methods such as the turning-off method, and analyzes the main influencing factors of pollution and their influencing degree by calculating the change of surface water pollutant concentration by setting the presence or absence of influencing factors. It should be noted that in the case of high spatio-temporal variability of pollution characteristic indicators, the first method usually has difficulty in controlling the accuracy, and the measurement of pollution characteristic indicators is usually very expensive. For the second method, the parameter calibration in the physical model is usually optimal for the total pollution amount, and there is a lack of necessary theoretical basis and physical basis for the decomposition of various influencing factors and their contributions. The interpretation of the causes of pollution depends to a large extent on the model parameters. More importantly, the calibration of model parameters usually requires a large amount of measured data of meteorology, hydrology, pollution sources, underlying surface, etc. over many years. It can be seen that under the conditions of high spatio-temporal variability of pollution influencing factors such as pollutant emissions, underlying surface factors, and meteorological conditions in the case of multi-source composite pollution, the existing methods for analyzing the causes of surface water pollution cannot effectively achieve the defect of pollution cause analysis. Summary of the Invention

[0004] The present invention is made to solve the above problems, and aims to provide a method and system for analyzing the causes of surface water pollution based on the distribution of multi-source composite relationships, which can realize the analysis of the causes of surface water pollution only based on surface water monitoring data through the first-order and second-order partial derivative distribution of multi-source composite pollution factors, and provide a scientific basis for the construction of basin distributed pollution models and pollution control decision-making.

[0005] In order to achieve the above object, the present invention adopts the following solutions:

[0006] <Method>

[0007] The present invention provides a method for analyzing the causes of surface water pollution based on the distribution of multi-source composite relationships, which is characterized by including the following steps:

[0008] Step 1: Obtain the time series of hydrological, pollutant concentration, and indicator factor monitoring data for the surface water control section, where the length of the time series covers at least one flood season and one dry season.

[0009] Step 2: Perform data interpolation and normalization on the time series, which will be used as the data for subsequent steps.

[0010] Step 3: Express the pollutant concentration under the combined influence of multiple factors (the measured data after interpolation and normalization, the same below) as a linear superposition of the first-order terms and second-order partial derivative terms of various influencing factors; the change in pollutant concentration in the river section is expressed as:

[0011]

[0012] In the formula, c is the pollutant concentration, x and y are the position coordinates, t is the time step, and λ is the influencing factor; and are the first-order partial derivative term and the second-order partial derivative term respectively. i and j represent different influencing factors. When i = j, it is the self-second-order partial derivative, and when they are not equal, it is the co-second-order partial derivative term. n is the total number of influencing factors, and O is the lumped error term;

[0013] The direct relationship between the change in the influencing factor and the change in the pollutant concentration is described by the first-order partial derivative term, and the influence of the complex chemical and physical change process (combined action of multiple factors) of the pollutant formation process on the pollutant concentration is described by the second-order partial derivative term;

[0014] Step 4: Use the implicit difference method to approximately estimate the differential partial derivative terms based on the measured data. The first-order partial derivative term and the second-order partial derivative term are respectively expressed as:

[0015]

[0016]

[0017] In the formula, m + 1, m, and m - 1 represent the (m + 1)-th day, the m-th day, and the (m - 1)-th day respectively, and λ i,m+1 -λ i,m represents the change amount of the influencing factor i between the (m + 1)-th day and the m-th day;

[0018] The change mechanism of the pollutant under the combined action of multiple factors is expanded into a form of linear superposition to determine the influence degree of various factors on the change in pollutant concentration;

[0019] Step 5: Use multiple linear estimation to determine the linear relationship between pollutant concentration and the first and second derivatives of influencing factors. The linear coefficient therein is the factor reflecting the influence of the influencing factor on the pollutant. The larger the value of the linear coefficient, the greater the influence, and vice versa. Thus, the causes of surface water pollution are analyzed based on the linear coefficients of each influencing factor.

[0020] Preferably, the method for analyzing the causes of surface water pollution based on multi-source composite relationship distribution provided by the present invention may further have the following characteristics: Among the factors obtained in Step 1, the hydrological factors include at least flow rate, sediment concentration, or sediment transport rate; the pollutant index factors include at least pH value and temperature; the concentration of other substances related to the target pollutant can also affect the concentration change of the target pollutant, so it can also be used as a factor.

[0021] Preferably, the method for analyzing the causes of surface water pollution based on multi-source composite relationship distribution provided by the present invention may further have the following characteristics: In Step 1, the pollutants mainly include ammonia nitrogen (NH3-N), total phosphorus (TP), permanganate index (I Mn ), total nitrogen (TN), dissolved oxygen (DO), and chlorophyll a (chl_a) measured by the water quality automatic station.

[0022] Preferably, the method for analyzing the causes of surface water pollution based on multi-source composite relationship distribution provided by the present invention may further have the following characteristics: In Step 2, in the case of missing data in hydrology, pollutant concentration, and index factor data, the Lagrange polynomial interpolation method is used to interpolate data based on 6 sampling data before and after the interpolation position; after completing the data interpolation, the data is normalized to between 0 and 1 by linear normalization:

[0023]

[0024] In the formula: x and x * are the values after normalizing the original data, min x and max x are the minimum and maximum values of the variables to which the original data belongs, respectively.

[0025] Preferably, the method for analyzing the causes of surface water pollution based on multi-source composite relationship distribution provided by the present invention may further have the following characteristics: In Step 3, without considering the influence of the interaction of various factors on the change of pollutant concentration, the first-order partial derivative term spreading-linear fitting method can be directly used to determine the lumped influence of various influencing factors on the water quality causes.

[0026] <System>

[0027] Furthermore, the present invention also provides a system for analyzing the causes of surface water pollution based on multi-source composite relationship distribution, which can automatically implement the above <Method>. It is characterized by including:

[0028] A data acquisition module that acquires the time series of hydrological, pollutant concentration, and index factor monitoring data of surface water control sections, where the length of the time series covers at least one flood season and one dry season;

[0029] A preprocessing module that performs data interpolation and normalization on the time series as the data for subsequent steps;

[0030] An influence relationship description module that expresses the pollutant concentration under the combined influence of multiple factors as a linear superposition of the first-order terms and second-order partial derivative terms of various influencing factors; the change in pollutant concentration in the river section is expressed as:

[0031]

[0032] In the formula, c is the pollutant concentration, x and y are position coordinates, t is the time step, and λ is the influencing factor; and are the first-order partial derivative term and the second-order partial derivative term respectively. i and j represent different influencing factors. When i = j, it is the self-second-order partial derivative, and when they are not equal, it is the co-second-order partial derivative term. n is the total number of influencing factors, and O is the lumped error term; the direct relationship between the change in the influencing factor and the change in the pollutant concentration is described by the first-order partial derivative term, and the influence of the complex chemical and physical change processes intertwined by multiple factors in the formation process of the pollutant on the pollutant concentration is described by the second-order partial derivative term;

[0033] An influence degree determination module that uses the implicit difference method to approximately estimate the differential partial derivative terms based on the measured data. The first-order partial derivative term and the second-order partial derivative term are respectively expressed as:

[0034]

[0035]

[0036] In the formula, m + 1, m, and m - 1 represent the (m + 1)th day, the mth day, and the (m - 1)th day respectively, and λ i,m+1 -λ i,m represents the change amount of the influencing factor i between the (m + 1)th day and the mth day; the change mechanism of the pollutant under the combined action of multiple factors is spread into a form of linear superposition to determine the influence degree of various factors on the change in pollutant concentration;

[0037] A pollution cause analysis module that uses multiple linear estimation to determine the linear relationship between the pollutant concentration and the first-order and second-order derivatives of the influencing factors. The linear coefficient therein is the factor reflecting the influence of the influencing factor on the pollutant. The larger the value of the linear coefficient, the greater the influence, and vice versa. Thus, the analysis of the causes of surface water pollution is realized according to the linear coefficients of each influencing factor;

[0038] A control module, which is communicatively connected to a data acquisition module, a preprocessing module, an influence relationship description module, an influence degree determination module, and a pollution cause analysis module, and controls their operations.

[0039] Preferably, the surface water pollution cause analysis system based on multi-source composite relationship distribution provided by the present invention may further have the following characteristics: in the influence relationship description module, without considering the influence of various factor interactions on the change of pollutant concentration, the method of first-order partial derivative term distribution - linear fitting can be directly used to determine the overall influence of various influencing factors on the water quality cause.

[0040] Preferably, the surface water pollution cause analysis system based on multi-source composite relationship distribution provided by the present invention may further include: an input display module, which is communicatively connected to the data acquisition module, the preprocessing module, the influence relationship description module, the influence degree determination module, the pollution cause analysis module, and the control unit, and is used to allow a user to input operation instructions and perform corresponding displays.

[0041] Preferably, the surface water pollution cause analysis system based on multi-source composite relationship distribution provided by the present invention may further have the following characteristics: the input display module displays various possible hydrological factors, pollutants, and pollutant index factors for an operator to select, and the data acquisition module obtains the time series of surface water control section hydrology, pollutant concentration, and index factor monitoring data based on the selected pollutants and factors.

[0042] Preferably, the surface water pollution cause analysis system based on multi-source composite relationship distribution provided by the present invention may further have the following characteristics: the input display module can generate a graph or table showing the relationship between pollutant concentration and each influencing factor and the pollution cause analysis result according to the result of the pollution cause analysis module for the operator to view.

[0043] Functions and effects of the invention

[0044] The surface water pollution cause analysis method and system based on multi-source composite relationship distribution provided by the present invention, based on surface water monitoring data, expand the change of pollutant concentration under the multi-factor composite influence into a linear superposition of the first-order partial derivative term and the second-order partial derivative term of the influencing factor. The first-order partial derivative term represents the direct relationship between the change of the influencing factor and the change of the pollutant concentration, and the second-order partial derivative term represents the influence of the complex chemical and physical change process of the multi-factor interweaving in the formation process of the pollutant on the pollutant concentration. After determining the first-order partial derivative term and the second-order partial derivative term at different time periods by the difference method, the linear coefficients of each first-order partial derivative term and second-order partial derivative term are determined by the multiple linear regression method. The linear coefficients reflect the influence degree of various influencing factors and the interaction between factors on the change of surface water quality, thereby analyzing the pollution cause of surface water and being able to provide scientific support for surface water pollution control decision-making and physical model construction. Description of the Drawings

[0045] Figure 1 This is the normalized dissolved oxygen time series (Jiangjunyan section of Lanjiang River in Zhejiang Province) involved in the embodiment of the present invention;

[0046] Figure 2 This is the influencing factor for the change in the concentration of dissolved oxygen in surface water involved in the embodiment of the present invention;

[0047] Figure 3 This is the comparison chart of the calculation results and the measured results involved in the embodiment of the present invention. Detailed Embodiment

[0048] The following will describe in detail the specific implementation of the method and system for analyzing the causes of surface water pollution based on multi-source composite relationship distribution and release according to the present invention with reference to the drawings.

[0049] <Example>

[0050] The method for analyzing the causes of surface water pollution based on multi-source composite relationship distribution and release adopted in this embodiment includes the following steps:

[0051] Step 1: Obtain the time series of hydrological (flow, sediment concentration or sediment transport rate) and pollutant concentration and index factor (pH value, temperature) monitoring data of the surface water control section, and the length of the time series covers at least one flood season and one dry season.

[0052] Step 2: Interpolate and normalize the time series data of each hydrological, pollutant concentration and parameter index: In the case of missing hydrological, pollutant concentration and parameter index data, based on the 6 sampling data before and after the interpolation position, use the non-missing data to construct a polynomial to interpolate this position. After completing the interpolation of the missing data, use the linear normalization method to normalize the data between 0 and 1.

[0053]

[0054] In the formula: x and x * are the values after normalizing the original data respectively, and min x and max x are the minimum and maximum values of the variables to which the original data belongs.

[0055] Taking the Jiangjunyan section of Lanjiang River in Lanxi City, Zhejiang Province as an example, among the dissolved oxygen monitoring data in 2020, a total of 16 days of data were missing, and the time series of dissolved oxygen concentration after data interpolation and normalization is as Figure 1 shown.

[0056] Step 3: Express the pollutant concentration under the influence of multi-factor compounding (the measured data after interpolation and normalization, the same below) as a linear superposition of the first-order terms and second-order partial derivative terms of various influencing factors. The change in the pollutant concentration in the river cross-section can be expressed as:

[0057]

[0058] In the formula, c is the pollutant concentration, x and y are position coordinates, t is the time step, and λ is the influencing factor; and are the first-order partial derivative term and the second-order partial derivative term respectively; i and j represent different influencing factors. In the case of i = j, it is the self-second-order partial derivative, and in the case of inequality, it is the co-second-order partial derivative term. n is the total number of influencing factors, and O is the lumped error term.

[0059] Among them, the first-order partial derivative term describes the direct relationship between the change in the influencing factor and the change in the pollutant concentration, and the second-order partial derivative term describes the influence of the complex chemical and physical change processes intertwined by multiple factors in the pollutant formation process on the pollutant concentration.

[0060] Step 4: Adopt the implicit difference method to approximately estimate the decoupled differential partial derivative terms based on the measured data. The first-order partial derivative term and the second-order partial derivative term are respectively approximated by difference using equations (3) and (4);

[0061]

[0062]

[0063] In the formula, i and j represent different influencing factors, m + 1, m, and m - 1 respectively represent the (m + 1)-th day, the m-th day, and the (m - 1)-th day, and λ i,m+1 -λ i,m represents the change in the influencing factor i between the (m + 1)-th day and the m-th day, and the rest are similar. Expand the change mechanism of the pollutant under the multi-factor compound action into a linear superposition form to determine the influence degree of various factors on the change in the pollutant concentration.

[0064] The relationship between the influencing factors of the change in the dissolved oxygen concentration in surface water is as Figure 2 shown. According to Figure 2 the change relationship of the dissolved oxygen concentration, equation (4) can be expanded as:

[0065]

[0066] In the formula, C and C i,0 are respectively the current-day concentration and the previous-day concentration of dissolved oxygen, C IMn represents the permanganate index concentration, T represents the water body temperature, pH represents the water body pH value, and C Nwhere \(C\) is the concentration of nitrogen oxides (NH3-N), and \(Q\) is the flow rate.

[0067] Step 5: Use multiple linear estimation to determine the linear relationship between pollutant concentration and the first and second derivatives of influencing factors. Fitting based on the measured data of Lanxi surface water in 2020, we get:

[0068] C i = C i,0 + 0.2474ΔT + 1.14×10 -7 ΔQ - 7.42ΔC IMn - 1.487ΔC N + 0.004ΔpH + 5.87×10 -4 ΔT 2 + 8.29×10 -8 ΔQ 2 + 0.007ΔC N 2 + 0.041ΔC IMn 2

[0069] + 4.44×10 -6 ΔTΔQ + 0.0087ΔTΔC IMn + 0.0035ΔTΔC N + 6.74×10 -6 ΔC N ΔQ + 9.12×10 -6 ΔC IMn ΔQ

[0070] As Figure 3 shown, based on the above formula, comparing the calculated results and the measured results of the dissolved oxygen concentration at the Jiangjunyan section from January to October 2021, it can be seen that there is good consistency between the calculated values and the measured values, indicating that the method proposed in the present invention can accurately analyze the pollution causes and their influencing degrees.

[0071] In this embodiment, the main factor affecting the dissolved oxygen concentration in the river is the first derivative of the concentration of organic matter (I Mn ), followed by temperature, and the third is the concentration of oxygen-consuming factors (NH3-N). Among them, the influence of organic matter (I Mn ) significantly exceeds other factors. The influence of temperature on other pollutants is also the largest among various factors. It can be seen that the method proposed in the present invention effectively realizes the analysis of the main influencing factors and the influencing degrees of the concentration changes of pollutants.

[0072] Furthermore, in this embodiment, a surface water pollution cause analysis system based on multi-source composite relationship distribution and release that can automatically implement the above method of the present invention is also provided. The system includes a data acquisition module, a preprocessing module, an influence relationship description module, an influence degree determination module, a pollution cause analysis module, and an input display module.

[0073] The data acquisition module executes the content described in step 1 above, and acquires the time series of hydrology, pollutant concentration, and index factor monitoring data of the surface water control section. The length of the time series covers at least one flood season and one dry season.

[0074] The preprocessing module executes the content described in step 2 above, performs data interpolation and normalization processing on the time series, and uses it as the data for subsequent steps.

[0075] The influence relationship description module executes the content described in step 3 above, represents the pollutant concentration under the multi-factor composite influence as a linear superposition of the first-order terms and second-order partial derivative terms of various influence factors; describes the direct relationship between the change of the influence factor and the change of the pollutant concentration through the first-order partial derivative term, and describes the influence of the complex chemical and physical change processes intertwined by multiple factors in the formation process of the pollutant on the pollutant concentration through the second-order partial derivative term.

[0076] The influence degree determination module executes the content described in step 4 above, uses the implicit difference method to approximately estimate the differential partial derivative term based on the measured data; expands the change mechanism of the pollutant under the multi-factor composite action into a linear superposition form to determine the influence degree of various factors on the change of the pollutant concentration.

[0077] The pollution cause analysis module executes the content described in step 5 above, uses multiple linear estimation to determine the linear relationship between the pollutant concentration and the first-order and second-order derivatives of the influence factors, and realizes the analysis of the causes of surface water pollution according to the linear coefficients of each influence factor.

[0078] The input display module is used to allow the user to input operation instructions and perform corresponding displays. For example, the input display module can display various possible hydrological factors, pollutants, and pollutant index factors for the operator to select. The data acquisition module acquires the time series of hydrology, pollutant concentration, and index factor monitoring data of the surface water control section based on the selected pollutants and factors; the input display module can also display the input, output data, and processing process of each module in the form of charts according to the operation instructions for the operator to view.

[0079] The control module is communicatively connected to the data acquisition module, the preprocessing module, the influence relationship description module, the influence degree determination module, the pollution cause analysis module, and the input display module to control their operations.

[0080] The above embodiments are only illustrative examples of the technical solutions of the present invention. The method and system for analyzing the causes of surface water pollution based on multi-source composite relationship distribution and interpretation involved in the present invention are not limited only to the content described in the above embodiments, but are subject to the scope defined by the claims. Any modification, supplement or equivalent replacement made by those skilled in the art to which the present invention pertains on the basis of this embodiment is within the scope protected by the claims of the present invention.

Claims

1. A method for analyzing the causes of surface water pollution based on the distribution and interpretation of multi-source composite relationships, characterized in that, It includes the following steps: Step 1: Obtain the time series of hydrological, pollutant concentration, and index factor monitoring data of the surface water control section, and the length of the time series should cover at least one flood season and one dry season; Step 2: Perform data interpolation and normalization processing on the time series as the data for subsequent steps; Step 3: Express the pollutant concentration under the combined influence of multiple factors as a linear superposition of the first-order terms and second-order partial derivative terms of various influencing factors; the change in pollutant concentration in the river section is expressed as: In the formula, c is the pollutant concentration, x and y are position coordinates, t is the time step, and λ is the influencing factor; and are the first-order partial derivative term and the second-order partial derivative term respectively. i and j represent different influencing factors. When i = j, it is the self-second-order partial derivative; when they are not equal, it is the co-second-order partial derivative term. n is the total number of influencing factors, and O is the lumped error term; Describe the direct relationship between the change in the influencing factor and the change in pollutant concentration through the first-order partial derivative term, and describe the influence of the complex chemical and physical change processes intertwined by multiple factors during the formation of pollutants on the pollutant concentration through the second-order partial derivative term; Step 4: Use the implicit difference method to approximately estimate the differential partial derivative terms based on the measured data. The first-order partial derivative term and the second-order partial derivative term are respectively expressed as: Where m + 1, m, and m - 1 represent the (m + 1)-th day, the m-th day, and the (m - 1)-th day respectively, and λ i,m+1 -λ i,m represents the change in the influence factor i between the (m + 1)-th day and the m-th day; Spread the change mechanism of pollutants under the combined action of multiple factors into a form of linear superposition to determine the influence degree of various influencing factors on the change in pollutant concentration; Step 5: Use multiple linear estimation to determine the linear relationship between the pollutant concentration and the first-order and second-order derivatives of the influencing factors. The linear coefficient therein is the factor reflecting the influence of the influencing factor on the pollutant. The larger the value of the linear coefficient, the greater the influence, and vice versa. Thus, analyze the causes of surface water pollution according to the linear coefficients of each influencing factor.

2. The method for analyzing the causes of surface water pollution based on the distribution of multi-source composite relationships according to claim 1, characterized in that: Among them, In step 1, the hydrological factors at least include flow rate, sediment concentration, or sediment transport rate; the pollutant index factors at least include pH value and temperature.

3. The method for analyzing the causes of surface water pollution based on the distribution of multi-source composite relationships according to claim 1, characterized in that: Among them, In step 1, the pollutants mainly include ammonia nitrogen NH3-N, total phosphorus TP, permanganate index I measured by the water quality automatic station Mn , total nitrogen TN, dissolved oxygen DO and chlorophyll a.

4. The method for analyzing the causes of surface water pollution based on the distribution of multi-source composite relationships according to claim 1, characterized in that: Among them, In step 2, in the case of missing data in hydrology, pollutant concentration, and index factor data, use the Lagrange polynomial interpolation method to interpolate data based on 6 sampling data before and after the interpolation position; after completing data interpolation, normalize the data to between 0 and 1 by linear normalization: Where: x and x * are the values after normalizing the original data, min x and max x are the minimum and maximum values of the variables to which the original data belongs, respectively.

5. The method for analyzing the causes of surface water pollution based on the distribution of multi-source composite relationships according to claim 1, characterized in that: Among them, In step 3, without considering the influence of the interaction of various factors on the change in pollutant concentration, the method of first-order partial derivative term spread-linear fitting can be directly used to determine the overall influence of various influencing factors on the water quality formation.

6. A surface water pollution cause analysis system based on multi-source composite relationship distribution and interpretation, characterized in that It includes: A data acquisition module that obtains the time series of hydrological, pollutant concentration, and index factor monitoring data of the surface water control section, and the length of the time series should cover at least one flood season and one dry season; A preprocessing module that performs data interpolation and normalization processing on the time series as the data for subsequent steps; The influence relationship description module represents the pollutant concentration under the combined influence of multiple factors as a linear superposition of the first-order terms and second-order partial derivative terms of various influencing factors; the change in the pollutant concentration in the river cross-section is expressed as: In the formula, c is the pollutant concentration, x and y are position coordinates, t is the time step, and λ is the influencing factor; and are the first-order partial derivative term and the second-order partial derivative term respectively. i and j represent different influencing factors. In the case of i = j, it is the self-second-order partial derivative, and in the case of inequality, it is the co-second-order partial derivative term. n is the total number of influencing factors, and O is the lumped error term. The direct relationship between the change of influencing factors and the change of pollutant concentration is described by the first-order partial derivative term, and the influence of the complex chemical and physical change processes intertwined by multiple factors on the pollutant concentration during the formation process of pollutants is described by the second-order partial derivative term; The influence degree determination module uses the implicit difference method to approximately estimate the differential partial derivative terms based on the measured data. The first-order partial derivative term and the second-order partial derivative term are respectively expressed as: Wherein, m + 1, m, and m - 1 respectively represent the (m + 1)-th day, the m-th day, and the (m - 1)-th day, and λ i,m+1 -λ i,m represents the change amount of the influence factor i on the (m + 1)-th day and the m-th day; the change mechanism of the pollutant under the combined action of multiple factors is spread into a form of linear superposition to determine the influence degree of various influence factors on the change of the pollutant concentration; The pollution cause analysis module uses multiple linear estimation to determine the linear relationship between the pollutant concentration and the first-order and second-order derivatives of the influencing factors. The linear coefficient therein is the factor reflecting the influence of the influencing factor on the pollutant. The larger the value of the linear coefficient, the greater the influence, and vice versa. Thus, the analysis of the causes of surface water pollution is realized according to the linear coefficients of various influencing factors; The control module is communicatively connected to the data acquisition module, the preprocessing module, the influence relationship description module, the influence degree determination module, and the pollution cause analysis module, and controls their operations.

7. The surface water pollution cause analysis system based on multi-source composite relationship release according to claim 6, characterized in that: Among them, In the influence relationship description module, without considering the influence of the interaction of various factors on the change of pollutant concentration, the lumped influence of various influencing factors on the water quality cause can be directly determined by the method of first-order partial derivative term spreading - linear fitting.

8. The system for analyzing the causes of surface water pollution based on multi-source composite relationship distribution according to claim 6, characterized in that, It further includes: The input display module is communicatively connected to the data acquisition module, the preprocessing module, the influence relationship description module, the influence degree determination module, the pollution cause analysis module, and the control module, and is used for allowing the user to input operation instructions and performing corresponding displays.

9. The surface water pollution cause analysis system based on multi-source composite relationship release according to claim 8, characterized in that: Among them, The input display module displays various possible hydrological factors, pollutants, and pollutant index factors for the operator to select, and the data acquisition module obtains the time series of hydrological, pollutant concentration, and index factor monitoring data of the surface water control section based on the selected pollutants and factors.

10. The surface water pollution cause analysis system based on multi-source composite relationship release according to claim 9, characterized in that: Among them, The input display module can generate a graph or table showing the relationship between the pollutant concentration and each influencing factor and the pollution cause analysis result according to the result of the pollution cause analysis module for the operator to view.

Images