Collaborative method and device for watershed simulation and downstream monitoring of non-point source pollution

Abstract

The present invention provides a method and device for coordinating watershed simulation and downstream monitoring of non-point source pollution, the method comprising: obtaining current attenuation factor data of a target monitoring point; inputting the current attenuation factor data into a pre-constructed distribution coefficient regression model to obtain the distribution coefficient of each water outlet to the target monitoring point; inputting the distribution coefficient of each water outlet to the target monitoring point and the current watershed simulation concentration value of each water outlet into a pre-constructed fitting linear model to obtain the predicted concentration value of the target monitoring point. The method and device for coordinating watershed simulation and downstream monitoring of non-point source pollution provided by the present invention establish a numerical relationship between the watershed simulation of non-point source pollution and the downstream monitoring point, and extend the watershed simulation concentration value to the downstream monitoring point to determine the predicted concentration value of the monitoring point, providing a way to effectively verify the watershed simulation results and evaluate the effects of the mitigation measures.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural information processing, and in particular to a collaborative method and device for non-point source pollution watershed simulation and downstream monitoring. Background Art

[0002] Simulation and monitoring of non-point source pollution are two common technical means in pollution control management. Non-point source pollution simulation usually requires the selection of a relatively independent watershed. Through various types of driving data and models, combined with the monitoring of pollutant input and output in the simulated watershed, the model is established and the parameters are calibrated. However, since the simulation area is often located upstream of the entire large watershed, it is not a key pollution monitoring area and lacks long-term pollution monitoring data.

[0003] Monitoring points for non-point source pollution are set up to determine the severity of non-point source pollution and to evaluate the effectiveness of upstream mitigation measures. Monitoring points are generally set up in farmland and water bodies downstream of the basin. Since the downstream areas where the monitoring points are located are usually complex due to water system input or incomplete basic data, non-point source pollution simulation cannot be performed simultaneously.

[0004] There is a causal relationship between the simulation output of the upstream watershed of agricultural non-point source pollution and the pollution monitoring of the downstream monitoring points. That is, although the simulation output and pollution monitoring are closely related, there is a lack of a coordinated mechanism to achieve a quantitative correlation between the two, which is mainly manifested in:

[0005] On the one hand, the existing non-point source pollution analysis model can simulate pollution under different conditions by setting different scenario parameters, but its simulation range is limited to the watershed and cannot be extrapolated to downstream monitoring points;

[0006] On the other hand, although the pollution level at the monitoring point is directly affected by the output intensity of the upstream basin, it cannot be directly used as verification data for model simulation results. Summary of the invention

[0007] The present invention provides a method and device for coordinating non-point source pollution watershed simulation and downstream monitoring, so as to solve the defect in the prior art that the non-point source pollution watershed simulation results and the monitoring data of the monitoring points cannot be coordinated and quantified, and realize the effective coordination between the two.

[0008] In the first aspect, the present invention provides a collaborative method for non-point source pollution watershed simulation and downstream monitoring, including: obtaining current attenuation factor data of a target monitoring point; inputting the current attenuation factor data into a pre-constructed distribution coefficient regression model to obtain the distribution coefficient of each water outlet to the target monitoring point; inputting the distribution coefficient of each water outlet to the target monitoring point and the current watershed simulation concentration value of each water outlet into a pre-constructed fitting linear model to obtain the predicted concentration value of the target monitoring point.

[0009] According to a collaborative method for non-point source pollution watershed simulation and downstream monitoring provided by the present invention, before the allocation coefficient of each water outlet to the target monitoring point and the current watershed simulation concentration value of each water outlet are input into a pre-constructed fitting linear model, the method also includes: obtaining historical watershed monitoring concentration values ​​of multiple water outlets in the target pollution watershed collected at different sampling time points, and historical monitoring concentration values ​​detected at multiple monitoring points; using the historical watershed monitoring concentration values ​​of the multiple water outlets, linearly fitting the historical monitoring concentration values ​​of each of the monitoring points, respectively, to construct the fitting linear model.

[0010] According to a collaborative method for non-point source pollution watershed simulation and downstream monitoring provided by the present invention, the functional expression of the fitted linear model is:

[0011]

[0012] Among them, a k,1 ,a k,2 ,…,a k,L are the distribution coefficients of the 1st to Lth water outlets to the kth monitoring point; are the historical simulated concentration values ​​of the watershed at the sampling point at the tth time from the 1st outlet to the Lth outlet; b is the background coefficient; is the historical monitoring concentration value of the kth monitoring point at the tth sampling point.

[0013] According to a collaborative method for non-point source pollution watershed simulation and downstream monitoring provided by the present invention, before inputting the current attenuation factor data into a pre-constructed distribution coefficient regression model, the method also includes: collecting attenuation factor data for multiple monitoring points respectively, and constructing an independent variable matrix of the distribution coefficient regression model; determining the distribution coefficient matrix of the fitted linear model; using the distribution coefficient matrix as the dependent variable matrix of the distribution coefficient regression model, and constructing the distribution coefficient regression model in combination with the independent variable matrix.

[0014] According to a collaborative method for non-point source pollution watershed simulation and downstream monitoring provided by the present invention, the distribution coefficient matrix of the fitted linear model is expressed as:

[0015]

[0016] Among them, a k,l is the allocation coefficient of the l-th outlet to the k-th monitoring point, k∈[1,K], l∈[1,L], K represents the total number of monitoring points, and L represents the total number of outlets.

[0017] According to a collaborative method for non-point source pollution watershed simulation and downstream monitoring provided by the present invention, the attenuation factor data includes at least one of the following indicator data: the river distance between the target monitoring point and the nearest outlet, terrain data, planting data and meteorological data.

[0018] According to a collaborative method for non-point source pollution watershed simulation and downstream monitoring provided by the present invention, after obtaining the predicted concentration value of the target monitoring point, it also includes: determining the effectiveness evaluation value of the non-point source pollution mitigation measures based on the comparison result of the predicted concentration value and the current monitoring concentration value of the target monitoring point.

[0019] In the second aspect, the present invention also provides a collaborative device for non-point source pollution watershed simulation and downstream monitoring, including: a data acquisition unit, used to obtain current attenuation factor data of a target monitoring point; a data analysis unit, used to input the current attenuation factor data into a pre-constructed distribution coefficient regression model to obtain the distribution coefficient of each water outlet to the target monitoring point; a concentration prediction unit, used to input the distribution coefficient of each water outlet to the target monitoring point and the current watershed simulation concentration value of each water outlet into a pre-constructed fitting linear model to obtain the predicted concentration value of the target monitoring point.

[0020] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of the collaborative method for non-point source pollution watershed simulation and downstream monitoring as described in any one of the above-mentioned methods are implemented.

[0021] In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the collaborative method for nonpoint source pollution watershed simulation and downstream monitoring as described in any of the above-mentioned methods.

[0022] The collaborative method and device for non-point source pollution watershed simulation and downstream monitoring provided by the present invention establish a numerical relationship between the watershed simulation of non-point source pollution and the downstream monitoring points, and extend the watershed simulation concentration values ​​to the downstream monitoring points to determine the predicted concentration values ​​of the monitoring points, thereby providing a way to effectively verify the watershed simulation results and evaluate the effectiveness of mitigation measures. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 It is a schematic flow chart of the collaborative method of non-point source pollution watershed simulation and downstream monitoring provided by the present invention;

[0025] Figure 2 It is a schematic diagram of the distribution of the simulated watershed and each monitoring point provided by the present invention;

[0026] Figure 3 It is a schematic diagram of a method for obtaining a predicted concentration value of a monitoring point provided by the present invention;

[0027] Figure 4 It is a schematic diagram of the structure of the collaborative device for non-point source pollution watershed simulation and downstream monitoring provided by the present invention;

[0028] Figure 5 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] It should be noted that in the description of the embodiments of the present invention, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "include one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements. The orientation or position relationship indicated by the terms "upper", "lower" and the like is based on the orientation or position relationship shown in the drawings, which is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. Unless otherwise clearly specified and limited, the terms "installed", "connected" and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, or it can be a connection between two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0031] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable under appropriate circumstances, so that the embodiments of the present application can be implemented in an order other than those illustrated or described here, and the objects distinguished by "first", "second", etc. are generally of one type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" in the specification and claims represents at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0032] The collaborative method of non-point source pollution watershed simulation and downstream monitoring provided by the present invention is mainly to establish a quantitative association between the simulation results of non-point source pollution related to the target watershed (i.e., the watershed simulation concentration value) and the monitoring data of the downstream monitoring points of the target watershed (i.e., the predicted concentration value of the monitoring point), so that the watershed simulation results can be extended to the monitoring point prediction, in order to realize the verification of the watershed simulation results based on the comparison between the current monitoring concentration value and the predicted concentration value of the downstream monitoring point.

[0033] To achieve this technical solution, the collaborative method of non-point source pollution watershed simulation and downstream monitoring provided by the present invention can establish the relationship between the simulated concentration value of the outlet watershed and the predicted concentration value of the monitoring point through two-step fitting, including:

[0034] In the first step, assuming that there is a linear correlation between the pollution intensity at the monitoring point in the target watershed and the pollution intensity at the outlet, parameter fitting can be performed using historical data to obtain the distribution coefficient and background coefficient of the fitted linear model.

[0035] Since the distribution coefficient is the fitting result of historical data, it does not correspond to the actual physical and chemical processes related to the diffusion and digestion of pollution, so it cannot ensure the stability of the fitting parameters during the modeling and verification period. In view of this, the second step of fitting is required.

[0036] In the second step, the physical and chemical variables related to the diffusion and dissipation of non-point source pollution are extracted as dependent variables. The distribution coefficient regression model constructed during the modeling period is used to fit the obtained distribution coefficient. The distribution coefficient is used to construct a fitted linear model. The obtained fitted linear model can remain stable during the modeling and verification periods.

[0037] Combine the following Figure 1-Figure 5 The collaborative method and device for non-point source pollution watershed simulation and downstream monitoring provided by the embodiments of the present invention are described.

[0038] Figure 1 It is a flow chart of the collaborative method of non-point source pollution watershed simulation and downstream monitoring provided by the present invention, such as Figure 1As shown, including but not limited to the following steps:

[0039] Step 101: Obtain current attenuation factor data of a target monitoring point.

[0040] First, for each outlet of the target basin, relevant factors that affect the diffusion and reduction of upstream pollution are selected, such as the distance from the river channel to the basin outlet, topography, planting, meteorology and other O indicators.

[0041] Assume that the total number of downstream monitoring points in the target basin is K, and the target monitoring point is the kth of the K monitoring points. At the target monitoring point, relevant data of the above O indicators are collected, and the current attenuation factor data obtained, then the current attenuation factor data between the lth outlet and the target monitoring point can be expressed as

[0042] in, It is the current attenuation factor data affecting pollution diffusion and reduction at the lth outlet collected at the target monitoring point (i.e. the kth monitoring point).

[0043] Step 102: inputting the current attenuation factor data into a pre-built distribution coefficient regression model to obtain the distribution coefficient of each water outlet to the target monitoring point;

[0044] The expression of the pre-built partition coefficient regression model can be simplified as shown in Formula 1:

[0045]

[0046] Where, f(x) is the distribution coefficient regression model function; a kl is the distribution coefficient of the lth outlet to the target monitoring point.

[0047] Step 103: input the distribution coefficient of the pollution output of each water outlet to the target monitoring point and the current basin simulation concentration value of each water outlet into a pre-built fitting linear model to obtain the predicted concentration value of the target monitoring point.

[0048] The expression of the pre-built fitted linear model can be simplified as shown in Formula 2:

[0049]

[0050] Among them, a k,1 ,a k,2 …a k,Lare the distribution coefficients of the pollution output from the 1st outlet to the Lth outlet of the upstream basin to the kth monitoring point (i.e., the target monitoring point), indicating the degree of influence of the pollution of the upstream basin on the target monitoring point; b is the background coefficient of the pollution at the monitoring point, indicating the initial pollution state. If historical monitoring data can provide this value, it will be directly used, otherwise it will be determined by the estimated value of the regression model; to are the simulated concentration values ​​of the watershed collected at the t-th sampling point from the 1st outlet to the L-th outlet respectively; is the predicted concentration value of the target monitoring point at the sampling point at time t.

[0051] Without considering the time sampling point, the above formula 2 can be expressed as formula 3:

[0052]

[0053] exist to For the first outlet to the Lth outlet, under the current watershed simulation concentration value collected, the obtained It is the predicted concentration value of the target monitoring point.

[0054] Optionally, after the predicted concentration value of the target monitoring point is obtained, the pollution concentration reduction effect can be accurately evaluated based on the comparison between the current monitoring concentration value actually detected at the target monitoring point and the predicted concentration value.

[0055] The collaborative method and device for non-point source pollution watershed simulation and downstream monitoring provided by the present invention establish a numerical relationship between the watershed simulation of non-point source pollution and the downstream monitoring points, and extend the watershed simulation concentration values ​​to the downstream monitoring points to determine the predicted concentration values ​​of the monitoring points, thereby providing a way to effectively verify the watershed simulation results and evaluate the effectiveness of mitigation measures.

[0056] Based on the content of the above embodiment, as an optional embodiment, before the distribution coefficient of each water outlet to the target monitoring point and the current watershed simulation concentration value of each water outlet are input into the pre-built fitting linear model, it also includes:

[0057] Obtain historical monitoring concentration values ​​of multiple water outlets in the target polluted watershed collected at different sampling time points, as well as historical monitoring concentration values ​​detected at multiple monitoring points;

[0058] The historical monitoring concentration values ​​of the watershed of the plurality of water outlets are used to perform linear fitting on the historical monitoring concentration values ​​of each of the monitoring points to construct the fitted linear model.

[0059] Figure 2is a schematic diagram of the distribution of the simulated watershed and monitoring points provided by the present invention. Figure 2 Only one outlet in the upstream simulated basin is shown, and only three monitoring points are set on the main river channel corresponding to the outlet, that is, the non-point source pollution degree of each monitoring point is mainly affected by one upstream basin, but it is not regarded as a specific limitation of the present invention.

[0060] When the input of non-point source pollution monitored by the monitoring point is affected by one or more upstream watersheds, the coordination of watershed model simulation and downstream non-point source pollution monitoring points can be implemented in three steps, among which steps 1 and 2 are the modeling period, and step 3 is the prediction / verification period.

[0061] In step 1, a fitted linear model is constructed mainly by fitting the linear relationship between the basin output concentration (i.e., the historical basin monitoring concentration value of the outlet) and the monitoring value of each monitoring point (i.e., the historical monitoring concentration value detected by the monitoring point), and finally obtaining the model parameters of the fitted linear model in the modeling period, which specifically includes but is not limited to the following steps:

[0062] Firstly, the time period that coincides with the monitoring time of the historical monitoring concentration values ​​output from each outlet of the target polluted basin and the monitoring time of the historical monitoring concentration values ​​monitored at the monitoring point is taken as the modeling period.

[0063] Obtain the historical basin monitoring concentration values ​​of each outlet (or multiple outlets) of the target polluted basin at different sampling time points during the modeling period, as shown in Formula 4:

[0064]

[0065] At the same time, the historical monitoring concentration values ​​collected at multiple monitoring points at each sampling time point are obtained, as shown in Formula 5:

[0066]

[0067] in, is the historical basin monitoring concentration value collected at the Lth outlet at the Nth sampling time point; is the historical monitoring concentration value collected by the Kth monitoring point at the Nth sampling time point; L is the total number of outlets, K is the total number of monitoring points, and N is the total number of sampling time points.

[0068] It should be noted that if there is a hysteresis effect on the monitoring point from the output of the basin outlet (assuming the delay time is Δt), the time correspondence between the above historical basin monitoring concentration value and the historical monitoring concentration value should be adjusted, expressed as That is, transform formula 5 into formula 6:

[0069]

[0070] For each monitoring point, a linear fitting is performed to obtain a fitted linear model, as shown in Formula 2.

[0071] Accordingly, by performing linear fitting on each monitoring point in turn, the coefficient matrix of the fitted linear model can be obtained, as shown in Formula 7:

[0072]

[0073] By deleting the background coefficient in Formula 7, the distribution coefficient matrix can be obtained, and its expression is shown in Formula 8:

[0074]

[0075] Among them, a k,l is the distribution coefficient of the l-th outlet to the k-th monitoring point, k∈[1,K], l∈[1,L].

[0076] Based on the content of the above embodiment, as an optional embodiment, before inputting the current attenuation factor data into the pre-built distribution coefficient regression model, it also includes:

[0077] Collecting attenuation factor data at multiple monitoring points respectively, and constructing an independent variable matrix of the distribution coefficient regression model;

[0078] determining a partition coefficient matrix for the fitted linear model;

[0079] The distribution coefficient matrix is ​​used as the dependent variable matrix of the distribution coefficient regression model, and combined with the independent variable matrix to construct the distribution coefficient regression model.

[0080] On the basis of step 1 described in the above embodiment, step 2 is continued to be performed during the modeling period to establish a partition coefficient regression model, including but not limited to the following steps:

[0081] First, at the kth monitoring point, take its distribution coefficient for L outlets as the dependent variable, expressed as shown in Formula 9:

[0082]

[0083] According to the relevant factors affecting upstream pollution diffusion and attenuation selected in the above embodiment, including but not limited to at least one of the following indicator data: the river distance between the target monitoring point and the nearest outlet, terrain data, planting data and meteorological data. The attenuation factor data corresponding to L outlets are extracted at k monitoring points to establish the independent variable matrix of the distribution coefficient model, as shown in Formula 10:

[0084]

[0085] Then, in combination with the distribution coefficient matrix obtained in the above step 1 (ie, Formula 8), a distribution coefficient regression model can be established, as shown in Formula 1.

[0086] It should be noted that the specific form of the above distribution coefficient regression model can be determined by comparing the fitting results of different types of equations. Since the influence of the dependent variable on the distribution coefficient is relatively stable, R can be selected. 2 The largest model was used as the final partition coefficient regression model.

[0087] Based on the content of the above embodiment, as an optional embodiment, after obtaining the predicted concentration value of the target monitoring point, the method further includes:

[0088] The effectiveness evaluation value of the non-point source pollution mitigation measures is determined based on the comparison result of the predicted concentration value and the current monitoring concentration value of the target monitoring point.

[0089] Figure 3 Schematic diagram of a method for obtaining a predicted concentration value of a monitoring point provided by the present invention. Figure 3 As shown, the entire acquisition method mainly includes two stages, namely the modeling stage and the verification stage.

[0090] The modeling phase mainly includes the following steps:

[0091] Step 1.1, the measured values ​​or conventional non-point source pollution analysis models (such as SWAT models) can be used to obtain the historical monitoring concentration values ​​of all outlets in the target basin, and the historical monitoring concentration values ​​of all monitoring points in the target basin can be obtained at the same time. The above data are used for fitting to obtain a fitted linear model, and the distribution coefficient and background coefficient related to the fitted linear model are extracted.

[0092] Step 2 1.2, using the distribution coefficient as the dependent variable and the attenuation factor data as the independent variable, modeling is performed to obtain the distribution coefficient regression model.

[0093] The verification process mainly includes the following steps:

[0094] Step 2.1, extract the dependent variable data of the distribution coefficient at the target monitoring point to obtain the current attenuation factor data; calculate the L distribution coefficients of the L water outlets to the target monitoring point according to the distribution coefficient regression model described in Formula 1.

[0095] Use the conventional SWAT model to analyze the non-point source pollution of the target polluted watershed, or use direct measurement to obtain the current simulated concentration value of each water outlet in L. to

[0096] Step 2.2, after one-to-one correspondence between the current basin simulated concentration value and the L distribution coefficients of the L outlets to the target monitoring point, input them into the pre-built fitted linear model, and the predicted concentration value of the target monitoring point can be obtained.

[0097] After obtaining the predicted concentration value of the target monitoring point, it can be compared with the current monitoring concentration value actually measured at the target monitoring point. If the predicted concentration value is greater than the current monitoring concentration value, it means that the effect of the non-point source pollution mitigation measures is positive; if the predicted concentration value is less than or equal to the current monitoring concentration value, it means that the effect of the non-point source pollution mitigation measures is negative.

[0098] Optionally, the effect evaluation value may also be determined based on the ratio of the difference between the predicted concentration value and the current monitored concentration value to any standard concentration value.

[0099] The collaborative method for non-point source pollution watershed simulation and downstream monitoring provided by the present invention extracts physical and chemical variables related to the diffusion and dissipation of non-point source pollution as dependent variables, uses the distribution coefficient matrix obtained by fitting during the modeling period to model the distribution coefficient, and the final fitted linear model can remain stable during the modeling period and the verification period.

[0100] Figure 4 Schematic diagram of the structure of the collaborative device for non-point source pollution watershed simulation and downstream monitoring provided by the present invention, such as Figure 4 As shown, it mainly includes:

[0101] A data acquisition unit 41 is used to obtain current attenuation factor data of a target monitoring point;

[0102] The data analysis unit 42 is used to input the current attenuation factor data into a pre-built distribution coefficient regression model to obtain the distribution coefficient of each water outlet to the target monitoring point;

[0103] The concentration prediction unit 43 is used to input the distribution coefficient of each water outlet to the target monitoring point and the current basin simulation concentration value of each water outlet into a pre-built fitting linear model to obtain the predicted concentration value of the target monitoring point.

[0104] It should be noted that the collaborative device for non-point source pollution watershed simulation and downstream monitoring provided in an embodiment of the present invention can, during specific operation, execute the collaborative method for non-point source pollution watershed simulation and downstream monitoring described in any of the above embodiments, which will not be elaborated in this embodiment.

[0105] The collaborative device for non-point source pollution watershed simulation and downstream monitoring provided by the present invention establishes a numerical relationship between the watershed simulation of non-point source pollution and the downstream monitoring points, extends the watershed simulation concentration values ​​to the downstream monitoring points to determine the predicted concentration values ​​of the monitoring points, and provides a way to effectively verify the watershed simulation results and evaluate the effectiveness of mitigation measures.

[0106] Figure 5 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 5 As shown, the electronic device may include: a processor 510, a communication interface 520, a memory 530 and a communication bus 540, wherein the processor 510, the communication interface 520 and the memory 530 communicate with each other through the communication bus 540. The processor 510 may call the logic instructions in the memory 530 to execute the collaborative method of non-point source pollution watershed simulation and downstream monitoring, the method comprising: obtaining the current attenuation factor data of the target monitoring point; inputting the current attenuation factor data into a pre-constructed distribution coefficient regression model to obtain the distribution coefficient of each water outlet to the target monitoring point; inputting the distribution coefficient of each water outlet to the target monitoring point and the current watershed simulation concentration value of each water outlet into a pre-constructed fitting linear model to obtain the predicted concentration value of the target monitoring point.

[0107] In addition, the logic instructions in the above-mentioned memory 530 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0108] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the collaborative method of non-point source pollution watershed simulation and downstream monitoring provided by the above-mentioned methods, and the method includes: obtaining current attenuation factor data of the target monitoring point; inputting the current attenuation factor data into a pre-constructed distribution coefficient regression model to obtain the distribution coefficient of each water outlet to the target monitoring point; inputting the distribution coefficient of each water outlet to the target monitoring point and the current watershed simulation concentration value of each water outlet into a pre-constructed fitting linear model to obtain the predicted concentration value of the target monitoring point.

[0109] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it is implemented to perform the collaborative method of non-point source pollution watershed simulation and downstream monitoring provided in the above-mentioned embodiments, the method comprising: obtaining current attenuation factor data of the target monitoring point; inputting the current attenuation factor data into a pre-constructed distribution coefficient regression model to obtain the distribution coefficient of each water outlet to the target monitoring point; inputting the distribution coefficient of each water outlet to the target monitoring point and the current watershed simulation concentration value of each water outlet into a pre-constructed fitting linear model to obtain the predicted concentration value of the target monitoring point.

[0110] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0111] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A collaborative approach for watershed simulation and downstream monitoring of non-point source pollution, It is characterized in that include: Obtain current attenuation factor data of the target monitoring point; Inputting the current attenuation factor data into a pre-built distribution coefficient regression model to obtain the distribution coefficient of each water outlet to the target monitoring point; Input the distribution coefficient of each water outlet to the target monitoring point and the current watershed simulated concentration value of each water outlet into a pre-built fitted linear model to obtain the predicted concentration value of the target monitoring point; Before inputting the distribution coefficient of each water outlet to the target monitoring point and the current watershed simulated concentration value of each water outlet into the pre-built fitted linear model, the method further includes: Obtain historical monitoring concentration values ​​of multiple water outlets in the target polluted watershed collected at different sampling time points, as well as historical monitoring concentration values ​​detected at multiple monitoring points; Using the historical watershed monitoring concentration values ​​of the multiple water outlets, linear fitting is performed on the historical monitoring concentration values ​​of each monitoring point to construct the fitted linear model; The functional expression of the fitted linear model is: Among them, a k,1 ,a k,2 ,…,a k,L are the distribution coefficients of the 1st to Lth water outlets to the kth monitoring point; are the historical simulated concentration values ​​of the watershed at the sampling point at the tth time from the 1st outlet to the Lth outlet; b is the background coefficient; is the historical monitoring concentration value of the kth monitoring point at the tth sampling point; Before inputting the current attenuation factor data into the pre-built partition coefficient regression model, the method further includes: Collecting attenuation factor data at multiple monitoring points respectively, and constructing an independent variable matrix of the distribution coefficient regression model; determining a partition coefficient matrix for the fitted linear model; Using the distribution coefficient matrix as the dependent variable matrix of the distribution coefficient regression model, and combining it with the independent variable matrix, constructing the distribution coefficient regression model; The expression of the distribution coefficient matrix of the fitted linear model is: Among them, a k,l is the allocation coefficient of the l-th outlet to the k-th monitoring point, k∈[1,K], l∈[1,L], K represents the total number of monitoring points, and L represents the total number of outlets.

2. The collaborative method for watershed simulation and downstream monitoring of non-point source pollution according to claim 1, It is characterized in that The attenuation factor data includes at least one of the following indicator data: the river distance between the target monitoring point and the nearest water outlet, terrain data, planting data and meteorological data.

3. The collaborative method for watershed simulation and downstream monitoring of non-point source pollution according to claim 1, It is characterized in that After obtaining the predicted concentration value of the target monitoring point, the method further includes: The effectiveness evaluation value of the non-point source pollution mitigation measures is determined based on the comparison result of the predicted concentration value and the current monitoring concentration value of the target monitoring point.

4. A collaborative device for non-point source pollution watershed simulation and downstream monitoring, It is characterized in that The device is used to perform the collaborative method of non-point source pollution watershed simulation and downstream monitoring as claimed in any one of claims 1 to 3, and the device comprises: A data acquisition unit, used to obtain current attenuation factor data of a target monitoring point; A data analysis unit, used for inputting the current attenuation factor data into a pre-built distribution coefficient regression model to obtain the distribution coefficient of each water outlet to the target monitoring point; The concentration prediction unit is used to input the distribution coefficient of each water outlet to the target monitoring point and the current basin simulation concentration value of each water outlet into a pre-built fitting linear model to obtain the predicted concentration value of the target monitoring point.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, It is characterized in that When the processor executes the computer program, it implements the collaborative method steps of non-point source pollution watershed simulation and downstream monitoring as described in any one of claims 1 to 3.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, It is characterized in that When the computer program is executed by a processor, the steps of the collaborative method for non-point source pollution watershed simulation and downstream monitoring as described in any one of claims 1 to 3 are implemented.

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