Method for accurately identifying pollution source of karst underground river system

Abstract

The invention discloses a karst underground river system pollution source accurate identification method. The method comprises the steps that historical monitoring data, geological and hydrological data, emission source data and position data of a research area are collected; performing region division based on the geological and hydrological data and the position data to obtain an influence region, and performing conformal prediction on the historical monitoring data based on emission source data of the influence region to obtain influence data; correcting the influence data by adopting the position data to obtain pollution source data, and establishing a pollution source identification model based on the pollution source data; and inputting to-be-identified data into the pollution source identification model, and outputting an identification result. The method not only can improve the identification precision of the pollution source of the karst underground river system, but also has good interpretability, and can be directly applied to the accurate identification system of the pollution source of the karst underground river system.

Description

Technical Field

[0001] The present invention relates to the fields of environmental science and hydrology, and in particular to a method for accurately identifying pollution sources in a karst underground river system. Background Art

[0002] Karst groundwater systems are highly sensitive and complex to the transmission of pollutants due to their unique geological structure and hydrological characteristics. These systems are usually composed of complex underground river networks, caves and fissures. Once pollutants enter, they will spread rapidly and are difficult to track and control. Traditional pollution source identification methods, such as simple monitoring and chemical analysis, often cannot accurately reveal the location and characteristics of pollution sources, especially in highly heterogeneous environments such as karst areas. In addition, these methods are usually time-consuming and labor-intensive, and it is difficult to achieve real-time monitoring and rapid response. Therefore, developing a method that can quickly and accurately identify pollution sources in karst underground river systems is crucial for the effective management and protection of these fragile water ecosystems. It is in this context that the present invention proposes an innovative method for accurate identification of pollution sources, which aims to improve the accuracy and efficiency of identification through the comprehensive application of advanced technologies such as geographic information systems (GIS), hydrogeological models, and machine learning algorithms. Summary of the invention

[0003] The purpose of the present invention is to provide a method for accurately identifying pollution sources in a karst underground river system.

[0004] To achieve the above object, the present invention is implemented according to the following technical solutions:

[0005] The present invention comprises the following steps:

[0006] Collect historical monitoring data, geological and hydrological data, emission source data and location data for the study area;

[0007] Performing regional division based on the geological and hydrological data and the location data to obtain an impact area, and performing conformal prediction on the historical monitoring data based on the emission source data of the impact area to obtain impact data;

[0008] Using the position data to correct the impact data to obtain pollution source data, and establishing a pollution source identification model based on the pollution source data;

[0009] The data to be identified is input into the pollution source identification model, and the identification result is output.

[0010] Furthermore, the method of collecting historical monitoring data, geological and hydrological data, emission source data and location data of the study area includes:

[0011] The historical monitoring data include the types and concentrations of pollutants. The geological and hydrological data are obtained through the GIS system. The geological and hydrological data include the boundaries of the underground river system and the underground river flow rate. The emission source data include the historical emission sources and the corresponding emission source list. The location data include the location of the emission source and the monitoring point.

[0012] Furthermore, a method for obtaining an impact area by performing regional division based on the geological and hydrological data and the location data includes:

[0013] The area within 5 km upstream of the monitoring point is divided into identification areas based on geological and hydrological data and location data.

[0014] The location data of emission sources in the identified area are used to form data points, and the data points are clustered to obtain c clusters:

[0015]

[0016] in, is the objective function, n is the number of data points, c is the number of clusters, η ij is the membership of the ith data point to the jth cluster, indicating the spatial correlation of the data point to the cluster, ‖·‖ indicates the Euclidean distance, M is the fuzzy factor, 1.5<M<2.5, Λ j is the fuzzy penalty coefficient of the jth cluster, X i is the i-th data point, u j is the center point of cluster j, C j is the set of data points in cluster j, |C j | is the number of data points in cluster j, h is the bandwidth parameter, 0.4<h<1, is the i-th noise term, and the membership degree is calculated as:

[0017]

[0018] Among them, δ is used to avoid the denominator being zero, 0<δ<0.001, and the cluster center update formula is:

[0019]

[0020] in is the center point of cluster j after iteration s+1 steps, is the membership of data point i to cluster j after iteration s+1 steps,

[0021] The data points are divided into the clusters with the highest membership, and the c clusters obtained by clustering are used as the influence areas.

[0022] Furthermore, the method of performing conformal prediction on the historical monitoring data based on the emission source data of the impact area to obtain the impact data includes:

[0023] Fitting historical monitoring data based on emission source data in the affected area:

[0024] The emission source data is used as the characteristic data and the historical monitoring data is used as the target value to establish a regression model. The quantile loss function of the regression model is:

[0025]

[0026] in is the quantile loss of the kth feature data for the qth quantile, s k and are the target value and predicted value of the kth feature data, q is the target quantile,

[0027] Regularized loss function for regression model:

[0028]

[0029] Where n is the number of feature data, T is the number of leaf nodes, γ is the regularization parameter, λ is the coefficient of the L2 regularization term, and w j is the weight of leaf node j,

[0030] Feature data is selected based on split gain, split gain of decision tree:

[0031]

[0032] Among them I L and I R are the sample sets of the left subtree and the right subtree after the decision tree splits, I is the sample set of the decision tree before splitting, ζ is the penalty coefficient, and are the first-order derivatives of the quantile loss function of the left subtree and the right subtree, respectively. and are the second-order derivatives of the quantile loss function for the left and right subtrees, respectively. and are the first and second derivatives of the quantile loss function before splitting,

[0033] The SHAP value of the emission source data on the target value is calculated as the influence, and the emission source data with an influence greater than a preset influence threshold is taken as the influence data.

[0034] Furthermore, the method of using the position data to correct the impact data to obtain pollution source data includes:

[0035] Using impact data as independent variables and historical monitoring data as dependent variables, the distance data of emission sources relative to the nearest underground river system boundary and monitoring points are calculated based on the location data of emission sources and monitoring points, and a geographic regression model is established:

[0036]

[0037] Among them, Y i is the i-th dependent variable, is the historical monitoring data of the i-th monitoring point, X ij is the jth independent variable of the ith dependent variable, j is the index of the emission source in the impact data, p is the number of independent variables, β0(u i ,v i ) is the intercept term of the i-th monitoring point, β j (u i ,v i ) is the jth independent variable at the ith monitoring point (u i ,v i ) is the spatial location function, ε i is the error term, (u i ,v i ) is the location data of the i-th monitoring point, β flow (u i ,v i ) is the underground river velocity (u i ,v i ), V i is the underground river velocity at the ith monitoring point,

[0038] The spatial geographic location function is estimated using local least squares:

[0039]

[0040] in, is β(u o ,v o ) at the estimated point (u o ,v o ) is the local least squares estimate, β(u o ,v o ) is the estimated point (u o ,v o ) is the spatial geographic location function matrix, β(u o ,v o )=(β0(u o ,v o ),β1(u o ,v o ),...,β p (u o ,v o ))T , X T is the transpose of the independent variable matrix X, W(u o ,v o ) is the spatial weight matrix, where the element w i (u o ,v o ) is the i-th monitoring point at the estimated point (u o ,v o ), Y is the dependent variable matrix,

[0041] Determine the spatial weights through the kernel function:

[0042]

[0043] where w i (u j ,v j ) is the i-th monitoring point at the j-th emission source (u j ,v j ) spatial weight, d river,ij is the distance data from the jth emission source to the boundary of the underground river system at the nearest ith monitoring point, d mon,ij is the distance data from the ith monitoring point to the jth emission source, h1 and h2 are the d values ​​selected according to the AICc information criterion. river,ij and d mon,ij The bandwidth parameter,

[0044] The product of the spatial weight and the emission source data is taken as the pollution index and normalized, and the emission source with a pollution index greater than the preset pollution threshold is regarded as the pollution source.

[0045] Furthermore, a method for establishing a pollution source identification model based on the pollution source data includes:

[0046] A data set is established using impact data, location data, historical detection data and pollution source data, in which impact data, location data and historical detection data are feature variables and pollution source data is the target variable. The mapping relationship between the feature variables and the target variables in the data set is learned through a machine learning algorithm to establish a pollution source identification model.

[0047] In a second aspect, an embodiment of the present application further provides an electronic device, including:

[0048] A processor; and a memory arranged to store computer executable instructions, which when executed cause the processor to perform the method steps described in the first aspect.

[0049] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method steps described in the first aspect.

[0050] The beneficial effects of the present invention are:

[0051] The present invention is a method for accurately identifying pollution sources in a karst underground river system. Compared with the prior art, the present invention has the following technical effects:

[0052] The method for accurately identifying pollution sources in a karst underground river system provided by the present invention realizes accurate identification of pollution sources in a karst underground river system by comprehensively utilizing a variety of data and advanced mathematical models. It is efficient and accurate and has important application value for environmental protection and water resources management. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 This is a flow chart of the steps of a method for accurately identifying pollution sources in a karst underground river system according to the present invention;

[0054] Figure 2 It is a schematic diagram of the structure of an electronic device in an embodiment of this specification. DETAILED DESCRIPTION

[0055] The present invention is further described below by means of specific embodiments. The illustrative embodiments and descriptions of the present invention are used to explain the present invention but are not intended to limit the present invention.

[0056] The present invention provides a method for accurately identifying pollution sources in a karst underground river system, comprising the following steps:

[0057] like Figure 1 As shown, in this embodiment, the following steps are included:

[0058] Collect historical monitoring data, geological and hydrological data, emission source data and location data for the study area;

[0059] In the actual assessment, historical monitoring data: the pollutant type is heavy metals, and the concentration is 0.05 mg / L; geological and hydrological data: the boundary of the underground river system is connected by the points (109.8°, 22.8°), (110.2°, 22.6°), (110.4°, 23.4°) and (109.6°, 23.2°), and the underground river flow rate is 0.5 m / s; emission source data: historical emission sources: emission source list: Factory A heavy metal emissions 100 kg / year, factory Factory B has a heavy metal emission of 50 kg / year, Factory C has a heavy metal emission of 70 kg / year, and Factory D has a heavy metal emission of 40 kg / year; Location data: Emission source location: Factory A, (110.12°, 23.12°), Factory B, (110.13°, 23.13°), Factory C, (110.14°, 23.14°), Factory D, (110.15°, 23.15°), Monitoring point location: 110.1° east longitude, 23.1° north latitude;

[0060] Performing regional division based on the geological and hydrological data and the location data to obtain an impact area, and performing conformal prediction on the historical monitoring data based on the emission source data of the impact area to obtain impact data;

[0061] In the actual assessment, factories A, B and C are within the identification area, factory A is impact area 1, factories B and C are impact area 2, and the impact data are the emission source data of factories A, B and C;

[0062] Using the position data to correct the impact data to obtain pollution source data, and establishing a pollution source identification model based on the pollution source data;

[0063] In the actual assessment, the pollution indexes of factories A, B, and C are 1, 0.3, and 0.21, the preset pollution threshold is 0.25, and the pollution source data are factory A and factory B;

[0064] The data to be identified is input into the pollution source identification model, and the identification result is output.

[0065] In this embodiment, the method of collecting historical monitoring data, geological and hydrological data, emission source data and location data of the study area includes:

[0066] The historical monitoring data include the types and concentrations of pollutants. The geological and hydrological data are obtained through the GIS system. The geological and hydrological data include the boundaries of the underground river system and the underground river flow rate. The emission source data include the historical emission sources and the corresponding emission source list. The location data include the location of the emission source and the monitoring point.

[0067] In this embodiment, the method for obtaining the affected area by performing regional division based on the geological and hydrological data and the location data includes:

[0068] The area within 5 km upstream of the monitoring point is divided into identification areas based on geological and hydrological data and location data.

[0069] The location data of emission sources in the identified area are used to form data points, and the data points are clustered to obtain c clusters:

[0070]

[0071] in, is the objective function, n is the number of data points, c is the number of clusters, η ij is the membership of the ith data point to the jth cluster, indicating the spatial correlation of the data point to the cluster, ‖·‖ indicates the Euclidean distance, M is the fuzzy factor, 1.5<M<2.5, Λ j is the fuzzy penalty coefficient of the jth cluster, X i is the i-th data point, u j is the center point of cluster j, C j is the set of data points in cluster j, |C j | is the number of data points in cluster j, h is the bandwidth parameter, 0.4<h<1, is the i-th noise term, and the membership degree is calculated as:

[0072]

[0073] Among them, δ is used to avoid the denominator being zero, 0<δ<0.001, and the cluster center update formula is:

[0074]

[0075] in is the center point of cluster j after iteration s+1 steps, is the membership of data point i to cluster j after iteration s+1 steps,

[0076] The data points are divided into the clusters with the highest membership, and the c clusters obtained by clustering are used as the influence areas.

[0077] In this embodiment, the method of performing conformal prediction on the historical monitoring data based on the emission source data of the impact area to obtain the impact data includes:

[0078] Fitting historical monitoring data based on emission source data in the affected area:

[0079] The emission source data is used as the characteristic data and the historical monitoring data is used as the target value to establish a regression model. The quantile loss function of the regression model is:

[0080]

[0081] in is the quantile loss of the kth feature data for the qth quantile, s k and are the target value and predicted value of the kth feature data, q is the target quantile,

[0082] Regularized loss function for regression model:

[0083]

[0084] Where n is the number of feature data, T is the number of leaf nodes, γ is the regularization parameter, λ is the coefficient of the L2 regularization term, and w j is the weight of leaf node j,

[0085] Feature data is selected based on split gain, split gain of decision tree:

[0086]

[0087] Among them I L and I R are the sample sets of the left subtree and the right subtree after the decision tree splits, I is the sample set of the decision tree before splitting, ζ is the penalty coefficient, and are the first-order derivatives of the quantile loss function of the left subtree and the right subtree, respectively. and are the second-order derivatives of the quantile loss function for the left and right subtrees, respectively. and are the first and second derivatives of the quantile loss function before splitting,

[0088] The SHAP value of the emission source data on the target value is calculated as the influence, and the emission source data with an influence greater than a preset influence threshold is taken as the influence data. The preset influence threshold is 0.65.

[0089] In this embodiment, the method of using the position data to correct the impact data to obtain the pollution source data includes:

[0090] Using impact data as independent variables and historical monitoring data as dependent variables, the distance data of emission sources relative to the nearest underground river system boundary and monitoring points are calculated based on the location data of emission sources and monitoring points, and a geographic regression model is established:

[0091]

[0092] Among them, Y i is the i-th dependent variable, is the historical monitoring data of the i-th monitoring point, X ijis the jth independent variable of the ith dependent variable, j is the index of the emission source in the impact data, p is the number of independent variables, β0(u i ,v i ) is the intercept term of the i-th monitoring point, β j (u i ,v i ) is the jth independent variable at the ith monitoring point (u i ,v i ) is the spatial location function, ε i is the error term, (u i ,v i ) is the location data of the i-th monitoring point, β flow (u i ,v i ) is the underground river velocity (u i ,v i ), V i is the underground river velocity at the ith monitoring point,

[0093] The spatial geographic location function is estimated using local least squares:

[0094]

[0095] in, is β(u o ,v o ) at the estimated point (u o ,v o ) is the local least squares estimate, β(u o ,v o ) is the estimated point (u o ,v o ) is the spatial geographic location function matrix, β(u o ,v o )=(β0(u o ,v o ),β1(u o ,v o ),...,β p (u o ,v o )) T , X T is the transpose of the independent variable matrix X, W(u o ,v o ) is the spatial weight matrix, where the element w i (u o ,v o ) is the i-th monitoring point at the estimated point (u o ,v o ), Y is the dependent variable matrix,

[0096] Determine the spatial weights through the kernel function:

[0097]

[0098] where w i (u j ,v j ) is the value of the ith monitoring point at the jth emission source (u j ,v j ) spatial weight, d river,ij is the distance data from the jth emission source to the boundary of the underground river system at the nearest ith monitoring point, d mon,ij is the distance data from the ith monitoring point to the jth emission source, h1 and h2 are the d values ​​selected according to the AICc information criterion. river,ij and d mon,ij The bandwidth parameter,

[0099] The product of the spatial weight and the emission source data is taken as the pollution index and normalized, and the emission source with a pollution index greater than the preset pollution threshold is regarded as the pollution source.

[0100] In this embodiment, the method for establishing a pollution source identification model based on the pollution source data includes:

[0101] A data set is established using impact data, location data, historical detection data and pollution source data, in which impact data, location data and historical detection data are feature variables and pollution source data is the target variable. The mapping relationship between the feature variables and the target variables in the data set is learned through a machine learning algorithm to establish a pollution source identification model.

[0102] Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. Figure 2 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and a memory. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage. Of course, the electronic device may also include hardware required for other services.

[0103] The processor, network interface and memory can be interconnected through an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 Only one bidirectional arrow is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0104] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0105] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a device for accurately identifying the pollution source of the karst underground river system at the logical level. The processor executes the program stored in the memory and is specifically used to execute any of the above-mentioned methods for accurately identifying the pollution source of the karst underground river system.

[0106] The above application Figure 1The embodiment shown discloses a method for accurately identifying pollution sources in a karst underground river system, which can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The steps of the method disclosed in the embodiment of the present application can be directly embodied as being executed by a hardware decoding processor, or executed by a combination of hardware and software modules in a decoding processor. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0107] The electronic device may also perform Figure 1 A method for accurately identifying pollution sources in karst underground river systems Figure 1 The functions of the illustrated embodiment will not be described in detail in the embodiments of the present application.

[0108] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs, and the one or more programs include instructions. When the instructions are executed by an electronic device including multiple application programs, any one of the aforementioned methods for accurately identifying pollution sources in a karst underground river system is executed.

[0109] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0110] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0111] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0112] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0113] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0114] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0115] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0116] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0117] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0118] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for accurately identifying pollution sources in a karst underground river system, characterized in that: The following steps are involved: Collect historical monitoring data, geological and hydrological data, emission source data and location data for the study area; Performing regional division based on the geological and hydrological data and the location data to obtain an impact area, and performing conformal prediction on the historical monitoring data based on the emission source data of the impact area to obtain impact data; Using the position data to correct the impact data to obtain pollution source data, and establishing a pollution source identification model based on the pollution source data; The data to be identified is input into the pollution source identification model, and the identification result is output.

2. According to claim 1, a method for accurately identifying pollution sources in a karst underground river system is characterized in that: Methods for collecting historical monitoring data, geological and hydrological data, emission source data, and location data for the study area, including: The historical monitoring data include the types and concentrations of pollutants. The geological and hydrological data are obtained through the GIS system. The geological and hydrological data include the boundaries of the underground river system and the underground river flow rate. The emission source data include the historical emission sources and the corresponding emission source list. The location data include the location of the emission source and the monitoring point.

3. According to claim 1, a method for accurately identifying pollution sources in a karst underground river system is characterized in that: The method for obtaining the affected area by performing regional division based on the geological and hydrological data and the location data comprises: The area within 5 km upstream of the monitoring point is divided into identification areas based on geological and hydrological data and location data. The location data of emission sources in the identified area are used to form data points, and the data points are clustered to obtain c clusters: in, is the objective function, n is the number of data points, c is the number of clusters, η ij is the membership of the ith data point to the jth cluster, indicating the spatial correlation of the data point to the cluster, ‖·‖ indicates the Euclidean distance, M is the fuzzy factor, 1.5<M<2.5, Λ j is the fuzzy penalty coefficient of the jth cluster, X i is the i-th data point, u j is the center point of cluster j, C j is the set of data points in cluster j, |C j | is the number of data points in cluster j, h is the bandwidth parameter, 0.4<h<1, is the ith noise term, The calculation formula of membership degree is: Among them, δ is used to avoid the denominator being zero, 0<δ<0.001, and the cluster center update formula is: in is the center point of cluster j after iteration s+1 steps, is the membership of data point i to cluster j after iteration s+1 steps, The data points are divided into the clusters with the highest membership, and the c clusters obtained by clustering are used as the influence areas.

4. According to claim 1, a method for accurately identifying pollution sources in a karst underground river system is characterized in that: The method for performing conformal prediction on the historical monitoring data based on the emission source data of the impact area to obtain the impact data comprises: Fitting historical monitoring data based on emission source data in the affected area: The emission source data is used as the characteristic data and the historical monitoring data is used as the target value to establish a regression model. The quantile loss function of the regression model is: in is the quantile loss of the kth feature data for the qth quantile, s k and are the target value and predicted value of the kth feature data, q is the target quantile, Regularized loss function for regression model: Where n is the number of feature data, T is the number of leaf nodes, γ is the regularization parameter, λ is the coefficient of the L2 regularization term, and w j is the weight of leaf node j, Feature data is selected based on split gain, split gain of decision tree: Among them I L and I R are the sample sets of the left subtree and the right subtree after the decision tree splits, I is the sample set of the decision tree before splitting, ζ is the penalty coefficient, and are the first-order derivatives of the quantile loss function of the left subtree and the right subtree, respectively. and are the second-order derivatives of the quantile loss function for the left and right subtrees, respectively. and are the first and second derivatives of the quantile loss function before splitting, The SHAP value of the emission source data on the target value is calculated as the influence, and the emission source data with an influence greater than a preset influence threshold is taken as the influence data.

5. According to claim 1, a method for accurately identifying pollution sources in a karst underground river system is characterized in that: The method of using the position data to correct the impact data to obtain pollution source data includes: Using impact data as independent variables and historical monitoring data as dependent variables, the distance data of emission sources relative to the nearest underground river system boundary and monitoring points are calculated based on the location data of emission sources and monitoring points, and a geographic regression model is established: Among them, Y i is the i-th dependent variable, is the historical monitoring data of the i-th monitoring point, X ij is the jth independent variable of the ith dependent variable, j is the index of the emission source in the impact data, p is the number of independent variables, β0(u i ,v i ) is the intercept term of the i-th monitoring point, β j (u i ,v i ) is the jth independent variable at the ith monitoring point (u i ,v i ) is the spatial location function, ε i is the error term, (u i ,v i ) is the location data of the i-th monitoring point, β flow (u i ,v i ) is the underground river velocity (u i ,v i ), V i is the underground river velocity at the ith monitoring point, The spatial geographic location function is estimated using local least squares: in, is β(u o ,v o ) at the estimated point (u o ,v o ) is the local least squares estimate, β(u o ,v o ) is the estimated point (u o ,v o ) is the spatial geographic location function matrix, β(u o ,v o )=(β0(u o ,v o ),β1(u o ,v o ),...,β p (u o ,v o )) T , X T is the transpose of the independent variable matrix X, W(u o ,v o ) is the spatial weight matrix, where the element w i (u o ,v o ) is the i-th monitoring point at the estimated point (u o ,v o ), Y is the dependent variable matrix, Determine the spatial weights through the kernel function: where w i (u j ,v j ) is the i-th monitoring point at the j-th emission source (u j ,v j ) spatial weight, d river,ij is the distance data from the jth emission source to the boundary of the underground river system at the nearest ith monitoring point, d mon,ij is the distance data from the ith monitoring point to the jth emission source, h1 and h2 are the d values ​​selected according to the AICc information criterion. river,ij and d mon,ij The bandwidth parameter, The product of the spatial weight and the emission source data is taken as the pollution index and normalized, and the emission source with a pollution index greater than the preset pollution threshold is regarded as the pollution source.

6. According to claim 1, a method for accurately identifying pollution sources in a karst underground river system is characterized in that: The method for establishing a pollution source identification model based on the pollution source data includes: A data set is established using impact data, location data, historical detection data and pollution source data, in which impact data, location data and historical detection data are feature variables and pollution source data is the target variable. The mapping relationship between the feature variables and the target variables in the data set is learned through a machine learning algorithm to establish a pollution source identification model.

7. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions, wherein when the executable instructions are executed, the processor executes the method according to any one of claims 1 to 6.

8. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, enables the electronic device to execute the method according to any one of claims 1 to 6.

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