Intelligent monitoring and tracing system and method for groundwater pollution based on big data

Abstract

The invention discloses an intelligent groundwater pollution monitoring and tracing system and method based on big data, and relates to the field of intelligent pollution monitoring and tracing, and the system comprises a water flow module, a geology module, an analysis module, an acquisition module and a display module. Analyzing to obtain fracture characteristic normalization parameters, permeability K and permeability correction coefficients T of the control volume, obtaining adjusted permeability Kt of the control volume, analyzing pollution information of the water quality sensor of the groundwater pollution traceability signal, obtaining groundwater pollution characteristic similarity of a pollution source and a water quality monitoring position, and calculating the groundwater pollution traceability signal according to the groundwater pollution characteristic similarity. The main pollution source and the secondary pollution source corresponding to each water quality monitoring position are judged and analyzed, each pollution source point establishes a connection relation with the corresponding water quality monitoring point and displays the connection relation, the traceability result can be visually and clearly presented, and it can be guaranteed that personnel rapidly and accurately position the pollution sources.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring and traceability of pollution, and specifically to an intelligent monitoring and traceability system and method for groundwater pollution based on big data. Background Art

[0002] With the acceleration of urbanization and industrialization, a large amount of domestic sewage and industrial wastewater are directly discharged without effective treatment, resulting in an increasingly deepening degree of groundwater pollution and an expanding pollution range. Traditional groundwater pollution monitoring mainly relies on laboratory analysis and manual sampling, which not only consumes a large amount of material resources, time, and manpower, but also cannot accurately trace the pollution source. Therefore, an intelligent monitoring and traceability system and method for groundwater pollution based on big data have emerged as the times require.

[0003] When the existing intelligent monitoring and traceability system and method for groundwater pollution based on big data are in operation, they cannot accurately monitor and trace groundwater pollution in multiple dimensions, nor can they analyze the correlation between pollution sources and water quality monitoring points, and even less can they intuitively and clearly present the traceability results. As a result, it is difficult for governance personnel to accurately locate the real pollution source, and a large amount of governance resources will be invested in unnecessary areas. At the same time, poor information communication seriously affects the promotion efficiency of governance work.

[0004] In order to solve the above defects, a technical solution is provided now. Summary of the Invention

[0005] In order to solve the technical problems proposed in the above background art, the present invention is proposed. Embodiments of the present invention provide an intelligent monitoring and traceability system and method for groundwater pollution based on big data.

[0006] The object of the present invention can be achieved by the following technical solutions: An intelligent monitoring and traceability system for groundwater pollution based on big data, including: a water flow module, a geology module, an analysis module, a collection module, and a display module. The water flow module is used to collect water flow parameters and transmit them to the analysis module. The geology module is used to collect geology parameters and transmit them to the analysis module. The collection module is used to collect pollution information and location coordinate information and transmit them to the analysis module. The output ends of the collection module, the geology module, and the water flow module are respectively connected to the input end of the operation unit in the analysis module. The analysis module analyzes fracture characteristics, permeability coefficient, water flow path, similarity of groundwater pollution characteristics, and determination of groundwater pollution sources. The display module connects and displays the pollution source points of the main pollution source and the secondary pollution source with the water quality monitoring points in different colors. The connection line of the main pollution source is red, and the connection line of the secondary pollution source is orange. The input end of the display module is connected to the output end of the operation unit in the analysis module. The analysis module includes an operation unit, a sorter, and a data repository. All the operation processes within the analysis module are executed by the operation unit. The data repository is responsible for retaining the transient data generated during the operation of the analysis module. The sorter conducts classification operations on the transient data and, after completion, transfers the data to the data repository. The transient data includes the fracture feature normalization parameters of the control volume, the adjusted permeability of the control volume, multiple water flow paths with different orientations starting from the pollution source, and the similarity of groundwater pollution characteristics between the pollution source and the water quality monitoring location. The geological parameters include fracture information, fracture filling information, porosity, specific surface area, average particle size, average particle density, and average spherical coefficient of the particles. The water flow parameters include the head difference of the control volume and the average boundary area.

[0007] Furthermore, the intelligent monitoring and tracing method for groundwater pollution based on big data includes the following steps: Step 1: Calculation of fracture features of the control volume. The operation unit divides the monitoring area into regular cuboid control volumes. The geological module acquires the geological parameters of the control volume and transmits them to the operation unit in the analysis module, and analyzes to obtain the fracture feature normalization parameters of the control volume, which are stored in the data repository in the analysis module. Step 2: Solution and correction of the permeability coefficient. The operation unit analyzes and calculates the geological parameters to obtain the permeability K of the control volume. The data repository in the analysis module transmits the fracture feature normalization parameters of the control volume to the operation unit in the analysis module. The operation unit analyzes to obtain the permeability correction coefficient T of the control volume and the adjusted permeability Kt of the control volume. Step 3: Analysis of water flow paths. The water flow module acquires the water flow parameters of the control volume and transmits them to the operation unit in the analysis module, and acquires the position coordinates of each pollution source through the acquisition module, and analyzes to obtain multiple water flow paths with different orientations starting from each pollution source. Step 4: Sending of tracing signals and analysis of similarity of groundwater pollution characteristics. The acquisition module acquires the pollution information of the water quality sensor and transmits it to the operation unit in the analysis module. The operation unit analyzes and correspondingly sends out groundwater pollution tracing signals. The acquisition module acquires the pollution information of the pollution source and transmits it to the operation unit in the analysis module. The acquisition module acquires the pollution information of the water quality sensor that sends out the groundwater pollution tracing signal and transmits it to the operation unit in the analysis module. The operation unit analyzes to obtain the similarity δ of groundwater pollution characteristics between the pollution source and the water quality monitoring location. Step 5: Determination of groundwater pollution sources. The operation unit in the analysis module analyzes and determines the main pollution sources and secondary pollution sources corresponding to each water quality monitoring location through the multiple water flow paths starting from the pollution sources in the data repository in the analysis module, the position coordinates of each pollution source in the acquisition module, and the position coordinates of the water quality monitoring location. Step 6: Result display. The operation unit in the analysis module constructs connection relationships between each pollution source point of the determined main pollution sources and secondary pollution sources and the corresponding water quality monitoring points respectively. The connection line between the main pollution source and the water quality monitoring point is set to red, and the connection line between the secondary pollution source and the water quality monitoring point is set to orange. The display module makes corresponding adjustments to the displayed content according to these instructions generated by the operation unit.

[0008] Furthermore, the analysis steps for the fracture characteristic normalization parameters of the control volume are as follows: Step 103: Mark the fracture angle dip infiltration velocity modulus value of the control volume, the similarity difference value of the fracture filling composition of the control volume, and the similarity difference value of the fracture filling structure of the control volume as p1, p2, and p3 respectively. Use the formula p1 norm = [p1 - min(p1)] / [max(p1) - min(p1)] to obtain the normalized value p1 of the fracture angle dip infiltration velocity modulus of the control volume norm , where min(p1) and max(p1) are the minimum and maximum values of the fracture angle dip infiltration velocity modulus value respectively. Use the formula to obtain the normalized value p2 of the similarity of the fracture filling composition of the control volume norm , use the formula to obtain the normalized value p3 of the similarity of the fracture filling structure of the control volume norm , and mark the normalized value p1 of the fracture angle dip infiltration velocity modulus norm , the normalized value p2 of the similarity of the fracture filling composition norm , and the normalized value p3 of the similarity of the fracture filling structure norm as the fracture characteristic normalization parameters of the control volume and store them in the data repository in the analysis module.

[0009] Furthermore, the analysis steps for the fracture angle dip infiltration velocity modulus value of the control volume, the similarity difference value of the fracture filling composition of the control volume, and the similarity difference value of the fracture filling structure of the control volume are as follows: Step 101: The operation unit divides the monitoring area into regular cuboid control volumes, divides them at equal intervals Δx, Δy, and Δz in the x, y, and z directions respectively to form cuboid control volumes one by one, and marks the vertices of the control volumes as grid nodes; Step 102: The geological module obtains the geological parameters of the control volume and transmits them to the operation unit in the analysis module. The geological parameters include fracture information and fracture filling information. The operation unit uses an image processing algorithm to identify the fracture boundaries of each fracture in the control volume, fits an elliptical shape, calculates the major axis and minor axis parameters, connects the two endpoints of the major axis to obtain the major axis line of the ellipse, draws a line perpendicular to the major axis through the center of the ellipse to obtain the reference plane of the plane where the ellipse is located, calculates the angle between the major axis line of the ellipse and the x-axis as the fracture dip angle θ, the angle between the reference plane and the horizontal plane as the fracture inclination angle ɑ, obtains the fracture water flow direction, and constructs a water flow direction vector in the three-dimensional space coordinate system. , and constructs a fracture dip vector by adding the calculated fracture dip angle. , constructs a unit vector parallel to the z-axis. , according to the formula according to the formula , according to the vector projection formula , the projection vector of the water flow direction vector in the fracture plane , obtains the modulus of this projection vector , which is marked as the fracture angle inclination seepage velocity modulus value of the control volume. The fracture angle inclination seepage velocity modulus values of the control volume are averaged to obtain the fracture angle inclination seepage velocity modulus value of the control volume. The operation unit irradiates each fracture filling in the control volume with X-rays to obtain the corresponding diffraction pattern, and performs characteristic comparison on the diffraction pattern to determine the types of various minerals in each fracture filling. Based on the intensity and area parameters of the diffraction peaks, the relative content of each type of mineral is obtained. The difference in the relative content of the corresponding minerals between any two fracture fillings in the control volume is squared, and the sum of these squared values of all minerals is added, and then the square root of the sum is taken and the maximum value is marked as the fracture filling composition similarity difference value of the control volume. The operation unit processes the diffraction pattern of the fracture filling in the control volume, groups the diffraction peaks according to the characteristics of the intensity and symmetry of the diffraction peaks, combines the diffraction peak angle positions, uses the Bragg's law formula, and compares with the standard crystal structure database to obtain the corresponding crystal plane index. The direction vectors of the corresponding crystal planes in the crystal coordinate system are constructed with the crystal plane index, and the angles between the two vectors in each direction of the fracture filling crystal plane in the control volume are calculated through vector operations and averaged, which is marked as the fracture filling structure similarity difference value of the control volume.

[0010] Further, the analysis steps for adjusting the permeability of the control volume are as follows: Step 201: The geological module obtains the geological parameters of the control volume and transmits them to the operation unit in the analysis module. The geological parameters include porosity and specific surface area, which are respectively marked as ψ and ɑ. According to the formula K = ψ 3 / [C×(1 - ψ) 2 ×ɑ 2, perform calculations to obtain the permeability K of the control volume, where C is the permeability constant coefficient of the control volume; Step 202: The data repository in the analysis module transmits the fracture feature normalization parameters of the control volume to the arithmetic unit in the analysis module. The arithmetic unit adds up the fracture feature normalization parameters of the control volume to obtain the integrated fracture feature quantity of the control volume. If the integrated fracture feature quantity of the control volume is greater than or equal to 2, then classify this control volume into the fracture super-dynamic strong effect control volume set A1. If the integrated fracture feature quantity of the control volume is greater than 1.5 and less than 2, then classify this control volume into the fracture medium-dynamic medium effect control volume set A2. If the integrated fracture feature quantity of the control volume is equal to 1.5, then classify this control volume into the fracture critical equilibrium state control volume set A3. If the integrated fracture feature quantity of the control volume is less than 1.5, then classify this control volume into the fracture low-dynamic latent type control volume set A4. For the control volume sets A1, A2, A3, and A4, the permeability correction coefficients correspond to t1, t2, t3, and t4 respectively, where t1 > t2 > t3 > t4, denoted as the permeability correction coefficient T of the control volume; Step 203: The arithmetic unit multiplies the permeability correction coefficient T of the control volume by the permeability K of the control volume to obtain the adjusted permeability Kt of the control volume.

[0011] Furthermore, the analysis steps for the permeability constant coefficient of the control volume are as follows: The geology module obtains the geological parameters of the control volume and transmits them to the arithmetic unit in the analysis module. The geological parameters include the average particle size, average particle density, and average spherical coefficient of the particles, denoted as dp, ρm, and ζ respectively, and together with the porosity ψ of the control volume, according to the set formula C = 180×(1 - ψ) / (dp 2 ×ρm 2 ×ζ 2 ), the permeability constant coefficient C of the control volume is obtained, where the spherical coefficient of the particle is the square root of the ratio of the surface area of a sphere with the same volume as the particle to the actual surface area of the particle.

[0012] Furthermore, the analysis steps for multiple water flow paths with different directions starting from each pollution source are as follows: Step 301: The water flow module obtains the water flow parameters of the control volume and transmits them to the operation unit in the analysis module. The water flow parameters include the head difference Δhx of the control volume in the x direction, the average boundary area Ax in the x direction, and the adjusted permeability Kt of the control volume. The calculation module calculates the water flow transport flux LZx of the control volume in the x direction according to the formula LZx = -Kt × Ax × Δhx / Δx, where Δx is the boundary coordinate difference of the control volume in the x direction. By solving according to the same method, the water flow transport flux LZy of the control volume in the y direction and the water flow transport flux LZz of the control volume in the z direction are obtained, and the water flow transport flux vector of the control volume is constructed. And according to the formula, the water flow transport flux modulus of the control volume is obtained. ; Step 302: The acquisition module obtains the position coordinates of each pollution source and transmits this pollution information to the operation unit in the analysis module. Starting from the control volume where each pollution source is located, according to the direction determined by the water flow transport flux vector, multiple adjacent control volumes that can be flowed into by water are found. On each branch, the operation of continuously finding the next adjacent control volume that meets the water inflow condition is repeated. By analogy, the control volumes along the way are connected in sequence according to the water flow direction, and multiple water flow paths with different directions starting from each pollution source are obtained.

[0013] Further, the steps for analyzing the similarity of groundwater pollution characteristics between the pollution source and the water quality monitoring point are as follows: Step 401: The acquisition module obtains the pollution information of the water quality sensor and transmits it to the operation unit in the analysis module. The operation unit sums the values of the organic pollutant concentration and heavy metal concentration of the groundwater to obtain the pollution-weighted concentration value of the water quality monitoring. If the pollution-weighted concentration value of the water quality monitoring is greater than the set threshold TG1, the water quality sensor corresponding thereto issues a groundwater pollution source tracing signal. Step 402: The acquisition module obtains the pollution information of the pollution source and transmits it to the operation unit in the analysis module. The operation unit marks the values of the median lethal concentration, relative abundance of Pseudomonas, stable nitrogen isotope ratio, and hydroelectric conductivity of the groundwater of the pollution source as λ1, λ2, λ3, and λ4 respectively, and constructs the multi-dimensional ecological chemical characteristic vector of the groundwater of the pollution source. , =(λ1, λ2, λ3, λ4). The acquisition module obtains the pollution information of the water quality sensor that issues the groundwater pollution source tracing signal and transmits it to the operation unit in the analysis module. The operation unit marks the values of the median lethal concentration, relative abundance of Pseudomonas in groundwater, stable nitrogen isotope ratio in groundwater, and hydroelectric conductivity in groundwater of the water quality sensor as τ1, τ2, τ3, and τ4 respectively, and constructs the multi-dimensional ecological chemical characteristic vector of the groundwater of the water quality sensor. , =(τ1, τ2, τ3, τ4), according to the set formula, calculate the similarity δ of the groundwater pollution characteristics between the pollution source and the water quality monitoring site.

[0014] Furthermore, the determination and analysis steps of the groundwater pollution source are as follows: Step 502: Arrange the water flow transfer through-values between each pollution source and the water quality monitoring site in ascending order to obtain a matrix , where n is the total number of water flow transfer through-values. If the water flow transfer through-value between the pollution source and the water quality monitoring site is located between W 4n / 5 and W n , and the similarity δ of the groundwater pollution characteristics between the pollution source and the water quality monitoring site is greater than the set threshold XQ1, then the corresponding pollution source is the main pollution source of the water quality monitoring site. If the water flow transfer through-value between the pollution source and the water quality monitoring site is located between W n / 2 and W 4n / 5 , excluding the numerical point where the water flow transfer through-value is equal to W 4n / 5 , and the similarity δ of the groundwater pollution characteristics between the pollution source and the water quality monitoring site is greater than the set threshold XQ1, then the corresponding pollution source is the secondary pollution source of the water quality monitoring site.

[0015] Furthermore, the analysis steps of the water flow transfer through-value between the pollution source and the water quality monitoring site are as follows: Step 501: The data storage library in the analysis module transmits multiple water flow paths with different directions starting from each pollution source to the operation unit in the analysis module. The acquisition module transmits the position coordinates of each pollution source and the position coordinates of the water quality monitoring site to the operation unit in the analysis module. The operation unit analyzes the water flow paths from each pollution source to the water quality monitoring site, sums up the water flow transfer through-modulus in the water flow paths to obtain the total water flow transfer through-modulus of each water flow path, and obtains the maximum value in the total water flow transfer through-modulus of each water flow path, which is marked as the water flow transfer through-value W between the pollution source and the water quality monitoring site.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. The present invention divides the monitored area into regular cuboid control volumes through the operation unit. The geological module acquires the geological parameters of the control volumes and transmits them to the operation unit in the analysis module, and analyzes to obtain the fracture characteristic normalization parameters of the control volumes. The operation unit analyzes and calculates the geological parameters to obtain the permeability K of the control volumes, analyzes to obtain the permeability correction coefficient T of the control volumes, and further analyzes to obtain the adjusted permeability Kt of the control volumes. The operation unit analyzes and correspondingly issues groundwater pollution tracing signals. The acquisition module acquires the pollution information of the pollution sources and transmits it to the operation unit in the analysis module. The operation unit analyzes the pollution information of the water quality sensors of the groundwater pollution tracing signals to obtain the similarity of the groundwater pollution characteristics between the pollution sources and the water quality monitoring locations, and can accurately perform groundwater pollution monitoring and tracing in multiple dimensions.

[0017] 2. The present invention analyzes multiple water flow paths with different directions starting from the pollution sources, the position coordinates of each pollution source, and the position coordinates of the water quality monitoring locations through the operation unit in the analysis module, and determines the main pollution sources and secondary pollution sources corresponding to each water quality monitoring location. The operation unit in the analysis module constructs connection relationships between each pollution source point of the determined main pollution sources and secondary pollution sources and the corresponding water quality monitoring points respectively. The display module adjusts the display content accordingly based on these instructions generated by the operation unit, can analyze the correlation relationship between the pollution sources and the water quality monitoring points, and can more intuitively and clearly present the tracing results, and can ensure that personnel quickly and accurately locate the real pollution sources. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. The following drawings are not deliberately drawn to scale in actual size, and the focus is on showing the gist of the present invention.

[0019] Figure 1 It is a schematic diagram of the module connection of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts also belong to the scope of protection of the present invention.

[0021] Such as Figure 1As shown in the figure, the intelligent monitoring and tracing system for groundwater pollution based on big data includes a water flow module, a geology module, an analysis module, a collection module, and a display module. The water flow module is used to collect water flow parameters and transmit them to the analysis module. The geology module is used to collect geology parameters and transmit them to the analysis module. The collection module is used to collect pollution information and location coordinate information and transmit them to the analysis module. The output ends of the collection module, the geology module, and the water flow module are respectively connected to the input end of the operation unit in the analysis module. The analysis module analyzes the fracture characteristics, permeability coefficient, water flow path, similarity of groundwater pollution characteristics, and determination of groundwater pollution sources. The display module connects and displays the pollution source points of the main pollution source and the secondary pollution source and the water quality monitoring points in different colors. The connection line of the main pollution source is red, and the connection line of the secondary pollution source is orange. The input end of the display module is connected to the output end of the operation unit in the analysis module.

[0022] The analysis module includes an operation unit, a sorter, and a data storage repository. All the operation processes in the analysis module are responsible for being executed by the operation unit. The data storage repository is responsible for retaining the transient data generated during the operation of the analysis module. The sorter conducts classification operations on the transient data. After completion of the classification, the data is transferred to the data storage repository.

[0023] The transient data includes the fracture characteristic normalization parameters of the control volume, the adjusted permeability of the control volume, multiple water flow paths with different orientations starting from the pollution source, and the similarity of groundwater pollution characteristics between the pollution source and the water quality monitoring location; the geology parameters include fracture information, fracture filling information, porosity, specific surface area, average particle size of particles, average particle density, and average spherical coefficient of particles. The water flow parameters include the head difference of the control volume and the average boundary area.

[0024] The intelligent monitoring and tracing method for groundwater pollution based on big data includes the following steps: Step 1: Calculation of fracture characteristics of the control volume. The operation unit divides the monitoring area into regular cuboid control volumes. The geology module obtains the geology parameters of the control volume and transmits them to the operation unit in the analysis module. The fracture characteristic normalization parameters of the control volume are analyzed and stored in the data storage repository in the analysis module.

[0025] Step 2: Solution and correction of the permeability coefficient. The operation unit analyzes and calculates the geology parameters to obtain the permeability coefficient K of the control volume. The data storage repository in the analysis module transmits the fracture characteristic normalization parameters of the control volume to the operation unit in the analysis module. The operation unit analyzes to obtain the permeability correction coefficient T of the control volume and the adjusted permeability Kt of the control volume.

[0026] Step 3: Water flow path analysis. The water flow module obtains the water flow parameters of the control volume and transmits them to the arithmetic unit in the analysis module, and obtains the position coordinates of each pollution source through the acquisition module, and analyzes to obtain multiple water flow paths with different directions starting from each pollution source.

[0027] Step 4: Emission of the pollution source tracing signal and analysis of the similarity of groundwater pollution characteristics. The acquisition module obtains the pollution information of the water quality sensor and transmits it to the arithmetic unit in the analysis module. The arithmetic unit analyzes and correspondingly emits a groundwater pollution source tracing signal. The acquisition module obtains the pollution information of the pollution source and transmits it to the arithmetic unit in the analysis module. The acquisition module obtains the pollution information of the water quality sensor that emits the groundwater pollution source tracing signal and transmits it to the arithmetic unit in the analysis module. The arithmetic unit analyzes to obtain the similarity δ of the groundwater pollution characteristics between the pollution source and the water quality monitoring location.

[0028] Step 5: Determination of the groundwater pollution source. The arithmetic unit in the analysis module analyzes and determines the main pollution sources and secondary pollution sources corresponding to each water quality monitoring location through the multiple water flow paths starting from the pollution sources in the data storage library in the analysis module, the position coordinates of each pollution source in the acquisition module, and the position coordinates of the water quality monitoring location.

[0029] Step 6: Result display. The arithmetic unit in the analysis module constructs a connection relationship between each pollution source point of the determined main pollution source and secondary pollution source and the corresponding water quality monitoring point respectively. The connection line connecting the main pollution source and the water quality monitoring point is set to red, and the connection line connecting the secondary pollution source and the water quality monitoring point is set to orange. The display module adjusts the display content according to these instructions generated by the arithmetic unit to visually and clearly present the association relationship between different types of pollution sources and water quality monitoring points.

[0030] Among them, the calculation of the fracture characteristics of the specific control volume includes the following steps: Step 101: The arithmetic unit divides the monitoring area into regular cuboid control volumes, divides them at equal intervals Δx, Δy, and Δz in the x, y, and z directions respectively to form cuboid control volumes one by one. The vertices of the control volumes are marked as grid nodes, and the node coordinates are indexed by integers (a, b, c), where a represents the node number in the x direction, b represents the node number at the end side in the y direction, and c represents the node number at the end in the z direction.

[0031] Step 102: The geological module obtains the geological parameters of the control volume and transmits them to the arithmetic unit in the analysis module. The geological parameters include fracture information and fracture filling information. The arithmetic unit uses an image processing algorithm to identify the fracture boundaries of each fracture in the control volume, fits an elliptical shape, calculates the major axis and minor axis parameters, connects the two endpoints of the major axis to obtain the major axis straight line of the ellipse, draws a straight line perpendicular to the major axis through the center of the ellipse to obtain the reference plane of the plane where the ellipse is located, calculates the angle between the major axis straight line of the ellipse and the x-axis as the fracture dip angle θ, and the angle between the reference plane and the horizontal plane as the fracture inclination angle ɑ, obtains the fracture water flow direction, and constructs a water flow direction vector in the three-dimensional space coordinate system. And construct a fracture dip vector by adding the calculated fracture dip angle. Which is (cosθcosɑ, sinθcosɑ, sinɑ), and construct a unit vector parallel to the z-axis. =(0, 0, 1), according to the formula according to the formula , according to the vector projection formula , the projection vector of the water flow direction vector in the fracture plane , obtain the modulus of this projection vector , marked as the modulus value of the fracture angle dip infiltration velocity of the control volume. Calculate the average value of the modulus values of the fracture angle dip infiltration velocity of the control volume to obtain the modulus value of the fracture angle dip infiltration velocity of the control volume. The arithmetic unit irradiates each fracture filling in the control volume with X-rays to obtain the corresponding diffraction pattern, and performs characteristic comparison on the diffraction pattern to determine the types of various minerals in each fracture filling. According to the intensity and area parameters of the diffraction peaks, obtain the relative content of various minerals. Square the difference between the relative contents of the corresponding minerals of any two fracture fillings in the control volume, add up all these squared values of the minerals, then take the square root of the sum, and take the maximum value, marked as the similarity difference value of the fracture filling composition of the control volume. The arithmetic unit processes the diffraction pattern of the fracture filling in the control volume, groups the diffraction peaks according to the characteristics of the intensity and symmetry of the diffraction peaks, combines the diffraction peak angle positions, applies the Bragg's law formula, and compares with the standard crystal structure database to obtain the corresponding crystal plane indices. Construct the direction vectors of the corresponding crystal planes in the crystal coordinate system with the crystal plane indices, calculate the angles between the two vectors in each direction of the crystal plane of the fracture filling in the control volume through vector operations, and take the average value, marked as the similarity difference value of the fracture filling structure of the control volume.

[0032] ​It should be noted that when the angle between the water flow direction and the fracture dip is smaller, the water flow can flow more smoothly along the long axis direction of the fracture. The larger the fracture dip angle, the greater the component force of the fluid along the fracture direction under the action of gravity; the smaller the dissimilarity value of the fracture filling composition, the more similar the distribution of mineral components in the fracture filling, and vice versa, the greater the difference; the smaller the dissimilarity value of the fracture filling structure, the more similar the distribution of crystal plane directions in the fracture filling, and vice versa, the greater the difference.

[0033] Step 103: Mark the fracture angle infiltration velocity modulus value of the control volume, the dissimilarity value of the fracture filling composition of the control volume, and the dissimilarity value of the fracture filling structure of the control volume as p1, p2, and p3 respectively. Use the formula p1 norm = [p1 - min(p1)] / [max(p1) - min(p1)] to obtain the normalized value p1 of the fracture angle infiltration velocity modulus of the control volume norm , where min(p1) and max(p1) are the minimum and maximum values of the fracture angle infiltration velocity modulus value respectively. Use the formula p2 norm = |p2 - max(p2)| / [max(p2) - min(p2)] to obtain the normalized value p2 of the fracture filling composition similarity of the control volume norm , where min(p2) and max(p2) are the minimum and maximum values of the dissimilarity value of the fracture filling composition respectively. Use the formula p3 norm = |p3 - max(p3)| / [max(p3) - min(p3)] to obtain the normalized value p3 of the fracture filling structure similarity of the control volume norm , where min(p3) and max(p3) are the minimum and maximum values of the dissimilarity value of the fracture filling structure respectively. Mark the normalized value p1 of the fracture angle infiltration velocity modulus norm , the normalized value p2 of the fracture filling composition similarity norm , and the normalized value p3 of the fracture filling structure similarity norm as the normalized fracture characteristic parameters of the control volume and store them in the data repository in the analysis module.

[0034] Among them, the solution and correction of the permeability coefficient include the following steps: Step 201: The geological module obtains the geological parameters of the control volume and transmits them to the operation unit in the analysis module. The geological parameters include porosity and specific surface area, which are marked as ψ and ɑ respectively. According to the formula K = ψ 3 / [C × (1 - ψ) 2 × ɑ 2 , calculate to obtain the permeability K of the control volume, where C is the permeability constant coefficient of the control volume.

[0035] Step 202: The data repository in the analysis module transmits the fracture feature normalization parameters of the control volume to the arithmetic unit in the analysis module. The arithmetic unit adds up the fracture feature normalization parameters of the control volume to obtain the integrated fracture feature quantity of the control volume. If the integrated fracture feature quantity of the control volume is greater than or equal to 2, then this control volume is classified into the fracture super-dynamic strong effect control volume set A1. If the integrated fracture feature quantity of the control volume is greater than 1.5 and less than 2, then this control volume is classified into the fracture medium-dynamic medium effect control volume set A2. If the integrated fracture feature quantity of the control volume is equal to 1.5, then this control volume is classified into the fracture critical equilibrium state control volume set A3. If the integrated fracture feature quantity of the control volume is less than 1.5, then this control volume is classified into the fracture low-dynamic latent type control volume set A4. For the control volume sets A1, A2, A3, and A4, the permeability correction coefficients correspond to t1, t2, t3, and t4 respectively, where t1 > t2 > t3 > t4, denoted as the permeability correction coefficient T of the control volume; Step 203: The arithmetic unit multiplies the permeability correction coefficient T of the control volume by the permeability K of the control volume to obtain the adjusted permeability Kt of the control volume.

[0036] Among them, the permeability constant coefficient C of the control volume includes the following steps: The geology module obtains the geological parameters of the control volume and transmits them to the arithmetic unit in the analysis module. The geological parameters include the average particle size, average particle density, and average spherical coefficient of the particles, respectively marked as dp, ρm, ζ, and with the porosity ψ of the control volume, according to the set formula C = 180×(1 - ψ) / (dp 2 ×ρm 2 ×ζ 2 ), the permeability constant coefficient C of the control volume is obtained, where the spherical coefficient of the particle is the square root of the ratio of the surface area of a sphere with the same volume as the particle to the actual surface area of the particle.

[0037] Among them, the multiple water flow paths starting from the pollution source include the following steps: Step 301: The water flow module obtains the water flow parameters of the control volume and transmits them to the arithmetic unit in the analysis module. The water flow parameters include the head difference Δhx in the x direction of the control volume, the average boundary area Ax in the x direction, and the adjusted permeability Kt of the control volume. The calculation module calculates the water flow transport flux LZx in the x direction of the control volume according to the formula LZx = -Kt×Ax×Δhx / Δx, where Δx is the boundary coordinate difference in the x direction of the control volume. By solving according to the same method, the water flow transport flux LZy in the y direction of the control volume and the water flow transport flux LZz in the z direction of the control volume are obtained, and the water flow transport flux vector of the control volume is constructed , , and according to the formula , the water flow transport flux of the control volume is obtained . It should be noted that the included angle between the water flow transport flux vector and the outer normal direction of the control volume surface is an acute angle, and the water flow is flowing out of the control volume. The included angle between the water flow transport flux vector and the outer normal direction of the control volume surface is an obtuse angle, and the water flow is flowing into the control volume.

[0038] Step 302: The acquisition module obtains the position coordinates of each pollution source and transmits this pollution information to the operation unit in the analysis module. Starting from the control volume where each pollution source is located, according to the direction determined by the water flow transport flux vector, find multiple adjacent control volumes into which the water flow can flow. On each branch, continuously repeat the operation of finding the next adjacent control volume that meets the water flow inflow condition, and so on. Connect the control volumes along the way in the water flow direction in sequence to obtain multiple water flow paths with different directions starting from each pollution source.

[0039] Among them, the similarity of groundwater pollution characteristics between the pollution source and the water quality monitoring point includes the following steps: Step 401: The acquisition module obtains the pollution information of the water quality sensor and transmits it to the operation unit in the analysis module. The operation unit sums the values of the organic pollutant concentration and heavy metal concentration in the groundwater to obtain the pollution weight concentration value of the water quality monitoring. If the pollution weight concentration value of the water quality monitoring is greater than the set threshold TG1, the water quality sensor corresponding thereto issues a groundwater pollution tracing signal; It should be noted that the organic pollutant concentration is the sum of the concentrations of polycyclic aromatic hydrocarbons, pesticides, petroleum substances, and halogenated hydrocarbons; the heavy metal concentration is the sum of the concentrations of mercury, cadmium, lead, chromium, and arsenic.

[0040] Step 402: The acquisition module obtains the pollution information of the pollution source and transmits it to the operation unit in the analysis module. The operation unit marks the values of the median lethal concentration, relative abundance of Pseudomonas, stable nitrogen isotope ratio, and hydroelectric conductivity of the groundwater of the pollution source as λ1, λ2, λ3, and λ4 respectively, and constructs the multi-dimensional ecological chemical characteristic vector of the groundwater of the pollution source , =(λ1, λ2, λ3, λ4). The acquisition module obtains the pollution information of the water quality sensor that issues the groundwater pollution tracing signal and transmits it to the operation unit in the analysis module. The operation unit marks the values of the median lethal concentration, relative abundance of Pseudomonas in the groundwater, stable nitrogen isotope ratio in the groundwater, and hydroelectric conductivity in the groundwater of the water quality sensor as τ1, τ2, τ3, and τ4 respectively, and constructs the multi-dimensional ecological chemical characteristic vector of the groundwater of the water quality sensor , =(τ1, τ2, τ3, τ4), According to the set formula , calculate to obtain the similarity δ of the groundwater pollution characteristics between the pollution source and the water quality monitoring location.

[0041] It should be noted that the stable nitrogen isotope ratio is the ratio of 15 N to 14 the content of

[0042] Among them, the determination of the groundwater pollution source includes the following steps: Step 501: The data repository in the analysis module transmits multiple water flow paths with different directions starting from each pollution source to the operation unit in the analysis module. The acquisition module transmits the position coordinates of each pollution source and the position coordinates of the water quality monitoring location to the operation unit in the analysis module. The operation unit analyzes to obtain the water flow paths from each pollution source to the water quality monitoring location, and sums the water flow transport modulus in the water flow paths to obtain the total water flow transport modulus of each water flow path, and obtains the maximum value in the total water flow transport modulus of each water flow path, which is marked as the water flow transport value W between the pollution source and the water quality monitoring location; Step 502: Arrange the water flow transport values between each pollution source and the water quality monitoring location in ascending order to obtain a matrix , where n is the total number of water flow transport values. If the water flow transport value between the pollution source and the water quality monitoring location is between W 4n / 5 and W n , and the similarity δ of the groundwater pollution characteristics between the pollution source and the water quality monitoring location is greater than the set threshold XQ1, then the corresponding pollution source is the main pollution source of the water quality monitoring location. If the water flow transport value between the pollution source and the water quality monitoring location is between W n / 2 and W 4n / 5 , excluding the numerical point where the water flow transport value is equal to W 4n / 5 , and the similarity δ of the groundwater pollution characteristics between the pollution source and the water quality monitoring location is greater than the set threshold XQ1, then the corresponding pollution source is the secondary pollution source of the water quality monitoring location, and the other pollution sources correspond to none.

[0043] The above is the description of the present invention and should not be considered as a limitation thereof. Although several exemplary embodiments of the present invention have been described, those skilled in the art will easily understand that many modifications can be made to the exemplary embodiments without departing from the novel teachings and advantages of the present invention. Therefore, all such modifications are intended to be included within the scope of the present invention as defined by the claims. It should be understood that the above is the description of the present invention and should not be considered as limited to the specific embodiments disclosed, and the modifications to the disclosed embodiments and other embodiments are intended to be included within the scope of the appended claims. The present invention is defined by the claims and their equivalents.

Claims

1. Intelligent monitoring and tracing system for groundwater pollution based on big data, including: The water flow module, geological module, analysis module, acquisition module and display module are characterized by: The water flow module is used to collect water flow parameters and transmit them to the analysis module, the geological module is used to collect geological parameters and transmit them to the analysis module, the collection module is used to collect pollution information and location coordinate information and transmit them to the analysis module, the output ends of the collection module, the geological module, and the water flow module are respectively connected to the input end of the operation unit in the analysis module, the analysis module analyzes the fracture characteristics, permeability coefficient, water flow path, similarity of groundwater pollution characteristics and determination of groundwater pollution sources, the display module connects and displays the pollution source points of the main pollution source and the secondary pollution source with the water quality monitoring points in different colors, the main pollution source connection line is red, and the secondary pollution source connection line is orange, and the input end of the display module is connected to the output end of the operation unit in the analysis module; The analysis module includes a computing unit, a sorter and a data repository. All computing processes in the analysis module are executed by the computing unit. The data repository is responsible for storing transient data generated during the operation of the analysis module. The sorter performs classification operations on the transient data and transfers the data to the data repository after the classification is completed. The transient data include the normalized parameters of the fracture characteristics of the control volume, the adjusted permeability of the control volume, multiple water flow paths in different directions starting from the pollution source, and the similarity of groundwater pollution characteristics between the pollution source and the water quality monitoring point; the geological parameters include fracture information, fracture filling information, porosity, specific surface area, average particle size of particles, average density of particles, and average spherical coefficient of particles; the water flow parameters include the control volume head difference and the average boundary area.

2. The intelligent monitoring and tracing method of groundwater pollution based on big data is characterized by The method is applied to realize the intelligent monitoring and tracing system for groundwater pollution based on big data as described in claim 1, comprising the following steps: Step 1: Calculation of fracture characteristics of the control volume. The operation unit divides the monitoring area into regular rectangular control volumes. The geological module obtains the geological parameters of the control volume and transmits them to the operation unit in the analysis module. The fracture characteristic normalization parameters of the control volume are obtained by analysis and stored in the data repository in the analysis module. Step 2: Solving and correcting the permeability coefficient. The operation unit analyzes and calculates the geological parameters to obtain the permeability K of the control volume, and the data repository in the analysis module transmits the fracture characteristic normalization parameters of the control volume to the operation unit in the analysis module. The operation unit analyzes and obtains the permeability correction coefficient T of the control volume, and analyzes and obtains the adjusted permeability Kt of the control volume; Step 3: Water flow path analysis: the water flow module obtains the water flow parameters of the control volume and transmits them to the computing unit in the analysis module, and obtains the position coordinates of each pollution source through the acquisition module, and analyzes and obtains multiple water flow paths with different directions starting from each pollution source; Step 4: Analyze the similarity between the source tracing signal and the groundwater pollution characteristics. The acquisition module obtains the pollution information of the water quality sensor and transmits it to the operation unit in the analysis module. The operation unit analyzes the corresponding groundwater pollution source tracing signal. The acquisition module obtains the pollution information of the pollution source and transmits it to the operation unit in the analysis module. The acquisition module obtains the pollution information of the water quality sensor that sends the groundwater pollution source tracing signal and transmits it to the operation unit in the analysis module. The operation unit analyzes to obtain the similarity between the pollution source and the groundwater pollution characteristics at the water quality monitoring point. Step 5: Determination of groundwater pollution sources: the computing unit in the analysis module analyzes and determines the main pollution sources and secondary pollution sources corresponding to each water quality monitoring point through multiple water flow paths with different directions from the pollution source in the data storage library in the analysis module, the location coordinates of each pollution source in the acquisition module, and the location coordinates of the water quality monitoring point; Step six: Result display. The operation unit in the analysis module establishes a connection relationship between each pollution source point of the determined main pollution source and secondary pollution source and the corresponding water quality monitoring point, sets the connection line between the main pollution source and the water quality monitoring point to red, and sets the connection line between the secondary pollution source and the water quality monitoring point to orange. The display module adjusts the display content accordingly based on these instructions generated by the operation unit.

3. The method for intelligent monitoring and tracing of groundwater pollution based on big data according to claim 2 is characterized in that: The crack characteristic normalization parameter analysis steps of the control volume are as follows: Step 103: Mark the fracture angle dip velocity modulus of the control volume, the fracture filling composition similar value of the control volume, and the fracture filling structure similar value of the control volume as p1, p2, and p3, respectively, and use the formula p1 norm =[p1-min(p1)] / [max(p1)-min(p1)], to obtain the normalized value p1 of the fracture angle infiltration velocity modulus of the control volume norm , where min(p1) and max(p1) are the minimum and maximum values ​​of the fracture angle seepage modulus, respectively. The formula is used to obtain the normalized value p2 of the fracture filling component of the control volume. norm , using the formula, to obtain the normalized value p3 of the fracture filling structure of the control volume norm , the fracture angle inclination velocity modulus normalized value p1 norm , fracture filling component normalized value p2 norm , fracture filling structure similar to normalized value p3 norm , the fracture characteristic normalization parameters marked as control volume, are stored in the data repository in the analysis module.

4. The method for intelligent monitoring and tracing of groundwater pollution based on big data according to claim 3 is characterized in that: The steps of analyzing the fracture angle dip permeability modulus of the control volume, the fracture filling component similarity value of the control volume, and the fracture filling structure similarity value of the control volume are as follows: Step 101: the computing unit divides the monitoring area into regular rectangular control volumes, and divides them into regular rectangular control volumes with equal intervals Δx, Δy and Δz in the x, y and z directions respectively, to form rectangular control volumes, and the vertices of the control volumes are marked as grid nodes; Step 102: The geological module obtains geological parameters of the control volume and transmits them to the operation unit in the analysis module. The geological parameters include fracture information and fracture filling material information. The operation unit uses an image processing algorithm to identify fracture boundaries of each fracture in the control volume, fits an ellipse shape, calculates the major axis and minor axis parameters, connects the two end points of the major axis to obtain the major axis straight line of the ellipse, draws a straight line perpendicular to the major axis through the center of the ellipse, obtains a reference plane with the plane where the ellipse is located, calculates the angle between the major axis straight line of the ellipse and the x-axis as the fracture inclination angle θ, and the angle between the reference plane and the horizontal plane as the fracture inclination angle ɑ, obtains the fracture water flow direction, and constructs the water flow direction vector in the three-dimensional space coordinate system. , and add the calculated fracture inclination angle to construct the fracture inclination vector , construct a unit vector parallel to the z-axis , according to the formula , according to the vector projection formula , the projection vector of the water flow direction vector in the fracture surface , get the modulus of the projection vector , marked as the fracture angle dip permeability modulus value of the control volume, the fracture angle dip permeability modulus value of the control volume is averaged to obtain the fracture angle dip permeability modulus value of the control volume, the computing unit uses X-rays to irradiate each fracture filling in the control volume to obtain a corresponding diffraction spectrum, and performs feature comparison on the diffraction spectrum to determine the types of various minerals in each fracture filling, and obtains the relative content of each type of mineral based on the intensity and area parameters of the diffraction peak, and squares the difference in the relative content of corresponding minerals between any two fracture fillings in the control volume, adds these square values ​​of all minerals, and then takes the sum The square root is taken and the maximum value is taken, which is marked as the pseudo-anomalous value of the crack filling component in the control volume. The operation unit processes the diffraction pattern of the crack filling in the control volume, groups the diffraction peaks according to the intensity and symmetry characteristics of the diffraction peaks, combines the angular position of the diffraction peaks, uses the Bragg law formula, and compares it with the standard crystal structure database to obtain the corresponding crystal plane index, and constructs the direction vector of the corresponding crystal plane in the crystal coordinate system with the crystal plane index. The angle between the two vectors in each direction of the crystal plane of the crack filling in the control volume is calculated by vector operation, and the average value is taken, which is marked as the pseudo-anomalous value of the crack filling structure in the control volume.

5. The method for intelligent monitoring and tracing of groundwater pollution based on big data according to claim 2 is characterized in that: The steps for adjusting the permeability analysis of the control volume are as follows: Step 201: The geological module obtains the geological parameters of the control volume and transmits them to the calculation unit in the analysis module. The geological parameters include porosity and specific surface area, which are marked as ψ and ɑ respectively. According to the formula K=ψ 3 / [C×(1-ψ) 2 × 2 ], and the permeability K of the control volume is calculated, where C is the permeability constant coefficient of the control volume; Step 202: The data repository in the analysis module transmits the normalized parameters of the crack characteristics of the control volume to the operation unit in the analysis module. The operation unit adds the normalized parameters of the crack characteristics of the control volume to obtain the crack integrated characteristic quantity of the control volume. If the crack integrated characteristic quantity of the control volume is greater than or equal to 2, the control volume is classified into the crack super-dynamic strong effect control volume set A1. If the crack integrated characteristic quantity of the control volume is greater than 1.5 and less than 2, the control volume is classified into the crack medium dynamic medium effect control volume set A2. If If the fracture integrated characteristic quantity of the control volume is equal to 1.5, the control volume is classified into the fracture critical equilibrium state control volume set A3. If the fracture integrated characteristic quantity of the control volume is less than 1.5, the control volume is classified into the fracture low dynamic latent type control volume set A4. The permeability correction coefficients of control volume set A1, control volume set A2, control volume set A3 and control volume set A4 correspond to t1, t2, t3 and t4 respectively, where t1>t2>t3>t4, which is marked as the permeability correction coefficient T of the control volume; Step 203: The calculation unit multiplies the permeability correction coefficient T of the control volume by the permeability K of the control volume to obtain the adjusted permeability Kt of the control volume.

6. The method for intelligent monitoring and tracing of groundwater pollution based on big data according to claim 5 is characterized in that: The permeability constant coefficient analysis steps of the control volume are as follows: The geological module obtains the geological parameters of the control volume and transmits them to the calculation unit in the analysis module. The geological parameters include the average particle size, the average density of the particles, and the average spherical coefficient of the particles, which are marked as dp, ρm, ζ, and the porosity ψ of the control volume. According to the set formula C=180×(1-ψ) / (dp 2 ×ρm 2 ×ζ 2 ), and obtain the permeability constant coefficient C of the control volume, where the spherical coefficient of the particle is the square root of the ratio of the surface area of ​​a sphere with the same particle volume to the actual surface area of ​​the particle.

7. The method for intelligent monitoring and tracing of groundwater pollution based on big data according to claim 2 is characterized in that: The steps for analyzing the multiple water flow paths in different directions from each pollution source are as follows: Step 301: The water flow module obtains the water flow parameters of the control volume and transmits them to the calculation unit in the analysis module. The water flow parameters include the head difference Δhx of the control volume in the x direction, the average boundary area in the x direction is Ax, and the adjusted permeability Kt of the control volume. The calculation module calculates the water flow transfer flux LZx in the x direction of the control volume according to the formula LZx=-Kt×Ax×Δhx / Δx, where Δx is the boundary coordinate difference in the x direction of the control volume. The water flow transfer flux LZy in the y direction of the control volume and the water flow transfer flux LZz in the z direction of the control volume are obtained by solving in the same way, and the water flow transfer flux vector LZy of the control volume is constructed. , and according to the formula, the water flow transport modulus of the control volume is obtained ; Step 302: The acquisition module obtains the location coordinates of each pollution source and transmits this pollution information to the computing unit in the analysis module. Starting from the control volume where each pollution source is located, multiple adjacent control volumes into which water can flow are found according to the direction determined by the water flow transfer flux vector. On each branch, the operation of finding the next adjacent control volume that meets the water flow inflow conditions is repeated continuously. Similarly, the control volumes along the way are connected in sequence according to the water flow direction to obtain multiple water flow paths with different directions starting from each pollution source.

8. The method for intelligent monitoring and tracing of groundwater pollution based on big data according to claim 2 is characterized in that: The steps for analyzing the similarity of groundwater pollution characteristics between the pollution source and the water quality monitoring site are as follows: Step 401: The acquisition module obtains the pollution information of the water quality sensor and transmits it to the operation unit in the analysis module. The operation unit takes the values ​​of the concentration of organic pollutants and heavy metals in the groundwater and sums them to obtain the pollution concentration value of the water quality monitoring. If the pollution concentration value of the water quality monitoring is greater than the set threshold TG1, the water quality sensor sends a groundwater pollution tracing signal accordingly; Step 402: The acquisition module obtains the pollution information of the pollution source and transmits it to the operation unit in the analysis module. The operation unit takes the median lethal concentration, relative abundance of Pseudomonas, stable nitrogen isotope ratio, and water conductivity of the groundwater of the pollution source and marks them as λ1, λ2, λ3, and λ4 respectively to construct a multidimensional ecological and chemical characteristic vector of the groundwater of the pollution source. , =(λ1,λ2,λ3,λ4),the acquisition module obtains the pollution information of the water quality sensor that sends the groundwater pollution source tracing signal and transmits it to the operation unit in the analysis module. The operation unit takes the groundwater median lethal concentration, the relative abundance of Pseudomonas in groundwater, the stable nitrogen isotope ratio of groundwater, and the water conductivity of groundwater of the water quality sensor, and marks them as τ1, τ2, τ3, and τ4 respectively, to construct the groundwater multidimensional ecological and chemical characteristic vector of the water quality sensor , =(τ1,τ2,τ3,τ4),According to the set formula, the similarity δ of groundwater pollution characteristics between the pollution source and the water quality monitoring point is calculated.

9. The method for intelligent monitoring and tracing of groundwater pollution based on big data according to claim 2 is characterized in that: The steps for determining and analyzing the groundwater pollution source are as follows: Step 502: Arrange the water flow flux values ​​of each pollution source and water quality monitoring point from small to large to obtain a matrix , n is the total number of water flow flux values. If the water flow flux value between the pollution source and the water quality monitoring point is located at W 4n / 5 To W n If the similarity δ between the pollution source and the groundwater pollution characteristics at the water quality monitoring site is greater than the set threshold XQ1, then the corresponding pollution source is the main pollution source at the water quality monitoring site. If the water flow transfer flux between the pollution source and the water quality monitoring site is between W n / 2 To W 4n / 5 , excluding the water transport flux equal to W 4n / 5 If the pollution source and the groundwater pollution characteristics similarity δ of the water quality monitoring site are greater than the set threshold XQ1, then the corresponding pollution source is the secondary pollution source of the water quality monitoring site.

10. The method for intelligent monitoring and tracing of groundwater pollution based on big data according to claim 9 is characterized in that: The steps for analyzing the water flow transfer flux between the pollution source and the water quality monitoring point are as follows: Step 501: The data repository in the analysis module transmits multiple water flow paths with different directions starting from each pollution source to the operation unit in the analysis module, and the acquisition module transmits the location coordinates of each pollution source and the location coordinates of the water quality monitoring point to the operation unit in the analysis module. The operation unit analyzes and obtains the water flow path from each pollution source to the water quality monitoring point, and sums the water flow transfer flux modulus in the water flow path to obtain the total water flow transfer flux modulus of each water flow path, and obtains the maximum value of the total water flow transfer flux modulus of each water flow path, which is marked as the water flow transfer flux value W of the pollution source and the water quality monitoring point.

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