Pollution source detection method and system based on water environment treatment

Abstract

The invention discloses a pollution source detection method and system based on water environment treatment, and relates to the technical field of intelligent detection, and the method comprises the following steps: combining a distribution range, emitting laser with a first Brillouin line width, adjusting the first Brillouin line width based on the feedback data of the laser, and obtaining a first Brillouin line width and a second Brillouin line width; and based on the constructed pollutant diffusion model, the three-point positioning method and the pollutant parameters of the current detection point, moving the coordinates of the pollution source, and obtaining the coordinates of a new pollution source. By detecting the concentration change of the trace pollutants and combining model inversion, the positioning precision is greatly improved, a closed loop mechanism which does not need to approach a polluted area and supports detection and updating at the same time is supported, three-dimensional positioning can be realized only by three detection points, a sensor network does not need to be densely arranged, the hardware cost and the maintenance burden are greatly reduced, and the method is suitable for emergency rapid deployment. The stable performance can be kept under the complex conditions of wind field change, topographic relief, water layering and the like.

Description

Technical Field

[0001] This invention relates to the field of intelligent detection technology, and in particular to a method and system for detecting pollution sources based on water environment management. Background Technology

[0002] In recent years, with the integrated development of new materials, the Internet of Things and artificial intelligence, water pollution monitoring has rapidly shifted from traditional laboratory analysis to online, intelligent and ecological approaches. Optical, electrochemical and biological sensors have become mainstream. Fluorescence-spectral organic matter monitors can achieve rapid early warning of sudden pollution in water sources. GIS, remote sensing and models fill data gaps and support diffusion simulation and health risk assessment of emerging pollutants such as PFAS, PPCPs and antibiotic resistance genes.

[0003] Currently, Chinese invention patent CN120233054A discloses a method for real-time source tracing of river water pollution. This method obtains the location of pollution sources in a river basin, the types of pollutants discharged by these sources, pollutant particle parameters and pollutant types, the river connectivity relationships in the river basin, and confirms the input and output flows of pollutants. Based on the output flow of pollutants, the theoretical pollutant level is obtained based on the theoretical water flow velocity and pollutant particle parameters of the river basin. Based on the measured pollutant concentration and measured water flow velocity, the measured pollutant level is obtained. Based on the measured pollutant level and the theoretical pollutant level, the source of the pollutant is determined. However, the related technology does not use a rapid detection method to locate the pollution source, which is not conducive to the rapid response of pollution source detection. It also does not establish a monitoring method that can migrate with dynamic scenarios, which is not conducive to the dynamism and adaptability of pollution source detection. Summary of the Invention

[0004] The technical problem solved by this invention is that: in related technologies, there is no method to locate the pollution source through rapid detection, which is not conducive to the rapid response of pollution source detection; and there is no monitoring method that can move with dynamic scenes, which is not conducive to the dynamism and adaptability of pollution source detection.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution. In the first aspect, a pollution source detection method based on water environment governance includes the following steps: Step S100, selecting any three non-collinear locations as detection points, and the detection points satisfying a first constraint condition; at the detection points, emitting a laser with a first Brillouin linewidth based on the distribution range; adjusting the first Brillouin linewidth based on the laser feedback data to obtain the spontaneous Brillouin linewidth of the detection point; wherein, the initial emission direction is represented as an arbitrary direction, and the emission direction of the detection point is determined by selecting the emission direction corresponding to the energy of the largest reflected wave. Step S200: Based on the constructed pollutant diffusion model and the three-point positioning method, obtain the polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point; Step S300: Based on the polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point and the pollutant parameters of the current detection point, the coordinates of the pollution source are moved to obtain the new coordinates of the pollution source.

[0006] As a preferred embodiment of the pollution source detection method based on water environment management described in this invention, step S100 includes the following sub-steps: step S101, select any water area to be detected and obtain the distribution range of the water area to be detected. Step S102: Obtain the geometric center point of the distribution range, select any three non-collinear positions as detection locations, calculate the sum of the Euclidean distances between the detection locations and the geometric center point, and set the sum of the Euclidean distances between the detection locations and the geometric center point as a first constraint condition that is greater than or equal to the first distance. Step S103: Jump to any monitoring point, and at that monitoring point, emit a laser with a first Brillouin linewidth, wherein the first Brillouin linewidth is matched based on the distribution range; Step S104: Receive feedback data from the water area, where the feedback data is represented as the Brillouin linewidth of the laser reflected back from the water area; Step S105: Calculate the saturation coefficient based on the feedback data and the first Brillouin linewidth, compare the saturation coefficient with the preset saturation threshold, and perform the first operation on the first Brillouin linewidth based on the comparison result. The first operation includes both control and non-control operations; When the first operation is a control operation, proceed to step S106; when the first operation is a non-control operation, proceed to step S107. Step S106: Continuously change the first Brillouin linewidth until the saturation corresponding to the continuously changed first Brillouin linewidth is greater than or equal to the preset saturation threshold, then jump to step S107. Step S107: Set the feedback data at this time to the self-published line width of the detection point; Step S108, repeat steps S101 to S107, to complete the self-released abyssal line width of each monitoring point in the water area to be tested.

[0007] As a preferred embodiment of the pollution source detection method based on water environment management described in this invention, step S200 includes the following sub-steps: step S201, assuming that the water area is polluted, setting the pollution parameters of the pollution source, setting the three-dimensional coordinates of the pollution source to (0, 0, 0), and transforming the ZP coordinates of each grid of the distribution boundary and the coordinates of the detection point from the ZP coordinate system to the three-dimensional Cartesian coordinate system. Step S202: Decompose the pollution parameters of the pollution source vertically and horizontally in a three-dimensional Cartesian coordinate system. Step S203: Obtain the historical flow parameters of the water body, and based on the historical flow parameters of the water body, obtain the longitudinal average flow velocity and the transverse average flow velocity; Step S204: Based on the pollution parameters of the pollution sources after vertical decomposition, the pollution parameters of the pollution sources after horizontal decomposition, the longitudinal average flow velocity, and the horizontal average flow velocity, obtain the concentration change equation. Step S205: Construct a spatiotemporal model of pollutant concentration based on the concentration change equation.

[0008] As a preferred embodiment of the pollution source detection method based on water environment governance described in this invention, step S205 further includes the following sub-steps: step S2051, constructing boundary conditions based on the three-dimensional Cartesian coordinates of each grid of the distribution boundary. Step S2052: Solve the concentration change equation according to the boundary conditions to obtain the influence expression of the pollution source; Step S2053: Construct a spatiotemporal model of pollutant concentration based on the influence expression of the pollution source.

[0009] As a preferred embodiment of the pollution source detection method based on water environment governance described in this invention, the method involves obtaining the coordinates of the detection point in a three-dimensional Cartesian coordinate system at any known origin, calculating the straight-line distance between any two coordinates, calculating the sum of the straight-line distances between the two coordinates, setting the sum of the straight-line distances between the two coordinates as a second constraint condition, setting the straight-line distance between any two coordinates as a third constraint condition, and obtaining the three-dimensional Cartesian coordinates of the detection point. The three-dimensional Cartesian coordinates of the detection point are obtained by calculating using the three-dimensional distance formula. Based on the three-dimensional distance formula, the third constraint condition, and the second constraint condition, a system of equations is constructed to obtain the polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point. The polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point includes polynomial expressions corresponding to the x-coordinate, the y-coordinate, and the z-coordinate, respectively.

[0010] As a preferred embodiment of the pollution source detection method based on water environment governance described in this invention, the current pollutant concentrations at three detection points are obtained. The method for obtaining the current pollutant concentrations at the three detection points includes matching the volume viscosity coefficient and shear viscosity coefficient of the corresponding water body with the corresponding self-published depth linewidth, and matching the corresponding pollutant concentrations based on the volume viscosity coefficient and shear viscosity coefficient of the water body.

[0011] As a preferred embodiment of the pollution source detection method based on water environment governance described in this invention, the method for obtaining the current pollutant concentration at the three detection points is to inversely deduce the three-dimensional Cartesian coordinates of the pollution source based on the current pollutant concentration at the detection point and the polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point. The three-dimensional Cartesian coordinate inverse method for pollution sources includes inputting the current pollutant concentration at the detection point into the spatiotemporal model of pollutant concentration to obtain the corresponding C value, calculating the average value of the C value, and setting the average value of the C value as the pollutant concentration of the pollution source.

[0012] As a preferred embodiment of the pollution source detection method based on water environment governance described in this invention, the pollution source coordinates are continuously moved from (0, 0, 0), and during the continuous movement, the horizontal and vertical coordinates of the pollution source coordinates are always distributed in the three-dimensional Cartesian coordinates of each grid on the distribution boundary, and the distribution is represented as greater than or equal to the corresponding minimum value and less than or equal to the corresponding maximum value. Obtain the C value corresponding to the coordinates of the pollution source after any movement, and calculate the difference between the C value and the average of the C values ​​to obtain the pollution concentration difference.

[0013] As a preferred embodiment of the pollution source detection method based on water environment management described in this invention, the method involves traversing all pollution concentration differences, selecting the pollution concentration difference with the smallest value, setting the three-dimensional Cartesian coordinates corresponding to the pollution concentration difference with the smallest value as the coordinates of the new pollution source, and replacing the original three-dimensional Cartesian coordinates of the pollution source with the coordinates of the new pollution source. Output the coordinates of the new pollution source.

[0014] Secondly, a pollution source detection system based on water environment governance includes a detection module, a calculation module, and a location module; The detection module selects any three non-collinear positions as detection locations, and the detection locations satisfy the first constraint condition. At the detection points, combined with the distribution range, a laser with a first Brillouin linewidth is emitted. Based on the feedback data of the laser, the first Brillouin linewidth is adjusted to obtain the spontaneous Brillouin linewidth of the detection point. The calculation module obtains the polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point based on the constructed pollutant diffusion model and the three-point positioning method. The positioning module moves the coordinates of the pollution source based on the polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point and the pollutant parameters of the current detection point, and obtains the new coordinates of the pollution source.

[0015] The beneficial effects of this invention are as follows: Since the release of the Brillouin linewidth, it is extremely sensitive to changes in environmental parameters and can detect changes in trace pollutant concentrations. Combined with model inversion, the positioning accuracy is greatly improved, which is superior to traditional infrared / visible light remote sensing. It does not require proximity to the polluted area; data can be obtained through laser remote sensing. It is particularly suitable for toxic, explosive, high-temperature, or inaccessible environments, such as chemical plant leaks, nuclear contaminated areas, and deep-sea hydrothermal vents. It supports a closed-loop mechanism of simultaneous detection and updating, enabling real-time tracking of mobile pollution sources, such as ship discharges and vehicle leaks. In contrast, traditional methods are mostly static snapshots and cannot respond to dynamic changes. Three-dimensional positioning can be achieved with only three detection points, eliminating the need for dense sensor networks, significantly reducing hardware costs and maintenance burdens, and making it suitable for rapid emergency deployment. Brillouin scattering responds to synchronous changes in temperature, pressure, and flow velocity. Through multi-parameter coupled modeling, it can maintain stable performance under complex conditions such as wind field changes, topographic relief, and water stratification. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the basic process of a pollution source detection method based on water environment management, provided as an embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0018] It should be understood that the step numbers used herein are for ease of description only and are not intended to limit the order in which the steps are performed. It should also be understood that the terminology used in this specification is for the purpose of describing specific embodiments only and is not intended to limit the invention.

[0019] As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0020] The terms “comprising” and “including” indicate the presence of the described feature, whole, step, operation, element and / or component, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0021] The term “and / or” refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0022] With the integrated development of new materials, the Internet of Things and artificial intelligence, water pollution monitoring is rapidly leaping from traditional laboratory analysis to online, intelligent and ecological directions. Optical, electrochemical and biological sensors have become mainstream. Fluorescence-spectral organic matter monitors can realize rapid early warning of sudden pollution in water sources. GIS, remote sensing and models fill data gaps and support diffusion simulation and health risk assessment of emerging pollutants such as PFAS, PPCPs and antibiotic resistance genes.

[0023] Based on this, embodiments of this application provide a pollution source detection method based on water environment governance.

[0024] The pollution source detection method based on water environment governance provided in this application relates to the field of intelligent detection. This method can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the pollution source detection method based on water environment governance, but is not limited to the above forms.

[0025] This application can also be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0026] Example, refer to Figure 1As an embodiment of the present invention, a pollution source detection method based on water environment governance is provided, including the following steps: Step S100, select any three non-collinear locations as detection locations, and the detection locations satisfy a first constraint condition; at the detection points, based on the distribution range, emit a laser with a first Brillouin linewidth; based on the feedback data of the laser, adjust the first Brillouin linewidth to obtain the spontaneous Brillouin linewidth of the detection point; wherein, the initial emission direction is represented as an arbitrary direction, and the emission direction of the detection point is determined by selecting the emission direction corresponding to the energy of the largest reflected wave. Step S200: Based on the constructed pollutant diffusion model and the three-point positioning method, obtain the polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point; Step S300: Based on the polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point and the pollutant parameters of the current detection point, the coordinates of the pollution source are moved to obtain the new coordinates of the pollution source.

[0027] Further preferably, the Brillouin linewidth is extremely sensitive to changes in environmental parameters and can detect changes in trace pollutant concentrations. Combined with model inversion, the positioning accuracy is significantly improved, outperforming traditional infrared / visible light remote sensing. It does not require proximity to the contaminated area; data can be acquired through laser remote sensing. It is particularly suitable for toxic, explosive, high-temperature, or inaccessible environments, such as chemical plant leaks, nuclear contaminated areas, and deep-sea hydrothermal vents. It supports a closed-loop mechanism of simultaneous detection and updating, enabling real-time tracking of mobile pollution sources, such as ship discharges and vehicle leaks. In contrast, traditional methods are mostly static snapshots and cannot respond to dynamic changes. Three-dimensional positioning can be achieved with only three detection points, eliminating the need for dense sensor networks, significantly reducing hardware costs and maintenance burdens, and making it suitable for rapid emergency deployment. Brillouin scattering responds to synchronous changes in temperature, pressure, and flow velocity. Through multi-parameter coupled modeling, it can maintain stable performance under complex conditions such as wind field changes, terrain undulations, and water stratification.

[0028] More preferably, in water bodies such as rivers and oceans, the elevation of the water surface is not equal, that is, the water surface can be regarded as a three-dimensional space. Therefore, selecting three non-collinear locations as detection points is beneficial to inferring the self-released abyssal line width corresponding to the pollution source.

[0029] More preferably, this application is based on the assumption that the water body has been polluted by industrial wastewater, domestic wastewater, etc. Therefore, it can bind the Brillouin linewidth of the pollution source with the volume viscosity coefficient and shear viscosity coefficient of the water body. Furthermore, it is determined that the volume viscosity coefficient and shear viscosity coefficient of the water body are not constant values ​​of pure water. In other words, the detection process of this application is meaningful. When the pollution of any water body to be tested is not serious, that is, when the calculated Brillouin linewidth of the pollution source is less than or equal to the first linewidth, the process jumps to the next water body to be tested. Since only three detection points are selected for reasoning, the rapid response of the detection is improved, which is a significant improvement compared to the prior art.

[0030] Step S100 includes the following sub-steps: Step S101, select any water area to be detected and obtain the distribution range of the water area to be detected; Step S102: Obtain the geometric center point of the distribution range, select any three non-collinear positions as detection locations, calculate the sum of the Euclidean distances between the detection locations and the geometric center point, and set the sum of the Euclidean distances between the detection locations and the geometric center point as a first constraint condition that is greater than or equal to the first distance. Step S103: Jump to any monitoring point, and at that monitoring point, emit a laser with a first Brillouin linewidth, wherein the first Brillouin linewidth is matched based on the distribution range; Step S104: Receive feedback data from the water area, where the feedback data is represented as the Brillouin linewidth of the laser reflected back from the water area; Step S105: Calculate the saturation coefficient based on the feedback data and the first Brillouin linewidth, compare the saturation coefficient with the preset saturation threshold, and perform the first operation on the first Brillouin linewidth based on the comparison result. The first operation includes both control and non-control operations; When the first operation is a control operation, proceed to step S106; when the first operation is a non-control operation, proceed to step S107. Step S106: Continuously change the first Brillouin linewidth until the saturation corresponding to the continuously changed first Brillouin linewidth is greater than or equal to the preset saturation threshold, then jump to step S107. Step S107: Set the feedback data at this time to the self-published line width of the detection point; Step S108, repeat steps S101 to S107, to complete the self-released abyssal line width of each monitoring point in the water area to be tested.

[0031] More preferably, the distribution range is represented as a spatial curved surface formed by the water plane, and the distribution range includes the distribution boundary. For the distribution boundary, the distribution boundary is automatically obtained by extracting code through the boundary layer.

[0032] More preferably, based on the finite element segmentation algorithm, the distribution range is automatically cut into various two-dimensional meshes.

[0033] More preferably, for the first Brillouin linewidth, a matching method is configured. The matching method includes: obtaining the distribution boundary; obtaining the straight-line distance between the pixel coordinates of any two grid points in the distribution boundary; sorting the straight-line distances in descending order; selecting the two grid points corresponding to the straight-line distance with the largest value and recording them as boundary points; obtaining the pixel coordinates of the boundary points; and converting the pixel coordinates of the boundary points from the pixel coordinate system to the ZP coordinate system (spatial rectangular coordinate system). Obtain the straight-line distance of the boundary point in the ZP coordinate system, and denote the straight-line distance of the boundary point in the ZP coordinate system as the effective medium length. Obtain the ZP coordinates of the geometric center point of any detection point. According to the calculation expression of the effective medium length and the scattered light intensity, obtain the scattered light intensity corresponding to the ZP coordinates of the geometric center point of the detection point. Retrieve the emission database, input the scattered light intensity corresponding to the ZP coordinates of the geometric center point of the detection point into the emission database, match the corresponding Brillouin linewidth, and denote the corresponding Brillouin linewidth as the first Brillouin linewidth.

[0034] More preferably, the expression for calculating the intensity of scattered light is: ; in, For effective medium length, The scattered light intensity corresponding to the ZP coordinates of the geometric center point of the detection point. g and e are both constants.

[0035] More preferably, the first value is set as the saturation threshold, and the saturation coefficient is calculated based on the feedback data and the first Brillouin linewidth. A calculation method is configured for the saturation coefficient, which includes calculating the first difference between the feedback data and the first Brillouin linewidth, setting the first difference to the first Brillouin linewidth as the first ratio, and setting the first saturation as the saturation coefficient. The larger the saturation coefficient value, the greater the matching degree between the emission parameters and the detection point.

[0036] More preferably, when the comparison result shows that the saturation coefficient is less than the preset saturation threshold, the first operation is set as a control operation; when the comparison result shows that the saturation coefficient is greater than or equal to the preset saturation threshold, the first operation is set as a step control operation.

[0037] More preferably, the continuous change of the first Brillouin linewidth is achieved by changing the incident light wavelength and incident angle, and the continuous change means continuously increasing or decreasing the first Brillouin linewidth. The first wavelength is set as the wavelength change amount, the first angle is set as the angle change amount, and the incident light wavelength is continuously increased or decreased according to the first wavelength, and the incident angle is continuously increased or decreased according to the first angle. In this process, the incident light wavelength and the incident angle change at the same frequency, meaning that a single modulation process includes adjusting the incident light wavelength once and adjusting the incident angle once.

[0038] Step S200 includes the following sub-steps: Step S201, assuming that the water area is polluted, set the pollution parameters of the pollution source, set the three-dimensional coordinates of the pollution source to (0, 0, 0), and transform the ZP coordinates of each grid of the distribution boundary and the coordinates of the detection point from the ZP coordinate system to the three-dimensional Cartesian coordinate system. Step S202: Decompose the pollution parameters of the pollution source vertically and horizontally in a three-dimensional Cartesian coordinate system. Step S203: Obtain the historical flow parameters of the water body, and based on the historical flow parameters of the water body, obtain the longitudinal average flow velocity and the transverse average flow velocity; Step S204: Based on the pollution parameters of the pollution sources after vertical decomposition, the pollution parameters of the pollution sources after horizontal decomposition, the longitudinal average flow velocity, and the horizontal average flow velocity, obtain the concentration change equation. Step S205: Construct a spatiotemporal model of pollutant concentration based on the concentration change equation.

[0039] More preferably, the pollution parameters are expressed as pollutant concentration and pollutant emission.

[0040] More preferably, the historical flow parameters include historical flow direction and historical flow velocity. The historical flow velocity is obtained by the image-based flow measurement method. The historical flow direction is represented as the direction of the vector corresponding to the pollutant at the assumed pollution source, which is tangent to the water surface, with the flow velocity decreasing from large to small, and along the central axis of the water body.

[0041] More preferably, the central axis of the water area is represented by a line connecting the midpoints of the water surface of the water source, excluding the boundaries of the shore, and the line is a curve parallel to any shore of the water area.

[0042] More preferably, the concentration change equation is: ; Where C represents the pollutant concentration at the pollution source, and t is the monitoring time point number, i.e., the data collected in the t-th instance according to the monitoring cycle. The monitoring cycle can be changed. The lateral diffusion coefficient is... The longitudinal diffusion coefficient is... The lateral average flow velocity, Where S is the longitudinal average flow velocity, and S is the compensation coefficient. Let be the sum of the compensation coefficients from each iteration. The longitudinal diffusion coefficient, transverse diffusion coefficient, and compensation coefficient are all constants. The symbol represents the partial derivative.

[0043] More preferably, for C, the equation of C is expressed as C=f(t)=ax+by, which represents the pollutant concentration at the pollution source location at time t. It is also expressed as the sum of the decomposition values ​​of the pollutant-corresponding vector on the x-axis and y-axis. The magnitude of the pollutant-corresponding vector is the pollutant concentration, and the direction of the pollutant-corresponding vector is tangent to the water surface, with the flow velocity decreasing from large to small, and along the central axis of the water area.

[0044] Step S205 also includes the following sub-steps: Step S2051, construct boundary conditions based on the three-dimensional Cartesian coordinates of each grid of the distribution boundary. Step S2052: Solve the concentration change equation according to the boundary conditions to obtain the influence expression of the pollution source; Step S2053: Construct a spatiotemporal model of pollutant concentration based on the influence expression of the pollution source.

[0045] More preferably, the expression for the impact of the pollution source includes the increase in mass concentration at other coordinate points of the pollution source, the increase in mass concentration at other points due to nearshore reflection, and the increase in mass concentration at other points due to offshore reflection.

[0046] More preferably, the boundary condition is expressed as follows: when x is 0 and t is 0, the value of C is C0; when t approaches infinity, C is 0. More preferably, the expression for the impact of the pollution source is: ; ; ; ; Where M represents the pollutant emission amount, b is the straight-line distance from the pollution source to the nearest shore, B and h are the maximum width and average depth of the water area, respectively, n is the number of reflections from the shore, x is the difference between the x-coordinate of other coordinate points and the pollution source, and y is the difference between the y-coordinate of other coordinate points and the pollution source. This represents the increase in mass concentration of the pollution source at other coordinate points. This represents the increase in mass concentration at other points due to near-shore reflection. This represents the increase in mass concentration at other points due to reflection from the far shore.

[0047] More preferably, the spatiotemporal model of pollutant concentration is as follows: .

[0048] Obtain the coordinates of the detection point in a three-dimensional Cartesian coordinate system with any known origin. Calculate the straight-line distance between any two coordinates. Calculate the sum of the straight-line distances between the two coordinates. Set the sum of the straight-line distances between the two coordinates as the second constraint condition. Set the straight-line distance between any two coordinates as the third constraint condition. Obtain the three-dimensional Cartesian coordinates of the detection point. The three-dimensional Cartesian coordinates of the detection point are calculated using the three-dimensional distance formula. Construct a system of equations based on the three-dimensional distance formula, the third constraint condition, and the second constraint condition to obtain the polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point. The polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point includes polynomial expressions for the x-coordinate, y-coordinate, and z-coordinate, respectively. The current pollutant concentrations at three monitoring points are obtained. The method for obtaining the current pollutant concentrations at the three monitoring points includes matching the volume viscosity coefficient and shear viscosity coefficient of the corresponding water body with the corresponding self-published water body linewidth, and matching the corresponding pollutant concentrations based on the volume viscosity coefficient and shear viscosity coefficient of the water body.

[0049] More preferably, the current pollutant concentration is expressed as the pollutant concentration at the current time point, adjusted according to the detection cycle; that is, the current pollutant concentration is different for different detection cycles.

[0050] More preferably, the method for matching the volume viscosity coefficient and the shear viscosity coefficient of the water body includes retrieving a water quality parameter lookup table, inputting the self-published nadir line width into the water quality parameter lookup table, and matching the volume viscosity coefficient and the shear viscosity coefficient of the water body corresponding to the self-published nadir line width.

[0051] More preferably, the pollutant concentration matching method includes retrieving a pollution parameter lookup table, inputting the volume viscosity coefficient and shear viscosity coefficient of the water body into the pollution parameter lookup table, obtaining the pollutant concentration corresponding to the volume viscosity coefficient and shear viscosity coefficient of the water body, and recording it as the current pollutant concentration.

[0052] The method for obtaining the current pollutant concentration at the three monitoring points is to inversely deduce the three-dimensional Cartesian coordinates of the pollution source based on the current pollutant concentration at the monitoring point and the polynomial corresponding to the three-dimensional Cartesian coordinates of the monitoring point. The three-dimensional Cartesian coordinate inverse method for pollution sources includes: inputting the current pollutant concentration at the detection point into the spatiotemporal model of pollutant concentration to obtain the corresponding C value; calculating the average value of the C value; and setting the average value of the C value as the pollutant concentration of the pollution source. The pollution source coordinates are continuously moved from (0, 0, 0), and during the continuous movement, the x and y coordinates of the pollution source coordinates are always distributed in the three-dimensional Cartesian coordinates of each grid on the distribution boundary, which is represented as greater than or equal to the corresponding minimum value and less than or equal to the corresponding maximum value. Obtain the C value corresponding to the coordinates of the pollution source after any movement, and calculate the difference between the C value and the average of the C values ​​to obtain the pollution concentration difference. Iterate through all pollution concentration differences, select the pollution concentration difference with the smallest value, set the three-dimensional Cartesian coordinates corresponding to the pollution concentration difference with the smallest value as the coordinates of the new pollution source, and replace the three-dimensional Cartesian coordinates of the original pollution source with the coordinates of the new pollution source. Output the coordinates of the new pollution source.

[0053] Further preferably, the Brillouin linewidth is extremely sensitive to changes in environmental parameters and can detect changes in trace pollutant concentrations. Combined with model inversion, the positioning accuracy is significantly improved, outperforming traditional infrared / visible light remote sensing. It does not require proximity to the contaminated area; data can be acquired through laser remote sensing. It is particularly suitable for toxic, explosive, high-temperature, or inaccessible environments, such as chemical plant leaks, nuclear contaminated areas, and deep-sea hydrothermal vents. It supports a closed-loop mechanism of simultaneous detection and updating, enabling real-time tracking of mobile pollution sources, such as ship discharges and vehicle leaks. In contrast, traditional methods are mostly static snapshots and cannot respond to dynamic changes. Three-dimensional positioning can be achieved with only three detection points, eliminating the need for dense sensor networks, significantly reducing hardware costs and maintenance burdens, and making it suitable for rapid emergency deployment. Brillouin scattering responds to synchronous changes in temperature, pressure, and flow velocity. Through multi-parameter coupled modeling, it can maintain stable performance under complex conditions such as wind field changes, terrain undulations, and water stratification.

[0054] Example 2: Three non-collinear detection points;

[0055] The laser emission parameters include a pulse energy of 120 mJ, a pulse width of 8 ns, an initial Brillouin linewidth of 650 MHz, and a saturation coefficient of 0.92 after four adjustments of the feedback data. Since 0.92 is greater than or equal to 0.85 (threshold), the Brillouin linewidth is locked. The relationship between the width of the Liyuan Line and the current pollutant concentration is as follows:

[0056] It is 0.047ms -1 , It is 0.183ms -1 , It is 0.11m 2 s -1 , It is 0.38m 2 s -1 k is 0.012, h is 1.7m, and... , , , Substituting k and h into the expression for the impact of the pollution source, we get , , The expression; Substituting the relationship between the self-published Liyuan linewidth and the current pollutant concentration into the spatiotemporal model, the coordinates at which the residual difference between the calculated and measured concentrations is minimized are obtained. With a search step size of 1m and traversing a 4.8km × 46km grid, the minimum pollution concentration difference ΔCmin = 0.18mg / L is found. -1 The coordinates of the new pollution source are (4438492, 4521945, 1.0m), and this point is 12m away from the nearest bank slope. The steps and experimental data for obtaining the first Brillouin linewidth include: using the Canny algorithm to extract edges from the binary image of the distribution range obtained in step S101 to obtain the boundary pixel set; Traverse the distance between any two points in the pixel coordinate system, take the maximum value, and then convert it to the ZP coordinate system according to the first ratio to obtain L. Substitute L into the calculation expression of the scattered light intensity to calculate the scattered light intensity corresponding to the geometric center of the detection point. In a laboratory temperature-controlled water bath (20±0.2℃), a standard Brillouin scattering instrument was used to establish the correspondence between the Brillouin linewidth and the scattered light intensity from 0 to 1200 MHz, and the correspondence was stored in the emission database.

[0057] The experimental data for obtaining the first Brillouin linewidth are as follows:

[0058] Example 3, the method for transforming the ZP coordinate system to a three-dimensional Cartesian coordinate system includes: the origin is the geometric center of the distribution range of the water area to be measured; the Z-axis is the direction along the water surface normal, and the positive direction of the Z-axis is the direction upward along the water surface normal; the P-axis is the projection of the main water flow direction in the horizontal plane, that is, the projection of the central axis of the water area in the horizontal plane, and the positive direction of the P-axis is the projection of the main water flow direction in the horizontal plane pointing downstream; the third axis is orthogonal to the P-axis in the horizontal plane, forming a right-handed coordinate system; The three-dimensional Cartesian coordinate system adopts the local NEU (North, East, Sky) system of the National Geodetic Coordinate System 2000 (CGCS2000). The positive direction of the X-axis is north; the positive direction of the Y-axis is east; and the positive direction of the Z-axis is sky. The transformation from the ZP coordinate system to the three-dimensional Cartesian coordinate system adopts the seven-parameter Bursa model. The transformation parameters are obtained by field measurement. A GNSS receiver is set up at the geometric center and continuously observed for 5 minutes to obtain the origin coordinates. The azimuth of the main direction is measured using a digital compass, thereby obtaining the unit vectors of the P axis, Z axis, and Q axis in the north-east plane. The calculation method for the second constraint condition includes finding the minimum sum of the pairwise straight-line distances between the three detection points to ensure the geometric strength of the spatial distribution of the three points. Given the coordinates of the three detection points in the three-dimensional Cartesian system, the distance between any two detection points is calculated using the Euclidean distance formula. Calculate the sum of the distances between the two detection points in each group, that is, the sum of the straight-line distances between the two coordinates, and set the second distance as the threshold of the sum of the straight-line distances between the two coordinates; Set the sum of the straight-line distances between two coordinates to be greater than or equal to the second distance as the second constraint condition.

[0059] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read Only Memory (EPROM), Programmable Red-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0060] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the protection scope of the present invention.

Claims

1. A pollution source detection method based on water environment management, characterized in that, The process includes the following steps: Step S100, select any three non-collinear locations as detection points, and the detection points satisfy the first constraint condition. At the detection points, based on the distribution range, emit a laser with a first Brillouin linewidth. Based on the feedback data of the laser, adjust the first Brillouin linewidth to obtain the spontaneous Brillouin linewidth of the detection point. The initial emission direction is represented as an arbitrary direction. The emission direction of the detection point is determined by selecting the emission direction corresponding to the energy of the largest reflected wave. The first constraint condition is expressed as follows: the sum of the Euclidean distances between the detection location and the geometric center point is greater than or equal to the first distance. Step S200: Based on the constructed pollutant diffusion model and the three-point positioning method, obtain the polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point, and construct a spatiotemporal model of pollutant concentration according to the polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point. Step S300: Calculate the coordinates of the new pollution source based on the spatiotemporal model of pollutant concentration.

2. The pollution source detection method based on water environment management as described in claim 1, characterized in that, Step S100 includes the following sub-steps: Step S101, select any water area to be detected and obtain the distribution range of the water area to be detected; Step S102: Obtain the geometric center point of the distribution range, select any three non-collinear positions as detection locations, and calculate the sum of the Euclidean distances between the detection locations and the geometric center point; Step S103: Jump to any monitoring point, and at that monitoring point, emit a laser with a first Brillouin linewidth, wherein the first Brillouin linewidth is matched based on the distribution range; Step S104: Receive feedback data from the water area, where the feedback data is represented as the Brillouin linewidth of the laser reflected back from the water area; Step S105: Calculate the saturation coefficient based on the feedback data and the first Brillouin linewidth, compare the saturation coefficient with the preset saturation threshold, and perform the first operation on the first Brillouin linewidth based on the comparison result. The first operation includes both control and non-control operations; When the first operation is a control operation, proceed to step S106; when the first operation is a non-control operation, proceed to step S107. Step S106: Continuously change the first Brillouin linewidth until the saturation corresponding to the continuously changed first Brillouin linewidth is greater than or equal to the preset saturation threshold, then jump to step S107. Step S107: Set the feedback data at this time to the self-published line width of the detection point; Step S108, repeat steps S101 to S107, to complete the self-released abyssal line width of each monitoring point in the water area to be tested.

3. The pollution source detection method based on water environment management as described in claim 2, characterized in that, Step S200 includes the following sub-steps: Step S201, set the pollution parameters of the pollution source, set the three-dimensional coordinates of the pollution source to (0, 0, 0), and transform the ZP coordinates of each grid of the distribution boundary and the coordinates of the detection points from the ZP coordinate system to the three-dimensional Cartesian coordinate system. Step S202: Decompose the pollution parameters of the pollution source vertically and horizontally in a three-dimensional Cartesian coordinate system. Step S203: Obtain the historical flow parameters of the water body, and based on the historical flow parameters of the water body, obtain the longitudinal average flow velocity and the transverse average flow velocity; Step S204: Based on the pollution parameters of the pollution sources after vertical decomposition, the pollution parameters of the pollution sources after horizontal decomposition, the longitudinal average flow velocity, and the horizontal average flow velocity, obtain the concentration change equation. Step S205: Construct a spatiotemporal model of pollutant concentration based on the concentration change equation.

4. The pollution source detection method based on water environment management as described in claim 3, characterized in that, Step S205 also includes the following sub-steps: Step S2051, construct boundary conditions based on the three-dimensional Cartesian coordinates of each grid of the distribution boundary. Step S2052: Solve the concentration change equation according to the boundary conditions to obtain the influence expression of the pollution source; Step S2053: Construct a spatiotemporal model of pollutant concentration based on the influence expression of the pollution source.

5. The pollution source detection method based on water environment management as described in claim 4, characterized in that, Obtain the coordinates of the detection point in a three-dimensional Cartesian coordinate system with any known origin. Calculate the straight-line distance between any two coordinates. Calculate the sum of the straight-line distances between the two coordinates. Set the sum of the straight-line distances between the two coordinates as the second constraint condition. Set the straight-line distance between any two coordinates as the third constraint condition. Obtain the three-dimensional Cartesian coordinates of the detection point. The three-dimensional Cartesian coordinates of the detection point are calculated using the three-dimensional distance formula. Construct a system of equations based on the three-dimensional distance formula, the third constraint condition, and the second constraint condition to obtain the polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point. The polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point includes polynomial expressions for the x-coordinate, y-coordinate, and z-coordinate.

6. The pollution source detection method based on water environment management as described in claim 5, characterized in that, The current pollutant concentrations at three monitoring points are obtained. The method for obtaining the current pollutant concentrations at the three monitoring points includes matching the volume viscosity coefficient and shear viscosity coefficient of the corresponding water body with the corresponding self-published water body linewidth, and matching the corresponding pollutant concentrations based on the volume viscosity coefficient and shear viscosity coefficient of the water body.

7. The pollution source detection method based on water environment management as described in claim 6, characterized in that, The method for obtaining the current pollutant concentration at the three monitoring points is to inversely deduce the three-dimensional Cartesian coordinates of the pollution source based on the current pollutant concentration at the monitoring point and the polynomial corresponding to the three-dimensional Cartesian coordinates of the monitoring point. The three-dimensional Cartesian coordinate inverse method for pollution sources includes inputting the current pollutant concentration at the detection point into the spatiotemporal model of pollutant concentration to obtain the corresponding C value, calculating the average value of the C value, and setting the average value of the C value as the pollutant concentration of the pollution source.

8. The pollution source detection method based on water environment management as described in claim 7, characterized in that, The pollution source coordinates are continuously moved from (0, 0, 0), and during the continuous movement, the x and y coordinates of the pollution source coordinates are always distributed in the three-dimensional Cartesian coordinates of each grid on the distribution boundary, which is represented as greater than or equal to the corresponding minimum value and less than or equal to the corresponding maximum value. Obtain the C value corresponding to the coordinates of the pollution source after any movement, and calculate the difference between the C value and the average of the C values ​​to obtain the pollution concentration difference.

9. The pollution source detection method based on water environment management as described in claim 8, characterized in that, Iterate through all pollution concentration differences, select the pollution concentration difference with the smallest value, set the three-dimensional Cartesian coordinates corresponding to the pollution concentration difference with the smallest value as the coordinates of the new pollution source, and replace the three-dimensional Cartesian coordinates of the original pollution source with the coordinates of the new pollution source. Output the coordinates of the new pollution source.

10. A pollution source detection system based on water environment management, the system being used to execute the pollution source detection method based on water environment management as described in claim 1, characterized in that, It includes a detection module, a calculation module, and a positioning module; The detection module selects any three non-collinear positions as detection locations, and the detection locations satisfy the first constraint condition. At the detection points, combined with the distribution range, a laser with a first Brillouin linewidth is emitted. Based on the feedback data of the laser, the first Brillouin linewidth is adjusted to obtain the spontaneous Brillouin linewidth of the detection point. The calculation module obtains the polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point based on the constructed pollutant diffusion model and the three-point positioning method. The positioning module moves the coordinates of the pollution source based on the polynomial corresponding to the three-dimensional Cartesian coordinates of the detection point and the pollutant parameters of the current detection point, and obtains the new coordinates of the pollution source.

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