River pollutant tracing system and method based on digital twinning

Abstract

The invention relates to the technical field of water pollution traceability, and particularly discloses a river pollutant traceability system and method based on digital twinning, and the system comprises a water body sampling module which is used for obtaining the current water quality data of a preset region of a target river; the model construction module is used for constructing a digital twinborn model according to the river topographic data and the current water quality data and by fusing the historical hydrological data, the real-time meteorological data and the drain outlet distribution topological graph; the data analysis module is used for performing spatial-temporal feature extraction on a pollutant diffusion path in the digital twinborn model by utilizing a graph convolutional neural network, applying dynamic correction pollution source position probability distribution in combination with Bayesian inference, and generating a traceability path thermodynamic diagram; and the pollution traceability module is used for obtaining a pollutant traceability result according to the confidence coefficient threshold of the traceability path thermodynamic diagram. According to the method, the efficiency and the accuracy of tracing the river pollutants in the complex dynamic environment can be remarkably improved, and technical support is provided for ecological safety and precise treatment of a drainage basin.

Description

Background Art

[0002] With the rapid development of industrialization and urbanization, the problem of water environmental pollution in rivers has become increasingly severe, and sudden pollutant discharge incidents occur frequently, posing a major threat to the ecological security of the basin and the health of residents. How to quickly and accurately identify pollution sources and take targeted treatment measures has become one of the core challenges in environmental monitoring and management. However, traditional river pollutant tracing technologies still have many limitations and are difficult to meet the efficient treatment requirements in complex dynamic environments.

[0003] The current mainstream technical means mainly include chemical analysis methods based on manual sampling, numerical simulation prediction methods, and fixed sensor monitoring methods. Chemical analysis methods rely on on-site sampling and laboratory testing, which are time-consuming and have limited spatial coverage, making it difficult to achieve real-time response to pollution incidents. Although numerical simulation prediction methods can simulate the diffusion path of pollutants, they rely on static parameter input (such as fixed hydrological and meteorological data) and are difficult to dynamically couple real-time monitoring data, resulting in the model accuracy being limited by initial conditions and boundary assumptions. In addition, the fixed sensor monitoring method mostly adopts an isolated deployment mode, with strong data heterogeneity, insufficient spatio-temporal resolution, and a lack of a deep fusion mechanism for multi-dimensional data, which is likely to cause uncertainty in the tracing results.

[0004] Therefore, there is an urgent need to provide a technical solution to solve the above problems. Summary of the Invention

[0005] To solve the above technical problems, the present invention provides a river pollutant tracing system and method based on digital twin.

[0006] In the first aspect, the present invention provides a river pollutant tracing system based on digital twin, and the technical solution of the system is as follows:

[0007] A water sampling module, configured to obtain the current water quality data of a preset area of a target river;

[0008] A model construction module, configured to construct a digital twin model of the target river according to the river channel terrain data and the current water quality data of the target river, and integrate historical hydrological data, real-time meteorological data, and the topological map of the sewage outlet distribution;

[0009] A data analysis module, configured to use a graph convolutional neural network to extract spatio-temporal features of the pollutant diffusion path in the digital twin model, and combine Bayesian inference to dynamically correct the probability distribution of the pollution source location to generate a tracing path heat map;

[0010] A pollution tracing module, configured to obtain the pollutant tracing result of the target river according to the confidence threshold of the tracing path heat map.

[0011] Further, the preset area includes: a pollution anomaly area and a key river node; the current water quality data includes: pollutant concentration, pH value, and dissolved oxygen data.

[0012] Further, it further includes: a drone remote sensing module; the drone remote sensing module is used for:

[0013] Using a hyperspectral imager to determine the spectral characteristics of pollutants based on spectral reflectance differences, and synchronously capturing the river flow field characteristics and river terrain data through a multispectral camera;

[0014] Based on the spatial overlay analysis of the pollutant spectral characteristics and the river flow field characteristics, real-time marking the longitude and latitude coordinates representing pollution anomalies to obtain the pollution anomaly area.

[0015] Further, the model construction module is specifically used for:

[0016] Performing spatial grid processing on the river terrain data and the sewage outlet distribution topological map to generate a three-dimensional spatial grid structure of the river;

[0017] Initializing the river cross-section flow rate and pollutant diffusion coefficient of the three-dimensional spatial grid structure based on the historical hydrological data, and coupling the hydrodynamic equation and the pollutant transport equation to construct a pollutant diffusion model;

[0018] Injecting the real-time meteorological data and the current water quality data into the pollutant diffusion model through a spatio-temporal data synchronization engine, dynamically correcting the river cross-section flow rate, pollutant diffusion coefficient, and instantaneous sewage discharge at the sewage outlet in the pollutant diffusion model to obtain the digital twin model and dynamically generating the pollutant diffusion path.

[0019] Further, the pollutant tracing result includes: the candidate location of the pollution source and the pollution diffusion prediction trend; the pollution tracing module is specifically used for:

[0020] Taking the area exceeding the confidence threshold in the tracing path heat map as the candidate location of the pollution source, and generating the pollution diffusion prediction trend including the spatio-temporal distribution map of pollutant concentration and the evolution curve of the influence range based on the time evolution parameters of the pollutant transport equation.

[0021] Further, it further includes: a visualization module; the visualization module is used for:

[0022] Outputting the pollutant tracing result to a target terminal for display.

[0023] In a second aspect, the present invention provides a method for tracing river pollutants based on digital twin, and the technical solution of this method is as follows:

[0024] Obtaining the current water quality data of the preset area of the target river;

[0025] Construct a digital twin model of the target river based on the river channel terrain data and the current water quality data of the target river, and integrate historical hydrological data, real-time meteorological data, and the topological map of the sewage outlet distribution.

[0026] Use a graph convolutional neural network to extract spatio-temporal features of the pollutant diffusion path in the digital twin model, and combine Bayesian inference to dynamically correct the probability distribution of the pollution source location to generate a heat map of the tracing path.

[0027] Obtain the pollutant tracing result of the target river according to the confidence threshold of the heat map of the tracing path.

[0028] Furthermore, the preset area includes: a pollution anomaly area and key river channel nodes; the current water quality data includes: pollutant concentration, pH value, and dissolved oxygen data.

[0029] Furthermore, it further includes:

[0030] Use a hyperspectral imager to determine the spectral characteristics of pollutants according to the spectral reflectance difference, and simultaneously capture the river channel flow field characteristics and river channel terrain data through a multispectral camera.

[0031] Based on the spatial overlay analysis of the boundary of the anomaly area and the river channel flow field characteristics, real-time mark the longitude and latitude coordinates representing the pollution anomaly to obtain the pollution anomaly area.

[0032] In a third aspect, the technical solution of an electronic device of the present invention is as follows:

[0033] It includes a memory, a processor, and a program stored on the memory and running on the processor. When the processor executes the program, it implements the steps of the method for tracing river pollutants based on digital twin of the present invention.

[0034] In a fourth aspect, the technical solution of a computer-readable storage medium provided by the present invention is as follows:

[0035] Instructions are stored in the computer-readable storage medium. When the computer-readable storage medium reads the instructions, the computer-readable storage medium executes the steps of the method for tracing river pollutants based on digital twin of the present invention.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0037] The technical solution of the present invention realizes real-time response, dynamic correction, and visual tracing of pollution sources, significantly improves the efficiency and accuracy of pollution source identification in a complex dynamic environment, and provides technical support for basin ecological security and precise governance.

[0038] Other advantages, objects and features of the present invention will be set forth in part in the following description, and in part will be obvious to those skilled in the art upon examination of the following, or may be learned from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings based on these drawings without creative efforts.

[0040] Figure 1 FIG. is a schematic structural diagram of an embodiment of a river pollutant tracing system based on digital twin of the present invention;

[0041] Figure 2 FIG. is a schematic flow diagram of an embodiment of a river pollutant tracing method based on digital twin of the present invention;

[0042] Figure 3 FIG. is a schematic structural diagram of an embodiment of an electronic device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] The following will clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0044] Figure 1 FIG. shows a schematic structural diagram of an embodiment of a river pollutant tracing system based on digital twin provided by the present invention. As Figure 1 shown, the system 100 includes:

[0045] A water sampling module 110 for obtaining current water quality data of a preset area of a target river.

[0046] Wherein, the target river is the river for which pollutant tracing needs to be carried out in this embodiment. The preset area includes: a pollution abnormal area and a key river node. A key river node refers to a sensitive area based on historical pollution event statistics and pollutant diffusion model prediction, specifically including a river return port, a confluence of tributaries, and a cross-section downstream of a sewage outlet. The current water quality data includes: pollutant concentration, pH value, and dissolved oxygen data. The water quality data of different areas (nodes) of the target river is different.

[0047] It should be noted that the water body sampling module 110 is configured with an autonomous navigation unit and a water quality sensor. The autonomous navigation unit generates a dynamic sampling path based on the coordinate positions of the preset area and in combination with the river channel terrain data, and controls the movement of the sampling device (water quality sensor). Each time the water quality sensor reaches a preset area, it triggers fixed-point water sample collection to obtain the current water quality data.

[0048] The model construction module 120 is used to construct a digital twin model of the target river according to the river channel terrain data and the current water quality data of the target river, and by integrating historical hydrological data, real-time meteorological data, and the sewage outlet distribution topology map.

[0049] Among them, the historical hydrological data can be obtained through a hydrological database or obtained by satellite remote sensing inversion. The sewage outlet distribution topology map includes: the geographical location of the sewage outlet, the discharge type (industrial / domestic sewage), the discharge volume statistics, the discharge time rule, etc. The sewage outlet distribution topology map is generated by using a visible light camera carried by an unmanned aerial vehicle to conduct an aerial survey of the river channel and combining a deep learning algorithm (such as YOLO) to identify the positions of the sewage outlets.

[0050] In an optional manner, the model construction module 120 is specifically used for:

[0051] Perform spatial grid processing on the river channel terrain data and the sewage outlet distribution topology map to generate a three-dimensional spatial grid structure of the river channel.

[0052] Among them, the three-dimensional spatial grid structure refers to including the geometric coordinates (x, y, z) of each grid unit, the topological connection relationship, and the sewage outlet marking information.

[0053] Specifically, the Delaunay triangulation algorithm or a finite element mesh generation tool (such as Gmsh) is used to divide the river channel into irregular triangular / tetrahedral grid units according to the river channel terrain data to ensure accurate fitting of complex terrains; map the position of each sewage outlet in the sewage outlet distribution topology map to the nearest grid node, and assign an initial discharge parameter (based on historical data or default value), and finally obtain the three-dimensional spatial grid structure of the river channel.

[0054] Initialize the river channel cross-section flow rate and pollutant diffusion coefficient of the three-dimensional spatial grid structure based on the historical hydrological data, and couple the hydrodynamic equation and the pollutant transport equation to construct a pollutant diffusion model.

[0055] Specifically, the cross-sectional average velocity recorded according to historical hydrological data is distributed to each grid cell of the three-dimensional spatial grid structure by spatial interpolation method to initialize the channel cross-sectional flow. Based on historical pollutant concentration monitoring data, the diffusion coefficient is calculated through an empirical formula and assigned according to the distribution of the river depth gradient to initialize the pollutant diffusion coefficient. The hydrodynamic equation uses the Saint-Venant equations to describe the river flow movement, and the pollutant transport equation uses the convection-diffusion equation to describe the pollutant migration. The pollutant diffusion model is used to dynamically simulate the migration, diffusion and transformation process of pollutants in the river by coupling physical mechanisms and real-time data, that is: the pollutant diffusion model is generated by coupling the hydrodynamic equation and the pollutant transport equation.

[0056] It should be noted that the hydrodynamic equation is: Q represents the channel cross-sectional flow, h represents the water level, A represents the cross-sectional area of the flow, S f represents the friction slope, g represents the acceleration due to gravity, and the hydrodynamic equation is used to calculate the flow velocity field, providing a carrier for pollutant migration. The pollutant transport equation is: u represents the flow velocity, D represents the pollutant diffusion coefficient, S represents the instantaneous pollutant discharge at the pollutant outlet, and the pollutant transport equation is used to simulate the combined effects of pollutant migration (convection) with the flow, natural diffusion and external input.

[0057] The real-time meteorological data and the current water quality data are injected into the pollutant diffusion model through the spatio-temporal data synchronization engine, dynamically correcting the channel cross-sectional flow, pollutant diffusion coefficient and instantaneous pollutant discharge at the pollutant outlet in the pollutant diffusion model, obtaining the digital twin model and dynamically generating the pollutant diffusion path.

[0058] Specifically: ① The real-time meteorological data is obtained through the API interface, including the wind speed v and rainfall R, and mapped to the three-dimensional grid of the river by spatial interpolation method; the current water quality data is the pollutant concentration C collected by the mobile device at the key nodes obs , and is associated with the corresponding grid cell through spatio-temporal indexing. ② The surface friction coefficient S is adjusted based on the wind speed v f , and the flow distribution is iteratively updated through the hydrodynamic equation to correct the channel cross-sectional flow Q; with the goal of minimizing the residual between C obs and the predicted concentration C sim of the model, the value of D is optimized by the gradient descent method to correct the pollutant diffusion coefficient D; if the downstream node of a sewage outlet satisfies C obs >C sim +3σ (σ is the standard deviation of historical concentration), then the instantaneous pollutant discharge S at the pollutant outlet is updated according to the Bayesian formula. ③ Only the local grid affected by the corrected parameters (such as the area within a radius of 50 meters around the pollutant outlet) is re-solved for the hydrodynamic equation and the pollutant transport equation to generate the updated pollutant concentration field C new , and C newThe spatio-temporal distribution, and output a dynamically updated pollutant diffusion path.

[0059] A data analysis module 130, configured to use a graph convolutional neural network to extract spatio-temporal features of the pollutant diffusion path in the digital twin model, and combine Bayesian inference to dynamically correct the probability distribution of the pollution source location, and generate a heat map of the traceability path.

[0060] Specifically: ① Map the pollutant diffusion path into a graph structure, where each node represents a river channel grid unit, and the node features include pollutant concentration, flow velocity, diffusion coefficient, and time stamp. The edge weights are calculated according to the water flow direction and distance between the grids. For example: w ij represents the edge weight between the i-th river channel grid unit and the j-th river channel grid unit, and d ij represents the Euclidean distance between the i-th river channel grid unit and the j-th river channel grid unit. ② Use a gated graph convolutional network for multi-layer message passing to output the spatio-temporal feature vector of each node (river channel grid unit). ③ Based on the spatio-temporal feature vector output by the gated graph convolutional network, generate an initial pollution source probability distribution P prior (x); Assume that the pollutant concentration C obs follows a Gaussian distribution N(C sim ,σ 2 ); Use Markov chain Monte Carlo (MCMC) sampling to iteratively update the posterior probability: P posterior (x) ∝ P(C obs |x)·P prior (x). ④ Normalize the posterior probability to the 0-1 interval, and render it to the three-dimensional river channel grid through color mapping (for example: red → high probability, blue → low probability) to generate a heat map of the traceability path.

[0061] It should be noted that the above process updates the posterior distribution of the pollution source location through Bayesian inference, which belongs to the problems of probability modeling and parameter estimation, rather than aiming to optimize the objective function. The above technical solution retains the spatial topology and time evolution correlation through a graph structure, and solves the limitation of traditional convolutional neural networks in modeling irregular river channels; the Bayesian framework fuses physical model prediction and real-time data to reduce the misjudgment rate caused by multi-source interference.

[0062] A pollution traceability module 140, configured to obtain the pollutant traceability result of the target river according to the confidence threshold of the heat map of the traceability path.

[0063] Among them, the pollutant traceability result includes: the candidate location of the pollution source and the predicted trend of pollution diffusion. The confidence threshold can be set according to the actual situation, such as 80%, and there is no limit here.

[0064] In an alternative approach, the pollution source tracing module 140 is specifically configured to:

[0065] Regard the areas in the heat map of the tracing path that exceed the confidence threshold as candidate positions of pollution sources, and generate the predicted pollution diffusion trend including the spatio-temporal distribution map of pollutant concentration and the evolution curve of the influence range based on the time evolution parameters of the pollutant transport equation.

[0066] Specifically: ① Extract all grid cells that exceed the confidence threshold from the heat map of the tracing path according to the confidence threshold. ② Use the DBSCAN algorithm to cluster the continuously high-probability regions (grid cells), and the cluster center is the candidate position of the pollution source. ③ Based on the pollutant transport equation, set the prediction time window, inject real-time meteorological forecast data (wind speed, rainfall) as boundary conditions, run the digital twin model, and output the concentration field at each time step. ④ Extract the areas where the concentration exceeds the safety standard to generate the spatio-temporal distribution map. ⑤ Statistically calculate the pollution area and the maximum influence distance at each time step, plot the curves of the pollution area and the maximum influence distance at the time step, mark the inflection points (such as the pollution peak time), and obtain the predicted pollution diffusion trend.

[0067] In an alternative approach, it further includes: a drone remote sensing module. The drone remote sensing module is equipped with a hyperspectral imager and a multispectral camera for cruising along the target river; the drone remote sensing module is used to:

[0068] Use the hyperspectral imager to determine the spectral characteristics of pollutants based on the spectral reflectance difference, and simultaneously capture the river flow field characteristics and river channel terrain data through the multispectral camera.

[0069] Among them, the specific steps of using the hyperspectral imager to determine the spectral characteristics of pollutants based on the spectral reflectance difference include: ① The hyperspectral imager continuously scans the river surface with a nanometer-level spectral resolution (such as 5 nm) to obtain hyperspectral cube data (spatial × spectral dimension) in the 400 - 2500 nm band. ② Calculate the spectral reflectance difference: ΔR(λ) = R target (λ) - R background (λ); ΔR(λ) represents the spectral reflectance difference at wavelength λ, which characterizes the reflectance difference between the target area and the background area in a specific band and is used to identify the abnormal reflection characteristics caused by pollutants. R target (λ) represents the spectral reflectance of the target area (suspected pollution area) at wavelength λ. R background (λ) represents the spectral reflectance of the background area (clean water body or non-polluted area) at wavelength λ. ③ Match the spectral library according to the spectral reflectance difference (such as the characteristic absorption peaks of heavy metals and petroleum pollutants) to determine the pollutant type and obtain the spectral characteristics of the pollutants.

[0070] Based on the spatial superposition analysis of the pollutant spectral characteristics and the river channel flow field characteristics, the longitude and latitude coordinates characterizing pollution anomalies are marked in real time to obtain the pollution anomaly area.

[0071] Specifically: ① Convert the pollutant spectral characteristics and the river channel flow field characteristics to the same geographic coordinate system, and ensure that the timestamp deviation between the spectral and flow field data is < 1 second through GPS synchronization. ② Starting from the boundary vertices of the abnormal area corresponding to the pollutant spectral characteristics, trace the possible source paths of the pollutants along the reverse flow direction of the flow field vector, and simulate the reverse movement trajectory of the particles. ③ For the grid cells passed by the tracing path, assign weights according to the following rules: the larger the absolute value of ΔR(λ), the higher the weight; if the flow direction is consistent with the tracing path direction, the weight increases. ④ Perform spatial clustering on the weight values (such as hot spot analysis Getis-Ord Gi* algorithm), identify significantly high-weight areas, extract the longitude and latitude of the geometric center points of the hot spot areas as the coordinates of the pollution anomaly area; if the hot spot areas are multiple discrete points, take the top N points with the highest weights (such as N = 5), and finally obtain the pollution anomaly area.

[0072] In an alternative manner, it further includes: a visualization module; the visualization module is used for:

[0073] Output the pollutant traceability result to the target terminal for display.

[0074] Wherein, the target terminal can be a visualization device such as a mobile phone, a computer, a tablet, and a smart bracelet, etc., without limitation here.

[0075] This embodiment aims at the limitations of traditional river pollutant traceability technologies, and proposes a river pollutant traceability system based on digital twin. The technical solution of this embodiment constructs a digital twin model by integrating multi-source data, extracts spatio-temporal features using a graph convolutional neural network, and combines Bayesian inference to apply dynamic correction to the probability distribution of the pollution source location, generating a high-precision traceability path heat map. This system breaks through the problems of long time consumption, limited accuracy, and strong data heterogeneity in the traditional method, realizes real-time response, dynamic correction, and visualization traceability of pollution sources, significantly improves the efficiency and accuracy of pollution source identification in complex dynamic environments, and provides technical support for basin ecological security and precise governance.

[0076] Figure 2 The flowchart of an embodiment of a river pollutant traceability method based on digital twin provided by the present invention is shown. As Figure 2 shown, the method includes the following steps:

[0077] S1. Obtain the current water quality data of a preset area of the target river;

[0078] S2. Constructing a digital twin model of the target river based on the river terrain data of the target river and the current water quality data, and integrating historical hydrological data, real-time meteorological data and a sewage outlet distribution topology map;

[0079] S3. Using a graph convolutional neural network to extract spatiotemporal features of the pollutant diffusion path in the digital twin model, dynamically correcting the probability distribution of the pollution source location in combination with Bayesian inference, and generating a heat map of the traceability path;

[0080] S4. Obtain the pollutant source tracing result of the target river according to the confidence threshold of the tracing path heat map.

[0081] In an optional manner, the preset area includes: abnormal pollution areas and key nodes of the river; the current water quality data includes: pollutant concentration, pH value and dissolved oxygen data.

[0082] In an optional manner, the method further includes:

[0083] Use a hyperspectral imager to determine the spectral characteristics of pollutants based on spectral reflectance differences, and use a multispectral camera to simultaneously capture river flow characteristics and river topography data;

[0084] Based on the spatial superposition analysis of the pollutant spectral characteristics and the river flow field characteristics, the longitude and latitude coordinates representing the pollution anomaly are marked in real time to obtain the pollution anomaly area.

[0085] In an optional manner, the step of constructing a digital twin model of the target river based on the river terrain data of the target river and the current water quality data, and integrating historical hydrological data, real-time meteorological data and a sewage outlet distribution topology map, includes:

[0086] Performing spatial grid processing on the river channel topography data and the sewage outlet distribution topology map to generate a three-dimensional spatial grid structure of the river channel;

[0087] Initialize the river cross-sectional flow and pollutant diffusion coefficient of the three-dimensional spatial grid structure based on the historical hydrological data, and couple the hydrodynamic equation with the pollutant transport equation to construct a pollutant diffusion model;

[0088] The real-time meteorological data and the current water quality data are injected into the pollutant diffusion model through the spatiotemporal data synchronization engine, and the river section flow, pollutant diffusion coefficient and instantaneous discharge of the sewage outlet in the pollutant diffusion model are dynamically corrected to obtain the digital twin model and dynamically generate the pollutant diffusion path.

[0089] In an alternative manner, the pollutant traceability result includes: candidate source positions and pollution diffusion prediction trends; the step of obtaining the pollutant traceability result of the target river according to the confidence threshold of the traceability path heat map includes:

[0090] Regarding the areas in the traceability path heat map that exceed the confidence threshold as candidate source positions, and generating the pollution diffusion prediction trend including the spatio-temporal distribution map of pollutant concentration and the evolution curve of the influence range based on the time evolution parameters of the pollutant transport equation.

[0091] In an alternative manner, it further includes:

[0092] Outputting the pollutant traceability result to a target terminal for display.

[0093] It should be noted that the beneficial effects of the above-described river pollutant traceability method based on digital twin are the same as those of the above-described river pollutant traceability system 100 based on digital twin, and will not be elaborated here. In addition, when the above-described system implements its functions, only the above-mentioned division of each functional module is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the system can be divided into different functional modules according to the actual situation to complete all or part of the functions described above. Additionally, the above-described system and method embodiments belong to the same concept, and the specific implementation process can be found in the method embodiments and will not be elaborated here.

[0094] Among them, the digital twin-based river pollutant traceability system of the present invention can be a computer program (including program code) running in a computer device. For example, the digital twin-based river pollutant traceability system of the present invention is an application software and can be used to execute the corresponding steps in the digital twin-based river pollutant traceability method of the present invention.

[0095] In some embodiments, the digital-twin-based river pollutant tracing system of the present invention can be implemented in a combination of software and hardware. As an example, the digital-twin-based river pollutant tracing system of the present invention can be a processor in the form of a hardware decoding processor, which is programmed to execute the digital-twin-based river pollutant tracing method of the present invention. For example, the processor in the form of a hardware decoding processor can employ one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), or other electronic components.

[0096] Among them, the modules involved in the embodiments of the present invention can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation on the module itself in some cases.

[0097] An electronic device according to an embodiment of the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the above-mentioned digital-twin-based river pollutant tracing method is implemented. That is to say, an electronic device according to an embodiment of the present invention may include, but is not limited to: a processor and a memory; the memory is used to store the computer program; the processor is used to execute the digital-twin-based river pollutant tracing method shown in any embodiment of the present invention by calling the computer program.

[0098] In an alternative embodiment, an electronic device is provided, as Figure 2 shown, Figure 2 the electronic device 4000 shown includes: a processor 4001 and a memory 4003. Among them, the processor 4001 and the memory 4003 are connected, such as connected through a bus 4002. Optionally, the electronic device 4000 may further include a transceiver 4004, and the transceiver 4004 can be used for data interaction between the electronic device and other electronic devices, such as data sending and / or data receiving, etc. It should be noted that in actual applications, the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present invention.

[0099] The processor 4001 can be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute various exemplary logical blocks, modules, and circuits described in connection with the disclosure of the present invention. The processor 4001 can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0100] The bus 4002 can include a path for transmitting information between the above components. The bus 4002 can be a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus 4002 can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 2 only a thick line is used in the figure to represent the bus 4002, but it does not mean that there is only one bus or one type of bus.

[0101] The memory 4003 can be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, or it can also be an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory), or other optical disc storage, optical disc storage (including compact discs, laser discs, optical discs, digital versatile discs, Blu-ray discs, etc.), magnetic disk storage media, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.

[0102] The memory 4003 is used to store the application program code (computer program) for executing the solution of the present invention, and is controlled by the processor 4001 for execution. The processor 4001 is used to execute the application program code stored in the memory 4003 to implement the content shown in the foregoing method embodiments.

[0103] Among them, the electronic device may also be a terminal device, and the terminal device may be any terminal device that can install an application and access a web page through the application, including at least one of a smart phone, a tablet computer, a notebook computer, a desktop computer, a smart speaker, a smart watch, a smart TV, and a smart vehicle device.

[0104] It should be noted that Figure 2 the illustrated electronic device is only an example, and should not impose any limitation on the functions and usage scope of the embodiments of the present invention.

[0105] A computer-readable storage medium according to an embodiment of the present invention has a computer program stored thereon, and when the computer program is executed by a processor, the above-mentioned river pollutant tracing method based on digital twin is implemented.

[0106] Optionally, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0107] In an exemplary embodiment, a computer program product or a computer program is further provided. The computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the electronic device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the electronic device executes the above-mentioned river pollutant tracing method based on digital twin.

[0108] It should be understood that the flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a portion of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0109] The computer-readable storage medium provided by the embodiments of the present invention may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0110] The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed by the electronic device, the electronic device is caused to execute the methods shown in the above embodiments.

[0111] The above description is only a preferred embodiment of the present invention and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of disclosure involved in the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, and should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above disclosure concept. For example, technical solutions formed by mutually replacing the above features with (but not limited to) technical features having similar functions disclosed in the present invention.

[0112] It should be noted that the terms "first", "second", etc. in the description and claims of this application are used to distinguish similar objects, rather than to limit a specific order or sequence. In appropriate cases, the order of use of similar objects can be interchanged so that the embodiments of the present application described herein can be implemented in an order other than the illustrated or described order.

[0113] Those skilled in the art know that the present invention can be implemented as a system, method, or computer program product. Therefore, the present invention can be specifically implemented in the following forms, namely: it can be completely hardware, can also be completely software (including firmware, resident software, microcode, etc.), and can also be in the form of a combination of hardware and software, which is generally referred to as "circuit", "module", or "system" herein. In addition, in some embodiments, the present invention can also be implemented in the form of a computer program product in one or more computer-readable media, which contains computer-readable program code.

[0114] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A river pollutant tracing system based on digital twin, characterized in that, Including: A water body sampling module for obtaining current water quality data of a preset area of a target river; A model construction module for constructing a digital twin model of the target river according to the river channel terrain data and the current water quality data of the target river, and integrating historical hydrological data, real-time meteorological data and the sewage outlet distribution topology map; A data analysis module for extracting spatio-temporal features of the pollutant diffusion path in the digital twin model by using a graph convolutional neural network, and dynamically correcting the probability distribution of the pollution source location by combining Bayesian inference to generate a heat map of the traceability path; A pollution traceability module for obtaining the pollutant traceability result of the target river according to the confidence threshold of the traceability path heat map.

2. The river pollutant tracing system based on digital twin according to claim 1, wherein, The preset area includes: a pollution anomaly area and key river channel nodes; the current water quality data includes: pollutant concentration, pH value and dissolved oxygen data.

3. The river pollutant tracing system based on digital twin according to claim 2, characterized in that, Also including: An unmanned aerial vehicle (UAV) remote sensing module; the UAV remote sensing module is used for: Using a hyperspectral imager to determine the spectral characteristics of pollutants according to the spectral reflectance difference, and synchronously capturing the river channel flow field characteristics and the river channel terrain data through a multispectral camera; Based on the spatial overlay analysis of the pollutant spectral characteristics and the river channel flow field characteristics, real-time marking the longitude and latitude coordinates representing pollution anomalies to obtain the pollution anomaly area.

4. The river pollutant tracing system based on digital twin according to claim 1, characterized in that, The model construction module is specifically used for: Performing spatial grid processing on the river channel terrain data and the sewage outlet distribution topology map to generate a three-dimensional spatial grid structure of the river channel; Initializing the river channel section flow rate and the pollutant diffusion coefficient of the three-dimensional spatial grid structure based on the historical hydrological data, and coupling the hydrodynamic equation and the pollutant transport equation to construct a pollutant diffusion model; Injecting the real-time meteorological data and the current water quality data into the pollutant diffusion model through a spatio-temporal data synchronization engine, and dynamically correcting the river channel section flow rate, the pollutant diffusion coefficient and the instantaneous sewage discharge amount at the sewage outlet in the pollutant diffusion model to obtain the digital twin model and dynamically generate the pollutant diffusion path.

5. The river pollutant tracing system based on digital twin according to claim 1, characterized in that, The pollutant traceability result includes: candidate pollution source locations and pollution diffusion prediction trends; the pollution traceability module is specifically used for: Taking the area exceeding the confidence threshold in the traceability path heat map as the candidate pollution source location, and generating the pollution diffusion prediction trend including the spatio-temporal distribution map of pollutant concentration and the evolution curve of the influence range based on the time evolution parameters of the pollutant transport equation.

6. The digital-twin-based river pollutant tracing system according to any one of claims 1 to 5, characterized in that, Also including: A visualization module; the visualization module is used for: Outputting the pollutant traceability result to a target terminal for display.

7. A method for tracing the source of river pollutants based on digital twins, characterized in that, Including: Obtaining current water quality data of a preset area of a target river; Constructing a digital twin model of the target river according to the river channel terrain data and the current water quality data of the target river, and integrating historical hydrological data, real-time meteorological data and the sewage outlet distribution topology map; Extracting spatio-temporal features of the pollutant diffusion path in the digital twin model by using a graph convolutional neural network, and dynamically correcting the probability distribution of the pollution source location by combining Bayesian inference to generate a heat map of the traceability path; Obtaining the pollutant traceability result of the target river according to the confidence threshold of the traceability path heat map.

8. The method for tracing the source of river pollutants based on digital twins according to claim 7, wherein The preset area includes: a pollution anomaly area and a key river node; the current water quality data includes: pollutant concentration, pH value, and dissolved oxygen data.

9. An electronic device, characterized in that, The electronic device includes a processor, the processor is coupled to a memory, and at least one computer program is stored in the memory. The at least one computer program is loaded and executed by the processor so that the electronic device implements the digital twin-based river pollutant tracing method as claimed in claim 7 or 8.

10. A computer-readable storage medium, characterized in that, At least one computer program is stored in the computer-readable storage medium. The at least one computer program is loaded and executed by a processor so that the computer-readable storage medium implements the digital twin-based river pollutant tracing method as claimed in claim 7 or 8.

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