Underground water pollution on-line monitoring method and device

Abstract

The invention relates to the technical field of underground water detection, in particular to an online monitoring method and device for underground water pollution. Establishing a quantization environment model of the underground water system based on the multi-source heterogeneous environment data of the target area; mapping the time sequence monitoring data of each monitoring point into a quantum state sequence in a high-dimensional Hilbert space to obtain environment observation input of each monitoring point; inputting the environment observation of each monitoring point into the quantization environment model, and generating quantum action modal data by using quantum gate operation; and integrating the monitored quantum action modal data of each target area, constructing a cross-regional groundwater pollution knowledge graph, and transmitting the knowledge graph to a preset terminal for display. According to the invention, comprehensive and visual decision support is provided for global pollution monitoring and treatment, efficient and accurate online monitoring and early warning are realized, and powerful technical support is provided for groundwater pollution prevention and control.

Description

Technical Field

[0001] The present invention relates to the technical field of groundwater detection, and particularly to an online monitoring method and device for groundwater pollution. Background Art

[0002] Groundwater pollution is an increasingly serious environmental problem globally. Its concealment, complexity, and long-term nature make it difficult for traditional monitoring methods to meet the requirements of precision and real-time. Current groundwater pollution monitoring technologies mainly rely on physicochemical sensors and numerical simulation methods. Although these methods can provide certain data support, they have significant limitations in dealing with multi-source heterogeneous environmental data. Firstly, the layout of traditional sensor networks is fixed and difficult to dynamically adapt to the complex changes in pollution diffusion, resulting in low monitoring efficiency. Secondly, numerical simulation methods have high computational complexity and limited accuracy when dealing with heterogeneous formations and the interaction of multiple pollutants, making it difficult to achieve real-time online monitoring. In addition, due to data island and heterogeneity problems in cross-regional groundwater pollution monitoring, it is difficult to construct a global pollution distribution model, further restricting the accurate identification and tracking of pollution sources. In recent years, the rapid development of quantum computing and quantum machine learning technologies has provided new ideas for solving the above problems. Quantum computing can efficiently process high-dimensional non-linear problems through the characteristics of quantum superposition and quantum entanglement, while quantum machine learning significantly improves the ability of data processing and model optimization through quantum state encoding and quantum gate operations. However, the existing technologies have not fully combined quantum computing with the actual needs of groundwater pollution monitoring, lacking an online monitoring method that can integrate multi-source heterogeneous environmental data and achieve reverse identification of pollution sources and prediction of diffusion paths. Therefore, there is an urgent need to develop an online monitoring method for groundwater pollution based on quantum reinforcement learning to break through the limitations of traditional technologies and achieve accurate and real-time pollution monitoring and early warning. Summary of the Invention

[0003] The present invention overcomes the deficiencies of the prior art and provides an online monitoring method and device for groundwater pollution.

[0004] To achieve the above object, the technical solution adopted by the present invention is as follows:

[0005] In the first aspect of the present invention, an online monitoring method for groundwater pollution is disclosed, including the following steps:

[0006] Establish a quantum environment model of the groundwater system based on multi-source heterogeneous environmental data of the target area;

[0007] Map the time-series monitoring data of each monitoring point into a quantum state sequence in a high-dimensional Hilbert space to obtain the environmental observation input of each monitoring point;

[0008] Input the environmental observations of each monitoring point into the quantized environmental model, and generate quantum action mode data by using quantum gate operations; the quantum action mode data includes the coordinates of potential pollution sources and the diffusion path cloud map;

[0009] Integrate the quantum action mode data monitored in each target area to construct a cross-regional groundwater pollution knowledge graph, and transmit the knowledge graph to a preset terminal for display.

[0010] Preferably, a quantized environmental model of the groundwater system is established based on multi-source heterogeneous environmental data of the target area, specifically:

[0011] Collect multi-source heterogeneous environmental data of the target area, including historical geological parameters, historical hydrological parameters and historical pollution data, and perform normalization preprocessing;

[0012] Map the normalized data to quantum states in a high-dimensional Hilbert space through a quantum encoder, where historical geological parameters are encoded as the ground state of quantum bits, historical hydrological parameters are encoded as quantum superposition states, and historical pollution data are encoded as quantum entanglement states;

[0013] If the historical geological parameters show heterogeneity, introduce a quantum annealing optimizer to solve the permeability coefficient distribution of the heterogeneous aquifer, and construct a quantum circuit to simulate the groundwater flow equation;

[0014] If the historical hydrological parameters show a multi-directional flow field, decompose the frequency components of the flow field through quantum Fourier transform, and construct the dynamic evolution of the pollutant transport equation;

[0015] If the historical pollution data indicates the existence of multiple pollution sources, establish a correlation model between the pollution sources through the quantum entanglement mechanism;

[0016] Simulate the dynamic evolution of the groundwater flow equation and the pollutant transport equation through a quantum circuit, and construct an environmental state space in the high-dimensional Hilbert space in combination with the quantum state;

[0017] Convert the quantum state information in the environmental state space into a classical probability distribution, and map it into the correlation model between pollution sources to generate a quantized environmental model including the positions of pollution sources, diffusion paths and interaction relationships.

[0018] Preferably, map the time-series monitoring data of each monitoring point to a quantum state sequence in a high-dimensional Hilbert space to obtain the environmental observation input of each monitoring point, specifically:

[0019] Real-time collect the time-series monitoring data of each monitoring point, including pollutant concentration, pH value and flow velocity data;

[0020] Divide the time-series monitoring data into fixed-time windows, with the data within each window serving as an observation unit, and perform normalization processing on each observation unit;

[0021] Input the normalized observation units into a quantum encoder, and map them to a quantum state sequence in a high-dimensional Hilbert space through quantum gate operations;

[0022] Perform quantum measurement operations on the generated quantum state sequence, extract its probability amplitude information and generate classical observation inputs. If the measurement results meet the preset quantum state fidelity threshold, it is determined that the mapping is completed; otherwise, adjust the quantum encoder parameters and re-execute the mapping process;

[0023] Align the mapped quantum state sequence with the environmental state space to obtain the environmental observation inputs for each monitoring point.

[0024] Preferably, input the environmental observation inputs of each monitoring point into the quantumized environmental model, and use quantum gate operations to generate quantum action mode data, specifically:

[0025] Map the environmental observation inputs of each monitoring point to quantum states in a high-dimensional Hilbert space through a quantum encoder, and align them with the environmental state space in the quantumized environmental model;

[0026] Embed the quantum phase estimation algorithm in the quantum circuit in the quantumized environmental model, simulate the evolution of pollutant concentration in time and space, and generate quantum state interference fringes as environmental observation inputs;

[0027] When the quantum state interference fringes show non-linear distortion, it is determined that an eigenvalue anomaly occurs; if the eigenvalue anomaly occurs at the same coordinate point in three consecutive identification periods, it is determined that the corresponding coordinate point is a potential pollution source, and the coordinates of the potential pollution source are obtained;

[0028] Based on the obtained coordinates of the potential pollution source, initialize the quantum state of the potential pollution source coordinate position in the quantum circuit, and map the potential pollution source coordinate position to the frequency domain through quantum Fourier transform;

[0029] If the hydrological parameters show that the water flow field at the coordinates of the potential pollution source is a unidirectional flow, simulate the diffusion process of pollutants along the flow direction through quantum rotation gates to construct a single-path evolution operator for pollutant diffusion dynamics; if the water flow field is a multi-directional flow, simulate the multi-path diffusion behavior of pollutants through quantum entanglement gates to construct a multi-path evolution operator for pollutant diffusion dynamics;

[0030] Based on the pollutant diffusion dynamics single-path evolution operator or the pollutant diffusion dynamics multi-path evolution operator, perform a flow direction feature extraction operation on the quantum state interference fringes in the quantized environmental model, extract the flow direction distribution information of the quantum state interference fringes, and combine the flow direction distribution information with the geographic information system to generate a diffusion path cloud map of potential pollution sources.

[0031] Preferably, embed the quantum phase estimation algorithm in the quantum circuit in the quantized environmental model to simulate the evolution of pollutant concentration in time and space, and generate quantum state interference fringes as the input of environmental observation, specifically:

[0032] Initialize the quantum phase estimation algorithm in the quantum circuit in the quantized environmental model, encode the evolution equation of pollutant concentration in time and space as a sequence of quantum phase gates, where the time evolution parameter is adjusted by the quantum rotation gate, and the space evolution parameter is configured by the quantum entanglement gate;

[0033] Map the environmental observation input of each monitoring point to a quantum state in a high-dimensional Hilbert space through a quantum encoder and input it into the quantum circuit;

[0034] Start the quantum phase estimation algorithm to simulate the evolution process of pollutant concentration in time and space, where: if the change in pollutant concentration gradient is greater than the preset gradient change threshold, increase the rotation angle of the quantum phase gate by a preset increment to capture the fast evolution feature, if the change in pollutant concentration gradient is not greater than the preset gradient change threshold, then reduce the rotation angle of the quantum phase gate by a preset decrement to improve the calculation efficiency;

[0035] Perform a quantum interference operation on the evolved quantum state to generate quantum state interference fringes. If the clarity of the quantum state interference fringes is lower than the preset clarity threshold, readjust the quantum phase gate parameters and iterate the simulation process.

[0036] Preferably, integrate the quantum action mode data monitored in each target area to construct a cross-regional groundwater pollution knowledge graph, specifically:

[0037] Receive the quantum action mode data monitored in each target area, and integrate the quantum action mode data across regions through the quantum federated learning framework;

[0038] Based on the integrated quantum action mode data, construct a cross-regional groundwater pollution knowledge graph, where the pollution source coordinates are used as nodes, the diffusion paths are used as edges, and the pollutant concentration is used as a node attribute. If there are isolated nodes or broken edges in the graph, they are complemented through the interpolation algorithm;

[0039] Perform quantum measurement operations on the knowledge graph, extract its topological structure and attribute information, and convert it into classical graph data. If the measurement result shows that the connectivity of the graph is lower than the preset threshold, readjust the parameters of the federated learning framework and iterate the integration process;

[0040] Combine the classical graph data with the geographic information system to generate a visualized cross-regional groundwater pollution knowledge graph.

[0041] The second aspect of the present invention discloses an on-line groundwater pollution monitoring device, which includes a memory and a processor. The memory stores an on-line groundwater pollution monitoring method program. When the on-line groundwater pollution monitoring method program is executed by the processor, the steps of any one of the on-line groundwater pollution monitoring methods are implemented.

[0042] The third aspect of the present invention discloses an on-line groundwater pollution monitoring system that implements any one of the on-line groundwater pollution monitoring methods. The system includes: a data acquisition module, a model construction module, a quantum action mode data generation module, a data integration module, and a monitoring report transmission module;

[0043] The data acquisition module is responsible for collecting the time-series monitoring data of each monitoring point;

[0044] The model construction module is responsible for establishing a quantum environment model of the groundwater system based on the multi-source heterogeneous environmental data of the target area;

[0045] The quantum action mode data generation module is responsible for generating quantum action mode data by using quantum gate operations;

[0046] The data integration module is responsible for integrating the quantum action mode data monitored in each target area;

[0047] The monitoring report transmission module is responsible for transmitting the knowledge graph to the preset terminal for display.

[0048] The fourth aspect of the present invention discloses a computer-readable storage medium, which includes an on-line groundwater pollution monitoring method program. When the on-line groundwater pollution monitoring method program is executed by the processor, the steps of any one of the on-line groundwater pollution monitoring methods are implemented.

[0049] The present invention solves the technical defects existing in the background art, and the present invention has the following beneficial effects: establishing a quantization environment model of the groundwater system based on the multi-source heterogeneous environmental data of the target area; mapping the time-series monitoring data of each monitoring point into a quantum state sequence in a high-dimensional Hilbert space to obtain the environmental observation input of each monitoring point; inputting the environmental observation of each monitoring point into the quantization environment model, and generating quantum action mode data by using quantum gate operations; integrating the quantum action mode data monitored in each target area to construct a cross-regional groundwater pollution knowledge map, and transmitting the knowledge map to a preset terminal for display. The present invention provides comprehensive and visual decision support for global pollution monitoring and control, realizes efficient and accurate online monitoring and early warning, and provides a strong technical guarantee for groundwater pollution prevention and control. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] 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 following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can also obtain the drawings of other embodiments without creative efforts.

[0051] Figure 1 It is a first method flow chart of an online groundwater pollution monitoring method;

[0052] Figure 2 It is a first method flow chart of an online groundwater pollution monitoring method;

[0053] Figure 3 It is a system block diagram of an online groundwater pollution monitoring system. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] In order to be able to more clearly understand the above-mentioned objects, features, and advantages of the present invention, the following will further describe the present invention in detail with reference to the drawings and specific embodiments. It should be noted that, without conflict, the embodiments of the present application and the features in the embodiments can be combined with each other.

[0055] Many specific details are set forth in the following description in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0056] As Figure 1 shown, the first aspect of the present invention discloses an online groundwater pollution monitoring method, including the following steps:

[0057] S102. Establish a quantized environmental model of the groundwater system based on multi-source heterogeneous environmental data of the target area;

[0058] S104. Map the time-series monitoring data of each monitoring point into a quantum state sequence in a high-dimensional Hilbert space to obtain the environmental observation input of each monitoring point;

[0059] S106. Input the environmental observation of each monitoring point into the quantized environmental model, and generate quantum action mode data by using quantum gate operations; the quantum action mode data includes the coordinates of potential pollution sources and the diffusion path cloud map;

[0060] S108. Integrate the quantum action mode data monitored in each target area, construct a cross-regional groundwater pollution knowledge graph, and transmit the knowledge graph to a preset terminal for display.

[0061] The present invention provides comprehensive and visual decision-making support for global pollution monitoring and control, realizes efficient and accurate online monitoring and early warning, and provides strong technical support for groundwater pollution prevention and control.

[0062] Preferably, establishing a quantized environmental model of the groundwater system based on multi-source heterogeneous environmental data of the target area specifically includes:

[0063] Collect multi-source heterogeneous environmental data of the target area, including historical geological parameters, historical hydrological parameters and historical pollution data, and perform normalization preprocessing;

[0064] Map the normalized data into quantum states in a high-dimensional Hilbert space through a quantum encoder, where historical geological parameters are encoded as the ground state of quantum bits, historical hydrological parameters are encoded as quantum superposition states, and historical pollution data are encoded as quantum entanglement states;

[0065] Among them, the quantum encoder is a computing module that maps classical data (such as geological parameters, hydrological parameters and pollution data) into quantum states. Its core function is to encode multi-source heterogeneous environmental data into quantum states in a high-dimensional Hilbert space through quantum gate operations (such as quantum rotation gates, quantum entanglement gates, etc.). The quantum encoder can encode different types of data into the ground state of quantum bits, quantum superposition states or quantum entanglement states respectively, so as to make full use of the parallelism and superposition of quantum computing and realize the efficient modeling and simulation of complex groundwater systems.

[0066] If the historical geological parameters show heterogeneity, introduce a quantum annealing optimizer to solve the permeability coefficient distribution of the heterogeneous aquifer, and construct a quantum circuit to simulate the groundwater flow equation;

[0067] Among them, heterogeneity refers to the characteristic that geological or hydrological parameters in the groundwater system show non-uniformity in spatial distribution. For example, there are significant differences in the permeability coefficient of the aquifer, lithology distribution, or groundwater flow velocity at different locations. This non-uniformity will lead to complex changes in the transport path and diffusion rate of pollutants in groundwater, and it is difficult for traditional numerical simulation methods to accurately depict its dynamic behavior.

[0068] It should be noted that if the historical geological parameters show heterogeneity, first, the permeability coefficient distribution of the heterogeneous aquifer is encoded as the Hamiltonian of the Ising model and input into the quantum annealing optimizer for solution. Through the quantum annealing process, the lowest energy state is found to obtain the optimal permeability coefficient distribution. Second, the solution result is mapped to the quantum gate parameters in the quantum circuit, and a quantum simulation circuit of the groundwater flow equation is constructed. Among them, the heterogeneous region simulates the spatial variation of the permeability coefficient through the quantum rotation gate, and the homogeneous region simulates the continuity of the flow field through the quantum entanglement gate. Then, start the quantum circuit to simulate the groundwater flow equation. If the fitting error between the simulation result and the historical hydrological data is lower than the preset threshold, it is determined that the quantum circuit construction is completed; otherwise, readjust the parameters of the quantum annealing optimizer and iterate the solution process.

[0069] If the historical hydrological parameters show a multi-directional flow field, the frequency components of the flow field are decomposed by the quantum Fourier transform to construct the dynamic evolution of the pollutant transport equation.

[0070] Among them, a multi-directional flow field refers to the characteristic that the groundwater flow direction shows multi-directional changes in space, usually caused by complex geological structures, terrain undulations, or external water source replenishment and other factors. The multi-directional flow field will cause pollutants to diffuse along different paths in groundwater, increasing the difficulty of source identification and diffusion path prediction.

[0071] It should be noted that if the historical hydrological parameters show a multi-directional flow field, the flow field data is decomposed into different frequency components by the quantum Fourier transform, and each frequency component is encoded as a quantum state in the quantum circuit. According to the spatial distribution characteristics of the frequency components, a dynamic evolution operator of the pollutant transport equation is constructed. Among them, the high-frequency components simulate the rapidly changing region through the quantum rotation gate, and the low-frequency components simulate the stable flow field through the quantum entanglement gate. Then, the quantum phase estimation algorithm is embedded in the quantum circuit to simulate the diffusion process of pollutants in the multi-directional flow field. If the fitting error between the simulation result and the historical pollution data is lower than the preset threshold, it is determined that the dynamic evolution operator construction is completed; otherwise, readjust the parameters of the quantum Fourier transform and iterate the decomposition process.

[0072] If the historical pollution data indicates the existence of multiple pollution sources, a correlation model between the pollution sources is established through the quantum entanglement mechanism.

[0073] It should be noted that if historical pollution data indicates the existence of multiple pollution sources, the positions and pollutant concentration data of each pollution source are encoded as quantum bits, and a correlation model between pollution sources is established through quantum entanglement gates. Secondly, a quantum phase estimation algorithm is embedded in the quantum circuit to simulate the interaction between multiple pollution sources. If the concentration changes between pollution sources show correlation, the entangled state information is extracted through quantum measurement operations. The extracted entangled state information is converted into a classical probability distribution to generate a correlation model between pollution sources. If the fitting error between the model and the historical pollution data is lower than the preset threshold, it is determined that the construction of the correlation model is completed; otherwise, the parameters of the quantum entanglement gates are readjusted and the simulation process is iterated.

[0074] Simulate the dynamic evolution of the groundwater flow equation and the pollutant transport equation through a quantum circuit and construct an environmental state space in a high-dimensional Hilbert space in combination with the quantum state;

[0075] Among them, the environmental state space refers to a classical probability distribution space formed by converting multi-source heterogeneous environmental data into quantum states in a high-dimensional Hilbert space through simulating the dynamic evolution of the groundwater flow equation and the pollutant transport equation by a quantum circuit and then through quantum measurement operations. This space contains key information such as the positions of pollution sources, diffusion paths, and interaction relationships, and can comprehensively reflect the dynamic characteristics of the groundwater system, providing high-dimensional and non-linear data support for pollution source identification and diffusion path analysis.

[0076] Convert the quantum state information in the environmental state space into a classical probability distribution and map it into the correlation model between pollution sources to generate a quantized environmental model containing the positions of pollution sources, diffusion paths, and interaction relationships.

[0077] In summary, the present invention can effectively process complex scenarios such as heterogeneous strata, multi-directional flow fields, and multiple pollution sources by constructing a high-precision quantized environmental model from multi-source heterogeneous environmental data, and improve the accuracy of pollution source location and diffusion path prediction through a quantum annealing optimizer, quantum Fourier transform, and quantum entanglement mechanism. At the same time, the conversion of quantum state information into a classical probability distribution ensures the interpretability and practicality of the model, providing efficient and reliable technical support for on-line monitoring of groundwater pollution and solving the limitations of traditional methods in dealing with high-dimensional non-linear problems.

[0078] Preferably, map the time-series monitoring data of each monitoring point into a quantum state sequence in a high-dimensional Hilbert space to obtain the environmental observation input of each monitoring point, as Figure 2 shown, specifically:

[0079] S202. Real-time collect the time-series monitoring data of each monitoring point, including pollutant concentration, pH value, and flow rate data;

[0080] S204. Divide the time-series monitoring data into fixed time windows, with the data within each window serving as an observation unit, and perform normalization processing on each observation unit;

[0081] S206. Input the normalized observation units into the quantum encoder, and map them into a quantum state sequence in a high-dimensional Hilbert space through quantum gate operations; among them, encode the pollutant concentration, pH value, and flow rate data into different degrees of freedom of quantum bits;

[0082] S208. Perform quantum measurement operations on the generated quantum state sequence, extract its probability amplitude information and generate classical observation inputs. If the measurement result meets the preset quantum state fidelity threshold, it is determined that the mapping is completed; otherwise, adjust the quantum encoder parameters and re-execute the mapping process;

[0083] Among them, the quantum measurement operation refers to the process of observing a quantum state to extract its classical information, and converting the probability amplitude information of the quantum state into a classical probability distribution through quantum measurement. In the present invention, the quantum measurement operation is used to project the quantum state sequence in a high-dimensional Hilbert space into classical observation inputs, so as to generate environmental observation data that can be used for subsequent analysis. The quantum measurement operation not only ensures the interpretability of quantum state information, but also can verify the accuracy of the mapping process through the fidelity threshold, providing a high-precision and high-efficiency data conversion means for groundwater pollution monitoring.

[0084] S210. Align the mapped quantum state sequence with the environmental state space to obtain the environmental observation inputs of each monitoring point.

[0085] In summary, the present invention maps the time-series monitoring data into a quantum state sequence in a high-dimensional Hilbert space, makes full use of the parallelism and superposition of quantum computing, and improves the data processing efficiency and accuracy. The quantum encoder encodes the pollutant concentration, pH value, and flow rate data into different degrees of freedom of quantum bits respectively, and generates classical observation inputs through quantum measurement operations, ensuring the integrity and interpretability of the data. It can effectively process complex and non-linear monitoring data, and provides high-dimensional and dynamic environmental observation inputs for subsequent pollution source identification and diffusion path prediction.

[0086] Preferably, input the environmental observation inputs of each monitoring point into the quantumized environmental model, and generate quantum action mode data by using quantum gate operations, specifically:

[0087] Map the environmental observation inputs of each monitoring point into quantum states in a high-dimensional Hilbert space through a quantum encoder, and align them with the environmental state space in the quantumized environmental model;

[0088] Embed the quantum phase estimation algorithm in the quantum circuit in the quantized environmental model to simulate the evolution of pollutant concentration in time and space, and generate the interference fringes of the quantum state as the input of environmental observation;

[0089] When the interference fringes of the quantum state show non - linear distortion, it is determined that there is an eigenvalue anomaly; if the eigenvalue anomaly appears at the same coordinate point for three consecutive recognition cycles, it is determined that the corresponding coordinate point is a potential pollution source, and the coordinates of the potential pollution source are obtained;

[0090] Among them, the eigenvalue anomaly refers to the non - linear distortion phenomenon that appears in the interference fringes of the quantum state, usually caused by the sudden change or complex change of pollutant concentration in time and space. This anomaly reflects the non - steady state characteristics in the quantum state evolution process and can be used as an identification mark of potential pollution sources.

[0091] Based on the obtained coordinates of the potential pollution source, initialize the quantum state at the position of the potential pollution source coordinates in the quantum circuit, and map the position of the potential pollution source coordinates to the frequency domain through the quantum Fourier transform;

[0092] It should be noted that the quantum Fourier transform is a quantum algorithm that converts the quantum state from the time domain to the frequency domain. Its core function is to decompose and encode the frequency components of the quantum state into the phase information of quantum bits through quantum gate operations (such as Hadamard gates and controlled - phase gates). In the present invention, the quantum Fourier transform is used to map the position of the potential pollution source coordinates to the frequency domain, so as to efficiently analyze the pollutant diffusion characteristics in the multi - directional flow field.

[0093] If the hydrological parameters show that the water flow field at the position of the potential pollution source coordinates is a one - way flow, simulate the diffusion process of pollutants along the flow direction through the quantum rotation gate to construct a single - path evolution operator for pollutant diffusion kinetics; if the water flow field is a multi - directional flow, simulate the multi - path diffusion behavior of pollutants through the quantum entanglement gate to construct a multi - path evolution operator for pollutant diffusion kinetics;

[0094] It should be noted that if the hydrological parameters show that the water flow field at the position of the potential pollution source coordinates is a one - way flow, simulate the diffusion process of pollutants along the flow direction through the quantum rotation gate to generate a single - path evolution operator for pollutant diffusion kinetics (i.e., the evolution path of pollutants in single - path diffusion); the rotation angle is dynamically adjusted by the flow velocity and the pollutant concentration gradient.

[0095] If the water flow field is a multi - directional flow, simulate the multi - path diffusion behavior of pollutants through the quantum entanglement gate, and embed the quantum Fourier transform in the quantum circuit to decompose the frequency components of the multi - path diffusion to generate a multi - path evolution operator for pollutant diffusion kinetics (i.e., the evolution path of pollutants in multi - path diffusion); the entangled paths are configured by the spatial correlation of the flow field direction.

[0096] Based on the pollutant diffusion dynamics single-path evolution operator or the pollutant diffusion dynamics multi-path evolution operator, perform a flow direction feature extraction operation on the quantum state interference fringes in the quantized environmental model, extract the flow direction distribution information of the quantum state interference fringes, and combine the flow direction distribution information with the geographic information system to generate a diffusion path cloud map of potential pollution sources.

[0097] In summary, the present invention maps environmental observations into quantum states and uses the quantum phase estimation algorithm to simulate the evolution of pollutant concentrations, achieving the accurate identification of potential pollution sources and the dynamic prediction of diffusion paths, providing a high-precision and visual dynamic analysis tool for groundwater pollution monitoring and improving the accuracy of pollution source tracking and diffusion prediction.

[0098] Preferably, the quantum phase estimation algorithm is embedded in the quantum circuit in the quantized environmental model to simulate the evolution of pollutant concentrations in time and space and generate quantum state interference fringes as the input of environmental observations, specifically:

[0099] Initialize the quantum phase estimation algorithm in the quantum circuit in the quantized environmental model, encode the evolution equation of pollutant concentrations in time and space as a sequence of quantum phase gates, where the time evolution parameter is adjusted by a quantum rotation gate and the space evolution parameter is configured by a quantum entanglement gate;

[0100] Map the environmental observation inputs at each monitoring point into quantum states in a high-dimensional Hilbert space through a quantum encoder and input them into the quantum circuit;

[0101] Start the quantum phase estimation algorithm to simulate the evolution process of pollutant concentrations in time and space, where: if the change in pollutant concentration gradient is greater than the preset gradient change threshold, increase the rotation angle of the quantum phase gate by a preset increment to capture the fast evolution characteristics, and if the change in pollutant concentration gradient is not greater than the preset gradient change threshold, reduce the rotation angle of the quantum phase gate by a preset decrement to improve the calculation efficiency;

[0102] Perform a quantum interference operation on the evolved quantum state to generate quantum state interference fringes. If the clarity of the quantum state interference fringes is lower than the preset clarity threshold, readjust the quantum phase gate parameters and iterate the simulation process.

[0103] Among them, the quantum interference operation refers to the process of superimposing and interfering with the quantum state through a sequence of quantum gates (such as Hadamard gates and phase gates) in the quantum circuit to generate quantum state interference fringes. This operation can convert the phase information of the quantum state into the intensity distribution of the interference fringes, thereby revealing the dynamic evolution characteristics of the quantum state.

[0104] In summary, by embedding the quantum phase estimation algorithm in the quantization environment model, the present invention simulates the evolution process of pollutant concentration in time and space, can efficiently capture the dynamic characteristics of pollutant diffusion, solves the limitations of traditional numerical simulations in dealing with complex non-linear problems, and provides an efficient and accurate dynamic analysis tool for groundwater pollution monitoring.

[0105] Preferably, the quantum action mode data monitored in each target area are integrated to construct a cross-regional groundwater pollution knowledge graph, specifically as follows:

[0106] Receive the quantum action mode data monitored in each target area, and integrate the quantum action mode data across regions through the quantum federated learning framework;

[0107] It should be noted that the quantum federated learning framework is a distributed data processing method that combines quantum computing and federated learning technologies. Its core function is to achieve collaborative modeling and integration of cross-regional data through quantum communication protocols while protecting data privacy. In the present invention, the quantum federated learning framework uses the quantum entanglement mechanism to establish an association model between regions, and solves the data heterogeneity problem through the quantum variational autoencoder, improving the construction efficiency and accuracy of the cross-regional groundwater pollution knowledge graph. This framework can not only efficiently integrate multi-source heterogeneous data, but also ensure the integrity and connectivity of the graph through quantum measurement operations, providing strong technical support for global pollution monitoring and governance. Among them, if multiple regions have shared boundaries or hydrological connectivity, an association model between regions is established through the quantum entanglement mechanism. If there is data heterogeneity between regions, the quantum variational autoencoder is used for data alignment.

[0108] Based on the integrated quantum action mode data, construct a cross-regional groundwater pollution knowledge graph, where the pollution source coordinates are used as nodes, the diffusion paths are used as edges, and the pollutant concentration is used as a node attribute. If there are isolated nodes or broken edges in the graph, they are completed through an interpolation algorithm;

[0109] Perform quantum measurement operations on the knowledge graph, extract its topological structure and attribute information, and convert it into classical graph data. If the measurement result shows that the connectivity of the graph is lower than the preset threshold, readjust the parameters of the federated learning framework and iterate the integration process;

[0110] Combine the classical graph data with the geographic information system to generate a visual cross-regional groundwater pollution knowledge graph.

[0111] In summary, the present invention integrates the quantum action mode data of each target area through the quantum federated learning framework, and uses the quantum entanglement mechanism and the quantum variational autoencoder to solve the problem of data heterogeneity between regions, and constructs a cross-regional groundwater pollution knowledge graph. The graph takes the pollution source coordinates as nodes, the diffusion paths as edges, and the pollutant concentration as node attributes, comprehensively reflecting the pollution distribution and propagation characteristics. The finally generated visual knowledge graph can provide intuitive and efficient decision-making support for cross-regional pollution monitoring and governance, and improve the accuracy and collaboration of global pollution analysis.

[0112] In this embodiment, the online groundwater pollution monitoring method may further include the following steps:

[0113] Obtain the environmental observation inputs (quantum state sequences) of each monitoring point; based on the quantum Feynman path integral principle, invert the propagation probability wave function of pollutants at each monitoring point according to the environmental observation inputs of each monitoring point;

[0114] Among them, the quantum Feynman path integral principle is a path summation theory based on quantum mechanics. Its core idea is to calculate the evolution probability of a quantum system by integrating over all possible propagation paths. In the present invention, the quantum Feynman path integral principle is used to invert the propagation probability wave function of pollutants at each monitoring point. Through quantum state superposition and path integration, it can efficiently characterize the complex diffusion behavior of pollutants in the groundwater system.

[0115] It should be noted that the quantum circuit is used to simulate the propagation process of pollutants between each monitoring point to generate the propagation probability wave function of pollutants; among them, the path weight is dynamically adjusted by the pollutant concentration gradient.

[0116] Construct a time-reversal symmetry model, import the propagation probability wave function into the time-reversal symmetry model for reverse evolution to the initial moment, and generate a pollution source tracing probability matrix;

[0117] Among them, the time-reversal symmetry model is a reverse evolution model based on quantum mechanics. Its core function is to reverse evolve the pollutant propagation probability wave function from the current moment to the initial moment through quantum gate operations, so as to reveal the source distribution of pollutants. The model simulates the reverse propagation process of pollutant concentration in time through the quantum phase estimation algorithm, and combines the quantum annealing optimizer to solve the multi-solution problem, improving the efficiency and accuracy of pollution source tracing.

[0118] If the tracing probability value of a certain coordinate point in the pollution source tracing probability matrix exceeds the preset probability threshold three times in a row, then determine that this point is a potential pollution emission location;

[0119] Based on the hierarchical clustering algorithm, spatial clustering analysis is carried out on potential pollution emission sites. If the clustering result shows that the boundary of the target area is complete and consistent with the hydrogeological conditions, the potential pollution emission sites are determined as pollution emission sites; otherwise, the path integral parameters are readjusted and the inversion process is iterated.

[0120] The target area of the determined pollution emission sites is combined with the geographic information system to generate a visual pollution emission point tracing report.

[0121] It should be noted that the present invention realizes the accurate identification of potential pollution emission sites by inversing the pollutant propagation probability wave function through quantum Feynman path integral and generating a pollution tracing probability matrix in combination with the time-reversal symmetry model.

[0122] In this embodiment, the online groundwater pollution monitoring method may further include the following steps:

[0123] Based on the cross-regional groundwater pollution knowledge graph, pairs of monitoring wells with potential pollution associations are identified, and the pollutant concentration data of each monitoring well are mapped into quantum states through a quantum encoder.

[0124] It should be noted that the node information of each monitoring well is extracted from the knowledge graph, including the source coordinates of pollution sources, pollutant concentrations, and diffusion paths. The spatial distance and hydrogeological connectivity between monitoring wells are analyzed. If the monitoring wells are located in the same aquifer or share the groundwater flow field, they are initially determined as potential associated pairs. Then, based on the time series data of pollutant concentrations, the correlation coefficient between pairs of monitoring wells is calculated. If the correlation coefficient exceeds the preset threshold, they are further confirmed as potential pollution associated pairs. Then, in combination with geological parameters and hydrogeological parameters, the physical rationality of the associated pairs is verified. If the associated pairs meet the hydrogeological conditions, they are finally determined as pairs of monitoring wells with potential pollution associations.

[0125] A Bell state measurement channel is established between pairs of monitoring wells using the quantum teleportation protocol. A CHSH inequality calculation module is embedded in the Bell state measurement channel. The probability amplitude information of the Bell state is extracted through quantum measurement operations, and the CHSH inequality violation value is calculated.

[0126] It should be noted that the pollutant concentration data of each monitoring well is mapped into a quantum state through a quantum encoder, where the pollutant concentration is encoded as the ground state or superposition state of a quantum bit; the quantum teleportation protocol is initialized between monitoring well pairs, and a Bell state is generated through a quantum entanglement gate, where: if there is a significant correlation in the change of pollutant concentration between monitoring well pairs, a strong-correlation Bell state is established through the quantum entanglement gate, and if there is data heterogeneity, a quantum variational autoencoder is used for data alignment and a weak-correlation Bell state is generated. A Bell state measurement module is embedded in the quantum circuit, and the probability amplitude information of the Bell state is extracted through quantum measurement operations. If the measurement result shows that the entanglement degree of the Bell state is lower than a preset threshold, the parameters of the quantum entanglement gate are readjusted and the generation process is iterated; finally, the generated Bell state measurement channel is used for subsequent CHSH inequality calculation and correlation strength analysis to ensure the accuracy and reliability of cross-regional pollution correlation determination.

[0127] Among them, the CHSH inequality is a form of Bell inequality used to quantify the non-locality of a quantum entangled state. Its core function is to judge whether there is a quantum entanglement phenomenon that cannot be explained by classical theory by measuring the correlation strength of a quantum system. The CHSH inequality judges whether it satisfies the limitations of the classical hidden variable theory by calculating the expected values of four relevant measurement results. If the calculation result exceeds the classical upper limit (i.e., violates the CHSH inequality), it indicates that there is quantum entanglement in the system. In the present invention, the CHSH inequality is used to determine the pollution correlation strength between monitoring well pairs. If the violation value exceeds a preset threshold, it indicates that there is a strong quantum correlation between the monitoring well pairs, providing a quantitative criterion for cross-regional pollution analysis.

[0128] If the CHSH inequality violation value exceeds the preset violation value threshold, it is determined that there is a strong pollution correlation between the monitoring well pairs. If the violation value is lower than the preset violation value threshold but higher than the baseline value, it is determined that there is a medium pollution correlation, otherwise it is determined that there is no significant correlation;

[0129] The correlation strength calculation result is combined with a geographic information system to generate a cross-regional pollution correlation strength distribution map.

[0130] It should be noted that the present invention establishes a Bell state measurement channel between monitoring well pairs through the quantum teleportation protocol and combines it with a CHSH inequality calculation module to achieve accurate determination of cross-regional pollution correlation strength, which can improve the efficiency and accuracy of cross-regional pollution correlation identification and provide strong technical support for pollution prevention and control.

[0131] The second aspect of the present invention discloses an on-line groundwater pollution monitoring device, which includes a memory and a processor. A groundwater pollution on-line monitoring method program is stored in the memory. When the groundwater pollution on-line monitoring method program is executed by the processor, the steps of any of the described groundwater pollution on-line monitoring methods are implemented.

[0132] As Figure 3 shown, the third aspect of the present invention discloses an on - line monitoring system for groundwater pollution, which realizes any of the described on - line monitoring methods for groundwater pollution. The system includes: a data acquisition module 10, a model construction module 20, a quantum action mode data generation module 30, a data integration module 40, and a monitoring report transmission module 50;

[0133] The data acquisition module 10 is responsible for collecting the time - series monitoring data of each monitoring point;

[0134] The model construction module 20 is responsible for establishing a quantum environment model of the groundwater system based on the multi - source heterogeneous environment data of the target area;

[0135] The quantum action mode data generation module 30 is responsible for generating quantum action mode data by using quantum gate operations;

[0136] The data integration module 40 is responsible for integrating the quantum action mode data monitored in each target area;

[0137] The monitoring report transmission module 50 is responsible for transmitting the knowledge graph to a preset terminal for display.

[0138] The fourth aspect of the present invention discloses a computer - readable storage medium, which includes a program for the on - line monitoring method of groundwater pollution. When the program for the on - line monitoring method of groundwater pollution is executed by a processor, the steps of any of the described on - line monitoring methods for groundwater pollution are realized.

[0139] In several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed with each other can be through some interfaces. The indirect coupling or communication connection of devices or units can be electrical, mechanical, or other forms.

[0140] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units; they can be located in one place or distributed to multiple network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0141] In addition, each functional unit in the embodiments of the present invention may be entirely integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the above integrated unit may be implemented in the form of hardware, or in the form of a combination of hardware and software functional units.

[0142] Those of ordinary skill in the art can understand that all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions. The foregoing program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps including the above method embodiments; and the foregoing storage medium includes: removable storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks and other various media that can store program codes.

[0143] Alternatively, if the above integrated unit of the present invention is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiments of the present invention essentially or the part that contributes to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the methods of the various embodiments of the present invention. And the foregoing storage medium includes: removable storage devices, ROM, RAM, magnetic disks, or optical disks and other various media that can store program codes.

[0144] The above is only the specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered by the protection scope of the present invention.

Claims

1. An online monitoring method for groundwater pollution, characterized in that, Including the following steps: Establish a quantization environment model of the groundwater system based on the multi-source heterogeneous environment data of the target area; Map the time-series monitoring data of each monitoring point into a quantum state sequence in a high-dimensional Hilbert space to obtain the environmental observation input of each monitoring point; Input the environmental observation of each monitoring point into the quantization environment model, and generate quantum action mode data by using quantum gate operations; Integrate the quantum action mode data monitored in each target area, construct a cross-regional groundwater pollution knowledge graph, and transmit the knowledge graph to a preset terminal for display.

2. The on-line monitoring method for groundwater pollution according to claim 1, characterized in that, Establish a quantization environment model of the groundwater system based on the multi-source heterogeneous environment data of the target area. Specifically: Collect the multi-source heterogeneous environment data of the target area, including historical geological parameters, historical hydrological parameters and historical pollution data, and perform normalization preprocessing; Map the normalized data into quantum states in a high-dimensional Hilbert space through a quantum encoder, where the historical geological parameters are encoded as the quantum bit ground state, the historical hydrological parameters are encoded as the quantum superposition state, and the historical pollution data are encoded as the quantum entanglement state; If the historical geological parameters show heterogeneity, introduce a quantum annealing optimizer to solve the permeability coefficient distribution of the heterogeneous aquifer, and construct a quantum circuit to simulate the groundwater flow equation; If the historical hydrological parameters show a multi-directional flow field, decompose the flow field frequency components through a quantum Fourier transform, and construct the dynamic evolution of the pollutant transport equation; If the historical pollution data indicates the existence of multiple pollution sources, establish a correlation model between the pollution sources through a quantum entanglement mechanism; Simulate the dynamic evolution of the groundwater flow equation and the pollutant transport equation through a quantum circuit, and construct an environmental state space in the high-dimensional Hilbert space in combination with the quantum state; Convert the quantum state information in the environmental state space into a classical probability distribution, and map it into the correlation model between the pollution sources to generate a quantization environment model including the pollution source location, diffusion path and interaction relationship.

3. The on-line monitoring method for groundwater pollution according to claim 1, characterized in that, Map the time-series monitoring data of each monitoring point into a quantum state sequence in a high-dimensional Hilbert space to obtain the environmental observation input of each monitoring point. Specifically: Collect the time-series monitoring data of each monitoring point in real time, including pollutant concentration, pH value and flow velocity data; Divide the time-series monitoring data into fixed time windows, and the data in each window is used as an observation unit, and each observation unit is normalized; Input the normalized observation unit into a quantum encoder, and map it into a quantum state sequence in a high-dimensional Hilbert space through quantum gate operations; Perform a quantum measurement operation on the generated quantum state sequence, extract its probability amplitude information and generate a classical observation input. If the measurement result meets the preset quantum state fidelity threshold, it is determined that the mapping is completed, otherwise, adjust the quantum encoder parameters and re-execute the mapping process; Align the mapped quantum state sequence with the environmental state space to obtain the environmental observation input of each monitoring point.

4. An on-line monitoring method for groundwater pollution according to claim 1, characterized in that, Input the environmental observation of each monitoring point into the quantization environment model, and generate quantum action mode data by using quantum gate operations. Specifically: Map the environmental observations at each monitoring point into quantum states in a high-dimensional Hilbert space through a quantum encoder, and align them with the environmental state space in the quantized environmental model; Embed the quantum phase estimation algorithm in the quantum circuit in the quantized environmental model, simulate the evolution of pollutant concentration in time and space, and generate the interference fringes of quantum states as the input of environmental observations; When the interference fringes of quantum states show non-linear distortion, it is determined that an eigenvalue anomaly occurs; If eigenvalue anomalies occur at the same coordinate point in three consecutive identification periods, it is determined that the corresponding coordinate point is a potential pollution source, and the coordinates of the potential pollution source are obtained; Based on the obtained coordinates of the potential pollution source, initialize the quantum state of the potential pollution source coordinate position in the quantum circuit, and map the potential pollution source coordinate position to the frequency domain through quantum Fourier transform; If the hydrological parameters show that the water flow field at the coordinates of the potential pollution source is a unidirectional flow, simulate the diffusion process of pollutants along the flow direction through a quantum rotation gate to construct a single-path evolution operator for pollutant diffusion dynamics; if the water flow field is a multi-directional flow, simulate the multi-path diffusion behavior of pollutants through a quantum entanglement gate to construct a multi-path evolution operator for pollutant diffusion dynamics; Based on the single-path evolution operator for pollutant diffusion dynamics or the multi-path evolution operator for pollutant diffusion dynamics, perform a flow direction feature extraction operation on the interference fringes of quantum states in the quantized environmental model, extract the flow direction distribution information of the interference fringes of quantum states, and combine the flow direction distribution information with a geographic information system to generate a diffusion path cloud map of the potential pollution source.

5. The on-line monitoring method for groundwater pollution according to claim 4, characterized in that, Embed the quantum phase estimation algorithm in the quantum circuit in the quantized environmental model, simulate the evolution of pollutant concentration in time and space, and generate the interference fringes of quantum states as the input of environmental observations, specifically: Initialize the quantum phase estimation algorithm in the quantum circuit in the quantized environmental model, encode the evolution equation of pollutant concentration in time and space as a sequence of quantum phase gates, where the time evolution parameter is adjusted by a quantum rotation gate and the space evolution parameter is configured by a quantum entanglement gate; Map the environmental observations at each monitoring point into quantum states in a high-dimensional Hilbert space through a quantum encoder, and input them into the quantum circuit; Start the quantum phase estimation algorithm to simulate the evolution process of pollutant concentration in time and space, where: if the change in pollutant concentration gradient is greater than the preset gradient change threshold, increase the rotation angle of the quantum phase gate by a preset increment to capture the fast evolution feature, if the change in pollutant concentration gradient is not greater than the preset gradient change threshold, reduce the rotation angle of the quantum phase gate by a preset decrement to improve the calculation efficiency; Perform a quantum interference operation on the evolved quantum state to generate interference fringes of quantum states. If the clarity of the interference fringes of quantum states is lower than the preset clarity threshold, readjust the parameters of the quantum phase gate and iterate the simulation process.

6. The on-line monitoring method for groundwater pollution according to claim 1, characterized in that, Integrate the quantum action mode data monitored in each target area to construct a cross-regional groundwater pollution knowledge graph, specifically: Receive the quantum action mode data monitored in each target area, and integrate the quantum action mode data across regions through a quantum federated learning framework; Based on the integrated quantum action modal data, construct a cross-regional groundwater pollution knowledge graph, where the pollution source coordinates are used as nodes, the diffusion paths are used as edges, and the pollutant concentrations are used as node attributes. If there are isolated nodes or broken edges in the graph, they are complemented through an interpolation algorithm; Perform quantum measurement operations on the knowledge graph, extract its topological structure and attribute information, and convert it into classical graph data. If the measurement results show that the connectivity of the graph is lower than the preset threshold, readjust the parameters of the federated learning framework and iterate the integration process; Combine the classical graph data with the geographic information system to generate a visualized cross-regional groundwater pollution knowledge graph.

7. An on-line monitoring method for groundwater pollution according to claim 1, characterized in that: The quantum action modal data includes potential pollution source coordinates and diffusion path cloud maps.

8. An on-line monitoring device for groundwater pollution, characterized in that, The on-line groundwater pollution monitoring device includes a memory and a processor. The on-line groundwater pollution monitoring method program is stored in the memory. When the on-line groundwater pollution monitoring method program is executed by the processor, the steps of the on-line groundwater pollution monitoring method according to any one of claims 1 to 7 are implemented.

9. An on-line monitoring system for groundwater pollution, characterized in that, Implement the on-line groundwater pollution monitoring method according to any one of claims 1-7. The system includes: a data acquisition module, a model construction module, a quantum action modal data generation module, a data integration module, and a monitoring report transmission module; The data acquisition module is responsible for collecting the time-series monitoring data of each monitoring point; The model construction module is responsible for establishing a quantum environment model of the groundwater system based on the multi-source heterogeneous environment data of the target area; The quantum action modal data generation module is responsible for generating quantum action modal data by using quantum gate operations; The data integration module is responsible for integrating the quantum action modal data monitored in each target area; The monitoring report transmission module is responsible for transmitting the knowledge graph to a preset terminal for display.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes an on-line groundwater pollution monitoring method program. When the on-line groundwater pollution monitoring method program is executed by the processor, the steps of the on-line groundwater pollution monitoring method according to any one of claims 1 to 7 are implemented.

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