River water environment treatment monitoring and analysis system based on artificial intelligence

Abstract

This invention discloses an artificial intelligence-based river water environment management monitoring and analysis system, belonging to the field of environmental management monitoring and analysis technology. It includes a dynamic sensing anchoring network module, a three-dimensional pollution source mapping module, an ecological metabolism simulation module, a management strategy generation module, a collaborative execution control module, and a real-time feedback optimization module. This invention, driven by multi-dimensional real-time data, achieves a fully intelligent upgrade from pollution identification to management execution, significantly improving management accuracy and response speed. Through autonomous mobile monitoring of the dynamic sensing network, spatiotemporal correlation modeling of the three-dimensional pollution map, dynamic simulation of ecological metabolic processes, and the generation and execution of multi-objective collaborative strategies, an intelligent management closed loop of perception, decision-making, and action optimization is formed. This enables river water environment management to shift from passive response to proactive prevention, and from experience-driven to data-driven, thereby improving overall management efficiency.

Description

Technical Field

[0001] This invention belongs to the field of environmental governance monitoring and analysis technology, specifically referring to an artificial intelligence-based river water environment governance monitoring and analysis system. Background Technology

[0002] With the acceleration of urbanization, the problem of river water pollution has become increasingly prominent. Traditional treatment methods rely heavily on manual monitoring and experience-based decision-making, which has problems such as delayed response, low accuracy of source tracing, and static treatment strategies.

[0003] However, existing river water environment management monitoring and analysis systems still have certain shortcomings. Existing technologies are based on single water quality indicators or static models, which cannot analyze the complex migration paths of pollutants among water, sediment, and biological phases. They lack quantitative modeling of dynamic processes such as sediment resuspension, bottom sediment, and water-body interaction, resulting in large errors in the calculation of pollution source contribution rates. Especially in scenarios with multiple pollution sources, it is difficult to accurately determine the responsible party, and the reliability of source tracing results is low. Ecological metabolism simulations mostly use linear models, ignoring the dynamic coupling of environmental factors such as biomass and temperature, and cannot accurately predict pollutant degradation rates and the self-purification capacity threshold of the ecosystem. At the same time, the assessment of ecological chain reactions does not combine nonlinear amplification effects and ecological response time constants, resulting in significant biases in the assessment of short-term abnormal events and long-term ecological evolution risks. The verification cycle of remediation schemes is too long, and the design of governance schemes often focuses on a single objective, lacking a synergistic balance of environmental, economic, and social objectives. Therefore, an artificial intelligence-based river water environment management monitoring and analysis system is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide an artificial intelligence-based river water environment management monitoring and analysis system to solve the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an artificial intelligence-based river water environment management monitoring and analysis system, comprising a dynamic perception anchoring network module, a three-dimensional pollution source tracing map module, an ecological metabolism simulation module, a management strategy generation module, a collaborative execution control module, and a real-time feedback optimization module;

[0006] The dynamic sensing anchoring network module collects multi-dimensional information about the river water body by deploying monitoring nodes whose positions can be dynamically adjusted, and generates a real-time sensing dataset.

[0007] The three-dimensional pollution source mapping module dynamically constructs a three-dimensional spatiotemporal map of pollutants based on a real-time sensing dataset and outputs pollution source information.

[0008] The ecological metabolism simulation module simulates the chain effects of aquatic ecosystems based on pollution source information, and assesses the risks of short-term anomalies and long-term ecological evolution.

[0009] The governance strategy generation module generates a multi-objective collaborative governance scheme for ecological restoration based on the results of ecological evolution risk assessment and pollution source information.

[0010] The collaborative execution control module transforms the collaborative governance scheme into specific control commands, drives the governance equipment to execute, and makes dynamic adjustments based on real-time feedback on the governance effect.

[0011] The real-time feedback optimization module performs feedback optimization based on the perceived full-process data of regulation and the final governance results.

[0012] Preferably, the dynamic sensing anchoring network module is wirelessly connected to the three-dimensional pollution source tracing map module, the three-dimensional pollution source tracing map module is wirelessly connected to the ecological metabolism simulation module and the governance strategy generation module, the ecological metabolism simulation module is wirelessly connected to the governance strategy generation module, the governance strategy generation module is wirelessly connected to the collaborative execution control module, the collaborative execution control module is wirelessly connected to the real-time feedback optimization module, and the real-time feedback optimization module is wirelessly connected to the dynamic sensing anchoring network module.

[0013] Preferably, the dynamic sensing and anchoring network module divides the monitoring area into sub-regions of high, medium, and low risk levels based on the characteristics of the river water, historical pollution data, and environmentally sensitive points. Fixed anchoring reference points are set at key nodes in the river, and precise coordinates are obtained through the BeiDou system. Mobile monitoring nodes are deployed on the main channel of the river. The nodes are equipped with autonomous navigation systems and underwater propulsion devices to move autonomously within the river.

[0014] When pollutant concentrations exceed the standard, the system automatically plans a movement path, quickly deploys mobile nodes to the forefront of pollution spread, sets node movement trigger conditions, acquires hydrodynamic parameters and biological characteristic parameters in real time, increases sampling density in high-pollution areas, reduces sampling frequency in clean areas, and integrates multi-dimensional data into a structured real-time sensing dataset.

[0015] Preferably, the three-dimensional pollution source tracing map module includes the following three-dimensional spatiotemporal map construction steps: based on a real-time sensing dataset, scanning is performed using a three-dimensional fluorescence spectrometer to obtain the excitation wavelength. With the emission wavelength The three-dimensional fluorescence spectral data were used to obtain the original fluorescence intensity matrix. The water quality fingerprint was obtained by standardizing the original fluorescence intensity matrix. Based on a hydrodynamic model, the fluorescent fingerprint features of water quality are spatiotemporally correlated with parameters such as water velocity, flow direction, and water depth.

[0016] A three-dimensional spatial topology is constructed by using a graph neural network. Water bodies, sediment interfaces, and biota are used as graph nodes, and pollutant migration paths are used as graph edges to construct a three-dimensional spatiotemporal map of pollutants. Sediment resuspension dynamic parameters, such as the Stokes number St, are introduced to dynamically adjust the graph edge weights.

[0017] Preferably, the three-dimensional pollution source mapping module obtains pollutant migration patterns from real-time sensing datasets and constructs a basic diffusion model. Based on the linear relationship between standardized water quality fingerprint calculation and pollutant concentration, the following is achieved:

[0018] ,

[0019] In the formula, denoted by , where represents the pollutant concentration considering the standardized water quality fingerprint, and k represents the correlation coefficient of the standardized water quality fingerprint.

[0020] Preferably, in the three-dimensional pollution source tracing map module, the pollution source contribution rate step is based on water quality fingerprints. The contribution rate of pollution sources is calculated as follows:

[0021] ,

[0022] In the formula, This indicates that among the observed water quality fingerprints, the pollution sources... The posterior probability, Indicates the source of pollution Under the condition of discharge, the probability of observing water quality fingerprints is integrated and standardized to obtain water quality fingerprints, pollutant concentrations have a linear relationship and pollution source contribution rate to generate pollution source information.

[0023] Preferably, the ecological metabolism simulation module simulates pollutant migration and transformation steps by: receiving pollution source information and simulating pollutant migration and transformation, which is achieved as follows:

[0024] ,

[0025] In the formula, Indicates the rate of change in pollutant concentration. C represents a small change over time, and C represents the pollutant concentration. This represents the fundamental degradation rate constant of pollutants. Indicates the current biomass. Indicates the initial biomass. This represents the temperature sensitivity coefficient, where T represents the water temperature. Indicates the reference water temperature.

[0026] Preferably, in the ecological metabolism simulation module, the aquatic ecosystem chain reaction impact step is as follows: based on the pollutant concentration change rate, where the square of the pollutant concentration change rate represents the impact intensity, the aquatic ecosystem chain impact index is implemented as follows:

[0027] ,

[0028] In the formula, Indicating the cascading impact index of aquatic ecosystems, Indicates the influence coefficient. This represents the response time constant of an aquatic ecosystem.

[0029] Preferably, the ecological metabolism simulation module assesses the risks of short-term anomalies and long-term ecological evolution by setting a preset risk threshold. Calculate relative risk Risks accumulate over time , The calibration coefficient represents a comprehensive risk assessment that combines short-term and long-term risk accumulation, resulting in:

[0030] ,

[0031] In the formula, R represents the overall risk index.

[0032] Preferably, the governance strategy generation module receives the comprehensive risk index and pollution source information, spatially aligns the risk assessment results with the pollution source information, classifies the risk areas based on the severity of the comprehensive risk index and the characteristics of the pollution sources, and defines core governance objectives according to the risk assessment results and water environment management needs.

[0033] Based on historical governance cases and real-time data, a governance strategy library containing engineering, management and ecological measures is constructed. According to risk classification and pollution source type, candidate strategies that match the current risk scenario are selected from the strategy library. The selected candidate strategies are combined to generate multiple collaborative governance solutions.

[0034] Preferably, the collaborative execution control module decomposes the collaborative governance scheme into executable device control commands, and sends the commands to the governance device control system in real time through a secure communication network. After receiving the commands, the device executes the operation, and the device execution status is transmitted back to the control center in real time, including execution success, failure status, and actual parameter values.

[0035] By using the real-time sensing dataset of the dynamic sensing anchor network module, key indicators, including water quality indicators, ecological indicators, and equipment operation indicators, are continuously collected. The real-time collected data is compared with the expected effects of the treatment plan to evaluate the efficiency of equipment execution and generate an effect evaluation report, which includes the degree of deviation, the scope of impact, and key influencing factors. Based on the effect evaluation results, the control strategy is dynamically adjusted.

[0036] Preferably, the real-time feedback optimization module integrates the real-time monitoring data of the dynamic sensing anchoring network module throughout the entire process, including water quality indicators, ecological response and equipment operating status, with the expected goals of the governance strategy generation module to establish a multi-dimensional effect evaluation system; based on the evaluation results, it identifies governance deviations and key influencing factors, and drives the collaborative execution control module to dynamically adjust equipment parameters or iteratively optimize strategies.

[0037] Compared with the prior art, the beneficial effects of the present invention are:

[0038] 1. This invention takes multi-dimensional real-time data as its core to achieve intelligent upgrade of the entire process from pollution identification to treatment execution, significantly improving treatment accuracy and response speed. Through autonomous mobile monitoring of dynamic sensing networks, spatiotemporal correlation modeling of three-dimensional pollution maps, dynamic simulation of ecological metabolic processes, and generation and execution of multi-objective collaborative strategies, an intelligent governance closed loop of perception, decision-making and action optimization is formed, enabling river water environment management to shift from passive response to proactive prevention, and from experience-driven to data-driven, thereby improving overall governance efficiency.

[0039] 2. This invention incorporates water bodies, sediments, and biological phases into a three-dimensional spatiotemporal map through fluorescent fingerprint standardization and graph neural network modeling, enabling the visualization and deduction of pollutant migration paths. Standardized water quality fingerprints eliminate background interference, and the graph edge weights are dynamically corrected by combining hydrodynamic models. Sediment resuspension dynamic parameters are introduced to achieve quantitative simulation of the interaction process between bottom sediment and water. The pollution source contribution rate calculation is combined with Bayesian probability and dynamic bias correction, which reduces the error rate of responsibility determination in multi-source pollution superposition scenarios.

[0040] 3. This invention incorporates the biomass metabolic capacity coefficient and temperature sensitivity into the pollutant degradation model to achieve real-time prediction of pollutant concentration changes. Through multidimensional coupling modeling of biomass, temperature, and concentration, it quantifies the self-purification capacity threshold of the ecosystem to pollutants, thereby reducing the error rate of pollution event impact assessment. The chain reaction index combines the square of the concentration change rate with the ecological response time constant to overcome the limitations of linear assessment and accurately capture the nonlinear response characteristics of the ecosystem. The comprehensive risk assessment model simultaneously considers short-term anomalies and long-term cumulative effects, shortening the effectiveness verification cycle of ecological restoration solutions.

[0041] 4. This invention constructs a strategy library that includes engineering, management, and ecological measures through a multi-objective collaborative framework, achieving Pareto optimal selection of governance solutions. Based on risk classification and pollution source characteristics, the matching algorithm improves the efficiency of solution generation and the adaptability of strategies. The phased implementation plan and detailed resource allocation ensure the optimal deployment of governance measures in spatial, temporal, and cost dimensions. Through the fusion analysis of historical case libraries and real-time data, the prediction accuracy of new solutions is improved. Attached Figure Description

[0042] Figure 1 This is a schematic diagram of the artificial intelligence-based river water environment management monitoring and analysis system of the present invention;

[0043] Figure 2 The present invention describes the operation flow of the artificial intelligence-based river water environment management monitoring and analysis system. Figure 1 ;

[0044] Figure 3 The present invention describes the operation flow of the artificial intelligence-based river water environment management monitoring and analysis system. Figure 2 ;

[0045] Figure 4 The present invention describes the operation flow of the artificial intelligence-based river water environment management monitoring and analysis system. Figure 3 . Detailed Implementation

[0046] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Example:

[0048] Please see Figures 1-4 As shown, the present invention provides a technical solution including a dynamic sensing and anchoring network module, a three-dimensional pollution source tracing map module, an ecological metabolism simulation module, a governance strategy generation module, a collaborative execution control module, and a real-time feedback optimization module;

[0049] The dynamic sensing anchoring network module collects multi-dimensional information about the river water body by deploying monitoring nodes whose positions can be dynamically adjusted, and generates a real-time sensing dataset.

[0050] The three-dimensional pollution source mapping module dynamically constructs a three-dimensional spatiotemporal map of pollutants based on a real-time sensing dataset, infers the migration, diffusion and transformation paths in water bodies, sediments and biota, and outputs pollution source information;

[0051] The ecological metabolism simulation module simulates the chain effects of aquatic ecosystems based on pollution source information, and assesses the risks of short-term anomalies and long-term ecological evolution.

[0052] The governance strategy generation module generates a multi-objective collaborative governance scheme for ecological restoration based on the results of ecological evolution risk assessment and pollution source information.

[0053] The collaborative execution control module transforms the collaborative governance scheme into specific control commands, drives the governance equipment to execute, and makes dynamic adjustments based on real-time feedback on the governance effect.

[0054] The real-time feedback optimization module performs feedback optimization based on the perceived full-process data of regulation and the final governance results.

[0055] In this embodiment, the dynamic sensing anchoring network module is wirelessly connected to the three-dimensional pollution source tracing map module, the three-dimensional pollution source tracing map module is wirelessly connected to the ecological metabolism simulation module and the governance strategy generation module, the ecological metabolism simulation module is wirelessly connected to the governance strategy generation module, the governance strategy generation module is wirelessly connected to the collaborative execution control module, the collaborative execution control module is wirelessly connected to the real-time feedback optimization module, and the real-time feedback optimization module is wirelessly connected to the dynamic sensing anchoring network module.

[0056] In this embodiment, the dynamic sensing and anchoring network module divides the monitoring area into sub-regions of high, medium and low risk levels based on the characteristics of the river water, historical pollution data and environmentally sensitive points. Fixed anchoring reference points are set at key nodes in the river, and accurate coordinates are obtained through the Beidou system. Mobile monitoring nodes are deployed on the main channel of the river. The nodes are equipped with autonomous navigation systems and underwater propulsion devices to move autonomously within the river.

[0057] This should be understood as follows: when pollutant concentration exceeds the standard, the system automatically plans a movement path and quickly deploys the moving node to the forefront of pollution diffusion. The node movement trigger conditions are set, including pollutant concentration thresholds, flow velocity changes, and sudden changes in meteorological conditions. During the node movement, basic water quality parameters such as water temperature, pH value, dissolved oxygen, and turbidity are collected simultaneously, and hydrodynamic parameters and biological characteristic parameters are obtained in real time. The sampling density is increased in high-pollution areas and the sampling frequency is reduced in clean areas.

[0058] Specifically, the collected data is transmitted back to the central processing system in real time through an underwater communication network. Through a data time alignment mechanism, the multi-dimensional data collected by different monitoring nodes are accurately matched on the time axis, and the multi-dimensional data is integrated into a structured real-time sensing dataset.

[0059] In this embodiment, the three-dimensional pollution source tracing map module includes the following steps for constructing a three-dimensional spatiotemporal map: based on a real-time sensing dataset, scanning is performed using a three-dimensional fluorescence spectrometer to obtain the excitation wavelength. With the emission wavelength The three-dimensional fluorescence spectral data were used to obtain the original fluorescence intensity matrix. The water quality fingerprint was obtained by standardizing the original fluorescence intensity matrix. , In the formula, Indicates standardized water quality fingerprints, This represents the background fluorescence signal at the excitation wavelength. and emission wavelength Background interference signals at the location, The integral value of the Raman peak of pure water is represented by a hydrodynamic model, such as the Navier-Stokes equation, which spatiotemporally correlates the fluorescent fingerprint characteristics of water quality with parameters such as water flow velocity, flow direction, and water depth.

[0060] This should be understood as constructing a three-dimensional spatial topology through a graph neural network, using water bodies, sediment interfaces, and biota as graph nodes, and pollutant migration paths as graph edges to construct a three-dimensional spatiotemporal map of pollutants, and introducing sediment resuspension dynamic parameters, such as the Stokes number St, to dynamically adjust the graph edge weights.

[0061] In this embodiment, the three-dimensional pollution source mapping module obtains pollutant migration patterns from real-time sensing datasets and constructs a basic diffusion model. , , Let represent the initial pollutant concentration, u represent the water flow velocity, D represent the pollutant diffusion coefficient, x represent the spatial location, and t represent time. Combining standardized water quality fingerprint calculations, a linear relationship exists between the pollutant concentration and the calculated concentration, resulting in:

[0062] ,

[0063] In the formula, This represents the pollutant concentration considering the standardized water quality fingerprint, where k represents the correlation coefficient of the standardized water quality fingerprint. Represented as , ensure when hour, It has reached its maximum value.

[0064] In this embodiment, the pollution source contribution rate step of the three-dimensional pollution source tracing map module is based on water quality fingerprinting. The contribution rate of pollution sources is calculated as follows:

[0065] ,

[0066] In the formula, This indicates that among the observed water quality fingerprints, the pollution sources... The posterior probability, Indicates the source of pollution Under the conditions of discharge, the probability of observing water quality fingerprints. , The squared Euclidean distance of the water fingerprint is represented by... Typical deviations in water quality fingerprints are represented. After integration and standardization, water quality fingerprints and pollutant concentrations show a linear relationship, and pollution source contribution rates are used to generate pollution source information.

[0067] In this embodiment, the ecological metabolism simulation module simulates the pollutant migration and transformation steps as follows: receiving pollution source information and simulating pollutant migration and transformation, which is implemented as follows:

[0068] ,

[0069] In the formula, Indicates the rate of change in pollutant concentration. Representing a small change over time, C represents the pollutant concentration, and C is expressed as... , Indicates the spatial location of the ecological monitoring point. This represents the fundamental degradation rate constant of pollutants. Indicates the current biomass. Indicates the initial biomass. This represents the temperature sensitivity coefficient, where T represents the water temperature. Indicates the reference water temperature.

[0070] In this embodiment, the ecological metabolism simulation module, the aquatic ecosystem chain reaction impact step: based on the pollutant concentration change rate, where the square of the pollutant concentration change rate represents the impact intensity, the aquatic ecosystem chain impact index is implemented as follows:

[0071] ,

[0072] In the formula, Indicating the cascading impact index of aquatic ecosystems, Indicates the influence coefficient. This represents the response time constant of an aquatic ecosystem.

[0073] In this embodiment, the ecological metabolism simulation module assesses the risks of short-term anomalies and long-term ecological evolution by setting a preset risk threshold. Calculate relative risk This represents the ratio of the current impact to the threshold risk, reflecting the degree of short-term anomaly and the accumulation of risk over time. , The calibration coefficient represents a comprehensive risk assessment that combines short-term and long-term risk accumulation, resulting in:

[0074] ,

[0075] In the formula, R represents the overall risk index.

[0076] In this embodiment, the governance strategy generation module receives the comprehensive risk index and pollution source information, aligns the risk assessment results with the pollution source information space, classifies the risk areas based on the severity of the comprehensive risk index and the characteristics of the pollution sources, and defines the core governance objectives according to the risk assessment results and water environment management needs.

[0077] Specifically, based on historical governance cases and real-time data, a governance strategy library containing engineering measures, management measures, and ecological measures is constructed. According to risk classification and pollution source type, candidate strategies that match the current risk scenario are selected from the strategy library. The selected candidate strategies are combined to generate multiple collaborative governance solutions, including short-term, medium-term, and long-term solutions.

[0078] In this embodiment, the collaborative execution control module decomposes the collaborative governance scheme into executable equipment control instructions, which are then finely broken down according to equipment type, spatial location, and time sequence. The instructions are sent to the governance equipment control system in real time through a secure communication network. After receiving the instructions, the equipment executes the operation, and the equipment execution status is transmitted back to the control center in real time, including execution success, failure status, and actual parameter values.

[0079] This should be understood as follows: by using the real-time sensing dataset of the dynamic sensing anchor network module, key indicators, including water quality indicators, ecological indicators, and equipment operation indicators, are continuously collected. The real-time collected data is compared with the expected effects of the treatment plan to evaluate the efficiency of equipment execution and generate an effect evaluation report, which includes the degree of deviation, the scope of impact, and key influencing factors. Based on the effect evaluation results, the control strategy is dynamically adjusted.

[0080] In this embodiment, the real-time feedback optimization module integrates the real-time monitoring data of the dynamic sensing anchoring network module throughout the entire process, including water quality indicators, ecological response and equipment operating status, with the expected goals of the governance strategy generation module to establish a multi-dimensional effect evaluation system; based on the evaluation results, it identifies governance deviations and key influencing factors, and drives the collaborative execution control module to dynamically adjust equipment parameters or iteratively optimize strategies.

[0081] Working principle: By deploying fixed anchor reference points and mobile monitoring nodes, a dynamic monitoring network covering the entire area is formed. Fixed nodes provide reference coordinates and basic data support, while mobile nodes autonomously navigate to the pollution diffusion front based on conditions such as pollutant concentration thresholds, flow velocity changes, or sudden meteorological changes, and dynamically adjust the sampling density. During the movement, key water quality parameters such as water temperature, pH, and dissolved oxygen are collected simultaneously and transmitted back to the central system in real time through an underwater communication network. A data time alignment mechanism accurately matches heterogeneous data from multiple nodes on the time axis and integrates them into a structured real-time sensing dataset.

[0082] Based on real-time sensing datasets, water quality characteristics are scanned using a 3D fluorescence spectrometer to extract standardized water quality fingerprints to eliminate background interference. Combining hydrodynamic models and graph neural networks, water bodies, sediment interfaces, and biota are modeled as graph nodes, with pollutant migration paths as graph edges, constructing a 3D spatiotemporal map. By introducing sediment resuspension kinetic parameters to dynamically correct graph edge weights, the migration paths of pollutants among water, sediment, and organisms are deduced. Pollution source contribution rate calculation integrates water quality fingerprint similarity and a Bayesian probability model to output accurate pollution source information. After receiving pollution source information, the metabolic processes of pollutants in the aquatic ecosystem are simulated. The pollutant concentration change rate is quantified through a dynamic model, and the influence of environmental factors such as biomass and temperature on the degradation rate is considered to predict the transformation paths of pollutants in different ecological niches. Furthermore, based on the square of the pollutant concentration change rate and the ecological response time constant, a chain reaction index of the aquatic ecosystem is calculated to distinguish between short-term abnormal events and long-term ecological evolution risks. A comprehensive risk index is generated by combining preset risk thresholds. The ecological risk assessment results are integrated with the pollution source data. Pollution source information is used to classify risk areas into high, medium, and low levels through spatial alignment technology. Based on multi-objective collaboration, suitable strategies are selected from historical governance case libraries and real-time data to generate combined solutions including engineering, management, and ecological measures. Short-term emergency, medium-term remediation, and long-term recovery plans are formulated in stages, detailing spatial layout, time series, and resource requirements, and outputting executable governance solution reports. The governance solution is broken down into equipment control commands, and tasks are assigned according to type, location, and time series. Commands are sent to governance equipment in real time through a secure communication network, and execution status feedback is received synchronously. Combined with real-time data from the dynamic sensing module, the governance effect is compared with the expected goals, and a deviation analysis report is generated. Based on the feedback results, equipment parameters or execution strategies are dynamically adjusted, and full-process data is integrated to establish a multi-dimensional evaluation system to quantify governance deviations and key influencing factors. A closed-loop feedback mechanism drives the collaborative execution control module to adjust equipment parameters or iterate governance strategies, and the governance strategy library and equipment control logic are updated synchronously. Based on risk thresholds and dynamic weight adjustments, the multi-objective collaborative balance is optimized.

[0083] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their likenesses.

[0084] The present invention and its embodiments have been described above. This description is not restrictive, and the accompanying drawings are only one embodiment of the present invention; the actual structure is not limited thereto. In conclusion, if those skilled in the art are inspired by this description and design similar structures and embodiments without departing from the spirit of the invention, such designs should fall within the protection scope of the present invention.

Claims

1. A river water environment management monitoring and analysis system based on artificial intelligence, characterized in that: It includes a dynamic perception and anchoring network module, a three-dimensional pollution source map module, an ecological metabolism simulation module, a governance strategy generation module, a collaborative execution control module, and a real-time feedback optimization module; The dynamic sensing anchoring network module collects multi-dimensional information about the river water body by deploying monitoring nodes whose positions can be dynamically adjusted, and generates a real-time sensing dataset. The three-dimensional pollution source mapping module dynamically constructs a three-dimensional spatiotemporal map of pollutants based on a real-time sensing dataset and outputs pollution source information. The ecological metabolism simulation module simulates the chain effects of aquatic ecosystems based on pollution source information, and assesses the risks of short-term anomalies and long-term ecological evolution. The governance strategy generation module generates a multi-objective collaborative governance scheme for ecological restoration based on the results of ecological evolution risk assessment and pollution source information. The collaborative execution control module transforms the collaborative governance scheme into specific control commands, drives the governance equipment to execute, and makes dynamic adjustments based on real-time feedback on the governance effect. The real-time feedback optimization module performs feedback optimization based on the perceived full-process data of regulation and the final governance effect; The three-dimensional pollution source tracing mapping module includes the following steps for constructing a three-dimensional spatiotemporal map: based on a real-time sensing dataset, scanning is performed using a three-dimensional fluorescence spectrometer to obtain the excitation wavelength. With the emission wavelength The three-dimensional fluorescence spectral data were used to obtain the original fluorescence intensity matrix. The water quality fingerprint was obtained by standardizing the original fluorescence intensity matrix. Based on the hydrodynamic model, the fluorescent fingerprint features of water quality are spatiotemporally correlated with water flow velocity, flow direction and water depth parameters. The water body, sediment interface and biota are used as graph nodes and the pollutant migration path is used as graph edges to construct a three-dimensional spatiotemporal map of pollutants. The three-dimensional pollution source mapping module obtains pollutant migration patterns from real-time sensing datasets and constructs a basic diffusion model. The calculation shows a linear relationship between the pollutant concentration and the concentration, which is achieved as follows: ， In the formula, The value represents the pollutant concentration considering the standardized water quality fingerprint, and k represents the correlation coefficient of the standardized water quality fingerprint. The three-dimensional pollution source tracing mapping module includes a pollution source contribution rate step based on water quality fingerprinting. The contribution rate of pollution sources is calculated as follows: ， In the formula, This indicates that among the observed water quality fingerprints, the pollution sources... The posterior probability, Indicates the source of pollution Under the condition of discharge, the probability of observing water quality fingerprints is integrated and standardized to obtain water quality fingerprints, pollutant concentrations have a linear relationship and pollution source contribution rate to generate pollution source information; The ecological metabolism simulation module assesses short-term anomalies and long-term ecological evolution risks, with a preset risk threshold of [missing information]. Calculate relative risk Long-term risk accumulation , The calibration coefficient represents a comprehensive risk assessment that combines short-term and long-term risk accumulation, resulting in: ， In the formula, R represents the overall risk index.

2. The river water environment management monitoring and analysis system based on artificial intelligence according to claim 1, characterized in that: The dynamic sensing and anchoring network module divides the monitoring area into high, medium, and low-risk sub-regions based on river water characteristics, historical pollution data, and environmentally sensitive points. Fixed anchoring reference points are set at key river nodes, and precise coordinates are obtained through the BeiDou system. Mobile monitoring nodes are deployed on the main river channel. Each node is equipped with an autonomous navigation system and underwater propulsion device to move autonomously within the river. When pollutant concentration exceeds the standard, the system automatically plans a movement path and quickly deploys the mobile node to the pollution diffusion front. Node movement trigger conditions are set, and hydrodynamic and biological characteristic parameters are acquired in real time. Sampling density is increased in high-pollution areas, and sampling frequency is reduced in clean areas. Multi-dimensional data is integrated into a structured real-time sensing dataset.

3. The river water environment management monitoring and analysis system based on artificial intelligence according to claim 1, characterized in that: The ecological metabolism simulation module simulates pollutant migration and transformation steps: receiving pollution source information and simulating pollutant migration and transformation, which is achieved as follows: ， In the formula, Indicates the rate of change in pollutant concentration. C represents a small change over time, and C represents the pollutant concentration. This represents the fundamental degradation rate constant of pollutants. Indicates the current biomass. Indicates the initial biomass. This represents the temperature sensitivity coefficient, where T represents the water temperature. Indicates the reference water temperature.

4. The river water environment management monitoring and analysis system based on artificial intelligence according to claim 3, characterized in that: The ecological metabolism simulation module describes the chain reaction impact steps in the aquatic ecosystem: based on the pollutant concentration change rate, the square of the pollutant concentration change rate represents the impact intensity, and the aquatic ecosystem chain impact index is implemented as follows: ， In the formula, Indicating the cascading impact index of aquatic ecosystems, Indicates the influence coefficient. This represents the response time constant of an aquatic ecosystem.

5. The river water environment management monitoring and analysis system based on artificial intelligence according to claim 1, characterized in that: The governance strategy generation module receives the comprehensive risk index and pollution source information, aligns the risk assessment results with the pollution source information space, classifies the risk areas based on the severity of the comprehensive risk index and the characteristics of the pollution sources, defines core governance objectives based on the risk assessment results and water environment management needs, constructs a governance strategy library containing engineering measures, management measures and ecological measures based on historical governance cases and real-time data, selects candidate strategies that match the current risk scenario from the strategy library according to the risk classification and pollution source type, and combines the selected candidate strategies to generate multiple collaborative governance solutions.

6. The river water environment management monitoring and analysis system based on artificial intelligence according to claim 1, characterized in that: The collaborative execution control module decomposes the collaborative governance plan into executable equipment control commands. These commands are sent to the governance equipment control system in real time via a secure communication network. After receiving the commands, the equipment executes the operation, and the equipment execution status is transmitted back to the control center in real time, including execution success, failure status, and actual parameter values. Through the real-time sensing dataset of the dynamic sensing anchoring network module, key indicators, including water quality indicators, ecological indicators, and equipment operation indicators, are continuously collected. The real-time collected data is compared with the expected effect of the governance plan to evaluate the equipment execution efficiency and generate an effect evaluation report, including the degree of deviation, the scope of impact, and key influencing factors. Based on the effect evaluation results, the control strategy is dynamically adjusted.

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