Artificial intelligence-based river regulation data analysis and management system and method
By using an AI-based river management data analysis and management system, combined with deep learning algorithms and water layer segmentation, the problem of ignoring the stratification characteristics of pollutants in existing technologies has been solved. This enables accurate prediction and targeted treatment of river water quality deterioration risks, improving both governance and decision-making efficiency.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- SHANGHAI QUANQI WATER ENG DESIGN CO LTD
- Filing Date
- 2025-11-21
- Publication Date
- 2026-04-28
AI Technical Summary
Existing technologies cannot accurately locate pollution sources and diffusion paths, and ignore the vertical stratification of pollutants, resulting in superficial data analysis, a lack of predictive and insightful capabilities, and fragmented data on sedimentation, floating matter, and water quality, making it difficult to form a comprehensive judgment.
An AI-based river management data analysis and management system is adopted, which uses modules for water layer division, sedimentation collection, floating collection, water quality collection, diffusion analysis, and decision base, combined with deep learning algorithms to construct an impact diffusion model, thereby achieving multi-dimensional data fusion analysis and accurate prediction.
It has enabled accurate prediction and targeted management of the risk of river water quality deterioration, improved decision-making efficiency and resource utilization efficiency, changed the passive management model to proactive prevention, and realized the automatic mapping from complex data to management decisions.
Smart Images
Figure CN121936699A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of intelligent management technology, specifically to a data analysis and management system and method for river management based on artificial intelligence. Background Technology
[0002] With the rapid advancement of industrialization and urbanization, waterways face multiple problems, including siltation and blockage, pollution from floating debris, eutrophication, and black and odorous water bodies. Waterways are dynamic, three-dimensional, and organic ecosystems, where the distribution, migration, and transformation patterns of pollutants vary significantly across different water layers. Sensor technology and wireless communication networks enable massive data collection, cloud computing and edge computing provide powerful computing support, and breakthroughs in AI algorithms such as deep learning in time series prediction and pattern recognition provide the technological foundation for extracting patterns from complex data and predicting risks.
[0003] Traditional methods typically monitor water quality only at the surface or a fixed depth, ignoring the vertical stratification of pollutants. This makes it impossible to accurately locate the precise position and diffusion path of pollution sources. Data analysis is superficial, with existing systems mostly limited to simple data statistics, display, and threshold alarms, lacking predictive and insightful capabilities. Sedimentation data, floating debris data, and water quality data are fragmented, making it difficult to form a comprehensive judgment. Summary of the Invention
[0004] (a) Technical problems to be solved
[0005] In view of the above-mentioned shortcomings of the existing technology, the present invention provides a river management data analysis and management system and method based on artificial intelligence, which can effectively solve the problems of the existing technology.
[0006] (II) Technical Solution
[0007] To achieve the above objectives, the present invention provides the following technical solution:
[0008] This invention discloses an artificial intelligence-based river management data analysis and management system, comprising:
[0009] The main control module is used to control the data flow editing and command transmission of each functional module; it realizes inter-module communication through cloud platform or edge computing architecture, supports remote monitoring and distributed data processing, and has log recording and performance analysis functions for system operation status evaluation and optimization.
[0010] The water layer division module is used to define and divide the water layers of the target water area. Based on hydrological characteristics, the water area is divided into multiple independent or interactive water layers, and a unique identifier is assigned to each water layer. The water layer division module performs clustering based on the geographic information system data, water depth measurement data and flow velocity distribution data of the target water area, and defines the water layers as surface, middle and bottom layers. Each water layer has a dynamic boundary adjustment function to adapt to seasonal hydrological changes.
[0011] The sedimentation acquisition module is used to acquire data on the accumulation of obstructions in a specified water layer through a sensor network;
[0012] The floating acquisition module is used to acquire data on the coverage of floating objects in a specified water layer through image acquisition equipment or buoy sensors.
[0013] The water quality acquisition module is used to acquire water quality data in real time from the designated water layer where the sedimentation acquisition module and the floating acquisition module are located, including pH value, dissolved oxygen, turbidity, chemical oxygen demand and biological indicators;
[0014] The diffusion analysis unit is used to extract several features based on the feedback data from the sedimentation collection module, the floating collection module, and the water quality collection module, and input them into the pre-built impact diffusion model. The model outputs the probability of water quality deterioration risk of a certain water layer in the future preset period, forming a risk dataset.
[0015] The early warning module is used to compare each data point in the risk dataset with a preset standard threshold data one by one. If the risk probability exceeds the threshold, it is judged as abnormal data, and an alarm signal is generated according to the degree of threshold violation of the abnormal data.
[0016] The decision library module stores predefined dredging plans, floating debris removal strategies, or water purification measures, outputs management decision content that matches the corresponding water layer, and triggers the execution mechanism or display interface to respond through the main control module.
[0017] The data indexing module is used to index the data based on the water layer identifier and threshold violation information of the abnormal data, and to make associated decisions with the decision base module.
[0018] Furthermore, the blockage accumulation data in the sedimentation acquisition module includes sediment thickness, composition distribution, and accumulation rate; the floating debris coverage data in the floating acquisition module includes floating debris type, density, and movement trajectory.
[0019] Furthermore, the diffusion analysis unit has sub-modules deployed at its lower levels, including a feature extraction module, a model building module, and a data extraction module. The 52 module interacts with the feature extraction module and the data extraction module via a wireless network.
[0020] The feature extraction module is used to extract several feature coefficients from the data obtained from the sedimentation collection module, the floating collection module, and the water quality collection module.
[0021] The model building module is used to build an impact diffusion model through deep learning algorithms. The model takes feature coefficients as input and outputs the probability of water quality deterioration risk of the corresponding water layer in the future preset period. The deep learning algorithm includes a convolutional neural network, which is used to learn the nonlinear relationship between water quality parameters and deterioration trend.
[0022] The data extraction module is used to obtain the deterioration risk probability of multiple water layers from the feature extraction module, and encapsulate the water layer identifier, deterioration risk probability value and timestamp data into a structured risk dataset.
[0023] Furthermore, the process of the feature extraction module extracting several feature coefficients includes the following steps:
[0024] The raw water quality data acquired by the water quality acquisition module is preprocessed, including data cleaning, outlier removal and normalization, in order to eliminate noise and dimensional effects.
[0025] Based on the preprocessed data, statistical features, time-series features, and frequency domain features are included. The statistical features include the mean, variance, skewness, and kurtosis of water quality parameters within a specified time window. The time-series features include the trend, volatility, and autocorrelation function calculated through a sliding window. The frequency domain features include the spectral energy and dominant frequency components extracted through fast Fourier transform or wavelet transform.
[0026] A feature selection algorithm is used to screen out key feature coefficients from the statistical features. The feature selection algorithm includes principal component analysis, mutual information evaluation, or recursive feature elimination to reduce dimensionality and retain features that are highly correlated with water quality deterioration. The feature coefficients are used as input vectors for the impact diffusion model.
[0027] Furthermore, the construction and operation process of the influence diffusion model in the model construction module is as follows:
[0028] The model architecture is constructed using a convolutional neural network framework, and historical water quality data is used as training samples. The historical water quality data includes sedimentation data, floating object data and related water quality indicators of multiple water layers to adapt to the processing of spatial and temporal data. During the model training process, the network weights are optimized through the backpropagation algorithm to minimize the prediction error, and the cross-validation method is used to evaluate the model's generalization ability to ensure that the model can accurately capture the nonlinear relationship between water quality deterioration and feature coefficients.
[0029] After the model is built, during the deployment phase, the feature extraction module obtains the feature coefficients of the current water layer from the water quality acquisition module in real time and inputs them into the pre-trained impact diffusion model. The model calculates and outputs the probability of deterioration risk of the corresponding water layer in the future preset period through forward propagation.
[0030] Furthermore, the expression in the model building module that influences the calculation of the probability of deterioration risk in the diffusion model is as follows:
[0031]
[0032] In the formula, P represents the probability of the corresponding water layer deteriorating in future cycles, with a value range of [0,1], u0 represents the model bias term, which is also obtained through training and optimization, e represents the natural constant, n represents the total number of feature coefficients, and w i The model weights corresponding to the i-th feature coefficients are obtained by optimizing them based on historical data during the training phase using a deep learning algorithm. i This represents the i-th characteristic coefficient.
[0033] Furthermore, the main control module is interconnected with the water layer division module and the decision base module via a wireless network; the water layer division module is interconnected with the sedimentation acquisition module and the floating acquisition module via a wireless network; the water quality acquisition module is interconnected with the sedimentation acquisition module, the floating acquisition module, and the diffusion analysis unit via a wireless network; the diffusion analysis unit is interconnected with the early warning module via a wireless network; and the data indexing module is interconnected with the early warning module and the decision base module via a wireless network.
[0034] The AI-based data analysis and management method for river management includes the following steps:
[0035] Step 1: Based on the hydrological characteristics of the target water area, the water layer is defined and divided using geographic information system data and cluster analysis. The water area is divided into multiple independent or interactive water layers, and a unique identifier is assigned to each water layer to adapt to seasonal changes and dynamic boundary adjustments.
[0036] Step 2: In the divided water layers, acquire blockage accumulation data, floating debris coverage data, and corresponding water quality data of the specified water layers through sensor networks and image acquisition devices. The blockage accumulation data includes sediment thickness and accumulation rate, the floating debris coverage data includes type and density, and the water quality data includes pH value, dissolved oxygen, and chemical oxygen demand.
[0037] Step 3: Extract several feature coefficients from the collected water quality data, construct an impact diffusion model using deep learning algorithms, and output the probability of deterioration risk of the corresponding water layer in the future preset period, with the feature coefficients as input.
[0038] Step 4: Integrate the deterioration risk probabilities of multiple water layers into a structured risk dataset, including water layer identifiers, risk probability values, and timestamps;
[0039] Step 5: Compare each data point in the risk dataset with the preset standard threshold data one by one. If the risk probability exceeds the threshold, it is judged as abnormal data, and a graded alarm signal is generated according to the degree of threshold violation.
[0040] Step 6: Based on the water layer identifier and violation information of the abnormal data, index the predefined decision base through semantic matching or fuzzy logic, and output the management decision content that matches the corresponding water layer.
[0041] Furthermore, in step 2, the data acquisition device adopts an Internet of Things (IoT) deployment method, including a multi-node sensor array and a wireless communication unit. It acquires data on obstruction accumulation through sonar or laser rangefinders and identifies floating object coverage data through cameras combined with computer vision algorithms.
[0042] Furthermore, the management decision-making content in step 6 is linked with external governance execution equipment, including automatically starting the dredging robot, adjusting the sluice gate or releasing the purification agent, and transmitting the execution result data back to the management end through a feedback mechanism for system adaptive learning.
[0043] (III) Beneficial Effects
[0044] Compared with the known prior art, the technical solution provided by this invention has the following beneficial effects:
[0045] 1. By constructing an impact diffusion model and using artificial intelligence deep learning algorithms to fuse and analyze multi-dimensional, real-time collected data, it is possible to output the probability of deterioration risk in a specific future period. This enables management departments to identify high-risk areas and intervene in advance before water quality or siltation problems actually occur or worsen, thus moving the governance checkpoint forward and helping to change the logic of river management.
[0046] 2. Through a systematic module and step design, water layer division, siltation, floating, water quality collection, feature extraction, risk prediction, early warning and decision indexing are organically linked. Through data-driven approach, the original physical monitoring data is automatically transformed into management decision content with direct guiding significance. It realizes the automatic mapping from complex data to executable solutions. Through a highly integrated and intelligent closed-loop processing flow, information silos are avoided and the decision-making efficiency from monitoring to action is greatly improved.
[0047] 3. By introducing the concept of water layer division, the target water area is defined in a three-dimensional and refined manner, enabling data collection and analysis to accurately locate different water depths. The system can output management decisions that match specific water layers, achieving targeted governance. The refined management model based on three-dimensional spatial structure effectively improves resource utilization efficiency and the actual effect of governance measures. Attached Figure Description
[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.
[0049] Figure 1 This is a schematic diagram of the overall framework of the river management data analysis and management system in this invention;
[0050] Figure 2 This is a schematic diagram of the feature extraction module in this invention.
[0051] The numbers in the diagram represent: 1. Main control module; 2. Water layer division module; 3. Sedimentation collection module; 4. Floating collection module; 5. Water quality collection module; 6. Diffusion analysis unit; 61. Feature extraction module; 62. Model building module; 63. Data extraction module; 7. Early warning module; 8. Decision base module; 9. Data indexing module. Detailed Implementation
[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.
[0053] The present invention will be further described below with reference to embodiments.
[0054] This embodiment of the river management data analysis and management system based on artificial intelligence, such as Figure 1 and Figure 2 As shown, it includes:
[0055] Main control module 1 is used for overall control of data flow editing and instruction transmission of various functional modules; it realizes inter-module communication through cloud platform or edge computing architecture, supports remote monitoring and distributed data processing, and has log recording and performance analysis functions for system operation status evaluation and optimization.
[0056] The water layer division module 2 is used to define and divide the water layers of the target water area. Based on hydrological characteristics, the water area is divided into multiple independent or interactive water layers, and a unique identifier is assigned to each water layer. The water layer division module 2 performs clustering based on the geographic information system data, water depth measurement data and flow velocity distribution data of the target water area, and defines the water layers as surface, middle and bottom layers. Each water layer has a dynamic boundary adjustment function to adapt to seasonal hydrological changes.
[0057] The sedimentation acquisition module 3 is used to acquire data on the accumulation of obstructions in a specified water layer through a sensor network; the obstruction accumulation data includes sediment thickness, composition distribution and accumulation rate; the floating object coverage data in the floating acquisition module 4 includes floating object type, density and movement trajectory.
[0058] The floating acquisition module 4 is used to acquire data on the coverage of floating objects in a specified water layer through image acquisition equipment or buoy sensors.
[0059] The water quality acquisition module 5 is used to acquire water quality data in real time from the designated water layer where the sedimentation acquisition module 3 and the floating acquisition module 4 are located, including pH value, dissolved oxygen, turbidity, chemical oxygen demand and biological indicators.
[0060] The diffusion analysis unit 6 is used to extract several features based on the feedback data from the sedimentation collection module 3, the floating collection module 4 and the water quality collection module 5, and input them into the pre-built impact diffusion model. The model outputs the probability of water quality deterioration risk of a certain water layer in the future preset period, forming a risk dataset.
[0061] The diffusion analysis unit 6 has sub-modules deployed below it, including a feature extraction module 61, a model building module 62, and a data extraction module 63. 52 interacts with the feature extraction module 61 and the data extraction module 63 via a wireless network.
[0062] The feature extraction module 61 is used to extract several feature coefficients from the data obtained by the sedimentation collection module 3, the floating collection module 4 and the water quality collection module 5;
[0063] The model building module 62 is used to build an impact diffusion model through a deep learning algorithm. The model takes feature coefficients as input and outputs the probability of water quality deterioration risk of the corresponding water layer in the future preset period. The deep learning algorithm includes a convolutional neural network, which is used to learn the nonlinear relationship between water quality parameters and deterioration trend.
[0064] The data extraction module 63 is used to obtain the deterioration risk probability of multiple water layers from the feature extraction module, and encapsulate the water layer identifier, deterioration risk probability value and timestamp data into a structured risk dataset.
[0065] The process of feature extraction module 61 extracting several feature coefficients includes the following steps:
[0066] The raw water quality data acquired by the water quality acquisition module is preprocessed, including data cleaning, outlier removal and normalization, in order to eliminate noise and dimensional effects.
[0067] Based on the preprocessed data, statistical features, time-series features, and frequency domain features are included. The statistical features include the mean, variance, skewness, and kurtosis of water quality parameters within a specified time window. The time-series features include the trend, volatility, and autocorrelation function calculated through a sliding window. The frequency domain features include the spectral energy and dominant frequency components extracted through fast Fourier transform or wavelet transform.
[0068] Feature selection algorithms are used to screen out key feature coefficients from the statistical features. Feature selection algorithms include principal component analysis, mutual information evaluation, or recursive feature elimination to reduce dimensionality and retain features that are highly correlated with water quality deterioration. The feature coefficients are used as input vectors for the impact diffusion model.
[0069] The early warning module 7 is used to compare each data in the risk dataset with the preset standard threshold data one by one. If the risk probability exceeds the threshold, it is judged as abnormal data, and an alarm signal is generated according to the degree of threshold violation of the abnormal data.
[0070] The decision library module 8 is used to store predefined dredging plans, floating debris removal strategies, or water purification measures, output management decision content that matches the corresponding water layer, and trigger the execution mechanism or display interface to respond through the main control module 1.
[0071] Data indexing module 9 is used to index the water layer identifier and threshold violation information of abnormal data to make associated decisions in decision base module 8.
[0072] The main control module 1 is connected to the water layer division module 2 and the decision base module 8 via a wireless network. The water layer division module 2 is connected to the sedimentation acquisition module 3 and the floating acquisition module 4 via a wireless network. The water quality acquisition module 5 is connected to the sedimentation acquisition module 3, the floating acquisition module 4 and the diffusion analysis unit 6 via a wireless network. The diffusion analysis unit 6 is connected to the early warning module 7 via a wireless network. The data index module 9 is connected to the early warning module 7 and the decision base module 8 via a wireless network.
[0073] Compared with existing technologies, the use of artificial intelligence deep learning methods to refine the water layer division and integrate and analyze multi-source data has achieved a fundamental shift from passive monitoring to proactive risk prediction. It can output the probability of deterioration of specific water layers in advance based on dynamic characteristic coefficients and automatically associate them with precise governance decisions. This has completely changed the traditional river management model that relies on human experience and has a delayed response, and improved the predictability, accuracy and overall efficiency of governance.
[0074] At other levels, this embodiment also provides an artificial intelligence-based river management data analysis and management method, including the following steps:
[0075] Step 1: Based on the hydrological characteristics of the target water area, the water layer is defined and divided using geographic information system data and cluster analysis. The water area is divided into multiple independent or interactive water layers, and a unique identifier is assigned to each water layer to adapt to seasonal changes and dynamic boundary adjustments.
[0076] Step 2: Within the defined water layers, data on obstruction accumulation, floating debris coverage, and corresponding water quality are acquired using a sensor network and image acquisition equipment. Obstruction accumulation data includes sediment thickness and accumulation rate; floating debris coverage data includes type and density; and water quality data includes pH, dissolved oxygen, and chemical oxygen demand. The acquisition equipment is deployed using an IoT approach, comprising a multi-node sensor array and a wireless communication unit. It acquires obstruction accumulation data via sonar or laser rangefinders, identifies floating debris coverage data using cameras combined with computer vision algorithms, and performs real-time data upload and redundancy verification.
[0077] Step 3: Extract several feature coefficients from the collected water quality data, construct an impact diffusion model using deep learning algorithms, and output the probability of deterioration risk of the corresponding water layer in the future preset period using the feature coefficients as input. The deep learning algorithm includes convolutional neural networks or recurrent neural networks, which are used to learn the nonlinear relationship between water quality parameters and deterioration trends, and train and optimize the model using historical data.
[0078] Step 4: Integrate the deterioration risk probabilities of multiple water layers into a structured risk dataset, including water layer identifiers, risk probability values, and timestamps, to achieve unified data management and fast access;
[0079] Step 5: Compare each data point in the risk dataset with the preset standard threshold data one by one. If the risk probability exceeds the threshold, it is judged as abnormal data, and a graded alarm signal is generated according to the degree of threshold violation.
[0080] Step 6: Based on the water layer identifier and violation information of the abnormal data, index the predefined decision library through semantic matching or fuzzy logic, and output the management decision content that matches the corresponding water layer; the management decision content is linked with the external governance execution equipment, including automatically starting the dredging robot, adjusting the water gate or releasing the purification agent, and the execution result data is sent back to the management end through the feedback mechanism for the system to adaptively learn.
[0081] This embodiment provides a process for constructing and operating an influence diffusion model as follows:
[0082] The model architecture is constructed using a convolutional neural network framework, and historical water quality data is used as training samples. The historical water quality data includes sedimentation data, floating object data and related water quality indicators of multiple water layers to adapt to the processing of spatial and temporal data. During the model training process, the network weights are optimized through the backpropagation algorithm to minimize the prediction error, and the cross-validation method is used to evaluate the model's generalization ability to ensure that the model can accurately capture the nonlinear relationship between water quality deterioration and feature coefficients.
[0083] After the model is built, during the deployment phase, the feature extraction module 61 obtains the feature coefficients of the current water layer from the water quality acquisition module in real time and inputs them into the pre-trained impact diffusion model. The model calculates and outputs the probability of deterioration risk of the corresponding water layer in the future preset period through forward propagation. This probability represents the degree of possibility of water quality deterioration in the water layer and serves as the basis for the risk dataset. In addition, the model supports a regular update mechanism, incorporating the latest monitoring data through incremental learning to dynamically adapt to changes in the river environment and improve prediction accuracy. The entire construction and working logic does not require manual intervention, realizing end-to-end automated risk prediction.
[0084] The expression for calculating the probability of deterioration risk using the impact diffusion model is as follows:
[0085]
[0086] In the formula, P represents the probability of the corresponding water layer deteriorating in future cycles, with a value range of [0,1], u0 represents the model bias term, which is also obtained through training and optimization, e represents the natural constant, n represents the total number of feature coefficients, and w i The model weights corresponding to the i-th feature coefficients are obtained by optimizing them based on historical data during the training phase using a deep learning algorithm. i represents the i-th feature coefficient; this formula maps the linear combination of feature coefficients to probability values through the sigmoid function, reflecting the nonlinear risk of water layer deterioration; during model training, gradient descent is used to minimize the cross-entropy loss function, and regularization techniques are combined to prevent overfitting, so as to ensure prediction accuracy and generalization ability.
[0087] In summary, this invention achieves refined monitoring of the three-dimensional space of the river channel through intelligent water layer segmentation and collaborative collection of data on siltation, floating debris, and water quality. Combined with an impact diffusion model constructed using deep learning, it predicts the probability of future cyclical deterioration risks for each water layer using characteristic coefficients, transforming traditional passive management into proactive prevention. Multi-level intelligent early warning is achieved based on threshold comparison of risk datasets, and a pre-set expert decision-making database is matched using semantic indexing. Integrating IoT sensing and AI analysis reduces the cost of manual inspections. By accurately locating abnormal water layers and matching management solutions, it avoids resource waste in overall management and improves management efficiency.
[0088] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims
1. A river management data analysis and management system based on artificial intelligence, characterized in that: include: The main control module is used for overall control of data stream editing and instruction transmission of various functional modules; The water layer division module is used to define and divide the water layers of the target water area. It divides the water area into multiple independent water layers based on hydrological characteristics and assigns a unique identifier to each water layer. The sedimentation acquisition module is used to acquire data on the accumulation of obstructions in a specified water layer through a sensor network; The floating acquisition module is used to acquire data on the coverage of floating objects in a specified water layer; The water quality acquisition module is used to acquire water quality data in real time from the designated water layer where the sedimentation acquisition module and the floating acquisition module are located, including pH value, dissolved oxygen, turbidity, chemical oxygen demand and biological indicators; The diffusion analysis unit is used to extract several features based on the feedback data from the sedimentation collection module, the floating collection module, and the water quality collection module, and input them into the pre-built impact diffusion model. The model outputs the probability of water quality deterioration risk of a certain water layer in the future preset period, forming a risk dataset. The early warning module is used to compare each data point in the risk dataset with a preset standard threshold data one by one. If the risk probability exceeds the threshold, it is judged as abnormal data, and an alarm signal is generated according to the degree of threshold violation of the abnormal data. The decision library module is used to store predefined dredging plans, floating debris removal strategies, or water purification measures. The data indexing module is used to index the data based on the water layer identifier and threshold violation information of the abnormal data, and to make associated decisions with the decision base module.
2. The river management data analysis and management system based on artificial intelligence according to claim 1, characterized in that, The obstruction accumulation data in the sedimentation acquisition module includes sediment thickness, composition distribution, and accumulation rate; the floating object coverage data in the floating acquisition module includes floating object type, density, and movement trajectory.
3. The river management data analysis and management system based on artificial intelligence according to claim 1, characterized in that, The diffusion analysis unit has sub-modules deployed below it, including a feature extraction module, a model building module, and a data extraction module. The 52 module interacts with the feature extraction module and the data extraction module via a wireless network. The feature extraction module is used to extract several feature coefficients from the data obtained from the sedimentation collection module, the floating collection module, and the water quality collection module. The model building module is used to build an impact diffusion model through deep learning algorithms. The model takes feature coefficients as input and outputs the probability of water quality deterioration risk of the corresponding water layer in the future preset period. The data extraction module is used to obtain the deterioration risk probability of multiple water layers from the feature extraction module, and encapsulate the water layer identifier, deterioration risk probability value and timestamp data into a structured risk dataset.
4. The river management data analysis and management system based on artificial intelligence according to claim 3, characterized in that, The process of extracting several feature coefficients by the feature extraction module includes the following steps: The raw water quality data acquired by the water quality acquisition module is preprocessed; Based on the preprocessed data, statistical features, time-series features, and frequency domain features are included. The statistical features include the mean, variance, skewness, and kurtosis of water quality parameters within a specified time window. The time-series features include the trend, volatility, and autocorrelation function calculated through a sliding window. The frequency domain features include the spectral energy and dominant frequency components extracted through fast Fourier transform or wavelet transform. A feature selection algorithm is used to filter out key feature coefficients from the statistical features, and the feature coefficients are used as input vectors for the influence diffusion model.
5. The river management data analysis and management system based on artificial intelligence according to claim 3, characterized in that, The construction and operation process of the influence diffusion model in the model construction module is as follows: The model architecture is constructed using a convolutional neural network framework, and historical water quality data is used as training samples. The historical water quality data includes sedimentation data, floating object data and related water quality indicators of multiple water layers. During the model training process, the network weights are optimized through the backpropagation algorithm to minimize the prediction error. After the model is built, during the deployment phase, the feature extraction module obtains the feature coefficients of the current water layer from the water quality acquisition module in real time and inputs them into the pre-trained impact diffusion model. The model calculates and outputs the probability of deterioration risk of the corresponding water layer in the future preset period through forward propagation.
6. The river management data analysis and management system based on artificial intelligence according to claim 3, characterized in that, The expression for calculating the probability of deterioration risk in the diffusion model within the model building module is as follows: In the formula, P represents the probability of the corresponding water layer deteriorating in future cycles, u0 represents the model bias term, e represents the natural constant, n represents the total number of characteristic coefficients, and w i x represents the model weight corresponding to the i-th feature coefficient. i This represents the i-th characteristic coefficient.
7. The river management data analysis and management system based on artificial intelligence according to claim 1, characterized in that, The main control module interacts with the water layer division module and the decision base module via a wireless network. The water layer division module interacts with the sedimentation acquisition module and the floating acquisition module via a wireless network. The water quality acquisition module interacts with the sedimentation acquisition module, the floating acquisition module, and the diffusion analysis unit via a wireless network. The diffusion analysis unit interacts with the early warning module via a wireless network. The data index module interacts with the early warning module and the decision base module via a wireless network.
8. A method for river management data analysis and management based on artificial intelligence, wherein the method is an implementation method of the river management data analysis and management system based on artificial intelligence as described in any one of claims 1-7, characterized in that, Includes the following steps: Step 1: Based on the hydrological characteristics of the target water area, the water layers are defined and divided using geographic information system data and cluster analysis. The water area is divided into multiple independent or interactive water layers, and a unique identifier is assigned to each water layer. Step 2: In the divided water layers, acquire data on blockage accumulation, floating debris coverage, and water quality of the corresponding water layers through a sensor network and image acquisition equipment; Step 3: Extract several feature coefficients from the collected water quality data, construct an impact diffusion model using deep learning algorithms, and output the probability of deterioration risk of the corresponding water layer in the future preset period, with the feature coefficients as input. Step 4: Integrate the deterioration risk probabilities of multiple water layers into a structured risk dataset, including water layer identifiers, risk probability values, and timestamps; Step 5: Compare each data point in the risk dataset with the preset standard threshold data one by one. If the risk probability exceeds the threshold, it is judged as abnormal data, and a graded alarm signal is generated according to the degree of threshold violation. Step 6: Based on the water layer identifier and violation information of the abnormal data, index the predefined decision base through semantic matching or fuzzy logic, and output the management decision content that matches the corresponding water layer.
9. The artificial intelligence-based river management data analysis and management method according to claim 8, characterized in that, In step 2, the data acquisition device adopts an Internet of Things (IoT) deployment method, including a multi-node sensor array and a wireless communication unit. It acquires data on obstruction accumulation through sonar or laser rangefinders and identifies floating object coverage data through cameras combined with computer vision algorithms.
10. The artificial intelligence-based river management data analysis and management method according to claim 8, characterized in that, The management decision-making process in step 6 is linked with external governance execution equipment, including automatically starting the dredging robot, adjusting the sluice gate, or adding purification agents, and transmitting the execution result data back to the management terminal through a feedback mechanism.