Water conservancy project ecological construction method and system based on artificial intelligence

Abstract

The invention discloses a water conservancy project ecological construction method and system based on artificial intelligence, and relates to the technical field of ecological system simulation, and the method comprises the steps: collecting historical ecological event data of each region of a water conservancy project, analyzing the causal association between events based on an artificial intelligence model, and calculating and dynamically updating the association degree index between any two regions; constructing a regional association network based on the association degree indexes, calculating core scores of nodes in the network to identify a core region, and dynamically improving the monitoring sampling frequency of ecological data for the core region; and when an abnormal state is monitored in a single region, predicting the abnormal risk probability of other regions, and generating and recommending an optimal coping plan. Compared with the prior art, the method has the advantages that by constructing a dynamic regional correlation analysis system, the mutual influence between ecological regions is quantified, the monitoring resources are distributed uniformly to be intelligently and optimally configured based on the network core degree, and the monitoring efficiency and the capturing capacity of key information are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of ecosystem simulation, in particular to a water conservancy project ecological construction method and system based on artificial intelligence. BACKGROUND

[0002] With the continuous expansion of water conservancy projects and the increasing complexity of their impact on the surrounding ecological environment, the monitoring, evaluation and regulation of water conservancy project ecosystems have become a crucial and extremely challenging task. The existing technical solutions generally use fixed sensor networks in each subzone to set threshold triggers for alarm monitoring mode. This mode usually relies on a unified, pre-set monitoring frequency, and does not fully consider the differences in the roles played by different ecological regions in the system and their dynamic ecological connections with each other, resulting in the risk of uneconomical allocation of monitoring resources or insufficient capture of key information. At the same time, when an ecological indicator anomaly occurs in a single region, the existing system often responds to the alarm point in isolation, lacking effective prediction ability for the spread of abnormal states through hydrological, biological chains or material circulation pathways in the regional network.

[0003] Further, current water conservancy project ecological management decisions largely rely on manual experience analysis and static plans. This method is difficult to quantitatively and dynamically depict and utilize the causal and correlation relationships between complex regions, resulting in the inability to foresee the chain effects at the early stage of risk occurrence. In addition, when facing the problem of coordinated regulation of multiple regions and multiple targets, such as balancing ecological restoration effects, project operation safety and regulation costs, traditional methods are difficult to automatically generate globally optimal or near-optimal systematic response strategies, the decision-making process is lagging and inefficient, limiting the forward-looking and precision of ecological protection and restoration work. SUMMARY

[0004] To solve the above technical problems, a water conservancy project ecological construction method and system based on artificial intelligence are provided, which solve at least one of the technical problems mentioned in the background technology.

[0005] To achieve the above purposes, the technical solution adopted by the present application is: A water conservancy project ecological construction method based on artificial intelligence, comprising: Constructing a regional recognition correlation analysis system: collecting historical ecological event data of each region of the water conservancy project, analyzing the causal correlation between events based on an artificial intelligence model, and calculating and dynamically updating the correlation index between any two regions, wherein the higher the causal correlation between the historical events of the two regions, the higher the correlation index. Identifying core areas and increasing monitoring sampling frequency: based on the correlation index, a regional correlation network is constructed, the core score of the nodes in the network is calculated to identify the core areas, and the monitoring sampling frequency of ecological data is dynamically increased for the core areas; Abnormal state prediction and intelligent response: when an abnormal state is monitored in a single area, based on the regional correlation network and the current abnormal state, the abnormal risk probability of the remaining areas is predicted through a graph neural network model, and based on the prediction result, the optimal response plan is generated and recommended through a reinforcement learning model.

[0006] Preferably, the historical ecological event data of each area of the water conservancy project is collected, the causal correlation between events is analyzed based on an artificial intelligence model, and the correlation index between any two areas is calculated and dynamically updated, which specifically includes: Collect and preprocess data: collect historical ecological event data of each area of the water conservancy project, the event data includes event type, occurrence time, occurrence area and event intensity, and the data is cleaned, labeled and standardized pretreated; Calculate the directed causal correlation degree: for any two areas i and j, based on the time series causal inference model, the causal influence strength of area i on area j is calculated , the probability gain of area i state change leading to area j state change is calculated based on the probabilistic graph model , and the weights of the two and expert experience are fused , the directed causal correlation degree is calculated by formula , where α, β, γ are weight coefficients, and α+β+γ=1, Norm() represents the normalization function; Generate and update the correlation matrix: based on the calculated bidirectional directed causal correlation degree, the undirected correlation index is calculated by formula , all constitute the correlation matrix A, and the online learning mechanism is adopted to periodically dynamically update the correlation matrix A combined with real-time data.

[0007] Preferably, the causal influence strength of area i on area j calculated based on the time series causal inference model is specifically: Obtain the ecological index time series Xi(t) and Xj(t) of area i and area j, where t represents the time point, perform stationarity test on the time series, if the sequence is not stationary, perform difference processing until the stationarity test is passed, and adopt normalization processing to eliminate the dimension effect; Construct a vector autoregressive model of area j, the model includes: Restricted model: ; Complete model:​​ ; where p is the optimal lag order determined by AIC criterion, 、 are model coefficients, 、 is model residual; The F statistic is calculated by comparing the residual sum of squares of the restricted model and the full model through F test:

[0008] where 、 are the residual sum of squares of the restricted model and the full model respectively, T is the sample size, and the significance probability calculated based on the observed value of the F statistic and F distribution when testing the null hypothesis that "region i is not the Granger cause of region j" The causal influence strength is quantified as:

[0009] The probability gain of region i state change leading to region j state change is calculated based on the probabilistic graphical model Specifically: The ecological state of each region is discretized into a finite state set S = {normal, abnormal}, and a Bayesian network structure is constructed based on historical event data using a constrained learning algorithm, wherein the nodes represent regional state variables and the directed edges represent causal directions; The conditional probability distribution of each node in the Bayesian network is learned based on historical data using maximum likelihood estimation method, and then the probability gain is calculated through causal intervention:

[0010] where is the marginal probability of region j being abnormal, is the conditional probability of region j being abnormal when region i is intervened to force its state to be abnormal; When there are multiple ecological indicators, the time series causal influence strength and state probability gain corresponding to each indicator are calculated respectively, and the weighted average value of the calculation results of each indicator is taken as the final and .

[0011] Preferably, the region association network is constructed based on the correlation degree indicator, the coreness score of the nodes in the network is calculated to identify the core regions, and the monitoring sampling frequency of ecological data is dynamically improved for the core regions, specifically including: Each water conservancy project ecological region is taken as a node, and the correlation degree indicator To determine the weights of the edges connecting node i and node j, construct a weighted undirected graph G=(V,E,A), where V is the set of nodes, E is the set of edges, and A is the edge weight. Compute each node in the weighted undirected graph G Comprehensive core score Its calculation formula is ,in , , These are the weighting coefficients. They are nodes Normalized degree centrality, betweenness centrality, and eigenvector centrality, with threshold values ​​set. , will satisfy The region corresponding to the node is identified as the core region; For each core area Dynamically calculate its monitoring frequency The calculation formula is: ,in Let η be the base monitoring frequency, and μ be the core area frequency boosting coefficient and anomaly propagation sensitivity coefficient, respectively. I() is the indicator function, which is defined when any condition satisfies... The value is 1 when the associated neighbor region j is in an abnormal state, and 0 otherwise. As the correlation threshold, the final calculated frequency will be... The instructions are then sent to the monitoring equipment in the corresponding areas for execution.

[0012] Preferably, when an abnormal state is detected in a single area, based on the area association network and the current abnormal state, a graph neural network model is used to predict the probability of abnormal risk in other areas, and based on the prediction results, a reinforcement learning model is used to generate and recommend the optimal response plan, specifically including: When the monitoring system detects a certain area When ecological indicators exceed preset thresholds, an abnormal state signal is triggered, marking the state of that area as abnormal, and obtaining the ecological state feature vectors of all areas at the current moment. and the correlation matrix A; The state feature vector The correlation matrix A is input into a pre-trained graph neural network prediction model, and graph convolution operations are performed. Information is propagated layer by layer, and finally the probability vector of abnormal risks for all regions in the future time period Δt is output. ,in I is the identity matrix. for The degree matrix, Let be the trainable weight matrix of the l-th layer, and σ be the activation function; Current system status The input is fed into a reinforcement learning decision model, which, based on the objective of maximizing cumulative reward, uses a policy... Output the optimal response action sequence The reward function is defined as follows: ,in These are the weighting coefficients. For the cost of action, To constrain violations and penalties, the aforementioned action sequence will ultimately be recommended to managers as a response plan. Let be the probability of abnormal risk in region i within the future time period Δt.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention introduces and integrates artificial intelligence technologies such as causal inference, complex network analysis, graph neural networks, and reinforcement learning to construct a closed-loop system of "dynamic correlation analysis - proactive monitoring - intelligent prediction and decision-making." This system first dynamically quantifies and updates the ecological correlation between regions through causal analysis of historical ecological events, thereby connecting discrete monitoring points into a dynamically evolving "ecological correlation network." Then, by identifying core areas in the network, it achieves adaptive and optimized allocation of monitoring resources. Finally, when anomalies occur, it uses the network structure and current state to predict the risk diffusion path and, with the help of a reinforcement learning model, generates an optimal response plan that balances effectiveness and cost from a global perspective, thereby achieving refined, proactive, and intelligent construction and maintenance of the water conservancy project ecosystem. Attached Figure Description

[0014] Figure 1 This is a flowchart of the AI-based water conservancy engineering ecosystem construction method proposed in this solution; Figure 2 The flowchart shows the method proposed in this scheme for calculating and dynamically updating the correlation index between any two regions. Figure 3 This is a flowchart of the method proposed in this scheme for identifying core areas and dynamically increasing the monitoring sampling frequency; Figure 4 The flowchart for the method proposed in this solution to generate and recommend the optimal response plan is shown. Detailed Implementation

[0015] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0016] Reference Figure 1 As shown, an artificial intelligence-based method for constructing an ecological system for water conservancy projects includes: A regional correlation analysis system was constructed: historical ecological event data of various regions of the water conservancy project were collected, and causal relationships between events were analyzed based on artificial intelligence models. The correlation index between any two regions was calculated and dynamically updated. The higher the causal relationship between historical events between two regions, the higher the correlation index. Historical ecological event data, such as sudden changes in water quality and fluctuations in biological populations, were used to drive the artificial intelligence model for in-depth analysis. Through methods such as time-series causal inference and probabilistic graphical models, the model not only identified the direction of causal relationships between events but also calculated specific numerical indicators representing the intensity of influence. This correlation index is not static but is periodically updated through an online learning mechanism as new data flows in, ensuring that it reflects the latest dynamic interactions of the ecosystem and providing an accurate, time-varying relational basis for subsequent network construction and decision-making. Identifying core areas and increasing monitoring sampling frequency accordingly: A regional correlation network is constructed based on correlation indicators. The coreity score of nodes in the network is calculated to identify core areas, and the monitoring sampling frequency of ecological data is dynamically increased for these core areas. By comprehensively calculating network centrality indicators such as degree centrality and betweenness centrality of each node in the graph, core areas that occupy pivotal or bridging positions in the entire ecological network can be scientifically identified. Monitoring resources for these core areas are then optimized by dynamically increasing their data sampling frequency based on their coreity score and the abnormal states of their associated neighbors. This ensures that limited monitoring resources can prioritize coverage of key points that have the greatest impact on system stability and the highest information value, achieving a shift from "uniform monitoring" to "precise monitoring." Anomaly Prediction and Intelligent Response: When an anomaly is detected in a single area, the system predicts the probability of anomalies in other areas based on the area's correlation network and the current anomaly status using a graph neural network model. Based on the prediction results, an optimal response plan is generated and recommended through a reinforcement learning model. When an anomaly alarm is triggered in a certain area, the system takes the real-time status data of the entire network and the pre-constructed area correlation network as input and calls the pre-trained graph neural network model. This model simulates the propagation process of anomalies along the network edges through graph convolution operations, thereby outputting the potential risk probability of other areas in the future. On this basis, the reinforcement learning decision model takes into account the above risk probabilities, available control measures and their costs, ecological constraints, etc., and generates a set of optimal action sequences aimed at minimizing global risk and total cost through simulation and learning, such as adjusting gate opening and starting purification equipment. These are then recommended to managers in the form of visual suggestions, realizing a leap from passive response to proactive and intelligent control.

[0017] Specifically, refer to Figure 2 As shown, historical ecological event data are collected from various regions of the water conservancy project. Based on an artificial intelligence model, causal relationships between events are analyzed, and the correlation index between any two regions is calculated and dynamically updated. Specifically, this includes: Data collection and preprocessing: Collect historical ecological event data for each area under the jurisdiction of the water conservancy project. The event data includes event type, occurrence time, occurrence area and event intensity. The data is then cleaned, labeled and standardized for preprocessing. Calculate the directed causal correlation degree: For any two regions i and j, calculate the causal influence strength of region i on region j based on the time-series causal inference model. Based on a probabilistic graphical model, the probability gain of a change in state in region j caused by a change in state in region i is calculated. And integrate the weights of both and expert experience. Through formula Calculate the directed causal relationship degree , where α, β, γ are weight coefficients and satisfy α+β+γ=1, and Norm() represents the normalization function; Generating and updating the correlation matrix: Based on the calculated bidirectional directed causal correlation, using the formula... Calculate the undirected correlation index ,all A correlation degree matrix A is constructed, and an online learning mechanism is used to periodically and dynamically update the correlation degree matrix A in combination with real-time data. The periodic dynamic update adopts a time decay mechanism, which multiplies the undirected correlation degree index calculated based on the historical ecological event data in the most recent period and the undirected correlation degree index before the update by 0.5 and then adds them together to obtain the updated undirected correlation degree index.

[0018] This solution presents a specific technical path for the quantification and dynamic evolution of regional correlations. First, systematic data preprocessing provides high-quality, standardized input for model analysis. Second, when calculating directed causal correlations, it innovatively integrates three types of information: time-series-based causal influence strength, state probability-based causal intervention gain, and expert experience. Through weighted summation, it achieves multi-dimensional and robust quantification of complex causal relationships between regions. Finally, after generating the undirected correlation matrix for network construction, a dynamic update strategy combining periodic recalculation and time decay mechanisms is introduced. This strategy, during each update, performs a weighted average of the results calculated based on the latest data and the historical matrix. This allows for the absorption of new knowledge while retaining some historical memory, enabling the correlation matrix to smoothly and adaptively track the true evolution of ecosystem interactions and avoid misleading drastic changes caused by short-term data fluctuations. By integrating temporal causal inference, probabilistic graphical models, and expert knowledge to calculate directed causal correlations, this approach overcomes the limitations of single models, resulting in more comprehensive and robust quantitative correlation indicators. It simultaneously captures both temporal guiding relationships and probabilistic dependencies between events. The designed dynamic update mechanism, particularly the introduction of time decay update rules, enables the correlation matrix to continuously learn the latest ecological interaction patterns while maintaining a memory of long-term stable relationships. This enhances the model's ability to distinguish between slow ecosystem evolution and short-term noise, ensuring the timeliness and stability of the correlation network model. This scientific and dynamic correlation analysis system lays a reliable and adaptive data foundation for subsequent accurate identification of core areas and intelligent prediction of abnormal propagation.

[0019] Specifically, the causal influence strength of region i on region j is calculated based on the time-series causal inference model. Specifically: Obtain the time series of ecological indicators Xi(t) and Xj(t) for regions i and j, where t represents the time point. Perform a stationarity test on the time series. If the series is non-stationary, perform differencing until it passes the stationarity test. Then, use normalization to eliminate the influence of dimensions. Construct a vector autoregressive model for region j, the model including: Constrained model: ; Complete model: ; Where p is the optimal lag order determined by the AIC criterion. , These are the model coefficients. , For model residuals; The F-statistic is calculated by comparing the sum of squared residuals of the restricted model and the complete model using the F-test.

[0020] in , Let be the sum of squared residuals for the restricted model and the complete model, respectively, and T be the sample size. When testing the null hypothesis that "region i is not a Granger cause of region j," the significance probability is calculated based on the observed values ​​of the F-statistic and the F-distribution. The strength of causal influence is quantified as follows:

[0021] The probability gain of a change in state in region j caused by a change in state in region i is calculated based on a probabilistic graphical model. Specifically: The ecological state of each region is discretized into a finite set of states S={normal, abnormal}. Based on historical event data, a Bayesian network structure is constructed using a constrained learning algorithm, where nodes represent regional state variables and directed edges represent causal directions. Based on historical data, the conditional probability distribution of each node in the Bayesian network is learned using the maximum likelihood estimation method, and then the probability gain is calculated through causal intervention.

[0022] in Let be the marginal probability of an anomaly occurring in region j. The conditional probability that region j will become abnormal when region i is intervened in and its state is forced to be abnormal; When multiple ecological indicators exist, the time-series causal influence intensity and state probability gain corresponding to each indicator are calculated separately, and the weighted average of the calculated results for each indicator is taken as the final result. and .

[0023] The temporal causal inference model focuses on the sequential guiding relationship in the time dimension. It uses statistical tests to determine whether historical information of region i can significantly improve the prediction of the future of region j, thus quantifying the strength of the former's causal influence on the latter in the time series. The probabilistic graphical model reveals causal relationships at the state level. By constructing a probabilistic dependency structure between variables and using the concept of "intervention" to simulate the probability change of the state of region j when the state of region i is forcibly altered, it quantifies the strength of the former's causal effect on the latter in the state space. Finally, for the complex situation where multiple ecological indicators coexist, such as water temperature, pH, and dissolved oxygen, this scheme adopts a strategy of calculating each indicator separately and then weighting and fusing them. It calculates the time series causal influence strength and state probability gain of each indicator separately, and then synthesizes them into a single value. This design ensures the comprehensive capture and quantification of multi-dimensional and multi-modal ecological associations. The dual-model fusion calculation method combines the guiding nature of time series analysis with the probabilistic nature of state-space analysis, forming a complementary and verifying mechanism. This makes the final quantified causal correlation more scientific and robust, overcoming the risk of misjudgment that may arise from discrepancies in data characteristics or model assumptions that can occur with single methods. By calculating the causal strength of multiple ecological indicators separately and then fusing them, this method can comprehensively consider the physical, chemical, and biological processes reflected by different ecological parameters, avoiding a one-sided or erroneous understanding of complex inter-regional ecological relationships caused by relying on a single indicator. This collectively ensures that the subsequently constructed regional correlation network can more realistically and comprehensively reflect the intricate interactions between multiple regions and factors within the ecosystem.

[0024] Reference Figure 3As shown, a regional association network is constructed based on the correlation index. The coreity score of nodes in the network is calculated to identify core regions. The monitoring and sampling frequency of ecological data is dynamically increased for core regions. Specifically, this includes: Taking the ecological zone of each water conservancy project as a node, and using correlation indicators... To determine the weights of the edges connecting node i and node j, construct a weighted undirected graph G=(V,E,A), where V is the set of nodes, E is the set of edges, and A is the edge weight. Compute each node in the weighted undirected graph G Comprehensive core score Its calculation formula is ,in , , These are the weighting coefficients. They are nodes Normalized degree centrality, betweenness centrality, and eigenvector centrality, with threshold values ​​set. , will satisfy The region corresponding to the node is identified as the core region; For each core area Dynamically calculate its monitoring frequency The calculation formula is: ,in Let η be the base monitoring frequency, and μ be the core area frequency boosting coefficient and anomaly propagation sensitivity coefficient, respectively. I() is the indicator function, which is defined when any condition satisfies... The value is 1 when the associated neighbor region j is in an abnormal state, and 0 otherwise. As the correlation threshold, the final calculated frequency will be... The instructions are then sent to the monitoring equipment in the corresponding areas for execution.

[0025] The system transforms quantified correlation indicators into a computable network topology, enabling intelligent scheduling of monitoring resources. First, by constructing a weighted undirected graph using correlation indicators as edge weights, abstract ecological relationships are intuitively and structurally represented as a complex network. When identifying core areas, it doesn't rely solely on a single centrality indicator but innovatively integrates degree centrality, betweenness centrality, and eigenvector centrality. These measures a region's direct influence, control over information or risk propagation paths, and the importance of its neighboring nodes, respectively, thus comprehensively identifying key areas that truly occupy pivotal positions within the network. Finally, the dynamic calculation formula for monitoring frequency achieves dual optimization: on one hand, the base frequency is proportionally increased based on the node's own centrality score, ensuring that highly important areas receive more monitoring resources; on the other hand, abnormal states of associated neighbors are introduced as a sensitivity coefficient. When closely associated neighbors of a core area exhibit abnormalities, their monitoring frequency is immediately further increased, achieving proactive and enhanced monitoring of potential risk propagation links. By integrating three centrality indicators for comprehensive evaluation, the limitations of using a single indicator to identify core areas are overcome, resulting in more scientific and comprehensive identification. This allows for the accurate discovery of key areas within the ecological network that simultaneously possess high connectivity, high control, and high influence. The designed dynamic monitoring frequency formula achieves a fundamental shift in monitoring resource allocation from "static uniformity" to "dynamic precision." It not only allocates resources based on the inherent importance of regions but also responds sensitively to the real-time risk status of the network. This significantly improves the utilization efficiency of limited monitoring resources, ensuring focused monitoring of the most influential key points for overall ecological stability and the weakest links most likely to experience risk transmission. Consequently, it enhances the system's overall sensitivity to ecological disturbances and its early warning capabilities.

[0026] Reference Figure 4 As shown, when an abnormal state is detected in a single area, based on the regional correlation network and the current abnormal state, a graph neural network model is used to predict the probability of abnormal risks in other areas. Based on the prediction results, a reinforcement learning model is used to generate and recommend the optimal response plan, which specifically includes: When the monitoring system detects a certain area When ecological indicators exceed preset thresholds, an abnormal state signal is triggered, marking the state of that area as abnormal, and obtaining the ecological state feature vectors of all areas at the current moment. and the correlation matrix A; The state feature vector The correlation matrix A is input into a pre-trained graph neural network prediction model, and graph convolution operations are performed. Information is propagated layer by layer, and finally the probability vector of abnormal risks for all regions in the future time period Δt is output. ,in I is the identity matrix. for The degree matrix, Let be the trainable weight matrix of the l-th layer, and σ be the activation function; Current system status The input is fed into a reinforcement learning decision model, which, based on the objective of maximizing cumulative reward, uses a policy... Output the optimal response action sequence The reward function is defined as follows: ,in These are the weighting coefficients. For the cost of action, To constrain violations and penalties, the action sequence was ultimately recommended to managers as a response plan. Let be the probability of abnormal risk in region i within the future time period Δt.

[0027] An intelligent closed-loop response process of "perception-prediction-decision" was constructed. When an anomaly is triggered in a specific area, the system first integrates the current ecological state characteristics of the entire network and the correlation matrix reflecting the inherent connections between regions as input for prediction. Through a pre-trained graph neural network model, utilizing its unique graph convolution operation, the system simulates the multi-level propagation and aggregation process of anomaly information along weighted correlation edges between network nodes, thereby outputting a quantified, future-oriented probability distribution vector of anomaly risk, realizing the prediction of the spatiotemporal diffusion of risk. Subsequently, this prediction result, together with the current state of the system, constitutes the decision environment state of the reinforcement learning model. Through its policy network, guided by a reward function jointly defined by predicted risk, action cost, and constraint compliance, the model explores and outputs the optimal sequence of response actions that maximizes long-term cumulative rewards, ultimately transforming the abstract prediction into a concrete and executable regulatory planning recommendation. By modeling and extrapolating the interconnected network using graph neural networks, the system can overcome the limitations of traditional isolated alarms and proactively predict the cascading risks and probabilities that a single anomaly may trigger, thus achieving a shift from "post-event response" to "pre-event warning." Secondly, the introduction of reinforcement learning decision-making models enables the system to automatically and rapidly generate globally near-optimal response solutions in complex multi-objective decision spaces. This effectively overcomes the lag, one-sidedness, and suboptimal nature of decisions based on human experience and static contingency plans, significantly improving the scientific, systematic, and timely response to ecological emergencies, and providing powerful intelligent decision support for the ecological safety and stable operation of water conservancy projects.

[0028] Furthermore, this solution also proposes an artificial intelligence-based water conservancy engineering ecosystem construction system, including: The data acquisition and storage module is used to collect and store ecological monitoring data and historical event data of various areas of the water conservancy project in real time; The correlation analysis engine is used to construct and dynamically update the regional identification correlation analysis system. The core area identification and monitoring optimization module is used to identify core areas and increase the monitoring sampling frequency in a targeted manner, and to identify core areas and generate dynamic monitoring instructions based on the correlation analysis system. The anomaly prediction and intelligent planning module is used to perform anomaly state prediction and intelligent response, perform anomaly state propagation prediction and generate intelligent response planning; The human-computer interaction and execution interface is used to display core areas, early warning information, and response plans, and to send planning instructions to the water conservancy project control system.

[0029] The correlation analysis engine specifically includes: The data preprocessing unit is used to clean, label, and format historical and real-time ecological event data; The causal association calculation unit is used to calculate the directed causal association degree between any two regions based on the time-series causal inference model and the probabilistic graphical model; The dynamic update unit is used to generate a correlation matrix A based on the directed causal correlation degree and to dynamically update it using an online learning algorithm.

[0030] The core area identification and monitoring optimization module specifically includes: The association network construction unit is used to construct a regional association network in the form of a weighted undirected graph G based on the association degree matrix A. The core calculation and identification unit is used to calculate the comprehensive core score of each node in the network and compare it with a threshold to identify the core region. The monitoring frequency scheduling unit is used to dynamically calculate the monitoring frequency of each area based on the core score and the abnormal status of related neighbors, and generate corresponding sensor control commands.

[0031] The anomaly prediction and intelligent planning module specifically includes: The anomaly monitoring and triggering unit is used to monitor ecological indicators in various regions in real time, trigger the prediction process and integrate the current system status information when an anomaly is detected. The graph neural network prediction unit is used to load a pre-trained graph neural network model, input the current state, and predict the probability vector of abnormal risk in each region. The reinforcement learning decision unit is used to take the current system state and prediction results as input, call the reinforcement learning decision model to calculate the optimal response planning action sequence, and make recommendations through human-computer interaction and execution interface.

[0032] In summary, the advantages of this invention are as follows: By constructing a dynamic regional correlation analysis system, the mutual influence between ecological regions is quantified, realizing the intelligent optimization configuration of monitoring resources from uniform distribution to network core degree, significantly improving monitoring efficiency and the ability to capture key information; by using graph neural networks to predict the propagation risk of abnormal states in the associated network, it overcomes the limitation of traditional methods that can only provide alarms for isolated points, enhancing the system's forward-looking perception of ecological chain reactions; finally, by integrating multi-objective constraints and predictive information through a reinforcement learning model, it automatically generates a globally near-optimal response plan, upgrading the decision-making mode from relying on static plans and human experience to data-driven, system-autonomous intelligent decision-making, thereby achieving more precise, proactive, and adaptive regulation and construction of the water conservancy project ecosystem as a whole.

[0033] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for constructing an ecological system for water conservancy projects based on artificial intelligence, characterized in that, include: Construct a regional identification correlation analysis system: collect historical ecological event data of various regions of water conservancy projects, analyze the causal relationship between events based on artificial intelligence models, calculate and dynamically update the correlation index between any two regions. The higher the causal relationship between historical events between two regions, the higher the correlation index. Identify core areas and increase monitoring sampling frequency accordingly: Construct a regional association network based on the correlation index, calculate the coreity score of nodes in the network to identify core areas, and dynamically increase the monitoring sampling frequency of ecological data for the core areas. Anomaly prediction and intelligent response: When an anomaly is detected in a single area, based on the area association network and the current anomaly, the probability of anomaly risk in other areas is predicted by a graph neural network model. Based on the prediction results, the optimal response plan is generated and recommended by a reinforcement learning model.

2. The method for constructing an ecological system for water conservancy projects based on artificial intelligence according to claim 1, characterized in that, The collection of historical ecological event data from various regions of water conservancy projects, analysis of causal relationships between events based on artificial intelligence models, and calculation and dynamic updating of correlation indicators between any two regions specifically include: Historical ecological event data are collected from various areas under the jurisdiction of water conservancy projects. The event data includes event type, occurrence time, occurrence area and event intensity. The data is then cleaned, labeled and standardized preprocessed. For any two regions i and j, the causal influence strength of region i on region j is calculated based on the time-series causal inference model, the probability gain of the state change of region i leading to the state change of region j is calculated based on the probabilistic graphical model, and the two and expert experience weights are combined to calculate the directed causal correlation degree. Based on the calculated bidirectional directional causal correlation degree, an undirected correlation degree index is calculated. All undirected correlation degree indices constitute a correlation degree matrix, and an online learning mechanism is used to periodically and dynamically update the correlation degree matrix in combination with real-time data.

3. The method for constructing an ecological system for water conservancy projects based on artificial intelligence according to claim 2, characterized in that, The calculation of the causal influence strength of region i on region j based on the time-series causal inference model is specifically as follows: Obtain the time series of ecological indicators for region i and region j, perform a stationarity test on the time series, and if the series is non-stationary, perform differencing until the stationarity test is passed, and use normalization to eliminate the influence of dimensions. Construct a vector autoregressive model for region j, the model including: The F-statistic is calculated by comparing the sums of squared residuals using the F-test. The causal influence strength is calculated based on the observed values ​​of the F-statistic and the significance probability calculated from the F-distribution. The specific method for calculating the probability gain of a change in the state of region j caused by a change in the state of region i based on the probabilistic graphical model is as follows: The ecological state of each region is discretized into a finite set of states S={normal, abnormal}. Based on historical event data, a Bayesian network structure is constructed using a constrained learning algorithm, where nodes represent regional state variables and directed edges represent causal directions. The conditional probability distribution of each node in the Bayesian network is learned using the maximum likelihood estimation method based on historical data, and then the probability gain is calculated through causal intervention. When multiple ecological indicators exist, the time series causal influence intensity and state probability gain corresponding to each indicator are calculated separately, and the weighted average of the calculation results of each indicator is taken as the final time series causal influence intensity and state probability gain.

4. The method for constructing an ecological system for water conservancy projects based on artificial intelligence according to claim 3, characterized in that, The specific steps of constructing a regional association network based on the correlation index, calculating the core scores of nodes in the network to identify core regions, and dynamically increasing the monitoring and sampling frequency of ecological data for the core regions include: A weighted undirected graph is constructed with each water conservancy project's ecological area as a node and the correlation index as the weight of the edge connecting node i and node j. Calculate the comprehensive coreness score of each node in the weighted undirected graph, set a threshold, and identify the regions corresponding to nodes whose comprehensive coreness scores are greater than or equal to the threshold as core regions. The monitoring frequency for each core area is dynamically calculated, and the calculated monitoring frequency is then sent to the monitoring equipment in the corresponding area for execution.

5. The method for constructing an ecological system for water conservancy projects based on artificial intelligence according to claim 4, characterized in that, When an abnormal state is detected in a single area, based on the area's correlation network and the current abnormal state, a graph neural network model is used to predict the probability of abnormal risks in other areas. Based on the prediction results, a reinforcement learning model is used to generate and recommend the optimal response plan, specifically including: When the monitoring system detects that the ecological indicators of a certain area exceed the preset threshold, it triggers an abnormal state signal, marks the state of that area as abnormal, and obtains the ecological state feature vector and correlation matrix of all areas at the current time. The state feature vector and correlation matrix are input into a pre-trained graph neural network prediction model. Information is propagated layer by layer through graph convolution operations, and finally the abnormal risk probability vector of all regions in the future time period is output. The current system state is input into a reinforcement learning decision model. Based on the goal of maximizing cumulative reward, the model outputs the optimal action sequence and finally recommends the action sequence as a response plan to the management personnel.

6. An artificial intelligence-based ecological construction system for water conservancy projects, characterized in that, The method for implementing the AI-based ecological construction method for water conservancy projects as described in any one of claims 1-5 includes: The data acquisition and storage module is used to collect and store ecological monitoring data and historical event data of various areas of the water conservancy project in real time; The correlation analysis engine is used to execute the construction of the regional identification correlation analysis system, and to construct and dynamically update the regional identification correlation analysis system. The core area identification and monitoring optimization module is used to identify the core area and increase the monitoring sampling frequency in a targeted manner, and to identify the core area based on the correlation analysis system and generate dynamic monitoring instructions. An anomaly prediction and intelligent planning module is used to perform the anomaly state prediction and intelligent response, perform anomaly state propagation prediction and generate intelligent response planning; The human-computer interaction and execution interface is used to display core areas, early warning information, and response plans, and to send planning instructions to the water conservancy project control system.

7. The artificial intelligence-based water conservancy engineering ecological construction system according to claim 6, characterized in that, The correlation analysis engine specifically includes: The data preprocessing unit is used to clean, label, and format historical and real-time ecological event data; The causal association calculation unit is used to calculate the directed causal association degree between any two regions based on the time-series causal inference model and the probabilistic graphical model; The dynamic update unit is used to generate a correlation matrix A based on the directed causal correlation degree and to dynamically update it using an online learning algorithm.

8. The artificial intelligence-based water conservancy engineering ecological construction system according to claim 6, characterized in that, The core area identification and monitoring optimization module specifically includes: The association network construction unit is used to construct a regional association network in the form of a weighted undirected graph G based on the association degree matrix A. The core calculation and identification unit is used to calculate the comprehensive core score of each node in the network and compare it with a threshold to identify the core region. The monitoring frequency scheduling unit is used to dynamically calculate the monitoring frequency of each area based on the core score and the abnormal status of associated neighbors, and generate corresponding sensor control commands.

9. A water conservancy engineering ecological construction system based on artificial intelligence according to claim 6, characterized in that, The anomaly prediction and intelligent planning module specifically includes: The anomaly monitoring and triggering unit is used to monitor ecological indicators in various regions in real time, trigger the prediction process and integrate the current system status information when an anomaly is detected. The graph neural network prediction unit is used to load a pre-trained graph neural network model, input the current state, and predict the probability vector of abnormal risk in each region. The reinforcement learning decision unit is used to take the current system state and prediction results as input, call the reinforcement learning decision model to calculate the optimal response planning action sequence, and make recommendations through the human-computer interaction and execution interface.

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