Intelligent water conservancy dynamic monitoring and early warning method based on deep learning

Abstract

The invention discloses an intelligent water conservancy dynamic monitoring and early warning method based on deep learning. The method comprises the following steps: S1, collecting hydrological data of a water conservancy system in real time; s2, performing dynamic modeling on the hydrological data by using an adaptive non-stationary Gaussian process, and identifying and processing non-stationary characteristics of the hydrological data; s3, based on the optimized dynamic prediction model, time-varying characteristics among hydrological indexes are modeled in a collaborative manner, and a dynamic prediction result is generated; s4, combining the dynamic prediction result with a physical rule base by using a neural symbol reasoning technology to generate a causal reasoning result; s5, based on a causal reasoning result, assessing the disaster risk of the water conservancy system, and generating an early warning scheme; and S6, through a real-time feedback mechanism, automatically adjusting the optimized dynamic prediction model parameters and causal reasoning rules, and continuously optimizing the prediction precision and the emergency response effect. Dynamic prediction and disaster early warning of the water conservancy system are optimized in real time by using deep learning and neural symbol reasoning technologies.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy monitoring and early warning, and particularly to a smart water conservancy dynamic monitoring and early warning method based on deep learning. Background Art

[0002] With the frequent occurrence of global climate change and extreme weather events, water conservancy management faces unprecedented challenges. The frequency and intensity of floods, droughts and other hydrological disasters are increasing continuously, which makes the monitoring and early warning of water conservancy systems particularly important. Traditional water conservancy monitoring systems rely on static models and manual experience, and often cannot reflect the changing trend of hydrological data in real time. Especially when sudden weather events occur, the response ability and prediction accuracy of the system have significant limitations.

[0003] Existing water conservancy monitoring methods are usually based on single physical models or statistical models. The limitation of these methods is that they often assume that hydrological data is stationary or only effective under specific conditions. For example, classical hydrological models are often trained based on historical data, but these models fail to fully consider the non-stationarity and time-variability of hydrological data. Hydrological data, such as water level, flow rate, precipitation, etc., often show obvious seasonal changes, trend changes and the influence of sudden events, which makes traditional models face great challenges in dealing with complex hydrological changes. As the amount of data increases, the processing ability and adaptability of traditional models become increasingly insufficient, and it is difficult to provide accurate decision-making support for water conservancy management.

[0004] In addition, most traditional water conservancy monitoring systems use experience-based threshold judgment to predict disaster risks. This method relies on manually set rules and lacks the ability to automatically identify complex interrelationships between hydrological data. Although some advanced statistical methods such as regression analysis and time series analysis have been applied to hydrological prediction, these methods usually ignore the dynamic non-linear characteristics of hydrological systems and the complex causal relationships between various indicators. Therefore, their assessment of disaster risks is usually relatively rough and cannot accurately capture the potential risks of sudden changes and extreme events.

[0005] In recent years, the application of deep learning and artificial intelligence technologies in the water conservancy field has gradually attracted attention. Deep learning can automatically extract features from a large amount of hydrological data through complex neural network structures, and perform dynamic modeling and prediction. Compared with traditional methods, deep learning has strong non-linear modeling capabilities and can better handle complex relationships and time-varying characteristics in hydrological data. In particular, the time series prediction model based on deep learning can identify the changing trends of hydrological indicators and perform real-time prediction based on a large amount of historical data. However, the existing water conservancy monitoring systems based on deep learning still face problems such as insufficient interpretability of the model, difficulty in parameter tuning, and insufficient ability to fuse multi-source data. Deep learning models are usually regarded as black boxes, and their prediction processes lack transparency, resulting in difficulty in providing operational decision-making support for water conservancy management.

[0006] In terms of disaster warning, most of the existing technologies adopt single statistical analysis methods or rule-based inference systems. These methods cannot process complex multi-source data in a short time, nor can they make dynamic adjustments when the data stream and model parameters change. Traditional hydrological disaster warning systems rely on preset empirical rules and thresholds, and the system will trigger an alarm only when the hydrological data reaches a certain set value. This method seems powerless in the face of disasters with strong suddenness and rapid changes, often unable to capture the precursors of disasters in time, resulting in response delays when disasters occur.

[0007] Although some new integrated models, Bayesian networks, and rule-based inference systems have been applied in water conservancy disaster warning, their performance is still limited by the construction and maintenance of the rule base. These systems usually require the setting and updating of rules based on the expert experience in the hydrological field, which has great subjectivity and limitations, and cannot flexibly respond to environmental changes or data fluctuations in emergency situations. Especially in emergency response, the warning systems based on traditional methods usually make decisions based on empirical data, lacking the ability of dynamic analysis and decision support for real-time data.

[0008] In addition, traditional water conservancy monitoring and warning methods cannot effectively cope with the increasingly complex dynamic changes of hydrological systems and the demand for disaster risk prediction. The existing technologies have significant deficiencies in dealing with emergencies, real-time adjustment of prediction results, and optimization of warning plans. The prediction and decision-making of water conservancy systems rely on static models and artificially set rules, unable to automatically adapt to changes in hydrological data, and having weak fusion capabilities for multi-source information, unable to achieve efficient warning and emergency response for disasters.

[0009] Therefore, how to provide a dynamic monitoring and warning method for intelligent water conservancy based on deep learning is an urgent problem to be solved by those skilled in the art. Summary of the Invention

[0010] An object of the present invention is to propose a dynamic monitoring and early warning method for intelligent water conservancy based on deep learning. The present invention makes full use of deep learning and neuro-symbolic reasoning technologies, combines an adaptive non-stationary Gaussian process, and optimizes the dynamic prediction and disaster early warning of the water conservancy system in real time. By intelligently processing the time-varying characteristics of hydrological data and combining a physical rule base to deduce the causal relationship between hydrological indicators, the present invention can accurately calculate the probability of disasters occurring and generate an early warning plan that is adjusted in real time. It has the advantages of high precision, intelligence, and real-time response, can effectively improve the prediction accuracy of the water conservancy system, the emergency response efficiency, and significantly enhance the disaster prevention and control ability.

[0011] The dynamic monitoring and early warning method for intelligent water conservancy based on deep learning according to an embodiment of the present invention includes the following steps: S1. Real-time collect hydrological data of the water conservancy system through a sensor network and remote sensing technology, where the hydrological data includes water level, flow rate, precipitation, temperature, soil humidity, and evaporation; S2. Use an adaptive non-stationary Gaussian process to perform dynamic modeling on the hydrological data, identify and process the non-stationary characteristics of the hydrological data, automatically adjust the kernel function to adapt to the seasonal changes, trend changes, and emergencies of the hydrological data, and generate an optimized dynamic prediction model; S3. Based on the optimized dynamic prediction model, co-model the time-varying characteristics between hydrological indicators, predict the changes of hydrological indicators in real time, and generate a dynamic prediction result; S4. Use neuro-symbolic reasoning technology to combine the dynamic prediction result with a physical rule base, establish a causal relationship model in the water conservancy system, deduce the mutual influence between hydrological indicators, and generate a causal reasoning result; S5. Based on the causal reasoning result output by neuro-symbolic reasoning, evaluate the disaster risk of the water conservancy system, calculate the probability of disasters occurring, and generate an early warning plan; S6. Through a real-time feedback mechanism, automatically adjust the parameters of the optimized dynamic prediction model and the causal reasoning rules according to the real-time monitoring results, and continuously optimize the prediction accuracy and emergency response effect.

[0012] Optionally, the S2 specifically includes: S21. Perform dynamic modeling on hydrological data through an adaptive non-stationary Gaussian process, identify and process the non-stationary characteristics in the hydrological data, and define a non-stationary kernel function for modeling the dynamic characteristics of hydrological data changing with time, where t represents the current time point, represents the adjacent time point of the current time point, and the non-stationary kernel function captures the non-stationarity of time series data by comparing the hydrological data of adjacent time points: ; Among them, represents the amplitude of the non-stationary kernel function, and exp represents the exponential function. represents the smoothness of data change. represents the amplitude coefficient, and P represents the period parameter; S22. For the parameter set of the non-stationary kernel function perform optimization. Using the maximum marginal likelihood estimation method, the optimization objective is to maximize the marginal likelihood function: ; where represents the conditional probability density function, y represents the observed hydrological data, X represents the time point matrix, represents the parameter set of the non-stationary kernel function, K represents the covariance matrix between training data points, and n represents the total number of data points. represents the determinant of the covariance matrix; S23. Based on the optimized parameter set of the non-stationary kernel function, calculate the prediction mean and prediction variance through the prediction formula of the Gaussian process: ; ; where represents the prediction mean, represents the prediction variance, represents the covariance matrix between the prediction points and the training data points, represents the covariance matrix between the prediction points, represents the noise variance, and I represents the identity matrix; S24. Generate an optimized dynamic prediction model according to the optimized non-stationary kernel function. The optimized dynamic prediction model automatically adjusts the prediction results according to the hydrological data characteristics in different time periods.

[0013] Optionally, the specific steps of S3 include: S31. Based on the optimized dynamic prediction model, extract the time-varying characteristics between hydrological indicators, and identify the periodic and sudden characteristics of each hydrological indicator through time series analysis; S32. Adopt a collaborative modeling method. By jointly optimizing the mutual relationship between hydrological indicators, considering the time-varying characteristics and the internal connection of hydrological data, define the time-varying characteristic function of each hydrological indicator , representing the predicted value of the i-th hydrological indicator at the current time point t: ; where represents the baseline predicted value of the i-th hydrological indicator at the current time point t, representing the stationary part of the hydrological indicator, and R represents the total number of period parameters. Denote the seasonal amplitude of the \(i\)-th hydrological index on the \(r\)-th cycle parameter, Denote the \(r\)-th cycle parameter of the \(i\)-th hydrological index, Denote the trend change coefficient of the \(i\)-th hydrological index, and \(M\) denote the total number of emergencies, Denote the influence coefficient of the \(i\)-th hydrological index on the \(m\)-th emergency, Denote the attenuation coefficient of the \(i\)-th hydrological index on the \(m\)-th emergency, Denote the time point of the \(m\)-th emergency; S33. Based on the time-varying characteristic function , by calculating the correlation matrix between different hydrological indices, the correlation matrix describes the correlation between each hydrological index at different time points: ; Among them, Denote the correlation coefficient between the \(i\)-th hydrological index and the \(j\)-th hydrological index at the current time point \(t\), Denote the predicted value of the \(j\)-th hydrological index at the current time point \(t\), \(\overline{}\) denote the average value of the \(i\)-th hydrological index, \(\overline{}\) denote the average value of the \(j\)-th hydrological index, Denote the standard deviation of the \(i\)-th hydrological index, Denote the standard deviation of the \(j\)-th hydrological index, and \(N\) denote the total number of hydrological indices; S34. Through the correlation matrix, weight-adjust the time-varying characteristic functions of each hydrological index to generate a dynamic prediction result: ; Among them, Denote the dynamic prediction result of the \(i\)-th hydrological index.

[0014] Optionally, the specific content of S4 includes: S41. Combine the real-time hydrological data obtained from the sensor network and remote sensing technology with the dynamic prediction result as the input of the neuro-symbolic reasoning system; S42. In the neuro-symbolic reasoning system, construct a physical rule base, the physical rule base contains the known physical laws and historical experience rules in the water conservancy system, the physical laws include the relationship between flow and water level and the relationship between precipitation and basin runoff, and the historical experience rules are based on the knowledge accumulation of past water conservancy management and disaster response, and are used to assist in reasoning the potential causal relationship between hydrological indices; S43. The neuro-symbolic reasoning system engine docks the dynamic prediction result with the physical rule base, and through symbolic logical reasoning and the learning ability of the neural network, identifies the causal relationship between hydrological indices; S44. The neuro-symbolic reasoning system establishes a causal model based on the derived causal relationships, analyzes the mutual influences and variation laws of hydrological indicators. The causal model depicts the correlation and interdependence of each hydrological indicator based on the hydrological knowledge in the physical rule base and the variation trend of dynamic prediction data. S45. Based on the causal model, the neuro-symbolic reasoning system evaluates the influence intensity between different hydrological indicators and generates causal reasoning results.

[0015] Optionally, the specific steps of S5 are as follows: S51. Based on the causal reasoning results and hydrological indicators, calculate the probability of disaster occurrence P(D|X), where D represents the occurrence of a disaster event, X represents the set of hydrological indicators, and use the weighted summation formula to calculate the probability of disaster occurrence: ; where, represents the conditional probability of disaster occurrence given the i-th hydrological indicator, N represents the total number of hydrological indicators, represents the weight of the i-th hydrological indicator; S52. Define a disaster risk assessment function. The disaster risk assessment function combines the probability of disaster occurrence and environmental factors to calculate the disaster risk level: ; where, RL represents the disaster risk level, , and represent constant weight coefficients, represents the influence weight of the q-th environmental factor, Q represents the total number of environmental factors, represents the probability of disaster occurrence based on historical data; The value range of RL is RL ∈ [0, 1], and the closer it is to 1, the higher the risk; S53. Based on the disaster risk level RL, dynamically generate a corresponding early warning plan , where represents the routine monitoring and management measures in the case of low risk, represents the scheduling plan and emergency resource allocation in the case of medium risk, represents the emergency response and disaster avoidance measures in the case of high risk; S54. Update the disaster risk level based on real-time monitoring data. Each time when a new data stream arrives, recalculate the probability of disaster occurrence , and output the final early warning plan.

[0016] Optionally, the specific steps of S6 are as follows: S61. Input the real-time data into the optimized dynamic prediction model based on the real-time monitoring results, and calculate the prediction error through the feedback mechanism. : ; wherein, represents the dynamic prediction value, y(t) represents the actual observed value, and t represents the current time point; S62. Use the prediction error to update the parameters of the optimized dynamic prediction model online: ; wherein, represents the updated parameter, represents the parameter before update, represents the learning rate, represents the gradient of the prediction error with respect to the parameter; S63. Retrain based on the updated parameter to better adapt to the new hydrological data stream; S64. Dynamically adjust the causal inference rules according to the changes in the real-time monitoring data and historical data, and retrain the adjusted causal inference rules to reduce the bias in the causal inference process and continuously optimize the prediction accuracy and emergency response effect: ; wherein, represents the updated causal inference rule, represents the causal inference rule before update, represents the learning rate for causal rule update, represents the gradient of the causal inference error.

[0017] The beneficial effects of the present invention are as follows: First, by using the adaptive non-stationary Gaussian process to dynamically model the hydrological data, the present invention can effectively handle the non-stationary characteristics in the hydrological data, especially the impacts of seasonal variations, trend changes, and emergencies. Different from the traditional static models, the model of the present invention can automatically adjust according to the real-time changes in the hydrological data, ensuring the accuracy and adaptability of the prediction results.

[0018] Second, based on the optimized dynamic prediction model, the present invention can co-model the time-varying characteristics between hydrological indicators, predict the changes in hydrological indicators in real time, and generate more accurate dynamic prediction results. Different from the traditional methods that rely on simple statistical analysis, the present invention can capture the non-linear and time-varying characteristics of the hydrological data, enhancing the response ability of the hydrological system to complex changes.

[0019] In addition, in terms of disaster risk assessment, the present invention utilizes neuro-symbolic reasoning technology to combine dynamic prediction results with a physical rule base, deriving causal relationships between hydrological indicators. Through this method, the present invention can effectively identify the mutual influences between hydrological data, providing a more accurate basis for calculating the probability of disasters, and then generating an early warning plan that conforms to real-time changes. Compared with traditional early warning systems based on empirical rules, the system of the present invention can predict and evaluate disaster risks more flexibly and accurately.

[0020] Finally, through a real-time feedback mechanism, the present invention can continuously optimize the dynamic prediction model and causal inference rules according to real-time monitoring data, continuously improving the prediction accuracy and emergency response effect. Different from traditional water conservancy monitoring systems that rely on static rules and models, the present invention can be adjusted and optimized in real time during the process of data change, ensuring that emergency response measures can be activated in a timely manner when emergencies occur and effectively coping with complex hydrological changes. Brief Description of the Drawings

[0021] The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the drawings: Figure 1 is a flowchart of the intelligent water conservancy dynamic monitoring and early warning method based on deep learning proposed by the present invention. Detailed Embodiments

[0022] Now, the present invention will be further described in detail with reference to the drawings. These drawings are all simplified schematic diagrams, only illustrating the basic structure of the present invention in a schematic manner, so they only show the components related to the present invention.

[0023] Refer to Figure 1 , the intelligent water conservancy dynamic monitoring and early warning method based on deep learning includes the following steps: S1. Real-time collect hydrological data of the water conservancy system through a sensor network and remote sensing technology, where the hydrological data includes water level, flow rate, precipitation, temperature, soil humidity, and evaporation; S2. Use an adaptive non-stationary Gaussian process to perform dynamic modeling on the hydrological data, identify and process the non-stationary characteristics of the hydrological data, automatically adjust the kernel function to adapt to seasonal changes, trend changes, and emergencies of the hydrological data, and generate an optimized dynamic prediction model; S3. Based on the optimized dynamic prediction model, co-model the time-varying characteristics between hydrological indicators, predict the changes of hydrological indicators in real time, and generate dynamic prediction results; S4. Use neuro-symbolic reasoning technology to combine the dynamic prediction results with a physical rule base, establish a causal relationship model in the water conservancy system, derive the mutual influences between hydrological indicators, and generate causal inference results; S5. Based on the causal inference results output by neuro-symbolic reasoning, evaluate the disaster risk of the water conservancy system, calculate the probability of disaster occurrence, and generate an early warning plan; S6. Through a real-time feedback mechanism, automatically adjust the parameters of the optimized dynamic prediction model and causal inference rules according to the real-time monitoring results, and continuously optimize the prediction accuracy and emergency response effect.

[0024] In this embodiment, the S2 specifically includes: S21. Dynamically model the hydrological data through an adaptive non-stationary Gaussian process, identify and process the non-stationary characteristics in the hydrological data, and define a non-stationary kernel function for modeling the dynamic characteristics of hydrological data changing over time, where t represents the current time point, represents the adjacent time point of the current time point, and the non-stationary kernel function captures the non-stationarity of the time series data by comparing the hydrological data of adjacent time points: ; Among them, represents the amplitude of the non-stationary kernel function, exp represents the exponential function, represents the smoothness of data change, represents the amplitude coefficient, and P represents the period parameter; S22. Optimize the parameter set of the non-stationary kernel function, and adopt the maximum marginal likelihood estimation method, with the optimization objective of maximizing the marginal likelihood function: ; Among them, represents the conditional probability density function, y represents the observed hydrological data, X represents the time point matrix, represents the parameter set of the non-stationary kernel function, K represents the covariance matrix between training data points, n represents the total number of data points, represents the determinant of the covariance matrix; S23. Based on the optimized parameter set of the non-stationary kernel function, calculate the prediction mean and prediction variance through the prediction formula of the Gaussian process: ; ; Among them, represents the prediction mean, represents the prediction variance, represents the covariance matrix between the prediction point and the training data points, represents the covariance matrix between prediction points, represents the noise variance, and I represents the identity matrix; S24. Generate an optimized dynamic prediction model based on the optimized non-stationary kernel function. The optimized dynamic prediction model automatically adjusts the prediction results according to the hydrological data characteristics in different time periods.

[0025] In this embodiment, the specific steps of S3 are as follows: S31. Based on the optimized dynamic prediction model, extract the time-varying characteristics between hydrological indicators, and identify the periodic and sudden characteristics of each hydrological indicator through time series analysis. S32. Adopt a collaborative modeling method. By jointly optimizing the mutual relationship between hydrological indicators, considering the time-varying characteristics and the internal connection of hydrological data, define the time-varying characteristic function of each hydrological indicator , representing the predicted value of the i-th hydrological indicator at the current time point t: ; Where represents the baseline predicted value of the i-th hydrological indicator at the current time point t, representing the stationary part of the hydrological indicator, R represents the total number of periodic parameters, represents the seasonal amplitude of the i-th hydrological indicator on the r-th periodic parameter, represents the r-th periodic parameter of the i-th hydrological indicator, represents the trend change coefficient of the i-th hydrological indicator, M represents the total number of emergencies, represents the influence coefficient of the i-th hydrological indicator on the m-th emergency, represents the attenuation coefficient of the i-th hydrological indicator on the m-th emergency, represents the time point of the m-th emergency; S33. Based on the time-varying characteristic function , by calculating the correlation matrix between different hydrological indicators, the correlation matrix describes the correlation between each hydrological indicator at different time points: ; Where represents the correlation coefficient between the i-th hydrological indicator and the j-th hydrological indicator at the current time point t, represents the predicted value of the j-th hydrological indicator at the current time point t, ̄ represents the average value of the i-th hydrological indicator, ̄ represents the average value of the j-th hydrological indicator, represents the standard deviation of the i-th hydrological indicator, represents the standard deviation of the j-th hydrological indicator, and N represents the total number of hydrological indicators; S34. Through the correlation matrix, perform weighted adjustment on the time-varying characteristic functions of each hydrological indicator to generate a dynamic prediction result: ; Among them, represents the dynamic prediction result of the i-th hydrological index.

[0026] In this embodiment, S4 specifically includes: S41. Combine the real-time hydrological data obtained from the sensor network and remote sensing technology with the dynamic prediction results as the input of the neuro-symbolic reasoning system; S42. In the neuro-symbolic reasoning system, construct a physical rule base, which includes the known physical laws in the water conservancy system and historical experience rules. The physical laws include the relationship between flow and water level and the relationship between precipitation and basin runoff. The historical experience rules are based on the knowledge accumulation of past water conservancy management and disaster response, and are used to assist in reasoning the potential causal relationships between hydrological indices; S43. The neuro-symbolic reasoning system engine docks the dynamic prediction results with the physical rule base, and identifies the causal relationships between hydrological indices through symbolic logical reasoning and the learning ability of neural networks; S44. The neuro-symbolic reasoning system establishes a causal model based on the derived causal relationships, analyzes the mutual influences and variation laws of hydrological indices. The causal model depicts the relevance and interdependence of each hydrological index based on the hydrological knowledge in the physical rule base and the variation trend of dynamic prediction data; S45. Based on the causal model, the neuro-symbolic reasoning system evaluates the influence intensity between different hydrological indices and generates causal reasoning results.

[0027] In this embodiment, S5 specifically includes: S51. Calculate the probability of disaster occurrence P(D|X) based on the causal reasoning results and hydrological indices, where D represents the occurrence of a disaster event and X represents the set of hydrological indices. Use the weighted summation formula to calculate the probability of disaster occurrence: ; Among them, represents the conditional probability of disaster occurrence given the i-th hydrological index, N represents the total number of hydrological indices, represents the weight of the i-th hydrological index; S52. Define a disaster risk assessment function, which combines the probability of disaster occurrence and environmental factors to calculate the disaster risk level: ; Among them, RL represents the disaster risk level, , and represent constant weight coefficients, denotes the influence weight of the q-th environmental factor, and Q represents the total number of environmental factors. represents the probability of disaster occurrence based on historical data; The value range of RL is RL ∈ [0, 1]. The closer it is to 1, the higher the risk; S53. Dynamically generate corresponding early warning plans based on the disaster risk level RL , where represents the conventional monitoring and management measures in the case of low risk, represents the dispatching plan and emergency resource allocation in the case of medium risk, represents the emergency response and disaster avoidance measures in the case of high risk; S54. Update the disaster risk level based on real-time monitoring data. Each time a new data stream arrives, recalculate the probability of disaster occurrence , and output the final early warning plan.

[0028] In this embodiment, the S6 specifically includes: S61. Based on the real-time monitoring results, input the real-time data into the optimized dynamic prediction model, and calculate the prediction error through the feedback mechanism : ; where represents the dynamic prediction value, y(t) represents the actual observed value, and t represents the current time point; S62. Use the prediction error to update the parameters of the optimized dynamic prediction model online: ; where represents the updated parameter, represents the parameter before update, represents the learning rate, represents the gradient of the prediction error with respect to the parameter; S63. Retrain based on the updated parameter to better adapt to the new hydrological data stream; S64. Dynamically adjust the causal inference rules according to the changes in real-time monitoring data and historical data, and retrain the adjusted causal inference rules to reduce the deviation in the causal inference process and continuously optimize the prediction accuracy and emergency response effect: ; where represents the updated causal inference rule, represents the causal inference rule before update, represents the learning rate of causal rule update, The gradient representing the causal inference error.

[0029] Example 1: To verify the feasibility of the present invention in implementation, the present invention is applied to the flood monitoring and early warning system of a certain reservoir. The reservoir is located in a certain area in southern China and is a typical multi-purpose reservoir mainly used for irrigation, water supply and flood control. With the frequent occurrence of extreme climate events in recent years, the accuracy and real-time performance of the hydrological data monitoring and flood early warning system of the reservoir have become increasingly important. Traditional hydrological prediction methods are limited by fixed models and fail to capture the non-stationarity and sudden events in hydrological data well, resulting in some flood events not being warned in time, thus affecting the efficiency of disaster prevention and control. To address this problem, the present invention combines methods such as adaptive non-stationary Gaussian processes and neuro-symbolic reasoning techniques to optimize the hydrological prediction model in real time, and improves the prediction accuracy through a real-time feedback mechanism, and finally generates accurate disaster risk assessment and early warning plans.

[0030] In the actual application of the reservoir, first, a set of advanced sensor network and remote sensing technology system is deployed to collect hydrological data around the reservoir in real time, including key indicators such as water level, flow rate, precipitation, temperature, soil humidity and evaporation. We use the adaptive non-stationary Gaussian process to dynamically model the collected hydrological data, identify and process the non-stationarity in the hydrological data, especially seasonal changes, trend changes and sudden events. In this process, the automatic adjustment function of the kernel function can flexibly adapt to the changes in hydrological data, enabling the model to accurately capture the change characteristics in different time periods, thus generating an optimized dynamic prediction model.

[0031] For example, within a certain period of time, the water level and flow rate of the reservoir show certain seasonal fluctuations. Through the adjustment of the non-stationary Gaussian process model, we can capture this fluctuation in real time and predict the water level and flow rate trends in the future. In the process of model optimization, we also combined historical data and real-time sensor data to further improve the accuracy and timeliness of the prediction.

[0032] Next, based on the optimized dynamic prediction model, we co-modeled the time-varying characteristics between hydrological indicators. For example, the relationship between precipitation and flow rate, the relationship between flow rate and water level, etc. By jointly optimizing the mutual relationship between various hydrological indicators, more accurate dynamic prediction results are generated. These results provide accurate data support for subsequent disaster risk assessment.

[0033] In the actual application process, neuro-symbolic reasoning technology has played an important role. By combining dynamic prediction results with a physical rule base, we can generate accurate causal reasoning results based on known physical laws in the water conservancy system (such as the relationship between flow and water level, the relationship between precipitation and basin runoff, etc.) and historical experience rules. These reasoning results can reveal the potential mutual influences between hydrological indicators, thus providing a more scientific basis for disaster risk assessment.

[0034] Specifically for disaster risk assessment, based on the causal reasoning results, we conducted real-time assessment of the flood risk of the reservoir system. For example, during an extreme precipitation event in June 2024, the precipitation around the reservoir increased sharply, reaching 120 mm / h. Through real-time monitoring data and the optimized prediction model, the system issued a flood warning 2 hours in advance, notifying the reservoir management department to take corresponding dispatching measures in advance and avoiding the occurrence of floods. The calculation of the disaster occurrence probability shows that based on the optimized model, the probability of flood occurrence has been reduced from the original 30% to 15%, successfully reducing the disaster occurrence probability.

[0035] In the subsequent emergency response, the system, through a real-time feedback mechanism, automatically adjusts the parameters of the dynamic prediction model and causal reasoning rules according to the monitoring data, further optimizing the prediction accuracy and emergency response effect. During the same precipitation event, the real-time data feedback mechanism helped the system dynamically adjust the prediction model of the impact of precipitation on flow changes, making the prediction results more accurate, thus providing a more reliable basis for the implementation of emergency measures.

[0036] Through a period of actual operation, the application of the present invention has achieved remarkable results, greatly enhancing the disaster prevention ability and emergency response efficiency of the reservoir.

[0037] Table 1 Data Sheet of Reservoir Flood Monitoring and Warning System Water level (m) Discharge (m³ / s) Precipitation (mm) Soil moisture (%) Predicted water level (m) Predicted discharge (m³ / s) Flood warning time (hours) Probability of flood occurrence (%) 12.5 250 90 28 12.6 255 1.0 12 12.7 270 110 32 12.8 275 2.0 15 13.0 300 120 35 13.1 310 3.0 18 13.2 350 150 40 13.4 370 2.0 10 13.5 400 160 42 13.6 410 1.5 8 13.7 450 170 45 13.9 460 1.0 6 It can be seen from the data in Table 1 above that the system issued an effective warning in advance before the prediction of flood occurrence, and through the optimized model, successfully reduced the probability of flood occurrence. The prediction results are accurate and effective, providing reliable support for the flood prevention and control and emergency response of the reservoir.

[0038] This embodiment demonstrates the great advantages of the present invention through actual data, indicating that the application of this method in the water conservancy system not only improves the accuracy of disaster prediction and risk assessment, but also enhances the intelligence and real-time response ability of the system, providing strong technical support for water conservancy management and disaster prevention and control.

[0039] The above are only the preferred specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention should cover within the protection scope of the present invention any equivalent substitution or change made according to the technical solution and inventive concept of the present invention.

Claims

1. A smart water conservancy dynamic monitoring and early warning method based on deep learning, characterized in that: The steps include: S1. Real-time collection of hydrological data of the water conservancy system through sensor networks and remote sensing technology, wherein the hydrological data includes water level, flow, precipitation, temperature, soil moisture and evaporation; S2. Dynamically modeling the hydrological data using an adaptive non-stationary Gaussian process, identifying and processing the non-stationary characteristics of the hydrological data, automatically adjusting the kernel function to adapt to seasonal changes, trend changes and emergencies in the hydrological data, and generating an optimized dynamic prediction model; S3. Based on the optimized dynamic prediction model, collaboratively model the time-varying characteristics of hydrological indicators, predict the changes of hydrological indicators in real time, and generate dynamic prediction results; S4. Using neural symbolic reasoning technology, the dynamic prediction results are combined with the physical rule base to establish a causal relationship model in the water conservancy system, deduce the mutual influence between hydrological indicators, and generate causal reasoning results; S5. Based on the causal reasoning results output by neural symbolic reasoning, the disaster risk of the water conservancy system is evaluated, the probability of disaster occurrence is calculated, and an early warning plan is generated; S6. Through the real-time feedback mechanism, according to the real-time monitoring results, the optimized dynamic prediction model parameters and causal reasoning rules are automatically adjusted to continuously optimize the prediction accuracy and emergency response effect.

2. The smart water conservancy dynamic monitoring and early warning method based on deep learning according to claim 1 is characterized in that: The S2 specifically includes: S21. Dynamically model hydrological data through adaptive non-stationary Gaussian processes, identify and process non-stationary characteristics in hydrological data, and define non-stationary kernel functions It is used to model the dynamic characteristics of hydrological data over time, where t represents the current time point, Represents the adjacent time points of the current time point. The non-stationary kernel function captures the non-stationarity of time series data by comparing the hydrological data of adjacent time points: ； in, represents the amplitude of the non-stationary kernel function, exp represents the exponential function, Indicates the smoothness of data changes. represents the amplitude coefficient, and P represents the period parameter; S22, parameter set for the non-stationary kernel function Optimize and use the maximum marginal likelihood estimation method. The optimization goal is to maximize the marginal likelihood function: ； in, represents the conditional probability density function, y represents the observed hydrological data, X represents the time point matrix, represents the parameter set of the non-stationary kernel function, K represents the covariance matrix between training data points, n represents the total number of data points, represents the determinant of the covariance matrix, The transpose of the vector representing the observed hydrological data; S23. Based on the optimized parameter set of the non-stationary kernel function, the prediction mean and prediction variance are calculated using the prediction formula of the Gaussian process: ； ； in, represents the predicted mean, represents the prediction variance, Represents the covariance matrix of the prediction point and the training data point, represents the covariance matrix between prediction points, represents the noise variance, I represents the identity matrix, Represents the transpose of the covariance matrix between the prediction point and the training data point; S24. Generate an optimized dynamic prediction model according to the optimized non-stationary kernel function, wherein the optimized dynamic prediction model automatically adjusts the prediction results according to the characteristics of the hydrological data in different time periods.

3. The smart water conservancy dynamic monitoring and early warning method based on deep learning according to claim 1 is characterized in that: The S3 specifically includes: S31. Based on the optimized dynamic prediction model, the time-varying characteristics between hydrological indicators are extracted, and the periodicity and burst characteristics of each hydrological indicator are identified through time series analysis; S32. Adopt collaborative modeling method, jointly optimize the relationship between hydrological indicators, consider the time-varying characteristics and the intrinsic connection of hydrological data, and define the time-varying characteristic function of each hydrological indicator. , represents the predicted value of the i-th hydrological indicator at the current time point t: ; in, represents the baseline forecast value of the ith hydrological indicator at the current time point t, representing the stable part of the hydrological indicator, R represents the total number of periodic parameters, represents the seasonal amplitude of the i-th hydrological indicator on the r-th period parameter, represents the rth period parameter of the ith hydrological indicator, represents the trend change coefficient of the i-th hydrological indicator, M represents the total number of emergencies, represents the influence coefficient of the i-th hydrological index on the m-th emergency event, represents the attenuation coefficient of the ith hydrological indicator in the mth emergency event, represents the time point of the mth emergency event; S33, based on time-varying characteristic function , by calculating the correlation matrix between different hydrological indicators, the correlation matrix describes the correlation between various hydrological indicators at different time points: ; in, represents the correlation coefficient between the ith hydrological index and the jth hydrological index at the current time point t, represents the predicted value of the jth hydrological indicator at the current time point t, ̄ represents the average value of the i-th hydrological index, ̄ represents the average value of the jth hydrological index, represents the standard deviation of the ith hydrological index, represents the standard deviation of the jth hydrological indicator, and N represents the total number of hydrological indicators; S34. Through the correlation matrix, the time-varying characteristic functions of each hydrological index are weighted and adjusted to generate dynamic prediction results: ; in, Represents the dynamic prediction result of the i-th hydrological indicator.

4. The smart water conservancy dynamic monitoring and early warning method based on deep learning according to claim 1 is characterized in that: The S4 specifically includes: S41. Combine the real-time hydrological data obtained from sensor networks and remote sensing technology with the dynamic prediction results as the input of the neural symbolic reasoning system; S42. In the neural symbolic reasoning system, a physical rule base is constructed, wherein the physical rule base contains known physical laws and historical empirical rules in the water conservancy system, wherein the physical laws include the relationship between flow and water level and the relationship between precipitation and basin runoff, and the historical empirical rules are based on the knowledge accumulation of past water conservancy management and disaster response, and are used to assist in reasoning about the potential causal relationship between hydrological indicators; S43, the neural symbolic reasoning system engine connects the dynamic prediction results with the physical rule base, and identifies the cause-effect relationship between hydrological indicators through symbolic logic reasoning and the learning ability of neural networks; S44, the neural symbolic reasoning system establishes a causal model based on the derived causal relationship to analyze the mutual influence and change rules of the hydrological indicators. The causal model is based on the hydrological knowledge in the physical rule library and the change trend of the dynamic prediction data to describe the correlation and interdependence of various hydrological indicators; S45. Based on the causal model, the neural symbolic reasoning system evaluates the impact intensity between different hydrological indicators and generates causal reasoning results.

5. The method for dynamic monitoring and early warning of smart water conservancy based on deep learning according to claim 1 is characterized in that: The S5 specifically includes: S51. Based on the causal reasoning results and hydrological indicators, the probability of disaster occurrence P(D|X) is calculated, where D represents the occurrence of a disaster event and X represents a set of hydrological indicators. The probability of disaster occurrence is calculated using the weighted summation formula: ; in, represents the conditional probability of disaster occurrence given the i-th hydrological index, N represents the total number of hydrological indicators, represents the weight of the i-th hydrological indicator; S52. Define a disaster risk assessment function, which combines the probability of disaster occurrence with environmental factors to calculate the disaster risk level: ; Among them, RL represents the disaster risk level, , and represents the constant weight coefficient, represents the impact weight of the qth environmental factor, Q represents the total number of environmental factors, represents the probability of disaster occurrence based on historical data; The value range of RL is RL∈[0,1], the closer to 1, the higher the risk; S53. Dynamically generate corresponding early warning plans based on disaster risk level RL ,in Indicates routine monitoring and management measures in low-risk situations, Indicates the dispatch plan and emergency resource allocation under medium-risk conditions. Indicates emergency response and disaster avoidance measures in high-risk situations; S54. Update the disaster risk level based on real-time monitoring data. Upon arrival, recalculate the probability of disaster occurrence , output the final early warning plan.

6. The smart water conservancy dynamic monitoring and early warning method based on deep learning according to claim 1 is characterized in that: The S6 specifically includes: S61. Based on the real-time monitoring results, the real-time data is input into the optimized dynamic prediction model, and the prediction error is calculated through the feedback mechanism. : ; in, represents the dynamic prediction value, y(t) represents the actual observation value, and t represents the current time point; S62. Using prediction errors Update the parameters of the optimized dynamic prediction model online: ; in, represents the updated parameters, Indicates the parameters before updating. represents the learning rate, represents the gradient of the prediction error with respect to the parameter; S63, based on updated parameters Retrain to better adapt to new hydrological data streams; S64. Dynamically adjust causal reasoning rules based on changes in real-time monitoring data and historical data, and retrain the adjusted causal reasoning rules to reduce deviations in the causal reasoning process and continuously optimize prediction accuracy and emergency response effects: ; in, represents the updated causal inference rule, represents the causal inference rule before updating, represents the learning rate for causal rule updates, represents the gradient of the causal inference error.

Images