Intelligent adjustment and water level early warning method and system based on big data

Abstract

According to the intelligent adjustment and water level early warning method and system based on the big data, accurate prediction and intelligent management of water level changes are achieved through real-time data collection, prediction model construction, integration of historical and real-time water level data and meteorological information and dynamic adjustment of a dam gate, and meanwhile, the intelligent adjustment and water level early warning method and system based on the big data are provided. Through multi-level early warning levels and an automatic response strategy, potential water level threats are rapidly dealt with, the flood risk is reduced, and the defects that in the prior art, real-time performance is insufficient, model prediction precision is not high, a dynamic adjustment mechanism is lacked, risk assessment and early intervention capacity are not comprehensive, and response to extreme events is not sound are overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to an intelligent regulation and water level early warning method and system based on big data. Background Art

[0002] In traditional water level monitoring and early warning systems, there are deficiencies such as insufficient real-time performance, low model prediction accuracy, lack of dynamic regulation mechanism, incomplete risk assessment and early intervention capabilities, and imperfect response to extreme events. These systems usually rely on fixed-frequency data collection and empirical models, and fail to effectively analyze large-scale historical data. Summary of the Invention

[0003] Based on the above technical problems, the present invention proposes an intelligent regulation and water level early warning method and system based on big data, and the technical solutions adopted are as follows:

[0004] An intelligent regulation and water level early warning method based on big data, the method comprising:

[0005] S1: Periodically detect and collect water level data at different positions through a water level sensor, preprocess the water level data, and store the preprocessed water level data in a big database;

[0006] S2: Establish a water level prediction model, analyze the historical water level data stored in the big database, combine the real-time water level data and real-time meteorological data, predict the water level change in a future period of time, and obtain a predicted water level result;

[0007] S3: Perform intelligent regulation according to the predicted water level result, the intelligent regulation including: dynamically adjusting the opening and closing degree and time of the dam gate;

[0008] S4: When the predicted water level result at a certain future moment reaches the set water level early warning threshold, issue a warning of the corresponding level.

[0009] Preferably, the S1 includes:

[0010] S11: Set the collection period of the water level sensor, deploy the water level sensor at the mid-upper reaches and confluence points of tributaries of rivers, the inlet and outlet of lakes, and the flood discharge sluice gates of reservoirs, and collect water level data through the water level sensor;

[0011] S12: Preprocess the water level data, the preprocessing including data cleaning and data format conversion, and store the preprocessed water level data in the big database.

[0012] Preferably, the S2 includes:

[0013] S21: Obtain historical water level data and real-time water level data from a large database, extract the characteristics of the water level change trend from the historical water level data and the real-time water level data, and use it as the first feature. Using the chi-square test method, with the first feature as the dependent variable, screen out the independent variable that has the greatest impact on the first feature, and use the independent variable as the second feature. The second feature represents the value of the rainfall within the same period as the water level sensor, and input the first feature and the second feature into the water level prediction model;

[0014] S22: Use ARIMA as the basis of the water level prediction model, and predict the water level change within a certain period in the future through the first feature and the second feature to obtain the predicted water level result.

[0015] Preferably, the S3 includes:

[0016] S31: Confirm the target water level, and dynamically adjust the opening time of the dam gate according to the predicted water level result to control the inflow and outflow of water and keep it within the target water level;

[0017] S32: During the opening process of the dam gate, according to the water level decline trend, through the water level prediction model, perform a secondary prediction on the water level predicted in advance at the same moment. If the secondary predicted water level result reaches the target water level, the dam gate remains unchanged. If the secondary predicted water level result still exceeds the target water level, dynamically adjust the opening height of the dam gate.

[0018] Preferably, the S4 includes:

[0019] S41: Set the water level warning levels, which include: warning water level, alarm water level, and emergency water level;

[0020] S42: When the predicted water level result reaches the warning water level, it means that the water level has reached the initial warning standard, and the warning system issues a primary warning. At the same time, the water level sensor switches from periodically collecting water level data to collecting water level data in real time; when the predicted water level result reaches the alarm water level, it means that the water level has reached the height where preliminary protective actions need to be taken, and the warning system issues an intermediate alarm to remind relevant personnel to prepare flood control materials and evacuate residents temporarily; when the predicted water level result reaches the emergency water level, it means that the water level is about to reach the height that will cause catastrophic impacts, and the warning system issues a high-level alarm, and at the same time reminds relevant personnel to conduct emergency shelters for residents.

[0021] An intelligent regulation and water level warning system based on big data, the system includes:

[0022] Water level data collection and storage system: Periodically detect and collect water level data at different locations through water level sensors, preprocess the water level data, and store the preprocessed water level data in a large database;

[0023] Water level prediction and analysis system: Establish a water level prediction model, analyze historical water level data stored in a large database, and combine real-time water level data and real-time meteorological data to predict the water level change in a future period of time and obtain the predicted water level result;

[0024] Intelligent dam regulation system: According to the predicted water level result, perform intelligent regulation, and the intelligent regulation includes: dynamically adjusting the opening and closing degree and time of the dam gate;

[0025] Water level early warning release system: When the predicted water level result at a certain future moment reaches the set water level early warning threshold, issue an early warning of the corresponding level.

[0026] Preferably, the water level data collection and storage system includes:

[0027] Data acquisition system: Set the acquisition period of the water level sensor, deploy the water level sensor at the mid-upper reaches and confluence points of rivers, the inlet and outlet of lakes, and the flood discharge gate of the reservoir, and collect water level data through the water level sensor;

[0028] Water level data preprocessing and storage system: Preprocess the water level data, and the preprocessing includes data cleaning and data format conversion, and store the preprocessed water level data in a large database.

[0029] Preferably, the water level prediction and analysis system includes:

[0030] Feature extraction and selection system: Obtain historical water level data and real-time water level data from the large database, extract the water level change trend feature from the historical water level data and real-time water level data as the first feature, use the chi-square test method, take the first feature as the dependent variable, screen out the independent variable that has the greatest influence on the first feature, and use the independent variable as the second feature. The second feature represents the numerical value of the rainfall within the same period as the water level sensor, and input the first feature and the second feature into the water level prediction model;

[0031] Water level prediction system: Use ARIMA as the basis of the water level prediction model, and predict the water level change in a future period of time through the first feature and the second feature to obtain the predicted water level result.

[0032] Preferably, the intelligent dam regulation system includes:

[0033] Dam intelligent regulation system: Confirm the target water level, and dynamically adjust the opening time of the dam gate according to the predicted water level result to control the inflow and outflow of water and keep it within the target water level;

[0034] Dam Dynamic Prediction and Adjustment System: During the opening process of the dam gate, according to the water level decline trend, through the water level prediction model, the water level predicted in advance at the same moment is re-predicted. If the result of the secondary predicted water level reaches the target water level, the dam gate remains unchanged. If the result of the secondary predicted water level still exceeds the target water level, the opening height of the dam gate is dynamically adjusted.

[0035] Preferably, the water level early warning release system includes:

[0036] Water Level Early Warning Level Setting System: Set the water level early warning levels, which include: warning water level, alarm water level and emergency water level;

[0037] Intelligent Water Level Early Warning Response System: When the predicted water level result reaches the warning water level, it means that the water level has reached the initial warning standard, and the early warning system issues a primary warning. At the same time, the water level sensor changes from periodically collecting water level data to collecting water level data in real time; when the predicted water level result reaches the alarm water level, it means that the water level has reached the height where preliminary protective actions need to be taken, and the early warning system issues an intermediate alarm to remind relevant personnel to prepare flood control materials and evacuate residents temporarily; when the predicted water level result reaches the emergency water level, it means that the water level is about to reach the height that will cause catastrophic impacts, and the early warning system issues a high-level alarm, and at the same time reminds relevant personnel to conduct emergency shelters for residents.

[0038] Advantages of the present invention: The above intelligent regulation and water level early warning solution based on big data realizes the accurate prediction and intelligent management of water level changes through real-time data collection, construction of prediction models, and flexible early warning and response mechanisms. It effectively integrates historical and real-time water level data and meteorological information, dynamically adjusts the dam gate, and improves the real-time performance and accuracy of the system. At the same time, through multi-level early warning levels and automated response strategies, this solution can quickly respond to potential water level threats, reduce flood risks, and provide strong support for water resource management and infrastructure safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 A method for intelligent regulation and water level early warning based on big data according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0040] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only for illustrating and explaining the present invention, and are not used to limit the present invention.

[0041] An embodiment of the present invention, a method for intelligent regulation and water level early warning based on big data, the method includes:

[0042] S1: Periodically detect and collect water level data at different locations through a water level sensor, preprocess the water level data, and store the preprocessed water level data in a large database;

[0043] S2: Establish a water level prediction model, analyze the historical water level data stored in the large database, and combine the real-time water level data and real-time meteorological data to predict the water level change in a future period of time to obtain the predicted water level result;

[0044] S3: According to the predicted water level result, perform intelligent adjustment, and the intelligent adjustment includes: dynamically adjusting the opening and closing degree and time of the dam gate;

[0045] S4: When the predicted water level result at a certain future moment reaches the set water level warning threshold, issue a warning of the corresponding level.

[0046] The working principle and effects of the above technical solution are as follows: This intelligent adjustment and water level warning method based on big data realizes efficient water level management and warning through an integrated multi-step cooperation mechanism. First, the system regularly obtains water level data at multiple locations through a water level sensor, and after data preprocessing, stores it in a large database. Then, the system establishes a water level prediction model, and uses historical data and real-time water level and meteorological data to accurately predict future water level changes. Guided by the prediction results, the system can intelligently adjust the opening and closing degree and time of the dam gate to optimize water resource management. Finally, when the predicted water level exceeds the predetermined warning threshold, the system can immediately issue a warning of the corresponding level to ensure a timely response to potential water level threats. First, through real-time data collection and the establishment of a large database, it provides the ability to monitor and analyze water level changes in detail. The water level prediction model that combines historical data with real-time meteorological information enhances the prediction accuracy and promotes early intervention to prevent potential risks. Second, the intelligent adjustment function allows the dam gate to be dynamically adjusted according to the prediction results, optimizing water flow management and improving water resource utilization efficiency. Finally, the flexible warning system can quickly issue an alarm when the water level approaches or exceeds the safety threshold, enabling relevant departments and personnel to take countermeasures in a timely manner and reducing the losses caused by floods. This integrated solution significantly improves the response speed, accuracy, and effectiveness of water level management, providing more reliable safety guarantees for basin management.

[0047] In an embodiment of the present invention, the S1 includes:

[0048] S11: Set the collection period of the water level sensor, deploy the water level sensor at the mid-upper reaches and confluence points of rivers, the inlet and outlet of lakes, and the flood discharge gate of the reservoir, and collect water level data through the water level sensor;

[0049] S12: Preprocess the water level data, where the preprocessing includes data cleaning and data format conversion, and store the preprocessed water level data in a large database.

[0050] The working principle and effects of the above technical solution are as follows: First, the system ensures comprehensive water level data acquisition by setting the acquisition period of water level sensors and installing sensors at key hydrological positions such as the upper and middle reaches of rivers, confluence points of tributaries, inlets and outlets of lakes, and flood discharge sluice gates of reservoirs. These sensors collect water level information at the set period. Subsequently, the collected raw water level data is sent to a preprocessing step, including data cleaning to remove noise and errors, and format conversion to unify the data structure, effectively improving the quality and consistency of the data. The preprocessed data is stored in a large database. By reasonably setting the acquisition period of water level sensors and strategically deploying them at key hydrological positions, the comprehensiveness and representativeness of water level data are ensured. The periodic acquisition method guarantees real-time updates, facilitating timely monitoring of the dynamic changes in water level. The preprocessing steps of data cleaning and format conversion further improve the accuracy and consistency of the data, removing potential errors and irregular inputs. Finally, storing high-quality data in a large database provides a reliable data information source for subsequent analysis and decision support. This method significantly enhances the system's sensitivity and response ability to water level changes, supporting more refined hydrological management practices.

[0051] In one embodiment of the present invention, S2 includes:

[0052] S21: Obtain historical water level data and real-time water level data from the large database, extract the water level change trend characteristics from the historical water level data and real-time water level data as the first feature, use the chi-square test method, with the first feature as the dependent variable, screen out the independent variable that has the greatest impact on the first feature, and use the independent variable as the second feature. The second feature represents the value of rainfall within the same period as the water level sensor, and input the first feature and the second feature into the water level prediction model;

[0053] S22: Use ARIMA as the basis of the water level prediction model, and predict the water level change within a future period through the first feature and the second feature to obtain the predicted water level result.

[0054] Moreover, the water level prediction model predicts the water level based on the following formula:

[0055]

[0056] where X k represents the predicted water level value (m) at the k-th moment, X t represents the water level value at the t-th moment, and R tRepresents the value corresponding to the rainfall at time t (millimeters).

[0057] The working principle and effects of the above technical solution are as follows: First, the system obtains historical and real-time water level data from the large database, extracts the trend characteristics of the water level change as the first feature. The chi-square test method is used to analyze the data, and the factor that has the greatest impact on this trend feature is selected as the independent variable. The independent variable is usually the rainfall data synchronized with the water level sensor, as the second feature. This feature combination is introduced into the ARIMA model to build a prediction model for future water level changes through this comprehensive analysis framework. The system can generate water level prediction results that are both based on historical laws and adapted to current environmental changes. By using big data analysis and the ARIMA model to predict water level changes, this method has significant advantages. First, the trend of water level change and rainfall characteristics generated from historical data and real-time data extracted from the large database ensure the richness and accuracy of the model data input. Using the chi-square test provides a scientific quantitative standard for feature selection, ensuring that the model focuses on the most influential variables. Taking the ARIMA model as the basis for prediction can effectively capture the time series characteristics of the data, especially performing well in dealing with time series data with seasonal and differential trends. The ARIMA model combines the extracted trend characteristics with rainfall, enabling the prediction of water level changes to more deeply reflect the dynamic changes in a complex environment, improving the accuracy and reliability of the prediction. This integrated method strengthens the scientific nature and practicality of decision-making support in water level early warning and management.

[0058] In the calculation formula of the water level prediction model, taking the log logarithmic function of the rainfall is considered to reduce bias. The rainfall may show very large differences in magnitude, especially across regions or seasons. Through logarithmic transformation, the data range is reduced, which helps to improve the stability of the data in analysis and modeling. This formula predicts the water level change in the future period by combining the water level trend and rainfall impact, and the design purpose is to improve the accuracy and stability of the prediction. The formula continues the past water level change trend and provides a basic estimate of the water level change. Using logarithmic transformation for rainfall can smooth the impact of rainfall outliers and better capture the complex non-linear impact of rainfall on the water level.

[0059] In one embodiment of the present invention, the S3 includes:

[0060] S31: Confirm the target water level, and dynamically adjust the opening time of the dam gate according to the predicted water level result to control the inflow and outflow of water and maintain it within the target water level;

[0061] And, the opening time of the dam is obtained through the following formula:

[0062]

[0063] Among them, H 1 represents the current water level (m), H 2 represents the target water level, A represents the area of the dam, and Q 2 represents the water discharge flow rate (m³ / h) when the gate is opened, and Q 1 represents the flow rate into the reservoir, and the calculated opening time is executed through the execution system;

[0064] S32: During the opening process of the dam gate, according to the water level decline trend, through the water level prediction model, the water level predicted in advance at the same moment is predicted again. If the result of the secondary predicted water level reaches the target water level, the dam gate remains unchanged. If the result of the secondary predicted water level still exceeds the target water level, the opening height of the dam gate is dynamically adjusted.

[0065] The working principle and effects of the above technical solution are as follows: First, the system sets and confirms the target water level based on the water level prediction result. On this basis, the opening time of the dam gate is dynamically adjusted to ensure that the water inflow or outflow keeps the water level within the preset target. During the gate operation, the system continuously monitors the water level change and conducts real-time secondary prediction through the water level prediction model. If the predicted water level reaches the target water level, the opening state of the gate remains unchanged; if the predicted water level still exceeds the target range, the opening height of the gate is adjusted immediately. By dynamically adjusting the opening time and degree of the dam gate in real time, precise control of the target water level is achieved. First, the prediction-driven adjustment strategy ensures the predictability and scientific nature of water flow management and improves the control ability of the water level. Second, the continuous water level monitoring and secondary prediction functions form a closed-loop feedback system, making the response to hydrological changes more flexible and real-time, and effectively reducing the risks brought by sudden water level changes. This method not only optimizes the utilization of water resources, ensures the safety of dam operation, but also improves the adaptability to changing environments and extreme events, and is an important tool for modern intelligent water conservancy project management.

[0066] In an embodiment of the present invention, the S4 includes:

[0067] S41: Set the water level warning levels, and the water level warning levels include: warning water level, alarm water level, and emergency water level;

[0068] S42: When the predicted water level reaches the warning water level, it indicates that the water level has reached the initial warning standard. The warning system issues a primary warning, and at the same time, the water level sensor switches from periodically collecting water level data to collecting water level data in real time. When the predicted water level reaches the alarm water level, it means that the water level has reached a height where preliminary protective actions need to be taken. The warning system issues an intermediate alarm to remind relevant personnel to prepare flood control materials and evacuate residents temporarily. When the predicted water level reaches the emergency water level, it indicates that the water level is about to reach a height that will cause catastrophic impacts. The warning system issues a high-level alarm and at the same time reminds relevant personnel to conduct emergency shelters for residents.

[0069] The working principle and effects of the above technical solution are as follows: First, the system sets multiple water level warning levels, including the warning water level, the alarm water level, and the emergency water level, so as to trigger corresponding countermeasures at different water level change stages. When the predicted water level reaches the warning water level, the system immediately issues a primary warning and adjusts the water level sensor collection method to the real-time mode to ensure more accurate data support. When the water level reaches the alarm water level, the system upgrades to an intermediate alarm to remind relevant personnel to prepare flood control materials and evacuate residents in low-risk areas in a timely manner. When the water level approaches the emergency water level, the system enters the high-level alarm state to guide relevant personnel to implement the emergency shelter plan for residents. Through this progressive warning mechanism, the system can deploy appropriate measures in advance, strengthen the response speed and protection ability to water level changes, and protect life and property safety to the greatest extent. Through flexible warning level settings, namely the warning water level, the alarm water level, and the emergency water level, the accuracy and responsiveness of water level risk management are effectively improved. When the water level changes, the system can not only adjust the collection frequency of the sensor in time to strengthen monitoring, but also gradually upgrade the warning measures, from preliminary monitoring, reminding to prepare for flood control to emergency shelter, providing clear action guidelines for relevant personnel. This hierarchical warning mechanism ensures the early detection and response to potential risks, not only optimizes resource allocation, reduces the situation of being caught off guard by floods, but also significantly improves the efficiency of personnel and property safety protection, and enhances the ability to resist and mitigate the impacts of extreme hydrological events.

[0070] An embodiment of the present invention, an intelligent regulation and water level warning system based on big data, the system includes:

[0071] Water level data collection and storage system: Periodically detect and collect water level data at different positions through water level sensors, preprocess the water level data, and store the preprocessed water level data in a big database;

[0072] Water level prediction and analysis system: Establish a water level prediction model, analyze the historical water level data stored in the big database, combine the real-time water level data and real-time meteorological data, predict the water level change in the future for a period of time, and obtain the predicted water level result;

[0073] Intelligent Dam Regulation System: According to the predicted water level results, it conducts intelligent regulation, and the intelligent regulation includes: dynamically adjusting the opening and closing degree and time of the dam gates;

[0074] Water Level Early Warning Release System: When the predicted water level results at a certain future moment reach the set water level early warning threshold, it issues warnings of corresponding levels.

[0075] The working principle and effects of the above technical solutions are as follows: This intelligent regulation and water level early warning method based on big data realizes efficient water level management and early warning through integrating a multi-step cooperation mechanism. First, the system regularly obtains water level data at multiple locations through water level sensors, and after data preprocessing, stores it in a big database. Then, the system establishes a water level prediction model, and uses historical data, real-time water level, and meteorological data to accurately predict future water level changes. Guided by the prediction results, the system can intelligently adjust the opening and closing degree and time of the dam gates to optimize water resource management. Finally, when the predicted water level exceeds the predetermined early warning threshold, the system can immediately issue warnings of corresponding levels to ensure timely response to potential water level threats. First, through real-time data collection and the establishment of a big database, it provides the ability to monitor and analyze water level changes in detail. The water level prediction model that combines historical data with real-time meteorological information enhances the prediction accuracy and promotes early intervention to prevent potential risks. Second, the intelligent regulation function allows dynamically adjusting the dam gates according to the prediction results, optimizing water flow management, and improving water resource utilization efficiency. Finally, the flexible early warning system can quickly issue alarms when the water level approaches or exceeds the safety threshold, enabling relevant departments and personnel to take countermeasures in a timely manner and reducing losses caused by floods. This integrated solution significantly improves the response speed, accuracy, and effectiveness of water level management, providing more reliable safety guarantees for basin management.

[0076] In an embodiment of the present invention, the water level data collection and storage system includes:

[0077] Data Collection System: Set the collection period of the water level sensors, deploy the water level sensors at the mid-upper reaches and confluence points of rivers, the inlet and outlet of lakes, and the flood discharge gate of the reservoir, and collect water level data through the water level sensors;

[0078] Water Level Data Preprocessing and Storage System: Preprocess the water level data, and the preprocessing includes data cleaning and data format conversion, and store the preprocessed water level data in the big database.

[0079] The working principle and effects of the above technical solution are as follows: First, the system sets the acquisition period of the water level sensors and installs sensors at key hydrological positions such as the upper and middle reaches of rivers, confluence points of tributaries, inlet and outlet of lakes, and flood discharge gates of reservoirs, so as to ensure comprehensive water level data is obtained. These sensors collect water level information at the set period. Subsequently, the collected original water level data is sent to a preprocessing link, including data cleaning to remove noise and errors, and format conversion to unify the data structure, effectively improving the quality and consistency of the data. The preprocessed data is stored in a large database. By reasonably setting the acquisition period of the water level sensors and strategically deploying them at key hydrological positions, the comprehensiveness and representativeness of the water level data are ensured. The periodic acquisition method guarantees real-time update, which helps to monitor the dynamic changes of the water level in a timely manner. The preprocessing steps of data cleaning and format conversion further improve the accuracy and consistency of the data, removing potential errors and irregular inputs. Finally, the high-quality data is stored in the large database, providing a reliable data information source for subsequent analysis and decision support. This method significantly enhances the sensitivity and response ability of the system to water level changes, supporting more refined hydrological management practices.

[0080] In one embodiment of the present invention, the water level prediction and analysis system includes:

[0081] Feature extraction and selection system: Obtain historical water level data and real-time water level data from the large database, extract the water level change trend features from the historical water level data and real-time water level data as the first feature, use the chi-square test method, take the first feature as the dependent variable, screen out the independent variable that has the greatest impact on the first feature, and use the independent variable as the second feature. The second feature represents the numerical value of the rainfall within the same period as the water level sensor, and input the first feature and the second feature into the water level prediction model;

[0082] Water level prediction system: Based on ARIMA as the water level prediction model, predict the water level change in a future period through the first feature and the second feature to obtain the predicted water level result.

[0083] The working principle and effects of the above technical solution are as follows: First, the system obtains historical and real-time water level data from a large database, extracts the trend characteristics of water level changes as the first feature. The chi-square test method is used to analyze the data, and the factor that has the greatest impact on this trend feature is selected as the independent variable, which is usually the rainfall data synchronized with the water level sensor, as the second feature. This feature combination is introduced into the ARIMA model to build a prediction model for future water level changes through this comprehensive analysis framework. The system can generate water level prediction results that are both based on historical laws and adapted to current environmental changes. This method has significant advantages in predicting water level changes by using big data analysis and the ARIMA model. First, the water level change trend and rainfall characteristics generated from historical data and real-time data extracted from the large database ensure the richness and accuracy of the model data input. Using the chi-square test provides a scientific quantitative standard for feature selection, ensuring that the model focuses on the most influential variables. Taking the ARIMA model as the basis for prediction can effectively capture the time series characteristics of the data, especially performing well in dealing with time series data with seasonal and differential trends. The ARIMA model combines the extracted trend characteristics with rainfall, enabling the prediction of water level changes to more deeply reflect the dynamic changes in a complex environment, improving the accuracy and reliability of the prediction. This integrated method strengthens the scientificity and practicality of decision-making support in water level warning and management.

[0084] In an embodiment of the present invention, the intelligent dam regulation system includes:

[0085] Dam intelligent regulation system: Confirm the target water level, and dynamically adjust the opening degree and time of the dam gate according to the predicted water level result to control the inflow and outflow of water and maintain it within the target water level;

[0086] Dam dynamic prediction and adjustment system: During the opening process of the dam gate, according to the water level decline trend, through the water level prediction model, perform a secondary prediction on the water level predicted in advance at the same moment. If the secondary predicted water level result reaches the target water level, the dam gate remains unchanged. If the secondary predicted water level result still exceeds the target water level, dynamically adjust the opening height of the dam gate.

[0087] The working principle and effects of the above technical solution are as follows: First, the system sets and confirms the target water level based on the water level prediction result. On this basis, it dynamically adjusts the opening time of the dam gate to ensure that the water inflow or outflow keeps the water level within the preset target. During the gate operation, the system continuously monitors the water level change and conducts real-time secondary prediction through the water level prediction model. If the predicted water level reaches the target water level, the opening state of the gate remains unchanged; if the predicted water level still exceeds the target range, the opening height of the gate is immediately adjusted. By dynamically adjusting the opening time and degree of the dam gate in real time, precise control of the target water level is achieved. First, the prediction-driven adjustment strategy ensures the predictability and scientificity of water flow management and improves the control ability of the water level. Second, the continuous water level monitoring and secondary prediction functions form a closed-loop feedback system, making the response to hydrological changes more flexible and real-time, and effectively reducing the risks brought by sudden water level changes. This method not only optimizes the utilization of water resources, ensures the safety of dam operation, but also improves the adaptability to changing environments and extreme events, and is an important tool for modern intelligent water conservancy project management.

[0088] In one embodiment of the present invention, the water level early warning release system includes:

[0089] Water level early warning level setting system: Set the water level early warning levels, which include: warning water level, alarm water level, and emergency water level;

[0090] Intelligent water level early warning response system: When the predicted water level result reaches the warning water level, it indicates that the water level has reached the initial warning standard, and the early warning system issues a primary warning. At the same time, the water level sensor switches from periodically collecting water level data to real-time collecting water level data; when the predicted water level result reaches the alarm water level, it indicates that the water level has reached the height where preliminary protective actions need to be taken, and the early warning system issues an intermediate alarm to remind relevant personnel to prepare flood control materials and temporarily evacuate residents; when the predicted water level result reaches the emergency water level, it indicates that the water level is about to reach the height that will cause catastrophic impacts, and the early warning system issues a high-level alarm and at the same time reminds relevant personnel to conduct emergency shelters for residents.

[0091] The working principle and effects of the above technical solution are as follows: First, the system sets multiple water level warning levels, including the warning water level, the alarm water level, and the emergency water level, so as to trigger corresponding countermeasures at different water level change stages. When the predicted water level reaches the warning water level, the system immediately issues a primary warning and adjusts the water level sensor acquisition method to the real-time mode to ensure more accurate data support. When the water level reaches the alarm water level, the system upgrades to a medium-level alarm, reminding relevant personnel to prepare flood control materials and evacuate residents in low-risk areas in a timely manner. When the water level approaches the emergency water level, the system enters the high-level alarm state, guiding relevant personnel to execute the emergency shelter plan for residents. Through this progressive warning mechanism, the system can deploy appropriate measures in advance, strengthen the response speed and protection ability to water level changes, and maximize the protection of life and property safety. By flexibly setting warning levels, namely the warning water level, the alarm water level, and the emergency water level, the accuracy and responsiveness of water level risk management are effectively improved. When the water level changes, the system can not only adjust the acquisition frequency of the sensor in a timely manner to strengthen monitoring, but also gradually upgrade the warning measures, from preliminary monitoring, reminding to prepare for flood control to emergency shelter, providing clear action guidelines for relevant personnel. This hierarchical warning mechanism ensures the early detection and response to potential risks, not only optimizing resource allocation, reducing the situation of being caught off guard by the flood, but also significantly improving the effectiveness of personnel and property safety protection, and enhancing the ability to resist and mitigate the impact of extreme hydrological events.

[0092] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention also intends to include these changes and modifications.

Claims

1. An intelligent regulation and water level warning method based on big data, characterized in that: The method comprises: S1: Periodically detecting and collecting water level data at different locations through a water level sensor, preprocessing the water level data, and storing the preprocessed water level data in a large database; S2: Establish a water level prediction model, analyze the historical water level data stored in the big database, combine the real-time water level data and real-time meteorological data, predict the water level changes in the future, and obtain the predicted water level results; S3: Perform intelligent adjustment according to the predicted water level result, wherein the intelligent adjustment includes: dynamically adjusting the opening and closing degree and time of the dam gate; S4: When the water level predicted at a certain moment in the future reaches the set water level warning threshold, a warning of the corresponding level is issued.

2. According to the big data-based intelligent regulation and water level early warning method of claim 1, it is characterized in that: The S1 includes: S11: Setting a collection period of a water level sensor, and deploying the water level sensor at the middle and upper reaches of rivers and the confluence of tributaries, the inlet and outlet of lakes, and the flood discharge gate of a reservoir, and collecting water level data through the water level sensor; S12: Preprocessing the water level data, including data cleaning and data format conversion, and storing the preprocessed water level data in a large database.

3. The method for intelligent regulation and water level early warning based on big data according to claim 1 is characterized in that: The S2 includes: S21: Obtain historical water level data and real-time water level data from a large database, and extract water level change trend characteristics from the historical water level data and the real-time water level data, and use them as the first feature. Using the chi-square test method, take the first feature as the dependent variable, screen out the independent variable that has the greatest impact on the first feature, and use the independent variable as the second feature. The second feature represents the value of rainfall in the same period as the water level sensor, and the first feature and the second feature are put into the water level prediction model; S22: Using ARIMA as the basis of the water level prediction model, the first and second features are used to predict the water level changes in the future to obtain the predicted water level results.

4. The method for intelligent regulation and water level early warning based on big data according to claim 1 is characterized in that: The S3 includes: S31: confirming the target water level, and dynamically adjusting the opening time of the dam gate according to the predicted water level result to control the inflow and outflow of water to maintain it within the target water level; S32: During the opening process of the dam gate, according to the water level drop trend, the water level prediction model is used to make a second prediction of the pre-predicted water level at the same time. If the second predicted water level result reaches the target water level, the dam gate remains unchanged. If the second predicted water level result still exceeds the target water level, the opening height of the dam gate is dynamically adjusted.

5. The method for intelligent regulation and water level early warning based on big data according to claim 1 is characterized in that: The S4 includes: S41: Setting water level warning levels, the water level warning levels include: warning water level, alarm water level and emergency water level; S42: When the predicted water level reaches the warning level, it means that the water level has reached the initial level of warning standard, and the warning system issues a primary warning. At the same time, the water level sensor switches from periodically collecting water level data to collecting water level data in real time. When the predicted water level reaches the alarm level, it means that the water level has reached a height where preliminary protective actions need to be taken. The warning system issues an intermediate alarm to remind relevant personnel to prepare flood control materials and temporarily evacuate residents. When the predicted water level reaches the emergency level, it means that the water level is about to reach a height that will cause catastrophic effects. The warning system issues a senior alarm and reminds relevant personnel to provide emergency shelter for residents.

6. An intelligent regulation and water level warning system based on big data, characterized in that: The system comprises: Water level data collection and storage system: periodically detect and collect water level data at different locations through water level sensors, pre-process the water level data, and store the pre-processed water level data in a large database; Water level prediction and analysis system: Establish a water level prediction model, analyze the historical water level data stored in the big database, combine the real-time water level data and real-time meteorological data, predict the water level changes in the future, and obtain the predicted water level results; Intelligent dam regulation system: intelligent regulation is performed based on the predicted water level results, including: dynamic adjustment of the opening and closing degree and time of the dam gate; Water level warning release system: When the water level predicted at a certain moment in the future reaches the set water level warning threshold, a warning of the corresponding level will be issued.

7. The method for intelligent regulation and water level early warning based on big data according to claim 6 is characterized in that: The water level data collection and storage system comprises: Data acquisition system: setting the acquisition cycle of the water level sensor, and deploying the water level sensor at the middle and upper reaches of the river and the confluence of tributaries, the inlet and outlet of the lake, and the flood discharge gate of the reservoir, and collecting water level data through the water level sensor; Water level data preprocessing and storage system: preprocess the water level data, including data cleaning and data format conversion, and store the preprocessed water level data in a large database.

8. The method for intelligent regulation and water level early warning based on big data according to claim 6 is characterized in that: The water level prediction and analysis system comprises: Feature extraction and selection system: obtain historical water level data and real-time water level data from a large database, extract water level change trend characteristics from the historical water level data and the real-time water level data, and use them as the first feature. Use the chi-square test method, take the first feature as the dependent variable, screen out the independent variable that has the greatest impact on the first feature, and use the independent variable as the second feature. The second feature represents the value of rainfall in the same period as the water level sensor, and the first and second features are put into the water level prediction model; Water level prediction system: ARIMA is used as the basis of the water level prediction model. The first and second characteristics are used to predict the water level changes in the future to obtain the predicted water level results.

9. The method of intelligent regulation and water level early warning based on big data according to claim 6, characterized in that: The intelligent dam regulation system comprises: Dam intelligent regulation system: confirms the target water level and dynamically adjusts the opening and closing time of the dam gates based on the predicted water level results to control the inflow and outflow of water and keep it within the target water level; Dam dynamic prediction and adjustment system: During the opening process of the dam gate, according to the water level drop trend, the water level prediction model is used to make a second prediction of the pre-predicted water level at the same time. If the second predicted water level reaches the target water level, the dam gate remains unchanged. If the second predicted water level still exceeds the target water level, the opening height of the dam gate is dynamically adjusted.

10. The method for intelligent regulation and water level early warning based on big data according to claim 6, characterized in that: The water level early warning release system comprises: Water level warning level setting system: setting water level warning levels, including warning water level, alarm water level and emergency water level; Intelligent water level early warning response system: When the predicted water level reaches the warning level, it means that the water level has reached the initial level of early warning standard. The early warning system issues a primary warning, and the water level sensor converts from periodically collecting water level data to real-time collection of water level data. When the predicted water level reaches the alarm level, it means that the water level has reached a height where preliminary protective actions need to be taken. The early warning system issues an intermediate alarm to remind relevant personnel to prepare flood control materials and temporarily evacuate residents. When the predicted water level reaches the emergency level, it means that the water level is about to reach a height that will cause catastrophic effects. The early warning system issues a high-level alarm and reminds relevant personnel to provide emergency shelter for residents.

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