Large lake water level adjusting method, device, equipment, storage medium and product

Abstract

The invention relates to the technical field of water level adjustment, in particular to a large lake water level adjustment method, device and equipment, a storage medium and a product. The method comprises the steps of determining a water level change range according to water level influence factors of a to-be-controlled great lake; based on the water level change range and preset benefit demand data, constructing an economic entropy weight model; determining an optimal water level according to the economic entropy weight model; based on the time sequence model, predicting the water level of the to-be-controlled large lake; based on the prediction result and the optimal water level, the current water level of the to-be-controlled great lake is adjusted, and the great lake water level regulation effect is improved.

Description

Technical Field

[0001] The present application relates to the field of water level regulation technology, and in particular to a large lake water level regulation method, device, equipment, storage medium and product. Background Art

[0002] The regulation of the water level of large lakes is a complex system engineering project, involving the needs of multiple stakeholders such as ecological protection, shipping needs, flood control safety, and residential water supply. Since the water level of large lakes is affected by a variety of natural factors and human activities, such as precipitation, evaporation, and river flow into the lake, these factors show obvious dynamic changes and time correlation. Therefore, accurately predicting water level changes and formulating reasonable regulation plans have become a major technical challenge in water level management. Existing large lake water level regulation plans mostly rely on empirical rules or analysis of single factors. This traditional method may be effective in simple scenarios, but with the increasing complexity of ecological environment, climate change and human activities, the effect of water level regulation is often limited. For example, relying solely on historical water level data for prediction may ignore the impact of long-term climate change, and water level regulation methods that only focus on a certain type of demand may also easily lead to other needs not being fully considered, thereby affecting the overall effect of water level regulation. Therefore, how to achieve real-time water level regulation under the influence of multiple factors and take into account the needs of all parties and improve the effect of large lake water level regulation has become an urgent problem to be solved. Summary of the invention

[0003] The main purpose of this application is to provide a method, device, equipment, storage medium and product for regulating the water level of a large lake, aiming to solve the technical problem of how to improve the effect of regulating the water level of a large lake.

[0004] To achieve the above objectives, the present application provides a method for regulating the water level of a large lake, the method comprising:

[0005] Determine the range of water level changes based on the factors affecting the water level of the large lake to be controlled;

[0006] Based on the water level variation range and the preset benefit demand data, an economic entropy weight model is constructed;

[0007] Determining the optimal water level according to the economic entropy weight model;

[0008] Predicting the water level of the large lake to be controlled based on the time series model;

[0009] Based on the prediction result and the optimal water level, the current water level of the large lake to be controlled is adjusted.

[0010] In one embodiment, after the step of predicting the water level of the large lake to be controlled based on the time series model, the method further includes:

[0011] Acquiring water level impact data of the large lake to be controlled, and normalizing the water level impact data to obtain normalized data;

[0012] Based on a preset statistical method, the normalized data is screened to obtain screened data;

[0013] Using the screening data as independent variables, constructing a multiple regression model;

[0014] Based on the multivariate regression model, the error of the prediction result is corrected.

[0015] In one embodiment, the method further comprises:

[0016] Conducting sensitivity analysis on the economic entropy weight model, the time series model, and the multivariate regression model;

[0017] Based on the results of sensitivity analysis, the model parameters were adjusted.

[0018] In one embodiment, the step of constructing an economic entropy weight model based on the water level variation range and the preset benefit demand data includes:

[0019] Converting the preset benefit demand data into quantitative indicators;

[0020] Based on the water level variation range and the quantitative index, a decision matrix is ​​constructed;

[0021] Standardizing the data in the decision matrix to obtain standardized data;

[0022] Determining the entropy value of the quantitative indicator according to the ratio of each element in the standardized data;

[0023] According to the entropy value, the weight of the corresponding indicator is determined, and based on the weight, the economic entropy weight model is constructed.

[0024] In one embodiment, the step of determining the optimal water level according to the economic entropy weight model includes:

[0025] Based on the economic entropy weight model, obtaining the weight of each demand indicator;

[0026] Combining the weight of each demand indicator with the standardized score under the corresponding water level change range;

[0027] Based on the combined results, the comprehensive score of each water level change range is determined;

[0028] The water level variation range corresponding to the maximum value of the comprehensive score is taken as the optimal water level.

[0029] In one embodiment, before the step of predicting the water level of the large lake to be controlled based on the time series model, the step further includes:

[0030] Acquire time series data affecting water level, and sort the time series data in chronological order;

[0031] Post-processing the time series data to obtain post-processed data, wherein the post-processing includes one or more of missing value filling processing and stabilization processing;

[0032] Extracting features of the post-processed data based on a preset data analysis algorithm;

[0033] Determining corresponding initial models and model parameters according to the characteristics of the post-processed data;

[0034] The initial model is fitted with the model parameters to obtain the time series model.

[0035] In addition, to achieve the above purpose, the present application also proposes a large lake water level regulating device, the large lake water level regulating device comprising:

[0036] The water level change module is used to determine the range of water level change according to the factors affecting the water level of the large lake to be controlled;

[0037] A model building module, used to build an economic entropy weight model based on the water level change range and preset benefit demand data;

[0038] A water level determination module, used to determine the optimal water level according to the economic entropy weight model;

[0039] A water level prediction module, used for predicting the water level of the large lake to be controlled based on a time series model;

[0040] The regulating module is used to regulate the current water level of the large lake to be controlled based on the prediction result and the optimal water level.

[0041] In addition, to achieve the above-mentioned objectives, the present application also proposes a large lake water level regulation device, which includes: a memory, a processor, and a large lake water level regulation program stored in the memory and executable on the processor, wherein the large lake water level regulation program is configured to implement the steps of the large lake water level regulation method as described above.

[0042] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, on which a large lake water level regulation program is stored. When the large lake water level regulation program is executed by a processor, the steps of the large lake water level regulation method described above are implemented.

[0043] In addition, to achieve the above objectives, the present application also proposes a computer program product, which includes a computer program, and when the computer program is executed by a processor, it implements the steps of the large lake water level regulation method as described above.

[0044] This application determines the range of water level changes based on the factors affecting the water level of the large lake to be controlled; constructs an economic entropy weight model based on the water level change range and preset interest demand data; determines the optimal water level based on the economic entropy weight model; predicts the water level of the large lake to be controlled based on the time series model; and adjusts the current water level of the large lake to be controlled based on the predicted results and the optimal water level. This application first comprehensively analyzes the factors affecting the water level, accurately determines the range of water level changes, and provides a scientific data basis for regulation; secondly, constructs an economic entropy weight model based on preset economic interest needs, balances the needs of multiple parties, and ensures the rationality and comprehensiveness of water level regulation; thirdly, uses the entropy weight model to determine the optimal water level and achieve the optimal water level decision; then, predicts the trend of water level changes through a time series model, which improves the accuracy and foresight of the prediction; finally, dynamically adjusts the current water level based on the predicted results and the optimal water level to achieve real-time regulation; through these steps, the overall regulation effect of the large lake water level is optimized. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 This is a flow chart of the first embodiment of the large lake water level regulation method of the present application;

[0046] Figure 2 This is a schematic diagram of a sub-process in the second embodiment of the large lake water level regulation method of the present application;

[0047] Figure 3 This is a schematic diagram of a sub-process in the third embodiment of the large lake water level regulation method of the present application;

[0048] Figure 4 This is a schematic diagram of the module structure of the large lake water level regulating device according to an embodiment of the present application;

[0049] Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the large lake water level regulation method in the embodiment of the present application.

[0050] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0051] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0053] It should be noted that the regulation of the water level of the large lakes is a complex system engineering, involving the needs of multiple stakeholders such as ecological protection, shipping needs, flood control safety, and water supply for residents. Since the water level of the large lakes is affected by a variety of natural factors and human activities, such as precipitation, evaporation, and river flow into the lake, these factors show obvious dynamic changes and time correlation. Therefore, accurately predicting water level changes and formulating reasonable regulation plans have become a major technical challenge in water level management. Existing large lake water level regulation plans mostly rely on empirical rules or analysis of single factors. This traditional method may be effective in simple scenarios, but with the increasing complexity of the ecological environment, climate change, and human activities, the effect of water level regulation is often limited. For example, relying solely on historical water level data for prediction may ignore the impact of long-term climate change, and the water level regulation method that only focuses on a certain type of demand may also easily lead to other needs not being fully considered, thereby affecting the overall effect of water level regulation. Therefore, how to achieve real-time water level regulation under the influence of multiple factors and take into account the needs of all parties and improve the effect of large lake water level regulation has become an urgent problem to be solved.

[0054] The main solutions of this application are: determine the range of water level changes based on the factors affecting the water level of the large lake to be controlled; construct an economic entropy weight model based on the water level change range and preset interest demand data; determine the optimal water level based on the economic entropy weight model; predict the water level of the large lake to be controlled based on the time series model; adjust the current water level of the large lake to be controlled based on the prediction results and the optimal water level.

[0055] This application first comprehensively analyzes the factors affecting water levels, accurately determines the range of water level changes, and provides a scientific data basis for regulation; secondly, an economic entropy weight model is constructed based on the preset economic benefit needs to balance the needs of multiple parties and ensure the rationality and comprehensiveness of water level regulation; thirdly, the entropy weight model is used to determine the optimal water level and achieve the optimal water level decision; then, the water level change trend is predicted through a time series model to improve the accuracy and foresight of the prediction; finally, the current water level is dynamically adjusted based on the prediction results and the optimal water level to achieve real-time regulation; through these steps, the overall regulation effect of the large lake water level is optimized.

[0056] It should be noted that the execution subject of the method of this embodiment can be a computing service device with data processing, network communication and program running functions, or it can be the above-mentioned large lake water level regulation device with the same or similar functions. This embodiment and the following embodiments will be described by taking the large lake water level regulation device as an example.

[0057] Based on this, the first embodiment of the method for regulating the water level of the large lake of this application is proposed, please refer to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the large lake water level regulation method of the present application.

[0058] In this embodiment, the method includes the following steps:

[0059] S1: Determine the range of water level changes based on the factors affecting the water level of the large lake to be controlled;

[0060] It should be noted that large lakes to be controlled refer to large lakes that currently need to be regulated, which may involve multiple uses such as water resource management, ecological protection, shipping and flood control. Water level influencing factors refer to various natural and human factors that affect the changes in the water level of large lakes, including but not limited to precipitation, evaporation, flow of rivers entering the lake, temperature changes, wind speed and human activities (such as water diversion and water diversion projects).

[0061] Specifically, we first need to collect and analyze the historical water level data of the large lakes to be controlled. These data may come from a variety of sources, including meteorological stations, environmental monitoring agencies or lake management departments. By analyzing the historical water level changes, we can identify the overall trend of the water level of the large lakes and the seasonal fluctuations and long-term changes therein. At the same time, for specific large lake areas, it is necessary to further analyze the key factors directly related to water level changes, such as precipitation and evaporation. These data usually have time series characteristics. By analyzing these influencing factors, the range of water level changes can be predicted more accurately.

[0062] Furthermore, it is necessary to combine other external factors to determine the range of water level changes. These external factors may include the operation of water conservancy projects, the demand for water levels from surrounding economic activities, and the potential impact of climate change. By introducing mathematical models (such as regression analysis or correlation analysis), these factors are combined with the actual water level changes of the Great Lakes to derive a reasonable range of water level changes. This range not only reflects the impact of natural factors, but also provides an important reference for subsequent regulation models.

[0063] By comprehensively considering the natural and human factors affecting the water level of the lake, the water level change range can be accurately determined, providing a more reliable data basis for water level regulation. This process helps reduce uncertainty in the regulation process and avoids the risk of regulation failure due to ignoring key influencing factors, thus laying a solid foundation for subsequent water level prediction and control. Accurate determination of the water level change range not only improves the accuracy of water level regulation, but also effectively responds to complex environments and climate changes, making water level management more scientific and accurate.

[0064] S2: constructing an economic entropy weight model based on the water level variation range and the preset benefit demand data;

[0065] It should be noted that the water level change range refers to the reasonable range of fluctuations in the water level of the Great Lakes determined based on the factors affecting the water level of the Great Lakes. The water level changes within this range must not only meet the limitations of natural conditions, but also meet the needs of regulation. The preset interest demand data refers to the demand data of various stakeholders related to the water level of the Great Lakes (such as ecological protection, shipping, flood control, and residential water supply). These demands are quantified into specific values ​​or indicators to measure the requirements of various stakeholders for different water levels. The economic entropy weight model is a multi-factor weighted model that uses the entropy weight method to assign weights to different interest demands. The entropy weight method assigns weights based on the degree of difference in each data, reflecting the importance of each demand to the overall water level regulation.

[0066] Specifically, we first need to convert the preset interest demand data into quantifiable indicators. For example, ecological protection may require that the water level of a large lake be maintained within a certain range to protect the habitat, while shipping requires sufficient water depth to ensure the passage of ships. These different needs must be expressed through reasonable quantitative indicators, such as the appropriate water level range for ecological protection, the minimum water depth for shipping, and the water level requirements for residents' water supply. Then, combined with the previously determined water level change range, these quantitative indicators are matched with the water level range to form a decision matrix.

[0067] Furthermore, the data in the decision matrix is ​​processed based on the entropy weight method. The core idea of ​​the entropy weight method is to determine the influence of each indicator on the decision by calculating the degree of discreteness of the data. First, each indicator is standardized to eliminate the influence of the dimension; then the entropy value of each indicator is calculated. The larger the entropy value, the lower the degree of discreteness of the data, indicating that the indicator has less influence on the overall decision; conversely, the smaller the entropy value, the more important the indicator plays in water level regulation. By calculating the weight of each indicator and applying it to the water level regulation model, an economic entropy weight model is constructed.

[0068] By constructing an economic entropy weight model, we can effectively balance the interests of multiple parties and ensure that the needs of various stakeholders are reasonably considered in the process of water level regulation. The introduction of the entropy weight method makes the weight distribution between different demands more scientific and avoids the deviation problem caused by subjectivity. The model can quantitatively analyze the importance of large lake water level regulation according to the needs of all parties, thereby ensuring that the decision-making of water level regulation is more accurate and reasonable. This not only optimizes the effect of water level regulation, but also improves the economic efficiency of regulation, and ultimately achieves the water level management goal of a win-win situation for all parties.

[0069] S3: Determine the optimal water level according to the economic entropy weight model;

[0070] It should be noted that the optimal water level refers to the water level that can meet the maximum benefit demand, calculated through the economic entropy weight model after comprehensively considering the interests of all parties. It can not only balance the requirements of ecology, shipping, flood control, etc., but also ensure the water level with the best economic benefits.

[0071] Specifically, we first obtain the weights of each interest demand indicator from the economic entropy weight model. These weights represent the importance of each party's needs in water level regulation. For example, ecological protection needs may have a higher weight, while the weight of shipping needs may be adjusted depending on the shipping season and changes in transportation demand. Through these weights, we can quantify the importance of each water level change range in meeting the needs of all parties.

[0072] Furthermore, these weights are used to make a comprehensive score with the previously determined water level range. Specifically, the performance of each water level range in each demand indicator (such as the degree to which the water level is suitable for navigation, the impact of the water level on ecological protection, etc.) is multiplied by the corresponding weight to obtain a comprehensive score for each water level range. The comprehensive score reflects the comprehensive degree to which the water level range meets the needs of all parties. Finally, by comparing the comprehensive scores of different water level ranges, the water level range with the highest score is selected as the optimal water level.

[0073] By determining the optimal water level based on the economic entropy weight model, the problem of how to find the optimal balance between multiple demands can be effectively solved. This process makes full use of the scientific weights assigned by the entropy weight method to ensure that the demands of each stakeholder are reasonably considered and avoid the limitation that a single demand excessively affects the regulation decision. The determination of the optimal water level can maximize the satisfaction of the interests of all parties, avoid the shortcomings of traditional empirical regulation methods that are not accurate enough or biased towards the demands of one party, and ultimately improve the scientificity and economy of water level regulation.

[0074] S4: predicting the water level of the large lake to be controlled based on the time series model;

[0075] It should be noted that the time series model is a statistical model used to analyze time series data (i.e. data arranged in chronological order), which can predict future data based on the patterns and trends of historical data. Common time series models include ARIMA, SARIMA, LSTM, etc., which can handle periodic, trending, and random water level changes. Water level prediction refers to the use of time series models to infer the changing trend of the water level of the Great Lakes in the future based on the historical water level data of the Great Lakes and their influencing factors, so as to facilitate early control decisions.

[0076] Specifically, we first need to collect and process the historical water level data of the Great Lakes. These data include past water level changes, related factors that affect water levels (such as precipitation, evaporation, and river flow into the lake), and are organized in chronological order. Next, the data is stabilized to ensure that the mean and variance of the data are stable over the entire time period. For water level changes with trends or seasonality, it is also necessary to use differential methods or seasonal decomposition methods to remove non-stationary components to ensure the applicability of the time series model.

[0077] Furthermore, after the data preprocessing is completed, a time series model suitable for the data characteristics is selected based on historical data. For example, if the water level changes show obvious periodicity, the SARIMA model can be used; if there is a complex nonlinear dynamic relationship, deep learning models such as LSTM can be considered. By modeling historical data, the model can extract trends, seasonality and random fluctuations in the time series, and then predict the water level changes in a certain period of time in the future by fitting the model. Finally, the prediction results are applied to subsequent water level regulation so that a response can be made in advance when the water level is too high or too low.

[0078] Predicting the water level of the Great Lakes based on the time series model can effectively solve the problem that it is difficult to predict the dynamic changes of the water level in advance. By analyzing the trends and periodic laws of historical data, the time series model can predict future water level changes in advance, allowing managers to formulate control plans in advance based on the prediction results to avoid the adverse effects of abnormal water level fluctuations on shipping, ecology, and flood control. This step improves the foresight and accuracy of water level control, ensures that water level management is more scientific and efficient, and enhances the dynamic response capability of the Great Lakes water level control.

[0079] S5: Based on the prediction result and the optimal water level, adjust the current water level of the large lake to be controlled.

[0080] It should be noted that the forecast results are based on the time series model's estimated value of the Great Lakes' water level changes over a period of time in the future, reflecting future water level trends and fluctuations. Current water level regulation refers to adjusting the Great Lakes' current water level by controlling dams, sluice gates and other means of regulation, so that it gradually approaches or remains within the optimal water level range.

[0081] Specifically, the prediction results of the time series model are first compared with the previously determined optimal water level. If the prediction results show that the future water level will deviate from the optimal water level, the system will decide on the control measures to be taken based on the size, direction and impact of the deviation. Common control measures include opening and closing dams or sluice gates, increasing drainage or water storage, etc., so as to adjust the water level to the optimal range through artificial control means.

[0082] Furthermore, the changes in the current water level are monitored in real time, and the control parameters are continuously adjusted according to the difference between the predicted results and the optimal water level. For example, when it is predicted that the water level will rise above the optimal water level, the system will pre-start drainage measures to reduce the rise in the water level and avoid the impact of excessive water levels on shipping or ecology. On the contrary, when it is predicted that the water level may drop below the optimal water level, the system can temporarily suspend drainage or increase water storage to maintain ecological balance and shipping needs. Throughout the process, water level regulation is a dynamic and continuous adjustment process that requires real-time monitoring, feedback and correction.

[0083] By combining the prediction results of the time series model with the optimal water level to adjust the current water level, the lag problem in water level management can be effectively solved. The beneficial effect of this step is that the water level can be adjusted dynamically and in advance to ensure that the water level of the lake is always kept within a reasonable range, thereby avoiding potential risks caused by water level fluctuations. Real-time adjustment can not only reduce the adverse effects of excessively high or low water levels on ecosystems and shipping, but also improve the flexibility and accuracy of the regulation process, making the water level management of the lake more efficient and intelligent.

[0084] This embodiment determines the range of water level changes based on the factors affecting the water level of the large lake to be controlled; constructs an economic entropy weight model based on the water level change range and preset interest demand data; determines the optimal water level based on the economic entropy weight model; predicts the water level of the large lake to be controlled based on the time series model; and adjusts the current water level of the large lake to be controlled based on the predicted results and the optimal water level. This embodiment first comprehensively analyzes the factors affecting the water level, accurately determines the range of water level changes, and provides a scientific data basis for regulation; secondly, constructs an economic entropy weight model based on preset economic interest needs, balances the needs of multiple parties, and ensures the rationality and comprehensiveness of water level regulation; thirdly, uses the entropy weight model to determine the optimal water level and achieve the optimal water level decision; then, predicts the trend of water level changes through the time series model, which improves the accuracy and foresight of the prediction; finally, dynamically adjusts the current water level based on the predicted results and the optimal water level to achieve real-time regulation; through these steps, the overall regulation effect of the large lake water level is optimized.

[0085] Based on the above first embodiment, a second embodiment of the large lake water level regulation method of the present application is proposed. Figure 2 , Figure 2 This is a schematic diagram of a sub-process in the second embodiment of the large lake water level regulation method of the present application.

[0086] like Figure 2 As shown, in this embodiment, after step S4, the following steps are further included:

[0087] S4a: obtaining the water level impact data of the large lake to be controlled, and normalizing the water level impact data to obtain normalized data;

[0088] S4b: Based on a preset statistical method, the normalized data is screened to obtain screened data;

[0089] S4c: using the screening data as independent variables to construct a multiple regression model;

[0090] S4d: Based on the multivariate regression model, the error of the prediction result is corrected.

[0091] It should be noted that normalization is a data preprocessing method that scales the data within a certain range to eliminate the dimensional differences between different data and enable the data to be compared and analyzed at the same scale. The preset statistical method is a statistical method pre-set according to the specific application scenario, usually including correlation analysis, principal component analysis (PCA), etc., which aims to screen out the most significant influencing factors on water level changes from multiple variables. The multiple regression model is a statistical analysis model that predicts the dependent variable (such as water level changes) through multiple independent variables (i.e. influencing factors) and is used to analyze the degree of influence of each variable on the dependent variable. Error correction refers to correcting the deviation of the time series model in the prediction process and improving the accuracy of the prediction results through the application of the multiple regression model.

[0092] Specifically, first, collect influencing data related to the water level changes of the large lakes to be controlled from various channels. Common water level influencing data include precipitation, evaporation, river flow into the lake, temperature, etc. The dimensions and ranges of these data may be different, so they need to be normalized. The purpose of normalization is to scale data of different ranges to the same scale range (such as 0 to 1), so as to facilitate subsequent analysis and comparison. Common methods of normalization include minimum-maximum normalization and Z-score normalization, and the specific choice depends on the characteristics of the data and the application scenario. After normalization, the data is screened based on the preset statistical method to select the most significant influencing factors on the water level change. Correlation analysis or principal component analysis (PCA) is usually used to screen data, the purpose of which is to remove redundant or noise data, so as to obtain the core influencing factors that play a dominant role in water level changes. For example, through correlation analysis, it is possible to find out the correlation between variables such as precipitation and evaporation and water level changes, and then select the variables with the highest correlation as independent variables.

[0093] Furthermore, the selected important influencing factors are used as independent variables to construct a multiple regression model. The core of this model is to predict the dependent variable (i.e., water level changes) through multiple independent variables. Through the training of historical data, the model can learn the degree of influence of each variable on water level changes, thereby establishing a multiple regression model for water level prediction. Finally, the multiple regression model is applied to the prediction results of the time series model to correct its errors. Although the time series model can capture the dynamic trend of water level changes, it may have deviations when dealing with complex relationships of multiple variables. Through the multiple regression model, the linear relationship between water level changes and various influencing factors can be analyzed, thereby correcting the prediction error of the time series model and improving the accuracy and reliability of the prediction.

[0094] By obtaining water level influencing data and normalizing it, we ensure that the data is analyzed at the same scale, thereby eliminating the impact of different data dimensions. Based on the preset statistical method, we screened out the most significant factors affecting water level changes, removed redundant data, and simplified the complexity of the model. By constructing a multivariate regression model, we further revealed the relationship between water level changes and various influencing factors, and by correcting the errors in the prediction results of the time series model, we greatly improved the accuracy of water level prediction. This step ensures that water level predictions are more accurate and reliable, and provides more scientific data support for large lake water level regulation, thereby significantly improving the effectiveness of water level management.

[0095] Based on the above first embodiment, in this embodiment, the method further includes:

[0096] S6: Perform sensitivity analysis on the economic entropy weight model, the time series model and the multivariate regression model;

[0097] S7: Adjust model parameters based on sensitivity analysis results.

[0098] It should be noted that sensitivity analysis is a method used to evaluate the impact of changes in parameters or input variables in the model on the output results. The purpose is to identify the parameters in the model that play a key role in the change of results.

[0099] Specifically, sensitivity analysis first evaluates the degree of influence of changes in key parameters or variables in each model on the model output results. For the economic entropy weight model, the main purpose of sensitivity analysis is to understand the impact of changes in different weights on the optimal water level. For example, by simulating the fluctuations in the weights of various stakeholders, the impact on the final optimal water level decision is evaluated. The more sensitive the change in weight, the more important the weight is to the water level regulation decision. In the time series model, sensitivity analysis mainly focuses on parameters such as the autoregressive order, difference order, and moving average order of the time series. By analyzing the impact of different parameter settings on the water level prediction results, it can be found which parameters are most critical to the accuracy of water level prediction, thereby improving the model prediction ability. For the multivariate regression model, sensitivity analysis mainly analyzes the degree of influence of each variable (such as precipitation, evaporation, flow, etc.) on the prediction results. By changing the value or range of each variable and observing the contribution and sensitivity of these variables to water level prediction, it can be revealed which factors play a key role in the prediction of water level changes.

[0100] Furthermore, after the sensitivity analysis is completed, the parameters of each model can be adjusted according to the analysis results. For the economic entropy weight model, the weight distribution of each stakeholder can be adjusted according to the weight sensitivity results. For example, if the demand weight of a certain party has too much or too little impact on the water level change, the weight can be redistributed according to the analysis results to make the model more balanced and reasonable. In the time series model, if some parameters (such as autoregression order and difference order) have a significant impact on the prediction accuracy, these parameters can be adjusted to improve the model's prediction accuracy for future water level changes. By adjusting these parameters, the model can better capture the periodicity and trend of the water level. For the multivariate regression model, sensitivity analysis can help determine the most critical influencing factors. For those independent variables that have a greater impact on water level changes, the model can increase their weights or refit related parameters to ensure that the model's prediction results are more accurate. At the same time, if the sensitivity of some independent variables is low, the model can simplify the processing of these variables, reduce the amount of calculation, and improve operation efficiency.

[0101] By conducting sensitivity analysis on the economic entropy weight model, time series model and multivariate regression model, the most critical parameters and variables for water level prediction and regulation in the model can be identified. Sensitivity analysis helps optimize the model parameter settings, making the model more accurate and efficient. Based on the results of sensitivity analysis, adjusting the parameters of each model can not only improve the scientificity and rationality of water level regulation decisions, but also significantly improve the accuracy of water level prediction. This process ensures the flexibility and adaptability of the water level regulation model in practical applications, can cope with complex environmental changes, and ultimately achieve better large lake water level management results.

[0102] This embodiment determines the range of water level changes based on the factors affecting the water level of the large lake to be controlled; constructs an economic entropy weight model based on the water level change range and preset interest demand data; determines the optimal water level based on the economic entropy weight model; predicts the water level of the large lake to be controlled based on the time series model; and adjusts the current water level of the large lake to be controlled based on the predicted results and the optimal water level. This embodiment first comprehensively analyzes the factors affecting the water level, accurately determines the range of water level changes, and provides a scientific data basis for regulation; secondly, constructs an economic entropy weight model based on preset economic interest needs, balances the needs of multiple parties, and ensures the rationality and comprehensiveness of water level regulation; thirdly, uses the entropy weight model to determine the optimal water level and achieve the optimal water level decision; then, predicts the trend of water level changes through the time series model, which improves the accuracy and foresight of the prediction; finally, dynamically adjusts the current water level based on the predicted results and the optimal water level to achieve real-time regulation; through these steps, the overall regulation effect of the large lake water level is optimized.

[0103] Based on the above second embodiment, a third embodiment of the large lake water level regulation method of the present application is proposed. Figure 3 , Figure 3 This is a schematic diagram of a sub-process in the third embodiment of the large lake water level regulation method of the present application.

[0104] In this embodiment, step S2 includes:

[0105] S21: converting the preset benefit demand data into quantitative indicators;

[0106] S22: constructing a decision matrix based on the water level variation range and the quantitative index;

[0107] S23: performing standardization processing on the data in the decision matrix to obtain standardized data;

[0108] S24: determining the entropy value of the quantitative index according to the ratio of each element in the standardized data;

[0109] S25: Determine the weight of the corresponding indicator according to the entropy value, and construct the economic entropy weight model based on the weight.

[0110] It should be noted that quantitative indicators are the conversion of the preset needs of various stakeholders into specific and measurable indicators for calculation and analysis in the model. The decision matrix is ​​a matrix formed by the performance of various quantitative indicators under different water level change ranges, which is used for entropy weight analysis. Standardization refers to the process of converting data of different dimensions into the same dimension for comparative analysis. In the entropy weight method, entropy is used to measure the amount of information of each indicator. The smaller the entropy value, the higher the importance of the indicator to the decision. The weight is the relative importance assigned to each indicator based on the entropy value, which is used to calculate the comprehensive score under different water level change ranges.

[0111] Specifically, first, the demand data of various stakeholders need to be quantified for use in the model. Different stakeholders may have different demands for water levels. For example, ecological protection may require that the water level be maintained within a certain range to protect the habitat, while shipping requires the water level to be high enough to ensure the passage of ships. These demands are converted into specific quantitative indicators, such as the water level range suitable for ecological protection, the minimum water level required for shipping, and the optimal water level range for residential water use. Each interest demand will be converted into a measurable value so that it can be analyzed in correspondence with the range of water level changes.

[0112] Furthermore, a decision matrix is ​​constructed based on the previously determined water level range and the converted quantitative indicators. The rows of the decision matrix represent different water level ranges, and the columns represent the scores of each quantitative indicator. In the decision matrix, each element represents the performance of a certain water level range on a certain indicator. For example, at a specific water level, a certain range may have a higher score for meeting shipping needs, but a lower score for meeting ecological needs. Since the various quantitative indicators in the decision matrix may have different dimensions or ranges, they need to be standardized. Standardization can be achieved by adjusting all data to the same range (such as between 0 and 1) through methods such as minimum-maximum normalization or Z-score normalization for subsequent analysis. The standardized matrix makes the indicators comparable, thereby ensuring the accuracy of subsequent entropy weight analysis. Based on the standardized data, the entropy value of each quantitative indicator is calculated. The entropy value is calculated based on the proportion of each element in the standardized data; based on the calculated entropy value, the weight of each quantitative indicator is determined. Finally, an economic entropy weight model is constructed based on these weights to integrate various demand indicators and optimize water level regulation decisions.

[0113] By converting the preset interest demand data of all parties into quantitative indicators and constructing a decision matrix, the needs of various stakeholders can be systematically processed. Standardization eliminates the differences in data dimensions and ensures that all data are analyzed at the same scale. The entropy weight method makes the decision-making process more scientific and objective by calculating entropy values ​​and assigning weights, avoiding the problem of bias in subjective weight allocation. Finally, by constructing an economic entropy weight model, it is possible to reasonably balance the needs of multiple parties, determine the optimal water level, and improve the scientific nature of the water level regulation of the Great Lakes and the rationality of decision-making.

[0114] Based on the above second embodiment, in this embodiment, step S3 includes:

[0115] S31: Based on the economic entropy weight model, obtain the weight of each demand indicator;

[0116] S32: combining the weight of each demand indicator with the standardized score under the corresponding water level change range;

[0117] S33: Determine the comprehensive score of each water level change range according to the combined result;

[0118] S34: taking the water level variation range corresponding to the maximum value of the comprehensive score as the optimal water level.

[0119] It should be noted that the weight of the demand indicator indicates the relative importance of the demands of different stakeholders in the decision-making process. The larger the weight, the greater the impact of the demand on water level regulation. The standardized score is the score of the performance of different water level ranges on each demand indicator after standardization, which is used to ensure that the data are compared on the same scale. The comprehensive score refers to the total score of each water level range obtained by combining the weight and the standardized score, which is used to determine the optimal water level.

[0120] Specifically, the weights of each demand indicator are first determined through the economic entropy weight model. These weights are calculated based on the entropy weight method and reflect the relative importance of the demands of various stakeholders to the overall water level regulation decision. For large lake water level regulation, common demand indicators include ecological protection, shipping, flood control, and residential water use. The weight distribution results show the priority of these demands in the regulation process. For example, if the importance of ecological protection is higher in a certain period, its weight value will increase accordingly. Next, the weight of each demand indicator is combined with the standardized score under different water level ranges. The standardized score is the score after the performance of the demand indicator is standardized under each water level range. The weighted score of the water level range is obtained by multiplying the weight of each demand indicator by its corresponding standardized score.

[0121] Furthermore, the weighted scores of all demand indicators are summed to obtain a comprehensive score for each water level range. The comprehensive score reflects the overall performance of each water level range under multiple demands. The higher the score of the water level range, the better the water level range can meet the comprehensive requirements of multiple demands. For example, if the comprehensive score of a water level range is high, it means that it is suitable for ecological protection and meets other demands such as shipping and flood control. Finally, by comparing the comprehensive scores of each water level range, the water level range with the highest score is selected. The water level range corresponding to the maximum comprehensive score is the optimal water level, because this water level can achieve the best balance among multiple demands and meet the requirements of different stakeholders. Taking this optimal water level as the goal of water level regulation can maximize the economic and ecological benefits of regulation.

[0122] By combining the weight of the demand index with the standardized score of the water level change range, the impact of multiple demands on water level regulation can be comprehensively considered, making the regulation decision more scientific and reasonable. The process of determining the maximum value of the comprehensive score ensures that the regulation plan is not limited to a single goal, but can balance the needs of all parties and find an optimal water level that is in the overall interest. This process greatly improves the accuracy and applicability of water level regulation, ensuring that the ecological, economic and social needs of the lake are met to the greatest extent. Ultimately, the optimal water level determined based on this model can significantly improve the efficiency and effectiveness of water level regulation and achieve a win-win situation for all parties.

[0123] Based on the above second embodiment, in this embodiment, before step S4, the following is further included:

[0124] S4A: Obtaining time series data affecting the water level, and sorting the time series data in chronological order;

[0125] S4B: performing post-processing on the time series data to obtain post-processed data, wherein the post-processing includes one or more of missing value filling processing and stabilization processing;

[0126] S4C: extracting features of the post-processed data based on a preset data analysis algorithm;

[0127] S4D: Determine the corresponding initial model and model parameters according to the characteristics of the post-processing data;

[0128] S4E: Fitting the initial model with the model parameters to obtain the time series model.

[0129] It should be noted that post-processing is the process of cleaning and adjusting the original time series data, which usually includes missing value filling and stabilization processing to ensure data quality and model applicability. Missing value filling processing refers to the reasonable completion of the missing parts in the data. Common methods include interpolation and mean value filling. Stabilization processing is to eliminate the trend and seasonal components in the data and ensure that the mean and variance of the time series data are stable over the entire time period. Common methods include difference method and logarithmic transformation. Feature extraction refers to extracting key features related to water level changes from post-processed data. These features will be used to select a suitable model and optimize parameters. The initial model and model parameters refer to the model type (such as ARIMA, SARIMA, etc.) and its initial parameters selected in time series analysis, which are used to fit the trend of water level changes.

[0130] Specifically, first, obtain time series data related to the change of water levels in the Great Lakes from historical records. These data are arranged in chronological order, usually including historical water levels, precipitation, evaporation and other factors that affect water level changes. Make sure that these data are sorted in chronological order of date or time to maintain the time dependency of the data. The sorted time series data lays the foundation for the construction of subsequent models. In the acquired and sorted time series data, there may be some missing data, anomalies or non-stationarity. In order to improve the accuracy of the model, these data need to be post-processed. Post-processing includes: Missing value filling: If there are some missing data, these missing values ​​need to be filled by interpolation, mean filling or regression to ensure the integrity of the data. Stationary processing: Many time series models require the data to be stationary, that is, the mean and variance of the data remain constant over time. For water level data with trends or seasonality, the non-stationary components can be removed by difference method (that is, one or more difference operations are performed on the data) or logarithmic transformation, so as to obtain data that is more suitable for modeling. After completing the post-processing of the data, feature extraction is performed on the data based on the preset data analysis algorithm. Feature extraction is to extract the most relevant feature information about water level changes by analyzing the trend, periodicity, and correlation of data. Common feature extraction methods include autocorrelation analysis (ACF) and partial autocorrelation analysis (PACF), which are used to identify the lag, periodicity, or trend of time series. These features will provide a reference for model selection and parameter setting.

[0131] Furthermore, according to the extracted features, the most suitable time series model is selected. For example, if the data shows significant seasonal changes, the seasonal ARIMA (SARIMA) model can be selected; if the trend is more obvious, the differential autoregressive integrated moving average model (ARIMA) can be used. At the same time, the initial parameters of the model, such as the autoregressive order, the differential order, the moving average order, etc., are determined according to the results of the feature analysis. The reasonable setting of these initial parameters is crucial for the fitting and prediction accuracy of the model. After selecting the initial model and determining the model parameters, the initial model is fitted with the processed time series data. The fitting process is to adjust the parameters in the model so that the model can explain the laws and trends in the historical data as accurately as possible. During the fitting process, statistical methods such as maximum likelihood estimation (MLE) can be used to optimize the model parameters to improve the prediction accuracy of the model. After the fitting is completed, the obtained time series model can be used to predict the future water level.

[0132] By processing the time series data that affect the water level and constructing a time series model, the accuracy of water level prediction can be effectively improved. Missing value filling ensures the integrity of the data, stabilization makes the data suitable for modeling, feature extraction provides a basis for model selection, and model fitting improves the accuracy of the model by adjusting parameters. Ultimately, through this time series model, the future water level changes of the Great Lakes can be predicted more accurately, providing a scientific basis for water level regulation. This step ensures the foresight and reliability of water level prediction, helps optimize the Great Lakes water level regulation strategy, and reduces the adverse effects caused by water level fluctuations.

[0133] This embodiment determines the range of water level changes based on the factors affecting the water level of the large lake to be controlled; constructs an economic entropy weight model based on the water level change range and preset interest demand data; determines the optimal water level based on the economic entropy weight model; predicts the water level of the large lake to be controlled based on the time series model; and adjusts the current water level of the large lake to be controlled based on the predicted results and the optimal water level. This embodiment first comprehensively analyzes the factors affecting the water level, accurately determines the range of water level changes, and provides a scientific data basis for regulation; secondly, constructs an economic entropy weight model based on preset economic interest needs, balances the needs of multiple parties, and ensures the rationality and comprehensiveness of water level regulation; thirdly, uses the entropy weight model to determine the optimal water level and achieve the optimal water level decision; then, predicts the trend of water level changes through the time series model, which improves the accuracy and foresight of the prediction; finally, dynamically adjusts the current water level based on the predicted results and the optimal water level to achieve real-time regulation; through these steps, the overall regulation effect of the large lake water level is optimized.

[0134] The present application also provides a large lake water level regulating device, please refer to Figure 4 , Figure 4 This is a schematic diagram of the module structure of the large lake water level regulating device according to an embodiment of the present application, and the large lake water level regulating device comprises:

[0135] The water level variation module 401 is used to determine the water level variation range according to the factors affecting the water level of the large lake to be controlled;

[0136] A model building module 402 is used to build an economic entropy weight model based on the water level variation range and preset benefit demand data;

[0137] A water level determination module 403, used to determine the optimal water level according to the economic entropy weight model;

[0138] A water level prediction module 404 is used to predict the water level of the large lake to be controlled based on a time series model;

[0139] The adjustment module 405 is used to adjust the current water level of the large lake to be controlled based on the prediction result and the optimal water level.

[0140] The large lake water level regulating device provided in the embodiment of the present application adopts the large lake water level regulating method in the above embodiment, which can solve the technical problem of how to improve the effect of large lake water level regulation. Compared with the prior art, the beneficial effects of the large lake water level regulating device provided in the embodiment of the present application are the same as the beneficial effects of the large lake water level regulating method provided in the above embodiment, and the other technical features in the large lake water level regulating device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0141] The present application provides a large lake water level regulation device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the large lake water level regulation method in the above-mentioned embodiment.

[0142] Reference below Figure 5 , which shows a schematic diagram of the structure of a large lake water level regulating device suitable for implementing the embodiment of the present application. The large lake water level regulating device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The large lake water level regulating device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0143] like Figure 5As shown, the large lake water level regulating device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. Various programs and data required for the operation of the large lake water level regulating device are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow the Great Lakes water level regulating device to communicate wirelessly or wired with other devices to exchange data. Although the Great Lakes water level regulating device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0144] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0145] The large lake water level regulating device provided by the present application adopts the large lake water level regulating method in the above embodiment, which can solve the technical problem of how to improve the effect of large lake water level regulation. Compared with the prior art, the beneficial effects of the large lake water level regulating device provided by the present application are the same as the beneficial effects of the large lake water level regulating method provided by the above embodiment, and the other technical features of the large lake water level regulating device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0146] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0147] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0148] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, the computer-readable program instructions being used to execute the large lake water level regulation method in the above-mentioned embodiment.

[0149] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above. The computer-readable storage medium may be included in the large lake water level regulating device; or may exist independently without being assembled into the large lake water level regulating device.

[0150] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the large lake water level regulating device, the large lake water level regulating device: determines the water level variation range according to the water level influencing factors of the large lake to be controlled; constructs an economic entropy weight model based on the water level variation range and the preset benefit demand data; determines the optimal water level according to the economic entropy weight model; predicts the water level of the large lake to be controlled based on the time series model; and adjusts the current water level of the large lake to be controlled based on the prediction result and the optimal water level. The computer program code for performing the operation of the present application can be written in one or more programming languages ​​or a combination thereof, and the programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code can be executed completely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or it can be connected to an external computer (for example, through the Internet using an Internet service provider).

[0151] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0152] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0153] The readable storage medium provided in this application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned large lake water level regulation method, and can solve the technical problem of how to improve the effect of large lake water level regulation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the large lake water level regulation method provided in the above-mentioned embodiment, and will not be repeated here.

[0154] An embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the large lake water level regulation method as described above.

[0155] The computer program product provided in this application can solve the technical problem of how to improve the effect of large lake water level regulation. Compared with the prior art, the beneficial effects of the computer program product provided in the embodiment of this application are the same as the beneficial effects of the large lake water level regulation method provided in the above embodiment, which will not be repeated here.

[0156] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the present application.

Claims

1. A method for regulating the water level of a large lake, characterized in that: The method comprises: Determine the range of water level changes based on the factors affecting the water level of the large lake to be controlled; Based on the water level variation range and the preset benefit demand data, an economic entropy weight model is constructed; Determining the optimal water level according to the economic entropy weight model; Predicting the water level of the large lake to be controlled based on the time series model; Based on the prediction result and the optimal water level, the current water level of the large lake to be controlled is adjusted.

2. The method according to claim 1, characterized in that After the step of predicting the water level of the large lake to be controlled based on the time series model, the method further includes: Acquiring water level impact data of the large lake to be controlled, and normalizing the water level impact data to obtain normalized data; Based on a preset statistical method, the normalized data is screened to obtain screened data; Using the screening data as independent variables, constructing a multiple regression model; Based on the multivariate regression model, the error of the prediction result is corrected.

3. The method according to claim 2, characterized in that The method further comprises: Conducting sensitivity analysis on the economic entropy weight model, the time series model, and the multivariate regression model; Based on the results of sensitivity analysis, the model parameters were adjusted.

4. The method according to claim 1, characterized in that The step of constructing an economic entropy weight model based on the water level variation range and the preset benefit demand data includes: Converting the preset benefit demand data into quantitative indicators; Based on the water level variation range and the quantitative index, construct a decision matrix; Standardizing the data in the decision matrix to obtain standardized data; Determining the entropy value of the quantitative indicator according to the ratio of each element in the standardized data; According to the entropy value, the weight of the corresponding indicator is determined, and based on the weight, the economic entropy weight model is constructed.

5. The method according to claim 1, characterized in that The step of determining the optimal water level according to the economic entropy weight model comprises: Based on the economic entropy weight model, obtaining the weight of each demand indicator; Combining the weight of each demand indicator with the standardized score under the corresponding water level change range; Based on the combined results, the comprehensive score of each water level change range is determined; The water level variation range corresponding to the maximum value of the comprehensive score is taken as the optimal water level.

6. The method according to claim 1, characterized in that Before the step of predicting the water level of the large lake to be controlled based on the time series model, the method further includes: Acquire time series data affecting water level, and sort the time series data in chronological order; Post-processing the time series data to obtain post-processed data, wherein the post-processing includes one or more of missing value filling processing and stabilization processing; Extracting features of the post-processed data based on a preset data analysis algorithm; Determining corresponding initial models and model parameters according to the characteristics of the post-processed data; The initial model is fitted with the model parameters to obtain the time series model.

7. A large lake water level regulating device, characterized in that: The device comprises: The water level change module is used to determine the range of water level change according to the factors affecting the water level of the large lake to be controlled; A model building module, used to build an economic entropy weight model based on the water level change range and preset benefit demand data; A water level determination module, used to determine the optimal water level according to the economic entropy weight model; A water level prediction module, used for predicting the water level of the large lake to be controlled based on a time series model; The regulating module is used to regulate the current water level of the large lake to be controlled based on the prediction result and the optimal water level.

8. A computer device, characterized in that: The device comprises: a memory, a processor, and a large lake water level regulation program stored in the memory and executable on the processor, wherein the large lake water level regulation program is configured to implement the steps of the large lake water level regulation method according to any one of claims 1 to 6.

9. A storage medium, characterized in that: The storage medium stores a large lake water level regulation program, and when the large lake water level regulation program is executed by the processor, the steps of the large lake water level regulation method as described in any one of claims 1 to 6 are implemented.

10. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of the large lake water level regulation method according to any one of claims 1 to 6 are implemented.

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