Method for quantitative characterization of water cycle change process of tributaries of yellow river
By constructing a deep learning-based model of the water cycle change in the Yellow River tributaries, the problem of the inability to quantitatively characterize the water cycle change process in the Yellow River tributaries has been solved, enabling scientific prediction and improved management of water resource evolution trends.
Patent Information
- Authority / Receiving Office
- WO · WO
- Patent Type
- Applications
- Current Assignee / Owner
- INNER MONGOLIA AGRICULTURAL UNIVERSITY
- Filing Date
- 2025-02-14
- Publication Date
- 2026-07-23
AI Technical Summary
Existing models of the Yellow River tributary water cycle cannot provide quantitative characterization, which makes it impossible to effectively understand and predict the evolution trend of water resources, and thus cannot provide a scientific basis for water resources management and protection, resulting in poor management effectiveness.
By collecting real-time data on the water cycle changes in the Yellow River tributaries, a deep learning-based water cycle change model is constructed. Data processing and model optimization are performed, and predictive analysis and quantitative characterization are conducted to formulate comprehensive management plans and achieve intelligent management.
It enables quantitative characterization of the water cycle changes in the Yellow River tributaries, allowing for a better understanding and prediction of water resource evolution trends, providing a scientific basis for water resource management and protection, and improving management effectiveness.
Smart Images
Figure CN2025077315_23072026_PF_FP_ABST
Abstract
Description
A quantitative characterization method for the water cycle changes in Yellow River tributaries
[0001] This application claims priority to Chinese Patent Application No. 202510089826.8, filed on January 20, 2025, entitled "A Quantitative Characterization Method for the Water Cycle Change Process of a Yellow River Tributary", the entire contents of which are incorporated herein by reference. Technical Field
[0002] This application relates to the field of water cycle technology in Yellow River tributaries, and in particular to a method for quantitative characterization of the water cycle change process in Yellow River tributaries. Background Technology
[0003] The Yellow River is China's second-largest river and one of its most important water sources, playing a vital role in ensuring ecological security and economic development in northern China. However, due to the impacts of human activities and climate change, the hydrological and water resource situation in the Yellow River basin is becoming increasingly severe. Problems such as water shortages and water pollution are seriously affecting the production, lives, and sustainable economic and social development of people along the Yellow River.
[0004] Chinese patent CN106898243A discloses a Yellow River water cycle model, which consists of a miniature electric sprayer and a thermoplastic molded hard plastic film shell. It uses the miniature electric sprayer to spray water onto a rectangular, color-printed, thermoplastic molded hard plastic film shell, which reflects the topographical features of the Yellow River basin (higher in the west and lower in the east), dynamically demonstrating the Yellow River water cycle through "sea surface evaporation," "wind transport," and "rainfall." Therefore, it expresses the geographical knowledge of the Yellow River more intuitively and vividly than printed geography textbooks, making learning fun and engaging, stimulating students' interest in learning Yellow River geography, and making it easier to understand and remember. However, this patent has the following drawbacks:
[0005] The relevant models cannot quantitatively characterize the water cycle changes in the Yellow River tributaries, which is not conducive to better understanding and predicting the evolution trend of water resources, and cannot provide a scientific basis for water resources management and protection, resulting in poor management of the Yellow River tributaries. Summary of the Invention
[0006] The purpose of this application is to provide a quantitative characterization method for the water cycle change process of the Yellow River tributaries. This method can quantitatively characterize the water cycle change process of the Yellow River tributaries, which is conducive to better understanding and predicting the evolution trend of water resources, providing a scientific basis for water resources management and protection, improving the management effect of the Yellow River tributaries, and solving the problems mentioned in the background technology.
[0007] To achieve the above objectives, this application provides the following solution:
[0008] A method for quantitatively characterizing the water cycle changes in Yellow River tributaries, comprising the following steps:
[0009] S1: Collect real-time data on the water cycle change process of the Yellow River tributaries based on time series, process the real-time data on the water cycle change process of the Yellow River tributaries based on time series, and determine the characteristic data of the water cycle change process of the Yellow River tributaries based on time series.
[0010] S2: Based on the need for quantitative characterization of the water cycle change process of the Yellow River tributaries, a deep learning-based model of the Yellow River tributary water cycle change is constructed. The deep learning-based model of the Yellow River tributary water cycle change is tested and optimized to determine the optimal deep learning-based model of the Yellow River tributary water cycle change.
[0011] S3: Based on the optimal deep learning-based Yellow River tributary water cycle change model, predictive analysis and quantitative characterization of the time series-based Yellow River tributary water cycle change process characteristics data are performed, the Yellow River tributary water cycle change process is simulated, and the quantitative characterization results of the time series-based Yellow River tributary water cycle change process are determined.
[0012] S4: Visualize and present a quantitative characterization report on the water cycle changes in the Yellow River tributaries, and formulate a comprehensive management plan for the water cycle changes in the Yellow River tributaries. Based on the comprehensive management plan for the water cycle changes in the Yellow River tributaries, conduct intelligent and comprehensive management of the water cycle changes in the Yellow River tributaries.
[0013] In one embodiment, in step S1, real-time data on the water cycle changes of the Yellow River tributaries based on time series are collected, and the following operations are performed:
[0014] Real-time monitoring and continuous data collection of water level, flow rate, precipitation, sediment content and evaporation of Yellow River tributaries were conducted to obtain hydrological data on the water cycle of Yellow River tributaries at different times.
[0015] Real-time monitoring and continuous data collection were conducted on the water depth, flow velocity, bottom sediment, cover material, and water quality of the Yellow River tributaries to obtain data on aquatic organism habitats in the Yellow River tributary water cycle at different times.
[0016] Among them, based on hydrological data of the Yellow River tributary water cycle and aquatic habitat data at different times, real-time data of the Yellow River tributary water cycle change process based on time series were determined.
[0017] In one embodiment, in S1, the real-time data on the water cycle changes of the Yellow River tributaries based on time series are processed by performing the following operations:
[0018] Obtain real-time data on the water cycle changes in the Yellow River tributaries based on time series.
[0019] Cleaning of real-time data on the water cycle changes in the Yellow River tributaries based on time series data, including:
[0020] Consistency checks are performed on real-time data of the water cycle changes in the Yellow River tributaries based on time series.
[0021] Based on the reasonable value range and interrelationship of each parameter in the real-time data of the Yellow River tributary water cycle change process based on time series, check whether the real-time data of the Yellow River tributary water cycle change process based on time series meets the requirements.
[0022] Remove inconsistent data that are outside the normal range, logically unreasonable, or contradictory from the real-time data of the Yellow River tributary water cycle change process based on time series.
[0023] Invalid and missing values were processed in real-time data on the water cycle changes of Yellow River tributaries based on time series.
[0024] In accordance with the requirements for data validity and completeness, check whether the real-time data on the water cycle change process of the Yellow River tributaries based on time series contains invalid or missing values.
[0025] Remove invalid and missing values from the real-time data of the Yellow River tributary water cycle change process based on time series;
[0026] We have identified real-time data on the water cycle changes in the Yellow River tributaries that are useful for quantitative characterization of these changes.
[0027] In one embodiment, in step S1, the real-time data on the water cycle changes of the Yellow River tributaries based on time series are processed, and the following operations are also performed:
[0028] To obtain real-time data on the water cycle changes in the Yellow River tributaries, which are useful for quantitative characterization of these changes;
[0029] Real-time data on the water cycle changes of Yellow River tributaries, which are useful for quantitative characterization of the water cycle changes, are converted and processed.
[0030] Eliminate dimensional differences among real-time data on the water cycle changes of Yellow River tributaries, which are useful for quantitative characterization of these changes;
[0031] Standardized real-time data on the water cycle changes in the Yellow River tributaries were determined.
[0032] In one embodiment, in step S1, the real-time data on the water cycle changes of the Yellow River tributaries based on time series are processed, and the following operations are also performed:
[0033] Obtain standardized real-time data on the water cycle changes in the Yellow River tributaries;
[0034] Feature extraction was performed on standardized real-time data of water cycle changes in Yellow River tributaries.
[0035] Extract features that can quantitatively characterize the water cycle changes in the Yellow River tributaries;
[0036] Characteristic data of the water cycle change process of the Yellow River tributaries based on time series were determined.
[0037] In one embodiment, in step S2, a deep learning-based model of the water cycle change in the Yellow River tributaries is constructed, and the following operations are performed:
[0038] Based on the need for quantitative characterization of the water cycle changes in the Yellow River tributaries, historical data on the water cycle changes in the Yellow River tributaries were collected.
[0039] Historical data on the water cycle changes of the Yellow River tributaries were divided to determine the training set and test set for the water cycle changes of the Yellow River tributaries.
[0040] Choose a convolutional neural network model framework suitable for the water cycle changes in the Yellow River tributaries;
[0041] Based on the training set of water cycle changes in the Yellow River tributaries, a selected convolutional neural network model framework suitable for the water cycle change process of the Yellow River tributaries was trained.
[0042] A deep learning-based model for the water cycle changes in the Yellow River tributaries was established.
[0043] In one embodiment, in step S2, the deep learning-based model of water cycle change in Yellow River tributaries is tested and optimized by performing the following operations:
[0044] Obtain a deep learning-based model of water cycle changes in the Yellow River tributaries;
[0045] Based on the test set of water cycle changes in Yellow River tributaries, the performance of the deep learning-based water cycle change model of Yellow River tributaries is tested and evaluated.
[0046] The performance test and evaluation results based on the Yellow River tributary water cycle variation model were determined.
[0047] Based on the performance test and evaluation results of the Yellow River tributary water cycle change model, the deep learning-based Yellow River tributary water cycle change model is analyzed.
[0048] A parameter adjustment and optimization scheme based on the Yellow River tributary water cycle change model was determined;
[0049] Based on the parameter adjustment and optimization scheme of the Yellow River tributary water cycle change model, the parameters of the deep learning-based Yellow River tributary water cycle change model are adjusted and iteratively optimized.
[0050] The optimal deep learning-based model for the water cycle variation of the Yellow River tributaries was determined.
[0051] In one embodiment, in S3, predictive analysis and quantitative characterization are performed on the characteristic data of the water cycle change process of the Yellow River tributaries based on time series data, and the following operations are performed:
[0052] Obtain characteristic data of water cycle changes in Yellow River tributaries based on time series;
[0053] The characteristic data of the water cycle change process of the Yellow River tributaries based on time series are input into the optimal deep learning-based Yellow River tributary water cycle change model;
[0054] Based on the optimal deep learning-based model of water cycle change in Yellow River tributaries, we predict, analyze, and quantitatively characterize the characteristic data of water cycle change in Yellow River tributaries based on time series, and simulate the water cycle change process of Yellow River tributaries.
[0055] The quantitative characterization results of the water cycle change process in the Yellow River tributaries based on time series were determined.
[0056] In one embodiment, during step S4, a quantitative characterization report on the water cycle changes in the Yellow River tributaries is visualized, and the following operations are performed:
[0057] Obtain quantitative characterization results of the water cycle change process in the Yellow River tributaries based on time series;
[0058] Based on the quantitative characterization results of the water cycle change process of the Yellow River tributaries based on time series, the characteristic data of the water cycle change process of the Yellow River tributaries based on time series are mined and analyzed, and combined with the quantitative characterization results of the water cycle change process of the Yellow River tributaries based on time series, a quantitative characterization report of the water cycle change process of the Yellow River tributaries is formed.
[0059] Among them, the quantitative characterization report on the water cycle changes in the Yellow River tributaries is presented in a visual format.
[0060] In one embodiment, in step S4, intelligent comprehensive management of the water cycle changes in the Yellow River tributaries is performed, including the following operations:
[0061] Obtain a quantitative characterization report on the water cycle changes in the Yellow River tributaries;
[0062] Based on the quantitative characterization report of the water cycle change process in the Yellow River tributaries, a comprehensive management plan for the water cycle change process in the Yellow River tributaries was formulated.
[0063] Based on the comprehensive management scheme for the water cycle changes of the Yellow River tributaries, intelligent comprehensive management is carried out on the water cycle changes of the Yellow River tributaries.
[0064] According to the specific embodiments provided in this application, the following technical effects are disclosed:
[0065] This application collects real-time data on the water cycle changes in Yellow River tributaries, processes this data to determine the characteristic data of the water cycle changes in Yellow River tributaries, constructs a water cycle change model for Yellow River tributaries based on the quantitative characterization requirements of the water cycle changes in Yellow River tributaries, tests and optimizes this model to determine the optimal model, predicts and quantitatively characterizes the characteristic data of the water cycle changes in Yellow River tributaries based on the optimal model, simulates the water cycle changes in Yellow River tributaries, determines the quantitative characterization results of the water cycle changes in Yellow River tributaries, visualizes the quantitative characterization report of the water cycle changes in Yellow River tributaries, and formulates a comprehensive management plan for the water cycle changes in Yellow River tributaries. This intelligent and comprehensive management of the water cycle changes in Yellow River tributaries allows for quantitative characterization of the water cycle changes in Yellow River tributaries, which is beneficial for better understanding and predicting the evolution trend of water resources, providing a scientific basis for water resource management and protection, and improving the management effectiveness of Yellow River tributaries. Attached Figure Description
[0066] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0067] Figure 1 is a flowchart of the quantitative characterization method for the water cycle change process of the Yellow River tributaries provided in the embodiments of this application. Detailed Implementation
[0068] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0069] The purpose of this application is to provide a quantitative characterization method for the water cycle change process of the Yellow River tributaries. This method can quantitatively characterize the water cycle change process of the Yellow River tributaries, which is conducive to better understanding and predicting the evolution trend of water resources, providing a scientific basis for water resources management and protection, and improving the management effect of the Yellow River tributaries.
[0070] To address the problem that existing models cannot quantitatively characterize the water cycle changes in the Yellow River tributaries, hindering a better understanding and prediction of water resource evolution trends and failing to provide a scientific basis for water resource management and protection, thus resulting in poor management of Yellow River tributaries, please refer to Figure 1. This embodiment provides the following technical solution:
[0071] A method for quantitatively characterizing the water cycle changes in the Yellow River tributaries includes the following steps:
[0072] S1: Collect real-time data on the water cycle changes of the Yellow River tributaries based on time series, process the real-time data on the water cycle changes of the Yellow River tributaries based on time series, and determine the characteristic data of the water cycle changes of the Yellow River tributaries based on time series.
[0073] In this embodiment, real-time data on the water cycle changes of the Yellow River tributaries based on time series are collected, and the following operations are performed:
[0074] Real-time monitoring and continuous data collection of water level, flow rate, precipitation, sediment content and evaporation of Yellow River tributaries were conducted to obtain hydrological data on the water cycle of Yellow River tributaries at different times.
[0075] Real-time monitoring and continuous data collection were conducted on the water depth, flow velocity, bottom sediment, cover material, and water quality of the Yellow River tributaries to obtain data on aquatic organism habitats in the Yellow River tributary water cycle at different times.
[0076] Among them, based on hydrological data of the Yellow River tributary water cycle and aquatic habitat data at different times, real-time data of the Yellow River tributary water cycle change process based on time series were determined.
[0077] It should be noted that water depth and flow velocity directly affect the survival and reproduction of aquatic organisms. The substrate includes the material of the riverbed, and the cover is the plants or objects on the riverbed. These factors affect the habitat and shelter of aquatic organisms. Water quality includes water temperature, pH value, dissolved oxygen content, etc., which directly affect the living environment of aquatic organisms. Among them, water temperature changes affect the metabolism and activities of aquatic organisms and are important environmental parameters.
[0078] In this embodiment, real-time data on the water cycle changes of the Yellow River tributaries based on time series are processed by performing the following operations:
[0079] Obtain real-time data on the water cycle changes in the Yellow River tributaries based on time series.
[0080] Cleaning of real-time data on the water cycle changes of Yellow River tributaries based on time series;
[0081] It should be noted that data cleaning refers to the final procedure for discovering and correcting identifiable errors in data files. This includes checking data consistency and handling invalid and missing values. By cleaning real-time data on the water cycle changes of Yellow River tributaries based on time series, inconsistent data, invalid values, and missing values can be removed from the real-time data. This allows for the identification of real-time data on the water cycle changes of Yellow River tributaries that are useful for quantitative characterization of the water cycle changes, thereby improving the accuracy and efficiency of subsequent processing of the real-time data on the water cycle changes of Yellow River tributaries.
[0082] Specifically, this involves performing consistency checks on real-time data of the water cycle changes in the Yellow River tributaries based on time series data.
[0083] Based on the reasonable value range and interrelationship of each parameter in the real-time data of the Yellow River tributary water cycle change process based on time series, check whether the real-time data of the Yellow River tributary water cycle change process based on time series meets the requirements.
[0084] Remove inconsistent data that are outside the normal range, logically unreasonable, or contradictory from the real-time data of the Yellow River tributary water cycle change process based on time series.
[0085] It's important to note that consistency checks examine the data based on the reasonable range of values for each parameter and their interrelationships. This involves verifying whether the data meets requirements and identifying data that exceeds the normal range, is logically illogical, or is contradictory. For example, a variable measured using a 1-7 scale showing a value of 0, or a negative weight, should be considered outside the normal range. Computer software such as SPSS, SAS, and Excel can automatically identify each variable value outside the defined range. Logically inconsistent answers may appear in various forms: for example, many respondents say they drive to work but report not owning a car; or respondents report being heavy buyers and users of a particular brand but simultaneously give very low scores on the familiarity scale. When inconsistencies are found, the questionnaire number, record number, variable name, and error category should be listed for further verification and correction.
[0086] Invalid and missing values were processed in real-time data on the water cycle changes of Yellow River tributaries based on time series.
[0087] In accordance with the requirements for data validity and completeness, check whether the real-time data on the water cycle change process of the Yellow River tributaries based on time series contains invalid or missing values.
[0088] Remove invalid and missing values from the real-time data of the Yellow River tributary water cycle change process based on time series;
[0089] We have identified real-time data on the water cycle changes in the Yellow River tributaries that are useful for quantitative characterization of these changes.
[0090] It should be noted that the real-time data on the water cycle changes of the Yellow River tributaries based on time series data may contain some invalid and missing values, which need to be handled appropriately. Commonly used handling methods include estimation, whole-case deletion, variable deletion, and pairwise deletion.
[0091] The simplest way to estimate is to replace invalid and missing values with the sample mean, median, or mode of a variable. This method is simple, but it does not fully consider the information already in the data and may have a large error. Another method is to estimate based on the respondents' answers to other questions and through correlation analysis or logical inference between variables. For example, the ownership of a certain product may be related to household income, and the probability of owning this product can be estimated based on the respondents' household income.
[0092] The whole-sample deletion method removes samples containing missing values. Since many questionnaires may contain missing values, this method may result in a significant reduction in the effective sample size, making it impossible to fully utilize the data already collected. Therefore, it is only suitable for situations where key variables are missing, or where the proportion of samples containing invalid or missing values is very small.
[0093] Among them, variable deletion can be considered if a variable has many invalid and missing values, and the variable is not particularly important to the problem being studied. This approach reduces the number of variables available for analysis, but does not change the sample size.
[0094] In this method, pairwise deletion uses a special code (usually 9, 99, 999, etc.) to represent invalid and missing values, while retaining all variables and samples in the dataset. However, in actual calculations, only samples with complete answers are used. Therefore, different analyses involve different variables, and their effective sample size will vary. This is a conservative approach that preserves the usable information in the dataset to the greatest extent possible.
[0095] Real-time data on the water cycle changes of Yellow River tributaries, which are useful for quantitative characterization of the water cycle changes, are converted and processed.
[0096] Eliminate dimensional differences among real-time data on the water cycle changes of Yellow River tributaries, which are useful for quantitative characterization of these changes;
[0097] Standardized real-time data on the water cycle changes in the Yellow River tributaries were determined;
[0098] It should be noted that data transformation is the process of changing data from one representation to another. This can eliminate the dimensional differences between real-time data on the water cycle of the Yellow River tributaries, which are useful for quantitative characterization of the water cycle, and facilitate better analysis of the real-time data on the water cycle of the Yellow River tributaries.
[0099] Feature extraction was performed on standardized real-time data of water cycle changes in Yellow River tributaries.
[0100] Extract features that can quantitatively characterize the water cycle changes in the Yellow River tributaries;
[0101] Characteristic data of the water cycle change process of the Yellow River tributaries based on time series were determined.
[0102] It should be noted that in machine learning, pattern recognition, and image processing, feature extraction begins with an initial set of measurement data and establishes derived values designed to provide information and avoid redundancy, thereby facilitating subsequent learning and generalization steps and, in some cases, leading to better interpretability. Feature extraction is related to dimensionality reduction, and the quality of the features has a crucial impact on generalization ability. Therefore, feature extraction from standardized real-time data on the water cycle changes of Yellow River tributaries can identify the characteristic data of the Yellow River tributary water cycle changes, facilitating subsequent predictive analysis and quantitative characterization of the characteristic data of the Yellow River tributary water cycle changes, and simulating the water cycle changes of Yellow River tributaries.
[0103] S2: Based on the need for quantitative characterization of the water cycle change process of the Yellow River tributaries, a deep learning-based model of the Yellow River tributary water cycle change is constructed. The deep learning-based model of the Yellow River tributary water cycle change is tested and optimized to determine the optimal deep learning-based model of the Yellow River tributary water cycle change.
[0104] In this embodiment, a deep learning-based model of the water cycle change in the Yellow River tributaries is constructed, and the following operations are performed:
[0105] Based on the need for quantitative characterization of the water cycle changes in the Yellow River tributaries, historical data on the water cycle changes in the Yellow River tributaries were collected.
[0106] Historical data on the water cycle changes of the Yellow River tributaries were divided to determine the training set and test set for the water cycle changes of the Yellow River tributaries.
[0107] Choose a convolutional neural network model framework suitable for the water cycle changes in the Yellow River tributaries;
[0108] Based on the training set of water cycle changes in the Yellow River tributaries, a selected convolutional neural network model framework suitable for the water cycle change process of the Yellow River tributaries was trained.
[0109] A deep learning-based model for the water cycle changes in the Yellow River tributaries was established.
[0110] In this embodiment, the deep learning-based model of water cycle changes in the Yellow River tributaries is tested and optimized by performing the following operations:
[0111] Obtain a deep learning-based model of water cycle changes in the Yellow River tributaries;
[0112] Based on the test set of water cycle changes in Yellow River tributaries, the performance of the deep learning-based water cycle change model of Yellow River tributaries is tested and evaluated.
[0113] The performance test and evaluation results based on the Yellow River tributary water cycle variation model were determined.
[0114] Based on the performance test and evaluation results of the Yellow River tributary water cycle change model, the deep learning-based Yellow River tributary water cycle change model is analyzed.
[0115] A parameter adjustment and optimization scheme based on the Yellow River tributary water cycle change model was determined;
[0116] Based on the parameter adjustment and optimization scheme of the Yellow River tributary water cycle change model, the parameters of the deep learning-based Yellow River tributary water cycle change model are adjusted and iteratively optimized.
[0117] The optimal deep learning-based model for the water cycle variation of the Yellow River tributaries was determined.
[0118] S3: Based on the optimal deep learning-based model of water cycle change in Yellow River tributaries, predictive analysis and quantitative characterization of the characteristic data of water cycle change process in Yellow River tributaries based on time series are performed, the water cycle change process of Yellow River tributaries is simulated, and the quantitative characterization results of water cycle change process in Yellow River tributaries based on time series are determined.
[0119] In this embodiment, predictive analysis and quantitative characterization of the time-series-based water cycle change process characteristics of the Yellow River tributaries are performed by performing the following operations:
[0120] Obtain characteristic data of water cycle changes in Yellow River tributaries based on time series;
[0121] The characteristic data of the water cycle change process of the Yellow River tributaries based on time series are input into the optimal deep learning-based Yellow River tributary water cycle change model;
[0122] Based on the optimal deep learning-based model of water cycle change in Yellow River tributaries, we predict, analyze, and quantitatively characterize the characteristic data of water cycle change in Yellow River tributaries based on time series, and simulate the water cycle change process of Yellow River tributaries.
[0123] The quantitative characterization results of the water cycle change process in the Yellow River tributaries based on time series were determined.
[0124] S4: Visualize and present a quantitative characterization report on the water cycle changes in the Yellow River tributaries, and formulate a comprehensive management plan for the water cycle changes in the Yellow River tributaries. Based on the comprehensive management plan for the water cycle changes in the Yellow River tributaries, conduct intelligent and comprehensive management of the water cycle changes in the Yellow River tributaries.
[0125] In this embodiment, a quantitative characterization report on the water cycle changes in the Yellow River tributaries is visualized and the following operations are performed:
[0126] Obtain quantitative characterization results of the water cycle change process in the Yellow River tributaries based on time series;
[0127] Based on the quantitative characterization results of the water cycle change process of the Yellow River tributaries based on time series, the characteristic data of the water cycle change process of the Yellow River tributaries based on time series are mined and analyzed, and combined with the quantitative characterization results of the water cycle change process of the Yellow River tributaries based on time series, a quantitative characterization report of the water cycle change process of the Yellow River tributaries is formed.
[0128] Among them, the quantitative characterization report on the water cycle changes in the Yellow River tributaries is presented in a visual format.
[0129] In this embodiment, the water cycle changes of the Yellow River tributaries are managed intelligently and comprehensively by performing the following operations:
[0130] Obtain a quantitative characterization report on the water cycle changes in the Yellow River tributaries;
[0131] Based on the quantitative characterization report of the water cycle change process in the Yellow River tributaries, a comprehensive management plan for the water cycle change process in the Yellow River tributaries was formulated.
[0132] Based on the comprehensive management scheme for the water cycle changes of the Yellow River tributaries, intelligent comprehensive management is carried out on the water cycle changes of the Yellow River tributaries.
[0133] The quantitative characterization method for the water cycle change process of the Yellow River tributaries can be widely applied to water resource management, ecological environment protection, and water conservancy project planning in the Yellow River Basin. In the process of water resource allocation, reasonable allocation measures can be taken based on the model prediction results to improve water resource utilization efficiency. In flood control and disaster reduction, the evolution of water level and flow in the Yellow River tributaries can be simulated to provide early warning of potential flood disaster risks. In river management and ecological protection projects, corresponding engineering measures can be formulated based on the model results to achieve the goal of protecting and improving the aquatic ecological environment.
[0134] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.
[0135] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.
Claims
1. A method for quantitatively characterizing the water cycle changes in the Yellow River tributaries, characterized in that, The quantitative characterization method for the water cycle changes in the Yellow River tributaries includes the following steps: S1: Collect real-time data on the water cycle change process of the Yellow River tributaries based on time series, process the real-time data on the water cycle change process of the Yellow River tributaries based on time series, and determine the characteristic data of the water cycle change process of the Yellow River tributaries based on time series. S2: Based on the need for quantitative characterization of the water cycle change process of the Yellow River tributaries, a deep learning-based model of the Yellow River tributary water cycle change is constructed. The deep learning-based model of the Yellow River tributary water cycle change is tested and optimized to determine the optimal deep learning-based model of the Yellow River tributary water cycle change. S3: Based on the optimal deep learning-based Yellow River tributary water cycle change model, predictive analysis and quantitative characterization of the time series-based Yellow River tributary water cycle change process characteristics data are performed, the Yellow River tributary water cycle change process is simulated, and the quantitative characterization results of the time series-based Yellow River tributary water cycle change process are determined. S4: Visualize and present a quantitative characterization report on the water cycle changes in the Yellow River tributaries, and formulate a comprehensive management plan for the water cycle changes in the Yellow River tributaries. Based on the comprehensive management plan for the water cycle changes in the Yellow River tributaries, conduct intelligent and comprehensive management of the water cycle changes in the Yellow River tributaries.
2. The method for quantitatively characterizing the water cycle changes in the Yellow River tributaries according to claim 1, characterized in that, In step S1, real-time data on the water cycle changes of the Yellow River tributaries based on time series are collected, and the following operations are performed: Real-time monitoring and continuous data collection of water level, flow rate, precipitation, sediment content and evaporation of Yellow River tributaries were conducted to obtain hydrological data on the water cycle of Yellow River tributaries at different times. Real-time monitoring and continuous data collection were conducted on the water depth, flow velocity, bottom sediment, cover material, and water quality of the Yellow River tributaries to obtain data on aquatic organism habitats in the Yellow River tributary water cycle at different times. Among them, based on hydrological data of the Yellow River tributary water cycle and aquatic habitat data at different times, real-time data of the Yellow River tributary water cycle change process based on time series were determined.
3. The method for quantitatively characterizing the water cycle changes in the Yellow River tributaries according to claim 2, characterized in that, In step S1, the real-time data on the water cycle changes of the Yellow River tributaries based on time series are processed, and the following operations are performed: Obtain real-time data on the water cycle changes in the Yellow River tributaries based on time series. Cleaning of real-time data on the water cycle changes in the Yellow River tributaries based on time series data, including: Consistency checks are performed on real-time data of the water cycle changes in the Yellow River tributaries based on time series. Based on the reasonable value range and interrelationship of each parameter in the real-time data of the Yellow River tributary water cycle change process based on time series, check whether the real-time data of the Yellow River tributary water cycle change process based on time series meets the requirements. Remove inconsistent data that are outside the normal range, logically unreasonable, or contradictory from the real-time data of the Yellow River tributary water cycle change process based on time series. Invalid and missing values were processed in real-time data on the water cycle changes of Yellow River tributaries based on time series. In accordance with the requirements for data validity and completeness, check whether the real-time data on the water cycle change process of the Yellow River tributaries based on time series contains invalid or missing values. Remove invalid and missing values from the real-time data of the Yellow River tributary water cycle change process based on time series; We have identified real-time data on the water cycle changes in the Yellow River tributaries that are useful for quantitative characterization of these changes.
4. The method for quantitatively characterizing the water cycle changes in the Yellow River tributaries according to claim 3, characterized in that, In step S1, the real-time data on the water cycle changes of the Yellow River tributaries based on time series are processed, and the following operations are also performed: To obtain real-time data on the water cycle changes in the Yellow River tributaries, which are useful for quantitative characterization of these changes; Real-time data on the water cycle changes of Yellow River tributaries, which are useful for quantitative characterization of the water cycle changes, are converted and processed. Eliminate dimensional differences among real-time data on the water cycle changes of Yellow River tributaries, which are useful for quantitative characterization of these changes; Standardized real-time data on the water cycle changes in the Yellow River tributaries were determined.
5. The method for quantitatively characterizing the water cycle changes in the Yellow River tributaries according to claim 4, characterized in that, In step S1, the real-time data on the water cycle changes of the Yellow River tributaries based on time series are processed, and the following operations are also performed: Obtain standardized real-time data on the water cycle changes in the Yellow River tributaries; Feature extraction was performed on standardized real-time data of water cycle changes in Yellow River tributaries. Extract features that can quantitatively characterize the water cycle changes in the Yellow River tributaries; Characteristic data of the water cycle change process of the Yellow River tributaries based on time series were determined.
6. The method for quantitatively characterizing the water cycle change process of the Yellow River tributaries according to claim 5, characterized in that, In step S2, a deep learning-based model of the water cycle change in the Yellow River tributaries is constructed, and the following operations are performed: Based on the need for quantitative characterization of the water cycle changes in the Yellow River tributaries, historical data on the water cycle changes in the Yellow River tributaries were collected. Historical data on the water cycle changes of the Yellow River tributaries were divided to determine the training set and test set for the water cycle changes of the Yellow River tributaries. Choose a convolutional neural network model framework suitable for the water cycle changes in the Yellow River tributaries; Based on the training set of water cycle changes in the Yellow River tributaries, a selected convolutional neural network model framework suitable for the water cycle change process of the Yellow River tributaries was trained. A deep learning-based model for the water cycle changes in the Yellow River tributaries was established.
7. The method for quantitatively characterizing the water cycle changes in the Yellow River tributaries according to claim 6, characterized in that, In step S2, the deep learning-based Yellow River tributary water cycle change model is tested and optimized by performing the following operations: Obtain a deep learning-based model of water cycle changes in the Yellow River tributaries; Based on the test set of water cycle changes in Yellow River tributaries, the performance of the deep learning-based water cycle change model of Yellow River tributaries is tested and evaluated. The performance test and evaluation results based on the Yellow River tributary water cycle variation model were determined. Based on the performance test and evaluation results of the Yellow River tributary water cycle change model, the deep learning-based Yellow River tributary water cycle change model is analyzed. A parameter adjustment and optimization scheme based on the Yellow River tributary water cycle change model was determined; Based on the parameter adjustment and optimization scheme of the Yellow River tributary water cycle change model, the parameters of the deep learning-based Yellow River tributary water cycle change model are adjusted and iteratively optimized. The optimal deep learning-based model for the water cycle variation of the Yellow River tributaries was determined.
8. The method for quantitatively characterizing the water cycle change process of the Yellow River tributaries according to claim 7, characterized in that, In step S3, predictive analysis and quantitative characterization are performed on the time-series-based characteristic data of the Yellow River tributary water cycle change process, and the following operations are performed: Obtain characteristic data of water cycle changes in Yellow River tributaries based on time series; The characteristic data of the water cycle change process of the Yellow River tributaries based on time series are input into the optimal deep learning-based Yellow River tributary water cycle change model; Based on the optimal deep learning-based model of water cycle change in Yellow River tributaries, we predict, analyze, and quantitatively characterize the characteristic data of water cycle change in Yellow River tributaries based on time series, and simulate the water cycle change process of Yellow River tributaries. The quantitative characterization results of the water cycle change process in the Yellow River tributaries based on time series were determined.
9. The method for quantitatively characterizing the water cycle change process of the Yellow River tributaries according to claim 8, characterized in that, In section S4, a quantitative characterization report on the water cycle changes in the Yellow River tributaries is visualized, and the following operations are performed: Obtain quantitative characterization results of the water cycle change process in the Yellow River tributaries based on time series; Based on the quantitative characterization results of the water cycle change process of the Yellow River tributaries based on time series, the characteristic data of the water cycle change process of the Yellow River tributaries based on time series are mined and analyzed, and combined with the quantitative characterization results of the water cycle change process of the Yellow River tributaries based on time series, a quantitative characterization report of the water cycle change process of the Yellow River tributaries is formed. Among them, the quantitative characterization report on the water cycle changes in the Yellow River tributaries is presented in a visual format.
10. The method for quantitatively characterizing the water cycle change process of the Yellow River tributaries according to claim 9, characterized in that, In S4, intelligent comprehensive management of the water cycle changes in the Yellow River tributaries is performed, and the following operations are carried out: Obtain a quantitative characterization report on the water cycle changes in the Yellow River tributaries; Based on the quantitative characterization report of the water cycle change process in the Yellow River tributaries, a comprehensive management plan for the water cycle change process in the Yellow River tributaries was formulated. Based on the comprehensive management scheme for the water cycle changes of the Yellow River tributaries, intelligent comprehensive management is carried out on the water cycle changes of the Yellow River tributaries.