Intelligent hydrological analysis method and system for hydraulic engineering

Abstract

The invention relates to the technical field of hydrological data analysis, in particular to an intelligent hydrological analysis method and system for a water conservancy project, and the method comprises the following steps: based on an input hydrological data signal, extracting a spectral range of basin precipitation, segmenting a time sequence interval of the signal, and calculating a frequency center point and a bandwidth value; and the distribution weight decomposition signal is adjusted through iteration. According to the method, through combination of high-frequency fragment screening and sparse characteristic extraction, capture of dynamic changes of signals is enhanced, variable processing resolution and detail description capability are improved, amplitude distribution division and probability density calculation are carried out, classification of abnormal signals, rule analysis of emergencies and dynamic extraction of flood discharge time and quantity distribution are enhanced, and the method is high in reliability and high in reliability. According to the method, time interval statistics is combined, influence characteristics of reservoir area leakage are comprehensively analyzed, support is provided for leakage risk assessment, the positioning and monitoring efficiency of an abnormal area is improved through extraction of characteristics of a signal diffusion area, and data support is provided for hydrological disaster early warning and resource optimization scheduling.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrological data analysis, and in particular to an intelligent hydrological analysis method and system for water conservancy projects. Background Art

[0002] The field of hydrological data analysis technology refers to the use of computer technology, data processing algorithms, and intelligent methods to collect, process, analyze, and predict hydrological data. This field is primarily used in scenarios such as hydrological monitoring, flood forecasting, water resources management, and watershed management. Through precise analysis of various hydrological variables, such as precipitation, runoff, evaporation, water level, and water quality, it provides a scientific basis for water conservancy project design, disaster prevention and mitigation, optimal water resource allocation, and environmental protection. In recent years, with the development of artificial intelligence and big data technologies, hydrological data analysis is gradually moving towards automation, intelligence, and precision, improving the ability to analyze complex hydrological phenomena.

[0003] Intelligent hydrological analysis for water conservancy projects combines intelligent algorithms and computing technologies to automatically process and deeply analyze hydrological data related to water conservancy projects. Its primary applications include improving the efficiency of water conservancy project planning, design, and operational management, optimizing water resource allocation, and enhancing early warning capabilities for hydrological disasters. This enables intelligent management and control of water conservancy projects throughout their entire life cycle and the efficient use of hydrological information.

[0004] Existing technologies suffer from coarse granularity in signal decomposition, limited capture of dynamic characteristics, and insufficient analysis of precipitation spectra and dynamic changes, making it difficult to accurately characterize complex hydrological phenomena. Abnormal signal identification relies on static models, making it difficult to quickly respond to sudden hydrological changes and delaying early warning of abnormal events. Reservoir leakage analysis lacks integrated processing across time and space, resulting in inaccurate assessments of the spread and impact of leaks, which can lead to reduced efficiency in water resource allocation and risk management. In locating abnormal areas, insufficient dynamic analysis of signal diffusion leads to delayed monitoring and positioning, limiting the ability to accurately respond to and manage abnormal areas in real time. Summary of the Invention

[0005] The purpose of the present invention is to solve the shortcomings of the prior art and to propose an intelligent hydrological analysis method and system for water conservancy projects.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for intelligent hydrological analysis of water conservancy projects, comprising the following steps:

[0007] S1: Based on the input hydrological data signal, extract the spectrum range of basin precipitation, divide the time series interval of the signal, calculate the frequency center point and bandwidth value, decompose the signal by iteratively adjusting the distribution weight, and generate a precipitation decomposition signal group;

[0008] S2: Based on the precipitation decomposition signal group, the decomposition signal is screened for high-frequency segments, the parts with prominent dynamic changes are extracted, a sparse matching dictionary is constructed, and signal grouping and sparse feature extraction operations are performed successively to generate a traffic sparse signal group;

[0009] S3: Based on the traffic sparse signal group, extract the signal segments with prominent amplitudes, divide the amplitude distribution range in sequence, calculate the probability density of the signal segments, complete the grouping and aggregation of abnormal signals, and generate the peak abnormal distribution value;

[0010] S4: Based on the abnormal distribution value of the flood peak, extract the flood discharge time of the abnormal segment, calculate the flood discharge volume distribution and time interval, calculate the dynamic characteristic distribution of the flood discharge signal, analyze the reservoir leakage impact corresponding to the signal, and generate the reservoir leakage dynamic characteristic value;

[0011] S5: Based on the dynamic characteristic value of leakage in the reservoir area, slidingly calculate the time interval and diffusion range of the leakage signal, group the aggregated signal segments in time and space, extract the diffusion area characteristics of the leakage signal, and generate the characteristic value of the hydrological anomaly area.

[0012] The precipitation decomposition signal group specifically includes the frequency center point, bandwidth value, and distribution weight; the flow sparse signal group includes high-frequency segments, sparse matching dictionaries, and dynamically changing parts; the flood peak anomaly distribution value includes signal segment probability density, amplitude distribution range, abnormal signal grouping and aggregation; the reservoir area leakage dynamic characteristic value includes flood discharge time, flood discharge volume distribution, and time interval statistics; the hydrological anomaly area characteristic value specifically includes leakage signal time interval, diffusion range, and diffusion area characteristics.

[0013] As a further solution of the present invention, the step of obtaining the precipitation decomposition signal group is specifically as follows:

[0014] S111: Input a hydrological data signal, extract the frequency spectrum range of its basin precipitation, calculate the frequency center point and bandwidth value of the signal, and obtain a set of frequency characteristic parameters;

[0015] S112: Using the frequency characteristic parameter set, the original signal is divided into multiple intervals according to the time series, an initial weight is assigned to each interval, and the weight is refined and adjusted to match the interval characteristics to obtain a weighted time series signal distribution set;

[0016] S113: Calculate the decomposition weight center point of each interval according to the weighted time series signal distribution set to obtain a precipitation decomposition signal group.

[0017] As a further solution of the present invention, the step of obtaining the traffic sparse signal group is specifically as follows:

[0018] S211: Input the precipitation decomposition signal group, filter the high-frequency segments in the signal, calculate the dynamic change amplitude of multiple segments one by one, extract the segments whose change amplitude exceeds a set threshold, and generate a high-frequency dynamic segment set;

[0019] S212: Constructing a sparse matching dictionary based on the high-frequency dynamic segment set, performing dynamic characteristic analysis on each segment, and eliminating segments with insignificant dynamic changes by determining the continuity of amplitude changes and the matching degree of frequency characteristics, thereby obtaining a sparse characteristic matching set;

[0020] S213: Using the segment features in the sparse feature matching set, calculating the sparse feature weights of multiple segments, performing grouping operations one by one, and generating a traffic sparse signal group.

[0021] As a further solution of the present invention, the step of obtaining the abnormal distribution value of the flood peak is specifically as follows:

[0022] S311: extracting signal segments with prominent amplitudes from the sparse traffic signal group, performing preliminary screening based on signal strength, and calculating amplitude distribution ranges of the screened segments in turn to obtain an amplitude distribution feature set;

[0023] S312: Calculate the probability density of each signal segment using the amplitude distribution feature set, and apply a probabilistic statistical method to determine the steady-state and non-steady-state features of the signal segment to obtain a signal segment probability density feature set;

[0024] S313: According to the signal segment probability density feature set, the formula is used:

[0025]

[0026] Calculate and aggregate the groups of abnormal signals to generate the peak abnormal distribution value;

[0027] Among them, A i represents the abnormal distribution value of the flood peak after aggregation, p j is the probability density of the signal segment, x j is the amplitude of the signal segment, μ is the average amplitude, σ is the standard deviation of the amplitude, and n represents the total number of signal segments involved in the calculation.

[0028] As a further solution of the present invention, the steps for obtaining the dynamic characteristic value of leakage in the reservoir area are specifically as follows:

[0029] S411: Based on the flood peak abnormal distribution value, select segments with larger abnormal amplitudes as the main body of the flood discharge signal, and perform preliminary classification according to their time sequence and amplitude to obtain a flood discharge time distribution set;

[0030] S412: using the flood discharge time distribution set, calculating the flow distribution of each flood discharge event, and evaluating the distribution of the total flood discharge by aggregating data of all events to obtain a flood discharge distribution dataset;

[0031] S413: combining the flood discharge distribution data set, counting the time intervals between differentiated flood discharge events, analyzing their dynamic change characteristics, and obtaining a dynamic characteristic distribution set of flood discharge signals;

[0032] S414: Calculating the correlation impact between each flood discharge event and reservoir leakage based on the dynamic characteristic distribution set of the flood discharge signal, and generating a dynamic characteristic value of reservoir leakage.

[0033] As a further solution of the present invention, the step of obtaining the characteristic value of the hydrological anomaly area is specifically as follows:

[0034] S511: Based on the dynamic characteristic value of leakage in the reservoir area, slidingly calculating the time interval and diffusion range of each leakage event, determining the correlation of multiple events through time series analysis, and generating leakage event time interval and diffusion range data;

[0035] S512: using the leakage event time interval and diffusion range data, performing temporal and spatial grouping on the aggregated signal segments, and using a clustering algorithm to classify the signals into differentiated temporal and spatial blocks to obtain classified temporal and spatial signal groups;

[0036] S513: Extracting the leakage signal diffusion area characteristics of multiple blocks based on the classified time-space signal group using the formula:

[0037]

[0038] Calculate the diffusion characteristics of leakage signals in multiple blocks and generate characteristic values of hydrological anomaly areas;

[0039] Among them, R i represents the characteristic value of the hydrological anomaly area in the i-th block, a j is the influence intensity of the signal fragment, d j is the diffusion distance of the signal fragment, δ i is the average diffusion distance of the block, G is the standard deviation of the diffusion distance, and n is the total number of signal segments involved in the calculation.

[0040] An intelligent hydrological analysis system for water conservancy projects, the intelligent hydrological analysis system for water conservancy projects being used to execute the above-mentioned intelligent hydrological analysis method for water conservancy projects, the system comprising:

[0041] The precipitation signal decomposition module extracts the spectrum range of basin precipitation based on the input hydrological data signal, calculates the frequency center point and bandwidth value, divides the time series interval, adjusts the weight decomposition signal, and generates a precipitation decomposition signal group;

[0042] The traffic sparse signal extraction module extracts high-frequency segments based on the precipitation decomposition signal group, screens signal segments with large dynamic changes, extracts sparse characteristic segments by grouping, constructs a dynamic sparse matching dictionary, and generates a traffic sparse signal group;

[0043] The flood peak anomaly distribution calculation module extracts signal segments with prominent amplitudes based on the flow sparse signal group, divides the amplitude distribution range, calculates the segment probability density, aggregates and groups the signal segments, and generates a flood peak anomaly distribution value;

[0044] The reservoir leakage dynamic characteristic analysis module extracts the flood discharge time segment based on the flood peak abnormal distribution value, calculates the time interval and flood discharge distribution characteristics, counts the dynamic characteristic change value, screens the leakage impact segment, and generates the reservoir leakage dynamic characteristic value;

[0045] The leakage diffusion area characteristic calculation module calculates the time interval and diffusion range based on the dynamic characteristic value of the reservoir leakage, groups the leakage signal by time and space, extracts the diffusion space distribution characteristics, and generates the hydrological abnormality area characteristic value.

[0046] Compared with the prior art, the advantages and positive effects of the present invention are:

[0047] In the present invention, the frequency characteristics of precipitation signals are accurately captured through spectrum extraction and time series segmentation of hydrological data, and the degree of refinement of signal decomposition is optimized. The combination of high-frequency segment screening and sparse feature extraction strengthens the capture of dynamic changes in signals, improves variable processing resolution and detail characterization capabilities. Amplitude distribution division and probability density calculation enhance the classification of abnormal signals and the analysis of the laws of emergencies. The dynamic extraction of flood discharge time and amount distribution, combined with time interval statistics, comprehensively analyzes the impact characteristics of reservoir leakage, and provides support for leakage risk assessment. The extraction of signal diffusion area characteristics improves the efficiency of positioning and monitoring of abnormal areas, and provides data support for hydrological disaster warning and resource optimization and scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a schematic diagram of the workflow of the present invention;

[0049] Figure 2 This is a flow chart of the steps for obtaining the precipitation decomposition signal group of the present invention;

[0050] Figure 3 Flowchart of the steps for obtaining a traffic sparse signal group according to the present invention;

[0051] Figure 4 This is a flow chart of the steps for obtaining the abnormal distribution value of the flood peak of the present invention;

[0052] Figure 5 Flowchart of the steps for obtaining the dynamic characteristic value of reservoir leakage in the present invention;

[0053] Figure 6 This is a flow chart of the steps for obtaining characteristic values of hydrological anomaly areas according to the present invention. DETAILED DESCRIPTION

[0054] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0055] In the description of the present invention, it should be understood that the terms "length," "width," "up," "down," "front," "back," "left," "right," "vertical," "horizontal," "top," "bottom," "inside," "outside," and the like, indicating positions or relationships, are based on the positions or relationships shown in the accompanying drawings and are intended only to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, in the description of the present invention, "plurality" means two or more, unless otherwise expressly and specifically defined.

[0056] Example 1

[0057] See also Figure 1 The present invention provides a technical solution: an intelligent hydrological analysis method for water conservancy projects, comprising the following steps:

[0058] S1: Based on the input hydrological data signal, extract the spectrum range of basin precipitation, divide the time series interval of the signal, calculate the frequency center point and bandwidth value, decompose the signal by iteratively adjusting the distribution weight, and generate a precipitation decomposition signal group;

[0059] S2: Based on the precipitation decomposition signal group, the high-frequency segments of the decomposed signal are screened, the parts with prominent dynamic changes are extracted, a sparse matching dictionary is constructed, and signal grouping and sparse feature extraction operations are performed successively to generate a sparse traffic signal group;

[0060] S3: Based on the sparse flow signal group, extract the signal segments with prominent amplitudes, divide the amplitude distribution range in sequence, calculate the probability density of the signal segments, complete the grouping and aggregation of abnormal signals, and generate the flood peak abnormal distribution value;

[0061] S4: Based on the abnormal distribution value of the flood peak, the flood discharge time of the abnormal segment is extracted, the flood discharge volume distribution and time interval are calculated, the dynamic characteristic distribution of the flood discharge signal is statistically analyzed, the leakage impact of the reservoir area corresponding to the signal is analyzed, and the dynamic characteristic value of the reservoir area leakage is generated;

[0062] S5: Based on the dynamic characteristic value of leakage in the reservoir area, the time interval and diffusion range of the leakage signal are calculated by sliding, the aggregated signal segments are grouped in time and space, the diffusion area characteristics of the leakage signal are extracted, and the characteristic value of the hydrological anomaly area is generated.

[0063] The precipitation decomposition signal group specifically includes the frequency center point, bandwidth value, and distribution weight. The flow sparse signal group includes high-frequency segments, sparse matching dictionaries, and dynamically changing parts. The flood peak anomaly distribution value includes the signal segment probability density, amplitude distribution range, abnormal signal grouping and aggregation. The reservoir leakage dynamic characteristic value includes flood discharge time, flood discharge volume distribution, and time interval statistics. The hydrological anomaly area characteristic value specifically includes the leakage signal time interval, diffusion range, and diffusion area characteristics.

[0064] See also Figure 2 , the specific steps for obtaining the precipitation decomposition signal group are:

[0065] S111: Input a hydrological data signal, extract the frequency spectrum range of its basin precipitation, calculate the frequency center point and bandwidth value of the signal, and obtain a set of frequency characteristic parameters;

[0066] First, the raw data signal is analyzed to determine its frequency and intensity. These data are obtained through precise measurement equipment, ensuring the accuracy and reliability of the data. Next, professional software analysis tools are used to calculate the center point and bandwidth values in the spectrum. The key to this step is to use a precise frequency analysis algorithm to ensure that the calculation of the frequency center point and bandwidth is accurate. The resulting set of frequency characteristic parameters provides the necessary basic information for the next step of signal processing.

[0067] S112: Using the frequency characteristic parameter set, the original signal is divided into multiple intervals according to the time series, an initial weight is assigned to each interval, and the weight is refined and adjusted to match the interval characteristics to obtain a weighted time series signal distribution set;

[0068] The boundaries of each time interval are determined by a data processing algorithm. The initial weight of each interval after segmentation is adjusted according to its characteristics. The weight adjustment is based on the statistical characteristics of the signal in each interval, such as amplitude and energy distribution. The weight allocation of each interval is performed through an iterative optimization algorithm to ensure that the weight of each partition can maximize the reflection of the precipitation characteristics of the interval, so that the weighted time series signal distribution set can more accurately represent the overall precipitation pattern.

[0069] S113: Based on the weighted time series signal distribution set, the formula is used:

[0070]

[0071] Calculate the decomposition weight center point of each interval to obtain the precipitation decomposition signal group;

[0072] Among them, W i represents the weighted frequency deviation of the i-th time series, f k Represents the frequency value of multiple segmentation intervals, f ci is the frequency center point, w k is the weight of the kth interval, and n represents the total number of intervals into which the time series is divided.

[0073] formula:

[0074]

[0075] The benefit of the formula is that by introducing the weight parameter w k , which strengthens the model's emphasis on different frequency components, allowing the calculation process to more accurately reflect the contribution of each frequency component to the precipitation signal.

[0076] Detailed explanation of the formula and the process of formula calculation and derivation:

[0077] Assume there are three frequency intervals, with frequency values of 10Hz, 12Hz, and 15Hz respectively. The corresponding weights are set to 0.2, 0.5, and 0.3 respectively. The frequency center point is set to 12Hz. Substitute into the formula for calculation:

[0078]

[0079]

[0080]

[0081] W i =3.5;

[0082] The results show that after considering the weighted frequency deviation of each interval weight, the total deviation value is 3.5, which reflects the combined influence of each frequency component in the overall precipitation signal. Further, this value will be used to optimize the parameter settings of the model to more accurately predict the precipitation decomposition signal group.

[0083] See also Figure 3 ,The specific steps for obtaining the traffic sparse signal group are:

[0084] S211: Inputting a precipitation decomposition signal group, filtering high-frequency segments in the signal, calculating the dynamic change amplitudes of multiple segments one by one, extracting segments whose change amplitudes exceed a set threshold, and generating a set of high-frequency dynamic segments;

[0085] First, it is necessary to conduct a comprehensive analysis of the frequency distribution of the input signal, identify the high-frequency interval, and select segments with significant dynamic changes through dynamic threshold screening technology. Based on this, a high-frequency dynamic segment set is constructed. This set contains all qualified signal segments. These segments show the situation where precipitation changes sharply in a short period of time. This method not only improves the data processing efficiency, but also ensures the accuracy of data analysis. Through this stage of processing, a signal segment set that accurately reflects the area where precipitation changes sharply can be obtained.

[0086] S212: Based on the high-frequency dynamic segment set, a sparse matching dictionary is constructed, and dynamic characteristics of each segment are analyzed. By judging the continuity of amplitude changes and the matching degree of frequency characteristics, segments with insignificant dynamic changes are eliminated to obtain a sparse characteristic matching set;

[0087] By constructing a sparse matching dictionary that matches the signal fragments, this dictionary allows for detailed characteristic analysis of each signal fragment, including the degree of matching between its frequency characteristics and dynamic change patterns. Signal fragments with insignificant dynamic characteristics are eliminated by setting a matching threshold. In this process, the construction of the sparse matching dictionary relies on a deep understanding of the signal spectrum and the results of previous signal processing. In this way, we can extract more representative fragments from the original set of high-frequency dynamic fragments, thereby ensuring that the extracted signal fragments can provide more accurate data support in subsequent analysis, and thus obtain a sparse feature matching set.

[0088] S213: Use sparse feature matching to match the fragment features in the set, applying the formula:

[0089]

[0090] Calculate the sparse characteristic weights of multiple segments, perform grouping operations one by one, and generate traffic sparse signal groups;

[0091] Among them, D i represents the sparse feature weight of the i-th segment, x ij is the jth dynamic value in the segment, μ i is the dynamic mean of the i-th segment, w j is a weight factor that reflects the criticality of multiple dynamic values within a segment, and n represents the number of reference dynamic values when calculating the sparse feature weight of the i-th segment.

[0092] formula:

[0093]

[0094] The benefit of the formula is that it provides an accurate method for quantifying the weights of sparse features. This method can effectively distinguish signal segments with different dynamic changes. It is particularly critical for hydrological analysis because it allows precise control over which data are important, thereby optimizing signal processing and data storage.

[0095] Detailed explanation of the formula and the process of formula calculation and derivation:

[0096] Given an example, let w j =0.5 represents weight, x ij =10 represents the dynamic value within the segment, μ i =8 represents the dynamic mean, and the sparse feature weight of each segment is calculated:

[0097]

[0098] This result, 1, indicates that the sparsity weight of segment i is 1, meaning that its dynamic variation matches the preset average level. For such segments, further analysis is required to determine their role and importance in the hydrological event. This result indicates that this segment has a high weight in the sparse matching dictionary, meaning that its dynamic variation characteristics are considered to have a high priority in the analysis. Further data processing and analysis will focus on these high-weighted segments.

[0099] See also Figure 4 , the specific steps for obtaining the flood peak abnormal distribution value are:

[0100] S311: extracting signal segments with prominent amplitudes from the sparse traffic signal group, performing preliminary screening based on signal strength, and calculating the amplitude distribution ranges of the screened segments in turn to obtain an amplitude distribution feature set;

[0101] This process involves intensity screening of the signal, using spectrum analysis methods to identify and calculate the amplitude of each signal segment, thereby screening out segments with prominent amplitudes, and calculating the amplitude distribution range of the screened signal segments in turn. This operation uses standard signal processing methods, such as peak detection and amplitude sorting, to ensure that only the most representative signals are selected for analysis. This operation is implemented through high-precision signal processing instruments and advanced analysis software, ensuring the accuracy and reliability of the results. The amplitude distribution feature set obtained through this process provides basic data for the next probability density calculation, and an amplitude distribution feature set is obtained.

[0102] S312: Calculate the probability density of each signal segment using the amplitude distribution feature set, apply probabilistic statistical methods to determine the steady-state and non-steady-state features of the signal segment, and obtain a signal segment probability density feature set;

[0103] A detailed analysis of the steady-state and non-steady-state characteristics of the signal segment is carried out using probabilistic statistical methods, mainly through kernel density estimation (KDE). The KDE method can effectively estimate the overall probability density function from limited sample data. Through this method, the steady-state and non-steady-state characteristics in the signal segment can be accurately identified, and the signal segments can be classified accordingly. This process not only requires high-precision data acquisition, but also relies on complex mathematical models and calculation processes to ensure that an accurate set of signal segment probability density characteristics is obtained through this comprehensive method, so as to carry out effective abnormal signal analysis and processing.

[0104] S313: Based on the signal segment probability density feature set, the formula is used:

[0105]

[0106] Calculate and aggregate the groups of abnormal signals to generate the peak abnormal distribution value;

[0107] Among them, A i represents the abnormal distribution value of flood peak after aggregation, p j is the probability density of the signal segment, x j is the amplitude of the signal segment, μ is the average amplitude, σ is the standard deviation of the amplitude, and n represents the total number of signal segments involved in the calculation.

[0108] formula:

[0109]

[0110] The benefit of the formula is that by introducing a Gaussian function to calculate the weights of signal segments, the sensitivity to segments with large signal amplitude deviations is enhanced. This is particularly helpful in highlighting the importance of abnormal signals when processing signals, and then effectively aggregating these signals, providing a mathematically precise expression for the identification of abnormal distribution of flood peaks.

[0111] Detailed explanation of the formula and the process of formula calculation and derivation:

[0112] Suppose there is a set of signal segments, where the amplitude x of each segment is j , the mean amplitude is μ, the standard deviation is σ, and the probability density of each segment is p j In practice, μ and σ can be obtained from signal data by statistical methods, p j Determined by the previous signal processing step. If there are three signal segments with amplitudes of 10, 12, and 8, probability densities of 0.2, 0.5, and 0.3, an average amplitude of 10, and a standard deviation of 2, then:

[0113]

[0114] Ai =0.2·e 0 +0.5·e -1 +0.3·e -1 ;

[0115] A i =0.2·1+0.5·0.3678+0.3·0.3678;

[0116] A i =0.2+0.1839+0.1103;

[0117] A i =0.4942;

[0118] The results show that the calculated flood peak anomaly distribution value is 0.4942, which indicates that after considering the amplitude and probability density of each signal segment, the anomaly of the overall signal is medium. This value is obtained through precise calculation and directly reflects the collective behavior of abnormal signals in flood peak events.

[0119] See also Figure 5 The specific steps for obtaining the dynamic characteristic value of reservoir leakage are as follows:

[0120] S411: Based on the abnormal distribution value of the flood peak, the segments with larger abnormal amplitude are selected as the main body of the flood discharge signal, and preliminary classification is performed according to their time sequence and amplitude to obtain the flood discharge time distribution set;

[0121] First, the fragments with larger amplitudes in the abnormal distribution values of flood peaks are screened out as the main body. These data are collected by the real-time monitoring system to ensure the authenticity and accuracy of the data. Preliminary classification and processing are carried out according to chronological order and amplitude through automated scripts. The threshold analysis method is used to eliminate noise and irrelevant data and retain significant flood discharge events. These flood discharge time distribution sets reflect the flood discharge situation in different time periods and provide a basic data set for subsequent analysis. This process ensures the accuracy and real-time nature of data processing. Relying on these accurate flood discharge time distribution data, flood discharge patterns can be effectively predicted and managed.

[0122] S412: using the flood discharge time distribution set, calculating the flow distribution of each flood discharge event, and evaluating the distribution of the total flood discharge by aggregating the data of all events to obtain a flood discharge distribution dataset;

[0123] It is calculated by analyzing the flow data of each flood discharge event. These flow data are usually monitored in real time by flow meters and water level gauges and automatically recorded by data acquisition systems. Statistical analysis is performed on these data to determine the average flow and maximum flow of each flood discharge event. Data clustering algorithms are used to classify flood discharge events according to flow size. This method allows for a more accurate understanding of flow distribution characteristics under different flood discharge conditions, which is crucial for reservoir management and flood discharge strategy formulation.

[0124] S413: combining the flood discharge distribution data set, calculating the time intervals between differentiated flood discharge events, analyzing their dynamic change characteristics, and obtaining a dynamic characteristic distribution set of the flood discharge signal;

[0125] This process involves complex data processing and analysis techniques, including time series analysis and dynamic pattern recognition. Through these analyses, the periodic and sudden characteristics of flood discharge events can be identified, providing a scientific basis for reservoir management. At the same time, the analysis results of these dynamic characteristics can also be used to optimize the design and operation strategies of the reservoir to ensure its safe and efficient operation.

[0126] S414: Based on the dynamic characteristics distribution set of the flood discharge signal, the formula is used:

[0127]

[0128] Calculate the correlation between each flood discharge event and reservoir leakage, and generate the dynamic characteristic value of reservoir leakage;

[0129] Among them, S i Represents the dynamic characteristic value of leakage in the reservoir area, d k represents the flow of the kth flood discharge, t k is the flood discharge time, T i is the leakage analysis time point of the target reservoir area, B is the standard deviation of the flood discharge time, and n is the total number of flood discharge events.

[0130] formula:

[0131]

[0132] The benefit of the formula is that it provides a method for quantitatively analyzing the temporal relationship between reservoir leakage and flood discharge activities by considering the time dispersion and weighted average of flood discharge volume, which helps to more accurately predict and control the leakage risk of the reservoir area.

[0133] Detailed explanation of the formula and the process of formula calculation and derivation:

[0134] Set d k =300 cubic meters / second is the flow rate of the kth flood discharge, t k =5 hours is the flood discharge time, T i= 3 hours is the target analysis time point, and B = 1.5 hours is the time dispersion.

[0135]

[0136] The results show that considering the proximity of the flood discharge event to the analysis time point, the leakage risk assessment value is 0.641, indicating that the leakage risk near this time point is low and the flood discharge control measures are effective.

[0137] See also Figure 6 , the specific steps for obtaining the characteristic values of hydrological anomaly areas are as follows:

[0138] S511: Based on the dynamic characteristic value of leakage in the reservoir area, the time interval and diffusion range of each leakage event are calculated by sliding, the correlation between multiple events is determined through time series analysis, and the leakage event time interval and diffusion range data are generated;

[0139] First, a sliding window is set. The size of the window is determined by the previous event records, which is usually based on the frequency and intensity of past events. The window size is calculated through data analysis. Next, the time interval and diffusion range of the leakage events in each window are calculated. This requires collecting the start and end time of each event, as well as the estimated value of the penetration depth or range. These data are obtained through on-site sensor monitoring or remote sensing data. Then, a mathematical model is used to perform statistical analysis on the time interval and diffusion range. The model may include but is not limited to linear regression analysis or nonlinear time series analysis. These analyses are used to determine the mean and standard deviation of the time interval and diffusion range, providing the necessary statistical parameters for the next cluster analysis. This process not only provides a grouping basis for the aggregated signal fragments, but also helps predict possible leakage behavior in the future, thereby optimizing monitoring and emergency response strategies.

[0140] S512: using the leakage event time interval and diffusion range data, temporally and spatially grouping the aggregated signal segments, and using a clustering algorithm to classify the signals into differentiated temporal and spatial blocks to obtain classified temporal and spatial signal groups;

[0141] Based on the time interval and diffusion range data, a clustering algorithm is used to classify the signals. The classification is based on signal intensity, duration, and impact range. These classification parameters are extracted from the data using data mining techniques to ensure that each category is statistically significant. The classification results are then integrated with geographic information system data to analyze the spatial distribution characteristics of each category. This step is performed using advanced spatial analysis tools such as ArcGIS or QGIS. Through these tools, analysts can observe the spatial clustering of different leakage events and demarcate potential high-risk areas, providing a scientific basis for the formulation of prevention and control measures.

[0142] S513: Extract the diffusion characteristics of the leakage signal in multiple blocks based on the classified temporal and spatial signal groups using the formula:

[0143]

[0144] Calculate the diffusion characteristics of leakage signals in multiple blocks and generate characteristic values of hydrological anomaly areas;

[0145] Among them, R i represents the characteristic value of the hydrological anomaly area in the i-th block, a j is the influence intensity of the signal fragment, d j is the diffusion distance of the signal fragment, δ i is the average diffusion distance of the block, G is the standard deviation of the diffusion distance, and n is the total number of signal segments involved in the calculation.

[0146] formula:

[0147]

[0148] The benefit of the formula is that it can quantitatively describe the diffusion degree and concentration trend of leakage signals in different areas, making it possible to more accurately identify and predict possible high-risk leakage areas, and then take more targeted monitoring and intervention measures.

[0149] Detailed explanation of the formula and the process of formula calculation and derivation:

[0150] Assume there are three signal segments with influence strengths a1 = 0.5, a2 = 0.3, a3 = 0.2, diffusion distances d1 = 100 m, d2 = 150 m, d3 = 120 m, average diffusion distance δ = 130 m, and standard deviation G = 20 m. Calculate each term:

[0151]

[0152] but

[0153] R i =0.5·0.3247+0.3·0.6065+0.2·0.8825≈0.16235+0.18195+

[0154] 0.1765=0.5208;

[0155] The results show that, considering the impact intensity and diffusion distance of each signal segment, the calculated hydrological anomaly area characteristic value of block i is 0.5208, which indicates that there is a moderate leakage risk in this block and further monitoring and analysis is required.

[0156] An intelligent hydrological analysis system for water conservancy projects, which is used to execute the above-mentioned intelligent hydrological analysis method for water conservancy projects, comprises:

[0157] The precipitation signal decomposition module extracts the spectrum range of basin precipitation based on the input hydrological data signal, calculates the frequency center point and bandwidth value, divides the time series interval, adjusts the weight decomposition signal, and generates a precipitation decomposition signal group;

[0158] The traffic sparse signal extraction module decomposes the signal group based on precipitation, extracts high-frequency segments, filters signal segments with large dynamic changes, extracts sparse characteristic segments by group, builds a dynamic sparse matching dictionary, and generates a traffic sparse signal group;

[0159] The flood peak anomaly distribution calculation module extracts signal segments with prominent amplitudes based on the sparse flow signal group, divides the amplitude distribution range, calculates the segment probability density, aggregates and groups the signal segments, and generates flood peak anomaly distribution values;

[0160] The reservoir leakage dynamic characteristic analysis module extracts flood discharge time segments based on the abnormal distribution value of flood peaks, calculates the time interval and flood discharge distribution characteristics, counts the dynamic characteristic change values, screens the leakage-affected segments, and generates the reservoir leakage dynamic characteristic values;

[0161] The leakage diffusion regional characteristic calculation module calculates the time interval and diffusion range based on the dynamic characteristic value of reservoir leakage, groups the leakage signals by time and space, extracts the diffusion spatial distribution characteristics, and generates the characteristic value of the hydrological anomaly area.

[0162] The above are merely preferred embodiments of the present invention and do not limit the present invention in any other form. Any technician familiar with the profession may use the technical content disclosed above to change or modify it into an equivalent embodiment with equivalent changes and apply it to other fields. However, any simple modification, equivalent change and modification made to the above embodiment based on the technical essence of the present invention without departing from the content of the technical solution of the present invention shall still fall within the scope of protection of the technical solution of the present invention.

Claims

1. An intelligent hydrological analysis method for water conservancy projects, characterized in that: The following steps are involved: S1: Based on the input hydrological data signal, extract the spectrum range of basin precipitation, divide the time series interval of the signal, calculate the frequency center point and bandwidth value, decompose the signal by iteratively adjusting the distribution weight, and generate a precipitation decomposition signal group; S2: Based on the precipitation decomposition signal group, the decomposition signal is screened for high-frequency segments, the parts with prominent dynamic changes are extracted, a sparse matching dictionary is constructed, and signal grouping and sparse feature extraction operations are performed successively to generate a traffic sparse signal group; S3: Based on the traffic sparse signal group, extract the signal segments with prominent amplitudes, divide the amplitude distribution range in sequence, calculate the probability density of the signal segments, complete the grouping and aggregation of abnormal signals, and generate the peak abnormal distribution value; S4: Based on the abnormal distribution value of the flood peak, extract the flood discharge time of the abnormal segment, calculate the flood discharge volume distribution and time interval, calculate the dynamic characteristic distribution of the flood discharge signal, analyze the reservoir leakage impact corresponding to the signal, and generate the reservoir leakage dynamic characteristic value; S5: Based on the dynamic characteristic value of leakage in the reservoir area, slidingly calculate the time interval and diffusion range of the leakage signal, group the aggregated signal segments in time and space, extract the diffusion area characteristics of the leakage signal, and generate the characteristic value of the hydrological anomaly area.

2. The intelligent hydrological analysis method for water conservancy projects according to claim 1, characterized in that: The precipitation decomposition signal group specifically includes the frequency center point, bandwidth value, and distribution weight; the flow sparse signal group includes high-frequency segments, sparse matching dictionaries, and dynamically changing parts; the flood peak anomaly distribution value includes signal segment probability density, amplitude distribution range, abnormal signal grouping and aggregation; the reservoir area leakage dynamic characteristic value includes flood discharge time, flood discharge volume distribution, and time interval statistics; the hydrological anomaly area characteristic value specifically includes leakage signal time interval, diffusion range, and diffusion area characteristics.

3. The intelligent hydrological analysis method for water conservancy projects according to claim 2, characterized in that: The steps for obtaining the precipitation decomposition signal group are specifically as follows: S111: Input a hydrological data signal, extract the frequency spectrum range of its basin precipitation, calculate the frequency center point and bandwidth value of the signal, and obtain a set of frequency characteristic parameters; S112: Using the frequency characteristic parameter set, the original signal is divided into multiple intervals according to the time series, an initial weight is assigned to each interval, and the weight is refined and adjusted to match the interval characteristics to obtain a weighted time series signal distribution set; S113: Calculate the decomposition weight center point of each interval according to the weighted time series signal distribution set to obtain a precipitation decomposition signal group.

4. The intelligent hydrological analysis method for water conservancy projects according to claim 3, characterized in that: The steps for obtaining the traffic sparse signal group are specifically as follows: S211: Input the precipitation decomposition signal group, filter the high-frequency segments in the signal, calculate the dynamic change amplitude of multiple segments one by one, extract the segments whose change amplitude exceeds a set threshold, and generate a high-frequency dynamic segment set; S212: Constructing a sparse matching dictionary based on the high-frequency dynamic segment set, performing dynamic characteristic analysis on each segment, and eliminating segments with insignificant dynamic changes by determining the continuity of amplitude changes and the matching degree of frequency characteristics, thereby obtaining a sparse characteristic matching set; S213: Using the segment features in the sparse feature matching set, calculating the sparse feature weights of multiple segments, performing grouping operations one by one, and generating a traffic sparse signal group.

5. The intelligent hydrological analysis method for water conservancy projects according to claim 4, characterized in that: The steps for obtaining the abnormal distribution value of the flood peak are specifically as follows: S311: extracting signal segments with prominent amplitudes from the sparse traffic signal group, performing preliminary screening based on signal strength, and calculating amplitude distribution ranges of the screened segments in turn to obtain an amplitude distribution feature set; S312: Calculate the probability density of each signal segment using the amplitude distribution feature set, and apply a probabilistic statistical method to determine the steady-state and non-steady-state features of the signal segment to obtain a signal segment probability density feature set; S313: According to the signal segment probability density feature set, the formula is used: Calculate and aggregate the groups of abnormal signals to generate the peak abnormal distribution value; Among them, A i represents the abnormal distribution value of flood peak after aggregation, p j is the probability density of the signal segment, x j is the amplitude of the signal segment, μ is the average amplitude, σ is the standard deviation of the amplitude, and n represents the total number of signal segments involved in the calculation.

6. The intelligent hydrological analysis method for water conservancy projects according to claim 5, characterized in that: The steps for obtaining the dynamic characteristic value of leakage in the reservoir area are specifically as follows: S411: Based on the flood peak abnormal distribution value, select segments with larger abnormal amplitudes as the main body of the flood discharge signal, and perform preliminary classification according to their time sequence and amplitude to obtain a flood discharge time distribution set; S412: using the flood discharge time distribution set, calculating the flow distribution of each flood discharge event, and evaluating the distribution of the total flood discharge by aggregating data of all events to obtain a flood discharge distribution dataset; S413: combining the flood discharge distribution data set, counting the time intervals between differentiated flood discharge events, analyzing their dynamic change characteristics, and obtaining a dynamic characteristic distribution set of flood discharge signals; S414: Calculating the correlation impact between each flood discharge event and reservoir leakage based on the dynamic characteristic distribution set of the flood discharge signal, and generating a dynamic characteristic value of reservoir leakage.

7. The intelligent hydrological analysis method for water conservancy projects according to claim 6, characterized in that: The steps for obtaining the characteristic value of the hydrological anomaly area are specifically as follows: S511: Based on the dynamic characteristic value of leakage in the reservoir area, slidingly calculating the time interval and diffusion range of each leakage event, determining the correlation of multiple events through time series analysis, and generating leakage event time interval and diffusion range data; S512: using the leakage event time interval and diffusion range data, performing temporal and spatial grouping on the aggregated signal segments, and using a clustering algorithm to classify the signals into differentiated temporal and spatial blocks to obtain classified temporal and spatial signal groups; S513: Extracting the leakage signal diffusion area characteristics of multiple blocks based on the classified time-space signal group using the formula: Calculate the diffusion characteristics of leakage signals in multiple blocks and generate characteristic values of hydrological anomaly areas; Among them, R i represents the characteristic value of the hydrological anomaly area in the i-th block, a j is the influence intensity of the signal fragment, d j is the diffusion distance of the signal fragment, δ i is the average diffusion distance of the block, G is the standard deviation of the diffusion distance, and n is the total number of signal segments involved in the calculation.

8. An intelligent hydrological analysis system for water conservancy projects, characterized in that: According to any one of claims 1 to 7, the intelligent hydrological analysis method for water conservancy projects comprises: The precipitation signal decomposition module extracts the spectrum range of basin precipitation based on the input hydrological data signal, calculates the frequency center point and bandwidth value, divides the time series interval, adjusts the weight decomposition signal, and generates a precipitation decomposition signal group; The traffic sparse signal extraction module extracts high-frequency segments based on the precipitation decomposition signal group, screens signal segments with large dynamic changes, extracts sparse characteristic segments by grouping, constructs a dynamic sparse matching dictionary, and generates a traffic sparse signal group; The flood peak anomaly distribution calculation module extracts signal segments with prominent amplitudes based on the flow sparse signal group, divides the amplitude distribution range, calculates the segment probability density, aggregates and groups the signal segments, and generates a flood peak anomaly distribution value; The reservoir leakage dynamic characteristic analysis module extracts the flood discharge time segment based on the flood peak abnormal distribution value, calculates the time interval and flood discharge distribution characteristics, counts the dynamic characteristic change value, screens the leakage impact segment, and generates the reservoir leakage dynamic characteristic value; The leakage diffusion area characteristic calculation module calculates the time interval and diffusion range based on the dynamic characteristic value of the reservoir leakage, groups the leakage signal by time and space, extracts the diffusion space distribution characteristics, and generates the hydrological abnormality area characteristic value.

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