An intelligent analysis method and system for hydrological data

Abstract

The present invention relates to the technical field of data analysis, and specifically to an intelligent analysis method and system for hydrological data, including the following steps: collecting water level, flow velocity, and precipitation data, calculating the change rate at adjacent moments, marking hydrological parameter mutation points, counting the time interval, predicting the duration of extreme events, and obtaining the distribution characteristics of hydrological mutation points. In the present invention, by analyzing the time interval and duration of data points, it is possible to master the distribution characteristics of hydrological mutations at an early stage, make responses in advance, analyze the propagation order and diffusion rate of mutation points in different river sections, enhance the accurate classification of the influence range and risk level of hydrological events, realize the effective management of the spatial risk of hydrological events, deepen the understanding of key hydrological influencing factors by screening key variables and evaluating the evolution relationship of variables over time, enhance the accuracy of short-term and long-term hydrological risk prediction, and provide a scientific basis for the dynamic early warning of hydrological abnormal events.

Description

Technical Field

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

[0002] The technical field of data analysis is used to extract valuable information and insights from raw data. This field relies on statistics, algorithm development, and machine learning techniques to process and analyze data, aiming to support the decision-making process, optimize business operations, and predict future events. Data analysis technology is widely applied in multiple fields such as business, scientific research, medical care, and environmental monitoring. The core content covers aspects such as data cleaning, data integration, data mining, and data visualization. Through a systematic analysis process, data analysis technology can process large datasets and extract key indicators and patterns.

[0003] Among them, the intelligent analysis method and system for hydrological data refer to technical solutions specifically designed to process and analyze hydrological environment data. The technical matters targeted by this patent theme cover the collection, processing, and analysis of hydrological data, mainly achieved through the comprehensive analysis of data such as water level, flow rate, and precipitation. The solution includes data collection devices, data storage and analysis systems, and software application programs for interpreting and processing data. This system integrates various hydrological data sources and uses specific processing techniques to ensure the accuracy and usability of data, supporting water resource management and predicting hydrological events.

[0004] In actual operation of the prior art, although it can process and analyze a large amount of hydrological data, it lacks in-depth analysis of the characteristics of mutation points and a systematic risk assessment method, which limits its effectiveness in predicting hydrological events and managing risks. For example, it fails to fully utilize key information such as the distribution characteristics and diffusion rate of mutation points, resulting in a less timely or accurate response to hydrological events. The prior art fails to achieve differential management for different river sections and time periods during processing, resulting in the inability to effectively distinguish various hydrological risk levels, and insufficient implementation of early warnings and response measures for major hydrological events in practical applications, affecting the overall efficiency and safety of water resource management. Summary of the Invention

[0005] The purpose of the present invention is to solve the drawbacks existing in the prior art, and to propose an intelligent analysis method and system for hydrological data.

[0006] To achieve the above purpose, the present invention adopts the following technical solution. An intelligent analysis method for hydrological data includes the following steps:

[0007] S1: Collect water level, flow velocity, and precipitation data, calculate the change rate between adjacent moments, mark the mutation points of hydrological parameters, count the time interval, predict the duration of extreme events, and obtain the distribution characteristics of hydrological mutation points;

[0008] S2: Based on the distribution characteristics of the hydrological mutation points, analyze the distribution density of the mutation points in different hydrological basins, count the propagation order of the mutation points among the upper, middle, and lower reaches of the river, analyze the diffusion rate of the mutation points, identify the influence range of the mutation points, divide the spatial risk levels of hydrological events, and obtain the spatial diffusion trend of the hydrological mutation points.

[0009] S3: Based on the spatial diffusion trend of the hydrological mutation points, screen key variables, analyze the change trends of the key variables in different time periods, evaluate the evolution relationship of the key variables over time, identify the key influencing factors, and obtain the key hydrological influencing factors.

[0010] S4: Use the key hydrological influencing factors to evaluate the contribution degree of multiple variables to hydrological risk prediction, monitor the short-term changes in short-term rainfall, river water level fluctuations, and velocity mutations, calculate the weight changes of multiple variables within a short time window, analyze the change probability of the hydrological state, and adjust the key variables according to the state transition probability to obtain the hydrological state evolution path.

[0011] As a further solution of the present invention, the distribution characteristics of the hydrological mutation points include the priority area of the change rate, the mutation time point, and the corresponding time interval. The spatial diffusion trend of the hydrological mutation points includes the mutation propagation sequence from upstream to downstream, the mutation density in multiple river reaches, and the corresponding spatial risk level. The key hydrological influencing factors include the change trend of precipitation, the key velocity threshold, and the time relationship affecting the water level. The hydrological state evolution path includes the dynamic weight of key variables, the state change probability in different stages, and the corresponding risk adjustment record.

[0012] As a further solution of the present invention, the steps for obtaining the distribution characteristics of the hydrological mutation points are specifically as follows:

[0013] S111: Collect water level, velocity, and precipitation data, calculate the values at adjacent times to obtain the change rate at adjacent times. According to the fluctuation of the change rate, identify the dispersion degree of the change rate and the data fluctuation interval, and generate an initial set of hydrological parameter change rate mutation points.

[0014] S112: Based on the initial set of hydrological parameter change rate mutation points, count the time interval between adjacent mutation points, screen the mutation points that conform to the time distribution law, and eliminate the mutation points with abnormal time intervals. Use the formula:

[0015] ;

[0016] Calculate the standardized time deviation of the mutation points, and screen the mutation points with reasonable time intervals according to the numerical values to obtain the time distribution set of hydrological mutation points.

[0017] Wherein, represents the mutation point The standardized time deviation represents the time interval between adjacent mutation points, represents the mean of the time intervals of the mutation points, represents the number of mutation points;

[0018] S113: Using the set of time distributions of the hydrological mutation points, combining the distribution density and continuity characteristics of the mutation points, predict the duration of extreme events, conduct segmented statistics on the prediction results, analyze the duration distribution of extreme events in the differential interval, and obtain the distribution characteristics of the hydrological mutation points.

[0019] As a further solution of the present invention, the steps for obtaining the spatial diffusion trend of the hydrological mutation points are specifically as follows:

[0020] S211: Based on the distribution characteristics of the hydrological mutation points, count the number of mutation points in multiple hydrological basins, and use the formula:

[0021] ;

[0022] Calculate the distribution density per unit area to obtain the hydrological mutation point distribution density;

[0023] Among them, represents the hydrological mutation point distribution density within the hydrological basin represents the number of mutation points within the hydrological basin represents the hydrological basin represents the area of the hydrological basin represents the hydrological basin represents the area of the basin represents the basin and the adjacent basin represents the weight relationship between them represents the hydrological adjacent basin represents the number of mutation points within it represents the hydrological adjacent basin represents the area of the hydrological adjacent basin represents the number of hydrological adjacent basins;

[0024] S212: Using the hydrological mutation point distribution density, analyze the diffusion path of the mutation points among multiple river sections according to the spatial distribution relationship of the upstream river section, middle river section, and downstream river section, and arrange them in time series to obtain the propagation order of the hydrological mutation points;

[0025] S213: Using the propagation order of the hydrological mutation points, calculate the propagation rate of the mutation points between different river sections, analyze the diffusion range, and combine the influence range of the mutation points and the characteristics of the hydrological basin to divide the spatial risk level of the hydrological event and obtain the spatial diffusion trend of the hydrological mutation points.

[0026] As a further solution of the present invention, the steps for obtaining the key hydrological influencing factors are specifically as follows:

[0027] S311: Based on the spatial diffusion trend of the hydrological mutation points, analyze the change amplitude of variables within different time intervals, screen the variables with prominent changes in multiple time periods, and obtain the key hydrological variables;

[0028] S312: Use the key hydrological variables to analyze the change trend of the key hydrological variables within the time interval, and use the formula:

[0029] ;

[0030] Calculate the change rate of the key variables, analyze the relationship between the key variables and time changes, and obtain the time evolution relationship of the key variables;

[0031] Wherein, represents the change rate of the key variable of, and respectively represent the time intervals and within the key variable values, represents the time interval, represents the key variable and the adjacent variable between the influence weights, is the number of adjacent variables;

[0032] S313: Based on the time evolution relationship of the key variables, evaluate the correlation degree between multiple variables, identify the influencing factors affecting the key variables, analyze the contribution degree of the influencing factors, and obtain the key hydrological influencing factors.

[0033] As a further solution of the present invention, the steps for obtaining the hydrological state evolution path are specifically as follows:

[0034] S411: Use the key hydrological influencing factors to monitor the short-term changes in short-term rainfall, river water level fluctuations and flow velocity mutations, calculate the weight changes of multiple variables within a short time window, and evaluate the influence degree of multiple variables on the hydrological state within the short time window based on the weight changes to obtain the variable influence coefficient;

[0035] S412: Based on the variable influence coefficient, analyze the change probability of the hydrological state within different time windows, and use the formula:

[0036] ;

[0037] Calculate the hydrological state change probability;

[0038] Wherein, represents the probability of hydrological state change, represents the weight of the th variable at time represents the variable weight at time ; represents the short - term fluctuation amplitude of the variable at time ; represents the total number of variables;

[0039] S413: Using the probability of hydrological state change, adjust the criticality of multiple variables, analyze the evolution process of the hydrological state, and obtain the hydrological state evolution path.

[0040] As a further solution of the present invention, the method further includes step S5:

[0041] S5: Through the hydrological state evolution path, analyze the evolution trends of the mutation points in time and space, identify the development direction of hydrological events, conduct dynamic early warning based on the mutation point trends, perform risk grading on hydrological abnormal events, and obtain the early warning results of hydrological abnormal events;

[0042] The early warning results of the hydrological abnormal events include the time evolution sequence of the mutation points, the spatial dynamic trend, and the risk level adjustment result.

[0043] As a further solution of the present invention, the method further includes step S5:

[0044] S5: Through the hydrological state evolution path, analyze the evolution trends of the mutation points in time and space, identify the development direction of hydrological events, conduct dynamic early warning based on the mutation point trends, perform risk grading on hydrological abnormal events, and obtain the early warning results of hydrological abnormal events;

[0045] The early warning results of the hydrological abnormal events include the time evolution sequence of the mutation points, the spatial dynamic trend, and the risk level adjustment result.

[0046] As a further solution of the present invention, the specific steps for obtaining the early warning results of the hydrological abnormal events are as follows:

[0047] S511: Based on the hydrological state evolution path, analyze the spatial and temporal distribution of the mutation points in different time windows, and use the formula:

[0048] ;

[0049] Calculate the density change of the mutation points in space to obtain the spatio - temporal change trend of the mutation points;

[0050] where, represents the density of the mutation points within the time window ; represents the spatial position of the nth mutation point at time t, represents the spatial position at time t, represents the length of the time window, and represents the number of mutation points;

[0051] S512: Using the spatio-temporal change trend of the mutation points, determine the development direction of the hydrological event, perform linear fitting based on the change direction of the mutation points within the differential time window, analyze the change trend, and combine with real-time data to identify the development trend of the hydrological event, and obtain the development direction of the hydrological event;

[0052] S513: Using the development direction of the hydrological event, calculate the change rate of the mutation point trend, dynamically set the risk level threshold, and perform risk classification on the hydrological anomaly event to obtain the early warning result of the hydrological anomaly event.

[0053] The hydrological data intelligent analysis system is used to execute the above hydrological data intelligent analysis method, and the system includes:

[0054] The hydrological mutation point identification module obtains river water level, water flow velocity, and precipitation data, calculates the change rate between adjacent moments, marks the mutation points, counts the time interval between adjacent mutation points, counts the occurrence frequency of the mutation points in the differential time period, and identifies the fluctuation relationship of the mutation points in the seasonal hydrological changes to obtain the distribution characteristics of the hydrological mutation points;

[0055] The hydrological mutation point spatial analysis module calculates the distribution density of the mutation points in the differential hydrological basins based on the distribution characteristics of the hydrological mutation points, counts the propagation order of the mutation points between the upper, middle, and lower reaches of the river, analyzes the diffusion rate of the mutation points, and divides the spatial risk level of the hydrological event according to the distribution of the mutation points to obtain the spatial diffusion trend of the hydrological mutation points;

[0056] The hydrological key variable extraction module uses the spatial diffusion trend of the hydrological mutation points to screen the key variables that affect the change of the mutation points during the development of the hydrological event, analyzes the change trend of the key variables in the differential time period, and analyzes the dynamic evolution relationship of the key variables in the hydrological event to obtain the key hydrological influencing factors;

[0057] The hydrological state evolution analysis module uses the key hydrological influencing factors to evaluate the impact of short-term rainfall, river water level fluctuations, and water flow velocity changes on the hydrological state evolution, calculates the hydrological state change rate within a short time window, calculates the state transition probability of the differential variables, and constructs a hydrological state evolution path based on the change of the variables on the time axis;

[0058] The hydrological anomaly event analysis module analyzes the evolution trends of mutation points in terms of time and space through the hydrological state evolution path, identifies the development directions of hydrological events, classifies the hydrological anomaly events, and conducts dynamic early warning based on the change trends of mutation points to obtain the early warning results of hydrological anomaly events.

[0059] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0060] In the present invention, by collecting and calculating the change rates of water level, flow velocity, and precipitation data, and by marking mutation points, extreme hydrological events can be effectively identified and predicted, and the time intervals and durations of data points are analyzed, enabling the distribution characteristics of hydrological mutations to be grasped at an early stage and responses to be made in advance. The analysis of the propagation order and diffusion rate of mutation points in different river reaches enhances the accurate classification of the influence range and risk level of hydrological events, realizing the effective management of the spatial risks of hydrological events. By screening key variables and evaluating the evolution relationships of variables over time, the understanding of key hydrological influencing factors is deepened, the accuracy of short-term and long-term hydrological risk prediction is enhanced, the development directions and risk levels of hydrological events are helped to be identified, and a scientific basis is provided for the dynamic early warning of hydrological anomaly events. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 is a schematic diagram of the working process of the present invention;

[0062] Figure 2 is a flowchart for obtaining the distribution characteristics of hydrological mutation points in the present invention;

[0063] Figure 3 is a flowchart for obtaining the spatial diffusion trend of hydrological mutation points in the present invention;

[0064] Figure 4 is a flowchart for obtaining the key hydrological influencing factors in the present invention;

[0065] Figure 5 is a flowchart for obtaining the hydrological state evolution path in the present invention;

[0066] Figure 6 is a flowchart for obtaining the early warning results of hydrological anomaly events in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0067] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, 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 used to limit the present invention.

[0068] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0069] Please refer to Figure 1 , the present invention provides a technical solution, an intelligent analysis method for hydrological data, including the following steps:

[0070] S1: Collect water level, flow velocity and precipitation data, calculate the change rate at adjacent times, mark the hydrological parameter mutation points, count the time interval, predict the duration of extreme events, and obtain the distribution characteristics of hydrological mutation points;

[0071] S2: Based on the distribution characteristics of hydrological mutation points, analyze the distribution density of mutation points in different hydrological basins, count the propagation order of mutation points between the upper, middle and lower reaches of the river, analyze the diffusion rate of mutation points, identify the influence range of mutation points, divide the spatial risk level of hydrological events, and obtain the spatial diffusion trend of hydrological mutation points;

[0072] S3: Based on the spatial diffusion trend of hydrological mutation points, screen key variables, analyze the change trend of key variables in different time periods, evaluate the evolution relationship of key variables over time, identify key influencing factors, and obtain hydrological key influencing factors;

[0073] S4: Use hydrological key influencing factors to evaluate the contribution degree of multiple variables to hydrological risk prediction, monitor the short-term changes of short-term rainfall, river water level fluctuations and flow velocity mutations, calculate the weight changes of multiple variables in a short time window, analyze the change probability of hydrological states in different time periods, and adjust the key nature of variables according to the state transition probability to obtain the hydrological state evolution path;

[0074] S5: Through the hydrological state evolution path, analyze the evolution trend of mutation points in time and space, identify the development direction of hydrological events, conduct dynamic early warning based on the trend of mutation points, classify the risks of hydrological abnormal events, and obtain the early warning results of hydrological abnormal events;

[0075] The distribution characteristics of hydrological mutation points include the priority area of change rate, the mutation time point, and the corresponding time interval. The spatial diffusion trend of hydrological mutation points includes the mutation propagation sequence from upstream to downstream, the mutation density of multiple river reaches, and the corresponding spatial risk level. The key hydrological influencing factors include the change trend of precipitation, the key flow velocity threshold, and the time relationship affecting water level. The evolution path of hydrological state includes the dynamic weight of key variables, the state change probability in different differentiation stages, and the corresponding risk adjustment record. The warning result of hydrological abnormal events includes the time evolution sequence of mutation points, the spatial dynamic trend, and the risk level adjustment result.

[0076] Please refer to Figure 2 , and the specific steps for obtaining the distribution characteristics of hydrological mutation points are as follows:

[0077] S111: Collect water level, flow velocity, and precipitation data, calculate the values at adjacent times, obtain the change rate at adjacent times, identify the dispersion degree of the change rate and the data fluctuation interval according to the fluctuation of the change rate, and generate the initial set of hydrological parameter change rate mutation points;

[0078] Obtain water level, flow velocity, and precipitation data. The data acquisition equipment should be installed at hydrological stations in different regions. A buoy-type water level gauge, an ultrasonic flow velocity meter, and an automatic rain gauge are used for data acquisition. The water level, flow velocity, and precipitation data at each monitoring point are recorded with a sampling period of 1 hour and stored in the hydrological database. Call the data at adjacent times and calculate the data change rate between two adjacent times. The calculation method of the water level change rate is the difference between the current water level value and the water level value at the previous time. The flow velocity change rate is calculated in the same way, and the precipitation change rate is calculated as the difference between the cumulative precipitation in the current period and the precipitation in the previous period. Due to the volatility of the data, small-amplitude change data need to be removed, and only the data points with obvious mutations are retained. The screening standard is based on the mean and standard deviation of the change amplitude of real-time data, and a threshold is set as the mutation determination standard. If the change rate is greater than the threshold then it is recorded as a mutation point. The threshold is determined according to the mean of the change amplitude of real-time hydrological data and the standard deviation, and the calculation method is:

[0079] ;

[0080] Among them, the mean is calculated through long-term hydrological data, and is calculated through the standard deviation formula. Set the average water level change rate of a certain river reach in the past year to be 0.05 m / h, and the standard deviation to be 0.02 m / h. Then the mutation determination threshold is:

[0081] ;

[0082] If the change rate of the data point is higher than 0.09, it is determined as a mutation point. By the above method, the mutation point data of each hydrological parameter is calculated and screened to generate an initial set of mutation points of the hydrological parameter change rate.

[0083] S112: Based on the initial set of mutation points of the hydrological parameter change rate, the time intervals between adjacent mutation points are statistically analyzed, the mutation points that conform to the time distribution law are screened, and the mutation points with abnormal time intervals are excluded. The formula is used:

[0084] ;

[0085] Calculate the standardized time deviation degree of the mutation point, and screen the mutation points with reasonable time intervals according to the numerical value to obtain the time distribution set of hydrological mutation points;

[0086] Among them, represents the standardized time deviation degree of the mutation point , represents the time interval between adjacent mutation points, represents the mean value of the mutation point time intervals, represents the number of mutation points;

[0087] Extract the time intervals between adjacent mutation points, calculate the mean value and standard deviation of all adjacent mutation point time intervals to identify the time distribution that conforms to the hydrological mutation law. The time interval is calculated as the time difference between the mutation point and the previous mutation point , that is:

[0088] ;

[0089] Calculate all mean value and standard deviation , set the time deviation screening threshold , calculate the mutation point time deviation degree as the screening criterion;

[0090] Calculation example: If the interval times of mutation points in a certain area are 3h, 5h, 8h, 6h, 7h in sequence, then:

[0091] ;

[0092] The standard deviation is:

[0093] ;

[0094] Given the mean value , standard deviation , substitute into the formula to calculate the time deviation degree:

[0095] ;

[0096] ;

[0097] ;

[0098] Set a threshold , since , then eliminate the mutation points with large time deviations to obtain a set of mutation points that conform to the time distribution law. This result indicates that there are abnormal points in the time intervals of the mutation points, and screening is required to ensure rationality. Combining this result, the screened mutation points are used for the prediction and analysis of subsequent extreme events.

[0099] S113: Use the time distribution set of hydrological mutation points, combine the distribution density and continuity characteristics of the mutation points to predict the duration of extreme events, and conduct segmented statistics on the prediction results to analyze the duration distribution of extreme events in the differential interval, so as to obtain the distribution characteristics of hydrological mutation points;

[0100] Analyze the distribution density of the mutation points, count the number of mutation points in the differential time window, and the time window value refers to the typical duration of regional hydrological events. For example, flood events generally last for 12 - 48 hours, and drought events can last for weeks or months. Set the time window , calculate the mutation point density within the time window , the formula is:

[0101] ;

[0102] Among them, is the number of mutation points within the time window ;

[0103] Set the set time window , if the number of mutation points within 24 hours in a certain area , then the mutation point density is:

[0104] ;

[0105] The higher the density, the greater the frequency of abnormal changes in the hydrological conditions. Based on the mutation point density, calculate the predicted duration of extreme events. Set the mutation points to be normally distributed during the event duration, then the predicted duration can be calculated using the mean of the mutation point time intervals and the standard deviation , that is:

[0106] ;

[0107] Calculation example: If , then:

[0108] Different intervals of mutation point density can correspond to different types of hydrological events. By obtaining the duration distribution of extreme events in each time interval, the distribution characteristics of hydrological mutation points can be obtained.

[0109] Please refer to Figure 3 The steps for obtaining the spatial diffusion trend of hydrological mutation points are specifically as follows:

[0110] S211: Based on the distribution characteristics of hydrological mutation points, count the number of mutation points in multiple hydrological basins, and use the formula:

[0111] ;

[0112] Calculate the distribution density per unit area to obtain the distribution density of hydrological mutation points;

[0113] Among them, represents the distribution density of hydrological mutation points in the hydrological basin within, represents the number of mutation points in the hydrological basin within, represents the area of the hydrological basin ; represents the weight relationship between the basin and the adjacent basin ; represents the number of mutation points in the adjacent hydrological basin within, represents the area of the adjacent hydrological basin ; represents the number of adjacent hydrological basins ;

[0114] Obtain the number of mutation points in each hydrological basin. The specific method is to use hydrological indicators such as flow, precipitation, and evaporation in multi-year monitoring data, process the data using the mutation point identification method, record the time and spatial location of the mutation, and within a certain basin area, count the mutation points in the hydrological data in the past 10 years and obtain the total number of mutation points. Suppose 40 mutation points are detected in a certain basin, and the basin area is 800 square kilometers. Calculate the distribution density per unit area, that is, the number of mutation points per unit area. This calculation needs to consider the influence of adjacent basins and comprehensively evaluate the spatial influence of mutation points in each region;

[0115] Substitute the data:

[0116] Target basin: Number of mutation points , Basin area square kilometers;

[0117] Adjacent basins: The numbers of mutation points are respectively , ;

[0118] The adjacent basin areas are respectively , ;

[0119] The influence weights are respectively , ;

[0120] Calculate:

[0121] ;

[0122] ;

[0123] ;

[0124] ;

[0125] ;

[0126] This result shows that the distribution density of mutation points in this basin is low. According to the statistical data of hydrological events, this value indicates that the occurrence frequency of hydrological mutations in this basin is low, the distribution of mutation points is sparse, and subsequently, the hydrological risk trend of this area can be further evaluated by combining the propagation order of hydrological mutation points.

[0127] S212: Adopt the distribution density of hydrological mutation points, analyze the diffusion path of mutation points among multiple river reaches according to the spatial distribution relationship of the upstream river reach, middle river reach and downstream river reach, and arrange them in time series to obtain the propagation order of hydrological mutation points;

[0128] According to the spatial distribution relationship of the upstream river reach, middle river reach and downstream river reach, count the diffusion path of mutation points among each river reach, analyze the change of the occurrence position according to the time series of mutation points, form the time axis of the propagation of mutation points. In a certain basin, the mutation point appeared in the upstream in 2015, spread to the middle reaches in 2017, and appeared in the downstream in 2020. By counting the positions of mutation points in each time period, calculating the number of mutation points by river reach stratification, and sorting according to the time dimension to obtain the propagation order. The cumulative number of mutation points in the upstream of a certain basin is 50, 80 in the middle reaches, and 120 in the downstream. Since the mutation points gradually increase from the upstream to the downstream, the propagation order is "upstream, middle reaches, downstream", and the propagation order of hydrological mutation points is obtained.

[0129] S213: Use the propagation order of hydrological mutation points to calculate the propagation rate of mutation points among different river reaches, analyze the diffusion range, combine the influence range of mutation points and the characteristics of the hydrological basin, divide the spatial risk level of hydrological events, and obtain the spatial diffusion trend of hydrological mutation points;

[0130] Calculate the propagation rate of mutation points between different river sections, analyze the diffusion range, and obtain the propagation rate per unit time by calculating the time and distance required for the mutation points to propagate between different river sections. The upstream mutation point appeared in 2015, the middle reaches mutated in 2017, and the downstream mutated in 2020. The basin distance from the upstream to the middle reaches is 80 kilometers, and the basin distance from the middle reaches to the downstream is 120 kilometers. Then the propagation rate of the mutation point from the upstream to the middle reaches is:

[0131] km / year;

[0132] The propagation rate from the middle reaches to the downstream is:

[0133] km / year;

[0134] According to the hydrological characteristics and propagation rate of each basin, divide the influence range of the mutation point. The influence range can be classified through the propagation rate threshold. When the propagation rate is lower than 20 km / year, it is a low-risk area; 20 - 50 km / year is a medium-risk area; and higher than 50 km / year is a high-risk area. Since the calculated propagation rate is 40 km / year, which belongs to the medium-risk area, it can be classified as a medium-risk level. Further, combine the basin characteristics to conduct a spatial risk level division of the area to obtain the spatial diffusion trend of the hydrological mutation point.

[0135] Please refer to Figure 4 , and the specific steps for obtaining the key hydrological influencing factors are as follows:

[0136] S311: Based on the spatial diffusion trend of the hydrological mutation point, analyze the change range of variables within different differential time intervals, screen out the variables that change significantly in multiple time periods to obtain the key hydrological variables;

[0137] It is necessary to extract the mutation point information from the existing hydrological data, use the hydrological monitoring data of different time stages, calculate the change rates of key hydrological variables such as water level, flow rate, and precipitation, and screen out the points with sudden increases or decreases in a short time as mutation points. Conduct statistics on the spatial distribution of mutation points in different hydrological basins and river sections, calculate the number of mutation points per unit area in the upstream, middle reaches, and downstream regions respectively, and compare the mutation point densities between different basins. Suppose the area of a certain basin is 100 square kilometers and the observed number of mutation points is 50, then the mutation point distribution density of this basin can be calculated as 0.5 points / square kilometer. Compare the distribution densities of different basins, and screen out the basins with higher densities as the key research objects. In the selected key basins, compare the mutation point distribution in each time interval. If the number of mutation points in a certain basin was 120 during 2010 - 2015 and increased to 180 during 2015 - 2020, then the change rate of the mutation point can be calculated as:

[0138] ;

[0139] Using the statistical regression method, analyze the relationship between the distribution density of mutation points and time to determine whether there is a stable growth trend or periodic fluctuation. Through the above analysis, variables that show significant changes in multiple time periods can be screened out. If the water level fluctuation exceeds ±2 meters or the flow rate change exceeds ±20% is defined as the mutation point threshold, then further calculations can be performed on the mutation point data that meet this standard to obtain the hydrological key variables.

[0140] S312: Using the hydrological key variables, analyze the change trend of the hydrological key variables within the time interval, using the formula:

[0141] ;

[0142] Calculate the change rate of the key variable, analyze the relationship between the key variable and time, and obtain the time evolution relationship of the key variable;

[0143] Among them, represents the change rate of the key variable of and respectively represent the values of the key variable and within the time interval of represents the time interval, represents the key variable and the adjacent variable between the influence weights, is the number of adjacent variables;

[0144] Formula details and formula calculation derivation process:

[0145] and respectively represent the values of the key variable and within the time interval of and are calculated from the hydrological monitoring data. Set the average water level of a certain basin to be 2.4 meters between 2000 and 2005, and 3.2 meters between 2005 and 2010, then: ;

[0146] represents the time interval, and the time interval is set to 5 years: ;

[0147] represents the key variable and the influence weight between adjacent variables represents the weighted sum of squares of changes between adjacent variables;

[0148] Calculate the first part:

[0149] ;

[0150] Adjacent variables represent variables that affect water level changes, such as flow rate and precipitation. During the calculation process, the monitoring data of flow rate and precipitation are taken. The change in the average flow rate between two time intervals is: , ;

[0151] The change in the average precipitation between two time intervals is: , ;

[0152] Set weights to measure the influence of adjacent variables on key variables. The flow rate weight , and the precipitation weight ;

[0153] Calculate the weighted sum of squares of changes:

[0154] ;

[0155] ;

[0156] ;

[0157] ;

[0158] Calculate the square root part:

[0159] ;

[0160] Calculate:

[0161] ;

[0162] This result shows that the change rate of the key variable is 109.45, indicating that the change rate of hydrological variables within the time interval is large. Compared with small values, this value represents a strong time evolution trend of the hydrological key variable, and this value can be used to further analyze the time dynamic pattern of the key variable and predict the probability of hydrological mutation.

[0163] ​S313: Based on the time evolution relationship of key variables, evaluate the correlation degree among multiple variables, identify the influencing factors affecting the key variables, analyze the contribution degree of the influencing factors, and obtain the key hydrological influencing factors.

[0164] Iteratively analyze the correlation between variables. For the changes of each variable in each time interval, calculate the correlation coefficient between variables, calculate the Pearson correlation coefficient between water level and flow rate, and between precipitation, and judge whether their correlation reaches a significant level. Set the correlation coefficient calculation between water level and flow rate to 0.85, and the correlation coefficient between water level and precipitation to 0.40. Then it can be determined that the relationship between water level and flow rate is closer. On this basis, screen out the variables that have a greater impact on the key variables, calculate the contribution rate of their influencing factors. Set the contribution rate of flow rate to the change of water level to 60%, the contribution rate of precipitation to 30%, and the remaining factors to 10%. Then it can be determined that the flow rate is the main influencing factor. Through the regression analysis method, further calculate the regression equation between variables, using the formula:

[0165] ;

[0166] where, represents the key variable , represents the flow rate, represents the precipitation, represents the remaining influencing factors;

[0167] Through this calculation process, the main influencing factors of the key variables can be identified, and the key hydrological influencing factors can be obtained.

[0168] Please refer to Figure 5 , the specific steps for obtaining the hydrological state evolution path are as follows:

[0169] S411: Use the key hydrological influencing factors to monitor the short-term changes of short-term rainfall, river water level fluctuations and velocity mutations, calculate the weight changes of multiple variables within a short time window, and evaluate the influence degree of multiple variables on the hydrological state within the short time window based on the weight changes to obtain the variable influence coefficient.

[0170] To obtain monitoring data on short-term rainfall, river water level fluctuations and flow rate mutations, it is necessary to collect hydrological data through real-time monitoring equipment. Rainfall can be obtained through rain gauges or remote sensing satellites, river water level fluctuations can be continuously monitored using river depth sounders, and flow rate mutations are detected using current meters or buoy speed measuring equipment. All data are stored in the form of time series and recorded once per minute to ensure the capture of short-term changes. The monitoring data are preprocessed and outliers are removed. The judgment standard for outliers is based on the range of three times the standard deviation of real-time data. If the rainfall data of a certain monitoring is 50mm, the average rainfall is 20mm, and the standard deviation is 5mm, then the data is an outlier and needs to be removed. The preprocessed data is used to calculate the weight changes of multiple variables in a short time window. The weight calculation adopts the normalization method, and the rainfall, water level fluctuation, and flow rate mutation are set to be expressed as 、 、 , and its weight calculation formula is:

[0171] ;

[0172] in, For variables The weight of Representative The data value of the variable, is the total number of variables;

[0173] In this way, the relative importance of each variable is calculated and the time is set , then the weight of rainfall is:

[0174] ;

[0175] The weight of water level fluctuation is:

[0176] ;

[0177] The weight of the flow velocity mutation is:

[0178] ;

[0179] Get the variable influence coefficient.

[0180] S412: Based on the variable influence coefficient, analyze the probability of change of hydrological status within the differentiated time window using the formula:

[0181] ;

[0182] Calculate the probability of hydrological state change;

[0183] in, represents the probability of hydrological state change, Representative variables in time The weight of Representative time The variable weights when Representative time The short-term fluctuation amplitude of the time variable, represents the total number of variables;

[0184] Parameter details:

[0185] Representative variables In time The normalized weight of the moment is obtained through normalization calculation. The normalization calculation method is as follows:

[0186] ;

[0187] in, Representative variables In time The original data value of

[0188] This data value is collected in real time by hydrological monitoring equipment, for example, short-term rainfall Recorded by automatic rain gauge, unit is mm;

[0189] River water level fluctuations Measured by a water level gauge, unit is m;

[0190] Sudden flow rate change Measured by a flow meter, the unit is m / s;

[0191] At some point The monitoring data is mm, m, m / s, calculate the normalized weight:

[0192] ;

[0193] ;

[0194] ;

[0195] Represents the weight of the previous time window, and the data source is the same ;

[0196] Set in The monitoring data corresponding to the time is , , then we can calculate:

[0197] ;

[0198] ;

[0199] ;

[0200] represents the variable the short - term fluctuation amplitude at time which is quantified by calculating the standard deviation within the current time window The calculation method is as follows:

[0201] ;

[0202] where represents the time window length, set to minutes, represents the mean value of the variable within the window. In the past 5 minutes, the observed rainfall data is 22, 24, 26, 25, 23 mm, then the mean value is:

[0203] ;

[0204] Calculate the short - term fluctuation amplitude:

[0205] ;

[0206] ;

[0207] Similarly, calculate the water level fluctuation and the flow velocity fluctuation , and set to obtain m, m / s;

[0208] Derivation process of the formula calculation:

[0209] Calculate the sum of the absolute values of the weight differences:

[0210] ;

[0211] ;

[0212] Calculate the square root of the sum of the squares of the weights:

[0213] ;

[0214] ;

[0215] Calculate the sum of the short - term fluctuation amplitudes:

[0216] ;

[0217] Calculation :

[0218] ;

[0219] The result shows that the probability of the change in the hydrological state in the current time window is 0.0044, which is a small value, indicating that the overall fluctuation of the hydrological state is relatively stable. Subsequently, this value can be compared with a preset change threshold to determine whether it is necessary to adjust the weights of key variables. If it exceeds the threshold, it means that a significant change has occurred in the hydrological state, and it is necessary to reallocate the variable weights to enhance the ability to identify changes in the hydrological state.

[0220] S413: Use the probability of the change in the hydrological state to adjust the criticality of multiple variables, analyze the evolution process of the hydrological state, and obtain the evolution path of the hydrological state;

[0221] Adjust the criticality of each variable and set a criticality adjustment threshold , if , it indicates that the hydrological state has changed and it is necessary to adjust the weights of key variables. In the previous time window, if the change amount of a certain variable exceeds 50% of the global change amount, the criticality of this variable is enhanced. If the change in the rainfall weight in a certain time window is 0.05, while the change amounts of the water level fluctuation and the velocity mutation are 0.02 and 0.01 respectively, then the proportion of the change amount of rainfall is:

[0222] ;

[0223] The result exceeds 50%, so the weight of rainfall needs to be increased by 10%. After adjustment, the weights are renormalized to obtain the evolution path of the hydrological state.

[0224] Please refer to Figure 6 , the specific steps for obtaining the warning result of hydrological abnormal events are as follows:

[0225] S511: Based on the evolution path of the hydrological state, analyze the spatial and temporal distribution of mutation points in different time windows, and use the formula:

[0226] ;

[0227] Calculate the density change of mutation points in space to obtain the spatio-temporal change trend of mutation points;

[0228] Among them, represents the density of mutation points in the time window , represents the th mutation point at time spatial position, Represents the spatial position at time , represents the time window length, represents the number of mutation points;

[0229] Parameter meaning and calculation process:

[0230] Represents the th mutation point's spatial position at time , in meters, which is obtained in real time by monitoring equipment, setting the position of the water level mutation point recorded by the water level sensor or the coordinate information of the hydrological anomaly point obtained by the remote sensing satellite;

[0231] Represents the spatial position of the same mutation point at time , in meters, and has the same acquisition method as ;

[0232] Represents the number of mutation points, which is obtained by statistically analyzing the monitoring data within a short time window. Set the monitoring river section to be 10 kilometers. If 10 water level mutation points are found within a certain time window, then ;

[0233] Represents the length of the time window, in seconds, set to 300 seconds, that is, 5 minutes. This value is set according to the typical time scale of hydrological state changes. The size of the time window affects the calculation accuracy of mutation points. A small window can improve the spatio-temporal resolution, while a large window can more comprehensively reflect the evolution trend of hydrological events;

[0234] Substitute the monitoring data for calculation. Set within a certain time window seconds, the number of mutation points , and their spatial coordinates are as follows:

[0235] Time : m, m, m, m, m;

[0236] Time : m, m, m, m, m;

[0237] Calculate the change in the position of the mutation point:

[0238] ;

[0239] ;

[0240] Calculate the sum of squares of the mutation point positions:

[0241] ;

[0242] ;

[0243] ;

[0244] Calculate the square root:

[0245] ;

[0246] Calculate the mutation point density:

[0247] ; The result shows that within this time window, the spatio-temporal distribution density of the mutation points is 0.000129, and the change amount of mutation points per unit space per second is low, indicating that the evolution of the hydrological event is relatively stable. If this value is much higher than the set reference value, it can be determined that the hydrological state has changed violently.

[0248] S512: Use the spatio-temporal change trend of mutation points to judge the development direction of hydrological events. Perform linear fitting based on the change direction of mutation points within different time windows, analyze the change trend, and combine real-time data to identify the development trend of hydrological events to obtain the development direction of hydrological events;

[0249] Judge the development direction of hydrological events, obtain the change of mutation point positions within different time windows, calculate their spatial displacement vectors, and set the positions of mutation points in a certain river section at time to be (10, 20), (15, 25), (20, 30), and at time become (12, 22), (18, 27), (24, 33), then calculate the displacement vector of each mutation point , and respectively obtain (2, 2), (3, 2), (4, 3). Then calculate the average direction vector of the mutation point group using the formula:

[0250] ;

[0251] Obtain the overall direction of the mutation point group. If the average directions in multiple time windows are consistent, it indicates that the hydrological event expands along this direction. It is necessary to calculate the spatial distribution variance of the mutation points to judge the concentration degree of the mutation points. Set the judgment criterion. If the variance of the mutation points is less than the set threshold, the mutation points change concentratedly and the direction of the hydrological event is stable. Set the threshold of the coordinate variance of the mutation points to 5. If the calculated distribution variance of the mutation points is 3, it indicates that the hydrological event develops along a stable direction, otherwise it is judged to develop diffusely, and obtain the development direction of the hydrological event.

[0252] S513: Utilize the development direction of the hydrological event, calculate the change rate of the mutation point trend, dynamically set the risk level threshold, classify the hydrological anomaly events, and obtain the early warning result of the hydrological anomaly events;

[0253] Calculate the change rate of the mutation point trend. The change rate of the mutation point trend is defined as the growth rate of the number of mutation points per unit time. Count the total change of the number of mutation points between and , calculate the growth ratio, and adopt the formula:

[0254] ;

[0255] Among them, represents the mutation point growth rate, represents the total number of mutation points within time , represents the total number of mutation points within time ;

[0256] If the set risk level threshold is , then when , it is determined as a high-risk area. Set the number of mutation points in a certain area to increase from 5 to 8 within 10 minutes, then calculate the growth rate , and it is determined as a high-risk area. In addition, further analyze the spatial distribution trend of the mutation points, calculate its distribution equilibrium degree. If the mutation points are highly concentrated in a certain area, the risk level is relatively high. Use the Gini coefficient to calculate its equilibrium, and classify the hydrological anomaly events according to the risk level to obtain the early warning result of the hydrological anomaly events.

[0257] The intelligent hydrological data analysis system is used to execute the above intelligent hydrological data analysis method. The system includes:

[0258] The hydrological mutation point identification module obtains the river water level, water flow velocity and precipitation data, calculates the change rate of adjacent moments, marks the mutation points, counts the time interval between adjacent mutation points, counts the occurrence frequency of the mutation points in different time periods, and identifies the fluctuation relationship of the mutation points in seasonal hydrological changes to obtain the distribution characteristics of the hydrological mutation points;

[0259] Based on the distribution characteristics of hydrological mutation points, the spatial analysis module of hydrological mutation points calculates the distribution density of mutation points in different hydrological basins, counts the propagation order of mutation points among the upper, middle, and lower reaches of the river, analyzes the diffusion rate of mutation points, and divides the spatial risk level of hydrological events according to the distribution of mutation points to obtain the spatial diffusion trend of hydrological mutation points;

[0260] The key hydrological variable extraction module uses the spatial diffusion trend of hydrological mutation points to screen the key variables that affect the change of mutation points during the development of hydrological events, analyzes the change trend of key variables in different time periods, analyzes the dynamic evolution relationship of key variables in hydrological events, and obtains the key hydrological influencing factors;

[0261] The hydrological state evolution analysis module uses the key hydrological influencing factors to evaluate the impact of short-term rainfall, river water level fluctuations, and water flow velocity changes on the evolution of hydrological states, calculates the hydrological state change rate within a short time window, calculates the state transition probability of different variables, and constructs a hydrological state evolution path based on the variable changes on the time axis;

[0262] The hydrological abnormal event analysis module analyzes the evolution trend of mutation points in time and space through the hydrological state evolution path, identifies the development direction of hydrological events, classifies hydrological abnormal events, and conducts dynamic early warning according to the change trend of mutation points to obtain the early warning results of hydrological abnormal events.

[0263] The above is only the preferred embodiment of the present invention, and it does not limit the present invention in other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution content of the present invention still belong to the protection scope of the technical solution of the present invention.

Claims

1. An intelligent analysis method for hydrological data, characterized in that Including the following steps: S1: Collect water level, flow velocity and precipitation data, calculate the change rate at adjacent times, mark the hydrological parameter mutation points, count the time intervals, predict the duration of extreme events, and obtain the distribution characteristics of hydrological mutation points; S2: Based on the distribution characteristics of the hydrological mutation points, analyze the distribution density of the mutation points in different hydrological basins, count the propagation order of the mutation points between the upper, middle and lower reaches of the river, analyze the diffusion rate of the mutation points, identify the influence range of the mutation points, and divide the spatial risk level of hydrological events to obtain the spatial diffusion trend of hydrological mutation points; S3: Based on the spatial diffusion trend of the hydrological mutation points, screen key variables, analyze the change trends of the key variables in different time periods, evaluate the evolution relationship of the key variables over time, and identify the key influencing factors to obtain the key hydrological influencing factors; S4: Use the key hydrological influencing factors to evaluate the contribution degree of multiple variables to hydrological risk prediction, monitor the short-term changes of short-term rainfall, river water level fluctuations and flow velocity mutations, calculate the weight changes of multiple variables within a short time window, analyze the change probability of the hydrological state, and adjust the key variables according to the state transition probability to obtain the hydrological state evolution path; S5: Through the hydrological state evolution path, analyze the evolution trends of the mutation points in time and space, identify the development direction of hydrological events, conduct dynamic early warning based on the mutation point trends, and classify the risks of hydrological abnormal events to obtain the early warning results of hydrological abnormal events.

2. The intelligent analysis method for hydrological data according to claim 1, wherein The distribution characteristics of the hydrological mutation points include the change rate region at adjacent times, the hydrological parameter mutation points, and the corresponding time intervals. The spatial diffusion trend of the hydrological mutation points includes the mutation propagation order from upstream to downstream, the multi-section mutation diffusion rate, and the corresponding spatial risk level. The key hydrological influencing factors include the precipitation change trend, the key flow velocity threshold, and the time relationship affecting the water level. The hydrological state evolution path includes the dynamic weight of the key variables, the hydrological state change probability in different stages, and the corresponding risk adjustment records. The early warning results of the hydrological abnormal events include the time evolution sequence of the mutation points, the spatial dynamic trend, and the risk level adjustment result.

3. The intelligent analysis method for hydrological data according to claim 1, wherein The specific steps for obtaining the distribution characteristics of the hydrological mutation points are as follows: S111: Collect water level, flow velocity and precipitation data, calculate the values at adjacent times to obtain the change rate at adjacent times, and according to the fluctuation of the change rate, identify the dispersion degree of the change rate and the data fluctuation interval to generate an initial set of hydrological parameter change rate mutation points; S112: Based on the initial set of hydrological parameter change rate mutation points, count the time intervals between adjacent mutation points, screen the mutation points that conform to the time distribution law, and eliminate the mutation points with abnormal time intervals. Use the formula: ； Calculate the standardized time deviation degree of the mutation points, and screen the mutation points with reasonable time intervals based on the numerical values to obtain the time distribution set of hydrological mutation points; Among them, represents the normalized time deviation of the mutation point, represents the time interval between adjacent mutation points, represents the mean value of the time intervals of the mutation points, represents the number of mutation points; S113: Use the time distribution set of hydrological mutation points, combine the distribution density and continuity characteristics of the mutation points, predict the duration of extreme events, and conduct segmented statistics on the prediction results to analyze the duration distribution of extreme events in different intervals to obtain the distribution characteristics of hydrological mutation points.

4. The intelligent analysis method for hydrological data according to claim 3, wherein The steps for obtaining the spatial diffusion trend of the hydrological mutation point are specifically as follows: S211: Based on the distribution characteristics of the hydrological mutation points, the number of mutation points in multiple hydrological basins is counted using the formula: ； Calculate the distribution density within the unit area to obtain the distribution density of hydrological mutation points; Among them, represents the distribution density of hydrological mutation points within the hydrological basin, represents the number of mutation points within the hydrological basin, represents the area of the hydrological basin , represents the weight relationship between the basin and the adjacent basin, represents the number of mutation points within the adjacent hydrological basin, represents the area of the adjacent hydrological basin , represents the number of adjacent hydrological basins ; S212: using the hydrological mutation point distribution density, analyzing the diffusion paths of the mutation points among multiple river sections according to the spatial distribution relationship among the upstream river section, the midstream river section, and the downstream river section, arranging them in time series, and obtaining the propagation order of the hydrological mutation points; S213: Using the propagation order of the hydrological mutation point, calculate the propagation rate of the mutation point between differentiated river sections, analyze the diffusion range, combine the impact range of the mutation point and the hydrological basin characteristics, divide the spatial risk level of the hydrological event, and obtain the spatial diffusion trend of the hydrological mutation point.

5. The intelligent analysis method for hydrological data according to claim 4, characterized in that The steps for obtaining the key hydrological influencing factors are specifically as follows: S311: Based on the spatial diffusion trend of the hydrological mutation point, analyze the variation range of the variables in the differentiated time intervals, screen the variables with prominent changes in multiple time periods, and obtain the key hydrological variables; S312: Using the key hydrological variables, analyze the changing trend of the key hydrological variables within the time interval, using the formula: ； Calculate the rate of change of key variables, analyze the relationship between key variables and time, and obtain the time evolution relationship of key variables; Among them, represents the change rate of the key variable , and represent the values of the key variable and within the time intervals respectively, represents the time interval, represents the influence weight between the key variable and the adjacent variable ; is the number of adjacent variables S313: Based on the time evolution relationship of the key variables, evaluate the correlation between multiple variables, identify the influencing factors affecting the key variables, analyze the contribution of the influencing factors, and obtain the key hydrological influencing factors.

6. The intelligent analysis method for hydrological data according to claim 5, wherein The steps for obtaining the hydrological state evolution path are specifically as follows: S411: Using the key hydrological influencing factors, monitoring short-term changes in rainfall, river water level fluctuations, and flow velocity mutations, calculating weight changes of multiple variables within a short time window, and evaluating the degree of influence of multiple variables on the hydrological state within the short time window based on the weight changes to obtain variable influence coefficients; S412: Based on the variable influence coefficient, the probability of change of the hydrological state within the differentiated time window is analyzed using the formula: ； Among them, represents the probability of hydrological state change, represents the weight of the th variable at time represents the variable weight at time ; represents the short-term fluctuation range of the variable at time ; represents the total number of variables. S413: Using the hydrological state change probability, adjusting the criticality of multiple variables, analyzing the evolution process of the hydrological state, and obtaining the hydrological state evolution path.

7. The intelligent analysis method of hydrological data according to claim 6, wherein The steps for obtaining the hydrological abnormality event warning result are specifically as follows: S511: Based on the hydrological state evolution path, analyze the spatial and temporal distribution of mutation points in differentiated time windows, using the formula: ； Calculate the density change of mutation points in space and obtain the spatiotemporal change trend of mutation points; Among them, represents the mutation point density within the time window ; represents the -th mutation point's spatial position at time ; represents the spatial position at time ; represents the time window length ; represents the number of mutation points. S512: Using the spatiotemporal change trend of the mutation point to determine the development direction of the hydrological event, performing linear fitting based on the change direction of the mutation point within the differentiated time window, analyzing the change trend, and combining the real-time data to identify the development trend of the hydrological event and obtain the development direction of the hydrological event; S513: Using the development direction of the hydrological event, calculate the rate of change of the mutation point trend, dynamically set the risk level threshold, perform risk classification on the hydrological abnormality event, and obtain the hydrological abnormality event warning result.

8. An intelligent hydrological data analysis system, characterized in that, The method for intelligent analysis of hydrological data according to any one of claims 1 to 7, wherein the system comprises: The hydrological mutation point identification module obtains river water level, water flow velocity and precipitation data, calculates the change rate between adjacent moments, marks the mutation points, counts the time intervals between adjacent mutation points, counts the occurrence frequencies of mutation points in different time periods, identifies the fluctuation relationship of mutation points in seasonal hydrological changes, and obtains the distribution characteristics of hydrological mutation points; Based on the distribution characteristics of the hydrological mutation points, the hydrological mutation point spatial analysis module calculates the distribution density of mutation points in different hydrological basins, counts the propagation order of mutation points among the upper, middle and lower reaches of the river, analyzes the diffusion rate of mutation points, and divides the spatial risk levels of hydrological events according to the distribution of mutation points, so as to obtain the spatial diffusion trend of hydrological mutation points; The hydrological key variable extraction module uses the spatial diffusion trend of the hydrological mutation points to screen the key variables that affect the change of mutation points during the development of hydrological events, analyzes the change trends of key variables in different time periods, and analyzes the dynamic evolution relationship of key variables in hydrological events, so as to obtain the key hydrological influencing factors; The hydrological state evolution analysis module uses the key hydrological influencing factors to evaluate the impacts of short-term rainfall, river water level fluctuations and water flow velocity changes on the evolution of hydrological states, calculates the change rate of hydrological states within a short time window, calculates the state transition probability of different variables, and constructs a hydrological state evolution path according to the changes of variables on the time axis; The hydrological abnormal event analysis module analyzes the evolution trends of mutation points in time and space through the hydrological state evolution path, identifies the development directions of hydrological events, classifies hydrological abnormal events, and conducts dynamic early warning according to the change trends of mutation points, so as to obtain the early warning results of hydrological abnormal events.

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