A method for intelligent identification and early warning of hydrological data

Abstract

This invention relates to the field of hydrological early warning technology, specifically to a method for intelligent identification and early warning of hydrological data. The method includes the following steps: identifying surge segments based on water pressure and water level sequences at monitoring points; selecting synchronous monitoring combinations to generate node groups; constructing disturbance propagation path chains to generate path information sets; selecting paths approaching the early warning boundary to generate a list; calculating the disturbance intensity index to compare risk levels; and generating early warning calibration results. This invention, based on the linkage analysis of hourly hydrostatic pressure values ​​and water level observations, selects monitoring point groups with abrupt change linkage characteristics by synchronously judging the duration difference between surge segments and the time difference of the disturbance start point. It also selects path sets based on the spatial proximity relationship between the path end and the early warning boundary point. This improves the spatial accuracy and response timeliness of hydrological risk level identification based on accurately characterizing disturbance features, strengthens the full-link triggering closed loop of abnormal disturbance identification, and enhances the ability to identify the early warning coverage under abrupt change conditions.

Description

Technical Field

[0001] This invention relates to the field of hydrological early warning technology, and in particular to a method for intelligent identification and early warning of hydrological data. Background Technology

[0002] The field of hydrological early warning technology involves utilizing multiple hydrological and meteorological techniques, including observation, remote sensing, data communication, and numerical simulation, to monitor, analyze trends, and predict disasters in real time for hydrological elements such as rainfall, water level, flow rate, and soil moisture. The aim is to identify and issue early warnings for hydrological disasters such as floods, flash floods, and urban waterlogging. This field integrates interdisciplinary fields such as hydrological and water resources engineering, hydraulics, remote sensing and mapping, artificial intelligence, geographic information systems, and the Internet of Things. It focuses on the acquisition, transmission, processing, modeling, and establishment of early warning response mechanisms for hydrological data. Its applications cover areas at risk of flooding, including river basins, reservoir basins, and urban drainage systems, to guide emergency dispatch, support decision-making, and enhance disaster prevention and mitigation capabilities.

[0003] Among them, the intelligent identification and early warning method for hydrological data refers to the automatic identification and early warning triggering of hydrological risks through accurate identification and dynamic analysis of various types of hydrological monitoring data. This method aims to achieve the fusion processing of multi-source hydrological data and intelligent assessment of risk levels by analyzing and identifying abnormal trends. Its applications include improving the timeliness and accuracy of early warnings, assisting in flood control and drought relief command and dispatch, and reducing personnel and property losses caused by sudden hydrological disasters.

[0004] Traditional early warning methods lack in-depth analysis of the linkage characteristics of water pressure and water level within a single monitoring point, making it difficult to accurately determine the physical coupling characteristics of short-term sudden changes from the data ontology level. In terms of multi-point linkage, they do not involve geographical sequence modeling of propagation paths, resulting in the inability to reconstruct the advancement process of disturbances along the river. Early warning assessments are mainly based on data trends and have not constructed a spatial proximity judgment mechanism based on the approach of the path end to the boundary. This means that some sudden disturbances have occurred but have not triggered effective early warnings, which can easily lead to a lag in response to key downstream areas. For example, in the case of a rapid rise in water pressure due to heavy rainfall, the impact of upstream anomalies on the boundary area cannot be perceived because the disturbance path chain has not been established, resulting in early warning blind spots. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method for intelligent identification and early warning of hydrological data.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a method for intelligent identification and early warning of hydrological data, comprising the following steps:

[0007] S1: Based on the hydrological monitoring points set up along the river section, collect the hourly hydrostatic pressure value and water level observation value of each monitoring point within a set period, identify the continuous unidirectional upward change segment, and generate a set of water pressure and water level linkage jump segments.

[0008] S2: Based on the information of each segment in the set of water pressure and water level linkage jump segments, calculate the difference in jump duration between adjacent monitoring points and the time difference of disturbance start point, screen the combination of monitoring points that meet the sudden change synchronization condition, and generate a linkage disturbance sudden change node group.

[0009] S3: Based on each group of monitoring points in the linked disturbance mutation node group, extract the direction of the river section, the latitude and longitude coordinates of the monitoring point and the disturbance start time in the time series, construct the disturbance propagation path chain, and generate a water pressure disturbance propagation path information set;

[0010] S4: Based on the path end monitoring point information in the water pressure disturbance propulsion path information set, extract the corresponding regional early warning boundary point coordinate reference set, measure the minimum straight-line distance from the path end point to the boundary point, compare it with the spatial near-critical distance, filter the path set that meets the conditions, and generate a list of propulsion paths near the early warning boundary.

[0011] The present invention is improved in that the set of water pressure and water level linkage leap segments includes the pressure increment value within the leap segment, the water level increase value for the corresponding time period, and the duration of continuous leap; the linkage disturbance mutation node group specifically includes the monitoring point number that meets the leap synchronization condition, the time consistency index between nodes, and the spatial continuity identifier of the river section; the water pressure disturbance propagation path information set includes the spatial distance value between each node in the path chain, the time delay value of disturbance propagation, and the path direction sequence; and the list of near-warning boundary propagation paths specifically refers to the geographic location information of the path terminal monitoring point, the boundary distance measurement value, and the corresponding warning area code.

[0012] The present invention is improved in that the specific steps for obtaining the set of water pressure and water level linkage jump segments are as follows:

[0013] S111: Based on the hydrological monitoring points set up along the river section, collect the hourly hydrostatic pressure value and water level observation value of each monitoring point within a set period, and perform the difference calculation of adjacent time periods for each set of hydrostatic pressure value sequences, determine the direction of change of the difference between adjacent time periods, identify the continuous unidirectional upward change segment, and generate a set of hydrostatic pressure change segments.

[0014] S112: Based on each segment in the set of hydrostatic pressure change segments, extract the corresponding water level observation value sequence, calculate the water level change rate for each segment sequence, and identify the key values ​​of the change rate within the segment by performing maximum value filtering on the water level change rate values, thereby generating a set of extreme values ​​of water level change rate.

[0015] S113: Based on the set of hydrostatic pressure change segments and the set of extreme values ​​of water level change rate, the change segments are correlated. By judging the relationship between the water level change rate and the pressure increase of the change segment, the jump segments that meet the predetermined conditions are selected to generate a set of water pressure and water level linkage jump segments.

[0016] The present invention is improved in that the step of obtaining the linked perturbation mutation node group is specifically as follows:

[0017] S211: Based on the information of each segment in the set of water pressure and water level linkage jump segments, extract the corresponding monitoring point identifier, river segment number and start and end time of the jump segment, calculate the jump duration of the jump segment within the monitoring point, establish a combination list between any adjacent monitoring points based on the spatial adjacency relationship of monitoring points within the river segment, sequentially call the jump duration value of the monitoring points within the combination, calculate the time difference, and generate a sequence of jump duration difference values ​​between adjacent monitoring points;

[0018] S212: Based on the start time of the leap segment of adjacent monitoring points, obtain the leap disturbance start time difference between each pair of monitoring points, construct the disturbance start time difference sequence, call the leap duration difference sequence of adjacent monitoring points and the disturbance start time difference sequence, and compare them with the set fluctuation duration tolerance value and the leap start time synchronization tolerance value respectively, and filter the monitoring point combination that is simultaneously less than the two tolerance values ​​to obtain the mutation synchronization satisfied combination index set;

[0019] S213: Call the number identifier of each monitoring point combination in the mutation synchronization satisfying combination index set, extract the river segment number corresponding to the monitoring point in the original leap segment information, construct a monitoring point spatial group that meets the mutation synchronization conditions, calculate and obtain the synchronization combination intensity value, filter according to whether the combination intensity value is within the set intensity range, obtain the monitoring point combination that passes the filter, and establish a linkage disturbance mutation node group.

[0020] The present invention is improved in that the step of obtaining the water pressure disturbance propulsion path information set is specifically as follows:

[0021] S311: Based on each group of monitoring points in the linked disturbance mutation node group, extract the direction vector, latitude and longitude coordinates and corresponding disturbance start time of the monitoring points in the river section, construct pairwise combinations between monitoring points and obtain the arrangement order along the river direction, calculate the spatial Euclidean distance value of adjacent monitoring points in the combination and the time difference of disturbance propagation start, and generate a set of river-side disturbance propagation distance difference and time difference value.

[0022] S312: Call the monitoring point combination in the set of the difference in the distance and time of the disturbance propagation along the river, determine whether the angle between the river section direction difference vector and the direction of the Euclidean line is within the geographical path direction consistency angle threshold range, and at the same time determine whether the disturbance propagation time meets the increasing trend, filter the monitoring point sequence combination that meets the dual conditions, and obtain the sequentially increasing propagation combination index set.

[0023] S313: Based on the sequentially increasing propagation combination index of the monitoring point combination index, establish a sequentially connected list of path segments, construct a disturbance propagation path chain, record the cumulative river propagation distance and response start and end time interval of the path segments, and extract the maximum river propagation length and minimum response time interval values ​​to obtain the water pressure disturbance propagation path information set.

[0024] The present invention is improved in that the step of obtaining the list of advancing paths near the early warning boundary is specifically as follows:

[0025] S411: Based on the path end monitoring point information in the water pressure disturbance propulsion path information set, extract the corresponding river segment number and end monitoring point coordinate value, retrieve the set of pre-set early warning boundary point coordinate references within the administrative region according to the river segment number, call the coordinate value to calculate the straight-line distance between the end monitoring point and all boundary points in the region, and generate the path end to boundary point distance information.

[0026] S412: Based on the distance information from the end of the path to the boundary point, extract the minimum distance value corresponding to each end monitoring point of the path, construct the minimum straight-line distance sequence of the end point, call the spatial proximity critical distance benchmark value to compare each value in the distance sequence, filter the path combination index that is less than the benchmark value, and obtain the spatial proximity path index set;

[0027] S413: Based on the path number identified in the spatial proximity path index set, extract the corresponding path segment information from the water pressure disturbance propulsion path information set, reorganize and construct a spatial propulsion path list and mark the shortest distance value between the end of the path and the warning boundary to obtain the propulsion path list near the warning boundary.

[0028] The present invention has an improvement, wherein the method further includes the following steps:

[0029] S5: For each path in the list of paths advancing towards the near warning boundary, extract the maximum water pressure change amplitude within the disturbance section of the monitoring point at the starting point of the path, the total duration of the path, and the number of monitoring points in the leap section of the path. Calculate the corresponding disturbance intensity index, mark the risk level according to the administrative division, and generate the warning level calibration result driven by spatial mutation.

[0030] The spatial mutation-driven early warning level calibration results specifically include level labels, corresponding regional identifiers, and disturbance intensity level indexes.

[0031] The present invention is improved in that the steps for obtaining the early warning level calibration result driven by spatial mutation are as follows:

[0032] S511: Based on each path in the list of paths advancing towards the near warning boundary, extract the water pressure change sequence of the disturbance segment corresponding to the monitoring point at the starting point of the path, identify the difference between the maximum and minimum values ​​in the sequence, and perform normalization processing to obtain the normalized water pressure change amplitude of each path and generate a path water pressure change amplitude sequence.

[0033] S512: Call the path information corresponding to the path water pressure change amplitude sequence, extract the start and end times of each path and calculate the span time, count the number of monitoring points marked as leap sections in the path, combine the three indicators to construct a multi-path corresponding dataset, calculate and obtain the disturbance intensity index value, and generate a disturbance intensity index value set.

[0034] S513: Based on the index corresponding to each path in the disturbance intensity index value set, retrieve the administrative division identifier to which the path belongs, and perform interval determination on the disturbance intensity index according to the hydrological risk level classification benchmark value range set within the administrative division, mark the corresponding risk level, and obtain the warning level calibration result driven by spatial mutation.

[0035] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0036] In this invention, based on the linkage analysis of hourly hydrostatic pressure values ​​and water level observations, and on the basis of judging the direction of change and extracting the rate of change, high-precision identification of the rise segment is achieved. By synchronously judging the duration difference between rise segments and the time difference of the disturbance starting point, a group of monitoring points with abrupt linkage characteristics is screened. Combining the geographical direction consistency and time series progression relationship between nodes in the disturbance propagation path chain, a quantifiable disturbance propagation path information set is formed. The path set is screened by the spatial proximity relationship between the end of the path and the warning boundary point. The disturbance intensity index is constructed by combining the water pressure change amplitude, duration and number of rise segments, and guides the risk level calibration decision. On the basis of accurately characterizing the disturbance features, the spatial accuracy and response timeliness of hydrological risk level identification are improved, the full-link triggering closed loop of abnormal disturbance identification is strengthened, and the ability to identify the warning coverage under abrupt change situation is enhanced. Attached Figure Description

[0037] Figure 1 This is a flowchart of the method of the present invention;

[0038] Figure 2 This is a flowchart illustrating the process of obtaining the set of water pressure and water level linkage jump segments in this invention;

[0039] Figure 3This is a flowchart illustrating the process of obtaining a group of linked perturbation mutation nodes according to the present invention;

[0040] Figure 4 This is a flowchart illustrating the process of obtaining the propulsion path information set due to water pressure disturbance in this invention.

[0041] Figure 5 This is a flowchart illustrating the process of obtaining a list of advance paths near the early warning boundary according to the present invention.

[0042] Figure 6 This is a flowchart illustrating the process of obtaining the early warning level calibration results driven by spatial mutation in this invention. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this invention clearer, the 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 merely illustrative and not intended to limit the invention.

[0044] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0045] Please see Figure 1 This invention provides a technical solution: a method for intelligent identification and early warning of hydrological data, comprising the following steps:

[0046] S1: Based on the hydrological monitoring points set up along the river section, collect the hourly hydrostatic pressure value and water level observation value of each monitoring point within a set period, perform adjacent time period difference calculation on each set of hydrostatic pressure value sequence, determine the direction of change of continuous difference, identify continuous unidirectional upward change segment, and extract the water level change rate from the water level observation value sequence corresponding to the change segment to generate a set of water pressure and water level linkage jump segments.

[0047] S2: Based on the information of each segment in the set of water pressure and water level linkage jump segments, extract the identification of the monitoring point, the river segment number and the start and end time period, calculate the difference in jump duration and the time difference of disturbance start between adjacent monitoring points, and determine whether it is simultaneously lower than the fluctuation duration tolerance value and the jump start time synchronization tolerance value. Select the monitoring point combination that meets the sudden change synchronization condition and generate the linkage disturbance sudden change node group.

[0048] The fluctuation duration tolerance value refers to the maximum allowable duration difference during the propagation of disturbances between adjacent monitoring points; the transition start time synchronization tolerance value is the maximum time difference between the start times of disturbances at monitoring points. Both are calibrated and standardized based on historical flood evolution patterns.

[0049] S3: Based on each group of monitoring points in the linked disturbance mutation node group, extract the river section direction, latitude and longitude coordinates of the monitoring point, and the disturbance start time in the time series. Calculate the Euclidean distance difference and disturbance propagation time difference between nodes in the river direction. Determine whether the geographical path direction consistency condition and the time-series incremental propagation condition are met simultaneously. Construct the disturbance propagation path chain and mark and archive the maximum river channel propagation distance and minimum response time interval in the path chain to generate a water pressure disturbance propagation path information set.

[0050] Euclidean distance difference is used to assess the spatial advance distance of monitoring points; disturbance propagation time difference is the time difference; maximum channel propagation distance and minimum response time interval are path cascade features used to characterize spatial advance efficiency;

[0051] S4: Based on the path end monitoring point information in the water pressure disturbance propulsion path information set, extract the corresponding regional early warning boundary point coordinate reference set, measure the minimum straight distance value from the path end point to the boundary point, compare it with the spatial near-critical distance, filter the path set that meets the conditions, and generate a list of propulsion paths near the early warning boundary.

[0052] The spatial proximity critical distance is the minimum allowable proximity value between the end point of the path and the warning boundary point set by the hydrological department, used to determine whether the path is close to the key prevention and control area;

[0053] S5: For each path in the list of paths advancing near the warning boundary, extract the maximum water pressure change amplitude in the disturbance section of the monitoring point at the starting point of the path, the total time span of the path, and the number of monitoring points in the leap section of the path. Calculate the corresponding disturbance intensity index and compare it with the hydrological risk level classification benchmark value. Mark the risk level according to the administrative division to which it belongs and generate the warning level calibration result driven by spatial mutation.

[0054] The set of pressure-level-level linked leap segments includes the pressure increment value within the leap segment, the water level increase value for the corresponding time period, and the duration of continuous leaps. The linked disturbance mutation node group specifically includes the monitoring point number that meets the leap synchronization condition, the time consistency index between nodes, and the spatial continuity identifier of the river segment. The water pressure disturbance propagation path information set includes the spatial distance value between each node in the path chain, the time delay value of disturbance propagation, and the path direction sequence. The list of propagation paths near the warning boundary specifically refers to the geographic location information of the monitoring point at the path terminal, the boundary distance measurement value, and the corresponding warning area code. The warning level calibration result driven by spatial mutation specifically includes the level label, the area identifier corresponding to the level, and the disturbance intensity level index.

[0055] Please see Figure 2 The specific steps for obtaining the set of water pressure and water level linkage jump segments are as follows:

[0056] S111: Based on the hydrological monitoring points set up along the river section, collect the hourly hydrostatic pressure value and water level observation value of each monitoring point within a set period, and perform the difference calculation of adjacent time periods for each set of hydrostatic pressure value sequences, determine the direction of change of the difference between adjacent time periods, identify the continuous unidirectional upward change segment, and generate a set of hydrostatic pressure change segments.

[0057] Based on three hydrological monitoring points set up along a typical section of the middle reaches of the Yangtze River, specifically the monitoring points... Monitoring points With monitoring points Hourly hydrostatic pressure and water level measurements were collected at each monitoring point within a set 24-hour period. Taking the data collected within a specific 6-hour period as an example, as shown in Table 1, and for each set of hydrostatic pressure value sequences, such as monitoring points... hydrostatic pressure value sequence The difference calculation between adjacent time periods is performed, specifically by subtracting the pressure value of the previous time period from the pressure value of the later time period to obtain a difference sequence. For example, in... The difference in time is ,exist The difference in time is And so on, to obtain the complete difference sequence. Next, the direction of change of the difference between adjacent time periods is determined. This process involves determining the sign of each value in the difference sequence. If the difference is positive for three or more consecutive time periods, the sequence is determined to be a unidirectional upward change. For example, if all differences in the sequence are positive, then from... to The pressure change direction throughout the entire period was unidirectionally upward. Subsequently, all continuous unidirectional upward pressure segments meeting this condition were identified. In this embodiment, the monitoring point... exist The hydrostatic pressure change over a period of time constitutes a continuous unidirectional upward change segment, denoted as The same processing is performed on all data from all monitoring points to generate a set of hydrostatic pressure variation segments.

[0058] Table 1. Hourly hydrological data for monitoring point Z01:

[0059] ;

[0060] As shown in Table 1, it displays the raw data of hydrostatic pressure and water level collected by monitoring point Z01 over six consecutive time periods, providing basic data support for subsequent calculations.

[0061] S112: Based on each segment in the set of hydrostatic pressure change segments, extract the corresponding water level observation value sequence, calculate the water level change rate for each segment sequence, and identify the key values ​​of the change rate within the segment by performing maximum value filtering on the water level change rate values, and generate a set of extreme values ​​of water level change rate.

[0062] Based on each segment in the set of hydrostatic pressure variation segments, such as those identified in the previous step. Extract its in The sequence of water level observations corresponding to the time period, according to the data in Table 1, is as follows: The rate of change of water level is calculated for this sequence of water level observations. The calculation method is to divide the difference in water level between each adjacent time point in the sequence by the time interval, where the time interval is 1 hour. For example... Water level change rate over time period Similarly, the complete water level change rate sequence is calculated as follows: By performing maximum value filtering on the water level change rate sequence, specifically, the action is to filter the sequence... Find the maximum value in the segment to identify the key value of the rate of change within the segment. This value is The key value of the rate of change of water level corresponding to the hydrostatic pressure variation range, and this key value Corresponding change segment identifier Perform associative storage, and apply the same calculation and filtering process to all other segments in the set of hydrostatic pressure change segments to generate a set of extreme values ​​of water level change rate.

[0063] S113: Based on the set of hydrostatic pressure change segments and the set of extreme values ​​of water level change rate, the change segments are correlated. By judging the relationship between the water level change rate and the pressure increase of the change segment, the jump segments that meet the predetermined conditions are selected to generate a set of water pressure and water level linkage jump segments.

[0064] Based on the set of hydrostatic pressure variation segments and the set of extreme values ​​of water level change rate, the variation segments are correlated, specifically by extracting the hydrostatic pressure variation segments. The key values ​​corresponding to the extreme values ​​of total pressure increase and water level change rate The total pressure increase is calculated by subtracting the pressure value at the beginning of the segment from the pressure value at the end of the segment. By analyzing the relationship between the rate of change of water level and the increase in pressure within a given range, leap sections meeting predetermined conditions are selected. These conditions are defined as a critical value for the rate of change of water level exceeding a baseline value, and an increase in pressure exceeding a baseline value. The baseline value for the rate of change of water level is set by referring to hydrological data of the river section during the non-flood season over the past five years. All water level change rate data are statistically analyzed, and their 95th percentiles are calculated. After removing abnormal data points affected by extreme weather events, 1.2 times the final calculated value is taken. For example, the 95th percentile of historical data is... The baseline value is then set to The baseline value for pressure increase is set by referring to the same historical dataset, statistically analyzing the pressure increase over a consecutive 6-hour period, and calculating its 90th percentile. For example, the historical statistical results are... Then the pressure increase benchmark value will be set to ,Will The calculated results for this segment are compared with the benchmark value, and the key value of its water level change rate is... Greater than And its pressure increased Greater than Therefore, this change segment Once selected as a jump segment, the same judgment and selection are performed on all changing segments to generate a set of water pressure and water level linked jump segments.

[0065] Please see Figure 3 The specific steps for obtaining the linked perturbation mutation node group are as follows:

[0066] S211: Based on the information of each segment in the set of water pressure and water level linkage jump segments, extract the corresponding monitoring point identifier, river segment number and start and end time of the jump segment, calculate the jump duration of the jump segment within the monitoring point, establish a combination list between any adjacent monitoring points based on the spatial adjacency relationship of monitoring points within the river segment, sequentially call the jump duration value of the monitoring points within the combination, calculate the time difference, and generate a sequence of jump duration difference values ​​between adjacent monitoring points;

[0067] Based on the information of each segment in the set of water pressure and water level linkage jump segments, such as including monitoring points exist The leap in time period, monitoring points exist The corresponding monitoring point identifiers for the time jump segments are extracted as follows: , The river section is numbered as follows And the start and end times of the leap phase are respectively , Calculate the duration of the rise segment within the monitoring point. Its duration is For each time period unit, Its duration is Each time period is based on the spatial adjacency of monitoring points within a river segment. This adjacency is predetermined according to the physical deployment order of the monitoring points along the river channel. and For adjacent monitoring points, create a list of combinations between any two adjacent monitoring points. (This is where combinations are created.) The jump duration values ​​of the monitoring points within the combination are called sequentially, which are 5 and 6 respectively, and the time difference value is calculated. For each time period, the same duration extraction and difference calculation are performed on all other adjacent monitoring point combinations within the river segment to generate a sequence of differences in the rise duration of adjacent monitoring points.

[0068] S212: Based on the start time of the leap segment of adjacent monitoring points, obtain the leap disturbance start time difference between each pair of monitoring points, construct the disturbance start time difference sequence, call the leap duration difference sequence of adjacent monitoring points and the disturbance start time difference sequence, and compare them with the set fluctuation duration tolerance value and the leap start time synchronization tolerance value respectively, and filter the monitoring point combination that is simultaneously less than the two tolerance values ​​to obtain the mutation synchronization satisfied combination index set;

[0069] Based on the start time of the jump segment of adjacent monitoring points, a combination of... For example, their starting times are respectively and Obtain the time difference of the start point of the jump disturbance between each pair of monitoring points, and calculate it as follows: For each time period unit, a disturbance initiation time difference sequence is constructed. The values ​​(1 time period unit) from the adjacent monitoring point rise duration difference sequence generated in the previous steps are compared with the values ​​(1 time period unit) from the disturbance initiation time difference sequence generated in this step, respectively, and then compared with the set fluctuation duration tolerance value and the jump initiation time synchronization tolerance value. The fluctuation duration tolerance value is set by analyzing the distribution of differences in the duration of rises of adjacent monitoring points caused by the same disturbance source in historical data, taking twice the standard deviation as the tolerance value. For example, if the historical difference standard deviation is 0.8 time period units, then the tolerance value is set to 1.6 time period units. The jump initiation time synchronization tolerance value is set based on the average flow velocity of the river section and the distance between monitoring points, estimating the theoretical propagation time, and adding a delay fluctuation amount (such as the 80th percentile) based on historical observation data statistics. For example, if the theoretical propagation time is 0.5 time period units and the historical delay fluctuation amount is 0.7 time period units, then the tolerance value is set to... Each time period unit compares the calculated value with the tolerance value, and the difference in jump duration. And the time difference of the start of the disturbance Since both differences are less than the corresponding tolerance values, this combination of monitoring points... The selected samples yield a set of combined indexes that satisfy the mutation synchronization requirement.

[0070] S213: Invoke the identifier of each monitoring point combination in the combined index set that satisfies the mutation synchronization condition, extract the river segment number corresponding to the monitoring point in the original uplift segment information, and construct a spatial group of monitoring points that meets the mutation synchronization condition, using the formula:

[0071] ;

[0072] The synchronous combination strength value is obtained through calculation. The combination strength value is then filtered based on whether it is within a set strength range. The filtered monitoring point combinations are then obtained, and a group of linked disturbance mutation nodes is established.

[0073] in, For monitoring points With monitoring points The synchronization strength value of the combination is used to measure the strength of the disturbance linkage characteristics between combinations. For monitoring points and The normalized value of the difference in the duration of the jump between monitoring points is obtained by calculating the start and end time differences of the jump segment at each monitoring point and then normalizing the results. For monitoring points and The normalized value of the time difference between the start points of the disturbances is obtained by recording the time difference between the start points of the jump disturbances and then normalizing it. , monitoring points , The normalized value of the linear distance in the river segment direction is obtained by measuring the original linear length of the river channel and performing a normalization transformation. , monitoring points , The normalized standard deviation of the jump duration was obtained by statistically analyzing the fluctuation range of the jump duration within the same monitoring point during the period and then normalizing it. For monitoring points and The normalized value of the propagation path length in the direction of the inter-river section is obtained by measuring the shortest reachable path length between two points along the river section and then normalizing it. The intensity interval is obtained by statistically analyzing the distribution range of the combined intensity values ​​corresponding to the synchronous mutation combination in historical disturbance events, calculating the upper and lower quartiles to form a reference interval, and setting the upper and lower bounds after removing extreme outliers as the standard for the current selected intensity interval.

[0074] The mutation synchronization call satisfies the identifier of each monitoring point combination in the combined index set, such as the combination... Extract monitoring points from the original leap segment information , Corresponding river section number A spatial group of monitoring points that meets the conditions for mutation synchronization is constructed using the formula:

[0075] ;

[0076] The synchronous combination strength value is obtained through calculation. The specific explanations of each parameter in this formula are as follows: For monitoring points With monitoring points The combined synchronous intensity value has the following components: the numerator is a measure of difference in the time dimension, calculated by adding the absolute value of the difference in rise duration to the absolute value of the difference in the start time of the disturbance; the denominator is a comprehensive measure of spatial and fluctuation characteristics, calculated by multiplying the square of the difference in linear distance between the two monitoring points by the square root of the sum of the standard deviations of their respective rise durations by the natural logarithm of the propagation path length between the two points plus one. The entire formula quantifies the linkage intensity of disturbance events between two monitoring points through the ratio of time difference to spatial characteristics. The smaller the time difference, the closer the spatial distance, or the smaller the historical fluctuations, the higher the intensity value. and This represents the numbers of the two monitoring points in the combination, here it is. and ,parameter For monitoring points and The normalized value of the difference in jump duration between the two values ​​is obtained by first calculating the difference in jump duration as follows: Each time period is then used to determine the distribution range of the difference in the historical dataset. Perform min-max normalization and calculate as follows: ,parameter For monitoring points and The normalized value of the time difference between the start points of the disturbances is obtained by recording the time difference between the start points of the jump disturbances. Each time period unit, based on the distribution range of this difference in the historical dataset. Normalize and calculate as ,parameter , monitoring points , The normalized value of the linear distance along the river segment is obtained by measuring the total length of the river segment. Monitoring points lie in Location, monitoring point lie in At this point, the normalized value is , ,parameter , monitoring points , The normalized standard deviation of the jump duration was obtained through statistical analysis. The duration sequence of 10 jump events within a period Calculate its standard deviation as ,right Duration sequence Calculate its standard deviation as Based on the historical standard deviation distribution range Normalization is performed to obtain , ,parameter For monitoring points and The normalized value of the propagation path length in the inter-river section is obtained by measuring the shortest reachable path length between two points along the river section. Based on the maximum path length between monitoring points within this river section Normalization is performed to obtain Substitute the above parameter values ​​into the formula to perform the calculation:

[0077] ;

[0078] The intensity range was determined by statistically analyzing the distribution range of the combined intensity values ​​corresponding to synchronous mutation combinations in 100 confirmed historical disturbance events, and then calculating the upper quartiles. and lower quartiles The intensity range is then set as follows: Based on the combined strength value The system filters monitoring points within the defined intensity range to obtain the selected combinations. Establish a group of linked perturbation mutation nodes. The advantage of this formula is that it synchronizes perturbations in the time domain ( and ) and proximity of spatial domains ( ) and historical fluctuation stability of a single monitoring point ( By combining these methods, a multi-dimensional linkage intensity evaluation index was constructed, which can more accurately identify real disturbance propagation events with physical correlation, rather than accidental temporal coincidences. The result of 4.80 indicates that the monitoring points... and The disturbance linkage characteristics between the two monitoring points are relatively strong and within a reasonable range, confirming that the two monitoring points constitute a linkage disturbance mutation node.

[0079] Please see Figure 4 The specific steps for obtaining the water pressure disturbance propulsion path information set are as follows:

[0080] S311: Based on each group of monitoring points in the linked disturbance mutation node group, extract the direction vector, latitude and longitude coordinates of the monitoring points in the river section and the corresponding disturbance start time, construct pairwise combinations between monitoring points and obtain the order of arrangement along the river direction, calculate the spatial Euclidean distance between adjacent monitoring points in the combination and the time difference of the disturbance propagation start point, and generate a set of river-side disturbance propagation distance difference and time difference.

[0081] Based on each group of monitoring points in the linked perturbation mutation node group, for example including and The combination of these parameters is used to extract the direction vector, latitude and longitude coordinates of the monitoring point in the river segment, and the corresponding disturbance start time, using the monitoring point as the basis for analysis. For example, its coordinates are The start time of the disturbance is Monitoring points The coordinates are The start time of the disturbance is The monitoring points were paired and their arrangement along the river was determined based on the river's course. lie in Upstream, in order The spatial Euclidean distance between adjacent monitoring points in the combination and the time difference between the start of disturbance propagation are calculated. The spatial Euclidean distance is converted based on latitude and longitude coordinates to obtain... The time difference between the start and end of the disturbance propagation is Every hour, the same calculation is performed on all adjacent monitoring point combinations in the node group to generate a set of differences in the propagation distance and time of disturbance along the river.

[0082] S312: Call the combination of monitoring points in the set of differences in the distance and time of disturbance propagation along the river, determine whether the angle between the river section direction difference vector and the direction of the Euclidean line is within the threshold range of the geographical path direction consistency angle, and at the same time determine whether the disturbance propagation time meets the increasing trend, filter the combination of monitoring point sequences that meet the dual conditions, and obtain the sequentially increasing propagation combination index set.

[0083] Use a combination of monitoring points that represent the difference in propagation distance and time along the river, for example, a combination of... Determine whether the angle between the river segment direction difference vector and the Euclidean line is within the geographical path direction consistency angle threshold range, where the river segment is within... The main current vector at that point is (Southeast direction), by point to The angle of the Euclidean line connecting the directions is The angle between the two is The threshold for the geographic path direction consistency angle is set based on the analysis of river channel curvature using historical remote sensing image data. An angle range that can accommodate normal river channel curvature variations is selected. For the slightly curved river section in the plain area, the 85th percentile value of the angle between the historical mainstream direction and the actual line connecting them is chosen and set as [the threshold value]. ,because Less than The first condition is met, and it is also determined whether the disturbance propagation time follows an increasing trend, meaning the disturbance start time at the downstream monitoring point must be later than that at the upstream monitoring point. The second condition is also met; therefore, we select monitoring point sequence combinations that satisfy both conditions. By combining and filtering, a set of sequentially increasing propagation combined indexes is obtained.

[0084] S313: Based on the sequentially increasing propagation combination index of the monitoring point combination index, establish a sequentially connected list of path segments, construct the disturbance propagation path chain, record the cumulative river propagation distance and response start and end time interval of the path segments, and extract the maximum river propagation length and minimum response time interval values ​​to obtain the water pressure disturbance propagation path information set.

[0085] Based on the sequentially ascending propagation of the composite index, the monitoring points are combined, for example, including... and The index is used to create a list of sequentially concatenated path segments. If both segments pass the screening, they can be connected in series to construct a perturbation propulsion path chain. It records the cumulative river propagation distance and response start and end time interval of the path segment, and sets... arrive The distance of the river channel is If the response time interval is 1.5 hours, then the total cumulative river propagation distance of this pathchain is... The total response start and end time interval is The time interval was calculated, and the maximum river propagation length and minimum response time interval were extracted from all constructed path chains. For example, in this event, a total of 3 path chains were constructed, with propagation lengths of 1 hour, 2 hours, 3 minutes, and 3 seconds respectively. , , The maximum river channel propagation length is The corresponding response time intervals are 2.5 hours, 1.8 hours, and 3.2 hours, respectively. The minimum response time interval is 1.8 hours, thus obtaining the water pressure disturbance propulsion path information set.

[0086] Please see Figure 5 The specific steps for obtaining the list of paths to advance near the warning boundary are as follows:

[0087] S411: Based on the path end monitoring point information set of the water pressure disturbance propulsion path information set, extract the corresponding river segment number and end monitoring point coordinate value, retrieve the set of pre-set early warning boundary point coordinate reference within the administrative region according to the river segment number, call the coordinate value to calculate the straight distance between the end monitoring point and all boundary points in the region, and generate the path end to boundary point distance information.

[0088] Based on the path end monitoring point information of the propulsion path information set by water pressure disturbance, the path chain For example, extract its end monitoring points. The information, the corresponding river section number is The coordinates of the end monitoring point are According to the river section number Retrieve the established coordinate reference set of early warning boundary points within the administrative region of City A to which it belongs. This reference set contains boundary points of three key protection areas, namely the boundary points... , , Calculate the coordinate values ​​of the end monitoring point With all boundary points in the region The straight-line distance between them is calculated. , , Generate distance information from the end of the path to the boundary point.

[0089] S412: Based on the distance information from the end of the path to the boundary point, extract the minimum distance value corresponding to each end monitoring point, construct the minimum straight-line distance sequence of the end point, call the spatial proximity critical distance benchmark value to compare each value in the distance sequence, filter the path combination index that is less than the benchmark value, and obtain the spatial proximity path index set;

[0090] Based on the distance information from the end of the path to the boundary point, extract the minimum distance value corresponding to each monitoring point at the end of the path. In other words, its corresponding minimum distance value is (to the boundary point) The distance is calculated by constructing a sequence of minimum straight-line distances to the endpoints. If other paths exist, the minimum distances to the endpoints are also included in this sequence. A spatial near-critical distance benchmark is used to compare each value in the distance sequence. This benchmark is set by comprehensively considering the average deployment time of emergency response teams within the administrative region (e.g., 30 minutes) and the average propagation speed of water in the corresponding river section (e.g., [missing information]). ), calculate the safe lead time required for early warning, benchmark value = average propagation speed Emergency response time = The calculated minimum distance value Compared with the benchmark value Comparison, because Filter the indexes corresponding to the path to obtain the set of spatially similar path indexes.

[0091] S413: Based on the path number identified in the spatial proximity path index set, extract the corresponding path segment information from the hydraulic disturbance propulsion path information set, reorganize and construct a spatial propulsion path list and mark the shortest distance value between the end of the path and the warning boundary to obtain the propulsion path list near the warning boundary;

[0092] Based on the path number identified in the spatial proximity path index set, for example, path The number is The path segment information corresponding to the given number is extracted from the hydraulic disturbance propulsion path information set, and a spatial propulsion path list is constructed by reorganizing the information. The shortest distance between the end of the path and the warning boundary is specifically marked in each entry of this list. and the corresponding warning boundary points Perform the same extraction and annotation operations on all paths that pass the screening to obtain a list of paths advancing towards the warning boundary.

[0093] Please see Figure 6 The specific steps for obtaining the early warning level calibration results driven by spatial mutation are as follows:

[0094] S511: Based on each path in the path list near the early warning boundary, extract the water pressure change sequence of the disturbance segment corresponding to the monitoring point at the starting point of the path, identify the difference between the maximum and minimum values ​​in the sequence, and perform normalization processing to obtain the normalized water pressure change amplitude of each path and generate a path water pressure change amplitude sequence.

[0095] Each path in the path list is advanced based on the proximity to the early warning boundary, such as path Extract the monitoring point corresponding to the starting point of the path. The sequence of water pressure changes during the disturbance segment, this sequence is Identify the maximum value within a sequence and minimum value The difference between them yields the amplitude of the water pressure change. The data was then normalized, referencing the water pressure change amplitude of all recorded disturbance events in the historical database, with a range of [range missing]. The normalized water pressure change amplitude is Obtain the normalized water pressure change amplitude for each path, perform the same operation on all paths in the list, and generate a sequence of path water pressure change amplitudes.

[0096] S512: Retrieve path information corresponding to the path water pressure change sequence, extract the start and end times of each path and calculate the span time, count the number of monitoring points identified as rise sections within the path, combine the three indicators to construct a multi-path corresponding dataset, using the formula:

[0097] ;

[0098] The disturbance intensity index value is obtained through calculation, and a set of disturbance intensity index values ​​is generated;

[0099] in, Representing a path The disturbance intensity index value is calculated from the path dataset. Representing a path The normalized value of the water pressure change amplitude within the disturbance section at the starting monitoring point is obtained by extracting the difference between the maximum and minimum values ​​and then normalizing it. Representing a path The number of monitoring points in the intermediate rise phase is obtained by counting the sequence markers of monitoring points within the counting path. Representing a path The continuous distribution ratio of monitoring points in the inner riser segment is obtained by comparing the number of riser segments with the total number of monitoring points along the path. Representing a path The normalized value of the total time span is obtained by normalizing the difference between the start and end times. This is the disturbance distribution adjustment coefficient constant, used to control the degree of influence of the spatial distribution standard deviation. Representing a path The normalized standard deviation of the spatial location of the monitoring points in the inner rise section is obtained by statistically analyzing the positional fluctuation of the monitoring points in the linear river section and normalizing it. The basis for setting the benchmark value range for hydrological risk level classification is the distribution range of disturbance intensity index with marked levels in various typical disturbance events in the historical river section. By constructing a disturbance level mapping table and combining the frequency distribution density, quantile value and spatial boundary distribution trend, multiple segmented level ranges are defined for level determination.

[0100] Call the path information corresponding to the path water pressure change amplitude sequence, using the path For example, extract its start and end times, starting point The starting time is ,end The end time of the disturbance is , set as The calculation span time is Each time period unit represents the number of monitoring points identified as jump segments within the statistical path. In this example... All are identified as leap segments, therefore the number is 3. A multi-path corresponding dataset is constructed by combining the three indicators, using the formula:

[0101] ;

[0102] The disturbance intensity index value is calculated, and the parameters in the formula are explained in detail below: Representing a path The perturbation intensity index value, whose numerator is composed of the intensity of the perturbation source ( ), the breadth of disturbance propagation ( ) and the continuity of disturbed nodes on the path ( The denominator represents the duration of the disturbance event, which is jointly determined by the factors of the two factors. ) and the discreteness of spatial distribution ( The entire formula, through a comprehensive evaluation of the "energy" and "form" of the disturbance, provides a quantitative strength index, in which... For path numbering, here it is ,parameter Representing a path The normalized value of the water pressure change amplitude within the disturbance section at the starting monitoring point is the value obtained from the aforementioned calculation. ,parameter Representing a path The number of monitoring points in the middle rise phase, its value is ,parameter Representing a path The continuous distribution ratio of monitoring points in the inner riser segment is obtained by the ratio of the number of riser segments to the total number of monitoring points along the path. Assuming the path chain passes through only these three monitoring points, then... ,parameter Representing a path The total time span normalized value is obtained by taking the difference between the start and end times. Each time period is a unit based on the total time span of historical events. Normalization is performed to obtain ,parameter This is the constant for the disturbance distribution adjustment coefficient, determined through regression analysis of historical data. It aims to balance the influence weights of time span and spatial dispersion on the final result. Experimental verification shows that when... Values At that time, the model showed the best distinguishing ability for different types of disturbance events, therefore, the setting was... ,parameter Representing a path The normalized standard deviation of the spatial location of the monitoring points in the inner rise segment was obtained by statistically analyzing the monitoring points in the rise segment. Normalized position value in the direction of the linear river segment (set up The normalized position is ), calculate its standard deviation as Based on the standard deviation range of the spatial location of historical events Normalization is performed to obtain Substitute the parameter values ​​into the formula:

[0103] ;

[0104] Perform the same calculation on all paths to generate a set of disturbance intensity index values. The advantage of this formula is that it considers not only the initial intensity of the disturbance source (…). It also innovatively incorporates the propagation characteristics of disturbances in the spatiotemporal dimensions. This allows the final intensity index to more comprehensively reflect the potential risks throughout the entire dynamic development process, rather than relying solely on static measurements at a single point. This result... It is a comprehensive dimensionless index. The larger the value, the stronger the disturbance event represented by the path and the greater the destructive potential.

[0105] S513: Based on the index corresponding to each path in the disturbance intensity index value set, retrieve the administrative division identifier to which the path belongs, and perform interval determination on the disturbance intensity index according to the hydrological risk level classification benchmark value range set within the administrative division, mark the corresponding risk level, and obtain the warning level calibration result driven by spatial mutation.

[0106] Based on the disturbance intensity index value set, each path corresponds to an index, for example, path The index value is 0.862. The search path is located in the administrative division of City A. Based on the hydrological risk level classification benchmark range set within the administrative division of City A, the disturbance intensity index is determined within this range. This benchmark range is based on 50 historical river disturbance events in City A, categorized into "general," "relatively severe," and "serious" according to their actual socio-economic impact and the degree of damage to water conservancy facilities. The disturbance intensity index corresponding to each category is then calculated. The distribution range is defined by constructing a disturbance level mapping table and combining it with the frequency distribution density, resulting in the following segmented level ranges: Level 1 (general risk) corresponds to... Level 2 (Severe Risk) Level 3 (Severe Risk) , path The disturbance intensity index of 0.862 is compared with this range. Therefore, its risk level is determined to be level three, and the corresponding risk level is marked as "level three (severe risk)". The same interval determination and labeling are performed on all values ​​in the index set to obtain the warning level labeling result driven by spatial mutation.

[0107] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for intelligent identification and early warning of hydrological data, characterized in that, Includes the following steps: S1: Based on the hydrological monitoring points set up along the river section, collect the hourly hydrostatic pressure value and water level observation value of each monitoring point within a set period, identify the continuous unidirectional upward change segment, and generate a set of water pressure and water level linkage jump segments. S2: Based on the information of each segment in the set of water pressure and water level linkage jump segments, calculate the difference in jump duration between adjacent monitoring points and the time difference of disturbance start point, screen the combination of monitoring points that meet the sudden change synchronization condition, and generate a linkage disturbance sudden change node group. S3: Based on each group of monitoring points in the linked disturbance mutation node group, extract the direction of the river section, the latitude and longitude coordinates of the monitoring point and the disturbance start time in the time series, construct the disturbance propagation path chain, and generate a water pressure disturbance propagation path information set; S4: Based on the path end monitoring point information in the water pressure disturbance propulsion path information set, extract the corresponding regional early warning boundary point coordinate reference set, measure the minimum straight-line distance from the path end point to the boundary point, compare it with the spatial near-critical distance, filter the path set that meets the conditions, and generate a list of propulsion paths near the early warning boundary. S5: For each path in the list of paths advancing towards the near warning boundary, extract the maximum water pressure change amplitude within the disturbance section of the monitoring point at the starting point of the path, the total duration of the path, and the number of monitoring points in the leap section of the path. Calculate the corresponding disturbance intensity index, mark the risk level according to the administrative division, and generate the warning level calibration result driven by spatial mutation. The spatial mutation-driven early warning level calibration results specifically include level labels, corresponding regional identifiers, and disturbance intensity level indexes.

2. The method for intelligent identification and early warning of hydrological data according to claim 1, characterized in that, The set of water pressure and water level linkage leap segments includes the pressure increment value within the leap segment, the water level increase value for the corresponding time period, and the duration of continuous leaps. The linkage disturbance mutation node group specifically includes the monitoring point number that meets the leap synchronization condition, the time consistency index between nodes, and the spatial continuity identifier of the river section. The water pressure disturbance propagation path information set includes the spatial distance value between each node in the path chain, the time delay value of disturbance propagation, and the path direction sequence. The list of near-warning boundary propagation paths specifically refers to the geographic location information of the path terminal monitoring point, the boundary distance measurement value, and the corresponding warning area code.

3. The method for intelligent identification and early warning of hydrological data according to claim 2, characterized in that, The specific steps for obtaining the set of water pressure and water level linkage jump segments are as follows: S111: Based on the hydrological monitoring points set up along the river section, collect the hourly hydrostatic pressure value and water level observation value of each monitoring point within a set period, and perform the difference calculation of adjacent time periods for each set of hydrostatic pressure value sequences, determine the direction of change of the difference between adjacent time periods, identify the continuous unidirectional upward change segment, and generate a set of hydrostatic pressure change segments. S112: Based on each segment in the set of hydrostatic pressure change segments, extract the corresponding water level observation value sequence, calculate the water level change rate for each segment sequence, and identify the key values ​​of the change rate within the segment by performing maximum value filtering on the water level change rate values, thereby generating a set of extreme values ​​of water level change rate. S113: Based on the set of hydrostatic pressure change segments and the set of extreme values ​​of water level change rate, the change segments are correlated. By judging the relationship between the water level change rate and the pressure increase of the change segment, the jump segments that meet the predetermined conditions are selected to generate a set of water pressure and water level linkage jump segments.

4. The intelligent identification and early warning method for hydrological data according to claim 3, characterized in that, The specific steps for obtaining the linked perturbation mutation node group are as follows: S211: Based on the information of each segment in the set of water pressure and water level linkage jump segments, extract the corresponding monitoring point identifier, river segment number and start and end time of the jump segment, calculate the jump duration of the jump segment within the monitoring point, establish a combination list between any adjacent monitoring points based on the spatial adjacency relationship of monitoring points within the river segment, sequentially call the jump duration value of the monitoring points within the combination, calculate the time difference, and generate a sequence of jump duration difference values ​​between adjacent monitoring points; S212: Based on the start time of the leap segment of adjacent monitoring points, obtain the leap disturbance start time difference between each pair of monitoring points, construct the disturbance start time difference sequence, call the leap duration difference sequence of adjacent monitoring points and the disturbance start time difference sequence, and compare them with the set fluctuation duration tolerance value and the leap start time synchronization tolerance value respectively, and filter the monitoring point combination that is simultaneously less than the two tolerance values ​​to obtain the mutation synchronization satisfied combination index set; S213: Call the number identifier of each monitoring point combination in the mutation synchronization satisfying combination index set, extract the river segment number corresponding to the monitoring point in the original leap segment information, construct a monitoring point spatial group that meets the mutation synchronization conditions, calculate and obtain the synchronization combination intensity value, filter according to whether the combination intensity value is within the set intensity range, obtain the monitoring point combination that passes the filter, and establish a linkage disturbance mutation node group.

5. The method for intelligent identification and early warning of hydrological data according to claim 4, characterized in that, The specific steps for obtaining the water pressure disturbance propulsion path information set are as follows: S311: Based on each group of monitoring points in the linked disturbance mutation node group, extract the direction vector, latitude and longitude coordinates and corresponding disturbance start time of the monitoring points in the river section, construct pairwise combinations between monitoring points and obtain the arrangement order along the river direction, calculate the spatial Euclidean distance value of adjacent monitoring points in the combination and the time difference of disturbance propagation start, and generate a set of river-side disturbance propagation distance difference and time difference value. S312: Call the monitoring point combination in the set of the difference in the distance and time of the disturbance propagation along the river, determine whether the angle between the river section direction difference vector and the direction of the Euclidean line is within the geographical path direction consistency angle threshold range, and at the same time determine whether the disturbance propagation time meets the increasing trend, filter the monitoring point sequence combination that meets the dual conditions, and obtain the sequentially increasing propagation combination index set. S313: Based on the sequentially increasing propagation combination index of the monitoring point combination index, establish a sequentially connected list of path segments, construct a disturbance propagation path chain, record the cumulative river propagation distance and response start and end time interval of the path segments, and extract the maximum river propagation length and minimum response time interval values ​​to obtain the water pressure disturbance propagation path information set.

6. The method for intelligent identification and early warning of hydrological data according to claim 5, characterized in that, The specific steps for obtaining the list of approach warning boundary advancement paths are as follows: S411: Based on the path end monitoring point information in the water pressure disturbance propulsion path information set, extract the corresponding river segment number and end monitoring point coordinate value, retrieve the set of pre-set early warning boundary point coordinate references within the administrative region according to the river segment number, call the coordinate value to calculate the straight-line distance between the end monitoring point and all boundary points in the region, and generate the path end to boundary point distance information. S412: Based on the distance information from the end of the path to the boundary point, extract the minimum distance value corresponding to each end monitoring point of the path, construct the minimum straight-line distance sequence of the end point, call the spatial proximity critical distance benchmark value to compare each value in the distance sequence, filter the path combination index that is less than the benchmark value, and obtain the spatial proximity path index set; S413: Based on the path number identified in the spatial proximity path index set, extract the corresponding path segment information from the water pressure disturbance propulsion path information set, reorganize and construct a spatial propulsion path list and mark the shortest distance value between the end of the path and the warning boundary to obtain the propulsion path list near the warning boundary.

7. The method for intelligent identification and early warning of hydrological data according to claim 6, characterized in that, The specific steps for obtaining the early warning level calibration results driven by spatial mutation are as follows: S511: Based on each path in the list of paths advancing towards the near warning boundary, extract the water pressure change sequence of the disturbance segment corresponding to the monitoring point at the starting point of the path, identify the difference between the maximum and minimum values ​​in the sequence, and perform normalization processing to obtain the normalized water pressure change amplitude of each path and generate a path water pressure change amplitude sequence. S512: Call the path information corresponding to the path water pressure change amplitude sequence, extract the start and end times of each path and calculate the span time, count the number of monitoring points marked as leap sections in the path, combine the three indicators to construct a multi-path corresponding dataset, calculate and obtain the disturbance intensity index value, and generate a disturbance intensity index value set. S513: Based on the index corresponding to each path in the disturbance intensity index value set, retrieve the administrative division identifier to which the path belongs, and perform interval determination on the disturbance intensity index according to the hydrological risk level classification benchmark value range set within the administrative division, mark the corresponding risk level, and obtain the warning level calibration result driven by spatial mutation.

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