River embankment leakage monitoring and early warning method and system based on GIS technology

Abstract

The application relates to the technical field of river embankment leakage early warning, and discloses a river embankment leakage monitoring and early warning method and system based on GIS technology. The method comprises the following steps: differentiating frequency sampling and neighboring point correlation analysis processing are performed on monitoring points in a digital embankment basic data set, real-time embankment monitoring data flow is obtained, a seepage pressure gradient field is constructed, and seepage path tracking processing is performed, embankment leakage characteristic data is obtained, combined analysis processing and seasonal change correction of water level difference, pressure gradient and seepage rate are performed, and leakage risk early warning threshold data is obtained; leakage diffusion rate evaluation and historical accuracy correction processing are performed on the leakage risk early warning threshold data, early warning information push scheme is obtained; correlation analysis and monitoring parameter optimization processing are performed on the early warning information push scheme and the real-time embankment monitoring data flow, and leakage monitoring control parameters are obtained. The application realizes accurate monitoring and timely early warning of embankment leakage.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of river embankment leakage early warning, and in particular to a river embankment leakage monitoring and early warning method and system based on GIS technology. BACKGROUND

[0002] River embankment is an important infrastructure for flood control projects, and its safety is directly related to the safety of the basin and the safety of people's lives and property. Traditional river embankment leakage monitoring mainly relies on manual patrol, single-point sensor monitoring and other means, and some areas have begun to use GIS technology for embankment safety management. These monitoring methods collect embankment leakage related parameters by laying water level gauges, osmotic pressure gauges and other sensing devices, and assess the safety of the embankment in combination with the experience of engineering personnel. At the same time, some areas have also established a preliminary early warning system to alarm abnormal situations by setting fixed thresholds.

[0003] However, the existing river embankment leakage monitoring technology has the following shortcomings: the monitoring device layout lacks scientific basis, often using uniform layout or experience layout, without fully considering the embankment structure characteristics and historical leakage conditions; the monitoring data collection frequency is fixed and cannot be dynamically adjusted according to the actual situation, which may miss important data during critical periods; the early warning threshold setting is too simple and does not consider seasonal changes and historical data, which can easily cause false alarms and missed alarms; there is a lack of correlation analysis between monitoring points, which cannot effectively identify the leakage development trend and potential risks. SUMMARY

[0004] The present application provides a river embankment leakage monitoring and early warning method and system based on GIS technology, which is used to realize accurate monitoring and timely early warning of embankment leakage by differentiating the layout and dynamic sampling of monitoring points, combining correlation analysis of multi-source data and seasonal correction.

[0005] In a first aspect, the application provides a GIS technology-based river embankment leakage monitoring and early warning method, which comprises: performing layered partition processing and historical leakage position weight calculation on three-dimensional terrain data of a river embankment to obtain a digital embankment basic data set; performing differential frequency sampling and adjacent point correlation analysis processing on monitoring points in the digital embankment basic data set to obtain a real-time embankment monitoring data stream; performing seepage pressure gradient field construction and seepage path tracking processing on the real-time embankment monitoring data stream to obtain embankment leakage characteristic data; performing combined analysis processing and seasonal change correction of water level difference, pressure gradient and seepage rate on the embankment leakage characteristic data to obtain leakage risk early warning threshold data; performing leakage diffusion rate evaluation and historical accuracy correction processing on the leakage risk early warning threshold data to obtain an early warning information pushing scheme; and performing correlation analysis and monitoring parameter optimization processing on the early warning information pushing scheme and the real-time embankment monitoring data stream to obtain leakage monitoring control parameters.

[0006] In a second aspect, the application provides a GIS technology-based river embankment leakage monitoring and early warning system, which comprises:

[0007] a layered module for performing layered partition processing and historical leakage position weight calculation on three-dimensional terrain data of a river embankment to obtain a digital embankment basic data set;

[0008] a correlation module for performing differential frequency sampling and adjacent point correlation analysis processing on monitoring points in the digital embankment basic data set to obtain a real-time embankment monitoring data stream;

[0009] a tracking module for performing seepage pressure gradient field construction and seepage path tracking processing on the real-time embankment monitoring data stream to obtain embankment leakage characteristic data;

[0010] a correction module for performing combined analysis processing and seasonal change correction of water level difference, pressure gradient and seepage rate on the embankment leakage characteristic data to obtain leakage risk early warning threshold data;

[0011] a correction module for performing leakage diffusion rate evaluation and historical accuracy correction processing on the leakage risk early warning threshold data to obtain an early warning information pushing scheme;

[0012] an optimization module for performing correlation analysis and monitoring parameter optimization processing on the early warning information pushing scheme and the real-time embankment monitoring data stream to obtain leakage monitoring control parameters.

[0013] In the technical scheme provided in the application, through hierarchical partition processing and historical leakage position weight calculation on the geographic space data and engineering geological data of the river embankment, scientific layout of the embankment monitoring point and accurate identification of the risk area are realized, the problem of many blind areas in the traditional monitoring method is effectively solved, and through differential frequency sampling and adjacent point correlation analysis processing on the monitoring points in the digital embankment basic data set, an adaptive data acquisition mechanism is established, and the pertinence and efficiency of data acquisition are improved. In the scheme, the real-time embankment monitoring data stream is subjected to seepage pressure gradient field construction and seepage path tracking processing, so that the identification of the leakage path is more accurate, and reliable data support is provided for early warning. Through combined analysis processing and seasonal change correction on the water level difference, pressure gradient and seepage rate of the embankment leakage characteristic data, multi-dimensional leakage risk assessment is realized, and the accuracy of early warning is greatly improved. The leakage diffusion rate of the leakage risk early warning threshold data is evaluated and the historical accuracy is corrected, a dynamic early warning threshold system is established, and the false positive and false negative rates are effectively reduced. Finally, through correlation analysis and monitoring parameter optimization processing on the early warning information pushing scheme and the real-time embankment monitoring data stream, adaptive adjustment of the monitoring parameters is realized, and the operation efficiency and reliability of the whole monitoring and early warning system are improved. The method combines GIS technology with traditional embankment monitoring technology, and constructs a complete embankment leakage monitoring and early warning system, which not only improves the spatial coverage range and time continuity of monitoring, but also enhances the timeliness and accuracy of early warning. BRIEF DESCRIPTION OF DRAWINGS

[0014] In order to more clearly illustrate the technical scheme of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0015] Figure 1 An embodiment of the river embankment leakage monitoring and early warning method based on GIS technology in the embodiment of the present application;

[0016] Figure 2 An embodiment of the river embankment leakage monitoring and early warning system based on GIS technology in the embodiment of the present application. DETAILED DESCRIPTION

[0017] The embodiment of the present application provides a river embankment leakage monitoring and early warning method and system based on GIS technology. The terms "first", "second", "third", "fourth" and the like (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" or "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific process of the embodiment of the present application is described below. Please refer to Figure 1 One embodiment of the river embankment leakage monitoring and early warning method based on GIS technology in the embodiment of the present application comprises:

[0019] Step S101, layering and partitioning the three-dimensional terrain data of the river embankment and calculating the historical leakage position weight to obtain a digital embankment basic data set;

[0020] Step S102, differentiating the frequency sampling of the monitoring points in the digital embankment basic data set and performing adjacent point correlation analysis processing to obtain real-time embankment monitoring data stream;

[0021] Step S103, constructing the seepage pressure gradient field and tracking the seepage path of the real-time embankment monitoring data stream to obtain embankment leakage characteristic data;

[0022] Step S104, performing combined analysis processing and seasonal change correction on the water level difference, pressure gradient and seepage rate of the embankment leakage characteristic data to obtain leakage risk early warning threshold data;

[0023] Step S105, performing leakage diffusion rate evaluation and historical accuracy correction processing on the leakage risk early warning threshold data to obtain an early warning information pushing scheme;

[0024] Step S106, performing correlation analysis and monitoring parameter optimization processing on the early warning information pushing scheme and the real-time embankment monitoring data stream to obtain leakage monitoring control parameters.

[0025] It can be understood that the execution subject of the present application can be a river embankment leakage monitoring and early warning system based on GIS technology, and can also be a terminal or a server, and the specific embodiments are not limited herein. The embodiment of the present application takes the server as the execution subject for example.

[0026] Specifically, the three-dimensional terrain data is obtained by measuring the spatial information of the embankment's elevation, slope, and orientation, etc. When performing hierarchical and zonal processing, the embankment is divided into embankment top layer, embankment body layer, and embankment foundation layer according to structural characteristics, and each layer is further divided into different regions according to engineering geological conditions. Meanwhile, combined with historical leakage event records, the leakage risk weight of each region is calculated to form a digital embankment foundation data set. For example, a certain section of embankment is 1000 meters long, the embankment top elevation is 15 meters obtained by elevation measurement, and the embankment foundation width is 40 meters. According to the division of a basic grid unit every 20 meters, a total of 50 basic units are divided, and historical records show that leakage events have occurred at units 25 and 26. The leakage risk weight values of these two units are set to 0.8, while the basic weight values of other units are 0.3. After obtaining the digital embankment foundation data set, the monitoring points therein are subjected to differential frequency sampling. Based on the risk weight values of the monitoring points, different sampling frequencies are set, with high-risk weight points having high sampling frequencies and low-risk weight points having low sampling frequencies. The data of adjacent monitoring points are subjected to correlation analysis, and when the data of a certain monitoring point fluctuate abnormally, the sampling frequencies of the surrounding monitoring points are automatically increased. Through this dynamic adjustment of the sampling strategy, real-time embankment monitoring data flow is formed. Taking units 25 and 26 mentioned above as examples, due to the high risk weight, the sampling frequencies of the monitoring points at these two locations are set to every 5 minutes, while the sampling frequencies of the monitoring points at other locations are set to every 30 minutes. When the water level of monitoring point 25 is detected to have a sudden change, the sampling frequencies of monitoring points 24 and 27 are automatically increased to every 5 minutes.

[0027] The obtained real-time embankment monitoring data flow is subjected to seepage pressure gradient field construction. First, the pressure data of each monitoring point is subjected to spatial interpolation to form a continuous pressure distribution field, and then the gradient of the pressure field is calculated to obtain the seepage pressure gradient field. The flow field theory is used to track the seepage path, and the potential leakage channel is identified by calculating the flow velocity vector and flow line. For example, at unit 25, the seepage pressure of monitoring point A is 50 kPa, the seepage pressure of adjacent monitoring point B is 30 kPa, and the distance between the two points is 10 meters. The pressure gradient is calculated to be 2 kPa / m, and according to this gradient value, the direction and speed of the seepage water flow can be predicted. Then, multi-dimensional analysis is performed on the embankment leakage characteristic data, including the combination analysis of water level difference, pressure gradient, and seepage rate. The water level difference reflects the water level change inside and outside the embankment, the pressure gradient represents the size and direction of the seepage force, and the seepage rate indicates the water flow speed. These three parameters are combined by weighting to obtain a comprehensive evaluation index. Meanwhile, the influence of seasonal factors on these parameters is considered, such as in the flood season and dry season, the same monitoring data may have different risk implications. Through these analyses and corrections, the leakage risk warning threshold data is obtained.

[0028] Then, the leakage diffusion rate of the leakage risk early warning threshold data is evaluated. According to the spatial distribution and development trend of the leakage point, the expansion speed of the influence range of the leakage is predicted. Meanwhile, the early warning accuracy is statistically analyzed by combining historical early warning records, so as to optimize the early warning parameters and avoid false positives and false negatives. For example, when the penetration pressure of a certain monitoring point exceeds the early warning threshold, the system calculates the leakage diffusion rate. If it is found that the leakage range expands at a speed of 0.5 meters per hour, and the historical data shows that the early warning accuracy under similar conditions reaches 90%, the corresponding level of early warning information is generated. Finally, the early warning information pushing scheme is associated with the real-time monitoring data stream for analysis, the early warning effect is evaluated, and the monitoring parameters are dynamically optimized. By analyzing the relationship between the early warning information sending time and the actual leakage event occurrence time, the early warning timeliness is calculated, and the sampling frequency and early warning threshold of the monitoring point are adjusted accordingly. For example, if it is found that the early warning information of a certain monitoring point can predict the occurrence of a leakage event 2 hours in advance on average, the sampling frequency of this point can be appropriately reduced to save monitoring resources; if the early warning of a certain monitoring point often lags behind the occurrence of the event, the sampling frequency or the early warning threshold needs to be increased. Through this continuous optimization process, the accuracy of monitoring and early warning is continuously improved.

[0029] In the embodiments of the present application, by performing hierarchical partitioning processing on the geographic spatial data and engineering geological data of the river embankment and calculating the weight of the historical leakage position, scientific layout of the embankment monitoring points and accurate identification of the risk area are realized, effectively solving the problem of multiple blind areas in the traditional monitoring method. Meanwhile, by performing differential frequency sampling on the monitoring points in the digital embankment basic data set and performing adjacent point association analysis processing, a self-adaptive data acquisition mechanism is established, and the pertinence and efficiency of data acquisition are improved. In the scheme, the penetration pressure gradient field of the real-time embankment monitoring data stream is constructed and the penetration path tracking processing is performed, so that the identification of the leakage path is more accurate, providing reliable data support for early warning. Through combined analysis processing and seasonal change correction of the water level difference, pressure gradient and penetration rate of the embankment leakage characteristic data, multi-dimensional leakage risk evaluation is realized, and the accuracy of early warning is greatly improved. The leakage diffusion rate of the leakage risk early warning threshold data is evaluated and the historical accuracy is corrected, a dynamic early warning threshold system is established, and the false positive and false negative rates are effectively reduced. Finally, through association analysis of the early warning information pushing scheme and the real-time embankment monitoring data stream and monitoring parameter optimization processing, adaptive adjustment of the monitoring parameters is realized, and the operation efficiency and reliability of the whole monitoring and early warning system are improved. The method combines GIS technology with traditional embankment monitoring technology to construct a complete embankment leakage monitoring and early warning system, which not only improves the spatial coverage and time continuity of monitoring, but also enhances the timeliness and accuracy of early warning.

[0030] In a specific embodiment, the process of step S101 can specifically include the following steps:

[0031] (1) The three-dimensional terrain data of the river embankment is processed in layers to obtain embankment structure level data, and the embankment structure level data is regionally meshed to obtain basic grid cell data;

[0032] (2) The basic grid cell data is subjected to geological attribute correlation analysis to obtain geological feature partition data, and the geological feature partition data is subjected to spatial distribution processing through Kriging interpolation to obtain geological spatial distribution data;

[0033] (3) The historical leakage event records are subjected to spatio-temporal clustering analysis to obtain historical leakage hotspot area data, and the historical leakage hotspot area data is subjected to density calculation to obtain leakage risk weight coefficients;

[0034] (4) The geological spatial distribution data and the leakage risk weight coefficients are subjected to superimposed analysis to obtain key monitoring area division data, and the key monitoring area division data is subjected to monitoring point layout calculation to obtain monitoring point position coordinate data;

[0035] (5) The monitoring point position coordinate data is subjected to monitoring equipment type matching to obtain a monitoring equipment configuration scheme, and the monitoring equipment configuration scheme is subjected to sampling frequency initialization setting to obtain monitoring point sampling parameters;

[0036] (6) The monitoring point sampling parameters and the key monitoring area division data are subjected to comprehensive processing to obtain a digital embankment basic data set.

[0037] Specifically, the three-dimensional terrain data of the river embankment is processed in layers. The three-dimensional terrain data includes elevation data, slope data and cross-section shape data of the embankment, wherein the elevation data records the elevation value of each point of the embankment, the slope data records the inclination of the embankment surface, and the cross-section shape data records the geometric characteristics of the embankment cross-section. The layered processing divides the embankment structure into three main levels from top to bottom, i.e. embankment top layer, embankment body layer and embankment foundation layer, each level having different engineering characteristics and seepage prevention requirements. After obtaining the embankment structure level data, each level is divided into regional grids, and the grid size is set based on the geometric characteristics of the embankment. Generally, the embankment top layer uses a 5m x 5m grid, the embankment body layer uses a 10m x 10m grid, and the embankment foundation layer uses a 20m x 20m grid, forming basic grid cell data. When performing geological attribute correlation analysis on the basic grid cell data, geological parameters such as soil type, permeability coefficient and water content need to be considered. The soil type reflects the physical properties of the embankment material, the permeability coefficient represents the water permeability of the material, and the water content represents the water content in the soil. By analyzing the spatial distribution of these parameters, grid cells with similar geological characteristics are classified into a category to form geological feature zoning data. Then, the geological feature zoning data is processed for spatial distribution using the Kriging interpolation algorithm. Kriging interpolation is an optimal linear unbiased estimation method based on variogram theory, which estimates the value of unknown points by analyzing the spatial correlation of known points, thereby obtaining continuous geological spatial distribution data.

[0038] The historical leakage event records are analyzed for spatio-temporal clustering. By analyzing the spatial location and occurrence time of historical leakage points, the spatio-temporal distribution of leakage events is identified. The spatio-temporal clustering uses a density-based clustering algorithm to cluster leakage events with similar spatial locations and occurrence times into a category, obtaining historical leakage hotspot region data. The density of each region is calculated by calculating the number of leakage events per unit area in each region, and the leakage risk weight coefficient reflecting the risk degree of the region is obtained by combining the severity of the leakage event. The geological spatial distribution data and the leakage risk weight coefficient are superimposed and analyzed to consider the influence of geological conditions and historical leakage on the safety of the embankment. The superimposed analysis uses a weighted superimposed method to assign different weights to different factors, and calculates the comprehensive risk score to divide the key monitoring areas and form key monitoring area division data. Based on this, the optimal layout position of the monitoring points is calculated according to the importance, area size and topographic features of the monitoring area, and the monitoring point coordinate data is obtained.

[0039] According to the monitoring point position coordinate data and monitoring requirements, a suitable monitoring device type is selected. The monitoring devices include water level meters, osmotic pressure meters, displacement meters, etc. The water level meters are used to measure the water level difference inside and outside the embankment, the osmotic pressure meters are used to measure the osmotic pressure inside the embankment, and the displacement meters are used to monitor the deformation of the embankment. Through device type matching, a monitoring device configuration scheme is formed. The monitoring device configuration scheme is initialized and set for sampling frequency, and the sampling time interval is determined based on the risk level of the monitoring area. Higher sampling frequency is used in high-risk areas, and lower sampling frequency is used in low-risk areas, to obtain the sampling parameters of the monitoring points. Finally, the monitoring point sampling parameters and key monitoring area division data are comprehensively processed, the deployment information of the monitoring devices, the sampling parameters and the area characteristics are associated, and a complete digital embankment basic data set is formed.

[0040] Taking a 2000-meter-long embankment as an example, through analysis of three-dimensional terrain data, the embankment height is 12 meters on average, the embankment top width is 8 meters, and the embankment base width is 45 meters. After being divided into three layers according to the layering requirements, the embankment top layer is divided into 400 5m x 5m grid units, the embankment body layer is divided into 200 10m x 10m grid units, and the embankment base layer is divided into 100 20m x 20m grid units. Through geological survey, it is found that the embankment is mainly composed of clay and sand, among which the permeability coefficient of the clay layer is 10^-7 cm / s and the permeability coefficient of the sand layer is 10^-4 cm / s. Historical records show that there have been three leakage events in the 500m to 700m section, and through spatiotemporal clustering analysis it is determined that this is a high-risk leakage area, and the leakage risk weight coefficient of this area is calculated to be 0.85, while the weight coefficients of other areas are between 0.2 and 0.4. According to the superposition analysis results, a monitoring point is arranged every 50 meters in the high-risk area, equipped with a water level meter and an osmotic pressure meter, and the sampling frequency is set to once every hour, and a monitoring point is arranged every 100 meters in other areas, with a sampling frequency of once every 4 hours. This differentiated monitoring strategy based on risk level ensures the rational allocation of monitoring resources and improves monitoring efficiency.

[0041] In a specific embodiment, the process of step S102 can specifically include the following steps:

[0042] (1) Initial parameter setting of sampling frequency for monitoring points in the digital embankment basic data set to obtain baseline sampling frequency data, and historical data change rate analysis of the baseline sampling frequency data to obtain a dynamic frequency adjustment coefficient;

[0043] (2) Importance classification processing of the dynamic frequency adjustment coefficient to obtain differentiated sampling parameters, and time window division of the differentiated sampling parameters to obtain a time-periodic sampling scheme;

[0044] (3) The adjacent monitoring point data is processed by a sliding window to obtain short-term data trend, and the short-term data trend is detected for mutation to obtain abnormal fluctuation marked data;

[0045] (4) The abnormal fluctuation marked data is analyzed for spatial correlation to obtain monitoring point group division data, and the monitoring point group division data is collected for data synchronization to obtain multi-point collaborative monitoring data;

[0046] (5) The multi-point collaborative monitoring data is subjected to data quality inspection to obtain effective data identification, and the effective data identification is subjected to time series reconstruction to obtain continuous monitoring sequence;

[0047] (6) The continuous monitoring sequence and the time period sampling scheme are comprehensively processed to obtain real-time embankment monitoring data stream.

[0048] Specifically, the risk level, geographical location and monitoring target of the monitoring point in the digital embankment basic data are sampled to set the initial parameters of the sampling frequency. The initial parameters of the sampling frequency include three key parameters: basic sampling interval, data collection time length and data precision requirement, and the reference sampling frequency data is obtained by calculation. Then the reference sampling frequency data is analyzed for historical data change rate, the change rate of the monitoring data in different time periods is calculated, the data fluctuation degree is evaluated, and the dynamic frequency adjustment coefficient is obtained. The dynamic frequency adjustment coefficient reflects the change characteristics of the monitoring data, and is used for dynamically adjusting the reference sampling frequency. When the dynamic frequency adjustment coefficient is subjected to importance classification processing of the monitoring point, the geographical location, historical data change rule and surrounding environmental factors of the monitoring point need to be considered. According to these factors, the monitoring points are classified, the higher level monitoring points adopt higher sampling frequency, and the lower level monitoring points adopt lower sampling frequency, so as to obtain differentiated sampling parameters. Then the differentiated sampling parameters are subjected to time window division, and are divided into several time periods according to 24 hours a day, each time period is set with corresponding sampling frequency, and a time period sampling scheme is formed. The time period sampling scheme fully considers the change characteristics of the embankment leakage risk in different time periods.

[0049] Then the data of adjacent monitoring points are processed by sliding window, the size of which is set according to the characteristics of the data, usually taking 5-10 data points as a window. The statistical characteristics of the data in each window are calculated, including the mean, standard deviation and rate of change, so as to obtain the short-term data trend. The mutation of the short-term data trend is detected, and the statistical test method is used to judge whether the data has significant change, and the data mutation point is marked, and the abnormal fluctuation marked data is obtained. Then the spatial correlation analysis is carried out on the abnormal fluctuation marked data, the correlation coefficient between different monitoring points is calculated, and the monitoring point group with similar data change characteristics is identified. The monitoring points with strong correlation are divided into the same group to form the monitoring point group division data. The data synchronization acquisition is carried out on the monitoring point group division data, so that the monitoring points in the same group can collect data at the same time, and the multi-point collaborative monitoring data is obtained. The collaborative monitoring data reflects the spatial propagation characteristics of the dike leakage.

[0050] The data quality inspection is carried out on the multi-point collaborative monitoring data, including data integrity check, outlier detection and consistency verification. By setting the inspection rules, the data meeting the quality requirements is screened out to obtain the effective data identification. The time series reconstruction is carried out on the effective data identification, the interpolation processing is carried out on the missing data, and the continuity of the data is ensured to obtain the continuous monitoring sequence. Finally, the continuous monitoring sequence and the time period sampling scheme are comprehensively processed, the monitoring data is organized and managed according to the characteristics of the time period, and the real-time dike monitoring data stream is formed.

[0051] Taking a certain embankment monitoring system as an example, the system is deployed on a 3-kilometer-long embankment with 30 monitoring points. The initial sampling frequency is set as follows: high-risk area (10 points) collects data every 10 minutes, medium-risk area (12 points) collects data every 30 minutes, and low-risk area (8 points) collects data every hour. Through analysis of historical data, it is found that during the period from 0 o'clock in the morning to 6 o'clock in the morning, the data change rate is low, with an average change of not more than 2%; while during the period from 6 o'clock in the morning to 10 o'clock in the evening, the data change rate increases significantly, with an average change of 5%. Based on this feature, the time window of the sampling scheme is divided: during the early morning period, the sampling frequency is reduced, with the high-risk area adjusted to every 20 minutes, the medium-risk area adjusted to every hour, and the low-risk area adjusted to every 2 hours; during the daytime period, the original sampling frequency is maintained. In the actual monitoring process, at 9 o'clock in the morning, the A15 monitoring point detects a 20-centimeter rise in water level within 15 minutes, triggering an abnormal fluctuation flag. Spatial correlation analysis shows that the adjacent A14 and A16 monitoring points also have a small amplitude of water level rise, with correlation coefficients of 0.85 and 0.82 respectively. These three monitoring points are divided into the same group, and the sampling frequency is increased to every 5 minutes for synchronous data collection. After data quality inspection, it is found that there is an abnormal data at 9:30 in A15 monitoring point, which is corrected through linear interpolation of the data before and after. The final monitoring data stream shows that the leakage anomaly in this area is controlled after 2 hours, and the water level gradually returns to normal.

[0052] In an embodiment, the process of performing step S103 can specifically include the following steps:

[0053] (1) performing spatial interpolation calculation on the real-time embankment monitoring data stream to obtain continuous pressure field data, and performing isopotential line extraction on the continuous pressure field data to obtain pressure contour data;

[0054] (2) performing gradient calculation on the pressure contour data to obtain pressure gradient vector data, and performing flow direction analysis on the pressure gradient vector data to obtain seepage flow field distribution data;

[0055] (3) performing numerical solution on the seepage flow field distribution data to obtain flow velocity component data, and performing streamline tracking on the flow velocity component data to obtain seepage path data;

[0056] (4) performing convergence point identification on the seepage path data to obtain potential leakage point position data, and performing intensity evaluation on the potential leakage point position data to obtain leakage intensity distribution data;

[0057] (5) performing propagation path analysis on the leakage intensity distribution data to obtain leakage diffusion law data, and performing characteristic parameter extraction on the leakage diffusion law data to obtain leakage characteristic index data;

[0058] (6) Comprehensive analysis of the leakage characteristic index data and the seepage path data to obtain the dike leakage characteristic data.

[0059] Specifically, when performing spatial interpolation calculation on the real-time dike monitoring data stream, the Kriging interpolation method is adopted to process the discrete monitoring point pressure data. The Kriging interpolation method considers the correlation of spatial positions, and the pressure value of an unknown point is calculated through the establishment of a variogram model, so as to obtain continuous distribution pressure field data. The continuous pressure field data is subjected to isopotential line extraction, and the points with the same pressure value are connected according to the preset pressure gradient interval to form pressure contour line data, which reflects the spatial distribution characteristics of the pressure field. Subsequently, gradient calculation is performed on the pressure contour line data to calculate the rate of change of the pressure field in space, and pressure gradient vector data is obtained. The pressure gradient vector data describes the size and direction of the pressure change, and is used for analyzing the motion trend of the seepage water flow. The flow direction analysis is performed on the pressure gradient vector data, the water flow motion direction is determined according to the Darcy law, and the seepage flow field distribution data is obtained, which represents the motion state of the water flow inside the dike.

[0060] When performing numerical solution on the seepage flow field distribution data, the finite difference method is adopted to solve the seepage equation. The solving process of the seepage equation can be expressed as:

[0061]

[0062] wherein, K x , K y , and K z represent the seepage coefficients (unit: m / s) in x, y, and z directions respectively, h represents the water head height (unit: m), S s represents the specific water storage rate (unit: m -1 / s), and t represents the time (unit: s). The flow velocity component data is obtained by solving the equation. The flow line tracing is performed on the flow velocity component data, the fourth-order Runge-Kutta method is adopted to calculate the water particle motion trajectory, and the seepage path data is obtained.

[0063] The convergence point identification is performed on the seepage path data, the intersection of the flow lines is searched, and the potential leakage point position is determined. The intensity evaluation is performed on the potential leakage point position data, and the calculation formula of the leakage intensity is as follows:

[0064]

[0065] wherein, L i represents the intensity index of the i-th leakage point, Q j represents the flow value (unit: m 3 / s) of the j-th monitoring point, d ij represents the distance (unit: m) from the leakage point i to the monitoring point j, and α j and βj respectively, n is the number of monitoring points in the influence range. The leakage intensity distribution data is calculated. The leakage diffusion law data is obtained by analyzing the propagation path of the leakage intensity distribution data to study the expansion process of the leakage influence range. The leakage characteristic index data is formed by extracting the characteristic parameters of the leakage diffusion law data, including diffusion rate, influence range and directionality, etc. Finally, the complete dike leakage characteristic data is obtained by comprehensive analysis of the leakage characteristic index data and the permeation path data.

[0066] Taking a river dike monitoring as an example, a monitoring section of 500 meters long is set up with 20 monitoring points. The pressure field data calculated by Kriging interpolation shows that there is a low pressure area 200 meters away from the dike toe, with a pressure value of 15 kPa, while the average pressure value of the surrounding area is 25 kPa. The calculation of pressure gradient shows that the pressure gradient of this area reaches 0.5 kPa / m, which is much higher than the average value of 0.1 kPa / m in other areas. When solving the seepage equation, the permeability coefficient K x = K y = 10 -5 m / s, K z = 10 -6 m / s, the specific storage ratio S s = 10 -4 m -1 , the maximum flow rate of this area is calculated to be 2 x 10 -4 m / s. The stream tracing result shows that many streamlines converge at 210 meters away from the dike toe, which is identified as a potential leakage point.

[0067] The intensity of the leakage point is evaluated, and the flow data of the surrounding five monitoring points are 0.015, 0.012, 0.010, 0.008 and 0.006 m 3 / s, respectively, with a distance of 5, 8, 10, 12 and 15 meters, respectively. The weight coefficients a j are all 1, and the distance attenuation coefficients b j are 0.1, and the leakage intensity index is calculated to be 0.038, which exceeds the warning value of 0.025. After 24 hours of continuous monitoring, the influence range of the leakage point spreads to all directions at a speed of 0.5 meters per hour, and the diffusion speed in the direction of the dike axis is faster, reaching 0.8 meters per hour.

[0068] In a specific embodiment, the process of performing step S104 can specifically include the following steps:

[0069] (1) Calculate the difference value of adjacent points for the water level data in the dike leakage characteristic data to obtain the water level gradient distribution data, and filter out the abnormal points of the water level gradient distribution data to obtain the water level mutation point data;

[0070] (2) Time series decomposition is performed on the pressure gradient data in the dike leakage feature data to obtain pressure change trend data, and critical value determination is performed on the pressure change trend data to obtain pressure abnormal area data;

[0071] (3) The seepage rate data in the dike leakage feature data is subjected to zonal statistics to obtain rate distribution feature data, and threshold classification is performed on the rate distribution feature data to obtain seepage risk grade data;

[0072] (4) Cross-validation is performed on the water level mutation point data and the pressure abnormal area data to obtain initial early warning criterion data, and the initial early warning criterion data is associated with the seepage risk grade data to obtain combined early warning index data;

[0073] (5) Historical seasonal fluctuation analysis is performed on the combined early warning index data to obtain seasonal change feature data, and a correction coefficient is calculated for the seasonal change feature data to obtain a seasonal correction factor;

[0074] (6) Weighted superposition analysis is performed on the combined early warning index data and the seasonal correction factor to obtain leakage risk early warning threshold data.

[0075] Specifically, the water level data in the dike leakage feature data is processed. The water level data contains the water level measurement values inside and outside the dike, and the water level difference between adjacent monitoring points reflects the potential risk of dike leakage. By calculating the water level difference between adjacent monitoring points, water level gradient distribution data is obtained. The water level gradient describes the degree of change in water level per unit distance, and the water level gradient distribution data is subjected to abnormal point screening, a statistical threshold is set, and points outside the normal fluctuation range are marked as abnormal points to obtain water level mutation point data. The pressure gradient data in the dike leakage feature data is subjected to time series decomposition processing, and the wavelet decomposition method is used to decompose the pressure gradient data into three components: trend item, periodic item and random item. The trend item reflects the long-term trend of pressure change, the periodic item embodies the periodic characteristics of pressure change, and the random item represents short-term fluctuations. By analyzing the combined characteristics of the three components, pressure change trend data is obtained. Critical value determination is performed on the pressure change trend data, and the areas exceeding the safety threshold are marked to obtain pressure abnormal area data.

[0076] The seepage rate data in the dike seepage feature data is statistically analyzed by partitioning, the monitoring area is divided into several sub-areas, the statistical characteristics of the seepage rate in each sub-area are calculated, including the average value, the standard deviation and the coefficient of variation, and the rate distribution characteristic data is obtained. The rate distribution characteristic data is classified by threshold, and the risk level is divided into three levels of low risk, medium risk and high risk according to the size of the seepage rate, and the seepage risk level data is obtained. Then the water level mutation point data and the pressure abnormal area data are cross-validated, the spatial correspondence and the time correlation between the two kinds of data are analyzed, the real abnormal area is determined, and the initial early warning criterion data is obtained. The initial early warning criterion data is associated with the seepage risk level data, the early warning criterion is matched with the seepage risk level, the severity of the abnormal situation is comprehensively evaluated, and the combined early warning index data is obtained.

[0077] The combined early warning index data is analyzed for historical seasonal fluctuations, and the change law of dike seepage features in different seasons is studied. The seasonal change characteristic data is obtained by statistically analyzing the seasonal change characteristics in the historical data, including the difference between flood season and dry season, the influence of temperature change, etc. The seasonal change characteristic data is corrected by calculating the correction coefficient, and the seasonal influence factor is established to adjust the early warning threshold, and the seasonal correction factor is obtained. Finally, the combined early warning index data and the seasonal correction factor are analyzed by weighted superposition, and the importance of each index and the seasonal influence are considered comprehensively, and the final seepage risk early warning threshold data is calculated.

[0078] Taking a river dike as an example, 30 water level monitoring points are arranged in the monitoring section, and the distance between adjacent points is 50 meters. It is found by calculation that the water level gradient between monitoring points A12 and A13 reaches 0.15 meters / meter, while the normal water level gradient is only 0.05 meters / meter, and this point is marked as a water level mutation point. At the same time, the time series decomposition of the pressure gradient data shows that the pressure change trend in this area has been rising continuously in the past 24 hours, with an upward rate of 2 kPa / hour, which exceeds the critical value of 1.5 kPa / hour, so it is marked as a pressure abnormal area. The partition statistical result of the seepage rate shows that the average seepage rate of this area is 3×10 -4 m / s, the standard deviation is 5×10 -5m / s, with a coefficient of variation of 0.17, belonging to the medium risk level. After cross-validation, it is confirmed that there is a water level mutation and pressure anomaly in this area at the same time, and the confidence of the initial early warning criterion reaches 85%. Historical data analysis shows that the infiltration risk in this area is generally higher in the rainy season (June-September) than in other seasons, and the historical average infiltration rate is 40% higher in the rainy season than in the dry season. Based on this feature, the seasonal correction factor is calculated to be 1.4 in the rainy season and 0.8 in the dry season. Finally, through weighted superposition analysis, the leakage risk early warning threshold of this area in the rainy season is determined: water level gradient threshold 0.12 m / m, pressure change rate threshold 1.8 kPa / hour, and infiltration rate threshold 2.5 x 10 -4 m / s. When the monitoring data exceeds these thresholds, the corresponding level of early warning signal will be triggered. This early warning method based on multi-dimensional data analysis and seasonal correction effectively improves the accuracy and applicability of early warning.

[0079] In a specific embodiment, the process of performing step S105 can specifically include the following steps:

[0080] (1) Time series segmentation is performed on the leakage risk early warning threshold data to obtain threshold change interval data, and diffusion rate calculation is performed on the threshold change interval data to obtain initial diffusion rate data;

[0081] (2) Spatial distribution analysis is performed on the initial diffusion rate data to obtain diffusion range prediction data, and risk level division is performed on the diffusion range prediction data to obtain regional risk distribution data;

[0082] (3) Accuracy statistics are performed on the historical early warning records to obtain early warning judgment standard data, and weight allocation is performed on the early warning judgment standard data to obtain early warning parameter correction data;

[0083] (4) Historical accuracy verification is performed on the regional risk distribution data to obtain risk assessment correction coefficients, and the risk assessment correction coefficients are assigned grades to obtain early warning level determination data;

[0084] (5) Time effectiveness analysis is performed on the early warning level determination data to obtain early warning time window data, and early warning priority sorting is performed on the early warning time window data to obtain hierarchical early warning sequence data;

[0085] (6) Comprehensive processing is performed on the hierarchical early warning sequence data and the early warning parameter correction data to obtain an early warning information pushing scheme.

[0086] Specifically, the leakage risk early warning threshold data is processed by time series segmentation. The time series segmentation adopts a sliding time window method, the early warning threshold data is divided into several time periods according to a fixed time length, the change characteristics of the threshold in each period are analyzed, and the threshold change interval data is obtained. The threshold change interval data is calculated by the diffusion rate, and the calculation formula is:

[0087]

[0088] wherein, V d represents the initial diffusion rate, ω i is the weight coefficient of the i th monitoring point, R i,t and R i,t+Δt respectively represent the risk value at time t and t+Δt, γ i is the spatial attenuation coefficient, D i is the distance from the monitoring point to the risk source (unit: m), and n is the number of monitoring points. The initial diffusion rate data is analyzed by spatial distribution, the variation law of diffusion rate in space is studied by using geographic statistics method, and the diffusion range prediction data is obtained. The diffusion range prediction data is divided into risk levels, the risk is divided into four levels according to the size of diffusion rate: slight risk, medium risk, large risk and major risk, and the regional risk distribution data is obtained.

[0089] Then, the accuracy of historical early warning records is statistically analyzed, the accuracy, false negative rate and false positive rate of historical early warning are calculated, and the early warning judgment standard data is obtained. The early warning judgment standard data is allocated by weight, the corresponding weight coefficient is set according to the importance and reliability of different early warning indexes, and the early warning parameter correction data is obtained. The regional risk distribution data is verified by historical accuracy, and the verification adopts the following formula:

[0090]

[0091] wherein, C r represents the risk assessment correction coefficient, λ j is the weight coefficient of the j th risk, P j and N j respectively represent the correct early warning rate and correct negative rate of the j th risk, A j is the prior probability of risk occurrence, and m is the number of risk types. The risk assessment correction coefficient is assigned by level, and the early warning level judgment data is obtained. The early warning level judgment data is analyzed by timeliness, the effective action time of early warning information is studied, and the early warning time window data is obtained. The early warning time window data is sorted by early warning priority, the priority is determined by risk level, influence range and emergency degree, and the hierarchical early warning sequence data is obtained. Finally, the hierarchical early warning sequence data and the early warning parameter correction data are comprehensively processed to form the final early warning information pushing scheme.

[0092] Taking a river embankment monitoring as an example, the embankment is 2000 meters long and 40 monitoring points are arranged. In a monitoring, the monitoring point numbered B15 detects that the leakage risk warning threshold exceeds the standard. Through time series analysis, it is found that the warning threshold of this point shows a continuous rising trend in the past 6 hours, and the segmented data shows that the threshold rising rate is 0.05 / hour in the 0-2 hour segment, 0.08 / hour in the 2-4 hour segment, and 0.12 / hour in the 4-6 hour segment.

[0093] When calculating the initial diffusion rate, five surrounding monitoring points are selected for analysis, and the weight coefficients are set as 0.3, 0.25, 0.2, 0.15 and 0.1 respectively. The spatial decay coefficient is uniformly set as 0.1, and the distances from each point to the risk source are 10, 15, 20, 25 and 30 meters respectively. The calculated initial diffusion rate is 0.085 / hour, indicating that the leakage influence range is gradually expanding. Spatial distribution analysis shows that the leakage influence range has spread about 50 meters from the point source to the surrounding in 6 hours. According to the diffusion rate, the area is divided into a large risk zone (inner circle 20 meters) and a medium risk zone (outer circle 30 meters). Historical warning record statistics show that the warning accuracy of this area is 85%, the false negative rate is 8%, and the false positive rate is 7%. Based on these data, the warning parameter weight is set as follows: permeation pressure accounts for 0.4, water level change accounts for 0.3, and permeation rate accounts for 0.3.

[0094] The historical accuracy verification shows that the risk assessment correction coefficient of the large risk zone is 1.2, and that of the medium risk zone is 0.9. The timeliness analysis determines that the warning time window is 12 hours, and the monitoring data needs to be updated every hour during this period. According to the risk level and diffusion trend, the priority of this warning event is set to level 2 (total 4 levels), which requires the warning information to be pushed within 30 minutes, and the tracking monitoring frequency to be maintained at 4 hours.

[0095] In a specific embodiment, the process of performing step S106 can specifically include the following steps:

[0096] (1) Comparing the warning information pushing scheme and the real-time embankment monitoring data stream in time series to obtain warning response time difference data, and grouping and counting the warning response time difference data to obtain monitoring response feature data;

[0097] (2) Conducting monitoring point sensitivity analysis on the monitoring response feature data to obtain monitoring point weight distribution data, and sampling frequency matching the monitoring point weight distribution data to obtain monitoring frequency adjustment parameters;

[0098] (3) The monitoring frequency adjustment parameters are subjected to monitoring cost constraint analysis to obtain monitoring resource allocation data, and the monitoring resource allocation data are subjected to monitoring point priority ranking to obtain monitoring point importance data;

[0099] (4) The monitoring point importance data are subjected to monitoring accuracy evaluation to obtain parameter adjustment coefficient data, and the parameter adjustment coefficient data are subjected to regional parameter compensation to obtain monitoring compensation parameter data;

[0100] (5) The monitoring compensation parameter data are subjected to monitoring point working state judgment to obtain monitoring state evaluation data, and the monitoring state evaluation data are subjected to monitoring point reliability analysis to obtain monitoring quality index data;

[0101] (6) The monitoring quality index data and monitoring response characteristic data are subjected to comprehensive analysis to obtain leakage monitoring control parameters.

[0102] Specifically, the early warning information pushing scheme and the real-time embankment monitoring data stream are subjected to time sequence comparison, the time difference between the early warning information sending time and the monitoring data abnormal occurrence time is analyzed, and early warning response time difference data are obtained. The time difference includes three parts of early warning information generation delay, transmission delay and response delay. The early warning response time difference data are subjected to grouping statistics, the monitoring points are divided into fast response group, medium response group and lag response group according to the length of time difference, the statistical characteristics of each group are calculated, including average response time, standard deviation and coefficient of variation, and monitoring response characteristic data are obtained. The monitoring response characteristic data are subjected to monitoring point sensitivity analysis to evaluate the response sensitivity of different monitoring points to leakage events. The sensitivity analysis considers three aspects of response time, data change amplitude and stability of the monitoring point, calculates the sensitivity index of each monitoring point, and obtains monitoring point weight distribution data. The monitoring point weight distribution data are subjected to sampling frequency matching, and the corresponding sampling frequency is set according to the sensitivity of the monitoring point. The points with high sensitivity adopt higher sampling frequency, and the points with low sensitivity adopt lower sampling frequency, and monitoring frequency adjustment parameters are obtained. Then, the monitoring frequency adjustment parameters are subjected to monitoring cost constraint analysis, considering factors such as equipment energy consumption, data transmission cost and storage cost, calculating the resource consumption under different sampling frequencies, obtaining monitoring resource allocation data. The monitoring resource allocation data are subjected to monitoring point priority ranking, considering the importance, response characteristics and resource consumption of the monitoring points, determining the priority level of each monitoring point, and obtaining monitoring point importance data.

[0103] The monitoring point importance data is subjected to monitoring accuracy evaluation, the accuracy and reliability of the monitoring data are analyzed, the measurement error and data stability index are calculated, and parameter adjustment coefficient data is obtained. The parameter adjustment coefficient data is subjected to regional parameter compensation, the compensation coefficient is set according to the environmental characteristics and monitoring conditions of different regions, the monitoring parameters are corrected, and monitoring compensation parameter data is obtained. Then the monitoring compensation parameter data is subjected to monitoring point working state judgment, the running state and data quality of the monitoring equipment are analyzed, the working efficiency of the monitoring point is evaluated, and monitoring state evaluation data is obtained. The monitoring state evaluation data is subjected to monitoring point reliability analysis, the data effective rate and stability index of the monitoring point are calculated, and monitoring quality index data is obtained. Finally, the monitoring quality index data and monitoring response characteristic data are subjected to comprehensive analysis, and complete leakage monitoring control parameters are formed.

[0104] Taking the monitoring of a river embankment as an example, 25 monitoring points are arranged on the embankment section. Through time series comparative analysis, it is found that the average early warning response time difference of monitoring point C08 is 15 minutes, the coefficient of variation is 0.2, and it belongs to the fast response group; the average response time difference of monitoring point C12 is 35 minutes, the coefficient of variation is 0.3, and it belongs to the medium response group; the average response time difference of monitoring point C15 is 60 minutes, the coefficient of variation is 0.4, and it belongs to the lag response group. Sensitivity analysis shows that the response speed of monitoring point C08 to water level change is fast, the data change amplitude is obvious, and the sensitivity index is calculated to be 0.85, and the sampling frequency is set to be once every 5 minutes. The sensitivity index of monitoring point C12 is 0.65, and the sampling frequency is set to be once every 15 minutes. The sensitivity index of monitoring point C15 is 0.45, and the sampling frequency is set to be once every 30 minutes.

[0105] The monitoring cost constraint analysis shows that the upper limit of the daily data transmission volume of each monitoring point is 1000, and the storage capacity limit is 10MB. According to the set sampling frequency, monitoring point C08 generates 288 data per day, occupying 2.5MB of storage space; C12 generates 96 data, occupying 0.8MB of storage space; C15 generates 48 data, occupying 0.4MB of storage space, all within the resource constraint range. The monitoring accuracy evaluation results show that the measurement error of C08 is within ±2%, and the data stability index is 0.92; the measurement error of C12 is within ±3%, and the stability index is 0.88; the measurement error of C15 is within ±4%, and the stability index is 0.85. According to these indexes, the sampling parameters are compensated: the compensation coefficient of C08 is 1.02, that of C12 is 1.05, and that of C15 is 1.08.

[0106] The working state judgment result shows that the data efficiency of C08 reaches 98%, and the equipment runs stably; the data efficiency of C12 is 95%, and there are occasional data missing; the data efficiency of C15 is 92%, and there is a certain degree of data fluctuation. Based on the above analysis, the final monitoring control parameters are determined: C08 adopts a high-frequency monitoring strategy, and real-time data transmission; C12 adopts a medium-frequency monitoring strategy, and data is summarized once an hour; C15 adopts a low-frequency monitoring strategy, and data is summarized once every two hours. This differentiated monitoring strategy not only ensures the monitoring accuracy of key points, but also optimizes the allocation of monitoring resources.

[0107] The above describes the river embankment leakage monitoring and early warning method based on GIS technology in the embodiments of the application. The following describes the river embankment leakage monitoring and early warning system based on GIS technology in the embodiments of the application. Please refer to Figure 2 An embodiment of the river embankment leakage monitoring and early warning system based on GIS technology in the embodiments of the application includes:

[0108] A hierarchical module is configured to perform hierarchical partition processing and historical leakage location weight calculation on three-dimensional terrain data of the river embankment, and obtain a digital embankment basic data set.

[0109] An association module is configured to perform differential frequency sampling and adjacent point association analysis processing on the monitoring points in the digital embankment basic data set, and obtain a real-time embankment monitoring data stream.

[0110] A tracking module is configured to perform seepage pressure gradient field construction and seepage path tracking processing on the real-time embankment monitoring data stream, and obtain embankment leakage characteristic data.

[0111] A correction module is configured to perform combined analysis processing and seasonal change correction of water level difference, pressure gradient, and seepage rate on the embankment leakage characteristic data, and obtain leakage risk early warning threshold data.

[0112] A correction module is configured to perform combined analysis processing and seasonal change correction of water level difference, pressure gradient, and seepage rate on the embankment leakage characteristic data, and obtain leakage risk early warning threshold data.

[0113] An optimization module is configured to perform association analysis and monitoring parameter optimization processing on the early warning information push scheme and the real-time embankment monitoring data stream, and obtain leakage monitoring control parameters.

[0114] Through the cooperation of the above-mentioned components, through the hierarchical partitioning processing of the geographic space data and the engineering geological data of the river embankment and the historical leakage position weight calculation, the scientific layout of the embankment monitoring point and the accurate identification of the risk area are realized, and the problem of many blind areas in the traditional monitoring method is effectively solved, and through the differential frequency sampling of the monitoring points in the digital embankment basic data set and the adjacent point correlation analysis processing, an adaptive data acquisition mechanism is established, and the pertinence and efficiency of data acquisition are improved. In the scheme, the real-time embankment monitoring data stream is subjected to the construction of the seepage pressure gradient field and the seepage path tracking processing, so that the identification of the leakage path is more accurate, and reliable data support is provided for early warning. Through the combination analysis processing and seasonal change correction of the water level difference, pressure gradient and seepage rate of the embankment leakage characteristic data, multi-dimensional leakage risk assessment is realized, and the accuracy of early warning is greatly improved. The leakage diffusion rate evaluation and historical accuracy correction processing are performed on the leakage risk early warning threshold data, a dynamic early warning threshold system is established, and the false alarm and missed alarm rates are effectively reduced. Finally, through the correlation analysis and monitoring parameter optimization processing of the early warning information pushing scheme and the real-time embankment monitoring data stream, the adaptive adjustment of the monitoring parameters is realized, and the operation efficiency and reliability of the whole monitoring and early warning system are improved. The method combines GIS technology with traditional embankment monitoring technology to build a complete embankment leakage monitoring and early warning system, which not only improves the spatial coverage range and time continuity of monitoring, but also enhances the timeliness and accuracy of early warning.

[0115] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, system and unit can refer to the corresponding processes in the foregoing method embodiments, which will not be described herein.

[0116] The integrated unit, if realized in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiments of the present application. The foregoing storage medium includes a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0117] The above-described embodiments are only used to illustrate the technical solutions of the present application, and not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for part of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A GIS technology-based river embankment leakage monitoring and early warning method, characterized in that, The river embankment leakage monitoring and early warning method based on the GIS technology comprises: The three-dimensional terrain data of the river embankment is subjected to layered partition processing and historical leakage position weight calculation to obtain a digital embankment basic data set; The monitoring points in the digital embankment basic data set are subjected to differential frequency sampling and adjacent point correlation analysis processing to obtain a real-time embankment monitoring data stream; The real-time embankment monitoring data stream is subjected to seepage pressure gradient field construction and seepage path tracking processing to obtain embankment leakage characteristic data; The embankment leakage characteristic data is subjected to combined analysis processing of water level difference, pressure gradient and seepage rate and seasonal change correction to obtain leakage risk early warning threshold data; The leakage risk early warning threshold data is subjected to leakage diffusion rate evaluation and historical accuracy correction processing to obtain an early warning information pushing scheme; The early warning information pushing scheme and the real-time embankment monitoring data stream are subjected to correlation analysis and monitoring parameter optimization processing to obtain leakage monitoring control parameters; The monitoring points in the digital embankment basic data set are subjected to differential frequency sampling and adjacent point correlation analysis processing to obtain a real-time embankment monitoring data stream, comprising: The monitoring points in the digital embankment basic data set are subjected to initial parameter setting of sampling frequency to obtain baseline sampling frequency data, and the baseline sampling frequency data is subjected to historical data change rate analysis to obtain a dynamic frequency adjustment coefficient; The dynamic frequency adjustment coefficient is subjected to monitoring point importance classification processing to obtain differential sampling parameters, and the differential sampling parameters are subjected to time window division to obtain a time period sampling scheme; Adjacent monitoring point data are subjected to sliding window processing to obtain short-time data change trend, and the short-time data change trend is subjected to sudden change detection to obtain abnormal fluctuation marker data; The abnormal fluctuation marker data are subjected to spatial correlation analysis to obtain monitoring point group division data, and the monitoring point group division data are subjected to data synchronous acquisition to obtain multi-point cooperative monitoring data; The multi-point cooperative monitoring data are subjected to data quality inspection to obtain effective data identification, and the effective data identification is subjected to time series reconstruction to obtain a continuous monitoring sequence; The continuous monitoring sequence and the time period sampling scheme are subjected to comprehensive processing to obtain a real-time embankment monitoring data stream; The early warning information pushing scheme and the real-time embankment monitoring data stream are subjected to correlation analysis and monitoring parameter optimization processing to obtain leakage monitoring control parameters, comprising: The early warning information pushing scheme and the real-time embankment monitoring data stream are subjected to time series comparison to obtain early warning response time difference data, and the early warning response time difference data are subjected to grouping statistics to obtain monitoring response characteristic data; The monitoring response characteristic data are subjected to monitoring point sensitivity analysis to obtain monitoring point weight distribution data, and the monitoring point weight distribution data are subjected to sampling frequency matching to obtain monitoring frequency adjustment parameters; The monitoring frequency adjustment parameters are subjected to monitoring cost constraint analysis to obtain monitoring resource allocation data, and the monitoring resource allocation data are subjected to monitoring point priority sorting to obtain monitoring point importance data; The monitoring point importance data is subjected to monitoring precision evaluation to obtain parameter adjustment coefficient data, and the parameter adjustment coefficient data is subjected to regional parameter compensation to obtain monitoring compensation parameter data; The monitoring compensation parameter data is subjected to monitoring point working state judgment to obtain monitoring state evaluation data, and the monitoring state evaluation data is subjected to monitoring point reliability analysis to obtain monitoring quality index data; The monitoring quality index data and the monitoring response characteristic data are subjected to comprehensive analysis to obtain leakage monitoring control parameters. 2.The GIS technology-based river embankment leakage monitoring and early warning method according to claim 1, characterized in that, The three-dimensional terrain data of the river embankment is subjected to hierarchical and zoned processing and historical leakage position weight calculation to obtain a digital embankment foundation data set, including: The three-dimensional terrain data of the river embankment is subjected to hierarchical processing to obtain embankment structure level data, and the embankment structure level data is subjected to regional grid division to obtain basic grid unit data; The basic grid unit data is subjected to geological property correlation analysis to obtain geological feature zoning data, and the geological feature zoning data is subjected to spatial distribution processing through Kriging interpolation to obtain geological spatial distribution data; The historical leakage event records are subjected to spatio-temporal clustering analysis to obtain historical leakage hotspot area data, and the historical leakage hotspot area data is subjected to density calculation to obtain leakage risk weight coefficients; The geological spatial distribution data and the leakage risk weight coefficients are subjected to superimposed analysis to obtain key monitoring area division data, and the key monitoring area division data is subjected to monitoring point layout calculation to obtain monitoring point position coordinate data; The monitoring point position coordinate data is subjected to monitoring equipment type matching to obtain a monitoring equipment configuration scheme, and the monitoring equipment configuration scheme is subjected to sampling frequency initialization setting to obtain monitoring point sampling parameters; The monitoring point sampling parameters and the key monitoring area division data are subjected to comprehensive processing to obtain a digital embankment foundation data set. 3.The GIS technology-based river embankment leakage monitoring and early warning method according to claim 1, characterized in that, The real-time embankment monitoring data stream is subjected to seepage pressure gradient field construction and seepage path tracking processing to obtain embankment leakage characteristic data, including: The real-time embankment monitoring data stream is subjected to spatial interpolation calculation to obtain continuous pressure field data, and the continuous pressure field data is subjected to equipotential line extraction to obtain pressure contour data; The pressure contour data is subjected to gradient calculation to obtain pressure gradient vector data, and the pressure gradient vector data is subjected to flow direction analysis to obtain seepage flow field distribution data; The seepage flow field distribution data is subjected to numerical solution to obtain flow velocity component data, and the flow velocity component data is subjected to streamline tracking to obtain seepage path data; The seepage path data is subjected to convergence point identification to obtain potential leakage point position data, and the potential leakage point position data is subjected to intensity evaluation to obtain leakage intensity distribution data; The leakage intensity distribution data is subjected to propagation path analysis to obtain leakage diffusion law data, and the leakage diffusion law data is subjected to characteristic parameter extraction to obtain leakage characteristic index data; The leakage characteristic index data and the seepage path data are subjected to comprehensive analysis to obtain embankment leakage characteristic data. 4.The GIS technology-based river embankment leakage monitoring and early warning method according to claim 1, characterized in that, The combined analysis and seasonal change correction of the dike leakage characteristic data are performed to obtain leakage risk early warning threshold data, including: Adjacent point difference calculation is performed on the water level data in the dike leakage characteristic data to obtain water level gradient distribution data, and abnormal point screening is performed on the water level gradient distribution data to obtain water level mutation point data; Time series decomposition is performed on the pressure gradient data in the dike leakage characteristic data to obtain pressure change trend data, and critical value judgment is performed on the pressure change trend data to obtain pressure abnormal area data; Partition statistics is performed on the permeation rate data in the dike leakage characteristic data to obtain rate distribution characteristic data, and threshold classification is performed on the rate distribution characteristic data to obtain permeation risk level data; Cross-validation is performed on the water level mutation point data and the pressure abnormal area data to obtain initial early warning criterion data, and the initial early warning criterion data is associated with the permeation risk level data to obtain combined early warning index data; History seasonal fluctuation analysis is performed on the combined early warning index data to obtain seasonal change characteristic data, and correction coefficient calculation is performed on the seasonal change characteristic data to obtain seasonal correction factor; Weighted superposition analysis is performed on the combined early warning index data and the seasonal correction factor to obtain leakage risk early warning threshold data. 5.The GIS technology based river embankment leakage monitoring and early warning method according to claim 1, characterized in that, The leakage risk early warning threshold data is subjected to leakage diffusion rate evaluation and historical accuracy correction processing to obtain an early warning information pushing scheme, including: Time series segmentation is performed on the leakage risk early warning threshold data to obtain threshold change interval data, and diffusion rate calculation is performed on the threshold change interval data to obtain initial diffusion rate data; Spatial distribution analysis is performed on the initial diffusion rate data to obtain diffusion range prediction data, and risk level division is performed on the diffusion range prediction data to obtain regional risk distribution data; Accuracy statistics are performed on historical early warning records to obtain early warning judgment standard data, and weight distribution is performed on the early warning judgment standard data to obtain early warning parameter correction data; The historical accuracy of the regional risk distribution data is verified to obtain a risk assessment correction coefficient, and the risk assessment correction coefficient is assigned a grade to obtain early warning level determination data; Time effectiveness analysis is performed on the early warning level determination data to obtain early warning time window data, and early warning priority sorting is performed on the early warning time window data to obtain hierarchical early warning sequence data; The hierarchical early warning sequence data and the early warning parameter correction data are subjected to comprehensive processing to obtain an early warning information pushing scheme.

6. A GIS technology-based river embankment leakage monitoring and early warning system for implementing the GIS technology-based river embankment leakage monitoring and early warning method according to any one of claims 1-5, characterized in that, The river dike leakage monitoring and early warning system based on GIS technology comprises: A hierarchical module for hierarchical partitioning and historical leakage location weight calculation of three-dimensional terrain data of a river dike to obtain a digital dike foundation data set; An association module for differential frequency sampling and adjacent point association analysis of monitoring points in the digital dike foundation data set to obtain real-time dike monitoring data stream; A tracking module is configured to perform osmotic pressure gradient field construction and osmotic path tracking processing on the real-time embankment monitoring data stream to obtain embankment leakage characteristic data; A correction module is configured to perform combined analysis processing and seasonal change correction on water level difference, pressure gradient and seepage rate of the embankment leakage characteristic data to obtain leakage risk early warning threshold data; A correction module is configured to perform leakage diffusion rate evaluation and historical accuracy correction processing on the leakage risk early warning threshold data to obtain early warning information pushing scheme; An optimization module is configured to perform correlation analysis and monitoring parameter optimization processing on the early warning information pushing scheme and the real-time embankment monitoring data stream to obtain leakage monitoring control parameters.

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