River channel dike leakage monitoring and early warning method and system based on GIS (Geographic Information System) technology

Abstract

The invention 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 a GIS technology. The method comprises the following steps: carrying out differential frequency sampling and adjacent point correlation analysis processing on monitoring points in a digital dike basic data set to obtain a real-time dike monitoring data flow, and carrying out seepage pressure gradient field construction and seepage path tracking processing; carrying out combined analysis processing and seasonal change correction on the water level difference, the pressure gradient and the permeation rate 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 push scheme; and performing correlation analysis and monitoring parameter optimization processing on the early warning information pushing scheme and the real-time dike monitoring data flow to obtain leakage monitoring control parameters. According to the invention, accurate monitoring and timely early warning of dike leakage are realized.

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 Art

[0002] River embankments are important infrastructure for flood control projects, and their safety is directly related to the flood control safety of the river basin and the safety of people's lives and property. Traditional river embankment leakage monitoring mainly relies on manual inspections and single-point sensor monitoring. Some areas have begun to use GIS technology for embankment safety management. These monitoring methods collect embankment leakage-related parameters by deploying sensing equipment such as water level gauges and osmometers, and evaluate the safety status of embankments based on the experience of engineering personnel. At the same time, some areas have also established preliminary early warning systems to alarm abnormal situations by setting fixed thresholds.

[0003] However, the existing river embankment leakage monitoring technology has the following shortcomings: the layout of monitoring equipment lacks scientific basis, and often adopts a uniform layout or empirical layout method, failing to fully consider the structural characteristics of the embankment and historical leakage conditions; the monitoring data collection frequency is fixed and cannot be dynamically adjusted according to actual conditions, resulting in the possibility of missing important data during critical periods; the warning threshold setting is too simple, and does not consider the impact of seasonal changes and historical data, which can easily lead to false alarms and missed reports; there is a lack of correlation analysis between the monitoring points, and it is impossible to effectively identify the development trend of leakage and potential risks. Summary of the invention

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

[0005] In the first aspect, the present application provides a river embankment leakage monitoring and early warning method based on GIS technology, and the river embankment leakage monitoring and early warning method based on GIS technology includes: performing layered and partitioning processing on the three-dimensional terrain data of the river embankment and calculating the weights of historical leakage positions to obtain a digital embankment basic data set; performing differentiated frequency sampling and adjacent point correlation analysis processing on the 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 of water level difference, pressure gradient and seepage rate and seasonal variation correction 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 push scheme; performing correlation analysis and monitoring parameter optimization processing on the early warning information push scheme and the real-time embankment monitoring data stream to obtain leakage monitoring control parameters.

[0006] In a second aspect, the present application provides a river embankment leakage monitoring and early warning system based on GIS technology, and the river embankment leakage monitoring and early warning system based on GIS technology includes:

[0007] The stratification module is used to perform stratification and partition processing on the three-dimensional terrain data of the river embankment and calculate the weight of the historical leakage position to obtain the basic data set of the digital embankment;

[0008] An association module is used to perform differential frequency sampling and adjacent point association analysis on the monitoring points in the digital levee basic data set to obtain a real-time levee monitoring data stream;

[0009] A tracking module, used for constructing a seepage pressure gradient field and tracking a seepage path for the real-time levee monitoring data stream to obtain levee leakage characteristic data;

[0010] A correction module is used to perform combined analysis and processing of water level difference, pressure gradient and infiltration rate and seasonal variation correction on the dike leakage characteristic data to obtain leakage risk warning threshold data;

[0011] A correction module, used to perform leakage diffusion rate assessment and historical accuracy correction processing on the leakage risk warning threshold data to obtain a warning information push plan;

[0012] The optimization module is used to perform correlation analysis and monitoring parameter optimization processing on the early warning information push scheme and the real-time levee monitoring data stream to obtain leakage monitoring control parameters.

[0013] In the technical solution provided by this application, by layering and partitioning the geographic spatial data and engineering geological data of the river embankment and calculating the weight of the historical leakage position, the scientific layout of the embankment monitoring points and the accurate identification of the risk area are realized, which effectively solves the problem of many monitoring blind spots in the traditional monitoring method. At the same time, by performing differentiated frequency sampling and adjacent point correlation analysis processing on the monitoring points in the digital embankment basic data set, an adaptive data collection mechanism is established to improve the pertinence and efficiency of data collection. In the scheme, the seepage pressure gradient field is constructed and the seepage path tracking processing is performed on the real-time embankment monitoring data stream, so that the identification of the leakage path is more accurate, providing reliable data support for early warning. By combining the analysis and processing of water level difference, pressure gradient and seepage rate and seasonal change correction of the embankment leakage characteristic data, a multi-dimensional leakage risk assessment is achieved, which greatly improves the accuracy of the early warning. The leakage risk early warning threshold data is evaluated for leakage diffusion rate and corrected for historical accuracy, and a dynamic early warning threshold system is established, which effectively reduces the false alarm and missed alarm rates. Finally, by analyzing the correlation between the early warning information push scheme and the real-time levee monitoring data stream and optimizing the monitoring parameters, the adaptive adjustment of the monitoring parameters was achieved, which improved the operating efficiency and reliability of the entire monitoring and early warning system. This method combines GIS technology with traditional levee monitoring technology to build a complete levee leakage monitoring and early warning system, which not only improves the spatial coverage and temporal continuity of monitoring, but also enhances the timeliness and accuracy of early warning. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

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

[0016] Figure 2 This is a schematic diagram of an embodiment of a river embankment leakage monitoring and early warning system based on GIS technology in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The embodiment of the present application provides a method and system for monitoring and early warning of river embankment leakage based on GIS technology. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are 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. Figure 1 In the embodiment of the present application, an embodiment of the river embankment leakage monitoring and early warning method based on GIS technology includes:

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

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

[0021] Step S103, constructing a seepage pressure gradient field and performing seepage path tracking processing on the real-time levee monitoring data stream to obtain levee leakage characteristic data;

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

[0023] Step S105, performing leakage diffusion rate assessment and historical accuracy correction processing on the leakage risk warning threshold data to obtain a warning information push plan;

[0024] Step S106: perform correlation analysis and monitoring parameter optimization processing on the early warning information push scheme and the real-time levee monitoring data stream to obtain leakage monitoring control parameters.

[0025] It is understandable that the execution subject of the present application can be a river embankment leakage monitoring and early warning system based on GIS technology, or a terminal or a server, which is not limited here. The present application embodiment is described by taking the server as the execution subject as an example.

[0026] Specifically, the three-dimensional terrain data is measured to obtain spatial information such as the elevation, slope, and direction of the levee. When the stratified and zoned processing is performed, the levee is divided into the top layer, the body layer, and the base layer according to the structural characteristics. Each layer is further divided into different areas according to the engineering geological conditions. At the same time, combined with the historical leakage event records, the leakage risk weight of each area is calculated to form a digital levee basic data set. For example, a section of the levee is 1,000 meters long. The elevation of the levee top is 15 meters and the width of the levee base is 40 meters. A basic grid unit is divided every 20 meters, and a total of 50 basic units are divided. Among them, 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 levee basic data set, the monitoring points are sampled with differential frequencies. Based on the risk weight value of each monitoring point, different sampling frequencies are set. The sampling frequency of points with high risk weight is high, and the sampling frequency of points with low risk weight is low. The data of adjacent monitoring points are analyzed for correlation. When the data of a certain monitoring point fluctuates abnormally, the sampling frequency of the surrounding monitoring points is automatically increased. Through this dynamically adjusted sampling strategy, a real-time levee monitoring data stream is formed. Taking units 25 and 26 mentioned above as an example, due to the high risk weight, the sampling frequency of the monitoring points at these two locations is set to once every 5 minutes, while the sampling frequency of monitoring points at other locations is once every 30 minutes. When a sudden change in water level is detected at monitoring point 25, the sampling frequency of monitoring points 24 and 27 is automatically increased to once every 5 minutes.

[0027] The seepage pressure gradient field is constructed for the acquired real-time levee monitoring data stream. First, the pressure data of each monitoring point is spatially interpolated 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 potential leakage channels are identified by calculating the flow velocity vector and streamline. For example, at unit 25, the seepage pressure of monitoring point A is 50kPa, and the seepage pressure of the adjacent monitoring point B is 30kPa. The distance between the two points is 10 meters. The pressure gradient is calculated to be 2kPa / m. According to this gradient value, the movement direction and speed of the seepage water flow can be predicted. Then, the levee leakage characteristic data is analyzed in multiple dimensions, including the combined analysis of water level difference, pressure gradient and seepage rate. The water level difference reflects the change of water level inside and outside the levee, the pressure gradient represents the magnitude and direction of the seepage force, and the seepage rate represents the speed of water flow. These three parameters are combined by weight to obtain a comprehensive evaluation index. At the same time, the influence of seasonal factors on these parameters is considered. For example, the same monitoring data may have different risk meanings in flood season and dry season. Through these analyses and corrections, leakage risk warning threshold data is obtained.

[0028] Then, the leakage risk warning threshold data is evaluated for leakage diffusion rate. According to the spatial distribution and development trend of the leakage point, the expansion speed of the leakage impact range is predicted. At the same time, combined with the historical warning records, the warning accuracy is statistically analyzed to optimize the warning parameters and avoid false alarms and missed alarms. For example, when the seepage pressure of a monitoring point exceeds the warning threshold, the system will calculate the leakage diffusion rate. If it is found that the leakage range is expanding at a rate of 0.5 meters per hour, and the historical data shows that the warning accuracy rate in similar situations reaches 90%, then the corresponding level of warning information is generated. Finally, the warning information push scheme is associated with the real-time monitoring data stream to evaluate the warning effect and dynamically optimize the monitoring parameters. By analyzing the relationship between the time of warning information issuance and the time of actual leakage event occurrence, the warning timeliness is calculated, and the sampling frequency and warning threshold of the monitoring point are adjusted accordingly. For example, if it is found that the warning information of a certain monitoring point predicts the occurrence of a leakage event 2 hours in advance on average, the sampling frequency of the point can be appropriately reduced to save monitoring resources; if the warning of a certain monitoring point often lags behind the event, the sampling frequency needs to be increased or the warning threshold needs to be adjusted. Through this continuous optimization process, the accuracy of monitoring and warning is continuously improved.

[0029] In the embodiment of the present application, by layering and partitioning the geographic spatial data and engineering geological data of the river embankment and calculating the weight of the historical leakage position, the scientific layout of the embankment monitoring points and the accurate identification of the risk area are realized, which effectively solves the problem of many monitoring blind spots in the traditional monitoring method. At the same time, by performing differentiated frequency sampling and adjacent point correlation analysis processing on the monitoring points in the digital embankment basic data set, an adaptive data collection mechanism is established to improve the pertinence and efficiency of data collection. In the scheme, the seepage pressure gradient field is constructed and the seepage path tracking processing is performed on the real-time embankment monitoring data stream, so that the identification of the leakage path is more accurate, providing reliable data support for early warning. By performing combined analysis and processing of water level difference, pressure gradient and seepage rate and seasonal change correction on the embankment leakage characteristic data, a multi-dimensional leakage risk assessment is achieved, which greatly improves the accuracy of the early warning. The leakage risk early warning threshold data is evaluated for leakage diffusion rate and corrected for historical accuracy, and a dynamic early warning threshold system is established, which effectively reduces the false alarm and missed alarm rates. Finally, by analyzing the correlation between the early warning information push scheme and the real-time levee monitoring data stream and optimizing the monitoring parameters, the adaptive adjustment of the monitoring parameters was achieved, which improved the operating efficiency and reliability of the entire monitoring and early warning system. This method combines GIS technology with traditional levee monitoring technology to build a complete levee leakage monitoring and early warning system, which not only improves the spatial coverage and temporal continuity of monitoring, but also enhances the timeliness and accuracy of early warning.

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

[0031] (1) The three-dimensional terrain data of the river embankment is processed in layers to obtain the embankment structure level data, and the embankment structure level data is divided into regional grids to obtain basic grid unit data;

[0032] (2) Performing geological attribute correlation analysis on the basic grid unit data to obtain geological feature partition data, and performing spatial distribution processing on the geological feature partition data through Kriging interpolation to obtain geological spatial distribution data;

[0033] (3) Perform spatiotemporal cluster analysis on historical leakage event records to obtain historical leakage hotspot area data, and perform density calculation on historical leakage hotspot area data to obtain leakage risk weight coefficients;

[0034] (4) Performing superposition analysis on the geological spatial distribution data and the leakage risk weight coefficient to obtain the key monitoring area division data, and performing monitoring point layout calculation on the key monitoring area division data to obtain the monitoring point location coordinate data;

[0035] (5) Matching the monitoring equipment type with the monitoring point location coordinate data to obtain a monitoring equipment configuration plan, and initializing the sampling frequency of the monitoring equipment configuration plan to obtain the monitoring point sampling parameters;

[0036] (6) Comprehensively process the sampling parameters of monitoring points and the division data of key monitoring areas 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 contains the elevation data, slope data and cross-sectional shape data of the embankment, where the elevation data records the elevation value of each point of the embankment, the slope data records the degree of inclination of the embankment surface, and the cross-sectional shape data records the geometric characteristics of the cross section of the embankment. The layered processing divides the embankment structure from top to bottom into three main layers: the embankment top layer, the embankment body layer and the embankment base layer. Each layer has different engineering characteristics and anti-seepage requirements. After obtaining the embankment structure layer data, each layer 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×5m grid, the embankment body layer uses a 10m×10m grid, and the embankment base layer uses a 20m×20m grid to form the basic grid unit data. When performing geological attribute correlation analysis on the basic grid unit 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 moisture content in the soil. By analyzing the spatial distribution of these parameters, grid cells with similar geological characteristics are grouped together to form geological feature partition data. Then, the Kriging interpolation algorithm is used to process the spatial distribution of geological feature partition data. Kriging interpolation is an optimal linear unbiased estimation method based on the theory of variogram. It estimates the value of unknown points by analyzing the spatial correlation of known points, thereby obtaining continuous geological spatial distribution data.

[0038] A spatiotemporal clustering analysis was conducted on the historical leakage event records. By counting the spatial location and occurrence time of the historical leakage points, the spatiotemporal distribution of the leakage events was identified. The spatiotemporal clustering adopted a density-based clustering algorithm to cluster leakage events with similar spatial locations and occurrence times into one category, and obtain the historical leakage hotspot area data. The density of these hotspot area data was calculated to calculate the number of leakage events per unit area in each area, and combined with the severity of the leakage events, the leakage risk weight coefficient reflecting the regional leakage risk level was obtained. The geological spatial distribution data and the leakage risk weight coefficient were superimposed and analyzed, and the impact of geological conditions and historical leakage on the safety of the levee was comprehensively considered. The superposition analysis adopted a weighted superposition method to assign different weights to different factors, and the comprehensive risk score was calculated. Based on this, the key monitoring areas were divided to form the key monitoring area division data. On this basis, according to the importance, area size and terrain characteristics of the monitoring area, the optimal layout of the monitoring points was calculated to obtain the location coordinate data of the monitoring points.

[0039] According to the location coordinate data of the monitoring point and the monitoring requirements, select the appropriate type of monitoring equipment. Monitoring equipment includes water level gauges, osmometers, displacement meters, etc. The water level gauge is used to measure the water level difference inside and outside the levee, the osmometer is used to measure the seepage pressure inside the levee, and the displacement meter is used to monitor the deformation of the levee. Through the matching of equipment types, a monitoring equipment configuration plan is formed. The sampling frequency of the monitoring equipment configuration plan is initialized, and the sampling time interval is determined based on the risk level of the monitoring area. A higher sampling frequency is used in high-risk areas and a lower sampling frequency is used in low-risk areas to obtain the sampling parameters of the monitoring points. Finally, the sampling parameters of the monitoring points and the division data of the key monitoring areas are comprehensively processed, and the deployment information, sampling parameters and regional characteristics of the monitoring equipment are associated to form a complete digital levee basic data set.

[0040] Taking a 2000-meter-long levee as an example, through the analysis of three-dimensional terrain data, the average levee height is 12 meters, the levee top width is 8 meters, and the levee base width is 45 meters. After being divided into three levels according to the stratification requirements, the top layer of the levee is divided into 400 5m×5m grid units, the levee body layer is divided into 200 10m×10m grid units, and the levee base is divided into 100 20m×20m grid units. Through geological surveys, it was found that this section of the levee is mainly composed of clay and sand, of which the clay layer has a permeability coefficient of 10^-7 cm / s and the sand layer has a permeability coefficient of 10^-4 cm / s. Historical records show that there have been three leakage events in the 500-700-meter section. After spatiotemporal clustering analysis, it was determined that this is a high-incidence area of ​​leakage. 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 results of the superposition analysis, a monitoring point is set up every 50 meters in the high-risk area, equipped with a water level meter and a piezometer, and the sampling frequency is set to once an hour. In other areas, a monitoring point is set up every 100 meters, and the sampling frequency is 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 executing step S102 may specifically include the following steps:

[0042] (1) Set the initial sampling frequency parameters for the monitoring points in the digital levee basic data set to obtain the benchmark sampling frequency data, and perform historical data change rate analysis on the benchmark sampling frequency data to obtain the dynamic frequency adjustment coefficient;

[0043] (2) The dynamic frequency adjustment coefficient is graded according to the importance of the monitoring points to obtain differentiated sampling parameters, and the differentiated sampling parameters are divided into time windows to obtain a time-divided sampling plan;

[0044] (3) Perform sliding window processing on the data of adjacent monitoring points to obtain the short-term data change trend, and perform mutation detection on the short-term data change trend to obtain abnormal fluctuation mark data;

[0045] (4) Performing spatial correlation analysis on the abnormal fluctuation mark data to obtain monitoring point group division data, and synchronously collecting data on the monitoring point group division data to obtain multi-point collaborative monitoring data;

[0046] (5) Perform data quality inspection on multi-point collaborative monitoring data to obtain valid data identifiers, and reconstruct the time series of valid data identifiers to obtain a continuous monitoring sequence;

[0047] (6) Comprehensively process the continuous monitoring sequence and time-segment sampling scheme to obtain a real-time levee monitoring data stream.

[0048] Specifically, the initial parameters of the sampling frequency are set according to the risk level, geographical location and monitoring target of the monitoring points in the digital levee basic data set. The initial parameters of the sampling frequency include three key parameters: basic sampling interval, data collection time and data accuracy requirements. The benchmark sampling frequency data is obtained by calculation. Then, the historical data change rate of the benchmark sampling frequency data is analyzed, the change rate of the monitoring data in different time periods is calculated, the degree of data fluctuation 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 to dynamically adjust the benchmark sampling frequency. When the dynamic frequency adjustment coefficient is graded for the importance of monitoring points, it is necessary to consider the geographical location of the monitoring points, the law of historical data changes and the surrounding environmental factors. According to these factors, the monitoring points are graded, and high-level monitoring points use higher sampling frequencies, while low-level monitoring points use lower sampling frequencies, so as to obtain differentiated sampling parameters. Then, the differentiated sampling parameters are divided into time windows, and the 24 hours of a day are divided into several time periods. The corresponding sampling frequency is set for each time period to form a time-divided sampling scheme. The time-divided sampling scheme fully considers the changing characteristics of the levee leakage risk in different time periods.

[0049] Then, the data of adjacent monitoring points are processed by sliding windows. The size of the sliding window is set according to the data characteristics, and 5-10 data points are usually taken as a window. The statistical characteristics of the data are calculated in each window, including the mean, standard deviation and rate of change, so as to obtain the short-term data change trend. The short-term data change trend is subjected to mutation detection, and statistical test methods are used to determine whether there are significant changes in the data, and the data mutation points are marked to obtain abnormal fluctuation marked data. Subsequently, the abnormal fluctuation marked data is subjected to spatial correlation analysis, the data correlation coefficients between different monitoring points are calculated, and groups of monitoring points with similar data changes are identified. Monitoring points with strong correlation are divided into the same group to form monitoring point group division data. Data synchronization is performed on the monitoring point group division data to ensure that the monitoring points in the same group collect data at the same time to obtain multi-point collaborative monitoring data. The collaborative monitoring data reflects the spatial propagation characteristics of levee leakage.

[0050] Data quality inspection is performed on multi-point collaborative monitoring data, including data integrity check, outlier detection and consistency verification. By setting inspection rules, data that meets quality requirements is screened out to obtain valid data identification. Time series reconstruction is performed on valid data identification, and missing data is interpolated to ensure data continuity and obtain a continuous monitoring sequence. Finally, the continuous monitoring sequence is comprehensively processed with the time-divided sampling scheme, and the monitoring data is organized and managed according to the characteristics of the time period to form a real-time levee monitoring data stream.

[0051] Take a levee monitoring system as an example. The system has 30 monitoring points on a 3-kilometer-long levee. The initial sampling frequency is set as follows: data is collected every 10 minutes in high-risk areas (10 points), every 30 minutes in medium-risk areas (12 points), and every hour in low-risk areas (8 points). Through the analysis of historical data, it is found that the data change rate is low from 0:00 to 6:00 in the morning, with an average change of no more than 2%; while the data change rate increases significantly from 6:00 in the morning to 10:00 in the evening, with an average change of 5%. Based on this feature, the sampling scheme is divided into time windows: the sampling frequency is reduced in the early morning, the high-risk area is adjusted to once every 20 minutes, the medium-risk area is adjusted to once every hour, and the low-risk area is adjusted to once every 2 hours; the original sampling frequency is maintained during the daytime. During the actual monitoring process, at 9:00 a.m. one day, the monitoring point numbered A15 detected that the water level rose by 20 cm in 15 minutes, triggering an abnormal fluctuation mark. Spatial correlation analysis showed that the adjacent A14 and A16 monitoring points also experienced a small increase in water levels, with correlation coefficients of 0.85 and 0.82, respectively. The three monitoring points were divided into the same group, and the sampling frequency was increased to once every 5 minutes, and data was collected synchronously. After data quality inspection, it was found that there was an abnormality in a data at 9:30 at the A15 monitoring point, which was corrected by linear interpolation of the previous and next data. The final monitoring data stream showed that the leakage anomaly in this area was controlled after 2 hours, and the water level gradually returned to normal.

[0052] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0053] (1) Perform spatial interpolation calculation on the real-time levee monitoring data stream to obtain continuous pressure field data, and extract equipotential lines from the continuous pressure field data to obtain pressure contour line 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) numerically solving the infiltration flow field distribution data to obtain velocity component data, and performing streamline tracing on the velocity component data to obtain infiltration path data;

[0056] (4) identifying the convergence point of the infiltration path data to obtain the potential leakage point location data, and conducting intensity assessment on the potential leakage point location data to obtain leakage intensity distribution data;

[0057] (5) Perform propagation path analysis on the leakage intensity distribution data to obtain leakage diffusion law data, and extract characteristic parameters from the leakage diffusion law data to obtain leakage characteristic index data;

[0058] (6) Comprehensively analyze the leakage characteristic index data and infiltration path data to obtain the embankment leakage characteristic data.

[0059] Specifically, when performing spatial interpolation calculations on the real-time levee monitoring data stream, the Kriging interpolation method is used to process the discrete monitoring point pressure data. The Kriging interpolation method takes into account the correlation of spatial positions. By establishing a variation function model, the pressure values ​​of unknown points are calculated to obtain continuously distributed pressure field data. Equipotential lines are extracted from the continuous pressure field data, and points with the same pressure value are connected according to the preset pressure gradient interval to form pressure contour data, which reflects the spatial distribution characteristics of the pressure field. Subsequently, the pressure contour data is gradient calculated to calculate the rate of change of the pressure field in space and obtain pressure gradient vector data. The pressure gradient vector data describes the magnitude and direction of the pressure change and is used to analyze the movement trend of the infiltration water flow. The pressure gradient vector data is subjected to flow direction analysis, and the direction of water flow movement is determined according to Darcy's law to obtain the infiltration flow field distribution data, which characterizes the movement state of the water flow inside the levee.

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

[0061]

[0062] Among them, K x , K y , K z They represent the permeability coefficients in the x, y, and z directions (unit: m / s), h represents the water head height (unit: m), S s Indicates specific water storage rate (unit: m -1 ), t represents time (unit: s). The velocity component data is obtained by solving the equation. The velocity component data is streamlined and the fourth-order Runge-Kutta method is used to calculate the motion trajectory of water particles to obtain the infiltration path data.

[0063] The convergence point of the infiltration path data is identified, and the intersection of the streamlines is searched to determine the potential leakage point location. The strength of the potential leakage point location data is evaluated, and the leakage strength calculation formula is:

[0064]

[0065] Among them, L i represents the intensity index of the i-th leakage point, Q j Indicates the flow value of the jth monitoring point (unit: m 3 / s), d ij represents the distance from leakage point i to monitoring point j (unit: m), α j and βj are the flow weight coefficient and distance attenuation coefficient, respectively, and n is the number of monitoring points within the impact range. The leakage intensity distribution data is calculated. The propagation path of the leakage intensity distribution data is analyzed to study the expansion process of the leakage impact range and obtain the leakage diffusion law data. The characteristic parameters of the leakage diffusion law data are extracted, including indicators such as diffusion rate, impact range and directionality, to form leakage characteristic index data. Finally, the leakage characteristic index data and the infiltration path data are comprehensively analyzed to obtain the complete levee leakage characteristic data.

[0066] Taking the monitoring of a river embankment as an example, the monitoring section is 500 meters long and has 20 monitoring points. The pressure field data obtained by Kriging interpolation calculation shows that there is an obvious low-pressure area 200 meters away from the embankment foot, with a pressure value of 15kPa, while the pressure value of the surrounding area is 25kPa on average. Calculating the pressure gradient found that the pressure gradient in this area reached 0.5kPa / m, which is much higher than the average value of 0.1kPa / m in other areas. When numerically solving the seepage equation, the permeability coefficient K is set x =K y =10 -5 m / s,K z =10 -6 m / s, specific water storage rate S s =10 -4 m -1 The maximum flow rate in this area is calculated to be 2×10 -4 The streamline tracing results showed that multiple streamlines converged at 210 meters from the embankment foot, which was identified as a potential leakage point.

[0067] The leakage point was evaluated for strength, and the flow data of the five surrounding monitoring points were 0.015, 0.012, 0.010, 0.008 and 0.006 m 3 / s, the distances are 5, 8, 10, 12 and 15 meters respectively, and the weight coefficient α is set j are all 1, and the distance attenuation coefficient β j The calculated leakage intensity index is 0.038, exceeding the warning value of 0.025. After 24 hours of continuous monitoring, the impact range of the leakage point spreads at a speed of 0.5 meters per hour, and the diffusion speed in the direction of the embankment axis is even faster, reaching 0.8 meters per hour.

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

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

[0070] (2) Decomposing the pressure gradient data in the levee leakage characteristic data into time series to obtain pressure change trend data, and making critical value judgment on the pressure change trend data to obtain pressure abnormal area data;

[0071] (3) Performing zoning statistics on the infiltration rate data in the embankment leakage characteristic data to obtain rate distribution characteristic data, and performing threshold classification on the rate distribution characteristic data to obtain infiltration risk level data;

[0072] (4) Cross-validate the water level mutation point data and the pressure abnormality area data to obtain initial warning criterion data, and associate the initial warning criterion data with the infiltration risk level data to obtain combined warning indicator data;

[0073] (5) Analyze the historical seasonal fluctuations of the combined early warning indicator data to obtain seasonal variation characteristic data, and calculate the correction coefficient of the seasonal variation characteristic data to obtain the seasonal correction factor;

[0074] (6) Perform weighted superposition analysis on the combined early warning indicator data and seasonal correction factors to obtain leakage risk warning threshold data.

[0075] Specifically, the water level data in the levee leakage characteristic data are processed. The water level data contains the water level measurements inside and outside the levee. The water level difference between adjacent monitoring points reflects the potential risk of levee leakage. The water level gradient distribution data is obtained by calculating the water level difference between adjacent monitoring points. The water level gradient describes the degree of change of the water level within a unit distance. The water level gradient distribution data is screened for abnormal points, and the statistical threshold is set. The points that exceed the normal fluctuation range are marked as abnormal points to obtain the water level mutation point data. The pressure gradient data in the levee leakage characteristic data are decomposed into time series, and the wavelet decomposition method is used to decompose the pressure gradient data into three components: trend term, periodic term and random term. The trend term reflects the long-term trend of pressure change, the periodic term reflects the periodic characteristics of pressure change, and the random term represents short-term fluctuations. By analyzing the combined characteristics of these three components, the pressure change trend data is obtained. The critical value of the pressure change trend data is determined, and the area exceeding the safety threshold is marked to obtain the pressure abnormal area data.

[0076] The infiltration rate data in the levee leakage characteristic data are subjected to zoning statistical analysis, the monitoring area is divided into several sub-areas, and the statistical characteristics of the infiltration rate in each sub-area are calculated, including the mean, standard deviation and coefficient of variation, to obtain the rate distribution characteristic data. The rate distribution characteristic data are threshold graded, and the risk level is divided into three levels: low risk, medium risk and high risk according to the size of the infiltration rate, to obtain the infiltration risk level data. Then, the water level mutation point data and the pressure abnormal area data are cross-validated, the spatial correspondence and temporal correlation between the two types 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 infiltration risk level data, the early warning criterion is matched with the infiltration risk level, the severity of the abnormal situation is comprehensively evaluated, and the combined early warning indicator data is obtained.

[0077] The historical seasonal fluctuation analysis of the combined early warning index data was conducted to study the changing patterns of levee leakage characteristics in different seasons. The seasonal variation characteristic data were obtained by statistically analyzing the seasonal variation characteristics in the historical data, including the differences between the flood season and the dry season, the impact of temperature changes, etc. The correction coefficient of the seasonal variation characteristic data was calculated, and the seasonal impact factor was established to adjust the early warning threshold and obtain the seasonal correction factor. Finally, the combined early warning index data and the seasonal correction factor were weighted superposition analysis, and the importance and seasonal impact of each indicator were comprehensively considered to calculate the final leakage risk early warning threshold data.

[0078] Taking a river embankment as an example, 30 water level monitoring points were set up in the monitoring section, with a distance of 50 meters between adjacent points. Through calculation, it was found that the water level gradient between monitoring points A12 and A13 reached 0.15 m / m, while the normal water level gradient was only 0.05 m / m, and this point was marked as a water level mutation point. At the same time, the time series decomposition of the pressure gradient data showed that the pressure change trend in the area showed a continuous upward trend in the past 24 hours, with an increase rate of 2 kPa / hour, exceeding the critical value of 1.5 kPa / hour, so it was marked as a pressure abnormality area. The zoning statistics of the infiltration rate showed that the average infiltration rate in the area was 3×10 -4 m / s, with a standard deviation of 5×10 -5m / s, with a coefficient of variation of 0.17, which is a medium risk level. After cross-validation, it was confirmed that there were both water level mutations and pressure anomalies in the area, and the confidence level of the initial warning criteria reached 85%. Historical data analysis shows that the infiltration risk in the rainy season (June-September) is generally higher in this area 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 for the rainy season is calculated to be 1.4, and that for the dry season is 0.8. Finally, through weighted overlay analysis, the leakage risk warning thresholds for the area in the rainy season were determined: the water level gradient threshold is 0.12 m / m, the pressure change rate threshold is 1.8 kPa / hour, and the infiltration rate threshold is 2.5×10 -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 executing step S105 may specifically include the following steps:

[0080] (1) Segment the leakage risk warning threshold data into time series to obtain threshold change interval data, and calculate the diffusion rate of the threshold change interval data to obtain initial diffusion rate data;

[0081] (2) Perform spatial distribution analysis on the initial diffusion rate data to obtain diffusion range prediction data, and classify the diffusion range prediction data into risk levels to obtain regional risk distribution data;

[0082] (3) Conduct accuracy statistics on historical warning records to obtain warning judgment standard data, and perform weight allocation on the warning judgment standard data to obtain warning parameter correction data;

[0083] (4) Verify the historical accuracy of regional risk distribution data to obtain risk assessment correction coefficients, and assign grades to the risk assessment correction coefficients to obtain warning level determination data;

[0084] (5) Performing timeliness analysis on the warning level determination data to obtain warning time window data, and sorting the warning time window data by warning priority to obtain graded warning sequence data;

[0085] (6) Comprehensively process the graded warning sequence data and warning parameter correction data to obtain a warning information push plan.

[0086] Specifically, the leakage risk warning threshold data is segmented into time series. The time series segmentation uses the sliding time window method to divide the warning threshold data into several time periods according to a fixed time length, analyze the change characteristics of the threshold in each time period, and obtain the threshold change interval data. The diffusion rate of the threshold change interval data is calculated, and the calculation formula is:

[0087]

[0088] Among them, V d represents the initial diffusion rate, ω i is the weight coefficient of the ith monitoring point, R i,t and R i,t+Δt They represent the risk values ​​at time t and time 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 spatial distribution analysis of the initial diffusion rate data is carried out, and the spatial variation law of the diffusion rate is studied using geographic statistical methods to obtain the diffusion range prediction data. The diffusion range prediction data is divided into risk levels, and the risks are divided into four levels according to the size of the diffusion rate: slight risk, medium risk, large risk and major risk, and the regional risk distribution data is obtained.

[0089] Subsequently, the accuracy of historical warning records was statistically analyzed, and the accuracy, missed alarm rate, and false alarm rate of historical warnings were calculated to obtain the standard data for warning judgment. The standard data for warning judgment was weighted, and the corresponding weight coefficients were set according to the importance and reliability of different warning indicators to obtain the correction data for warning parameters. The historical accuracy of regional risk distribution data was verified using the following formula:

[0090]

[0091] Among them, C r represents the risk assessment correction factor, λ j is the weight coefficient of the j-th risk, P j and N j They represent the correct warning rate and correct negation 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 coefficients are graded and assigned to obtain the warning level determination data. The timeliness of the warning level determination data is analyzed to study the effective time of the warning information and obtain the warning time window data. The warning time window data is sorted by warning priority, and the priority is determined by the risk level, impact scope and urgency, and the graded warning sequence data is obtained. Finally, the graded warning sequence data and the warning parameter correction data are comprehensively processed to form the final warning information push plan.

[0092] Take the monitoring of a river embankment as an example. The embankment section is 2,000 meters long and has 40 monitoring points. In one monitoring, the monitoring point numbered B15 detected that the leakage risk warning threshold exceeded the standard. Through time series analysis, it was found that the warning threshold of this point showed a continuous upward trend in the past 6 hours. The segmented data showed that the threshold rising rate in the 0-2 hour segment was 0.05 / hour, 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 were selected for analysis, and the weight coefficients were set to 0.3, 0.25, 0.2, 0.15 and 0.1 respectively, and the spatial attenuation coefficient was uniformly set to 0.1. The distances from each point to the risk source were 10, 15, 20, 25 and 30 meters respectively. The calculated initial diffusion rate was 0.085 / hour, indicating that the scope of the leakage was gradually expanding. Spatial distribution analysis showed that the leakage impact range spread from the point source to the surrounding area by about 50 meters within 6 hours. According to the diffusion rate, the area was divided into a large risk area (inner circle 20 meters) and a medium risk area (outer circle 30 meters). Statistics of historical warning records show that the warning accuracy rate in this area is 85%, the missed alarm rate is 8%, and the false alarm rate is 7%. Based on these data, the weights of the warning parameters are set: the infiltration pressure accounts for 0.4, the water level change accounts for 0.3, and the infiltration rate accounts for 0.3.

[0094] Historical accuracy verification shows that the risk assessment correction coefficient for high-risk areas is 1.2, and for medium-risk areas is 0.9. Timeliness analysis determines that the warning time window is 12 hours, during which monitoring data needs to be updated every hour. According to the risk level and diffusion trend, the priority of the warning event is set to level 2 (4 levels in total), requiring the warning information to be pushed within 30 minutes and maintaining a tracking monitoring frequency of once every 4 hours. This warning method based on multi-dimensional analysis ensures the timeliness and accuracy of the warning information.

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

[0096] (1) Compare the time series of the early warning information push scheme and the real-time levee monitoring data stream to obtain the early warning response time difference data, and group and count the early warning response time difference data to obtain the monitoring response characteristic data;

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

[0098] (3) Performing monitoring cost constraint analysis on monitoring frequency adjustment parameters to obtain monitoring resource allocation data, and sorting monitoring point priorities on the monitoring resource allocation data to obtain monitoring point importance data;

[0099] (4) Evaluate the monitoring accuracy of the monitoring point importance data to obtain parameter adjustment coefficient data, and perform regional parameter compensation on the parameter adjustment coefficient data to obtain monitoring compensation parameter data;

[0100] (5) judging the working state of the monitoring point based on the monitoring compensation parameter data to obtain monitoring state evaluation data, and performing monitoring point reliability analysis on the monitoring state evaluation data to obtain monitoring quality index data;

[0101] (6) Comprehensively analyze the monitoring quality index data and monitoring response characteristic data to obtain leakage monitoring control parameters.

[0102] Specifically, the early warning information push scheme and the real-time levee monitoring data stream are compared in time series, and the time difference between the time when the early warning information is issued and the time when the monitoring data abnormality occurs is analyzed to obtain the early warning response time difference data. The time difference includes three parts: early warning information generation delay, transmission delay and response delay. The early warning response time difference data are grouped and counted, and the monitoring points are divided into a fast response group, a medium response group and a delayed response group according to the length of the time difference. The statistical characteristics of each group, including the average response time, standard deviation and coefficient of variation, are calculated to obtain the monitoring response characteristic data. The monitoring point sensitivity analysis is performed on the monitoring response characteristic data to evaluate the response sensitivity of different monitoring points to leakage events. The sensitivity analysis considers the three aspects of the response time, data change amplitude and stability of the monitoring point, and calculates the sensitivity index for each monitoring point to obtain the monitoring point weight distribution data. The sampling frequency of the monitoring point weight distribution data is matched, and the corresponding sampling frequency is set according to the sensitivity of the monitoring point. The points with high sensitivity use a higher sampling frequency, and the points with low sensitivity use a lower sampling frequency to obtain the monitoring frequency adjustment parameters. Then, the monitoring cost constraint analysis is performed on the monitoring frequency adjustment parameters, and factors such as equipment energy consumption, data transmission cost, and storage cost are considered to calculate the resource consumption under different sampling frequencies to obtain the monitoring resource allocation data. The monitoring resource allocation data is prioritized, and the importance, response characteristics, and resource consumption of the monitoring points are comprehensively considered to determine the priority of each monitoring point and obtain the monitoring point importance data.

[0103] The monitoring accuracy of the monitoring point importance data is evaluated, the accuracy and reliability of the monitoring data are analyzed, the measurement error and data stability index are calculated, and the parameter adjustment coefficient data are obtained. The parameter adjustment coefficient data is subjected to regional parameter compensation. According to the environmental characteristics and monitoring conditions of different regions, the compensation coefficient is set, the monitoring parameters are corrected, and the monitoring compensation parameter data are obtained. Then, the working status of the monitoring point is judged on the monitoring compensation parameter data, the operating status and data quality of the monitoring equipment are analyzed, the working efficiency of the monitoring point is evaluated, and the monitoring status evaluation data is obtained. The monitoring point reliability analysis is performed on the monitoring status evaluation data, and the data efficiency and stability index of the monitoring point are calculated to obtain the monitoring quality index data. Finally, the monitoring quality index data and the monitoring response characteristic data are comprehensively analyzed to form a complete leakage monitoring control parameter.

[0104] Taking the monitoring of a river embankment as an example, 25 monitoring points were set up in this embankment section. Through time series comparative analysis, it was found that the average warning response time difference of monitoring point C08 was 15 minutes, with a coefficient of variation of 0.2, belonging to the rapid response group; the average response time difference of monitoring point C12 was 35 minutes, with a coefficient of variation of 0.3, belonging to the medium response group; the average response time difference of monitoring point C15 was 60 minutes, with a coefficient of variation of 0.4, belonging to the delayed response group. Sensitivity analysis shows that monitoring point C08 responds quickly to water level changes, and the data changes significantly. The sensitivity index is calculated to be 0.85, and the sampling frequency is set to once every 5 minutes. The sensitivity index of monitoring point C12 is 0.65, and the sampling frequency is set to once every 15 minutes. The sensitivity index of monitoring point C15 is 0.45, and the sampling frequency is set to once every 30 minutes.

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

[0106] The results of the working status judgment show that the data efficiency of C08 reaches 98%, and the equipment runs stably; the data efficiency of C12 is 95%, with occasional data loss; the data efficiency of C15 is 92%, with a certain degree of data fluctuation. Based on the above analysis, the final monitoring and 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 every 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 embodiment of the present application. The following describes the river embankment leakage monitoring and early warning system based on GIS technology in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the river embankment leakage monitoring and early warning system based on GIS technology includes:

[0108] The stratification module is used to perform stratification and partition processing on the three-dimensional terrain data of the river embankment and calculate the weight of the historical leakage position to obtain the basic data set of the digital embankment;

[0109] An association module is used to perform differential frequency sampling and adjacent point association analysis on the monitoring points in the digital levee basic data set to obtain a real-time levee monitoring data stream;

[0110] A tracking module, used for constructing a seepage pressure gradient field and tracking a seepage path for the real-time levee monitoring data stream to obtain levee leakage characteristic data;

[0111] A correction module is used to perform combined analysis and processing of water level difference, pressure gradient and infiltration rate and seasonal variation correction on the dike leakage characteristic data to obtain leakage risk warning threshold data;

[0112] A correction module, used to perform leakage diffusion rate assessment and historical accuracy correction processing on the leakage risk warning threshold data to obtain a warning information push plan;

[0113] The optimization module is used to perform correlation analysis and monitoring parameter optimization processing on the early warning information push scheme and the real-time levee monitoring data stream to obtain leakage monitoring control parameters.

[0114] Through the collaboration of the above components, the scientific layout of embankment monitoring points and the accurate identification of risk areas are achieved by layering and partitioning the geospatial data and engineering geological data of the river embankment and calculating the weight of the historical leakage position, effectively solving the problem of many monitoring blind spots in traditional monitoring methods. At the same time, by performing differentiated frequency sampling and adjacent point correlation analysis on the monitoring points in the digital embankment basic data set, an adaptive data collection mechanism is established to improve the pertinence and efficiency of data collection. In the scheme, the seepage pressure gradient field is constructed and the seepage path tracking process is performed on the real-time embankment monitoring data stream, making the identification of the leakage path more accurate and providing reliable data support for early warning. By combining the analysis and processing of water level difference, pressure gradient and seepage rate and seasonal change correction of the embankment leakage characteristic data, a multi-dimensional leakage risk assessment is achieved, which greatly improves the accuracy of the early warning. The leakage diffusion rate assessment and historical accuracy correction process are performed on the leakage risk early warning threshold data, and a dynamic early warning threshold system is established, which effectively reduces the false alarm and missed alarm rates. Finally, by analyzing the correlation between the early warning information push scheme and the real-time levee monitoring data stream and optimizing the monitoring parameters, the adaptive adjustment of the monitoring parameters was achieved, which improved the operating efficiency and reliability of the entire monitoring and early warning system. This method combines GIS technology with traditional levee monitoring technology to build a complete levee leakage monitoring and early warning system, which not only improves the spatial coverage and temporal 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 systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0116] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0117] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A river embankment leakage monitoring and early warning method based on GIS technology, characterized in that: The GIS-based river embankment leakage monitoring and early warning method includes: The three-dimensional terrain data of the river embankment is processed by layering and partitioning, and the weight of the historical leakage position is calculated to obtain the basic data set of the digital embankment; Performing differential frequency sampling and adjacent point correlation analysis on the monitoring points in the digital levee basic data set to obtain a real-time levee monitoring data stream; constructing a seepage pressure gradient field and performing seepage path tracking processing on the real-time levee monitoring data stream to obtain levee leakage characteristic data; Performing combined analysis and processing of water level difference, pressure gradient and infiltration rate and seasonal variation correction on the levee leakage characteristic data to obtain leakage risk warning threshold data; Perform leakage diffusion rate assessment and historical accuracy correction processing on the leakage risk warning threshold data to obtain a warning information push plan; The early warning information push scheme and the real-time levee monitoring data stream are subjected to correlation analysis and monitoring parameter optimization processing to obtain leakage monitoring control parameters.

2. The GIS-based river embankment leakage monitoring and early warning method according to claim 1 is characterized in that: The three-dimensional terrain data of the river embankment is processed by layering and partitioning and the weight of the historical leakage position is calculated to obtain a digital embankment basic data set, including: Performing layered processing on the three-dimensional terrain data of the river embankment to obtain embankment structure level data, and performing regional grid division on the embankment structure level data to obtain basic grid unit data; Performing geological attribute association analysis on the basic grid unit data to obtain geological feature partition data, and performing spatial distribution processing on the geological feature partition data through Kriging interpolation to obtain geological spatial distribution data; Performing spatiotemporal clustering analysis on historical leakage event records to obtain historical leakage hotspot area data, and performing density calculation on the historical leakage hotspot area data to obtain leakage risk weight coefficients; Performing a superposition analysis on the geological spatial distribution data and the leakage risk weight coefficient to obtain key monitoring area division data, and performing a monitoring point layout calculation on the key monitoring area division data to obtain monitoring point location coordinate data; Matching the monitoring point location coordinate data with the monitoring device type to obtain a monitoring device configuration scheme, and initializing the sampling frequency of the monitoring device configuration scheme to obtain monitoring point sampling parameters; The sampling parameters of the monitoring points and the division data of the key monitoring areas are comprehensively processed to obtain a digital levee basic data set.

3. The method for monitoring and early warning of river embankment leakage based on GIS technology according to claim 1 is characterized in that: The monitoring points in the digital levee basic data set are subjected to differentiated frequency sampling and adjacent point association analysis to obtain a real-time levee monitoring data stream, including: Setting initial sampling frequency parameters for monitoring points in the digital levee basic data set to obtain benchmark sampling frequency data, and performing historical data change rate analysis on the benchmark sampling frequency data to obtain a dynamic frequency adjustment coefficient; The dynamic frequency adjustment coefficient is subjected to a monitoring point importance classification process to obtain a differentiated sampling parameter, and the differentiated sampling parameter is divided into time windows to obtain a time-divided sampling scheme; Perform sliding window processing on the data of adjacent monitoring points to obtain the short-term data change trend, and perform mutation detection on the short-term data change trend to obtain abnormal fluctuation mark data; Performing spatial correlation analysis on the abnormal fluctuation mark data to obtain monitoring point group division data, and performing data synchronization acquisition on the monitoring point group division data to obtain multi-point collaborative monitoring data; Performing data quality inspection on the multi-point collaborative monitoring data to obtain valid data identifiers, and reconstructing the time series of the valid data identifiers to obtain a continuous monitoring sequence; The continuous monitoring sequence and the time-division sampling scheme are comprehensively processed to obtain a real-time levee monitoring data stream.

4. The method for monitoring and early warning of river embankment leakage based on GIS technology according to claim 1 is characterized in that: The process of constructing a seepage pressure gradient field and tracking a seepage path on the real-time levee monitoring data stream to obtain levee leakage characteristic data includes: Performing spatial interpolation calculation on the real-time levee monitoring data stream to obtain continuous pressure field data, and performing equipotential line extraction on the continuous pressure field data to obtain pressure contour line data; 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; Numerically solving the infiltration flow field distribution data to obtain velocity component data, and performing streamline tracing on the velocity component data to obtain infiltration path data; Performing convergence point identification on the infiltration path data to obtain potential leakage point location data, and performing intensity assessment on the potential leakage point location data to obtain leakage intensity distribution data; Performing propagation path analysis on the leakage intensity distribution data to obtain leakage diffusion law data, and extracting characteristic parameters from the leakage diffusion law data to obtain leakage characteristic index data; The leakage characteristic index data and the infiltration path data are comprehensively analyzed to obtain the embankment leakage characteristic data.

5. The method for monitoring and early warning of river embankment leakage based on GIS technology according to claim 1 is characterized in that: The combined analysis and processing of water level difference, pressure gradient and infiltration rate and seasonal variation correction of the levee leakage characteristic data to obtain leakage risk warning threshold data includes: Calculating adjacent point difference values ​​of water level data in the dike leakage characteristic data to obtain water level gradient distribution data, and screening abnormal points of the water level gradient distribution data to obtain water level mutation point data; Performing time series decomposition on the pressure gradient data in the dike leakage characteristic data to obtain pressure change trend data, and performing critical value determination on the pressure change trend data to obtain pressure abnormal area data; Performing zoning statistics on the infiltration rate data in the levee leakage characteristic data to obtain rate distribution characteristic data, and performing threshold classification on the rate distribution characteristic data to obtain infiltration risk level data; Cross-validating the water level mutation point data and the pressure abnormality area data to obtain initial early warning criterion data, and associating the initial early warning criterion data with the infiltration risk level data to obtain combined early warning indicator data; Performing a historical seasonal fluctuation analysis on the combined early warning indicator data to obtain seasonal variation characteristic data, and performing a correction coefficient calculation on the seasonal variation characteristic data to obtain a seasonal correction factor; A weighted superposition analysis is performed on the combined early warning indicator data and the seasonal correction factor to obtain leakage risk early warning threshold data.

6. The method for monitoring and early warning of river embankment leakage based on GIS technology according to claim 1 is characterized in that: The leakage risk warning threshold data is subjected to leakage diffusion rate evaluation and historical accuracy correction processing to obtain a warning information push scheme, including: Performing time series segmentation on the leakage risk warning threshold data to obtain threshold change interval data, and performing diffusion rate calculation on the threshold change interval data to obtain initial diffusion rate data; Performing spatial distribution analysis on the initial diffusion rate data to obtain diffusion range prediction data, and performing risk level classification on the diffusion range prediction data to obtain regional risk distribution data; Perform accuracy statistics on historical warning records to obtain warning judgment standard data, and perform weight distribution on the warning judgment standard data to obtain warning parameter correction data; Verifying the historical accuracy of the regional risk distribution data to obtain risk assessment correction coefficients, and assigning grades to the risk assessment correction coefficients to obtain warning level determination data; Performing timeliness analysis on the warning level determination data to obtain warning time window data, and performing warning priority sorting on the warning time window data to obtain graded warning sequence data; The hierarchical warning sequence data and the warning parameter correction data are comprehensively processed to obtain a warning information push plan.

7. The method for monitoring and early warning of river embankment leakage based on GIS technology according to claim 1 is characterized in that: The early warning information push scheme and the real-time levee monitoring data stream are subjected to correlation analysis and monitoring parameter optimization processing to obtain leakage monitoring control parameters, including: Performing a time series comparison between the early warning information push scheme and the real-time levee monitoring data stream to obtain early warning response time difference data, and performing group statistics on the early warning response time difference data to obtain monitoring response characteristic data; Performing monitoring point sensitivity analysis on the monitoring response characteristic data to obtain monitoring point weight distribution data, and performing sampling frequency matching on the monitoring point weight distribution data to obtain monitoring frequency adjustment parameters; Performing a monitoring cost constraint analysis on the monitoring frequency adjustment parameter to obtain monitoring resource allocation data, and performing monitoring point priority sorting on the monitoring resource allocation data to obtain monitoring point importance data; Performing monitoring accuracy evaluation on the monitoring point importance data to obtain parameter adjustment coefficient data, and performing regional parameter compensation on the parameter adjustment coefficient data to obtain monitoring compensation parameter data; Performing monitoring point working state judgment on the monitoring compensation parameter data to obtain monitoring state evaluation data, and performing monitoring point reliability analysis on the monitoring state evaluation data to obtain monitoring quality index data; The monitoring quality index data and the monitoring response characteristic data are comprehensively analyzed to obtain leakage monitoring control parameters.

8. A river embankment leakage monitoring and early warning system based on GIS technology, used to implement a river embankment leakage monitoring and early warning method based on GIS technology as described in any one of claims 1 to 7, characterized in that: The river embankment leakage monitoring and early warning system based on GIS technology includes: The stratification module is used to perform stratification and partition processing on the three-dimensional terrain data of the river embankment and calculate the weight of the historical leakage position to obtain the basic data set of the digital embankment; An association module is used to perform differential frequency sampling and adjacent point association analysis on the monitoring points in the digital levee basic data set to obtain a real-time levee monitoring data stream; A tracking module, used for constructing a seepage pressure gradient field and tracking a seepage path for the real-time levee monitoring data stream to obtain levee leakage characteristic data; A correction module is used to perform combined analysis and processing of water level difference, pressure gradient and infiltration rate and seasonal variation correction on the dike leakage characteristic data to obtain leakage risk warning threshold data; A correction module, used to perform leakage diffusion rate assessment and historical accuracy correction processing on the leakage risk warning threshold data to obtain a warning information push plan; The optimization module is used to perform correlation analysis and monitoring parameter optimization processing on the early warning information push scheme and the real-time levee monitoring data stream to obtain leakage monitoring control parameters.

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