A reservoir dam surface displacement self-adaptive monitoring method and system based on GNSS positioning

Abstract

The application discloses a reservoir dam body surface displacement adaptive monitoring method and system based on GNSS positioning, and particularly relates to the technical field of reservoir dam bodies, which responds to the offset coordinates of the acquired GNSS sampling points, calculates the initial value error based on the coordinates and a preset reference value, corrects the initial value error in combination with a pre-constructed inertia model, obtains the error offset, compensates the offset coordinates by applying the error offset, and determines the final offset check value; when the final offset check value exceeds the preset offset threshold, an alarm signal is output; the application corrects the measurement error by combining the pre-constructed inertia model, and fuses the optimized point layout strategy, thereby improving the accuracy and reliability of monitoring and reducing the false alarm rate.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of reservoir dam body monitoring, and particularly relates to a reservoir dam body surface displacement adaptive monitoring method and system based on GNSS positioning. BACKGROUND

[0002] As the core hub of water conservancy and hydropower projects, reservoir dams play an indispensable role in flood control, water supply, irrigation, and power generation. To effectively prevent dam collapse risks caused by factors such as structural aging, geological changes, or overloading, it is crucial to monitor the health status of dam structures in real time and accurately. Among them, dam surface displacement monitoring is the most direct technical means to assess the overall stability of the dam and predict potential structural abnormalities, which can provide key data support for dam safety warning and maintenance decision-making.

[0003] However, existing monitoring schemes have defects in spatial and temporal coverage. The layout of monitoring points is usually sparse and fixed, which cannot achieve continuous and comprehensive coverage of the dam surface, and is prone to form monitoring blind areas. At the same time, the data processing and analysis model is generally simple and lacks intelligent and adaptive capabilities, usually relying only on fixed thresholds for judgment or using simple linear models for trend prediction, which cannot effectively cope with the highly nonlinear and time-varying characteristics of dam displacement, nor can it eliminate the influence of external factors, resulting in low prediction accuracy and failing to provide a basis for dam health diagnosis and risk assessment.

[0004] Therefore, there is an urgent need for a reservoir dam body surface displacement adaptive monitoring method and system based on GNSS positioning. SUMMARY

[0005] The purpose of the present application is to provide a reservoir dam body surface displacement adaptive monitoring method and system based on GNSS positioning. The technical solution adopted is as follows:

[0006] A reservoir dam body surface displacement adaptive monitoring method based on GNSS positioning, comprising:

[0007] obtaining the offset coordinates of GNSS sampling points;

[0008] determining the final offset verification value based on the offset coordinates and a preset reference value, and combining a pre-constructed inertia model;

[0009] and when the components of the final offset verification value in the horizontal direction or the vertical direction exceed the corresponding preset offset threshold, outputting an alarm signal.

[0010] Preferably, the step of determining the final offset verification value comprises:

[0011] calculating a difference between the offset coordinate and the preset reference value to obtain an initial error;

[0012] correcting the initial error according to the pre-constructed inertial model to obtain an error offset;

[0013] and applying the error offset to the offset coordinate for compensation to obtain the final offset check value.

[0014] Preferably, the pre-constructed inertial model is an inertial model representing a functional relationship between a vertical direction component and a planar direction component of the initial error, and the functional relationship is corrected using a vertical height parameter of the reservoir dam body.

[0015] Preferably, the method further comprises, before performing the monitoring, determining the GNSS sampling points based on a three-dimensional structure diagram of the reservoir dam body, which comprises:

[0016] selecting an area with a surface area greater than a preset area threshold on the three-dimensional structure diagram as a priority position;

[0017] and arranging the GNSS sampling points in the priority position in the order of surface area, line area and point area.

[0018] The application also provides a reservoir dam body surface displacement adaptive monitoring system based on GNSS positioning, comprising:

[0019] a coordinate acquisition module for acquiring offset coordinates of GNSS sampling points;

[0020] a displacement adaptive solving module for determining a final offset check value based on the offset coordinates and a preset reference value, in combination with a pre-constructed inertial model, and comparing the final offset check value with a preset offset threshold;

[0021] and an alarm output module for outputting an alarm signal when the comparison result of the displacement adaptive solving module is that the final offset check value exceeds the preset offset threshold.

[0022] Preferably, the displacement adaptive solving module is further configured to:

[0023] calculating a difference between the offset coordinate and the preset reference value to obtain an initial error;

[0024] correcting the initial error according to the pre-constructed inertial model to obtain an error offset;

[0025] and applying the error offset to the offset coordinate for compensation to obtain the final offset check value.

[0026] Preferably, the pre-constructed inertial model is an inertial model representing a functional relationship between a vertical direction component and a plane direction component of the initial value error, and the functional relationship is corrected by using a vertical height parameter of the reservoir dam body.

[0027] Preferably, the system further comprises:

[0028] The sampling point arrangement module is configured to determine the GNSS sampling points based on a three-dimensional structure diagram of the reservoir dam body before performing the monitoring, and specifically configured to:

[0029] selecting, on the three-dimensional structure diagram, an area with a surface area greater than a preset area threshold as a priority position;

[0030] and arranging the GNSS sampling points in the priority position in the order of a surface area, a line area and a point area.

[0031] Beneficial effects

[0032] The application calculates the initial value error of the GNSS sampling points, constructs a pre-constructed inertial model associated with the plane direction and the vertical direction component according to the initial value error, corrects the initial value error to obtain an error offset by using the pre-constructed inertial model, compensates the offset coordinate, and obtains a final offset check value, so that the measurement error is actively corrected by constructing the pre-constructed inertial model, which can effectively filter out the noise and systematic deviation in the GNSS positioning signal, and improve the accuracy and reliability of the reservoir dam surface displacement monitoring result.

[0033] When determining the GNSS sampling points, the application selects an area with high air tightness and low area change rate as a priority position based on the three-dimensional structure diagram, and arranges the specific area in the priority position in the order of a surface area, a line area and a point area, realizes a scientific point layout, can ensure that the GNSS sampling points are arranged in the key area with stable structure and representative deformation, effectively avoids invalid data collection caused by non-structural disturbance, and provides a reliable foundation for subsequent displacement analysis.

[0034] The application processes the historical offset data composed of the final offset check value, extracts a trend change amount or an offset change amount according to signal stationarity, matches an analysis model to output a trend signal, calculates a future intersection point of the trend signal and corrects it to predict an alarm time, so that the depth analysis and trend prediction of the historical data are introduced, the internal law of displacement change can be dynamically identified, the future time point of risk occurrence can be predicted, and the monitoring system is endowed with the ability of active early warning. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 is a method flowchart of the application. DETAILED DESCRIPTION

[0036] In order to make the above objectives, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0037] Embodiment one

[0038] Please refer to Figure 1 The embodiment provides a reservoir dam surface displacement adaptive monitoring method based on GNSS positioning, which can combine historical offset data for joint analysis, evaluate whether the final offset check value generated after the reservoir dam body is offset exceeds the preset offset threshold, predict the trend signal or offset trend signal representing the displacement change trend through the analysis model, and calculate the final alarm time according to the trend signal or offset trend signal. Wherein, the final alarm time is calculated by extrapolation of the trend signal or offset trend signal, and the predicted displacement will reach the future time point when the preset offset threshold is corrected by combining the current displacement dynamic (such as speed and acceleration). Therefore, the embodiment can monitor the offset of the reservoir dam body and give the final alarm time.

[0039] Further explanation is that the trend signal is extracted from the historical offset data judged as continuous signal, which can represent the mathematical expression of continuous and smooth change of displacement with time, and is usually generated by sliding average or polynomial fitting method; the offset trend signal is generated by matching the analysis model (such as stable, linear or nonlinear model) for the historical offset data judged as non-continuous signal containing discrete points, which can represent the predictive mathematical expression of future displacement change trend.

[0040] The method specifically includes the following steps:

[0041] Step S100: obtaining the offset coordinates of GNSS sampling points;

[0042] Step S200: determining the final offset check value based on the offset coordinates and the preset reference value, and combining the pre-constructed inertia model;

[0043] Step S300: when the component of the final offset check value in the plane direction or the vertical direction exceeds the corresponding preset offset threshold, outputting an alarm signal.

[0044] Before step S100, the following steps are included: obtaining the three-dimensional structure diagram of the reservoir dam body, and determining the GNSS sampling points for monitoring the displacement change of the dam surface based on the three-dimensional structure diagram.

[0045] Specifically, in the process of determining the GNSS sampling points, in order to improve the signal-to-noise ratio and representativeness of the monitoring data, adaptive point selection optimization is needed, which is a process of dynamically screening and determining the best layout position of the GNSS sampling points according to the physical properties (such as air tightness, surface stability) carried by the three-dimensional structure diagram of the dam. On the three-dimensional structure diagram, the areas that meet the specific physical properties are screened out, and the areas whose air tightness is greater than the first preset air tightness threshold and the areas whose area change rate is less than the first preset area change rate threshold are screened out, wherein the area change rate is the ratio of the change amount of the area of a specific region of the dam surface to the initial area of the region within a certain time period, which is calculated by comparing remote sensing images or three-dimensional scanning data, and is used to quantify the surface structure stability of the region. The area that meets the air tightness condition usually means that it is less affected by external temperature, humidity and other environmental factors, and the thermal expansion and contraction effect is not obvious, so as to reduce the displacement noise caused by non-structural disturbance, and the area that meets the area change rate condition indicates that the dam surface structure at this place is stable and is not easy to occur local peeling or deformation, which is suitable for long-term stable displacement monitoring, and ensures that the monitoring is the overall and trend structure displacement rather than the local material change.

[0046] Further, the step of screening out the area whose air tightness is greater than the first preset air tightness threshold is achieved by analyzing the thermodynamic properties, which includes screening out the area whose temperature change similarity to the preset reference temperature change is greater than the preset temperature similarity threshold, which is used to determine whether the temperature change similarity of the dam surface area is high enough. Only the area whose similarity is greater than the threshold value meets the requirement of laying GNSS sampling points in terms of air tightness. For example, the temperature at a deep place inside the dam or a specific stable reference point can be selected as the reference temperature change curve, and the temperature change curve of the dam surface can be obtained by using infrared thermal imaging technology, and the similarity can be quantified by calculating the cross-correlation coefficient or Euclidean distance of the two curves. The area with high similarity indicates that it has good synchronization with the internal temperature change and is less affected by external environmental factors such as sunlight and wind cooling, which proves that it has the threshold condition of heat insulation performance, i.e. meets the condition of air tightness.

[0047] In the screened regions meeting the air tightness and stability conditions, they are collectively determined as the priority positions for setting GNSS sampling points. In the priority positions, to ensure that the sampling points can represent a large enough monitoring unit, regions with an area greater than a preset area threshold are preferentially selected, that is, the region set meeting both the conditions of air tightness greater than a first preset air tightness threshold and area variation rate less than a first preset area variation rate threshold on the three-dimensional structure diagram of the dam body is the preferred region for setting GNSS sampling points. To systematically cover stable regions of different geometric shapes, regions are selected in order of surface regions, line regions and point regions for setting GNSS sampling points. Specifically, GNSS sampling points are preferentially set on wide and flat dam surfaces, which are surface regions, regions with a large two-dimensional continuous surface area, and the highest priority for setting GNSS sampling points. If there is no surface region meeting the conditions, GNSS sampling points are set on structural boundary lines such as dam crest and dam slope, which are line regions, regions with obvious linear geometric features, and the secondary priority for setting GNSS sampling points. If neither the surface region nor the line region meets the conditions, GNSS sampling points are set at key positions such as structural corners and specific markers, which are point regions, key geometric feature points or structural nodes, and the lowest priority for setting GNSS sampling points. Through this hierarchical strategy, the comprehensiveness and efficiency of point layout are ensured.

[0048] In steps S100 and S200, after selecting the GNSS sampling points, the initial coordinates of the GNSS sampling points are obtained, and the initial coordinates are defined as preset reference values. The offset post-coordinate of the GNSS sampling points is obtained, and the offset post-coordinate is defined as a check value. The difference between the check value and the preset reference value is calculated, and the difference is defined as an initial value error. The initial value error is the vector difference between the offset post-coordinate and the initial coordinate (reference value) of a specific GNSS sampling point at a certain time, which reflects the original measurement displacement of the point and contains structural displacement and various measurement noises.

[0049] Furthermore, pre-built inertial models are constructed in both the planar and vertical directions based on the initial value error. The displacement of the reservoir dam is not isolated in different dimensions but exhibits physical coupling. Particularly due to gravity and the material properties of earth-rock dams, vertical displacement often triggers planar displacement. The step of constructing the pre-built inertial model aims to quantify this coupling. Specifically, a functional relationship is constructed, using the vertical component of the initial value error as input parameters and the planar component as output parameters. This functional relationship can be an empirical formula obtained through regression analysis of historical data or a mechanical response model established based on finite element analysis. Moreover, this response relationship is closely related to the geometric dimensions of the dam. Therefore, the vertical height parameter of the reservoir dam is used to correct the functional relationship to establish the pre-built inertial model. For example, for a taller dam, the same vertical displacement may lead to a more significant planar displacement. The vertical height parameter of the reservoir dam can be used as a correction coefficient or model variable to adjust the output of the functional relationship, thereby making the model more universal and accurate.

[0050] The inertial model is a computational model used to quantify the physical coupling relationship between the vertical and planar displacements of a reservoir dam. Its specific formula is as follows:

[0051]

[0052] In the formula, This represents the planar orientation error offset, which is the vertical displacement predicted by the model. The resulting, expected planar displacement; This represents the vertical initial value error, which is the difference between the offset coordinates and the initial coordinates measured by GNSS in the vertical direction. The vertical height of the dam body represents the vertical distance from the dam foundation to the dam crest. As a key geometric parameter affecting the degree of displacement coupling, it is used as a key input for correcting the inertial model to reflect the differences in displacement coupling effects of dams of different sizes. The reference height refers to the baseline dam height used for normalization calculations, which is a preset empirical constant. The model coefficients are dimensionless empirical coefficients obtained through regression analysis of historical data or finite element simulation, representing the basic coupling effect and the highly correlated gain effect, respectively. It is a hyperbolic tangent function used to perform nonlinear smoothing of the influence of the altitude parameter, making it sensitive to changes near the reference altitude, while tending to saturate when it is far from the reference altitude.

[0053] Further, the initial value error is corrected according to the pre-constructed inertia model to obtain an error offset, which represents the expected displacement based on the physical coupling relationship and is used to correct random errors or systematic deviations that may exist in the original measurement data. The error offset is applied to compensate the check value to obtain a final offset check value. The error offset is a plane displacement component that should theoretically occur according to the measured vertical displacement component, based on the inertia model.

[0054] In step S300, a preset offset threshold in the plane direction and a preset offset threshold in the vertical direction are set, and the components of the final offset check value in the plane direction and the vertical direction are compared with the preset offset threshold in the plane direction and the preset offset threshold in the vertical direction, respectively. If the component in any direction exceeds the corresponding preset offset threshold, an alarm signal is output to realize real-time risk early warning.

[0055] To further upgrade from real-time monitoring to trend prediction, the method further includes the following steps: collecting historical offset data composed of the final offset check value, which is a data set composed of the final offset check value arranged in time sequence and is the basis for displacement trend analysis and risk prediction; and classifying and organizing the historical offset data, for example, according to sampling points and time series.

[0056] According to the sampling time interval, the signal stationarity of the historical offset data is determined to distinguish displacement data of different properties and adopt the most suitable analysis strategy. If the sampling time interval is uniform and dense, and the data points present a smooth transition characteristic, the historical offset data is determined to be a continuous signal. This signal is suitable for trend analysis, so the trend change amount is extracted, which can quantify the parameters of the change trend, such as the change rate (speed) and the second-order change rate (acceleration) calculated by sliding average or polynomial fitting. For example, the change rate and acceleration are calculated by sliding average or polynomial fitting. If the sampling time interval between data points is large, or there is data interruption, jump, etc., the historical offset data is determined to be a non-continuous signal containing discrete points. At this time, simple trend fitting may be misleading, so the offset change amount is extracted, i.e., the displacement difference or mutation amplitude between discrete points is focused on, such as the displacement difference between two adjacent data points.

[0057] According to the trend change amount, a trend signal characterizing the displacement change trend is generated, and for non-continuous data, a matching analysis model is generated according to the offset change amount, and an offset trend signal is output by the analysis model, the analysis model including a nonlinear offset model, a linear offset model and a stable offset model, constituting an analysis model set. The step of matching the analysis model is specifically: comparing the offset change amount with a first preset model threshold and a second preset model threshold, and selecting a model from the analysis model set according to the comparison result, the first preset model threshold being a critical value for distinguishing whether the displacement is in a stable fluctuation state or enters a linear development state, and the second preset model threshold being a critical value for distinguishing whether the displacement is in a linear development state or enters a nonlinear acceleration state. For example, if the offset change amount is less than the first preset model threshold, it indicates that the displacement fluctuates randomly within an error range, and then the stable offset model is selected; if the offset change amount is between the first preset model threshold and the second preset model threshold, it indicates that the displacement presents a steady development trend, and then the linear offset model is selected; and if the offset change amount is greater than the second preset model threshold, it indicates that the displacement may be accelerating and there is a risk of instability, and then the nonlinear offset model is selected. The historical offset data generating the offset change amount is processed by using the selected analysis model to output the corresponding offset trend signal.

[0058] The analysis model is a decision model for adaptively selecting the most suitable offset model (stable, linear or nonlinear) from the analysis model set to generate the offset trend signal according to the discrete change amplitude of the historical offset data, and its specific formula is as follows:

[0059]

[0060] In the formula, represents the offset trend signal, which means a mathematical prediction of the dam displacement change in the future period of time; represents the time variable, which means the future time point relative to the current time; represents the offset change amount, which means the displacement difference between adjacent or specific time interval data points in the non-continuous historical offset data; represents the model threshold, which means a preset threshold for dividing different displacement change levels, is the boundary between stability and linear development, is the boundary between linear and nonlinear development; represents the historical offset data, which means a set of historical offset data points used to fit specific model parameters (such as velocity and acceleration); : which means that the stable offset model, the linear offset model and the nonlinear offset model are respectively represented; the model is a piecewise function, which is selected according to the input and the first preset model threshold and the second preset model threshold The corresponding sub-model is selected for calculation according to the comparison result.

[0061] The nonlinear displacement model is a calculation model used for describing the acceleration or deceleration change of dam displacement, and is usually selected when the displacement change exceeds the second preset model threshold. The mathematical form thereof is usually a quadratic or higher polynomial, for example wherein is the acceleration, is the initial velocity, is the initial displacement, and these parameters are fitted according to historical displacement data.

[0062] The linear displacement model is a calculation model used for describing the stable rate change of dam displacement, and is usually selected when the displacement change is between the first preset model threshold and the second preset model threshold. The mathematical form thereof is a linear function, for example wherein is the constant velocity, is the initial displacement, and these parameters are fitted according to historical displacement data.

[0063] The stable displacement model is a calculation model used for describing the random fluctuation of dam displacement within the measurement error range, and is usually selected when the displacement change is less than the first preset model threshold. The output thereof can be expressed as wherein is the mean value of historical displacement, is a random noise term with zero mean.

[0064] Based on the generated trend signal or the offset trend signal, a risk prediction is made, a future intersection point of the offset trend signal or the trend signal is calculated, the intersection point is the intersection point of the signal curve after extrapolation and the preset offset threshold, and the future intersection point is defined as a preliminary prediction value, which is a preliminary alarm time prediction value without correction of the current dynamic trend. To make the prediction more accurate, the shape of the signal is judged: if the signal used to calculate the intersection point shows a continuous upward trend, the intersection point is defined as a high point; if the signal shows a continuous downward trend, the intersection point is defined as a low point. Based on the high point or the low point, and in combination with the trend parameter in the current time period and the offset change speed, a correction parameter is generated, wherein the trend parameter is extracted from historical offset data or a trend signal and is used to describe the current displacement change rate, usually referred to as displacement speed. The trend parameter here can be the current displacement speed, and the offset change speed corresponds to the displacement acceleration. By combining the speed and acceleration information, it can be judged whether the trend is accelerating or decelerating, so as to generate a dynamic correction parameter. The correction parameter is used to adjust the preliminary prediction value to obtain a corrected prediction value, which is used to more accurately reflect the predicted alarm time considering the current dynamic trend; for example, if the displacement is accelerating, the intersection time of the preliminary prediction is advanced, and the corrected prediction value is defined as the final alarm time, providing a time window for the manager to respond to the risk.

[0065] Embodiment Two

[0066] The embodiment provides a reservoir dam surface displacement adaptive monitoring system based on GNSS positioning, which is used to execute the above-mentioned reservoir dam surface displacement adaptive monitoring method based on GNSS positioning. The system can accurately determine whether the dam surface displacement is abnormal by adaptively solving and checking the obtained GNSS sampling point coordinates, and timely output an alarm signal when the preset offset threshold is exceeded, thereby improving the automation level and reliability of reservoir dam safety monitoring. In specific implementation, the system can be deployed in a dedicated monitoring center server, a cloud computing platform or an embedded industrial computer, and communicates data with a plurality of GNSS receiving terminals arranged on the reservoir dam surface through wireless or wired network. The system can be divided into the following modules:

[0067] The sampling point arrangement module is configured to plan and determine the arrangement positions of the GNSS sampling points before the execution of the monitoring task. In a specific execution process, a three-dimensional structure diagram of the reservoir dam body is acquired or loaded, and based on the three-dimensional structure diagram, regions with a surface area greater than a preset area threshold on the dam surface are automatically or with human assistance identified and determined as priority positions. Selecting a region with a larger area as a priority position helps to ensure that the sampling points can represent the displacement characteristics of a wider region. In accordance with the priority order of the surface region, the line region and the point region, the GNSS sampling points are arranged in the priority positions in sequence, for example, the sampling points are arranged in a grid manner in the large-area region of the dam surface first, then the sampling points are arranged on the key linear structures of the dam crest and the dam foot, and finally the sampling points are supplemented on the key feature points of the spillway and the gate. The arrangement strategy ensures the comprehensiveness and representativeness of the monitoring points.

[0068] The coordinate acquisition module is configured to acquire the check values of the GNSS sampling points determined by the sampling point arrangement module in real time or periodically. The GNSS sampling points are in communication connection with the GNSS receiving terminals arranged on the dam surface. In the monitoring process, the three-dimensional coordinate data calculated by the terminals is continuously received, the coordinates acquired in real time are the check values reflecting the current state of the dam, and are transmitted to the subsequent displacement adaptive calculation module for processing.

[0069] The displacement adaptive solving module is configured to be responsible for adaptive solving of the obtained coordinates to determine the final displacement state. The specific working process is as follows: receiving the check value of the GNSS sampling point provided by the coordinate acquisition module, and at the same time, calling the preset reference value corresponding to the sampling point from the internal storage or database, the reference value is usually the coordinates of the dam body in the initial stable state, calculating the difference between the check value and the preset reference value to obtain the initial value error; in order to eliminate or weaken the interference of measurement noise, multipath effect and other factors on displacement judgment, a pre-constructed inertia model is called to correct the initial value error, the pre-constructed inertia model represents the internal functional relationship between the vertical direction component and the plane direction component of the initial value error, which is established based on the physical characteristics of the displacement of the reservoir dam under the action of water pressure, temperature and other loads, that is, the vertical displacement and the plane displacement often exist certain coupling correlation; further, the function relationship is also corrected by using the vertical height parameter of the reservoir dam, because the displacement response mode of dam body parts at different elevations may be different, by inputting the initial value error into the inertia model for calculation, an error offset closer to the real physical displacement can be obtained; the error offset is applied to the check value to compensate, thereby obtaining the final offset check value, which has higher reliability compared with the original measured coordinates; the final offset check value is decomposed into its components in the plane direction and the vertical direction, and the two components are compared with the corresponding preset offset threshold respectively, and the preset offset threshold is the safety warning line set according to the dam safety specification and historical monitoring data.

[0070] The alarm output module is configured to determine whether to trigger an alarm according to the comparison result of the displacement adaptive solving module. When the comparison result of the displacement adaptive solving module shows that the component of the final offset check value in the plane direction or the component in the vertical direction exceeds the corresponding preset offset threshold, the alarm output module is activated, and after being activated, an alarm signal is immediately output. The alarm signal can take various forms, such as popping up a highlighted warning window on the monitoring system interface, emitting a buzzing or voice alarm, sending an SMS or email to the designated operation and management personnel's mobile phone, so as to ensure that abnormal displacement can be discovered and handled in time.

[0071] Through the cooperative work of the above-mentioned sampling point arrangement module, coordinate acquisition module, displacement adaptive solving module and alarm output module, the application can realize automatic, intelligent and high-precision monitoring of the surface displacement of the reservoir dam body. By introducing the inertia model considering the physical characteristics of the dam body to adaptively correct the measurement data, the accuracy of the monitoring result and the reliability of the alarm are effectively improved, and the application is suitable for long-term safety monitoring scenarios of large-scale water conservancy hub projects.

[0072] The above only is the preferred embodiment of the present application, and is not used to limit the present application, for the person skilled in the art, the present application can have various changes and changes, any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application, should be included in the protection scope of the present application.

Claims

1. A reservoir dam surface displacement adaptive monitoring method based on GNSS positioning, characterized in that, The method comprises: obtaining offset coordinates of GNSS sampling points; determining a final offset check value based on the offset coordinates and a preset reference value in combination with a pre-constructed inertia model; and outputting an alarm signal when a component of the final offset check value in a planar direction or a vertical direction exceeds a corresponding preset offset threshold value; the step of determining the final offset check value comprises: calculating a difference between the offset coordinates and the preset reference value to obtain an initial value error; correcting the initial value error according to the pre-constructed inertia model to obtain an error offset; and compensating the offset coordinates by applying the error offset to obtain the final offset check value; the pre-constructed inertia model is an inertia model representing a functional relationship between a vertical direction component and a planar direction component of the initial value error, and the functional relationship is corrected by using a vertical height parameter of a reservoir dam body; the inertia model is a calculation model for quantifying a physical coupling relationship between vertical displacement and planar displacement of the reservoir dam body, and the formula is: ； wherein, represents the planar error offset, which means the model-predicted planar displacement caused by the vertical displacement induced by the dam; represents the vertical initial error, which means the difference between the offset coordinates measured by GNSS and the initial coordinates in the vertical direction; represents the vertical height of the dam body, which means the vertical distance from the dam foundation to the dam crest, and is used as a key input for correcting the inertia model to reflect the difference in displacement coupling effect of dams of different sizes; represents the reference height, which means the reference dam height for normalization calculation, and is a pre-set empirical constant; represents the model coefficient, which means a dimensionless empirical coefficient obtained by regression analysis of historical data or finite element simulation, and represents the base coupling effect and the height-dependent gain effect, respectively; is a hyperbolic tangent function, which is used for nonlinear smoothing of the influence of the height parameter, making it sensitive near the reference height and saturated far from the reference height.

2. The reservoir dam surface displacement adaptive monitoring method based on GNSS positioning according to claim 1, characterized in that, The method further comprises, before performing monitoring, determining the GNSS sampling points based on a three-dimensional structure diagram of the reservoir dam body, which comprises: selecting an area with a surface area greater than a preset area threshold value as a priority position on the three-dimensional structure diagram; and arranging the GNSS sampling points in the priority position in the order of a surface area, a line area and a point area.

3. A monitoring system applied to the reservoir dam surface displacement adaptive monitoring method based on GNSS positioning of claim 1, characterized in that, The method comprises: a coordinate acquisition module for obtaining offset coordinates of GNSS sampling points; a displacement adaptive solving module for determining a final offset check value based on the offset coordinates and a preset reference value in combination with a pre-constructed inertia model, and comparing the final offset check value with a preset offset threshold value; and an alarm output module for outputting an alarm signal when the comparison result of the displacement adaptive solving module is that the final offset check value exceeds the preset offset threshold value.

4. The GNSS positioning based reservoir dam surface displacement adaptive monitoring system according to claim 3, characterized in that, The displacement adaptive solving module is further configured to: calculate a difference between the offset coordinates and the preset reference value to obtain an initial value error; correct the initial value error according to the pre-constructed inertia model to obtain an error offset; and compensate the offset coordinates by applying the error offset to obtain the final offset check value.

5. The GNSS positioning based reservoir dam surface displacement adaptive monitoring system according to claim 4, characterized in that, The pre-constructed inertia model is an inertia model representing a functional relationship between a vertical direction component and a planar direction component of the initial value error, and the functional relationship is corrected by using a vertical height parameter of a reservoir dam body.

6. The GNSS positioning based reservoir dam surface displacement adaptive monitoring system according to claim 5, characterized in that, The system further comprises: a sampling point arrangement module for determining the GNSS sampling points based on a three-dimensional structure diagram of the reservoir dam body before performing monitoring, which is specifically configured to: select an area with a surface area greater than a preset area threshold value as a priority position on the three-dimensional structure diagram; and arrange the GNSS sampling points in the priority position in the order of a surface area, a line area and a point area.

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