Dam visual monitoring method and system based on three-dimensional deformation field reconstruction

Abstract

The application provides a dam visual monitoring method and system based on three-dimensional deformation field reconstruction, comprising: acquiring a line-of-sight observation value of an earth observation device for a monitoring point; decomposing the line-of-sight observation value to obtain a three-dimensional deformation component of the monitoring point; establishing a spatial deformation field of the monitoring point within a preset range around the monitoring point; acquiring a spatial deformation variable sequence of the monitoring point based on the spatial deformation field; determining a deformation velocity field and a deformation acceleration field of the monitoring point according to the spatial deformation variable sequence and a least square fitting algorithm; constructing a dynamic three-dimensional deformation field of the monitoring point according to the three-dimensional deformation component, the spatial deformation field, the deformation velocity field and the deformation acceleration field; rendering the dynamic three-dimensional deformation field based on a hierarchical detail control algorithm and an adaptive grid generation algorithm, and superimposing the dynamic three-dimensional deformation field into an initial dam model to obtain a target dam model. The application can realize multi-dimensional capture, dynamic trend description and intuitive visual monitoring of dam deformation.

Description

Technical Field

[0001] This application relates to the general field of image data processing or generation technology, and in particular to a method and system for visual monitoring of dams based on three-dimensional deformation field reconstruction. Background Technology

[0002] As a key water conservancy infrastructure for ensuring water resource regulation and flood control, dams are prone to major safety accidents such as structural instability if minor deformations of the dam body, slopes and surrounding structures are not monitored and warned in time. Therefore, accurate monitoring and intuitive presentation of dam deformation has become a core requirement for the safety management of water conservancy projects.

[0003] Existing dam monitoring solutions have the following limitations: Firstly, data processing is limited to a single dimension, only able to obtain deformation information in a single direction based on observation data. This fails to capture the complete spatial deformation state of monitoring points, resulting in a one-sided characterization of dam deformation and making it difficult to support a comprehensive assessment of dam structural stability. Secondly, dynamic characterization capabilities are insufficient. Existing solutions mostly focus on static deformation descriptions at a specific moment or time period, failing to effectively combine the spatial distribution of deformation with long-term variation patterns. This makes it impossible to reflect the dynamic evolution characteristics of deformation and to predict potential safety risks in advance. Thirdly, visualization presentation is inadequate. Existing rendering strategies lack targeted design, failing to flexibly adjust display accuracy according to actual observation needs and failing to accurately integrate multi-source deformation data with the dam model. This results in insufficient intuitiveness and practicality of the monitoring results presented to user terminals, making it difficult for staff to quickly identify risks and hazards in key areas and failing to meet the real-time, comprehensive, and accurate monitoring needs of dams. Summary of the Invention

[0004] This application provides a method and system for visual monitoring of dams based on three-dimensional deformation field reconstruction. The server acquires line-of-sight observations from dam monitoring points and Earth observation equipment. Then, through three-dimensional deformation component decomposition, the deformation field around the monitoring points is established. The deformation velocity field and acceleration field are determined based on the spatial deformation sequence and least squares fitting to construct a dynamic three-dimensional deformation field. Finally, the dynamic field is rendered using a hierarchical detail control algorithm and an adaptive mesh generation algorithm and superimposed onto the initial dam model and sent to the user terminal. This achieves multi-dimensional capture, dynamic trend characterization, and intuitive visual monitoring of dam deformation.

[0005] In a first aspect, this application provides a dam visualization monitoring method based on three-dimensional deformation field reconstruction, applied to a server of a dam monitoring system. The dam monitoring system further includes earth observation equipment and user terminals, and the server is connected to both the earth observation equipment and the user terminals. The method includes:

[0006] The monitoring points of the dam are obtained, and the line-of-sight observation values ​​of the Earth observation equipment for the monitoring points are obtained;

[0007] The line of sight is decomposed into the observed value to obtain the three-dimensional deformation components of the monitoring point. The three-dimensional deformation components include a first deformation component in the east-west direction, a second deformation component in the north-south direction, and a third deformation component in the vertical direction.

[0008] Establish the spatial deformation field of the monitoring point within a preset range;

[0009] Based on the spatial deformation field, obtain the spatial deformation sequence corresponding to the spatial deformation of the monitoring point at different time nodes; and determine the deformation velocity field and deformation acceleration field of the monitoring point according to the spatial deformation sequence and the least squares fitting algorithm.

[0010] The dynamic three-dimensional deformation field of the monitoring point is constructed based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, and the deformation acceleration field.

[0011] The dynamic three-dimensional deformation field is rendered based on the hierarchical detail control algorithm and the adaptive mesh generation algorithm, and superimposed on the initial dam model to obtain the target dam model; and the target dam model is sent to the user terminal for visualization.

[0012] Secondly, embodiments of this application provide a dam monitoring system, the system comprising:

[0013] The acquisition unit is used to acquire the monitoring points of the dam, and to acquire the line-of-sight observation values ​​of the Earth observation equipment for the monitoring points;

[0014] The processing unit is used to decompose the line of sight into observed values ​​to obtain the three-dimensional deformation components of the monitoring point. The three-dimensional deformation components include a first deformation component in the east-west direction, a second deformation component in the north-south direction, and a third deformation component in the vertical direction. It establishes a spatial deformation field of the monitoring point within a preset surrounding range; based on the spatial deformation field, it obtains a spatial deformation sequence corresponding to the spatial deformation of the monitoring point at different time nodes; and determines the deformation velocity field and deformation acceleration field of the monitoring point based on the spatial deformation sequence and a least-squares fitting algorithm; constructs a dynamic three-dimensional deformation field of the monitoring point based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, and the deformation acceleration field; and renders the dynamic three-dimensional deformation field based on a hierarchical detail control algorithm and an adaptive mesh generation algorithm, and superimposes it onto an initial dam model to obtain a target dam model.

[0015] The sending unit is used to send the target dam model to the user terminal for visualization.

[0016] Thirdly, embodiments of this application provide an electronic device including a processor, a memory, and one or more programs, the one or more programs being stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps in the first aspect of embodiments of this application.

[0017] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program / instructions stored thereon, which is executed by a processor to implement the steps of the method described in the first aspect above.

[0018] As can be seen, in this embodiment, the server acquires the monitoring points of the dam and the line-of-sight observation values ​​of the monitoring points from the Earth observation equipment; decomposes the line-of-sight observation values ​​to obtain the three-dimensional deformation components of the monitoring points; establishes a spatial deformation field of the monitoring points within a preset range around them; based on the spatial deformation field, acquires the spatial deformation sequence corresponding to the spatial deformation of the monitoring points at different time nodes; and determines the deformation velocity field and deformation acceleration field of the monitoring points based on the spatial deformation sequence and the least squares fitting algorithm; constructs a dynamic three-dimensional deformation field of the monitoring points based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, and the deformation acceleration field; renders the dynamic three-dimensional deformation field based on the hierarchical detail control algorithm and the adaptive mesh generation algorithm, and superimposes it onto the initial dam model to obtain the target dam model; and sends the target dam model to the user terminal for visualization. Thus, compared to existing dam monitoring technologies, this application achieves multi-dimensional capture, dynamic trend characterization, and intuitive visualization monitoring of dam deformation, solving the problems of one-sided deformation characterization, insufficient dynamism, and poor visualization effects in existing technologies, and can meet the needs of precise control over dam safety. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the steps of a dam visualization monitoring method based on three-dimensional deformation field reconstruction provided in this application embodiment;

[0021] Figure 2 This is a schematic diagram of a dam visualization interface based on InSAR satellite imagery provided in an embodiment of this application;

[0022] Figure 3This is another schematic diagram of a dam visualization interface based on InSAR satellite imagery provided in the embodiments of this application;

[0023] Figure 4 This is a schematic diagram of a level of detail provided in an embodiment of this application;

[0024] Figure 5 This is a schematic diagram of a dam visualization interface at the lowest level of detail provided in an embodiment of this application;

[0025] Figure 6 This is a schematic diagram of a dam visualization interface at the maximum level of detail provided in an embodiment of this application;

[0026] Figure 7 This is a radar chart illustrating the comprehensive performance of multiple dam visualization monitoring methods provided in this application embodiment;

[0027] Figure 8 This is a functional unit block diagram of a dam monitoring system provided in an embodiment of this application;

[0028] Figure 9 This is a structural block diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0029] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present application.

[0030] The terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0031] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0032] In the embodiments of this application, "and / or" describes the relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone; A and B exist simultaneously; B exists alone. Among them, A and B can be singular or plural.

[0033] In this embodiment, the symbol " / " can indicate that the preceding and following objects are in an "or" relationship. Alternatively, the symbol " / " can also represent a division sign, i.e., performing a division operation. For example, A / B can mean A divided by B.

[0034] In the embodiments of this application, "at least one item" or its similar expression refers to any combination of these items, including any combination of a single item or a plurality of items. "One or more" means one or more, while "multiple" means two or more. For example, "at least one item" of a, b, or c can represent the following seven cases: a, b, c; a and b; a and c; b and c; a, b, and c. Each of a, b, and c can be an element or a set containing one or more elements.

[0035] In the embodiments of this application, "equal to" can be used with "greater than" and is applicable to technical solutions used when "greater than" is used; it can also be used with "less than" and is applicable to technical solutions used when "less than" is used. When "equal to" is used with "greater than", it is not used with "less than"; when "equal to" is used with "less than", it is not used with "greater than".

[0036] Existing dam monitoring solutions have the following limitations: Firstly, data processing is limited to a single dimension, only able to obtain deformation information in a single direction based on observation data. This fails to capture the complete spatial deformation state of monitoring points, resulting in a one-sided characterization of dam deformation and making it difficult to support a comprehensive assessment of dam structural stability. Secondly, dynamic characterization capabilities are insufficient. Existing solutions mostly focus on static deformation descriptions at a specific moment or time period, failing to effectively combine the spatial distribution of deformation with long-term variation patterns. This makes it impossible to reflect the dynamic evolution characteristics of deformation and to predict potential safety risks in advance. Thirdly, visualization presentation is inadequate. Existing rendering strategies lack targeted design, failing to flexibly adjust display accuracy according to actual observation needs and failing to accurately integrate multi-source deformation data with the dam model. This results in insufficient intuitiveness and practicality of the monitoring results presented to user terminals, making it difficult for staff to quickly identify risks and hazards in key areas and failing to meet the real-time, comprehensive, and accurate monitoring needs of dams.

[0037] To address the aforementioned issues, this application provides a method and system for visual monitoring of dams based on three-dimensional deformation field reconstruction. The embodiments of this application will be described in detail below with reference to the accompanying drawings.

[0038] Please see Figure 1 , Figure 1 This is a flowchart illustrating the steps of a dam visualization monitoring method based on three-dimensional deformation field reconstruction, provided in an embodiment of this application. It is applied to the server of a dam monitoring system, such as... Figure 1 As shown, the method includes the following steps:

[0039] Step S110: Obtain the monitoring point of the dam, and obtain the line-of-sight observation value of the Earth observation device for the monitoring point.

[0040] The monitoring points focus on key areas of dam safety, including the main body of the dam (such as the dam crest, dam slope, and dam foundation), the dam slopes (such as the dam shoulder slopes and reservoir bank slopes), and the core locations of surrounding structures (such as spillways, secondary dams, and monitoring stations). These locations must be "functional locations" that are prone to safety risks, such as settlement points (monitoring vertical subsidence), deformation points (monitoring horizontal displacement), and sliding points (monitoring whether the slope has a tendency to become unstable and slide).

[0041] In this embodiment of the application, the Earth observation equipment can be an InSAR satellite (Synthetic Aperture Radar Interferometer) that can be used in conjunction with a GNSS monitoring station (Global Navigation Satellite System monitoring station) to achieve observation of the dam. InSAR satellites are suitable for acquiring deformation data of the dam area over a wide area and in all weather conditions, while GNSS monitoring stations can provide higher precision single-point observation data for key monitoring points.

[0042] Among them, line-of-sight observations are "raw physical data" directly acquired by Earth observation equipment, rather than processed deformation results. For InSAR satellites, this is the "phase difference of echo signals at different time points" or the "round-trip distance difference between the satellite and the monitoring point" obtained after the radar transmits and receives echoes to the monitoring point. For GNSS monitoring stations, this is the "real-time distance change" between the monitoring station and the satellite. This type of data usually contains interference such as terrain undulations and atmospheric delays, and needs to be processed in subsequent steps to be converted into usable deformation information.

[0043] Step S120: Decompose the line of sight to the observed value to obtain the three-dimensional deformation components of the monitoring point.

[0044] The three-dimensional deformation components include the first deformation component in the east-west direction, the second deformation component in the north-south direction, and the third deformation component in the vertical direction.

[0045] It is understandable that the line-of-sight observations are decomposed in order to transform the “raw data in one direction (line-of-sight observations)” acquired by the Earth observation equipment (InSAR satellite) into “three-dimensional deformation components” that can reflect the real spatial displacement of the monitoring point.

[0046] In one possible embodiment, the Earth observation device includes an InSAR satellite; the step of decomposing the line-of-sight observations to obtain the three-dimensional deformation components of the monitoring points includes: acquiring the topographic location information of the dam; determining, based on the topographic location information, whether the deformation of the monitoring points in the north-south direction is limited by the terrain, wherein the monitoring points include settlement points, deformation points, and sliding points of the dam body, dam slopes, and surrounding structures; if so, determining a first line-of-sight deformation variable based on the line-of-sight observations of the ascending InSAR satellite, and determining a second line-of-sight deformation variable based on the line-of-sight observations of the descending InSAR satellite; and, based on the first line-of-sight deformation variable, the second line-of-sight deformation variable, and satellite radar... The first deformation component in the east-west direction and the third deformation component in the vertical direction of the monitoring point are calculated from the incident angle and the satellite radar heading angle. If not, the first line-of-sight deformation time series is determined based on the line-of-sight observations of the ascending InSAR satellite, and the second line-of-sight deformation time series is determined based on the line-of-sight observations of the descending InSAR satellite. Based on the first line-of-sight deformation time series, the second line-of-sight deformation time series, the satellite radar incident angle, and the satellite radar heading angle, the first deformation component in the east-west direction, the second deformation component in the north-south direction, and the third deformation component in the vertical direction of the monitoring point are obtained using a least-squares fitting algorithm.

[0047] In a specific embodiment, the core of determining whether the deformation of the monitoring point in the north-south direction is limited by the terrain location information is based on the terrain location information of the dam, combined with whether there are "hard terrain conditions that hinder north-south displacement" in the physical space around the monitoring point. Specifically, this can be determined through the characteristics of the following actual scenarios:

[0048] (1) Valley topography: If the monitoring point is located in an east-west extending valley (such as a dam built in an east-west canyon), and there are continuous, tall mountains (or steep slopes) on the north and south sides of the valley, and the horizontal distance between the mountain and the monitoring point is less than the preset threshold (such as no open space within 500 meters), then the monitoring point has no physical space for north-south displacement and is judged as "restricted".

[0049] (2) Slope orientation and gradient: If the monitoring point is located on the east-west trending dam slope / reservoir bank slope (e.g., the slope surface faces north and south), and the slope gradient is greater than the preset value (e.g., 30°, steep slopes are unlikely to have north-south displacement perpendicular to the slope surface), the monitoring point can only slide along the slope surface (mostly east-west or along the slope), and the north-south displacement is very small, which is judged as "restricted".

[0050] (3) Surrounding buildings / terrain obstacles: If there are immovable hard obstacles (such as tall retaining walls, mountain protrusions, dense building clusters) on the north and south sides of the monitoring point (such as monitoring station buildings around the dam, auxiliary dam), and the distance between the obstacle and the monitoring point is less than the possible deformation range of the monitoring point (such as less than 10 meters, which cannot accommodate effective north-south displacement), it is judged as "restricted".

[0051] Understandably, if terrain limitations are not considered and the "three-dimensional components" are calculated uniformly for all monitoring points, "false results that do not match reality" may occur. For example, monitoring points in east-west valleys may not actually have north-south displacement, but if the algorithm is used to forcibly calculate "north-south deformation values," these values ​​are "false data fitted by the algorithm" and will mislead subsequent assessments of dam safety. However, after considering terrain limitations, ignoring the north-south components for the restricted monitoring points and retaining only the true and valid east-west and vertical components will allow the decomposition results to better match the actual deformation patterns of the dam and avoid interference from invalid data.

[0052] In one possible embodiment, the step of calculating the first deformation component of the monitoring point in the east-west direction and the third deformation component in the vertical direction based on the first line-of-sight deformation, the second line-of-sight deformation, the satellite radar incident angle, and the satellite radar heading angle includes:

[0053] The second deformation component of the monitoring point in the north-south direction is determined to be zero;

[0054] The fourth solution equation for the three-dimensional deformation components is established based on the first line-of-sight deformation, the satellite radar incident angle, and the satellite radar heading angle.

[0055] The fifth solution equation for the three-dimensional deformation components is established based on the second line-of-sight deformation, the satellite radar incident angle, and the satellite radar heading angle;

[0056] By jointly solving the fourth and fifth equations, the first deformation component of the monitoring point in the east-west direction and the third deformation component in the vertical direction are obtained.

[0057] Specifically, the formulas for calculating the three-dimensional deformation components corresponding to the fourth and fifth solution equations are as follows:

[0058] ;

[0059] in, The line-of-sight deformation of the InSAR satellite is expressed in mm. , , These represent the first deformation component in the east-west direction, the second deformation component in the north-south direction, and the third deformation component in the vertical direction, respectively, with units in mm; The radar incident angle has a range of 20°-50°, with a range of 30°-40° during ascent and 35°-45° during descent, and the difference between the observation angles of the two orbits is ≥25°. This is the radar heading angle, approximately -10° during ascent and approximately 190° during descent.

[0060] In one possible embodiment, obtaining the first deformation component in the east-west direction, the second deformation component in the north-south direction, and the third deformation component in the vertical direction of the monitoring point based on the first line-of-sight deformation time series, the second line-of-sight deformation time series, the satellite radar incident angle, and the satellite radar heading angle using a least-squares fitting algorithm includes: establishing multiple first solution equations for the three-dimensional deformation components based on the first line-of-sight deformation time series, the satellite radar incident angle, and the satellite radar heading angle; establishing multiple second solution equations for the three-dimensional deformation components based on the second line-of-sight deformation time series, the satellite radar incident angle, and the satellite radar heading angle; and solving the multiple first solution equations and the multiple second solution equations using the least-squares fitting algorithm to obtain the three-dimensional deformation components of the monitoring point.

[0061] Among them, the line-of-sight deformation time series contains multiple line-of-sight deformations corresponding to multiple time nodes, which can reflect the effective displacement value of a specific monitoring point along the satellite line of sight (after eliminating interference from terrain, atmosphere, etc.), and all data are bound to specific monitoring points (such as a point on the top of the dam or a point on the slope), clearly reflecting the displacement change trend of the monitoring point over time in different satellite line-of-sight directions.

[0062] As can be seen, in this embodiment, the north-south deformation of the monitoring point is determined based on the dam's topographic location information to see if it is restricted by the terrain (such as river valleys and mountains). If it is restricted, the line-of-sight deformation variables of the ascending and descending InSAR satellites are used, combined with the satellite radar incident angle and heading angle, to calculate the east-west and vertical components. If it is not restricted, multiple solution equations are established based on the time series of the line-of-sight deformation variables of the ascending and descending satellites, and the deformation components in the three directions are obtained through the least squares fitting algorithm to ensure that the decomposition results are consistent with the actual terrain and accurate.

[0063] Step S130: Establish the spatial deformation field of the monitoring point within a preset range.

[0064] In this embodiment, the spatial deformation field within a preset range around the monitoring point is obtained by selecting reference points around the monitoring point and combining the distance and weight between the reference points and the monitoring point, using interpolation methods such as radial basis function interpolation to obtain the deformation of more points within this range. The final constructed spatial deformation field is a set of deformation values ​​corresponding to each spatial location within a preset range (e.g., 500m) around the monitoring point, which can intuitively present the spatial distribution law of deformation in this area (e.g., large deformation value in the dam crest area and small deformation value in the dam foundation area).

[0065] In one possible embodiment, establishing the spatial deformation field of the monitoring point within a preset surrounding range includes: selecting multiple reference points located within the preset surrounding range of the monitoring point; determining the total number of reference points; determining multiple distances corresponding one-to-one between the monitoring point and the multiple reference points; and determining multiple reference weight coefficients corresponding one-to-one between the multiple reference points based on the multiple distances and a preset distance weight index; obtaining an interpolated deformation value at the monitoring point based on a preset radial basis function interpolation algorithm, based on the location information of the monitoring point, the total number of reference points, the multiple distances, and the multiple reference weight coefficients, wherein the interpolated deformation value characterizes the spatial deformation of the monitoring point; and obtaining the spatial deformation field of the monitoring point within the preset surrounding range based on the spatial deformation.

[0066] Specifically, based on a preset radial basis function interpolation algorithm, the formula for calculating the spatial deformation at the monitoring point is as follows:

[0067] ;

[0068] in, For monitoring points Spatial deformation, in mm; Let I be the reference weight coefficient for the i-th reference point, and let I be the total number of reference points. The least squares method is used to determine , , and , These are radial basis functions.

[0069] Among them, radial basis functions Using multiple quadratic functions ,in, This is a shape parameter that affects the smoothness of interpolation; its value ranges from 0.1 to 1.0. , This is the average distance from all reference points to the monitoring point. For reference point With the i-th monitoring point The Euclidean distance.

[0070] The formula for calculating the reference weight coefficient is as follows:

[0071] ;

[0072] in, Let be the distance from the i-th reference point to the monitoring point. This is the distance weighting index, with a value ranging from 1.5 to 2.5.

[0073] Specifically, if there is only one monitoring point within the surrounding preset range, the spatial deformation corresponding to that monitoring point directly constitutes the spatial deformation field within the surrounding preset range; if there are multiple monitoring points, all the spatial deformations corresponding to all monitoring points constitute the spatial deformation field.

[0074] As can be seen, in this embodiment, by selecting reference points around the monitoring point, determining the reference weight coefficient by combining distance and preset distance weight index, and then using an interpolation algorithm containing specific quadratic radial basis functions (shape parameters 0.1-1.0) to calculate the interpolated deformation value of the monitoring point, a spatial deformation field of a preset range (e.g., 500m) is constructed using the spatial deformation of one or more monitoring points. This not only accurately obtains the deformation of the monitoring point, but also intuitively presents the spatial distribution law of deformation in the area (e.g., the difference in deformation between the dam crest and the dam foundation), providing reliable spatial deformation data support for the subsequent construction of dynamic three-dimensional deformation field and dam visualization monitoring.

[0075] Step S140: Based on the spatial deformation field, obtain the spatial deformation sequence corresponding to the spatial deformation of the monitoring point at different time nodes; and determine the deformation velocity field and deformation acceleration field of the monitoring point according to the spatial deformation sequence and the least squares fitting algorithm.

[0076] Among them, the deformation velocity field refers to the set of "deformation degree per unit time" (i.e. deformation velocity) corresponding to each spatial location within the monitoring point and the surrounding preset range. It can intuitively reflect the spatial distribution of the deformation rate at different locations in the area (for example, the annual deformation velocity of a certain point on the dam crest is -30mm, and the annual deformation velocity of a certain point on the dam foundation is -5mm. Arranged according to spatial coordinates, they constitute the velocity field).

[0077] Among them, the deformation acceleration field is a set of "changes in deformation velocity per unit time" (i.e., deformation acceleration) corresponding to each spatial location, which is used to reflect the changing trend of deformation velocity (for example, if the deformation acceleration at a certain point is -2 mm / year², it means that its deformation velocity is accelerating year by year). Both are calculated by combining the spatial deformation variable sequence with least squares fitting, and the core is to present the "spatial distribution of deformation dynamic characteristics".

[0078] In one possible embodiment, the spatial deformation sequence includes multiple spatial deformations corresponding to the monitoring point at different time points, and the multiple spatial deformations are arranged in chronological order. Determining the deformation velocity field and deformation acceleration field of the monitoring point based on the spatial deformation sequence and the least squares fitting algorithm includes: for each spatial deformation in the spatial deformation sequence, establishing a third solution equation between the spatial deformation and a preset reference spatial deformation, deformation velocity, and deformation acceleration, resulting in multiple third solution equations; solving the multiple third solution equations based on the least squares fitting algorithm to obtain the deformation velocity and deformation acceleration of the monitoring point; determining the expected monitoring period for the dam; obtaining the deformation velocity field of the monitoring point based on the product of the deformation velocity and the expected monitoring period; and obtaining the deformation acceleration field of the monitoring point based on the product of the deformation acceleration and the square of the expected monitoring period.

[0079] Specifically, the formula for solving the third equation is as follows:

[0080] ;

[0081] Among them, D_ The spatial deformation of the monitoring point at time t, in mm; This is the predefined reference space deformation, in mm. The deformation rate is expressed in mm / year. The acceleration due to deformation is expressed in units of 1000 kJ / m². .

[0082] The preset reference spatial deformation can be obtained using ENVI SAR Scape software. ENVI SAR Scape is a professional synthetic aperture (SAR) data processing software, primarily used to process radar images acquired by InSAR satellites (such as Sentinel-1 and ALOS). It can perform functions such as SAR image registration, interferogram generation, phase unwrapping, and terrain correction. Ultimately, it can extract surface deformation information (such as subsidence and displacement) from radar data. It is a commonly used tool in the field of remote sensing deformation monitoring and can provide reliable basic deformation data support for dam monitoring. The preset reference spatial deformation can be selected from stable deformation values ​​at the initial stage of monitoring or from benchmark points in the region without significant deformation, serving as a comparison benchmark.

[0083] The multiple third solution equations are solved using the least squares fitting algorithm, and the corresponding formulas are as follows:

[0084]

[0085] in, They are respectively Spatial deformation of monitoring points at all times.

[0086] In this embodiment, the time series of spatial deformation at the monitoring points spans ≥ 1 year to ensure the reliability of statistical characteristics. Deformation velocity V reflects the linear trend of deformation, while deformation acceleration A reflects the nonlinear trend of deformation, with a prediction accuracy ≤ 3 mm. The deformation velocity field is obtained by multiplying the deformation velocity V by the expected monitoring years; the deformation acceleration field is obtained by multiplying the deformation acceleration A by the square of the expected monitoring years, where the expected monitoring years are in years.

[0087] As can be seen, in this embodiment, by combining the spatial deformation sequence of monitoring points arranged in time with the preset reference deformation (as a comparison benchmark) obtained by ENVI SARscape software, a third solution equation containing deformation velocity and acceleration is established. The reliability of the data is ensured by relying on the sequence with a span of ≥1 year. The velocity reflecting the linear deformation trend and the acceleration reflecting the nonlinear deformation trend are solved by least squares fitting. Then, the deformation velocity field and acceleration field are generated by combining the expected monitoring years, and the prediction accuracy is ≤3mm. This provides accurate temporal deformation characteristic data support for the subsequent construction of dynamic three-dimensional deformation field.

[0088] Step S150: Construct the dynamic three-dimensional deformation field of the monitoring point based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, and the deformation acceleration field.

[0089] Understandably, the essence of constructing the dynamic three-dimensional deformation field of the monitoring point is to combine the "static spatial deformation characteristics" (three-dimensional deformation components, spatial deformation field) with the "dynamic temporal evolution characteristics" (velocity field, acceleration field) to ultimately construct a dynamic three-dimensional deformation field that can reflect both the "deformation differences of the monitoring point and its surroundings at different locations" and "how these deformations change over time (speed, trend)". This provides a complete data model that "can see the spatial distribution and understand the temporal trend" for the visualization rendering in subsequent steps, and is a key transition to achieving "dynamic monitoring".

[0090] In one possible embodiment, constructing the dynamic three-dimensional deformation field of the monitoring point based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, and the deformation acceleration field includes: performing deformation statistics based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, and the deformation acceleration field to obtain multiple initial deformation feature parameters of the monitoring point, wherein the multiple deformation feature parameters include the deformation rate of the monitoring point, the maximum value, minimum value, average value, and standard deviation of the three-dimensional deformation components of the monitoring point; predicting multiple target deformation feature parameters of the monitoring point at different future time nodes based on a time-series fitting algorithm; and constructing the dynamic three-dimensional deformation field of the monitoring point based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, the deformation acceleration field, the multiple initial deformation feature parameters, and the multiple target deformation feature parameters.

[0091] Among them, time series fitting prediction can specifically be achieved by using time series fitting algorithms (such as linear fitting and exponential fitting) to estimate the "target deformation characteristic parameters" at different time points in the future based on the initial characteristic parameters. For example, it can predict the deformation rate and the maximum and minimum values ​​of the three-dimensional components of the monitoring points after 1 year and 3 years, so as to meet the need of "predicting risks in advance" for dam monitoring.

[0092] Furthermore, warning thresholds can be set based on historical data and design standards, and long-term forecasts and warnings of 12 / 24 / 36 / 48 / 72 hours can be made based on extrapolation predictions of time series fitting. Short-term warnings of 1 / 3 / 6 / 9 / 12 hours can be made based on real-time evaluation of the most recent observation data. Warnings can also be classified into normal, yellow, orange and red warnings.

[0093] As can be seen, in this embodiment, by integrating static three-dimensional deformation components, spatial deformation field and dynamic deformation velocity field and acceleration field, the initial deformation characteristic parameters such as deformation rate and maximum / minimum values ​​of three-dimensional components at monitoring points are first statistically analyzed. Then, a time-series fitting algorithm is used to predict future target characteristic parameters. Furthermore, historical data and design standards can be combined to set early warning thresholds, enabling long-term forecasts and early warnings of 12-72 hours and short-term early warnings of 1-12 hours, as well as graded (normal, yellow, orange, red) early warnings. The final constructed dynamic three-dimensional deformation field not only presents the spatial deformation distribution and depicts the temporal evolution trend, but also provides a complete data model for subsequent visualization rendering and can predict dam deformation risks in advance, meeting the early warning needs of safety monitoring.

[0094] Step S160: Render the dynamic three-dimensional deformation field based on the hierarchical detail control algorithm and the adaptive mesh generation algorithm, and overlay it onto the initial dam model to obtain the target dam model; and send the target dam model to the user terminal for visualization.

[0095] The core of the hierarchical detail control algorithm is to "adjust the level of model detail as needed". For example, when a user terminal views a dam, if the viewing angle is far (viewing the whole), the algorithm will automatically simplify the model details (such as omitting small textures on the dam surface) to reduce loading pressure and ensure smoothness; if the viewing angle is close (viewing a part), it will load high-precision details (such as dam cracks and deformation protrusions) to ensure clear observation and balance visualization smoothness and detail accuracy.

[0096] The adaptive mesh generation algorithm adjusts the mesh density according to the degree of deformation. It generates dense meshes for areas of significant dam deformation (such as the dam crest and slopes, where the deformation value is large) to accurately capture subtle deformations with more mesh nodes. For areas of gentle deformation (such as the dam foundation, where the deformation value is small), it generates sparse meshes to avoid redundant data. Ultimately, the rendered dynamic deformation field can accurately match the actual deformation of the dam while controlling the amount of data, making it convenient for users to present efficiently.

[0097] In one possible embodiment, the rendering of the dynamic 3D deformation field based on the hierarchical detail control algorithm and the adaptive mesh generation algorithm, and the superposition of the rendering field onto the initial dam model to obtain the target dam model, includes: obtaining the actual observation position of the dam observed by the user, and determining the actual observation distance between the actual observation position and the monitoring point; calculating the dynamic rendering display accuracy based on the hierarchical detail control algorithm according to the actual observation distance, a preset observation distance, and a preset maximum level of detail; determining the deformation gradient of the monitoring point and the maximum deformation gradient of the dam according to the dynamic 3D deformation field, wherein the maximum deformation gradient of the dam is the maximum deformation gradient of the monitoring point corresponding to the dam crest region; calculating the mesh density of the dam region corresponding to the monitoring point according to the deformation gradient, the maximum deformation gradient, a preset basic mesh density, and a gradient sensitivity coefficient; rendering the dynamic 3D deformation field according to the dynamic rendering display accuracy and the mesh density to obtain the processed target 3D deformation field; and superimposing the target 3D deformation field onto the initial dam model to obtain the target dam model.

[0098] The accuracy of dynamic rendering can be represented by the level of detail. The higher the level of detail, the smaller the displayed mesh density, which means the higher the accuracy of dynamic rendering.

[0099] Specifically, the formula for calculating the level of detail is as follows:

[0100] ;

[0101] Where LOD_level is the level of detail, and max_level is the preset maximum level of detail. This is the actual observation distance. To preset the observation distance, This is the data density correction factor. The level of detail can be set from 0 to 6, with a preset maximum level of detail of 6. The preset observation distance can be set to 100m.

[0102] It should be noted that in this embodiment, the level of detail and the preset observation distance need to be set according to the display accuracy requirements, and this embodiment does not limit this.

[0103] In this embodiment, the data density correction factor The calculation formula is:

[0104] ;

[0105] in, This represents the local data density, in units of... ; The baseline data density is expressed in units of... .

[0106] Among them, local area data density refers to the number of deformation monitoring data points per unit area within a specific local area of ​​the dam (such as a section of the dam crest or a section of the slope) (unit: points / This reflects the density of deformation data in that local area (the more data points, the higher the density, and the better the ability to capture subtle deformations). The baseline data density refers to the "basic reference data density" (unit: data points / ...) across the entire dam monitoring area. The minimum or standard data density is the minimum required to ensure the accuracy of overall dam deformation monitoring. There are two ways to obtain this data density: one is to refer to industry standards or dam design requirements (such as the recommended data density standards for monitoring similar dams in the water conservancy industry); the other is to calculate the average data density of the entire dam monitoring area (total number of data points ÷ total area of ​​dam monitoring) as a benchmark value. This application does not impose any restrictions on this.

[0107] Specifically, the formula for calculating the grid density of the monitoring points corresponding to the dam area is as follows:

[0108] ;

[0109] in, This represents the grid density, in units of ; To preset the basic mesh density, you can select... ; This is the gradient sensitivity coefficient, with a value ranging from 0.5 to 2.0; This is the normalized deformation gradient.

[0110] Among them, the normalized deformation gradient The calculation formula is as follows:

[0111] ;

[0112] ;

[0113] in, For the maximum deformation gradient, The deformation gradient at the monitoring point, For the spatial deformation of the monitoring points, , , These are the partial derivatives of the spatial deformation D along the three spatial coordinate axes: x (e.g., east-west), y (e.g., north-south), and z (e.g., vertical), representing the rate of change of D along the corresponding coordinate axes—for example... =3mm / m means that for every 1 meter moved along the x-direction, the spatial deformation D of the monitoring point will change by 3mm.

[0114] For example, in regions with a deformation gradient ≥ 0.5 mm / m, the mesh density increases by more than 50%, while in regions with a deformation gradient ≤ 0.1 mm / m, the mesh density can be reduced by 50%. This achieves adaptive adjustment of the mesh density, dynamically optimizing the mesh distribution based on the deformation gradient.

[0115] In some embodiments of this example, the method of rendering the dynamic three-dimensional deformation field according to the dynamic rendering display accuracy and the mesh density to obtain the processed target three-dimensional deformation field further includes: removing redundant objects. Specifically, removing redundant objects includes: removing objects outside the field of view using a frustum test, removing occluded objects using an occlusion test, and removing excessively distant detail objects using a distance test. The calculation formula is as follows:

[0116] ;

[0117] in, For the cone test, For occlusion testing, For distance testing, Visible is an indicator used to determine whether an object in the dam model needs to be visualized and rendered (usually a Boolean value, "true" means to retain and render, "false" means to cull and not render). Visible is "true" only when all three tests are "true": the frustum test (object is within the field of view), the occlusion test (object is not occluded by other structures), and the distance test (object is within a reasonable display range from the observation position). In this case, the corresponding object will be retained and rendered. If any one of the tests is not met, Visible is "false" and the object will be culled and not rendered redundantly.

[0118] Thus, rendering performance is optimized by using frustum culling and occlusion culling techniques, supporting multi-threaded parallel processing and making full use of the computing power of multi-core CPUs and GPUs.

[0119] In this embodiment, the 3D visualization display supports rotation operation: supports three-axis rotation with sensitive and smooth response; zoom operation: supports a zoom range of 150%-1000%; roaming operation: supports free viewpoint movement; slice display: supports viewing slices along any direction; measurement function: supports measurement of distance, area, volume, etc.; time playback: supports animation playback of deformation process with a frame rate ≥30fps.

[0120] As can be seen, in this embodiment, by combining hierarchical detail control and adaptive mesh generation algorithm, the dynamic rendering display accuracy is first calculated based on the actual observation distance and data density correction factor (simplifying details from a distance to ensure smoothness, and loading high-precision details up close to ensure clear observation). Then, the mesh density is calculated based on deformation gradient, maximum deformation gradient, and gradient sensitivity coefficient (densifying the mesh in areas of severe deformation to accurately capture deformation, and thinning the mesh in areas of gentle deformation to reduce data redundancy). This achieves a balance between rendering accuracy and data volume. Finally, the target dam model is superimposed on the initial model, and the dynamic deformation of the dam can be efficiently and accurately visualized on the user terminal, ensuring both smooth viewing and clearly reflecting the deformation differences in different areas.

[0121] The following section provides a specific example to further illustrate the dam visualization monitoring method based on three-dimensional deformation field reconstruction provided in this application.

[0122] First, assume the data includes 8.5 million InSAR satellite pixels and 15 GNSS monitoring points; the monitoring area is 12 × 8 km², with an elevation change of 600 m; deformation characteristics are: maximum subsidence of 45 mm and maximum horizontal displacement of 23 mm. Taking a typical pixel as an example (coordinates: 102.7°E, 25.3°N): obtain the InSAR satellite line-of-sight deformation of the ascending orbit at the monitoring point. Radar incident angle asc=36° asc=-10°; Line-of-sight deformation of the descending InSAR satellite D_LOS_desc=-8.3mm, radar incident angle desc = 41° desc = 190°.

[0123] Next, based on terrain constraints, we assume the north-south deformation component is... =0, based on the line-of-sight deformation variables of the ascending and descending InSAR satellites at the monitoring points, the equations for the three-dimensional deformation components of the monitoring points are established respectively: [ ]× =-12.5; [ ]× =-8.3; Combine the two equations to calculate the deformation components in the east-west and vertical directions. =2.8mm and =-18.7.

[0124] Then, 12 observation points within a 500m radius of the monitoring point were selected for interpolation: average point spacing: d_mean = 186m; shape parameter: c = 0.5 × 186 = 93m; distance weighting index: .

[0125] Taking the three observation points closest to the monitoring point as an example, the weights are calculated as follows: Point 1: distance 78m, weight Point 2: Distance 124m, weight Point 3: Distance 203m, weight Furthermore, the interpolation result is calculated based on the radial basis function interpolation algorithm: D(x,y,z)= Therefore, the interpolated deformation value (spatial deformation) at the monitoring point is -18.2 mm, which is 2.7% different from the deformation vector decomposition result of -18.7 mm.

[0126] Secondly, the time series of spatial shape variables are obtained based on spatial shape variable prediction: The unit is mm; and, the time series corresponding to the spatial shape variable time series is obtained: The unit is year. Furthermore, based on data obtained from the software ENVI SARscape... The deformation rate was obtained through least-squares fitting: The deformation acceleration is Deformation rate monitoring: -36.8 mm / year, acceleration: -2.4 mm / year².

[0127] Secondly, the user observes from a distance of 500m from the dam: line-of-sight distance (actual observation distance): =500m; Baseline distance (preset observation distance): =100m; Local data density: =2500 points / (Densely distributed in the dam area); Baseline density: Furthermore, the display precision of the dynamic 3D deformation field is adjusted based on the hierarchical detail control method:

[0128] ; .

[0129] Deformation gradient in the dam crest region (region of maximum deformation gradient): Maximum gradient: = Normalized gradient: Gradient sensitivity coefficient: Therefore, the mesh density is obtained as follows:

[0130] The unit is ;

[0131] In the stable region, , .

[0132] Finally, based on the above data, the maximum settlement monitoring result for the dam is -45.2 mm, with an accuracy of ±2.8 mm; the maximum horizontal displacement monitoring result is 23 mm, which can accurately capture the landslide displacement process. Additionally, rendering performance test results can be obtained, including: data loading time: 0.8 seconds (8.5 million points), mesh generation time: 1.2 seconds, real-time rendering frame rate: 35 fps, viewpoint switching response: <0.3 seconds, zoom operation latency: <0.2 seconds.

[0133] As can be seen, in this embodiment, the server acquires the line-of-sight observation values ​​of the dam monitoring points and the Earth observation equipment, and then performs three-dimensional deformation component decomposition, establishes the spatial deformation field around the monitoring points, determines the deformation velocity field and acceleration field based on the spatial deformation sequence and least squares fitting to construct a dynamic three-dimensional deformation field. Finally, the dynamic field is rendered using a hierarchical detail control algorithm and an adaptive mesh generation algorithm and superimposed on the initial dam model and sent to the user terminal, realizing multi-dimensional capture, dynamic trend characterization and intuitive visualization monitoring of dam deformation.

[0134] Please see Figure 2 and Figure 3 , Figure 2 This is a schematic diagram of a dam visualization interface based on InSAR satellite imagery provided in an embodiment of this application. Figure 3 This is another schematic diagram of a dam visualization interface based on InSAR satellite imagery provided in an embodiment of this application, such as... Figure 2 and Figure 3 As shown, Figure 2 A visual monitoring interface that presents an overall view of the dam. Figure 3 A high-precision visualization interface showing a close-up view of a local area of ​​the dam.

[0135] in, Figure 2 and Figure 3 This is the interface for the "Dam Deformation Monitoring Visualization and Interpretation System". Specifically, Figure 2 It is a normal interface from a distance. The center of the interface presents the overall layout of the dam body and the surrounding mountain and forest terrain with a simplified but complete 3D model, which is in line with the distance view logic of the layer detail control algorithm in the scheme (simplifying details to take into account the overall presentation and rendering smoothness). The data panel on the right is equipped with functional areas including deformation monitoring and early warning, deformation alarm statistics and GNSS monitoring and alarm, corresponding to the visualization design of the target dam model, and synchronously displaying dynamic 3D deformation field data to realize the linkage monitoring of spatial model and data indicators.

[0136] Figure 3It is a close-up view interface that focuses on local facilities such as factory buildings and power equipment around the dam. The 3D model in the middle of the interface is loaded with high-precision details such as building windows and equipment structures. It matches the close-up view rules of the hierarchical detail control algorithm (loading high-precision content at close range) and also fits the local refinement logic of the adaptive mesh generation algorithm (densifying the mesh in key areas to ensure accurate presentation). The data panel on the right is equipped with functional areas including deformation monitoring and early warning, deformation alarm statistics and GNSS monitoring alarm, which display the local monitoring results of the near-field area. It realizes the precise linkage between the visualization of local details and the interpretation of regional deformation data. The two respectively meet the needs of overall dam monitoring and refined monitoring of key local areas.

[0137] Please see Figure 4 , Figure 4 This is a schematic diagram of a level of detail provided in an embodiment of this application, such as... Figure 4 As shown, the intelligent detail control based on the LOD algorithm supports 6 levels of detail display, meaning the detail level can be set from 0 to 6, with the preset maximum detail level being 6.

[0138] The topmost block corresponds to LOD0, representing a detail level of 0, suitable for scenes with an observation distance d < 50m. It uses "full detail" display and loads 100% of the mesh to ensure accurate detail for close-range observation. The next block corresponds to LOD1-2, representing a detail level of 1-2, suitable for observation distances of 50-200m. It uses "medium detail" display and loads 50% of the mesh to balance detail rendering and rendering pressure. The next block corresponds to LOD3-4, representing a detail level of 3-4, suitable for observation distances of 200-1000m. It uses "simplified display" and loads only 25% of the mesh to reduce redundant data. The bottommost block corresponds to LOD5-6, representing a detail level of 5-6, suitable for distant scenes with d > 1000m. It only displays the outline and loads 10% of the mesh to ensure smooth rendering at distant viewpoints. Figure 4 These are the specific implementation rules of the hierarchical detail control algorithm, which clearly link the observation distance with the LOD level, level of detail, and mesh ratio.

[0139] As can be seen, in this embodiment, LOD_ =0: Displays all details, 100% mesh density, suitable for close-up viewing (<50m); LOD_ =1-2: Medium precision, 50% grid density, suitable for normal observation distance (50-200m); LOD_ =3-4: Simplified display, grid density 25%, suitable for long-distance browsing (200-1000m); LOD_ =5-6: Outline display, mesh density 10%, used for global overview (>1000m). This achieves intelligent detail control based on the LOD algorithm.

[0140] Please see Figure 5 and Figure 6 , Figure 5 This is a schematic diagram of a dam visualization interface at the lowest level of detail provided in an embodiment of this application. Figure 6 This is a schematic diagram of a dam visualization interface at the maximum level of detail provided in an embodiment of this application, such as... Figure 5 and Figure 6 As shown, both images are the deformation field visualization interfaces of the "Dam Deformation Monitoring Visualization and Interpretation System," corresponding to different applications at the Level of Detail (LOD) level: Figure 5 The interface is designed for the lowest level of detail, conforming to the rules of low LOD levels (such as LOD5-6) in the scheme. Its visualization area presents a large-scale deformation color distribution of the dam (with relatively coarse color block granularity), which is suitable for the needs of long-distance observation or quick overview of the overall deformation trend of the dam, while also taking into account the smoothness of rendering. Figure 6 For the interface with the highest level of detail, the rules of the high LOD level (such as LOD0) in the matching scheme are matched. The color blocks of the deformation color scale map of its visualization area are more refined and the granularity is smaller, which can present the subtle deformation differences in local areas. Through the difference in LOD level, the two realize the flexible visualization of the dam deformation field from "overall overview to local refinement", which corresponds to the practice of the hierarchical detail control algorithm in deformation field rendering in the scheme.

[0141] Please see Figure 7 , Figure 7 This is a radar chart illustrating the comprehensive performance of multiple dam visualization monitoring methods provided in this application embodiment, such as... Figure 7 As shown, this comprehensive performance radar chart compares the performance of traditional methods, commercial software, and the dam visualization monitoring method of this application across six core dimensions: processing speed, rendering efficiency, reconstruction accuracy, response time, data capacity, and ease of use. "Commercial software" refers to ENVI SAR scape—a professional commercial SAR data processing software commonly used for InSAR deformation monitoring, but it has limitations on single-track data scale, high cost, and often requires specialized GIS knowledge for operation. "Traditional methods" refers to the SBAS-InSAR satellite deformation monitoring method (small baseline set InSAR method), a traditional InSAR deformation processing technology with weaker performance, such as slow processing speed, deformation reconstruction accuracy of only 6.5mm, low rendering frame rate (12fps), and limited data scale support.

[0142] Among them, the dam visualization monitoring method of this application (green area) has the widest coverage and best performance across all dimensions, corresponding to its advantages in practical applications such as a processing speed of 4.25 million points / second, deformation reconstruction accuracy of 2.8mm, and rendering frame rate of 35fps; the performance coverage of commercial software (blue area) is in the middle; and the traditional method (orange area) has the narrowest performance coverage and the weakest performance across all dimensions. This radar chart intuitively demonstrates that the overall performance of this application is superior to that of traditional methods and commercial software, with an availability rate of ≥99.8% in practical applications, a cost reduction of more than 40% compared to commercial software, and the ability to operate without professional GIS knowledge.

[0143] Please see Figure 8 , Figure 8 A functional unit block diagram of a dam monitoring system provided in this application embodiment is shown below. Figure 8 As shown, the dam monitoring system 800 includes the following units:

[0144] The acquisition unit 810 is used to acquire the monitoring points of the dam, and to acquire the line-of-sight observation values ​​of the Earth observation equipment for the monitoring points;

[0145] The processing unit 820 is used to decompose the line of sight into observed values ​​to obtain the three-dimensional deformation components of the monitoring point. The three-dimensional deformation components include a first deformation component in the east-west direction, a second deformation component in the north-south direction, and a third deformation component in the vertical direction. It establishes a spatial deformation field of the monitoring point within a preset range. Based on the spatial deformation field, it obtains a spatial deformation sequence corresponding to the spatial deformation of the monitoring point at different time nodes. It also determines the deformation velocity field and deformation acceleration field of the monitoring point based on the spatial deformation sequence and a least-squares fitting algorithm. It constructs a dynamic three-dimensional deformation field of the monitoring point based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, and the deformation acceleration field. Finally, it renders the dynamic three-dimensional deformation field based on a hierarchical detail control algorithm and an adaptive mesh generation algorithm, and overlays it onto an initial dam model to obtain a target dam model.

[0146] The sending unit 830 is used to send the target dam model to the user terminal for visualization.

[0147] In one embodiment, the Earth observation device includes an InSAR satellite; the step of decomposing the line-of-sight observations to obtain the three-dimensional deformation components of the monitoring points includes: acquiring the topographic location information of the dam; determining, based on the topographic location information, whether the deformation of the monitoring points in the north-south direction is limited by the terrain, wherein the monitoring points include settlement points, deformation points, and sliding points of the dam body, dam slope, and surrounding structures; if so, determining a first line-of-sight deformation variable based on the line-of-sight observations of the ascending InSAR satellite, and determining a second line-of-sight deformation variable based on the line-of-sight observations of the descending InSAR satellite; and, based on the first line-of-sight deformation variable, the second line-of-sight deformation variable, and the satellite radar incident... The first deformation component in the east-west direction and the third deformation component in the vertical direction of the monitoring point are calculated from the angle and the satellite radar heading angle; if not, the first line-of-sight deformation time series is determined based on the line-of-sight observations of the ascending InSAR satellite, and the second line-of-sight deformation time series is determined based on the line-of-sight observations of the descending InSAR satellite; and, based on the first line-of-sight deformation time series, the second line-of-sight deformation time series, the satellite radar incident angle and the satellite radar heading angle, the first deformation component in the east-west direction, the second deformation component in the north-south direction and the third deformation component in the vertical direction of the monitoring point are obtained based on the least squares fitting algorithm.

[0148] In one embodiment, obtaining the first deformation component in the east-west direction, the second deformation component in the north-south direction, and the third deformation component in the vertical direction of the monitoring point based on the first line-of-sight deformation time series, the second line-of-sight deformation time series, the satellite radar incident angle, and the satellite radar heading angle using a least-squares fitting algorithm includes: establishing multiple first solution equations for the three-dimensional deformation components based on the first line-of-sight deformation time series, the satellite radar incident angle, and the satellite radar heading angle; establishing multiple second solution equations for the three-dimensional deformation components based on the second line-of-sight deformation time series, the satellite radar incident angle, and the satellite radar heading angle; and solving the multiple first solution equations and the multiple second solution equations using the least-squares fitting algorithm to obtain the three-dimensional deformation components of the monitoring point.

[0149] In one embodiment, establishing the spatial deformation field of the monitoring point within a preset surrounding range includes: selecting multiple reference points located within the preset surrounding range of the monitoring point; determining the total number of reference points; determining multiple distances corresponding one-to-one between the monitoring point and the multiple reference points; and determining multiple reference weight coefficients corresponding one-to-one between the multiple reference points based on the multiple distances and a preset distance weight index; obtaining an interpolated deformation value at the monitoring point based on a preset radial basis function interpolation algorithm, based on the location information of the monitoring point, the total number of reference points, the multiple distances, and the multiple reference weight coefficients, wherein the interpolated deformation value characterizes the spatial deformation of the monitoring point; and obtaining the spatial deformation field of the monitoring point within the preset surrounding range based on the spatial deformation.

[0150] In one embodiment, the spatial deformation sequence includes multiple spatial deformations corresponding to the monitoring point at different time points, and the multiple spatial deformations are arranged in chronological order. Determining the deformation velocity field and deformation acceleration field of the monitoring point based on the spatial deformation sequence and the least squares fitting algorithm includes: for each spatial deformation in the spatial deformation sequence, establishing a third solution equation between the spatial deformation and a preset reference spatial deformation, deformation velocity, and deformation acceleration, resulting in multiple third solution equations; solving the multiple third solution equations based on the least squares fitting algorithm to obtain the deformation velocity and deformation acceleration of the monitoring point; determining the expected monitoring period for the dam; obtaining the deformation velocity field of the monitoring point based on the product of the deformation velocity and the expected monitoring period; and obtaining the deformation acceleration field of the monitoring point based on the product of the deformation acceleration and the square of the expected monitoring period.

[0151] In one embodiment, constructing the dynamic three-dimensional deformation field of the monitoring point based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, and the deformation acceleration field includes: performing deformation statistics based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, and the deformation acceleration field to obtain multiple initial deformation feature parameters of the monitoring point, wherein the multiple deformation feature parameters include the deformation rate of the monitoring point, the maximum value, minimum value, average value, and standard deviation of the three-dimensional deformation components of the monitoring point; predicting multiple target deformation feature parameters of the monitoring point at different future time nodes based on a time-series fitting algorithm; and constructing the dynamic three-dimensional deformation field of the monitoring point based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, the deformation acceleration field, the multiple initial deformation feature parameters, and the multiple target deformation feature parameters.

[0152] In one embodiment, rendering the dynamic 3D deformation field based on the hierarchical detail control algorithm and the adaptive mesh generation algorithm, and superimposing it onto the initial dam model to obtain the target dam model, includes: obtaining the actual observation position of the dam observed by the user, and determining the actual observation distance between the actual observation position and the monitoring point; calculating the dynamic rendering display accuracy based on the hierarchical detail control algorithm according to the actual observation distance, a preset observation distance, and a preset maximum level of detail; determining the deformation gradient of the monitoring point and the maximum deformation gradient of the dam according to the dynamic 3D deformation field, wherein the maximum deformation gradient of the dam is the maximum deformation gradient of the monitoring point corresponding to the dam crest region; calculating the mesh density of the dam region corresponding to the monitoring point according to the deformation gradient, the maximum deformation gradient, a preset basic mesh density, and a gradient sensitivity coefficient; rendering the dynamic 3D deformation field according to the dynamic rendering display accuracy and the mesh density to obtain the processed target 3D deformation field; and superimposing the target 3D deformation field onto the initial dam model to obtain the target dam model.

[0153] As can be seen, in this embodiment, the server acquires the line-of-sight observation values ​​of the dam monitoring points and the Earth observation equipment, and then performs three-dimensional deformation component decomposition, establishes the spatial deformation field around the monitoring points, determines the deformation velocity field and acceleration field based on the spatial deformation sequence and least squares fitting to construct a dynamic three-dimensional deformation field. Finally, the dynamic field is rendered using a hierarchical detail control algorithm and an adaptive mesh generation algorithm and superimposed on the initial dam model and sent to the user terminal, realizing multi-dimensional capture, dynamic trend characterization and intuitive visualization monitoring of dam deformation.

[0154] Figure 9 This is a structural block diagram of an electronic device provided in an embodiment of this application. For example... Figure 9 As shown, electronic device 900 may include one or more of the following components: processor 901 and memory 902 coupled to processor 901, wherein memory 902 may store one or more computer programs, which may be configured to implement the methods described in the examples above when executed by one or more processors 901.

[0155] Processor 901 may include one or more processing cores. Processor 901 connects to various parts within the electronic device 900 using various interfaces and lines, and performs various functions and processes data of the electronic device 900 by running or executing instructions, programs, code sets, or instruction sets stored in memory 902, and by calling data stored in memory 902. Optionally, processor 901 may be implemented using at least one hardware form of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), or Programmable Logic Array (PLA). Processor 901 may integrate one or more of the following: Central Processing Unit (CPU), Graphics Processing Unit (GPU), and modem. It is understood that the aforementioned modem may also not be integrated into processor 901, but may be implemented separately through a communication chip.

[0156] The memory 902 may include random access memory (RAM) or read-only memory (ROM). The memory 902 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 902 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as touch functionality, sound playback functionality, image playback functionality, etc.), and instructions for implementing the above-described method examples. The data storage area may also store data created during the use of the electronic device 900.

[0157] It is understood that the electronic device 900 may include more or fewer structural elements than those shown in the above block diagram, such as a power module, physical buttons, WiFi (Wireless Fidelity) module, speaker, Bluetooth module, sensor, etc., without limitation.

[0158] This application also provides a computer storage medium storing a computer program / instructions thereon, which, when executed by a processor, implements some or all of the steps of any of the methods described in the above method embodiments.

[0159] This application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments.

[0160] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0161] In the several embodiments provided in this application, it should be understood that the disclosed methods, apparatuses, and systems can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for example, the division of units is merely a logical functional division, and there may be other division methods in actual implementation; for example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0162] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0163] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can be physically comprised separately, or two or more units can be integrated into one unit. The integrated unit described above can be implemented in hardware or in the form of hardware plus software functional units.

[0164] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute partial steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes: a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, volatile memory, or non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDRSDRAM), enhanced synchronous DRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DRRAM), etc., which are various media capable of storing program code.

[0165] While the present invention has been disclosed above, it is not limited thereto. Any person skilled in the art can easily conceive of variations or substitutions without departing from the spirit and scope of the present invention, and various modifications and alterations can be made, including combinations of the different functions and implementation steps described above, as well as software and hardware implementation methods, all of which are within the protection scope of the present invention.

Claims

1. A method for visual monitoring of dams based on three-dimensional deformation field reconstruction, characterized in that, A server is used in a dam monitoring system, the dam monitoring system also including earth observation equipment and user terminals, the server being connected to the earth observation equipment and the user terminals respectively; the method includes: The monitoring points of the dam are obtained, and the line-of-sight observation values ​​of the Earth observation equipment for the monitoring points are obtained; The line of sight is decomposed into the observed value to obtain the three-dimensional deformation components of the monitoring point. The three-dimensional deformation components include a first deformation component in the east-west direction, a second deformation component in the north-south direction, and a third deformation component in the vertical direction. Establish the spatial deformation field of the monitoring point within a preset range; Based on the spatial deformation field, obtain the spatial deformation sequence corresponding to the spatial deformation of the monitoring point at different time nodes; and determine the deformation velocity field and deformation acceleration field of the monitoring point according to the spatial deformation sequence and the least squares fitting algorithm. The dynamic three-dimensional deformation field of the monitoring point is constructed based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, and the deformation acceleration field. The dynamic 3D deformation field is rendered using a hierarchical detail control algorithm and an adaptive mesh generation algorithm, and then superimposed onto the initial dam model to obtain the target dam model; subsequently, the target dam model is sent to the user terminal for visualization. The Earth observation equipment includes an InSAR satellite; the process of decomposing the line-of-sight observations to obtain the three-dimensional deformation components of the monitoring point includes: Obtain the topographic location information of the dam; Based on the topographic location information, it is determined whether the deformation of the monitoring point in the north-south direction is limited by the terrain. The monitoring points include settlement points, deformation points, and sliding points of the dam body, dam slope, and surrounding buildings. If so, then the first line-of-sight deformation is determined based on the line-of-sight observations of the ascending InSAR satellite, and the second line-of-sight deformation is determined based on the line-of-sight observations of the descending InSAR satellite; and, The first deformation component of the monitoring point in the east-west direction and the third deformation component in the vertical direction are calculated based on the first line-of-sight deformation, the second line-of-sight deformation, the satellite radar incident angle and the satellite radar heading angle. If not, then determine the first line-of-sight deformation time series based on the line-of-sight observations of the ascending InSAR satellite, and determine the second line-of-sight deformation time series based on the line-of-sight observations of the descending InSAR satellite; and, Based on the first line-of-sight deformation time series, the second line-of-sight deformation time series, the satellite radar incident angle and the satellite radar heading angle, the first deformation component of the monitoring point in the east-west direction, the second deformation component in the north-south direction and the third deformation component in the vertical direction are obtained based on the least squares fitting algorithm.

2. The method according to claim 1, characterized in that, The step of obtaining the first deformation component in the east-west direction, the second deformation component in the north-south direction, and the third deformation component in the vertical direction of the monitoring point based on the first line-of-sight deformation time series, the second line-of-sight deformation time series, the satellite radar incident angle, and the satellite radar heading angle using a least-squares fitting algorithm includes: Based on the first line-of-sight deformation time series, the satellite radar incident angle, and the satellite radar heading angle, establish multiple first solution equations for the three-dimensional deformation components; Based on the second line-of-sight deformation time series, the satellite radar incident angle, and the satellite radar heading angle, establish multiple second solution equations for the three-dimensional deformation components; The three-dimensional deformation components of the monitoring point are obtained by solving the plurality of first solution equations and the plurality of second solution equations based on the least squares fitting algorithm.

3. The method according to claim 1 or 2, characterized in that, The establishment of the spatial deformation field of the monitoring point within a preset range includes: Select multiple reference points located within a preset range surrounding the monitoring point; Determine the total number of reference points; Determine multiple distances that correspond one-to-one between the monitoring point and the multiple reference points, and determine multiple reference weight coefficients that correspond one-to-one between the multiple reference points based on the multiple distances and a preset distance weight index; Based on the location information of the monitoring point, the total number of reference points, the multiple distances and the multiple reference weight coefficients, the interpolated deformation value at the monitoring point is obtained based on a preset radial basis function interpolation algorithm. The interpolated deformation value characterizes the spatial deformation of the monitoring point. The spatial deformation field of the monitoring point within a preset range is obtained based on the spatial deformation.

4. The method according to claim 3, characterized in that, The spatial shape variable sequence includes multiple spatial shape variables corresponding to the monitoring points at different time nodes, and the multiple spatial shape variables are arranged in chronological order. The step of determining the deformation velocity field and deformation acceleration field of the monitoring point based on the spatial deformation sequence and the least squares fitting algorithm includes: For each spatial deformation in the spatial deformation sequence, a third solution equation is established between the spatial deformation and the preset reference spatial deformation, deformation velocity and deformation acceleration, resulting in multiple third solution equations. The deformation velocity and deformation acceleration of the monitoring point are obtained by solving the multiple third equations based on the least squares fitting algorithm. Determine the expected monitoring period for the dam; The deformation velocity field of the monitoring point is obtained by multiplying the deformation velocity and the expected monitoring years, and the deformation acceleration field of the monitoring point is obtained by multiplying the deformation acceleration and the square of the expected monitoring years.

5. The method according to claim 4, characterized in that, The construction of the dynamic three-dimensional deformation field of the monitoring point based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, and the deformation acceleration field includes: Based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, and the deformation acceleration field, deformation statistics are performed to obtain multiple initial deformation characteristic parameters of the monitoring point. The multiple deformation characteristic parameters include the deformation rate of the monitoring point, the maximum value, minimum value, average value, and standard deviation of the three-dimensional deformation components of the monitoring point. Predict multiple target deformation characteristic parameters of the monitoring points at different future time nodes based on a time-series fitting algorithm; The dynamic three-dimensional deformation field of the monitoring point is constructed based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, the deformation acceleration field, the plurality of initial deformation feature parameters, and the plurality of target deformation feature parameters.

6. The method according to claim 5, characterized in that, The dynamic 3D deformation field is rendered using a hierarchical detail control algorithm and an adaptive mesh generation algorithm, and then superimposed onto the initial dam model to obtain the target dam model, including: Obtain the actual observation location of the dam as observed by the user, and determine the actual observation distance between the actual observation location and the monitoring point; Based on the actual observation distance, the preset observation distance, and the preset maximum level of detail, the dynamic rendering display accuracy is calculated using the level of detail control algorithm. The deformation gradient of the monitoring point and the maximum deformation gradient of the dam are determined based on the dynamic three-dimensional deformation field. The maximum deformation gradient of the dam is the maximum deformation gradient of the monitoring point corresponding to the dam crest region. The grid density of the dam area corresponding to the monitoring point is calculated based on the deformation gradient, the maximum deformation gradient, the preset basic grid density, and the gradient sensitivity coefficient. The dynamic three-dimensional deformation field is rendered according to the dynamic rendering display accuracy and the mesh density to obtain the processed target three-dimensional deformation field; The target three-dimensional deformation field is superimposed onto the initial dam model to obtain the target dam model.

7. A dam monitoring system, characterized in that, The system includes a server, Earth observation equipment, and user terminals. The Earth observation equipment includes InSAR satellites. The system includes: The acquisition unit is used to acquire the monitoring points of the dam, and to acquire the line-of-sight observation values ​​of the Earth observation equipment for the monitoring points; The processing unit is used to decompose the line of sight into observed values ​​to obtain the three-dimensional deformation components of the monitoring point. The three-dimensional deformation components include a first deformation component in the east-west direction, a second deformation component in the north-south direction, and a third deformation component in the vertical direction. It establishes a spatial deformation field of the monitoring point within a preset surrounding range; based on the spatial deformation field, it obtains a spatial deformation sequence corresponding to the spatial deformation of the monitoring point at different time nodes; and determines the deformation velocity field and deformation acceleration field of the monitoring point based on the spatial deformation sequence and a least-squares fitting algorithm; constructs a dynamic three-dimensional deformation field of the monitoring point based on the three-dimensional deformation components, the spatial deformation field, the deformation velocity field, and the deformation acceleration field; and renders the dynamic three-dimensional deformation field based on a hierarchical detail control algorithm and an adaptive mesh generation algorithm, and superimposes it onto an initial dam model to obtain a target dam model. A sending unit is used to send the target dam model to the user terminal for visualization. The processing unit is further configured to acquire the topographic location information of the dam; determine, based on the topographic location information, whether the deformation of the monitoring point in the north-south direction is restricted by the terrain, wherein the monitoring point includes settlement points, deformation points, and sliding points of the dam body, dam slope, and surrounding structures; if so, determine a first line-of-sight deformation variable based on the line-of-sight observations of the ascending InSAR satellite, and determine a second line-of-sight deformation variable based on the line-of-sight observations of the descending InSAR satellite; and calculate, based on the first line-of-sight deformation variable, the second line-of-sight deformation variable, the satellite radar incident angle, and the satellite radar heading angle, the east-west direction of the monitoring point. The first deformation component in the east-west direction and the third deformation component in the vertical direction are determined; if not, the first line-of-sight deformation time series is determined based on the line-of-sight observations of the ascending InSAR satellite, and the second line-of-sight deformation time series is determined based on the line-of-sight observations of the descending InSAR satellite; and, based on the first line-of-sight deformation time series, the second line-of-sight deformation time series, the satellite radar incident angle and the satellite radar heading angle, the first deformation component in the east-west direction, the second deformation component in the north-south direction and the third deformation component in the vertical direction of the monitoring point are obtained based on the least squares fitting algorithm.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer program includes executable instructions that, when executed by a processor, implement the method of any one of claims 1-6.

9. An electronic device, characterized in that, include: One or more processors; A memory for storing executable instructions of the processor, which, when executed by the one or more processors, cause the one or more processors to perform the method according to any one of claims 1-6.

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