A slope deformation monitoring method and device based on multi-source data fusion

Abstract

The disclosure provides a kind of based on multi-source data fusion's slope deformation monitoring method and device, it is related to dam slope safety monitoring technical field.The method comprises: in dam slope region, multi-source monitoring system is arranged by global satellite signal receiver, measuring robot and interferometric synthetic aperture radar;According to the slope deformation disturbance parameter of collection, the monitoring selection area division of dam slope region is carried out;The data fusion weight corresponding to each data fusion observation area is determined;Multi-source monitoring data in each data fusion observation area is collected;Based on the data fusion weight determined, the multi-source monitoring data in selection area is respectively carried out weighted fusion processing, and the slope deformation monitoring result is generated.The technical scheme is fused by regional division to realize multi-source monitoring data, improves the reliability, comprehensiveness and adaptability to environment of slope deformation monitoring data, and the stability, accuracy and precision of slope deformation monitoring result are improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of dam slope safety monitoring, and in particular to a slope deformation monitoring method and device based on multi-source data fusion. Background Art

[0002] Slope deformation monitoring in high mountain valleys presents significant technical challenges due to their complex geological environment and extreme natural conditions. These areas typically feature dramatically undulating terrain, dense vegetation, and are significantly affected by external forces such as rainfall and weathering, resulting in a high potential risk of geological disasters. As a crucial component of a dam project, the structural stability of the dam slope area is directly related to the overall safety and reliability of the dam. Under the long-term effects of natural conditions and external loads, the dam slope area is prone to cumulative deformation, which can lead to disasters such as landslides and collapses. Therefore, continuous and accurate deformation monitoring of the dam slope area is crucial for promptly identifying potential hazards and ensuring project safety. The dam slope monitoring technology in related technologies mainly relies on a single type of monitoring equipment. For example, the Global Navigation Satellite System (GNSS) is used for displacement monitoring, or the Interferometric Synthetic Aperture Radar (InSAR) is used to measure large-scale surface deformation. In some applications, a robotic total station (RTS) is also used to achieve high-precision displacement observation of key areas.

[0003] However, due to the complexity of the terrain and the limitations of the monitoring equipment itself, the relevant slope deformation monitoring methods still have certain shortcomings in practical applications. For example, a single type of monitoring equipment is significantly affected by environmental conditions. For example, GNSS signals are severely attenuated in highly obscured areas, affecting monitoring accuracy; InSAR interference signal-to-noise ratio is low in areas with lush vegetation or snow cover, which is prone to errors; measurement robots are limited by site layout conditions and meteorological environment, making it difficult to achieve efficient monitoring of the entire area; secondly, when faced with the diverse geological characteristics and disturbance conditions in the dam slope area, relevant monitoring strategies usually adopt a unified layout and data collection standard, failing to differentiate the characteristics of different regions, resulting in inefficient utilization of monitoring resources and difficulty in ensuring the accuracy and stability of monitoring results in complex environments. Therefore, how to achieve efficient, accurate, stable and reliable slope deformation monitoring in the complex and changeable dam slope area remains an issue that urgently needs attention and solution in the current technical field.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0005] The purpose of the embodiments of the present disclosure is to provide a slope deformation monitoring method based on multi-source data fusion, a slope deformation monitoring device based on multi-source data fusion, an electronic device, and a computer-readable storage medium. These methods can fuse multi-source monitoring data, thereby improving the reliability, comprehensiveness, and environmental adaptability of slope monitoring data, and enhancing the stability, accuracy, and precision of slope deformation monitoring results.

[0006] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by practice of the present disclosure.

[0007] According to a first aspect of an embodiment of the present disclosure, a slope deformation monitoring method based on multi-source data fusion is provided, comprising:

[0008] Deploy a monitoring coverage network consisting of global satellite signal receivers, measurement robots, and interferometric synthetic aperture radar in the dam slope area to be monitored, forming a multi-source monitoring system;

[0009] Based on the collected slope deformation disturbance parameters, the dam slope area is divided into monitoring areas to obtain multiple data fusion observation areas;

[0010] Determining the data fusion weight corresponding to each of the data fusion observation areas;

[0011] Periodically collecting multi-source monitoring data within each of the data fusion observation areas through the multi-source monitoring system;

[0012] Based on the data fusion weights of each data fusion observation area, weighted fusion processing is performed on the collected multi-source monitoring data to generate slope deformation monitoring results corresponding to each data fusion observation area.

[0013] In some example embodiments of the present disclosure, based on the aforementioned scheme, the dam slope area is divided into monitoring areas according to the collected slope deformation disturbance parameters to obtain multiple data fusion observation areas, including: preliminarily dividing the dam slope area according to a preset division grid to obtain multiple monitoring sub-areas; classifying the monitoring sub-areas based on the slope deformation disturbance parameters in each of the monitoring sub-areas, and merging and fusing the monitoring sub-areas belonging to the same classification to obtain multiple data fusion observation areas.

[0014] In some example embodiments of the present disclosure, based on the aforementioned scheme, the slope deformation disturbance parameters include the horizontal distance of the dam, the slope gradient, the degree of satellite signal obstruction, the vegetation coverage density and the potential risk level of geological hazards at each position coordinate in the dam slope area; based on the slope deformation disturbance parameters in each of the monitoring sub-areas, the monitoring sub-areas are classified, and the monitoring sub-areas belonging to the same classification are merged and fused to obtain multiple data fusion observation areas, including: determining the quantitative scores of the horizontal distance of the dam, the slope gradient, the degree of satellite signal obstruction, the vegetation coverage density and the potential risk level of geological hazards in each of the monitoring sub-areas; fusing the quantitative scores according to the weight ratio of each of the slope deformation disturbance parameters to obtain regional classification scores for each of the monitoring sub-areas; classifying the monitoring sub-areas according to the regional classification scores, and merging the monitoring sub-areas belonging to the same classification to obtain multiple data fusion observation areas.

[0015] In some example embodiments of the present disclosure, based on the aforementioned scheme, the method further includes: obtaining terrain elevation data of the dam slope area, and determining the horizontal distance of the dam from each position coordinate in the dam slope area to the dam body based on the terrain elevation data; performing slope calculation on each dam slope area through the terrain elevation data to determine the slope gradient of each position coordinate; obtaining a signal shielding test result based on a drone, and determining the degree of satellite signal shielding at each position coordinate based on the signal strength in the signal shielding test result; calculating a normalized vegetation index through multi-period remote sensing images of the dam slope area, and generating a vegetation coverage distribution map based on the normalized vegetation index, and determining the vegetation coverage density at each position coordinate through the vegetation coverage distribution map; determining the potential risk level of geological disasters at each position coordinate based on the historical geological disaster data and geological exploration results corresponding to the dam slope area.

[0016] In some example embodiments of the present disclosure, based on the aforementioned scheme, the determining of the data fusion weight corresponding to each of the data fusion observation areas includes: obtaining the environmental impact factors of each monitoring device in the multi-source monitoring system under the horizontal distance to the dam, slope gradient, satellite signal shielding degree, vegetation coverage density and potential risk level of geological disasters; calculating the environmental adaptability score of each of the monitoring devices in the current data fusion observation area through the slope deformation disturbance parameters and the environmental impact factors in the data fusion observation area; and normalizing the environmental adaptability score of each of the monitoring devices to obtain the data fusion weight corresponding to the data fusion observation area.

[0017] In some example embodiments of the present disclosure, based on the aforementioned scheme, before normalizing the environmental adaptability score of each of the monitoring devices to obtain the data fusion weight corresponding to the data fusion observation area, the method further includes: if it is determined that the environmental adaptability score of the current monitoring device is less than or equal to a preset score threshold, the environmental adaptability score of the current monitoring device is set to zero.

[0018] In some example embodiments of the present disclosure, based on the aforementioned scheme, the periodic collection of multi-source monitoring data within each of the data fusion observation areas by the multi-source monitoring system includes: determining the corresponding deployment density and acquisition frequency in the data fusion observation area according to the environmental adaptability scores of the global satellite signal receiver, measurement robot and interferometric synthetic aperture radar in the multi-source monitoring system respectively in the data fusion observation area; and collecting the multi-source monitoring data in each of the data fusion observation areas by the multi-source monitoring system under the deployment density and the acquisition frequency.

[0019] In some example embodiments of the present disclosure, based on the aforementioned scheme, the multi-source monitoring data includes monitoring point change monitoring data, surface deformation monitoring data and local high-precision monitoring data, and the collection of multi-source monitoring data in each data fusion observation area includes: collecting monitoring point change monitoring data in a geocentric coordinate system by a global satellite signal receiver arranged at each monitoring point in the data fusion observation area; collecting surface deformation monitoring data in an image coordinate system by an interferometric synthetic aperture radar arranged in the data fusion observation area; and collecting local high-precision monitoring data in a local coordinate system by a measuring robot arranged in the data fusion observation area.

[0020] In some example embodiments of the present disclosure, based on the aforementioned scheme, the method further includes: constructing a rotation matrix and a translation matrix based on the position coordinates of the selected reference monitoring point, and converting the monitoring point change monitoring data to a local coordinate system through the rotation matrix and the translation matrix to obtain unified point change monitoring data; solving a projection matrix based on the position coordinates and image coordinates of the reference monitoring point, and converting the surface deformation monitoring data to a local coordinate system through the projection matrix to obtain unified surface deformation monitoring data; performing deviation correction and alignment on the local high-precision monitoring data based on the position coordinates of the reference monitoring point to obtain unified local high-precision monitoring data; performing consistency verification on the unified monitoring point change monitoring data, the unified surface deformation monitoring data and the unified local high-precision monitoring data to obtain multi-source monitoring data within the data fusion observation area.

[0021] According to a second aspect of an embodiment of the present disclosure, a slope deformation monitoring device based on multi-source data fusion is provided, comprising:

[0022] The multi-source monitoring module is used to deploy a monitoring coverage network consisting of global satellite signal receivers, measurement robots, and interferometric synthetic aperture radar in the dam slope area to be monitored, forming a multi-source monitoring system;

[0023] A monitoring area division module is used to divide the dam slope area into monitoring areas according to the collected slope deformation disturbance parameters to obtain multiple data fusion observation areas;

[0024] A fusion weight module, used to determine the data fusion weight corresponding to each of the data fusion observation areas;

[0025] A multi-source monitoring data acquisition module, configured to periodically acquire multi-source monitoring data within each of the data fusion observation areas through the multi-source monitoring system;

[0026] The slope deformation analysis module is used to perform weighted fusion processing on the collected multi-source monitoring data based on the data fusion weights of each data fusion observation area, and generate slope deformation monitoring results corresponding to each data fusion observation area.

[0027] The technical solutions provided by the embodiments of the present disclosure may have the following beneficial effects:

[0028] The slope deformation monitoring method based on multi-source data fusion in the example embodiment of the present disclosure, on the one hand, by deploying a multi-source monitoring system composed of a global satellite signal receiver, a measurement robot and an interferometric synthetic aperture radar in the dam slope area, and combining the regional division according to the slope deformation disturbance parameters, can effectively achieve dynamic matching between the deployment of monitoring equipment and the characteristics of the slope area. Moreover, since the slope deformation disturbance parameters can reflect the differences in terrain changes, geological characteristics and environmental conditions in the area, combined with the adaptability of multiple types of monitoring equipment in different environments, combined with the data fusion weights, it can achieve targeted fusion of monitoring data according to the actual terrain and environmental conditions of different data fusion observation areas, thereby improving the monitoring coverage and data reliability in complex terrain, obstructed environment and high-risk areas, and making up for the monitoring blind spot problem caused by the limitation of a single monitoring means in related technologies. On the one hand, by weighting the data collected by the multi-source monitoring system according to the data fusion weights specific to each observation area, the advantages of data from different sources can be adaptively exerted according to the actual environmental characteristics. The determined data fusion weights not only take into account the environmental adaptability of each monitoring device under different slope deformation disturbance parameters, but also eliminate the interference of single monitoring data anomalies or deviations on the overall monitoring results through weighted fusion, thereby improving the stability and consistency of the slope deformation monitoring results; on the other hand, by periodically collecting multi-source monitoring data in each observation area and fusing the data based on unified standards, the deformation process of the slope area can be continuously tracked in the time dimension, improving the time continuity and change trend identification ability of the monitoring data, so that the system can capture abnormal signs in time at the early stage of slope deformation, which helps to form a complete and continuous record of slope deformation evolution. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0030] Figure 1 A schematic flow chart of a slope deformation monitoring method based on multi-source data fusion according to some embodiments of the present disclosure is shown schematically.

[0031] Figure 2 The following schematically illustrates a flow chart of determining a data fusion observation area by monitoring sub-areas according to some embodiments of the present disclosure.

[0032] Figure 3 A schematic diagram of a process for collecting slope deformation disturbance parameters according to some embodiments of the present disclosure is schematically shown.

[0033] Figure 4 The following schematically illustrates a flow chart of determining the data fusion weight of a data fusion observation area according to some embodiments of the present disclosure.

[0034] Figure 5 A schematic diagram of a scenario in which a data fusion observation area is obtained by dividing the dam slope area according to some embodiments of the present disclosure is schematically shown.

[0035] Figure 6 A schematic diagram of a slope deformation monitoring device based on multi-source data fusion according to some embodiments of the present disclosure is schematically shown.

[0036] Figure 7 A schematic structural diagram of a computer system of an electronic device according to some embodiments of the present disclosure is schematically shown.

[0037] Figure 8 A schematic diagram of a computer-readable storage medium according to some embodiments of the present disclosure is schematically shown.

[0038] In the drawings, the same or corresponding reference numerals denote the same or corresponding parts. DETAILED DESCRIPTION

[0039] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with this specification. Rather, they are merely examples of apparatus and methods consistent with certain aspects of this specification, as detailed in the appended claims.

[0040] Furthermore, the drawings are schematic illustrations only and are not necessarily drawn to scale. The block diagrams shown in the drawings are merely functional entities and do not necessarily correspond to physically separate entities. In other words, these functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0041] In this example embodiment, a slope deformation monitoring method based on multi-source data fusion is first provided. The slope deformation monitoring method based on multi-source data fusion can be applied to terminal devices or to servers. This embodiment does not make any special limitations on this, and the following description will take the execution of the method by the server as an example. Figure 1 The following schematically illustrates a flow chart of a slope deformation monitoring method based on multi-source data fusion according to some embodiments of the present disclosure. Figure 1As shown, the slope deformation monitoring method based on multi-source data fusion may include the following steps:

[0042] Step S110: deploying a monitoring coverage network consisting of global satellite signal receivers, measurement robots, and interferometric synthetic aperture radar in the dam slope area to be monitored, forming a multi-source monitoring system;

[0043] Step S120, dividing the dam slope area into monitoring areas based on the collected slope deformation disturbance parameters to obtain multiple data fusion observation areas;

[0044] Step S130, determining the data fusion weight corresponding to each of the data fusion observation areas;

[0045] Step S140, periodically collecting multi-source monitoring data within each of the data fusion observation areas through the multi-source monitoring system;

[0046] Step S150 : Based on the data fusion weights of the data fusion observation areas, weighted fusion processing is performed on the collected multi-source monitoring data to generate slope deformation monitoring results corresponding to the data fusion observation areas.

[0047] According to the slope deformation monitoring method based on multi-source data fusion in this example embodiment, on the one hand, by deploying a multi-source monitoring system consisting of a global satellite signal receiver, a measurement robot and an interferometric synthetic aperture radar in the dam slope area, and combining the regional division according to the slope deformation disturbance parameters, it is possible to effectively achieve dynamic matching between the deployment of monitoring equipment and the characteristics of the slope area. Moreover, since the slope deformation disturbance parameters can reflect the differences in terrain changes, geological characteristics and environmental conditions in the area, combined with the adaptability of multiple types of monitoring equipment in different environments, combined with the data fusion weights, it is possible to achieve targeted fusion of monitoring data according to the actual terrain and environmental conditions of different data fusion observation areas, thereby improving the monitoring coverage and data reliability in complex terrain, obstructed environments and high-risk areas, and making up for the monitoring blind spot problem caused by the limitation of a single monitoring means in related technologies. On the one hand, by weighting the data collected by the multi-source monitoring system according to the data fusion weights specific to each observation area, the advantages of data from different sources can be adaptively exerted according to the actual environmental characteristics. The determined data fusion weights not only take into account the environmental adaptability of each monitoring device under different slope deformation disturbance parameters, but also eliminate the interference of single monitoring data anomalies or deviations on the overall monitoring results through weighted fusion, thereby improving the stability and consistency of the slope deformation monitoring results; on the other hand, by periodically collecting multi-source monitoring data in each observation area and fusing the data based on unified standards, the deformation process of the slope area can be continuously tracked in the time dimension, improving the time continuity and change trend identification ability of the monitoring data, so that the system can capture abnormal signs in time at the early stage of slope deformation, which helps to form a complete and continuous record of slope deformation evolution.

[0048] Next, the slope deformation monitoring method based on multi-source data fusion in this exemplary embodiment will be further described.

[0049] In step S110, a monitoring coverage network consisting of global satellite signal receivers, measurement robots and interferometric synthetic aperture radar is deployed in the dam slope area to be monitored to form a multi-source monitoring system.

[0050] In an example embodiment of the present disclosure, the multi-source monitoring system can be laid out and planned based on the topographical features of the dam body and the slope areas on both sides of the dam. A global satellite signal receiver refers to a receiving station device equipped with a high-precision GNSS module, which is capable of receiving satellite signals in real time and performing high-precision three-dimensional positioning with the support of multi-frequency and multi-systems (such as GPS, Beidou, GLONASS, and Galileo). A surveying robot refers to a total station system with automatic target recognition and tracking functions, which can obtain high-precision angle and distance observation values ​​by actively locking the reflective prism without manual operation. Interferometric synthetic aperture radar includes an orbital InSAR observation system and a ground-based InSAR system, which is used to analyze surface deformation based on radar phase interferometry technology to form monitoring data covering a large area.

[0051] During actual deployment, global satellite signal receivers are preferably installed in open, unobstructed, and elevated locations to ensure GNSS signal reception quality. A grid layout strategy is then used to evenly distribute the receivers. The spacing between the receivers can be set within a range of tens to hundreds of meters, depending on the monitoring accuracy requirements. This embodiment does not impose any specific restrictions on this. In areas with severe local signal obstruction but active geological activity, coverage can be improved by installing relay signal modules or portable receiving nodes.

[0052] The measurement robot can be installed on a fixed base with good visual range, usually located at the foot of the slope or a relatively stable platform area to ensure the visual tracking capability of multiple monitoring prism targets. The prism targets are set on key slip surfaces, crack edges or important structures and arranged according to the optimal observation geometry to avoid extreme elevation angles or obstructions.

[0053] Interferometric synthetic aperture radar can determine the combined use of orbiting satellite InSAR or ground-based InSAR based on the scope of the slope area, terrain obstruction and vegetation distribution. The ground-based InSAR equipment can be located in an unobstructed position overlooking the monitoring area to ensure the maximum radar viewing angle and target reflection signal quality. Optionally, for areas with large areas of vegetation coverage or severe signal decoherence, artificial corner reflectors can be installed to enhance the coherence of radar echoes; for local areas of high and steep slopes, portable short-range measurement robots can be used to strengthen local high-frequency monitoring. Furthermore, under extreme environmental conditions, global satellite signal receivers and measurement robots can be equipped with protective covers and environmental monitoring modules to cope with the influence of natural factors such as rain, wind and snow, high temperatures, and strong winds, to ensure stable operation of the equipment.

[0054] In step S120, the dam slope area is divided into monitoring areas according to the collected slope deformation disturbance parameters to obtain multiple data fusion observation areas.

[0055] In an example embodiment of the present disclosure, the slope deformation disturbance parameter refers to a quantitative indicator that characterizes the environmental changes and deformation activity characteristics of the slope area. For example, the slope deformation disturbance parameter may include but is not limited to the horizontal distance of the dam at each location point, the slope gradient, the degree of satellite signal obstruction, the vegetation cover density and the potential risk level of geological disasters. Of course, other parameters may also be used, such as the current meteorological conditions, the visual range environment, etc. This example embodiment does not specifically limit the type of slope deformation disturbance parameter.

[0056] The horizontal distance of the dam refers to the horizontal straight-line distance from the monitoring point to the center of the dam body, which is used to assess the degree of influence of the region on the stability of the dam structure; the slope gradient can be calculated based on the Digital Elevation Model (DEM) to reflect the degree of surface inclination; the degree of satellite signal obstruction can be obtained through statistical analysis of the signal-to-noise ratio (SNR) of the GNSS signal receiver, which describes the degree to which the signal is blocked by obstacles; the vegetation cover density can be calculated using the Normalized Difference Vegetation Index (NDVI) extracted from remote sensing images to assess the impact of vegetation on the signal quality of monitoring methods (such as InSAR); the potential risk level of geological hazards is divided into levels based on historical disaster data, rock type distribution, and structural fracture development.

[0057] After obtaining the slope deformation disturbance parameters at each location, the dam slope area can be optionally divided into regular grid cells according to preset rules. The area of ​​each cell can be set to 10 meters × 10 meters, 20 meters × 20 meters or other suitable sizes according to engineering requirements. Subsequently, within each grid cell, a quantitative scoring process is performed based on the corresponding slope deformation disturbance parameters. For example, the closer the horizontal distance to the dam, the greater the slope gradient, the more severe the signal obstruction, the denser the vegetation, and the higher the risk level of the area, the higher the disturbance score. Different slope deformation disturbance parameters are given different weights based on their impact on the reliability, timeliness and coverage of the monitoring data. For example, for GNSS monitoring, the weight of the signal obstruction degree is higher than the weight of the vegetation cover density to reflect the different sensitivities of different monitoring technologies to environmental characteristics.

[0058] In an optional implementation, if the dam slope contains unique landforms (such as cliffs, slip zones, and gullies), the data fusion observation area can be modified in conjunction with the landform feature map to avoid division errors caused by sudden changes in topography. Furthermore, around highly dynamic areas (such as landslides with frequent deformation), dynamic disturbance parameter monitoring can be used to update the monitoring area division results in real time, improving the overall system's ability to respond quickly to sudden geological changes.

[0059] In step S130, the data fusion weight corresponding to each data fusion observation area is determined.

[0060] In an example embodiment of the present disclosure, the data fusion weight refers to the contribution ratio allocated to monitoring data from different sources in the multi-source data fusion process, which is used to balance the influence of various types of monitoring data in the fusion calculation. For example, the data fusion weight can be determined based on the adaptability, data accuracy and stability of each monitoring device in a specific environment; of course, the data fusion weight can also comprehensively consider the data sampling frequency, spatial resolution and real-time indicators of the monitoring equipment. This example embodiment does not specifically limit the specific setting method of the weight ratio.

[0061] Optionally, data fusion weights can be directly assigned according to preset rules. For example, the weight of GNSS data can be reduced and the weight of InSAR data can be increased in areas where satellite signals are severely blocked. A dynamic evaluation mechanism can also be used to adjust weights based on real-time data quality indicators, such as evaluating data stability through the variance of the change in the position of the monitoring point, and dynamically correcting the weight of each device to adapt to environmental changes. Of course, it is also possible to combine a weight optimization strategy based on a machine learning algorithm, use historical monitoring data to train a regression model, and automatically output the optimal fusion weight configuration scheme for each region. This example embodiment does not specifically limit the setting method of the data fusion weights.

[0062] The corresponding data fusion weights can be calculated by combining the applicability and data reliability of each monitoring device within each data fusion observation area with the weight setting method. By setting data fusion weights that match the terrain and environmental characteristics of each data fusion observation area, the contribution of data sources with high reliability and low error in specific environments can be highlighted in the subsequent data processing process, and the influence of potential noise sources can be suppressed, thereby improving the overall consistency of the fused data and monitoring accuracy.

[0063] In step S140, the multi-source monitoring data in each of the data fusion observation areas is periodically collected by the multi-source monitoring system.

[0064] In an example embodiment of the present disclosure, multi-source monitoring data refers to a set of observation data acquired from the observation area by different monitoring equipment and capable of reflecting the deformation state of the slope area. For example, the multi-source monitoring data may include monitoring point displacement data acquired by a GNSS receiver, three-dimensional coordinate change data measured by a surveying robot, and surface deformation interference pattern data acquired by an InSAR system. Of course, the multi-source monitoring data may also include other extended data types, such as remote sensing image change data, ground crack sensor monitoring data, or temperature and humidity environmental data. This embodiment does not specifically limit the source type of the multi-source monitoring data. In an optional implementation, the GNSS receiver may be set to a continuous acquisition mode to acquire high-frequency displacement data at time intervals of seconds or minutes. The surveying robot may regularly acquire spatial position information of control points according to a set inspection cycle. The InSAR system may acquire surface deformation image data every few days based on an orbit revisit cycle.

[0065] Periodic collection refers to the regular updating and collection of data in each observation area within a preset time period according to an established plan. For example, GNSS data can be set to real-time mobile collection, measurement robot data can be updated in batches on a daily or weekly basis, and InSAR data can be downloaded and processed regularly according to the satellite orbit cycle. Of course, the collection cycle can also be dynamically adjusted according to the monitoring stage. The sampling period can be extended when the slope is in a stable state, and shortened when abnormal change trends are discovered, so as to achieve a balance between resource optimization and sensitive capture of changing processes.

[0066] It is understandable that periodic collection can be set up in layers based on regional importance, risk level and equipment availability. For example, a high-frequency sampling strategy can be adopted for high-risk areas, and a low-frequency sampling strategy can be adopted for stable areas to rationally allocate monitoring resources. Of course, the sampling frequency can also be temporarily increased during special periods (such as heavy rainfall and large-scale construction activities) to enhance the monitoring capabilities of environmental disturbances.

[0067] By periodically collecting multi-source monitoring data in each data fusion observation area through a multi-source monitoring system, a continuous spatiotemporal data chain of the slope deformation process can be established. This not only ensures the temporal continuity of the monitoring data, but also can timely capture abnormal change trends in the slope status, improve the early identification capability of potential risk changes, and provide a complete and dynamic observation basis for subsequent data fusion processing and deformation trend analysis.

[0068] In step S150, based on the data fusion weights of each data fusion observation area, weighted fusion processing is performed on the collected multi-source monitoring data to generate slope deformation monitoring results corresponding to each data fusion observation area.

[0069] In an example embodiment of the present disclosure, weighted fusion processing refers to proportional weighted integration of data from different sources based on the data fusion weights determined for each monitoring data source in the current observation area, so as to generate a unified monitoring result reflecting the comprehensive deformation state. For example, the weighted fusion processing can adopt a weighted average method to perform superposition calculations on the spatial positions or displacements of different data sources according to the weight ratio; of course, the weighted fusion processing can also adopt a weighted least squares method (Weighted Least Squares, WLS) to model the errors of each data source and perform optimized fusion to minimize the overall error. This embodiment does not particularly limit the selection of a specific data fusion algorithm.

[0070] It is understandable that during the fusion process, data can be preprocessed first, such as unifying the coordinate reference, time synchronization, and anomaly elimination, to ensure the basic consistency of the fusion process; of course, a consistency verification step can be added after the fusion to judge the reliability of the fusion result by verifying the fitting residuals of different data sources, thereby further improving the accuracy and stability of the final monitoring data.

[0071] The contents of steps S110 to S150 are described in detail below.

[0072] In an exemplary embodiment of the present disclosure, the following steps may be performed to divide the dam slope area into monitoring zones based on the collected slope deformation disturbance parameters to obtain multiple data fusion observation zones. Specifically, the following steps may be performed:

[0073] The dam slope area can be preliminarily divided into regions according to the preset division grid to obtain multiple monitoring sub-regions; based on the slope deformation disturbance parameters in each monitoring sub-region, the monitoring sub-regions are classified, and the monitoring sub-regions belonging to the same classification are merged and fused to obtain multiple data fusion observation areas.

[0074] Among them, the preset division grid refers to a grid division structure for dividing the monitoring range that is set in advance according to a certain spatial scale and rules. For example, the division grid can adopt a square grid, a rectangular grid or a hexagonal honeycomb grid to ensure uniform division of the monitoring area; of course, the division grid can also adaptively adjust the grid unit size according to the change of terrain height difference, for example, a small-sized grid is used in an area with drastic terrain undulations, and a large-sized grid is used in an area with gentle terrain. This example embodiment does not specifically limit the specific shape and size of the division grid. Optionally, the division grid can be directly divided based on the latitude and longitude coordinate system, which is suitable for large-scale coarse division; it can also be plane-divided based on a projected coordinate system (such as the UTM coordinate system), which is suitable for local high-precision division requirements; of course, the division grid can also be automatically generated based on the dam design plan or the terrain DEM (Digital Elevation Model) to adapt to the actual project layout.

[0075] After determining the grid divisions, a preliminary division of the dam slope area can be performed. This involves dividing the dam slope coverage area into multiple independent small areas based on the location of the grid nodes, with each small area serving as a monitoring sub-area. Each monitoring sub-area has independent spatial boundaries and numbering identifiers, enabling independent data collection, parameter calculation, and subsequent classification processing. This preliminary regional division using a pre-set grid effectively and quickly establishes a standardized monitoring unit system across a large area, avoiding the subjective differences caused by manual divisions and providing a good spatial foundation for subsequent detailed processing and data management.

[0076] After obtaining the slope deformation disturbance parameters of each monitoring sub-area, classification is performed based on the parameter characteristics of each monitoring sub-area. The classification can adopt a rule-based classification method based on a set threshold. For example, a sub-area with a horizontal distance from the dam less than a certain value and a slope greater than a certain angle threshold is classified as a high deformation sensitive area. An unsupervised clustering algorithm (such as K-means, DBSCAN, etc.) can also be used to automatically divide similar feature areas without manually setting classification criteria in advance. This example embodiment does not specifically limit the classification method. Of course, the classification process can also be dominated by a single main control parameter (such as slope), or it can be comprehensively classified based on the combined characteristics of multiple disturbance parameters. For example, the principal component analysis (PCA) method can be used to extract the principal components of the disturbance parameters and then cluster them to improve the scientificity and stability of the classification.

[0077] After completing the sub-region classification, the monitoring sub-regions belonging to the same classification can be merged and fused to generate multiple data fusion observation areas. Merging and fusion refers to merging adjacent sub-regions with the same or similar classification results into a larger monitoring area, so that subsequent monitoring layout, data collection and fusion processing can be more targeted and regionally unified. In the merging and fusion process, an adjacency determination strategy can be adopted, that is, only spatially adjacent or similar sub-regions of the same type are merged to avoid merging to form unreasonable observation areas that are spatially discontinuous or span abnormal terrain features; of course, a fusion scale threshold can also be set. If the number of sub-regions in a certain category is less than the preset standard, it can be supplemented and optimized by expanding the fusion range or merging to the nearest neighbor category to ensure the rationality of the fusion results and the practicality of monitoring.

[0078] By classifying the slope deformation disturbance parameters in each monitoring sub-area and merging the monitoring sub-areas of the same classification, it is possible to achieve refined zoning management and differentiated monitoring deployment based on the differences in the natural environment and potential risks within the dam slope area, thereby improving the adaptability of the entire monitoring system in complex environments, rational resource allocation, and monitoring accuracy.

[0079] In an exemplary embodiment of the present disclosure, the slope deformation disturbance parameters may include the horizontal distance of the dam at each position coordinate in the dam slope area, the slope gradient, the degree of satellite signal obstruction, the vegetation cover density, and the potential risk level of geological disasters.

[0080] Among them, the horizontal distance of the dam refers to the shortest distance in the horizontal direction from each position point in the slope area to the dam body, which is used to measure the strength of the association between the position and the dam structure. For example, the horizontal distance of the dam can be calculated by extracting the shortest Euclidean distance between the coordinates of the position point and the center line of the dam; in complex terrain, it can also be corrected and calculated based on the projection distance or the distance along the slope path to improve the applicability of actual engineering projects; of course, the horizontal distance of the dam can also be quantified in a graded manner, such as 0-50 meters as the first level and 50-100 meters as the second level, which are divided in sequence for subsequent standardized scoring processing. This example embodiment does not specifically limit the specific calculation model of the horizontal distance of the dam.

[0081] The slope gradient refers to the degree of surface inclination at various locations within the slope area, usually expressed as an angle or percentage relative to the horizontal plane. For example, the slope gradient can be calculated based on the elevation difference and horizontal distance between adjacent pixels in a digital elevation model (DEM). Within a small local area, the slope value can also be directly calculated based on field measurement data using triangulation. Of course, the slope gradient can also be discretized according to slope grades, such as 0-15° for low slope, 15-30° for medium slope, and above 30° for high slope, to facilitate subsequent unified quantification. This example embodiment does not specifically limit the slope calculation method.

[0082] The degree of satellite signal obstruction refers to the degree of reduction in the GNSS signal reception quality at each location point in the slope area due to terrain obstruction, vegetation or artificial structures. For example, the degree of satellite signal obstruction can be determined by measuring the change in the signal-to-noise ratio (SNR) using an on-site signal quality tester; the degree of obstruction can also be calculated by simulating the obstruction angles in different directions based on three-dimensional terrain data and calculating the number of visible satellites. Of course, the degree of obstruction can be quantified as an obstruction coefficient of 0-1, where 0 indicates no obstruction and 1 indicates complete obstruction, which is used to standardize subsequent scoring. This example embodiment does not specifically limit the means of measuring obstruction.

[0083] Vegetation cover density refers to the degree of surface vegetation coverage at each location point in the slope area, which is usually obtained through remote sensing image analysis. For example, vegetation cover density can be calculated based on the Normalized Difference Vegetation Index (NDVI) to obtain the vegetation coverage percentage; it can also be based on pixel classification of high-resolution optical images or laser radar (LiDAR) point cloud data to extract vegetation height and infer coverage. Of course, vegetation cover density can be discretized into several levels according to the coverage rate range of 0-100% to meet different analysis requirements. This example embodiment does not specifically limit the method of extracting vegetation density.

[0084] The potential risk level of geological disasters refers to the disaster sensitivity index obtained by comprehensively evaluating historical disaster events, geological structural characteristics and geological survey results at each location point. For example, the potential risk level of geological disasters can be graded and defined based on the probability of occurrence of disasters such as landslides, collapses, and mudslides; it can also be quantitatively evaluated in combination with the regional ground stress field distribution, rock characteristics, and hydrogeological conditions. Of course, the geological disaster risk level can be divided into three levels: low, medium, and high, and different quantitative weights are assigned to each level to facilitate subsequent standardization processing. This example embodiment does not specifically limit the risk level classification standards.

[0085] Can be based on Figure 2The steps in the above are to classify the monitoring sub-areas based on the slope deformation disturbance parameters in each monitoring sub-area, and merge the monitoring sub-areas belonging to the same classification to obtain multiple data fusion observation areas. Figure 2 Specifically, it may include:

[0086] Step S210, determining a quantitative score of the horizontal distance to the dam, the slope gradient, the degree of satellite signal shielding, the vegetation coverage density, and the potential risk level of geological disasters in each monitoring sub-area;

[0087] Step S220: fusing the quantitative scores according to the weight ratios of the slope deformation disturbance parameters to obtain a regional classification score for each monitoring sub-region;

[0088] Step S230 , classifying the monitoring sub-regions according to the regional classification scores, and merging and fusing monitoring sub-regions belonging to the same classification to obtain multiple data fusion observation regions.

[0089] Quantitative scoring refers to converting each disturbance parameter into a standardized value based on a unified standard to reflect the potential impact of the parameter on regional stability. For example, a closer horizontal distance to the dam can be assigned a higher sensitivity score, and a larger slope can be assigned a higher deformation risk score. Of course, the quantitative scoring criteria can be adjusted and optimized based on actual engineering safety assessment experience. This example embodiment does not specifically limit the setting method of the quantitative scoring criteria. In optional implementations, a linear mapping method can also be used to proportionally convert the original parameter value into a scoring range of 0 to 100. Nonlinear scoring functions, such as the Sigmoid function, can also be used to highlight the sensitive change characteristics near specific parameter thresholds.

[0090] After completing the quantitative scoring of each disturbance parameter, the quantitative scores are fused according to the weight ratio of each slope deformation disturbance parameter to obtain the regional classification score of each monitoring sub-area. Fusion means that according to the importance of each disturbance parameter in affecting the stability of the slope, the quantitative scores are weighted and summed according to the set weights. For example, based on experience or historical monitoring analysis, the horizontal distance weight of the dam is determined to be 0.3, the slope weight is 0.3, the satellite signal obstruction weight is 0.15, the vegetation cover density weight is 0.15, and the potential risk level of geological disasters is 0.1, and a weighted calculation is performed; the weight ratio can also be dynamically adjusted according to the actual engineering needs of different monitoring areas. This example embodiment does not specifically limit the weight setting method.

[0091] After the regional classification score of each monitoring sub-area is determined, the monitoring sub-areas are classified according to the regional classification score, and the monitoring sub-areas belonging to the same classification are merged and integrated. Regional classification can be based on the classification score interval classification standard. For example, sub-areas with a score of 80 points or above are classified as high-risk areas, those with a score of 50-80 points are classified as medium-risk areas, and those with a score of 50 points or below are classified as low-risk areas. Classification thresholds can also be automatically determined based on the statistical quantile method to adapt to different data distribution forms. This example embodiment does not specifically limit the setting method of the classification standard.

[0092] Those skilled in the art will appreciate that classification can be combined with spatial adjacency determination to preferentially merge sub-regions with high spatial connectivity and consistent classification to form a spatially continuous fusion observation area; of course, a minimum area unit limit can also be set to appropriately absorb or merge isolated small sub-regions to optimize the spatial structure after fusion.

[0093] By determining the quantitative scores of the dam's horizontal distance, slope gradient, satellite signal obstruction, vegetation coverage density and potential risk level of geological hazards in each monitoring sub-area, and fusing them according to the weights of each parameter, and finally classifying and merging the monitoring sub-areas through regional classification scores, it is possible to achieve accurate zoning and differentiated management of the internal environmental characteristics of the dam slope area in a unified, quantitative and scientific manner, improve the environmental adaptability, resource allocation rationality and monitoring accuracy of the slope deformation monitoring system, and provide a reliable foundation for subsequent monitoring equipment deployment, data fusion processing and abnormal change identification.

[0094] In an exemplary embodiment of the present disclosure, Figure 3 The steps in the above are used to collect the slope deformation disturbance parameters, refer to Figure 3 Specifically, it may include:

[0095] Step S310, obtaining terrain elevation data of the dam slope area, and determining the horizontal distance from each position coordinate in the dam slope area to the dam body according to the terrain elevation data;

[0096] Step S320, calculating the slope of each dam slope area using the terrain elevation data to determine the slope of each position coordinate;

[0097] Step S330: Obtain a signal shielding test result based on the drone, and determine the satellite signal shielding degree at each position coordinate according to the signal strength in the signal shielding test result;

[0098] Step S340, calculating a normalized vegetation index (NVI) using multi-period remote sensing images of the dam slope area, generating a vegetation coverage distribution map based on the NVI, and determining the vegetation coverage density at each location coordinate using the vegetation coverage distribution map;

[0099] Step S350: determining the potential risk level of geological hazards at each location coordinate based on the historical geological hazard data and geological exploration results corresponding to the dam slope area.

[0100] Among them, terrain elevation data refers to a set of spatially distributed data that describes the elevation values ​​of various location points in the dam slope area. For example, terrain elevation data can be derived from a digital elevation model (DEM) obtained by satellite remote sensing, such as SRTM data, ASTER GDEM data, etc.; of course, terrain elevation data can also be obtained through unmanned aerial vehicle laser radar (Light Detection and Ranging, LiDAR) scanning to provide higher resolution and higher precision elevation information. This embodiment does not specifically limit the method for obtaining elevation data.

[0101] Optionally, terrain elevation data can use a DEM with a resolution of 30 meters for preliminary analysis; in key areas, a high-precision DEM with a resolution of less than 1 meter can be used to improve monitoring accuracy; of course, the quality of elevation data can also be improved through multi-source fusion (such as combining aerial surveys with ground measurements).

[0102] After the terrain elevation data is acquired, the horizontal distance of the dam is determined by measuring the shortest horizontal distance from each location point to the centerline of the dam body or the boundary contour based on the elevation information of each location coordinate. The horizontal distance can be calculated using the straight-line Euclidean distance in a projected coordinate system. For example, the horizontal distance is calculated by projecting the monitoring point to the nearest point on the dam centerline. Of course, in the case of complex and undulating dam slopes, the actual shortest path distance along the slope path can also be introduced for correction to improve the applicability of the project. This embodiment does not specifically limit the specific method for calculating the horizontal distance. In an optional implementation, the horizontal distance of the dam can be calculated in batches through a GIS (Geographic Information System) platform to improve the processing efficiency of large-scale monitoring points.

[0103] Slope refers to the rate of change of elevation of the surface in the horizontal direction, usually expressed as an angle with the horizontal plane or as a percentage. For example, the slope can be calculated by dividing the elevation difference between adjacent pixels by the horizontal distance and taking the arctangent value. Of course, in areas where micro-topography changes dramatically, a local plane fitting method can be used to estimate the slope value based on a third-order or higher-order neighborhood window to improve the description of small-scale terrain features. This example embodiment does not specifically limit the slope calculation method. Slope calculation can be performed on DEM data of different resolutions of 5 meters, 10 meters, and 30 meters to meet monitoring needs at different scales. Of course, according to the needs of slope stability analysis, aspect consistency correction can also be used to avoid slope distortion caused by data noise.

[0104] After slope calculation, the slope information at each location serves as a slope deformation disturbance parameter, directly influencing the classification of monitoring sub-areas and the development of subsequent monitoring strategies. By quantifying slope characteristics, steep sections, turning zones, and platform areas can be identified, providing a key basis for analyzing deformation hazards, deploying monitoring equipment, and building early warning models.

[0105] The drone signal obstruction test refers to the use of a drone equipped with GNSS signal receiving equipment to fly along a preset route in the dam slope area, and to record in real time indicators such as satellite signal strength, number of visible satellites, and signal-to-noise ratio (SNR) at each flight position to evaluate the impact of environmental factors such as terrain and vegetation on satellite signal transmission. For example, the signal obstruction test can complete a full coverage scan through multiple flights of a single drone; of course, multiple drones can also be used to fly in coordination to improve test efficiency. This example embodiment does not specifically limit the drone model, flight mode, and test frequency.

[0106] It is understandable that the drone signal obstruction test can be repeated under different meteorological conditions such as sunny, cloudy, and foggy days to capture the impact of weather changes on signal obstruction; of course, the test results at different altitudes can also be combined to evaluate the changing characteristics of the obstruction pattern at different flight altitudes.

[0107] After completing the signal obstruction test, the degree of satellite signal obstruction at each location can be determined based on the collected signal strength data and pre-set obstruction intensity grading standards. For example, areas with SNR below a certain threshold are classified as strongly obstructed, areas with medium to low SNR are classified as moderately obstructed, and areas with good SNR are classified as unobstructed. Optionally, the obstruction degree can be standardized to a range of 0-1 to facilitate subsequent quantitative processing. Of course, multiple levels of obstruction impact indicators can also be set based on the signal sensitivity requirements of different monitoring equipment to fine-tune the monitoring needs of different equipment types.

[0108] The Normalized Difference Vegetation Index (NDVI) is an indicator that characterizes the surface vegetation condition, calculated by calculating the reflectance of the red band (Red) and the near infrared band (NIR) in remote sensing images. The calculation formula is (NIR - Red) / (NIR + Red), where the value range is usually between -1 and +1. For example, an NDVI value close to 1 indicates high-density healthy vegetation, close to 0 indicates sparse vegetation or bare land, and close to -1 indicates water or snow. Of course, in order to adapt to different sensor characteristics, the band selection can be adjusted according to the image source, such as using the red edge band to enhance vegetation sensitivity. This example embodiment does not specifically limit the NDVI calculation band combination. In an optional implementation, the remote sensing image can be derived from medium- and high-resolution satellites such as Sentinel-2, Landsat-8, and GF-1. When higher accuracy is required, near-ground images collected by multispectral cameras mounted on drones can also be used to calculate NDVI.

[0109] After acquiring multiple remote sensing images, each image is subjected to radiometric calibration, atmospheric correction, and terrain correction to ensure comparability between images. NDVI is then calculated pixel by pixel, and multiple NDVI data are fused based on a set time window (such as the four seasons of the year or the growing season) to generate a vegetation coverage distribution map. A vegetation coverage distribution map is a layer that maps continuous NDVI values ​​to the spatial distribution of the surface, used to quantitatively describe the vegetation coverage at each location. For example, NDVI>0.6 can be set as a high-density vegetation area, 0.3 as a low-density vegetation area, and 0.6 as a low-density vegetation area. <NDVI≤0.6为中密度植被区,NDVI≤0.3为低密度植被区或裸地;当然,也可以根据实际植被类型与季节变化调整分类阈值,本示例实施例对于植被密度划分标准不作特别限定。可选的,植被覆盖度还可以通过时间序列NDVI均值或最大值合成(如Maximum Value Composite,MVC)方式生成,以突出稳定性更高的植被覆盖特征。

[0110] Based on the generated vegetation coverage distribution map, the vegetation coverage density value corresponding to each location coordinate is extracted to determine the vegetation density index of the location, which is used for subsequent disturbance parameter scoring, feasibility assessment of monitoring equipment deployment, and coherence prediction of InSAR images.

[0111] Historical geological disaster data refers to a collection of records of the time, location, scale, cause, and loss of geological disasters such as landslides, collapses, and debris flows that have occurred in the dam slope area and its surrounding areas. For example, historical geological disaster data can be derived from government geological disaster survey results, academic literature, engineering monitoring records, or local archival materials. Of course, historical event data can also be supplemented through ground surveys and aerial survey analysis to improve data integrity and accuracy. This example embodiment does not specifically limit the source channels of disaster data. In an optional implementation, indicators such as disaster frequency and scale level can be sorted out according to different disaster types to construct a disaster spatial distribution database to support subsequent risk quantification analysis.

[0112] Geological exploration results refer to basic data such as the geological structure, lithology, fracture development, and hydrogeological conditions of the dam slope area obtained through drilling, trenching, geological mapping, geophysical exploration, and remote sensing interpretation. For example, geological exploration can reveal key information such as potential weak interlayers, sliding surface distribution, and mechanical properties of rock and soil. Of course, comprehensive geophysical exploration (such as seismic wave reflection and resistivity imaging) can also be used in conjunction with direct exploration methods to improve the accuracy of underground structure interpretation. This example embodiment does not specifically limit the geological exploration technical means. In an optional implementation, the exploration data can be combined with three-dimensional geological modeling technology to establish a detailed three-dimensional geological model of the slope to more accurately determine the location and scale of potentially unstable structural units.

[0113] After acquiring historical geological disaster data and geological exploration results, spatial overlay analysis is performed in conjunction with a geographic information system (GIS) to assess the probability of disaster occurrence and the potential impact at each location, thereby determining the potential risk level of geological disasters at each location. Risk levels can be divided into low risk, medium risk, and high risk according to preset standards. For example, high-risk areas are areas with historical disaster records and adverse geological bodies, medium-risk areas are areas with certain geological hazards but no disasters, and low-risk areas are areas with stable geological conditions and no disaster records. Of course, risk classification can also be refined using semi-quantitative or quantitative risk assessment methods (such as a comprehensive model based on hazard, vulnerability, and exposure). This example embodiment does not specifically limit the risk level classification method.

[0114] By acquiring multi-source basic environmental data such as terrain elevation, signal obstruction, vegetation coverage, and geological disaster risk in the dam slope area and performing standardized quantitative processing, accurate and comprehensive environmental feature support can be provided for subsequent slope monitoring area division and data fusion, significantly improving the environmental adaptability and monitoring accuracy of the slope deformation monitoring system.

[0115] In an exemplary embodiment of the present disclosure, Figure 4 The steps in the above are used to determine the data fusion weights corresponding to each data fusion observation area. Figure 4 may specifically include:

[0116] In step S410, the environmental influence factors of each monitoring device in the multi-source monitoring system under dam horizontal distance, slope gradient, satellite signal blocking degree, vegetation coverage density and geological disaster potential risk level are obtained.

[0117] In step S420, the environmental adaptability score of each monitoring device in the current data fusion observation area is calculated by the slope deformation disturbance parameters in the data fusion observation area and the environmental influence factors.

[0118] In step S430, the environmental adaptability score of each monitoring device is normalized to obtain the data fusion weight corresponding to the data fusion observation area.

[0119] The environmental influence factor is a quantitative index of the performance of each monitoring device under different environmental characteristic parameters, which is used to reflect the adaptability and data reliability of the device in a specific environment. For example, the environmental influence factor can be obtained by statistical analysis of historical monitoring data. For example, if the GNSS receiving accuracy decreases by 20% in a large slope area, the corresponding slope influence factor is 0.8. The environmental influence factor can also be derived based on a theoretical performance model. For example, the decline trend of InSAR interference coherence in a dense vegetation area can be calculated according to the electromagnetic wave propagation theory. Of course, the environmental influence factor can be established by offline simulation or field sampling test, such as by carrying GNSS receivers and InSAR devices to conduct terrain shielding experiments and statistically analyzing the signal strength and monitoring error variation law. Of course, a qualitative or semi-quantitative score can also be given to the adaptability of the device under different environmental conditions according to the expert experience, and the present embodiment does not particularly limit the method of obtaining the environmental influence factor.

[0120] In specific implementation, for the dam horizontal distance parameter, the interference degree of GNSS in the near-dam strong reflection area multipath effect can be analyzed to set the environmental influence factor; for the slope gradient parameter, the stability and measurement error of the measuring robot in the high slope area can be evaluated to set the corresponding factor; for the satellite signal blocking degree, the GNSS positioning quality change can be measured to determine the influence factor; for the vegetation coverage density, the influence factor can be set by the change rate of the InSAR image coherence coefficient; and for the geological disaster potential risk level, the potential threat of ground disturbance to the stability and observation continuity of the monitoring device can be considered to set the corresponding influence factor.

[0121] The environmental adaptability score refers to a quantitative result of comprehensively evaluating the pros and cons of the monitoring performance of the device in the current region according to the specific environmental characteristics of the data fusion observation region and the environmental impact factors corresponding to each monitoring device. For example, the environmental adaptability score can be combined into a single score value by weighted multiplication or weighted summation of the impact factors corresponding to each environmental parameter; or a fuzzy comprehensive evaluation or machine learning regression model can be used to generate an adaptability score from multiple parameter characteristics. Of course, a linear model can be used, for example, adaptability score = ∑(disturbance parameter weight × impact factor value); of course, a nonlinear fusion method can also be used, such as setting a nonlinear amplification weight for important disturbance parameters to enhance the sensitivity of the score to extreme environmental conditions. The present example embodiment does not particularly limit the environmental adaptability score calculation method.

[0122] In the specific calculation process, the actual values of each disturbance parameter in the current observation region can be extracted, such as the average dam horizontal distance, the average slope, the average satellite signal blocking degree, the average vegetation coverage density, and the geological disaster potential risk level. Then, according to the actual values of each disturbance parameter, the corresponding factor value is found or interpolated in the monitoring device environmental impact factor table. Finally, each impact factor value can be combined by weighting according to the preset weight rule to obtain the environmental adaptability score of the monitoring device in the observation region.

[0123] Normalization refers to standardizing the adaptability scores of different monitoring devices with different numerical dimensions and large differences in numerical ranges to a unified scale interval, usually standardized to between 0 and 1, to facilitate direct comparison and fusion. For example, normalization can use the maximum value normalization method to standardize all device adaptability scores by the maximum value; or the Z-score standardization method can be used to adjust the score distribution to a standard normal distribution with a mean of 0 and a standard deviation of 1. Of course, a preset normalization formula can also be used, such as normalized score = original score / maximum score of all devices. A score threshold setting mechanism can also be introduced, and when the device adaptability score is below a certain level, it is directly assigned a value of zero to eliminate the interference of extremely low reliability data sources on the fusion weight. The present example embodiment does not particularly limit the normalization method.

[0124] After normalization, the data fusion weight corresponding to each type of monitoring device in each data fusion observation region is obtained. The fusion weight directly determines the contribution proportion of each data source to the final fusion result in the subsequent multi-source data fusion process. The data source with a high score has a larger proportion in the fusion, and the data source with a low score has a smaller proportion or is eliminated, so that the fusion result is more in line with the monitoring reliability and adaptability of each data source in the actual environment.

[0125] By acquiring the environmental influence factors of each monitoring device under different environmental characteristics, combining the environmental adaptability score calculated by the data fusion observation area of the slope deformation disturbance parameter, and then performing normalization processing to determine the data fusion weight, the contribution of each data source in the multi-source monitoring data fusion process can be dynamically adapted to the actual environmental conditions, and the rationality of data fusion, the accuracy and stability of monitoring results of the slope deformation monitoring system in complex and variable environments can be significantly improved.

[0126] In an optional embodiment, before the environmental adaptability score of each monitoring device is normalized to obtain the data fusion weight corresponding to the data fusion observation area, if it is determined that the environmental adaptability score of the current monitoring device is less than or equal to the preset score threshold, the environmental adaptability score of the current monitoring device can be set to zero.

[0127] The environmental adaptability score is a quantitative index reflecting the matching degree of the monitoring device in a specific data fusion observation area according to the environmental disturbance characteristics and the device performance, for example, the environmental adaptability score can be obtained by comprehensively considering the environmental influence factors corresponding to the dam horizontal distance, the slope gradient, the satellite signal shielding degree, the vegetation coverage density, and the geological disaster potential risk level. Of course, in order to adapt to different monitoring needs, the environmental adaptability score can also be calculated according to the actual monitoring accuracy requirement by setting a multi-dimensional feature weighted combination model, and the specific calculation method of the environmental adaptability score in the example embodiment is not particularly limited.

[0128] The score threshold is a preset limit value for screening the rationality and reliability of the device adaptability score, for example, the score threshold can be set to 0.3, that is, if the adaptability score of the device in the current area is less than or equal to 0.3, it is determined that the monitoring reliability of the device in the environment is insufficient, and the score threshold can also be set to 0.2. Specifically, the score threshold can be dynamically adjusted according to the monitoring accuracy requirement, the device type, and the environmental condition complexity, and of course, the score threshold can be uniformly set to a fixed value to simplify the system design, or it can be adaptively adjusted according to the environmental complexity of each data fusion observation area, for example, the score threshold requirement can be increased in a high-risk area, and the threshold can be appropriately relaxed in a low-risk area. The specific setting method of the score threshold in the example embodiment is not particularly limited.

[0129] In a specific implementation, firstly, the environmental adaptability score of each monitoring device in the current observation area is determined. If the score value is less than or equal to the preset score threshold, the environmental adaptability score of the monitoring device can be directly set to zero, that is, in the subsequent data fusion weight normalization process, the data contribution of the monitoring device is completely excluded. Through the zero processing, the monitoring device with significant performance degradation in a specific environment can be effectively prevented from participating in data fusion, and noise, error or distortion caused by low-quality data sources can be avoided, thereby improving the accuracy and stability of the overall fused data.

[0130] By introducing a score threshold screening mechanism before the environmental adaptability score normalization processing, and setting the score of the device that does not meet the threshold requirement to zero, the rigor and adaptability of the device screening in the multi-source data fusion process can be effectively enhanced, and the data fusion weight generated finally can better reflect the effectiveness and reliability of each monitoring device in the current environment, further improving the adaptability of the slope deformation monitoring system to complex and changeable environment, the robustness of the data fusion result and the sensitivity of the abnormal change identification.

[0131] In an example embodiment of the present disclosure, the multi-source monitoring data in each data fusion observation area can be periodically collected by the multi-source monitoring system through the following steps, which can specifically include:

[0132] The layout density and the acquisition frequency in the data fusion observation area can be determined according to the environmental adaptability scores of the global satellite signal receiver, the measurement robot and the interferometric synthetic aperture radar in the multi-source monitoring system in the data fusion observation area, and the multi-source monitoring data in each data fusion observation area can be collected by the multi-source monitoring system under the layout density and the acquisition frequency.

[0133] The environmental adaptability score refers to the performance quantization value calculated by evaluating the monitoring applicability of each monitoring device in the data fusion observation area under the conditions of dam horizontal distance, slope gradient, satellite signal shielding degree, vegetation coverage density and geological disaster potential risk level, etc. For example, the device with high adaptability score has good monitoring effect in the current environmental conditions and is suitable for higher density or increased sampling frequency. Of course, the device with low adaptability score has poor application effect in the area, and the layout density or sampling frequency can be appropriately reduced. The specific calculation logic of the adaptability score is not particularly limited in the example embodiment.

[0134] Deployment density refers to the number of monitoring devices arranged per unit area within the data fusion observation area. For example, the deployment density can be expressed as the number of GNSS receivers set up per square kilometer; the deployment density can also be described based on the number of sampling points on each monitoring profile line. Of course, a rule design in which the environmental adaptability score is positively correlated with the deployment density can also be adopted, that is, the higher the adaptability score, the higher the deployment density, so as to enhance the monitoring sensitivity; or, based on resource constraints, a hierarchical deployment strategy can be adopted to increase the density only in areas where the adaptability score is higher than a certain threshold, and adopt a sparse deployment in other areas. This example embodiment does not specifically limit the representation of the deployment density.

[0135] The acquisition frequency refers to the time interval or periodic indicator for updating and collecting data in the same observation area. For example, the acquisition frequency can be expressed as once a day, twice a week, etc. Of course, the acquisition frequency can also be described by a continuous sampling interval, such as once every 10 minutes. This example embodiment does not specifically limit the acquisition frequency setting method. Optionally, the acquisition frequency can also be dynamically adjusted according to the environmental adaptability score. For example, a high sampling frequency can be set in areas with high adaptability scores to improve the spatiotemporal continuity of the monitoring data. Of course, in areas with low adaptability scores, the acquisition frequency can be appropriately reduced to reduce resource consumption and improve the overall operating efficiency of the system.

[0136] During the actual data collection process, data collection is controlled based on the set deployment density and acquisition frequency. For example, GNSS receivers can be deployed at the set density in high-adaptability areas and continuously collect location information at a high frequency. Measurement robots regularly inspect key monitoring points in high-adaptability areas according to the set patrol plan. InSAR data, based on the orbit revisit cycle, prioritizes high-frequency data updates for observation areas with high adaptability scores. In an optional implementation, an automated data collection system can be combined to centrally schedule the collection plans and data return tasks of each device to ensure that data collection in each observation area meets the established density and frequency standards. Of course, an abnormal trigger mechanism can also be introduced. When displacement acceleration or abnormal changes are detected in a certain area, the data collection frequency and density in that area are automatically increased, forming a dynamic response collection system.

[0137] By dynamically determining the deployment density and collection frequency based on the environmental adaptability score of each monitoring device in the data fusion observation area, and collecting multi-source monitoring data accordingly, it is possible to adaptively optimize the monitoring resource allocation according to actual environmental conditions, so that the monitoring system can effectively reduce resource consumption and redundant burdens while ensuring the observation accuracy and timeliness of key areas, and significantly improve the environmental adaptability, monitoring sensitivity and overall operational efficiency of the dam slope deformation monitoring system.

[0138] In an exemplary embodiment of the present disclosure, multi-source monitoring data includes monitoring point change monitoring data, surface deformation monitoring data, and local high-precision monitoring data. The following steps can be used to collect the multi-source monitoring data within the data fusion observation area, which may specifically include:

[0139] The monitoring data of monitoring point changes in the geocentric coordinate system can be collected by global satellite signal receivers installed at each monitoring point in the data fusion observation area; the surface deformation monitoring data in the image coordinate system can be collected by interferometric synthetic aperture radar installed in the data fusion observation area; and the local high-precision monitoring data in the local coordinate system can be collected by a measuring robot installed in the data fusion observation area.

[0140] The monitoring point change monitoring data is collected and monitored by global satellite signal receivers installed at each monitoring point in the data fusion observation area. A GNSS receiver is a device capable of receiving and processing GNSS signals from the Global Navigation Satellite System (GNSS) and is used to measure the three-dimensional position changes of the monitoring point in the Earth-Centered, Earth-Fixed (ECEF) coordinate system in real time or periodically. For example, a GNSS receiver can support multiple observation systems, such as GPS, GLONASS, Galileo, and BeiDou, achieving sub-meter, decimeter, or even centimeter-level positioning accuracy. Of course, for deformation monitoring needs, dual-band or multi-band receivers can also be used to improve multipath interference resistance and observation accuracy. This example embodiment does not specifically limit the specific model and configuration of the GNSS receiver. A continuously operating reference station (CORS) mode can be used to operate at a fixed monitoring point for a long period of time to obtain high-precision time-series displacement data. Alternatively, a mobile monitoring mode can be used to periodically set up a GNSS receiver to perform short-term measurements at different monitoring points to achieve wide-area monitoring with limited resources.

[0141] In actual deployment, GNSS receivers can be installed at key locations within the dam slope monitoring sub-area, such as representative geological units, slip zones, crack concentration areas, and slope deformation warning areas, to collect change monitoring data at these locations. This data can be represented in a geocentric XYZ coordinate system for subsequent position change analysis, rate calculation, and deformation anomaly identification.

[0142] Surface deformation monitoring data is collected and monitored using an interferometric synthetic aperture radar (ISAR) system located in the data fusion observation area. ISAR is a remote sensing monitoring technology that can acquire surface images by transmitting and receiving microwave radar signals and perform interferometric processing based on phase differences between multi-temporal images to infer surface deformation information. For example, InSAR can be used for large-scale regional surface monitoring based on satellite-based platforms (such as Sentinel-1 and TerraSAR-X), or for high-precision continuous observation within a local area using ground-based synthetic aperture radar (GBSAR). To meet different monitoring needs, time series InSAR (TS-InSAR) or persistent scatterer InSAR (PS-InSAR) can be used to improve the temporal and spatial resolution and accuracy of monitoring data. This example embodiment does not specifically limit the specific configuration of the InSAR system or the interferometric processing method.

[0143] During the acquisition process, the surface deformation data output by the InSAR system is expressed in an image coordinate system (i.e., the radar-view projection coordinate system), requiring subsequent coordinate conversion to unify with other data sources. Surface deformation monitoring data can reflect large-scale changes in surface subsidence, uplift, or horizontal displacement within the monitoring area. It is particularly suitable for continuously monitoring slowly accumulating deformation processes and identifying anomalous trends.

[0144] Local high-precision monitoring data refers to monitoring data collected in a local coordinate system by a measuring robot positioned within the data fusion observation area. A measuring robot is a measuring device capable of performing high-precision angle and distance measurements based on an automated control system, thereby acquiring three-dimensional spatial coordinate data. For example, a measuring robot may be equipped with an Automatic Target Recognition (ATR) module, enabling rapid lock-on and continuous tracking measurement by identifying reflective prisms or natural feature targets. Of course, a measuring robot may also incorporate a three-dimensional laser scanning unit to collect high-density point cloud data in a local area, thereby enhancing the ability to capture fine-grained terrain changes. This example embodiment does not specifically limit the internal configuration of the measuring robot or the measurement method.

[0145] During data collection, the measuring robot measures the relative three-dimensional displacement of each monitoring prism or feature point in real time based on its own local coordinate system (typically with a fixed control point as the origin and defined horizontal and vertical axes). Data from this local coordinate system offers high measurement accuracy and timeliness, making it particularly suitable for high-frequency, high-precision dynamic monitoring of key areas experiencing significant local deformation, rapid change rates, or subtle spatial variations.

[0146] Specifically, the following steps may be used to unify the monitoring coordinate systems of multi-source monitoring data, which may include:

[0147] A rotation matrix and a translation matrix can be constructed based on the position coordinates of the selected reference monitoring points, and the monitoring point change monitoring data can be converted to the local coordinate system through the rotation matrix and the translation matrix to obtain unified point change monitoring data; a projection matrix can be solved based on the position coordinates and image coordinates of the reference monitoring points, and the surface deformation monitoring data can be converted to the local coordinate system through the projection matrix to obtain unified surface deformation monitoring data; the local high-precision monitoring data can be aligned and corrected for deviations based on the position coordinates of the reference monitoring points to obtain unified local high-precision monitoring data; the unified monitoring point change monitoring data, the unified surface deformation monitoring data and the unified local high-precision monitoring data can be checked for consistency to obtain multi-source monitoring data in the data fusion observation area.

[0148] Among them, the benchmark monitoring point refers to a specific monitoring point selected in the slope monitoring network, which is stable in location, has excellent observation conditions and is representative, and is used as a reference benchmark for coordinate conversion and data unification. For example, the benchmark monitoring point can be a GNSS continuous observation point located in a geologically stable area and has no significant displacement changes over a long period of time; of course, the best benchmark point can also be selected based on the long-term stability indicators of multiple candidate points. This example embodiment does not specifically limit the selection criteria for the benchmark monitoring point. In some optional implementations, a main benchmark point and a backup benchmark point can be set, which automatically switch when the main benchmark point fails to ensure the consistency and continuity of the monitoring data.

[0149] A rotation matrix refers to a three-dimensional orthogonal matrix that describes the attitude transformation relationship between a local coordinate system and a geocentric coordinate system, and is used to achieve directional alignment of the coordinate system. For example, the rotation matrix can be calculated using the known positional relationship between a reference monitoring point and its surrounding auxiliary points through a spatial three-point orientation or least squares fitting method. Of course, it can also be directly derived based on the transformation relationship between a known geographic coordinate system (such as WGS84) and a local engineering coordinate system. This example embodiment does not specifically limit the method for calculating the rotation matrix.

[0150] The translation matrix refers to a three-dimensional vector that describes the origin offset relationship between the local coordinate system and the geocentric coordinate system, and is used to achieve the unification of the coordinate origin position. For example, the translation matrix can be set to the position vector of the reference monitoring point in the geocentric coordinate system as the translation amount of the local coordinate system origin; of course, it can also be combined with the overall network adjustment calculation to obtain more accurate translation parameters. This example embodiment does not specifically limit the translation matrix calculation method.

[0151] Image coordinates refer to the pixel spatial positions of surface target points in radar images acquired by the InSAR system, and are typically expressed as row and column numbers or image coordinates (range, azimuth). For example, each surface point corresponds to a specific two-dimensional pixel position in the radar image, reflecting its spatial projection relationship within the radar field of view. Of course, image coordinates can be further associated with a geographic coordinate system through image geometric correction (such as orthorectification). This example embodiment does not specifically limit the acquisition and expression of image coordinates.

[0152] The projection matrix refers to the matrix mapping relationship that describes the transformation relationship between the image coordinate system and the local space rectangular coordinate system. For example, the projection matrix can be established based on the known positions of the reference monitoring points in the local coordinate system and their corresponding image coordinates through least squares fitting, homography matrix solution, or beam normal vector inversion. Of course, multi-point joint solution can also be performed based on the ground control point (GCP) system to improve accuracy. This example embodiment does not specifically limit the projection matrix solution method.

[0153] In the actual conversion process, we can first use the known reference point position information to establish a projection relationship model between local three-dimensional coordinates and image two-dimensional coordinates, and then apply the solved projection matrix to map the surface deformation data output by the InSAR system from the image coordinate system to the local three-dimensional coordinate system, so that the surface deformation monitoring data and other data sources maintain a unified spatial reference benchmark.

[0154] Local high-precision monitoring data refers to a data set collected by a measuring robot (TotalStation) set in the data fusion observation area, which describes the three-dimensional displacement changes of the monitoring prism or feature points based on the local coordinate system. For example, this type of data directly reflects the tiny displacement changes in the local space and has the characteristics of high precision and high temporal resolution; of course, local high-precision monitoring data may also be affected by the initial orientation error of the equipment, site drift or local measurement deviation, and requires unified benchmark correction. This example embodiment does not specifically limit the collection method of local high-precision monitoring data.

[0155] Deviation correction refers to identifying and correcting systematic errors in translation, rotation or scale of local data by comparing the actual measurement results of control points in local monitoring data with the standard positions of benchmark monitoring points. For example, the overall translation vector and rotation matrix can be estimated based on the offset of multiple common control points through the least squares adjustment method; of course, a rigid transformation model can also be used to perform only translation and rotation correction while ignoring scale changes. This example embodiment does not specifically limit the deviation correction model. In an optional implementation, rigid translation correction can be performed based on a single benchmark point, or multi-parameter joint fitting correction can be performed based on multiple benchmark points to improve overall correction accuracy and local consistency.

[0156] In the specific implementation, the spatial position of the benchmark monitoring point in the local high-precision monitoring data is analyzed correspondingly with its position in the unified local coordinate system, and the correction parameters are calculated and applied to align the local high-precision monitoring data to the unified spatial benchmark, so that all monitoring data are directly comparable in the same reference frame.

[0157] Consistency verification refers to the comparative analysis of displacement observation results of different data sources at the same or similar spatial positions and time nodes in a unified coordinate system to verify the spatial correspondence, temporal synchronization and deformation trend consistency between the data. For example, consistency verification can be performed by calculating the deviation between the displacements of the same target point from different data sources and evaluating whether the deviation is within a preset tolerance range; of course, it can also be performed by statistically analyzing the consistency of the overall deformation trends of each data source, such as linear regression fitting the deformation curve and calculating the correlation coefficient. This example embodiment does not specifically limit the specific method of consistency verification. In an optional implementation, a strict consistency threshold can be set for monitoring requirements in high-security areas; or a loose consistency standard can be used for preliminary screening of potential anomalies to improve processing efficiency.

[0158] In specific implementation, the point change data collected by the GNSS receiver can be used as a preliminary benchmark, and the InSAR surface deformation monitoring data and the local high-precision monitoring data of the surveying robot can be compared point by point. The displacement direction and amplitude can be checked one by one based on time alignment, and abnormal data points that exceed the reasonable deviation range can be identified and eliminated. At the same time, data sets with good consistency can be marked as input for subsequent data fusion processing.

[0159] By converting the monitoring point change monitoring data, surface deformation monitoring data and local high-precision monitoring data in different coordinate systems into a local coordinate system and performing consistency checking processing, the spatial deviation and systematic error introduced by the observation reference difference of multi-source monitoring data can be effectively eliminated, the consistency of data space reference and deformation trend is realized, and the accuracy of data fusion processing and the reliability of multi-source deformation information comprehensive analysis are significantly improved, thereby providing a high-precision, high-stability and high-reliability data basis for the slope deformation monitoring system.

[0160] Figure 5 A scene diagram illustrating division of a data fusion observation region in a dam slope region according to some embodiments of the present disclosure is schematically shown.

[0161] Reference Figure 5 As shown, the dam slope region can be divided into a plurality of monitoring sub-regions 510 by a preset division grid. The monitoring sub-regions 510 are formed by preliminarily dividing the entire dam slope region to be monitored according to the preset division grid, and each monitoring sub-region is distributed in a regular grid manner in space, which is used to support further regional classification processing based on disturbance parameters.

[0162] Subsequently, the slope deformation disturbance parameters in each monitoring sub-region 510, such as the dam horizontal distance, the slope gradient, the satellite signal shielding degree, the vegetation coverage density and the geological disaster potential risk level, can be collected, and the regional classification and merging method provided in the embodiments of the present disclosure is combined to classify and fuse each monitoring sub-region 510, thereby forming a plurality of data fusion observation regions. For example, Figure 5 As shown, a first data fusion observation region 520, a second data fusion observation region 530 and a third data fusion observation region 540 are finally generated.

[0163] For example, for the first data fusion observation region 520, it can schematically represent a region with a higher geological disaster potential risk level, a larger slope gradient and relatively less GNSS signal shielding. In this region, the GNSS receiver and the measurement robot both have a higher environmental adaptability score, so that these two types of data are given a higher fusion weight in the data fusion process, and a higher deployment density and collection frequency are set to enhance the timeliness response to sudden slip and sudden deformation; at the same time, since InSAR may be subject to multipath interference in the near-dam high reflection region, its data adaptability score is relatively low, so its weight is appropriately reduced in this region.

[0164] The second data fusion observation area 530 schematically represents an area where disturbance parameter characteristics fall between high and low risk, and the overall environmental adaptability score is at a medium level. Within this area, the adaptability scores of the three types of monitoring equipment are relatively close. Therefore, a weighted balancing strategy is adopted during the data fusion process. Data collected by GNSS, InSAR, and the surveying robot are weighted and fused according to the normalized score ratio. This ensures that the complementary advantages of each type of data are utilized during the fusion process, thereby establishing a stable deformation perception capability with moderate redundancy.

[0165] The third data fusion observation area 540 schematically represents an area with gentle terrain, weak geological disturbances, severe signal obstruction, and dense vegetation, resulting in low adaptability scores for GNSS receivers and surveying robots. InSAR, due to its wide-area coverage, enables large-scale, low-cost monitoring in such areas. Therefore, the weight of InSAR data is increased during the fusion process, while the participation of other equipment data is appropriately reduced. The frequency and deployment density of data acquisition are also reduced to control resource consumption and improve monitoring efficiency.

[0166] Through the above-mentioned differentiated data fusion strategy, customized monitoring strategy configuration can be achieved for different data fusion observation areas, thereby effectively improving the overall performance of the multi-source monitoring system in terms of spatial resource allocation, data fusion accuracy and disaster perception capability, and enhancing the robustness and precision adaptability of the monitoring system under multiple environmental conditions.

[0167] It should be noted that although the steps of the method disclosed herein are depicted in a particular order in the accompanying drawings, this does not require or imply that the steps must be performed in that particular order, or that all steps must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one, and / or one step may be decomposed into multiple steps.

[0168] In addition, in this exemplary embodiment, a slope deformation monitoring device based on multi-source data fusion is also provided. Figure 6 As shown, the slope deformation monitoring device 600 based on multi-source data fusion includes: a multi-source monitoring module 610, a monitoring area division module 620, a fusion weight module 630, a multi-source monitoring data acquisition module 640 and a slope deformation analysis module 650. Among them:

[0169] The multi-source monitoring module 610 is used to deploy a monitoring coverage network consisting of global satellite signal receivers, measurement robots, and interferometric synthetic aperture radar in the dam slope area to be monitored, forming a multi-source monitoring system;

[0170] A monitoring area division module 620 is configured to divide the dam slope area into monitoring areas based on the collected slope deformation disturbance parameters to obtain multiple data fusion observation areas;

[0171] A fusion weight module 630 is used to determine the data fusion weight corresponding to each of the data fusion observation areas;

[0172] A multi-source monitoring data acquisition module 640 is configured to periodically acquire multi-source monitoring data within each of the data fusion observation areas through the multi-source monitoring system;

[0173] The slope deformation analysis module 650 is used to perform weighted fusion processing on the collected multi-source monitoring data based on the data fusion weights of each data fusion observation area, and generate slope deformation monitoring results corresponding to each data fusion observation area.

[0174] In some example embodiments of the present disclosure, based on the aforementioned scheme, the monitoring area division module 620 is configured to: preliminarily divide the dam slope area according to a preset division grid to obtain multiple monitoring sub-areas; classify the monitoring sub-areas based on the slope deformation disturbance parameters in each of the monitoring sub-areas, and merge and fuse the monitoring sub-areas belonging to the same classification to obtain multiple data fusion observation areas.

[0175] In some example embodiments of the present disclosure, based on the aforementioned scheme, the slope deformation disturbance parameters include the horizontal distance of the dam, the slope gradient, the degree of satellite signal obstruction, the vegetation coverage density and the potential risk level of geological hazards at each position coordinate in the dam slope area; the monitoring area division module 620 is configured to: determine the quantitative scores of the horizontal distance of the dam, the slope gradient, the degree of satellite signal obstruction, the vegetation coverage density and the potential risk level of geological hazards in each of the monitoring sub-areas; fuse the quantitative scores according to the weight ratio of each of the slope deformation disturbance parameters to obtain a regional classification score for each of the monitoring sub-areas; classify the monitoring sub-areas according to the regional classification score, and merge the monitoring sub-areas belonging to the same classification to obtain multiple data fusion observation areas.

[0176] In some example embodiments of the present disclosure, based on the foregoing scheme, the slope deformation monitoring device 600 based on multi-source data fusion further comprises a slope deformation disturbance parameter acquisition module configured to: acquire topographic elevation data of the dam slope region, and determine the dam horizontal distance from each position coordinate in the dam slope region to the dam body according to the topographic elevation data; calculate the slope of each position coordinate by calculating the slope of the dam slope region through the topographic elevation data; acquire the signal shielding test results based on the unmanned aerial vehicle, and determine the satellite signal shielding degree at each position coordinate according to the signal strength in the signal shielding test results; calculate the normalized vegetation index through the multi-period remote sensing images of the dam slope region, and generate a vegetation coverage distribution map based on the normalized vegetation index, and determine the vegetation coverage density at each position coordinate through the vegetation coverage distribution map; determine the geological disaster potential risk level of each position coordinate according to the historical geological disaster data and the geological exploration results corresponding to the dam slope region.

[0177] In some example embodiments of the present disclosure, based on the foregoing scheme, the fusion weight module 630 is configured to: acquire the environmental influence factors of each monitoring device in the multi-source monitoring system under the dam horizontal distance, the slope, the satellite signal shielding degree, the vegetation coverage density and the geological disaster potential risk level; calculate the environmental adaptability score of each monitoring device in the current data fusion observation area through the slope deformation disturbance parameters in the data fusion observation area and the environmental influence factors; normalize the environmental adaptability score of each monitoring device to obtain the data fusion weight corresponding to the data fusion observation area.

[0178] In some example embodiments of the present disclosure, based on the foregoing scheme, before the environmental adaptability score of each monitoring device is normalized to obtain the data fusion weight corresponding to the data fusion observation area, the slope deformation monitoring device 600 based on multi-source data fusion further comprises an environmental adaptability score adjustment module configured to: if it is determined that the environmental adaptability score of the current monitoring device is less than or equal to the preset score threshold, the environmental adaptability score of the current monitoring device is set to zero.

[0179] In some example embodiments of the present disclosure, based on the foregoing scheme, the multi-source monitoring data acquisition module 640 is configured to: determine the layout density and the acquisition frequency corresponding to the data fusion observation area according to the environmental adaptability score of the global satellite signal receiver, the measurement robot and the interferometric synthetic aperture radar in the multi-source monitoring system in the data fusion observation area; acquire multi-source monitoring data in each data fusion observation area through the multi-source monitoring system under the layout density and the acquisition frequency.

[0180] In some example embodiments of the present disclosure, based on the aforementioned scheme, the multi-source monitoring data includes monitoring point change monitoring data, surface deformation monitoring data and local high-precision monitoring data, and the multi-source monitoring data acquisition module 640 is configured to: collect monitoring point change monitoring data in the geocentric coordinate system through a global satellite signal receiver set at each monitoring point in the data fusion observation area; collect surface deformation monitoring data in the image coordinate system through an interferometric synthetic aperture radar set in the data fusion observation area; and collect local high-precision monitoring data in the local coordinate system through a measuring robot set in the data fusion observation area.

[0181] In some example embodiments of the present disclosure, based on the aforementioned scheme, the slope deformation monitoring device 600 based on multi-source data fusion also includes a coordinate unification module, which is configured to: construct a rotation matrix and a translation matrix based on the position coordinates of the selected benchmark monitoring point, and convert the monitoring point change monitoring data to a local coordinate system through the rotation matrix and the translation matrix to obtain unified point change monitoring data; solve the projection matrix based on the position coordinates and image coordinates of the benchmark monitoring point, and convert the surface deformation monitoring data to a local coordinate system through the projection matrix to obtain unified surface deformation monitoring data; perform deviation correction and alignment on the local high-precision monitoring data based on the position coordinates of the benchmark monitoring point to obtain unified local high-precision monitoring data; perform consistency verification on the unified monitoring point change monitoring data, the unified surface deformation monitoring data and the unified local high-precision monitoring data to obtain multi-source monitoring data within the data fusion observation area.

[0182] The specific details of each module of the above-mentioned slope deformation monitoring device based on multi-source data fusion have been described in detail in the corresponding slope deformation monitoring method based on multi-source data fusion, and therefore will not be repeated here.

[0183] It should be noted that while the detailed description above mentions several modules or units within the slope deformation monitoring device based on multi-source data fusion, this division is not mandatory. In fact, depending on the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied within a single module or unit. Conversely, the features and functions of a single module or unit described above can be further divided and embodied by multiple modules or units.

[0184] In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above-mentioned slope deformation monitoring method based on multi-source data fusion is also provided.

[0185] Those skilled in the art can understand that the various aspects of the present disclosure can be implemented as a system, a method or a program product. Therefore, the various aspects of the present disclosure can be embodied as a whole hardware embodiment, a whole software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system" here.

[0186] The electronic device 700 according to this embodiment of the present disclosure will be described below with reference to Figure 7 Figure 7 The electronic device 700 shown is merely an example, and should not bring any limitation to the function and use range of the embodiments of the present disclosure.

[0187] As Figure 7 shown, the electronic device 700 is in the form of a general computing device. The components of the electronic device 700 can include, but are not limited to, the at least one processing unit 710 described above, the at least one storage unit 720 described above, a bus 730 connecting different system components (including the storage unit 720 and the processing unit 710), and a display unit 740.

[0188] The storage unit stores program code that can be executed by the processing unit 710, so that the processing unit 710 performs the steps according to various exemplary embodiments of the present disclosure described in the "Exemplary Method" section of the present specification. For example, the processing unit 710 can perform the steps S110 as shown in Figure 1 S110, a monitoring coverage network composed of a global satellite signal receiver, a survey robot and an interferometric synthetic aperture radar is laid out in a dam slope region to be monitored, forming a multi-source monitoring system; S120, the dam slope region is monitored and selected for division according to the collected slope deformation disturbance parameters, obtaining a plurality of data fusion observation regions; S130, the data fusion weight corresponding to each data fusion observation region is determined; S140, the multi-source monitoring data in each data fusion observation region is periodically collected through the multi-source monitoring system; S150, the collected multi-source monitoring data is respectively weighted and fused based on the data fusion weight of each data fusion observation region, generating the slope deformation monitoring result corresponding to each data fusion observation region.

[0189] The storage unit 720 can include a readable medium in the form of a volatile storage unit, such as a random access memory (RAM) 721 and / or a cache memory unit 722, and can further include a read-only memory (ROM) 723.

[0190] ​The storage unit 720 can also include a number of program modules 725 that are stored in the memory 724, including but not limited to an operating system, one or more application programs, other program modules, and program data, each of which or a combination of which can include implementation of a network environment.

[0191] The bus 730 can represent one or more of several types of bus structures, including a storage unit bus or bus controller, a peripheral bus, a graphics acceleration port, a processing unit bus, or a local bus using any of a variety of bus architectures.

[0192] The electronic device 700 can also communicate with one or more external devices 770 such as a keyboard or pointing device, a Bluetooth device, etc.; other devices that enable a user to interact with the electronic device 700; and / or one or more devices that enable the electronic device 700 to communicate with one or more other computing devices. Such communication can occur via an input / output (I / O) interface 750. Still yet, the electronic device 700 can communicate with one or more networks, such as a local area network (LAN), a general wide area network (WAN), and / or a public network, such as the Internet, via a network adapter 760. As depicted, the network adapter 760 communicates with the other components of the electronic device 700 via the bus 730. It should be appreciated that although the network adapter 760 is depicted as a single component, the network adapter 760 can comprise two or more components that work together to facilitate communications between the electronic device 700 and one or more other computing devices.

[0193] From the above description of the embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solutions according to the embodiments of the disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or a network, and includes a number of instructions to make a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) execute the methods according to the embodiments of the disclosure.

[0194] In exemplary embodiments of the present disclosure, a computer readable storage medium is also provided, on which a program product capable of implementing the method described above is stored. In some possible embodiments, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program codes for causing the terminal device to perform the steps described in the "Exemplary Method" section above according to various exemplary embodiments of the present disclosure when the program product is run on the terminal device.

[0195] Reference Figure 8 As shown, a program product 800 for implementing the slope deformation monitoring method based on multi-source data fusion described above according to embodiments of the present disclosure is described, which can adopt a portable compact disc read-only memory (CD-ROM) and include program codes, and can be run on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto, and in this document, the readable storage medium can be any tangible medium containing or storing a program, which can be used or combined with an instruction execution system, device or apparatus.

[0196] The program product can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium may, for example, be but is not limited to an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples (non-exhaustive list) of readable storage media include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0197] The computer readable signal medium can include a data signal propagated in a baseband or as a carrier wave in a propagated data signal, in which readable program codes are borne. Such a propagated data signal can take on many forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof. The readable signal medium can also be any readable medium other than the readable storage medium, which can send, propagate or transmit the program for use by or in connection with an instruction execution system, device or apparatus.

[0198] The program codes contained on the readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination thereof.

[0199] Program code for performing the operations of the present disclosure may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computing device, partially on the user's device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0200] Furthermore, the above-mentioned figures are merely illustrative of the processes included in the methods according to exemplary embodiments of the present disclosure and are not intended to be limiting. It is readily understood that the processes illustrated in the above-mentioned figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0201] Through the description of the above embodiments, it will be readily understood by those skilled in the art that the example embodiments described herein can be implemented via software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, or mobile hard drive) or on a network and includes several instructions to enable a computing device (such as a personal computer, server, touch terminal, or network device) to execute the methods according to the embodiments of the present disclosure.

[0202] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow from the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the claims.

[0203] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes can be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A slope deformation monitoring method based on multi-source data fusion, characterized in that: include: Deploy a monitoring coverage network consisting of global satellite signal receivers, measurement robots, and interferometric synthetic aperture radar in the dam slope area to be monitored, forming a multi-source monitoring system; The dam slope area is preliminarily divided into regions according to a preset division grid to obtain a plurality of monitoring sub-regions; the monitoring sub-regions are classified based on the slope deformation disturbance parameters in each of the monitoring sub-regions, and the monitoring sub-regions belonging to the same classification are merged and fused to obtain a plurality of data fusion observation regions; wherein the slope deformation disturbance parameters include the horizontal distance of the dam at each position coordinate in the dam slope area, the slope gradient, the degree of satellite signal obstruction, the vegetation cover density, and the potential risk level of geological hazards; Obtaining the environmental impact factors of each monitoring device in the multi-source monitoring system under the horizontal distance from the dam, slope gradient, satellite signal shielding degree, vegetation cover density, and potential risk level of geological disasters; calculating the environmental adaptability score of each monitoring device in the current data fusion observation area based on the slope deformation disturbance parameters and the environmental impact factors in the data fusion observation area; normalizing the environmental adaptability score of each monitoring device to obtain a data fusion weight corresponding to the data fusion observation area; Periodically collecting multi-source monitoring data within each of the data fusion observation areas through the multi-source monitoring system; Based on the data fusion weights of each data fusion observation area, weighted fusion processing is performed on the collected multi-source monitoring data to generate slope deformation monitoring results corresponding to each data fusion observation area.

2. The slope deformation monitoring method based on multi-source data fusion according to claim 1 is characterized in that: Based on the slope deformation disturbance parameters in each monitoring sub-area, the monitoring sub-areas are classified, and monitoring sub-areas belonging to the same classification are merged and fused to obtain multiple data fusion observation areas, including: Determining a quantitative score of the horizontal distance to the dam, the slope gradient, the degree of satellite signal obstruction, the vegetation cover density, and the potential risk level of geological hazards in each monitoring sub-area; According to the weight ratio of each slope deformation disturbance parameter, the quantitative scores are integrated to obtain a regional classification score for each monitoring sub-area; The monitoring sub-regions are classified according to the regional classification scores, and the monitoring sub-regions belonging to the same classification are merged and fused to obtain multiple data fusion observation regions.

3. The slope deformation monitoring method based on multi-source data fusion according to claim 2 is characterized in that: The method further comprises: Acquiring terrain elevation data of the dam side slope area, and determining the horizontal distance from each position coordinate in the dam side slope area to the dam body according to the terrain elevation data; Calculating the slope of each dam slope area using the terrain elevation data to determine the slope of each position coordinate; Obtaining a signal obstruction test result based on the drone, and determining the degree of satellite signal obstruction at each position coordinate based on the signal strength in the signal obstruction test result; Calculating a normalized vegetation index using multi-period remote sensing images of the dam slope area, generating a vegetation coverage distribution map based on the normalized vegetation index, and determining vegetation coverage density at each location coordinate using the vegetation coverage distribution map; The potential risk level of geological hazards at each location coordinate is determined based on historical geological hazard data and geological exploration results corresponding to the dam slope area.

4. The slope deformation monitoring method based on multi-source data fusion according to claim 1 is characterized in that: Before normalizing the environmental adaptability scores of the monitoring devices to obtain the data fusion weights corresponding to the data fusion observation areas, the method further includes: If it is determined that the environmental adaptability score of the current monitoring device is less than or equal to a preset score threshold, the environmental adaptability score of the current monitoring device is set to zero.

5. The slope deformation monitoring method based on multi-source data fusion according to claim 1 is characterized in that: The multi-source monitoring system periodically collects the multi-source monitoring data within each data fusion observation area, including: Determine the corresponding deployment density and acquisition frequency in the data fusion observation area according to the environmental adaptability scores of the global satellite signal receiver, the measurement robot, and the interferometric synthetic aperture radar in the multi-source monitoring system respectively in the data fusion observation area; The multi-source monitoring data in each of the data fusion observation areas is collected by the multi-source monitoring system under the deployment density and the collection frequency.

6. The slope deformation monitoring method based on multi-source data fusion according to claim 1 is characterized in that: The multi-source monitoring data includes monitoring point change monitoring data, surface deformation monitoring data and local high-precision monitoring data. The multi-source monitoring data collected from the data fusion observation area includes: Collecting monitoring data of changes in the monitoring point in the geocentric coordinate system by means of global satellite signal receivers arranged at each monitoring point in the data fusion observation area; Collecting surface deformation monitoring data in an image coordinate system by using an interferometric synthetic aperture radar arranged in the data fusion observation area; Local high-precision monitoring data in a local coordinate system is collected by a measuring robot arranged in the data fusion observation area.

7. The slope deformation monitoring method based on multi-source data fusion according to claim 6 is characterized in that: The method further comprises: Constructing a rotation matrix and a translation matrix based on the position coordinates of the selected reference monitoring points, and converting the monitoring point change monitoring data into a local coordinate system through the rotation matrix and the translation matrix to obtain unified point position change monitoring data; Solving a projection matrix based on the position coordinates and image coordinates of the reference monitoring point, and converting the surface deformation monitoring data into a local coordinate system through the projection matrix to obtain unified surface deformation monitoring data; Performing deviation correction and alignment on the local high-precision monitoring data based on the position coordinates of the reference monitoring point to obtain unified local high-precision monitoring data; The unified monitoring point change monitoring data, the unified surface deformation monitoring data and the unified local high-precision monitoring data are subjected to consistency verification to obtain multi-source monitoring data within the data fusion observation area.

8. A slope deformation monitoring device based on multi-source data fusion, characterized in that: include: The multi-source monitoring module is used to deploy a monitoring coverage network consisting of global satellite signal receivers, measurement robots, and interferometric synthetic aperture radar in the dam slope area to be monitored, forming a multi-source monitoring system; A monitoring area division module is configured to perform preliminary regional division of the dam slope area according to a preset division grid to obtain a plurality of monitoring sub-areas; classify the monitoring sub-areas based on the slope deformation disturbance parameters in each of the monitoring sub-areas, and merge and fuse the monitoring sub-areas belonging to the same classification to obtain a plurality of data fusion observation areas; wherein the slope deformation disturbance parameters include the horizontal distance to the dam, the slope gradient, the degree of satellite signal obstruction, the vegetation cover density, and the potential risk level of geological hazards at each position coordinate in the dam slope area; A fusion weight module is used to obtain the environmental impact factors of each monitoring device in the multi-source monitoring system under the horizontal distance of the dam, slope gradient, satellite signal shielding degree, vegetation cover density and potential risk level of geological disasters; calculate the environmental adaptability score of each monitoring device in the current data fusion observation area through the slope deformation disturbance parameters and the environmental impact factors in the data fusion observation area; normalize the environmental adaptability score of each monitoring device to obtain the data fusion weight corresponding to the data fusion observation area; A multi-source monitoring data acquisition module, configured to periodically acquire multi-source monitoring data within each of the data fusion observation areas through the multi-source monitoring system; The slope deformation analysis module is used to perform weighted fusion processing on the collected multi-source monitoring data based on the data fusion weights of each data fusion observation area, and generate slope deformation monitoring results corresponding to each data fusion observation area.

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