A method for monitoring deformation of a collapse landslide disaster body

Abstract

The present application relates to the technical field of geological disaster monitoring and early warning, and discloses a deformation monitoring method for collapse and landslide disaster bodies, which comprises the following steps: obtaining surface displacement data, crack data, deep deformation data, rainfall data and soil moisture content data, and constructing a synchronous monitoring data matrix; extracting surface displacement features, crack propagation features, deep shear deformation features and environmental induction features; constructing a dynamic model of the three-dimensional sliding surface of the disaster body based on the surface displacement features and the deep shear deformation features; adjusting the monitoring frequency; calculating the overall instability probability in combination with the multi-class features and the dynamic model of the three-dimensional sliding surface of the disaster body; determining the early warning level according to the overall instability probability and outputting the early warning result. The present application constructs a monitoring frequency triggering index, and dynamically adjusts the monitoring frequency according to the monitoring frequency triggering index, so that the monitoring data acquisition cycle can be adjusted according to the deformation state of the disaster body, thereby meeting the data acquisition requirements of different monitoring stages.

Description

Technical Field

[0001] This invention relates to the field of geological disaster monitoring and early warning technology, and in particular to a method for monitoring the deformation of landslide disaster bodies. Background Technology

[0002] Landslides and collapses are common geological hazards in mountainous areas, highway slopes, reservoir banks, and mine slopes. Their occurrence is typically accompanied by surface displacement, crack propagation, deep shear deformation, and changes in the hydrological environment. To monitor the deformation state of the hazard and provide risk warnings, it is usually necessary to deploy monitoring devices such as GPS satellite positioning equipment, crack monitoring equipment, inclinometers, rain gauges, and soil moisture monitoring equipment within the monitoring area for long-term continuous monitoring of the hazard.

[0003] In existing technologies, landslide hazard monitoring systems typically operate at a fixed sampling frequency. This means that monitoring equipment continuously collects surface displacement data, deep deformation data, and environmental monitoring data at preset time intervals, and then uploads the collected data to a monitoring platform for analysis and processing. In this technical solution, the monitoring frequency usually remains unchanged after system deployment. Regardless of whether the hazard body is in a stable state or undergoing deformation, the monitoring task is performed according to the same data acquisition cycle. When the monitored area remains stable for a long period, the monitoring system continues to collect data at a fixed frequency; when the hazard body's deformation activity intensifies, the monitoring system still uses the same sampling cycle to acquire monitoring data.

[0004] However, in the process of realizing the technical solution of this application, the inventors of this application discovered that the fixed monitoring frequency method in the prior art does not dynamically adjust the monitoring frequency in combination with the deformation state of the disaster body and the environmental change state, which results in the monitoring data acquisition cycle being unable to be adaptively configured according to the actual state changes of the disaster body, thus resulting in insufficient matching between the monitoring frequency and the deformation development state of the disaster body. Summary of the Invention

[0005] To overcome the above shortcomings, this invention provides a deformation monitoring method for landslide disaster bodies, aiming to improve the problem in the prior art that the monitoring data acquisition cycle cannot be adaptively configured according to the actual changes in the disaster body's state.

[0006] This invention provides the following technical solution: a method for monitoring the deformation of a landslide disaster body, comprising the following steps: S1. Obtain multi-source monitoring data of the landslide disaster body, including surface displacement data, crack data, deep deformation data, rainfall data, and soil moisture content data; S2. Perform time synchronization processing on the multi-source monitoring data to obtain a synchronized monitoring data matrix; S3. Extract surface displacement features, crack propagation features, deep shear deformation features, and environmental induced features based on the synchronous monitoring data matrix; S4. Construct a three-dimensional dynamic model of the sliding surface of the disaster body based on the surface displacement characteristics and deep shear deformation characteristics; S5. Based on the surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, and environmental induced characteristics, the monitoring frequency is adjusted to obtain the corresponding monitoring frequency. S6. Based on the surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, environmental induced characteristics, and the dynamic model of the three-dimensional sliding surface of the disaster body, a fusion analysis is performed to obtain the overall instability probability; S7. Determine the warning level based on the overall instability probability and output the corresponding warning result.

[0007] Preferably, in step S2, the step of performing time synchronization processing on the multi-source monitoring data includes: Obtain the collection timestamps corresponding to the multi-source monitoring data; establish a unified time reference; The timestamps of each collection are aligned according to the unified time reference. The time-aligned multi-source monitoring data are correlated and combined according to the synchronous monitoring time. A synchronous monitoring data matrix was obtained.

[0008] Preferably, in step S3, the step of extracting deep shear deformation features includes: Obtain deep deformation data corresponding to each inclined section; Calculate the horizontal displacement corresponding to each inclined section based on the deep deformation data; The local shear deformation rate is calculated based on the difference in horizontal displacement between adjacent inclined sections. The inclined section with the largest local shear deformation rate is identified as the dominant shear deformation section. The dominant shear depth is determined based on the dominant shear deformation segment. Deep shear deformation characteristics are formed based on the local shear deformation rate and the dominant shear depth.

[0009] Preferably, in step S3, the step of extracting surface displacement features includes: Obtain surface displacement data corresponding to various surface monitoring locations; Calculate the displacement amplitude corresponding to each surface monitoring location based on the surface displacement data; The displacement rate is calculated based on the change in displacement amplitude between adjacent monitoring times. Calculate displacement acceleration based on the change in displacement rate between adjacent monitoring times; The surface displacement characteristics are formed based on the displacement amplitude, displacement rate, and displacement acceleration.

[0010] Preferably, in step S3, the step of extracting environmentally induced features includes: Obtain rainfall data and soil moisture content data; The cumulative rainfall is calculated according to the preset time window. Calculate the change in soil moisture content between adjacent monitoring times; Environmentally induced characteristics are formed based on the cumulative rainfall and changes in soil moisture content.

[0011] Preferably, in step S4, the step of constructing a three-dimensional sliding surface dynamic model of the disaster body based on the surface displacement characteristics and deep shear deformation characteristics includes: Multiple advantageous shear depths are determined based on the deep shear deformation characteristics described above; Establish corresponding sliding surface control points based on the respective advantageous shear depths; Spatial interpolation calculations are performed based on the control points of each sliding surface to obtain the spatial distribution relationship of the sliding surface. A three-dimensional dynamic model of the sliding surface of the disaster body is established based on the spatial distribution relationship of the sliding surface. The dynamic model of the three-dimensional sliding surface of the disaster body is updated based on the surface displacement characteristics.

[0012] Preferably, in step S5, the step of adjusting the monitoring frequency based on the surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, and environmentally induced characteristics includes: State variables were extracted from the surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, and environmental induced characteristics, respectively. Construct monitoring frequency trigger indicators based on each state variable; The monitoring frequency trigger indicator is compared with a preset frequency threshold. Determine the corresponding monitoring frequency based on the comparison results; Subsequent monitoring data collection will be performed according to the stated monitoring frequency.

[0013] Preferably, the step of determining the corresponding monitoring frequency based on the comparison results includes: Record the monitoring frequency trigger indicators over multiple consecutive monitoring periods; Determine whether the monitoring frequency trigger indicator continuously meets the same frequency adjustment condition; When the same frequency adjustment condition is met consecutively, the corresponding monitoring frequency switching operation is executed; If the same frequency adjustment conditions are not met consecutively, the current monitoring frequency shall be maintained.

[0014] Preferably, in step S6, the step of performing fusion analysis based on the surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, environmental induced characteristics, and the three-dimensional sliding surface dynamic model of the disaster body includes: A fusion feature set is established, consisting of the surface displacement features, crack propagation features, deep shear deformation features, environmentally induced features, and a three-dimensional sliding surface dynamic model of the disaster body; Obtain the feature weights corresponding to each fusion feature according to the preset weight configuration; The fused features are weighted according to the aforementioned feature weights; The overall instability score is obtained based on the weighted calculation results; The overall instability probability is obtained based on the overall instability state score.

[0015] Preferably, in step S7, the step of determining the warning level based on the overall instability probability and outputting the corresponding warning result includes: Obtain the overall instability probability; The overall instability probability is compared with multiple preset warning level thresholds; Determine the warning level corresponding to the overall instability probability; Generate a warning result corresponding to the warning level; Output the warning results.

[0016] The present invention has the following beneficial effects: 1. This invention constructs a monitoring frequency trigger index and dynamically adjusts the monitoring frequency according to the monitoring frequency trigger index, so that the monitoring data acquisition cycle can be adjusted according to the deformation state of the disaster body, thereby taking into account the data acquisition needs of different monitoring stages.

[0017] 2. This invention performs time-synchronized processing of surface displacement data, crack data, deep deformation data, rainfall data, and soil moisture content data, and combines multiple features for fusion analysis, so that the overall instability probability can be calculated based on multi-source monitoring information, thereby improving the comprehensiveness of disaster state analysis.

[0018] 3. This invention establishes control points for the sliding surface based on the dominant shear depth and constructs a three-dimensional dynamic model of the sliding surface of the disaster body, enabling the correlation analysis between the deformation information inside the disaster body and the surface deformation information, thereby realizing the spatial dynamic representation of the deformation process of the disaster body. Attached Figure Description

[0019] Figure 1 This is an overall flowchart of a deformation monitoring method for landslide disaster bodies proposed in this invention; Figure 2This is a schematic diagram of a multi-source monitoring data acquisition and synchronization processing method proposed in this invention; Figure 3 This is a schematic diagram of a feature extraction process proposed in this invention; Figure 4 This is a schematic diagram illustrating the construction of a three-dimensional sliding surface dynamic model of a disaster body proposed in this invention; Figure 5 This is a schematic diagram of a dynamic adjustment process for monitoring frequency proposed in this invention; Figure 6 This is a schematic diagram illustrating the overall instability probability calculation and early warning determination proposed in this invention. Detailed Implementation

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Reference Figure 1 and Figure 2 This invention provides a method for monitoring the deformation of a landslide disaster body, comprising the following steps: S1. Obtain multi-source monitoring data of the landslide disaster body. The multi-source monitoring data includes surface displacement data, crack data, deep deformation data, rainfall data, and soil moisture content data.

[0022] Specifically, surface monitoring units, deep monitoring units, and environmental monitoring units are deployed within the monitoring area of ​​the landslide hazard. Surface monitoring units collect surface displacement and crack data, deep monitoring units collect deep deformation data, and environmental monitoring units collect rainfall and soil moisture data. Surface monitoring units can be located at the top, middle, and bottom of the slope, as well as in areas with developed cracks. Spatial coordinates of each monitoring location are obtained using GPS positioning equipment, and crack width information is obtained at the corresponding locations using crack gauges. For the first... Each surface monitoring location, at the monitoring time The corresponding spatial coordinates can be represented as: ; in, Indicates the first At the monitoring time, each surface monitoring location Corresponding spatial coordinates; , as well as These represent the corresponding horizontal coordinate, vertical coordinate, and elevation coordinate, respectively. Indicates the monitoring location number; This indicates the monitoring time. Surface displacement data is generated by continuously acquiring spatial coordinate information corresponding to different monitoring times, and crack data is generated by continuously acquiring crack width information corresponding to different monitoring times.

[0023] The deep monitoring unit acquires deep deformation data by setting up monitoring boreholes within the landslide hazard body and deploying array-type flexible inclinometers within these boreholes. The array-type flexible inclinometers are configured with multiple inclinometer segments along the borehole depth direction, each segment acquiring tilt state data at a corresponding depth location. Let the first... The inclination angle corresponding to each inclinometer segment is: The length of the inclined plane section is Then the horizontal displacement of the corresponding inclined section can be expressed as: ; in, Indicates the first Each inclinometer section at the monitoring time The corresponding horizontal displacement; Indicates the first The length of each inclined section; Indicates the first Each inclinometer section at the monitoring time The corresponding tilt angle; This indicates the inclinometer section number. Deep deformation data is generated by acquiring the horizontal displacement corresponding to each inclinometer section.

[0024] The environmental monitoring unit includes a rain gauge and a soil moisture meter. The rain gauge is used to acquire rainfall data within the monitoring area, and the soil moisture meter is used to acquire soil moisture content data at different locations within the monitoring area. (Regarding monitoring time...) Let the corresponding rainfall be The soil moisture content is This generates separate rainfall data sequences and soil moisture content data sequences. The surface monitoring unit, deep monitoring unit, and environmental monitoring unit are all connected to the intelligent edge gateway. The intelligent edge gateway receives data uploaded by each monitoring unit and stores surface displacement data, crack data, deep deformation data, rainfall data, and soil moisture content data in a unified manner, thus forming multi-source monitoring data corresponding to the landslide disaster body.

[0025] By acquiring surface displacement data, crack data, deep deformation data, rainfall data, and soil moisture content data from the surface, interior, and environmental areas of the landslide disaster body, subsequent analysis can simultaneously utilize surface deformation information, deep deformation information, and environmentally induced information, providing a data foundation for the construction of a synchronous monitoring data matrix and the analysis of the overall instability state.

[0026] Reference Figure 1 and Figure 2 Furthermore, S2, the multi-source monitoring data is processed for time synchronization to obtain a synchronized monitoring data matrix; Preferably, in step S2, the step of time synchronization processing of multi-source monitoring data includes: Obtain the collection timestamps corresponding to the multi-source monitoring data; establish a unified time benchmark; Align all data collection timestamps according to a unified time standard; The time-aligned multi-source monitoring data are correlated and combined according to the synchronous monitoring time. A synchronous monitoring data matrix was obtained.

[0027] Specifically, since surface displacement data, crack data, deep deformation data, rainfall data, and soil moisture content data originate from different monitoring units, and the sampling period, data upload period, and data generation time differ among these units, the intelligent edge gateway first obtains the acquisition timestamps corresponding to each multi-source monitoring data and establishes a unified time reference based on a unified time source. This unified time reference can be generated using BeiDou timing signals, Global Navigation Satellite System timing signals, or network timing signals, ensuring that the data output by each monitoring unit corresponds to a unified time reference system. Let the set of synchronous monitoring times corresponding to the unified time reference be: ; in, This represents the set of times during synchronous monitoring. Indicates the first Each time point is monitored synchronously.

[0028] After obtaining a unified time reference, time mapping is performed on the collection timestamps corresponding to each multi-source monitoring data to ensure that all types of monitoring data correspond to the target monitoring time in the set of synchronous monitoring times. When the collection time of a certain monitoring data is inconsistent with the target monitoring time, time alignment is performed using data corresponding to adjacent sampling times. Suppose a certain monitoring parameter is at the sampling time... The corresponding data value is At the sampling time The corresponding data value is The target monitoring time is And satisfy The data value corresponding to the target monitoring time is represented as: ; in, This represents the monitoring data after time alignment; Indicates the sampling time The corresponding data value; Indicates the sampling time The corresponding data value; Indicates the target monitoring time; as well as This indicates two sampling times adjacent to the target monitoring time.

[0029] After time alignment, surface displacement data, crack data, deep deformation data, rainfall data, and soil moisture content data corresponding to the same synchronous monitoring time are correlated and combined to form a synchronous monitoring data matrix. Let the monitoring time be... The corresponding synchronous monitoring data matrix is ​​represented as follows: ; in, This represents the synchronous monitoring data matrix; Indicates the monitoring time Corresponding surface displacement data; Indicates the monitoring time Corresponding crack data; Indicates the monitoring time Corresponding deep deformation data; Indicates the monitoring time Corresponding rainfall data; Indicates the monitoring time The corresponding soil moisture content data.

[0030] After the synchronous monitoring data matrix is ​​generated, the synchronous monitoring data matrix corresponding to each synchronous monitoring time is stored in chronological order to form a continuous monitoring data sequence, which provides a unified data input basis for subsequent surface displacement feature extraction, crack propagation feature extraction, deep shear deformation feature extraction and environmental induced feature extraction.

[0031] By establishing a unified time benchmark and performing time synchronization processing on various multi-source monitoring data, surface displacement data, crack data, deep deformation data, rainfall data, and soil moisture content data can be correlated and analyzed at the same monitoring time, thereby forming a synchronous monitoring data matrix and providing a unified data foundation for subsequent multi-source monitoring data fusion analysis.

[0032] Reference Figure 3 Furthermore, S3, based on the synchronous monitoring data matrix, extract surface displacement features, crack propagation features, deep shear deformation features, and environmentally induced features; Preferably, in step S3, the step of extracting deep shear deformation features includes: Obtain deep deformation data corresponding to each inclined section; Calculate the horizontal displacement corresponding to each inclined section based on the deep deformation data; The local shear deformation rate is calculated based on the difference in horizontal displacement between adjacent inclined sections. The inclined section with the largest local shear deformation rate is identified as the dominant shear deformation section. Determine the dominant shear depth based on the dominant shear deformation segment; Deep shear deformation characteristics are formed based on local shear deformation rate and dominant shear depth.

[0033] Preferably, in step S3, the step of extracting surface displacement features includes: Obtain surface displacement data corresponding to various surface monitoring locations; Calculate the displacement amplitude corresponding to each surface monitoring location based on surface displacement data; The displacement rate is calculated based on the change in displacement amplitude between adjacent monitoring times. Calculate displacement acceleration based on the change in displacement rate between adjacent monitoring times; The characteristics of surface displacement are formed based on displacement amplitude, displacement rate, and displacement acceleration.

[0034] Preferably, in step S3, the step of extracting environmentally induced features includes: Obtain rainfall data and soil moisture content data; The cumulative rainfall is calculated according to the preset time window. Calculate the change in soil moisture content between adjacent monitoring times; Environmentally induced characteristics are formed based on cumulative rainfall and changes in soil moisture content.

[0035] Specifically, after obtaining the synchronous monitoring data matrix, feature extraction processing is performed on the surface displacement data, crack data, deep deformation data, rainfall data, and soil moisture content data within the synchronous monitoring data matrix. For the surface displacement data, the spatial coordinate information of each surface monitoring location at different monitoring times is first obtained, and the displacement amplitude is calculated based on the coordinate changes between the current monitoring time and the baseline monitoring time. Let the... At the monitoring time, each surface monitoring location The corresponding displacement amplitude is The displacement rate between adjacent monitoring times is expressed as: ; in, Indicates the first At the monitoring time, each surface monitoring location The corresponding displacement rate; This indicates the displacement amplitude corresponding to the current monitoring moment; This indicates the displacement amplitude corresponding to the previous monitoring time. Indicates the current monitoring time; This indicates the previous monitoring time. Further, displacement acceleration is calculated based on the change in displacement rate between adjacent monitoring times, and the surface displacement characteristics are formed by combining displacement amplitude, displacement rate, and displacement acceleration. Simultaneously, for crack data, the crack width corresponding to each crack monitoring location is obtained, and the change in crack width and crack propagation rate between adjacent monitoring times are calculated. The change in crack width and crack propagation rate are then used as part of the crack propagation characteristics in subsequent analysis.

[0036] For deep deformation data, the horizontal displacement corresponding to each inclined section is calculated based on the inclination state data of each inclined section. Let the first inclined section be... The horizontal displacement corresponding to each inclinometer segment is The local shear deformation rate between adjacent inclined sections is expressed as: ; in, Indicates the first Local shear deformation rate corresponding to each inclined section; Indicates the first The horizontal displacement corresponding to each inclined section; This indicates the horizontal displacement corresponding to the adjacent previous inclinometer segment; Indicates the first The length of each inclined section is determined. Then, the local shear deformation rate corresponding to all inclined sections is compared, and the inclined section with the largest local shear deformation rate is identified as the dominant shear deformation section. Let the number corresponding to the dominant shear deformation section be denoted as . The corresponding dominant shear depth is expressed as: ; in, Indicates the dominant shear depth; Indicates the first The length of each inclined section; This indicates the number of the dominant shear deformation segment. Deep shear deformation characteristics are formed using local shear deformation rates and dominant shear depths, which are used to characterize areas of concentrated deformation within the disaster body and their depth locations.

[0037] For environmental monitoring data, rainfall data and soil moisture content data are acquired, and cumulative rainfall is calculated according to a preset time window. Let the cumulative rainfall within the time window be... It is represented as: ; in, Indicates the monitoring time Corresponding cumulative rainfall; Indicates the first time within the time window Rainfall corresponding to each sampling time; This indicates the number of data samples taken within the time window. Simultaneously, the change in soil moisture content between adjacent monitoring times is calculated, and the cumulative rainfall and the change in soil moisture content are used together as environmental induction characteristics to characterize the degree of influence of external environmental conditions on the deformation state of the landslide disaster body.

[0038] By extracting surface displacement features, crack propagation features, deep shear deformation features, and environmentally induced features, the multi-source monitoring data in the synchronous monitoring data matrix can be transformed into feature parameters with deformation characterization and environmental characterization capabilities, providing a feature basis for the subsequent construction of a three-dimensional sliding surface dynamic model of the disaster body and the analysis of the overall instability state.

[0039] Reference Figure 4 Furthermore, S4, a three-dimensional dynamic model of the sliding surface of the disaster body is constructed based on surface displacement characteristics and deep shear deformation characteristics; Preferably, in step S4, the step of constructing a three-dimensional dynamic model of the sliding surface of the disaster body based on surface displacement characteristics and deep shear deformation characteristics includes: Multiple dominant shear depths were determined based on the characteristics of deep shear deformation. Establish corresponding sliding surface control points based on each dominant shear depth; Spatial interpolation calculations are performed based on the control points of each sliding surface to obtain the spatial distribution relationship of the sliding surface. A three-dimensional dynamic model of the sliding surface of the disaster body is established based on the spatial distribution relationship of the sliding surface. The dynamic model of the three-dimensional sliding surface of the disaster body is updated based on the surface displacement characteristics.

[0040] Specifically, after obtaining the deep shear deformation characteristics, for each monitoring borehole, the corresponding dominant shear depth is extracted as the basis for slip surface identification. Since the dominant shear depth corresponding to different borehole locations reflects the location of the concentrated shear deformation area within the disaster body, the planar coordinate information of each monitoring borehole is correlated with the corresponding dominant shear depth to establish slip surface control points. Let the first... Each sliding surface control point is represented as: ; in, Indicates the first One sliding surface control point; Indicates the first The horizontal coordinates of each monitoring borehole; Indicates the first The longitudinal coordinates of each monitoring borehole; Indicates the first The dominant shear depth corresponding to each monitoring borehole; This indicates the control point number of the sliding surface. As the number of monitoring boreholes increases, multiple sets of sliding surface control points are formed, distributed within the monitoring area, to characterize the deep deformation features at different spatial locations.

[0041] After obtaining the control points of the sliding surface, spatial interpolation calculations are performed on each control point to establish the spatial distribution relationship of the sliding surface. Let any location within the monitoring area... The corresponding sliding surface depth function is expressed as: ; in, Indicates position The corresponding sliding surface depth value; Indicates the total number of control points on the sliding surface; Indicates the first Interpolation coefficients corresponding to each sliding surface control point; Indicates the first The spatial interpolation function corresponds to each sliding surface control point. By spatially fitting each sliding surface control point, the spatial distribution relationship of the sliding surface within the entire monitoring area is obtained, thereby determining the extension state and depth change state of the sliding surface within the spatial range.

[0042] After establishing the spatial distribution relationship of the sliding surfaces, a three-dimensional dynamic model of the sliding surfaces of the disaster body is constructed based on this relationship. Specifically, the horizontal coordinates of the monitoring area are used as the horizontal coordinate axis, and the sliding surface depth values ​​are used as the vertical coordinate axis. The sliding surface depths corresponding to each spatial location are connected to form a continuous sliding surface structure. Subsequently, the three-dimensional dynamic model of the sliding surfaces of the disaster body is dynamically updated based on the surface displacement characteristics. Let the first... The displacement vector corresponding to each surface monitoring location is: ; in, Indicates the first Displacement vectors corresponding to each surface monitoring location; Indicates the lateral displacement component; Represents the longitudinal displacement component; This represents the elevation displacement component. Based on the displacement vector changes corresponding to various surface monitoring locations, the spatial distribution relationship of the sliding surface within the corresponding area is corrected, so that the three-dimensional sliding surface dynamic model of the disaster body can simultaneously reflect the deep shear deformation state and the surface deformation state.

[0043] By establishing control points for the sliding surface based on the dominant shear depth and using spatial interpolation calculations based on these control points, a three-dimensional dynamic model of the sliding surface of the disaster body is formed. This enables the deep shear deformation characteristics and surface displacement characteristics to be expressed in a unified spatial coordinate system, thus providing a basis for the spatial distribution of the sliding surface for subsequent overall instability analysis.

[0044] Reference Figure 5 Furthermore, S5, based on surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, and environmental induced characteristics, the monitoring frequency is adjusted to obtain the corresponding monitoring frequency; Preferably, in step S5, the step of adjusting the monitoring frequency based on surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, and environmentally induced characteristics includes: State variables were extracted from surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, and environmental induced characteristics, respectively. Construct monitoring frequency trigger indicators based on each state variable; Compare the monitoring frequency trigger indicator with the preset frequency threshold; Determine the corresponding monitoring frequency based on the comparison results; Subsequent monitoring data collection will be performed according to the monitoring frequency.

[0045] Preferably, the steps for determining the corresponding monitoring frequency based on the comparison results include: Record the monitoring frequency trigger indicators over multiple consecutive monitoring periods; Determine whether the monitoring frequency trigger indicators continuously meet the same frequency adjustment conditions; When the same frequency adjustment condition is met consecutively, the corresponding monitoring frequency switching operation is executed; If the same frequency adjustment conditions are not met consecutively, the current monitoring frequency shall be maintained.

[0046] Specifically, after obtaining surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, and environmentally induced characteristics, state quantities reflecting the deformation state of the disaster body and the state of environmental change are extracted from each type of characteristic. Among these, the state quantities corresponding to surface displacement characteristics include displacement rate and displacement acceleration; those corresponding to crack propagation characteristics include crack propagation rate; those corresponding to deep shear deformation characteristics include local shear deformation rate and dominant shear depth; and those corresponding to environmentally induced characteristics include cumulative rainfall and changes in soil moisture content. Since the dimensions of different state quantities differ, they are first standardized to ensure they can participate in the same evaluation process. Then, a monitoring frequency trigger index is constructed based on each state quantity. Let the monitoring frequency trigger index be... Then it can be expressed as: ; in, Indicates the monitoring frequency trigger indicator; Represents the displacement rate state quantity; Represents the displacement acceleration state quantity; This represents a state quantity indicating the crack propagation rate. This represents the state quantity of local shear deformation rate; This indicates the cumulative rainfall status quantity; This represents a state quantity indicating the change in soil moisture content; to These represent the weighting coefficients of the corresponding state variables. The monitoring frequency trigger index is used to comprehensively reflect the current degree of deformation activity of the disaster body and the degree of environmental induction.

[0047] After obtaining the monitoring frequency trigger indicator, the indicator is compared with a preset frequency threshold. Specifically, low-frequency, medium-frequency, and high-frequency monitoring thresholds are preset, and different data acquisition frequencies correspond to different threshold ranges when the monitoring frequency trigger indicator falls within those ranges.

[0048] For example, when the monitoring frequency trigger indicator is in the low-frequency monitoring threshold range, the low-frequency monitoring mode is used; when the monitoring frequency trigger indicator reaches the medium-frequency monitoring threshold range, the medium-frequency monitoring mode is used; and when the monitoring frequency trigger indicator reaches the high-frequency monitoring threshold range, the high-frequency monitoring mode is used. After the monitoring frequency is determined, the intelligent edge gateway sends the corresponding data acquisition cycle parameters to each monitoring unit and performs subsequent monitoring data acquisition according to the adjusted monitoring frequency.

[0049] To avoid frequent monitoring frequency switching caused by a single abnormal data point, a continuous triggering determination mechanism is added to the monitoring frequency switching process. Let the number of continuous monitoring periods be... In continuous Record the sequence of monitoring frequency trigger indicators within each monitoring cycle: ; in, This indicates the sequence of monitoring frequency trigger indicators within a continuous monitoring period; Indicates the monitoring time The corresponding monitoring frequency trigger indicators. When the monitoring frequency trigger indicators meet the same frequency adjustment condition for multiple consecutive monitoring periods, the corresponding monitoring frequency switching operation is executed; when the same frequency adjustment condition is not met consecutively, the current monitoring frequency remains unchanged. This method reduces frequent adjustments caused by short-term fluctuations.

[0050] By utilizing surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, and environmental induced characteristics to construct monitoring frequency triggering indicators, and combining them with a continuous triggering judgment mechanism to adjust the monitoring frequency, the monitoring data acquisition frequency can be dynamically configured according to the real-time state of the disaster body, providing a data acquisition basis that matches the current state for subsequent disaster body deformation analysis.

[0051] Reference Figure 6Furthermore, S6, based on the surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, environmental induced characteristics, and the dynamic model of the three-dimensional sliding surface of the disaster body, a fusion analysis is performed to obtain the overall instability probability; Preferably, in step S6, the step of performing a fusion analysis based on surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, environmentally induced characteristics, and the dynamic model of the three-dimensional sliding surface of the disaster body includes: Establish a fusion feature set consisting of surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, environmentally induced characteristics, and a three-dimensional sliding surface dynamic model of the disaster body; Obtain the feature weights corresponding to each fusion feature according to the preset weight configuration; The fusion features are weighted according to their respective feature weights; The overall instability score is obtained based on the weighted calculation results; The overall instability probability is obtained based on the overall instability score.

[0052] Specifically, after obtaining surface displacement features, crack propagation features, deep shear deformation features, environmentally induced features, and a three-dimensional sliding surface dynamic model of the disaster body, the various features are first processed using a unified representation. Surface displacement features characterize the displacement activity state of the disaster body's surface; crack propagation features characterize the development state of surface cracks; deep shear deformation features characterize the shear deformation state inside the disaster body; environmentally induced features characterize changes in rainfall and soil moisture content; and the three-dimensional sliding surface dynamic model of the disaster body characterizes the spatial distribution of potential sliding surfaces. Based on these features, a fused feature set is established, denoted as: ; in, Represents the set of fused features; Indicates the characteristics of surface displacement; Indicates the characteristics of crack propagation; Indicates deep shear deformation characteristics; Indicates environmentally induced characteristics; This represents the characteristics corresponding to the three-dimensional sliding surface dynamic model of the disaster body. To enable features from different sources to participate in the unified analysis process, feature weights corresponding to each fused feature are obtained according to preset weight configurations. The preset weight configurations can be set based on the geological conditions of the monitoring area, the statistical results of historical monitoring data, and the type of monitoring object.

[0053] After obtaining the fusion features and their corresponding weights, the fusion features are weighted according to their respective weights to obtain the overall instability score. Let the overall instability score be KKK, then it is expressed as: ; in, The score indicates the overall instability state. The feature weights corresponding to surface displacement characteristics; Indicates the feature weights corresponding to the crack propagation characteristics; Indicates the feature weights corresponding to deep shear deformation features; This represents the feature weights corresponding to environmentally induced features; This represents the feature weights corresponding to the dynamic model of the three-dimensional sliding surface of the disaster body. The overall instability state score is used to comprehensively reflect the current deformation state, internal structural state, and environmentally induced state of the disaster body.

[0054] After obtaining the overall instability score, the overall instability probability is calculated based on the overall instability score. Let the overall instability probability be P, then it is expressed as: ; in, Indicates the overall probability of instability; The score indicates the overall instability state. This represents the probability mapping parameter, used for probabilistic mapping of the overall instability state score. Through the above calculation process, multi-source monitoring characteristics are transformed into a unified probabilistic expression, enabling comprehensive analysis of different monitoring data under the same evaluation system.

[0055] Furthermore, to reflect the development trend of the overall instability state, the overall instability probability corresponding to multiple consecutive monitoring periods can be recorded to form an overall instability probability sequence. When the overall instability probability continues to rise, it indicates that the disaster body is in a state of continuous deformation and development; when the overall instability probability remains stable, it indicates that the state of the disaster body changes little within the current monitoring period. The overall instability probability sequence serves as the data basis for subsequent early warning level determination.

[0056] By establishing a set of integrated features and combining them with surface displacement features, crack propagation features, deep shear deformation features, environmental induced features, and a dynamic model of the three-dimensional sliding surface of the disaster body for integrated analysis, the surface deformation state, internal deformation state, and environmental induced state of the disaster body can be evaluated in a unified manner. This allows us to obtain the overall instability probability that characterizes the overall state of the disaster body, providing an analytical basis for subsequent early warning level determination.

[0057] Reference Figure 6 Furthermore, S7 determines the warning level based on the overall instability probability and outputs the corresponding warning result; Preferably, in step S7, the step of determining the warning level based on the overall instability probability and outputting the corresponding warning result includes: Obtain the overall instability probability; The overall probability of instability is compared with multiple preset warning level thresholds; Determine the warning level corresponding to the overall probability of instability; Generate warning results corresponding to the warning level; Output the warning results.

[0058] Specifically, after obtaining the overall instability probability, it is input into the early warning determination unit as an evaluation parameter of the current state of the disaster body. The early warning determination unit pre-establishes multiple early warning level thresholds and determines the corresponding early warning level based on the range in which the overall instability probability falls. Let the overall instability probability be... The warning level determination function is expressed as: ;in, Indicates the warning level; Indicates the overall probability of instability; This represents the warning level mapping function. The warning level mapping function is used to determine the current warning level based on the correspondence between the overall instability probability and the preset warning level threshold.

[0059] In practice, a first-level early warning threshold can be set. Level II warning threshold Level 3 warning threshold and Level 4 warning threshold And satisfy: ; in, to These represent the warning level thresholds corresponding to different warning levels. Different warning levels correspond to different threshold ranges when the overall instability probability falls within those ranges. For example, when the overall instability probability is below the Level 1 warning threshold, it is determined to be in a normal monitoring state; when the overall instability probability reaches the corresponding warning level threshold range, it is determined to be in a warning state of that level. After the warning level is determined, a warning result data packet corresponding to the current warning level is generated. The warning result data packet includes warning level information, overall instability probability information, monitoring time information, and monitoring area identification information.

[0060] To reflect the continuous changes in the state of the disaster body, trend analysis can be performed on the overall instability probability across multiple consecutive monitoring periods. Let the overall instability probability sequence for N consecutive monitoring periods be represented as: ; in, This represents the overall instability probability sequence; Indicates the monitoring time The corresponding overall probability of instability; This indicates the number of monitoring periods. When the overall instability probability sequence remains within the same warning level range, the current warning level is maintained; when the overall instability probability sequence continuously enters a higher-level warning range, the corresponding warning level update operation is performed, thereby ensuring that the warning results are consistent with the process of changes in the state of the disaster body.

[0061] After the early warning result is generated, it is output to the monitoring terminal through an intelligent edge gateway or monitoring platform. The output includes the current early warning level, the overall probability of instability, the monitoring time, and the corresponding monitoring area information. Simultaneously, the early warning result can be stored in a historical early warning database, forming a disaster early warning record sequence, providing a data foundation for subsequent monitoring data tracing and status analysis.

[0062] By comparing the overall instability probability with multiple preset warning level thresholds and generating corresponding warning results based on the comparison results, the fusion analysis results of surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, environmental induced characteristics, and dynamic models of the three-dimensional sliding surface of the disaster body can be transformed into corresponding warning level information, thereby realizing the graded output of monitoring results of landslide disaster bodies.

[0063] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for monitoring the deformation of a landslide disaster body, characterized in that, Includes the following steps: S1. Obtain multi-source monitoring data of the landslide disaster body, including surface displacement data, crack data, deep deformation data, rainfall data, and soil moisture content data; S2. Perform time synchronization processing on the multi-source monitoring data to obtain a synchronized monitoring data matrix; S3. Based on the synchronous monitoring data matrix, extract surface displacement features, extract crack propagation features, extract deep shear deformation features, and extract environmental induced features; S4. Construct a three-dimensional dynamic model of the sliding surface of the disaster body based on the surface displacement characteristics and deep shear deformation characteristics; S5. Based on the surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, and environmental induced characteristics, the monitoring frequency is adjusted to obtain the corresponding monitoring frequency. S6. Based on the surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, environmental induced characteristics, and the dynamic model of the three-dimensional sliding surface of the disaster body, a fusion analysis is performed to obtain the overall instability probability; S7. Determine the warning level based on the overall instability probability and output the corresponding warning result.

2. The deformation monitoring method for a landslide disaster body according to claim 1, characterized in that, In step S2, the step of performing time synchronization processing on the multi-source monitoring data includes: Obtain the collection timestamps corresponding to the multi-source monitoring data; establish a unified time reference; The timestamps of each collection are aligned according to the unified time reference. The time-aligned multi-source monitoring data are correlated and combined according to the synchronous monitoring time. A synchronous monitoring data matrix was obtained.

3. The deformation monitoring method for a landslide disaster body according to claim 1, characterized in that, In step S3, the step of extracting deep shear deformation features includes: Obtain deep deformation data corresponding to each inclined section; Calculate the horizontal displacement corresponding to each inclined section based on the deep deformation data; The local shear deformation rate is calculated based on the difference in horizontal displacement between adjacent inclined sections. The inclined section with the largest local shear deformation rate is identified as the dominant shear deformation section. The dominant shear depth is determined based on the dominant shear deformation segment. Deep shear deformation characteristics are formed based on the local shear deformation rate and the dominant shear depth.

4. The deformation monitoring method for a landslide disaster body according to claim 1, characterized in that, In step S3, the step of extracting surface displacement features includes: Obtain surface displacement data corresponding to various surface monitoring locations; Calculate the displacement amplitude corresponding to each surface monitoring location based on the surface displacement data; The displacement rate is calculated based on the change in displacement amplitude between adjacent monitoring times. Calculate displacement acceleration based on the change in displacement rate between adjacent monitoring times; The surface displacement characteristics are formed based on the displacement amplitude, displacement rate, and displacement acceleration.

5. The deformation monitoring method for a landslide disaster body according to claim 1, characterized in that, In step S3, the step of extracting environmentally induced features includes: Obtain rainfall data and soil moisture content data; The cumulative rainfall is calculated according to the preset time window. Calculate the change in soil moisture content between adjacent monitoring times; Environmentally induced characteristics are formed based on the cumulative rainfall and changes in soil moisture content.

6. The deformation monitoring method for a landslide disaster body according to claim 1, characterized in that, In step S4, the step of constructing a three-dimensional sliding surface dynamic model of the disaster body based on the surface displacement characteristics and deep shear deformation characteristics includes: Multiple advantageous shear depths are determined based on the deep shear deformation characteristics described above; Establish corresponding sliding surface control points based on the respective advantageous shear depths; Spatial interpolation calculations are performed based on the control points of each sliding surface to obtain the spatial distribution relationship of the sliding surface. A three-dimensional dynamic model of the sliding surface of the disaster body is established based on the spatial distribution relationship of the sliding surface. The dynamic model of the three-dimensional sliding surface of the disaster body is updated based on the surface displacement characteristics.

7. The deformation monitoring method for a landslide disaster body according to claim 1, characterized in that, In step S5, the step of adjusting the monitoring frequency based on the surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, and environmentally induced characteristics includes: State variables were extracted from the surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, and environmental induced characteristics, respectively. Construct monitoring frequency trigger indicators based on each state variable; The monitoring frequency trigger indicator is compared with a preset frequency threshold. Determine the corresponding monitoring frequency based on the comparison results; Subsequent monitoring data collection will be performed according to the stated monitoring frequency.

8. The deformation monitoring method for a landslide disaster body according to claim 7, characterized in that, The step of determining the corresponding monitoring frequency based on the comparison results includes: Record the monitoring frequency trigger indicators over multiple consecutive monitoring periods; Determine whether the monitoring frequency trigger indicator continuously meets the same frequency adjustment condition; When the same frequency adjustment condition is met consecutively, the corresponding monitoring frequency switching operation is executed; If the same frequency adjustment conditions are not met consecutively, the current monitoring frequency shall be maintained.

9. The deformation monitoring method for a landslide disaster body according to claim 1, characterized in that, In step S6, the step of performing a fusion analysis based on the surface displacement characteristics, crack propagation characteristics, deep shear deformation characteristics, environmental induced characteristics, and the dynamic model of the three-dimensional sliding surface of the disaster body includes: A fusion feature set is established, consisting of the surface displacement features, crack propagation features, deep shear deformation features, environmentally induced features, and a three-dimensional sliding surface dynamic model of the disaster body; Obtain the feature weights corresponding to each fusion feature according to the preset weight configuration; The fused features are weighted according to the aforementioned feature weights; The overall instability score is obtained based on the weighted calculation results; The overall instability probability is obtained based on the overall instability state score.

10. The deformation monitoring method for a landslide disaster body according to claim 1, characterized in that, In step S7, the step of determining the early warning level based on the overall instability probability and outputting the corresponding early warning result includes: Obtain the overall instability probability; The overall instability probability is compared with multiple preset warning level thresholds; Determine the warning level corresponding to the overall instability probability; Generate a warning result corresponding to the warning level; Output the warning results.

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