Fiber Optic Sensing Landslide Dynamic Response Monitoring System

Abstract

The present invention discloses an optical fiber sensing landslide dynamic response monitoring system, which relates to the technical field of landslide monitoring. The key points of the solution are: an event monitoring module, a sampling optimization module, a landslide risk analysis module, and a warning level adjustment module. The present invention can quickly identify tiny displacement anomalies by comparing the offsets of adjacent data points in real time, provide instant markings for early geological activities, automatically adjust the sampling frequency according to real-time monitoring data, make data acquisition more refined, reduce the storage and processing of irrelevant data at the same time, effectively improve the operation efficiency of the system. By carefully comparing real-time data with historical landslide patterns and comprehensively analyzing key indicators such as surface displacement and soil humidity, the system can accurately calculate the probability of a landslide occurring, greatly improve the accuracy and timeliness of early warning, and effectively reduce the casualties and property losses caused by landslide disasters.

Description

Technical Field

[0001] The present invention relates to the technical field of landslide monitoring, and particularly to an optical fiber sensing landslide dynamic response monitoring system. Background Art

[0002] Landslide monitoring technology focuses on using various sensors and monitoring devices to predict and monitor landslide events, usually covering multiple disciplines such as geology, geophysics, and soil mechanics. Its main purpose is to collect and analyze data in potential landslide areas in real-time or near real-time for early warning. Technical means include but are not limited to surface displacement monitoring, soil moisture and pressure monitoring, and the use of remote sensing technology for analysis of regional terrain changes, in order to more accurately predict the occurrence time and scale of landslides, thereby helping to reduce casualties and property losses.

[0003] Among them, the optical fiber sensing landslide dynamic response monitoring system is a system that uses optical fiber sensing technology to monitor and analyze the dynamic response of landslides. Its main purpose is to real-time monitor surface displacement, stress changes, and other key indicators in landslide-prone areas. By the high sensitivity and anti-interference ability of optical fiber sensors, it can capture tiny environmental changes. The system can analyze the data collected from optical fiber sensors to real-time warn of the possibility of landslide occurrence, helping decision-makers take preventive measures, thereby effectively improving the ability to respond to landslide disasters.

[0004] In the prior art, data processing faces delays, and the ability of sensors to capture tiny changes is limited, directly affecting the timeliness and accuracy of early warning. In actual operation, due to the low degree of technology integration and sensitivity to environmental interference, it is unable to provide effective early warning at critical moments. Especially in complex environments, such as the sharp change in soil humidity caused by the rainy season, the response speed and accuracy of traditional technologies are insufficient to support effective disaster prevention. This limitation not only increases the monitoring cost but also causes significant casualties and property losses due to untimely response. Summary of the Invention

[0005] The present invention provides an optical fiber sensing landslide dynamic response monitoring system.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] An optical fiber sensing landslide dynamic response monitoring system, the system includes:

[0008] An event monitoring module, based on optical fiber sensor data, receives continuous surface displacement and vibration data, identifies abnormal points of the offset amplitude by real-time comparing the offsets of adjacent data points, marks them as geological activity events, and adjusts the sampling frequency to the optimal interval according to the event pattern to obtain event detection and positioning data;

[0009] Based on the event detection and location data, the sampling optimization module evaluates the change amplitude and duration of geological activities, determines whether the sampling frequency matches the current monitoring requirements, and optimizes the current sampling frequency and sampling density to obtain sampling configuration optimization information;

[0010] Based on the sampling configuration optimization information, the landslide risk analysis module compares the newly monitored geological activity data with known landslide patterns, analyzes the similarity between the amplitude change and historical patterns, and combines real-time surface displacement and soil moisture data to calculate the probability of landslide occurrence and obtain a landslide risk index;

[0011] Based on the landslide risk index, the early warning level adjustment module compares the current early warning level with the landslide risk level, evaluates the adjustment requirements of the early warning level, and adjusts the early warning level in real time according to the landslide probability to obtain the updated result of the early warning status.

[0012] The improvement of the present invention is that the step of identifying the abnormal points of the offset amplitude is specifically as follows:

[0013] Based on the fiber optic sensor data, continuous surface displacement and vibration data are received. For each pair of adjacent time point data, the formula is used:

[0014] ;

[0015] Calculate the offset between two time points , where is the surface displacement data at the first time point, is the vibration data at the first time point, is the surface displacement data at the second time point, is the vibration data at the second time point;

[0016] Based on the offset, an abnormal threshold is set, and whether the offset of each data point exceeds the abnormal threshold is compared to determine the abnormal points of the offset amplitude.

[0017] The improvement of the present invention is that the step of obtaining the event detection and location data is specifically as follows:

[0018] Perform spatial analysis on the abnormal points of the offset amplitude, locate each abnormal point on the map according to its geographical coordinates, and obtain a preliminary geological activity map;

[0019] Based on the preliminary geological activity map, adjust the sampling frequency to the optimal range, and use the formula:

[0020] ;

[0021] Calculate the new sampling frequency , to obtain event detection and location data, where is the current sampling frequency, is the adjustment coefficient, which is used to adjust the proportion of the sampling frequency according to the change rate or intensity of geological activities, is the attenuation factor, which is used to control the influence degree of the abnormal point density on the sampling frequency adjustment, is the abnormal point density index, which represents the density of abnormal points in the monitoring area and is used to adjust the sampling frequency according to the concentration degree of abnormal points.

[0022] The improvement of the present invention is that the evaluation steps of the change range and duration are specifically as follows:

[0023] Based on the event detection and location data, extract the start and end time points and the maximum change range of each geological activity event to obtain the preliminary geological activity record;

[0024] Analyze the preliminary geological activity record by using the formula:

[0025] ;

[0026] and

[0027] ;

[0028] Calculate the duration of the geological activity and the maximum change range , to obtain the duration and change range data, where, refers to the end time point of the geological activity, refers to the start time point of the geological activity, is all displacement data recorded during the geological activity;

[0029] Based on the duration and change range data, determine the average duration and average change range of the geological activity through statistical analysis to obtain the geological activity characteristic information.

[0030] The improvement of the present invention is that the acquisition steps of the sampling configuration optimization information are specifically as follows:

[0031] Evaluate the matching degree between the current sampling frequency and the monitoring requirements, and analyze whether the current sampling frequency can capture geological changes based on the change range and duration to obtain the frequency matching evaluation information;

[0032] Based on the frequency matching evaluation information, use the formula:

[0033] ;

[0034] Adjust the sampling frequency and sampling density to obtain the sampling configuration optimization information, where, is the optimized sampling frequency, represents the current sampling frequency, is a coefficient adjusted according to the sensitivity of the monitoring data, is the average change amplitude, is the average duration, is the current sampling density, is the target sampling density.

[0035] The improvement of the present invention is that the analysis step of the similarity between the amplitude change and the historical pattern is specifically as follows:

[0036] Based on the sampling configuration optimization information, collect newly monitored geological activity data, including the change amplitude and duration of the current geological event, to obtain an organized geological data set;

[0037] Based on the organized geological data set, compare it with the known landslide patterns, identify historical events similar to the current data pattern, and analyze the similarity between the amplitude change and the historical pattern to obtain a similarity analysis result.

[0038] The improvement of the present invention is that the obtaining step of the landslide risk index is specifically as follows:

[0039] Collect the real-time surface displacement and soil moisture data, integrate the data, and obtain a real-time updated geological data set;

[0040] Based on the real-time updated geological data set, use the formula:

[0041] ;

[0042] Calculate the probability of landslide occurrence , to obtain a landslide risk index, where represents the displacement value of the th data point, represents the soil moisture value of the th monitoring point, and are weight factors used to adjust the key importance of the displacement value and the soil moisture value in calculating the landslide probability, represents the total number of monitoring points.

[0043] The improvement of the present invention is that the obtaining step of the early warning status update result is specifically as follows:

[0044] Based on the landslide risk index, evaluate the relationship between the current early warning level and the landslide risk level, analyze whether it is necessary to adjust the early warning level, and obtain a preliminary risk early warning analysis log;

[0045] Based on the preliminary risk early warning analysis log, adjust the early warning level in real time according to the landslide probability, using the formula:

[0046] ;

[0047] Get the early warning status update result, where is the adjusted early warning level, represents the current early warning level, is the landslide risk index, is the threshold for early warning changes, is the adjustment coefficient.

[0048] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0049] In the present invention, by comparing the offsets of adjacent data points in real time, small displacement anomalies can be quickly identified, providing an immediate mark for early geological activities. The sampling frequency is automatically adjusted according to real-time monitoring data, making data collection more refined, while reducing the storage and processing of irrelevant data, effectively improving the operation efficiency of the system. By carefully comparing real-time data with historical landslide patterns and comprehensively analyzing key indicators such as surface displacement and soil humidity, the system can accurately calculate the probability of landslide occurrence, greatly improving the accuracy and timeliness of early warning, and effectively reducing the casualties and property losses caused by landslide disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for description in the embodiments. The drawings are only for the purpose of showing the implementation manner and are not considered as a limitation to the present invention.

[0051] Figure 1 is the system flowchart in the embodiment of the present invention;

[0052] Figure 2 is the flowchart for identifying abnormal points of the offset amplitude in the embodiment of the present invention;

[0053] Figure 3 is the flowchart for obtaining event detection and positioning data in the embodiment of the present invention;

[0054] Figure 4 is the flowchart for evaluating the change amplitude and duration in the embodiment of the present invention;

[0055] Figure 5 is the flowchart for obtaining sampling configuration optimization information in the embodiment of the present invention;

[0056] Figure 6 is the flowchart for analyzing the similarity between the amplitude change and the historical pattern in the embodiment of the present invention;

[0057] Figure 7 is the flowchart for obtaining the landslide risk index in the embodiment of the present invention;

[0058] Figure 8 This is a flowchart for obtaining the warning status update result in an embodiment of the present invention. Specific embodiments

[0059] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without making creative efforts belong to the scope of protection of the present invention.

[0060] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the technical field to which the present invention belongs; the terms used in the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The terms "including" and "having" in the specification and claims of the present invention and any variations thereof are intended to cover non-exclusive inclusions.

[0061] In the description of the embodiments of the present invention, technical terms such as "first" and "second" are only used to distinguish different objects and cannot be understood as indicating or implying relative importance or implicitly indicating the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present invention, "a plurality of" means two or more, unless otherwise specifically defined.

[0062] In the description of the embodiments of the present invention, the term "and / or" is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article generally represents an "or" relationship between the associated objects before and after.

[0063] In the description of the embodiments of the present invention, the term "a plurality of" refers to two or more (including two). Similarly, "a plurality of groups" refers to two or more groups (including two groups), and "a plurality of pieces" refers to two or more pieces (including two pieces).

[0064] In the description of the embodiments of the present invention, technical terms such as "center", "longitudinal", "transverse", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the indicated device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the embodiments of the present invention.

[0065] In the description of the embodiments of the present invention, unless otherwise clearly specified and limited, technical terms such as "installation", "connection", "connection", "fixation", etc. should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or integrated; it can also be a mechanical connection or an electrical connection; it can be directly connected, or indirectly connected through an intermediate medium, and can be the communication inside two components or the interaction relationship between two components. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0066] Embodiment

[0067] The embodiments of the present invention provide a fiber optic sensing landslide dynamic response monitoring system, as Figure 1 shown, including:

[0068] The event monitoring module, based on the fiber optic sensor data, receives continuous surface displacement and vibration data, identifies abnormal points of the offset amplitude by comparing the offsets of adjacent data points in real time, marks them as geological activity events, and adjusts the sampling frequency to the optimal interval according to the event pattern to obtain event detection and positioning data;

[0069] The sampling optimization module, based on the event detection and positioning data, evaluates the change amplitude and duration of geological activities, determines whether the sampling frequency matches the current monitoring requirements, and optimizes the current sampling frequency and sampling density to obtain sampling configuration optimization information;

[0070] The landslide risk analysis module, based on the sampling configuration optimization information, compares the newly monitored geological activity data with known landslide patterns, analyzes the similarity of the amplitude change with the historical pattern, and combines the real-time surface displacement and soil moisture data to calculate the probability of landslide occurrence to obtain a landslide risk index;

[0071] The warning level adjustment module, based on the landslide risk index, compares the current warning level with the landslide risk level, evaluates the adjustment requirement of the warning level, and adjusts the warning level in real time according to the landslide probability to obtain a warning status update result.

[0072] The event detection and positioning data includes geological event types, event occurrence frequencies, and event intensities. The sampling configuration optimization information includes sampling interval parameters, response time windows, and data compression ratios. The landslide risk index includes risk ratings, probability intervals, and results of influencing factor analyses. The warning status update result includes update frequencies, level change ranges, and emergency response trigger results.

[0073] As Figure 2 shown, the steps of identifying abnormal points of the offset amplitude are specifically as follows:

[0074] Based on the fiber optic sensor data, continuous surface displacement and vibration data are received. For each pair of adjacent time point data, the formula:

[0075] ;

[0076] is used to calculate the offset between two time points , where is the surface displacement data at the first time point, is the vibration data at the first time point, is the surface displacement data at the second time point, is the vibration data at the second time point;

[0077] The displacement and vibration signals of the surface are recorded by the fiber optic sensor. The signals are transmitted in digital form. The signals recorded at each time point are time - marked and arranged in chronological order to form time - series data. Then, the time - series data is pre - processed, including filtering of noise signals and cleaning of abnormal points, to ensure the reliability of the input data. Through point - by - point analysis of the time - series data, the displacement and vibration values at each time point are extracted. The recorded values can reflect the motion state of the surface at each time point, providing complete basic data for subsequent offset calculation, forming a time - series of surface displacement and vibration data. At the time point, the displacement and vibration data recorded by the sensor are , at the data at the time point is , and by substituting into the formula, it is calculated as:

[0078] ;

[0079] This result shows that from the time point to , the change amount of surface displacement and vibration is 7.07, indicating that obvious displacement and vibration have occurred on the surface between these two time points. If this change exceeds the preset abnormal threshold, it can be marked as an abnormal event.

[0080] Based on the offset, an abnormal threshold is set, and whether the offset of each data point exceeds the abnormal threshold is compared to determine the abnormal points of the offset amplitude;

[0081] When calculating, the surface displacement and vibration values of two consecutive time points need to be called. The offset data between each pair of time points is extracted respectively. By judging whether the offset exceeds the abnormal threshold range extracted from the historical data, the threshold is set based on the statistical analysis of historical monitoring data and the extraction of geological activity laws. The results of the offset calculation will be compared with the threshold point - by - point. The offset exceeding the threshold will be directly marked as abnormal to determine the abnormal points of the offset amplitude.

[0082] As shown Figure 3 below, the steps for obtaining event detection and location data are specifically as follows:

[0083] Conduct spatial analysis on the abnormal points of the offset amplitude, locate each abnormal point on the map according to its geographical coordinates, and obtain a preliminary geological activity map;

[0084] Locate each abnormal point on the map according to its geographical coordinates. Based on the captured abnormal point dataset, by retrieving the sensor recording timestamps and geographical location marking information corresponding to each data point, use the geographic information system to perform spatial mapping on the point data. First, classify the abnormal point data into independent regions or region clusters according to the timestamps and spatial markings, then determine their spatial coordinates by combining the geographical distribution patterns of the abnormal points. By invoking the rasterization function of the geographic information system, display each abnormal point in the form of spatial coordinates on a two-dimensional or three-dimensional map, and combine the point density statistics function to generate a preliminary geological activity map containing the point density and distribution characteristics.

[0085] Based on the preliminary geological activity map, adjust the sampling frequency to the optimal range, using the formula:

[0086] ;

[0087] Calculate the new sampling frequency , and obtain the event detection and location data, where is the current sampling frequency, and is the starting frequency set based on previous monitoring data and analysis results, is the adjustment coefficient, used to adjust the proportion of the sampling frequency according to the change rate or intensity of geological activities, is the attenuation factor, used to control the influence degree of the abnormal point density on the sampling frequency adjustment,

[0088] Collect the following data, the current sampling frequency Hz, the adjustment coefficient , the attenuation factor , the abnormal point density index , substitute into the formula to get:

[0089] ;

[0090] ;

[0091] ;

[0092] The results show that the new sampling frequency is 1.303 Hz, which is higher than the current frequency, indicating that the sampling frequency is increased in areas with denser abnormal activities to more accurately monitor and locate geological activity events.

[0093] As Figure 4 shown, the evaluation steps for the change range and duration are specifically as follows:

[0094] Based on the event detection and location data, extract the start and end time points and the maximum change range of each geological activity event to obtain the preliminary geological activity record;

[0095] Extract the parameter information related to geological activities from the detection data, including time series, displacement changes, and change ranges. By segmenting the time series data, identify the start and end time points of geological activities, divide the time series into multiple data segments, calculate the change rate for each segment, set a rate threshold, eliminate the segments with a change rate lower than the threshold, retain the segments with significant changes, further calculate the change range for the segments, and determine the maximum change point among them. At the same time, record the start and end times of each segment. To ensure the accuracy of the marked time boundary points, perform interpolation processing on the boundary data to ensure the continuity of the time points and displacement data. Finally, complete the extraction and recording of the start time, end time, and maximum change range of each geological activity event.

[0096] Analyze the preliminary geological activity record using the formulas:

[0097] ;

[0098] and

[0099] ;

[0100] Calculate the duration and the maximum change range of the geological activity to obtain the duration and change range data. Among them, refers to the time point when the geological activity ends, refers to the time point when the geological activity starts, is all the displacement data recorded during the geological activity;

[0101] The start time point of a certain geological activity event is , and the end time point is . According to the formula , the calculated duration is:

[0102] ;

[0103] The displacement change data recorded during this event is

[0104] , according to the formula , the maximum change amplitude is calculated as:

[0105] ;

[0106] The result shows that the duration of this geological activity event is 5 minutes, the maximum change amplitude is 3.8, and the data provides the basic input for subsequent statistical analysis.

[0107] Based on the duration and change amplitude data, the average duration and average change amplitude of geological activities are determined through statistical analysis to obtain the information on geological activity characteristics;

[0108] Data sets are established for the duration and change amplitude of all geological activity events respectively. The average values and data distribution ranges of the duration and change amplitude in the sets are calculated. Through the statistical analysis of the overall data distribution, it is judged whether the data characteristics conform to a certain regular distribution. If they conform to the regular distribution, the average values and data distribution ranges of the duration and change amplitude are directly recorded as the characteristic descriptions of geological activities. If they do not conform to the regular distribution, the median value and data interval range are used as the statistical characteristic descriptions. Finally, the average duration and change amplitude characteristics of geological activity events are determined and recorded as part of the information on geological activity characteristics.

[0109] As Figure 5 shown, the steps for obtaining the sampling configuration optimization information are specifically as follows:

[0110] Evaluate the matching degree between the current sampling frequency and the monitoring requirements. Based on the change amplitude and duration, analyze whether the current sampling frequency can capture geological changes to obtain the frequency matching evaluation information;

[0111] Extract the sampling parameter information based on the data of the change amplitude and duration, including the time interval, the number of sampling points and their coverage range. For the data, by calculating the time span and change characteristics of each geological change, extract the shortest duration and the maximum change amplitude of each change event, and analyze whether the data is completely captured at the current sampling frequency. For the time span, by comparing the sampling interval with the shortest duration of geological changes, determine whether the sampling has sufficient density to capture the complete geological changes. For the change amplitude, by extracting the displacement changes recorded at the sampling points and comparing them with the maximum change amplitude data, judge whether there is information loss. During the above analysis process, it is necessary to conduct cumulative analysis on the coverage range of data points in the time series to determine whether the sampling frequency covers the entire geological activity cycle. Finally, based on the comprehensive coverage evaluation results of the shortest duration and change amplitude, form the matching evaluation information on the sampling frequency and monitoring requirements.

[0112] Based on the frequency matching evaluation information, the formula is used:

[0113] ;

[0114] Adjust the sampling frequency and sampling density to obtain optimized sampling configuration information. Among them, is the optimized sampling frequency, represents the current sampling frequency, that is, the sampling frequency set and currently used by the system before optimization, is the coefficient adjusted according to the sensitivity of the monitoring data, is the average variation range, which refers to the average value of all variation ranges calculated in the analyzed geological activity events, is the average duration, which refers to the average value of all event durations calculated in the analyzed geological activity events, is the current sampling density, indicating the number of sampling points within the current area, is the target sampling density, adjusted according to the monitoring requirements and the characteristics of geological activities;

[0115] Current sampling frequency is 100Hz, the average variation range is 5.0 m / s, the average duration is 2.0 s, the current sampling density is 50 points per square meter, the target sampling density is 100 points per square meter, the adjustment coefficient is 0.1, substitute it into the formula for calculation:

[0116]

[0117] The result shows that based on the current geological activity data, the optimized sampling frequency should be adjusted to 187.5Hz, which will make the sampling configuration more accurately match the actual needs of geological activities.

[0118] As Figure 6 shown, the specific analysis steps for the similarity between the amplitude change and the historical pattern are as follows:

[0119] Based on the optimized sampling configuration information, collect the newly monitored geological activity data, including the variation range and duration of the current geological event, to obtain the organized geological data set;

[0120] Determine the content of geological activity data that needs to be collected after sampling optimization, including the magnitude of geological changes, event duration and time series records within the monitoring period. When collecting data, determine the distribution and density of data points according to the optimized sampling interval, and use sensors to record real-time data in the geological activity area. After eliminating abnormal data points and data with obvious noise interference, preliminarily calculate and mark the magnitude and duration of each geological event. Further classify and organize all collected monitoring data, arrange the data in chronological order to form a geological activity data set, and mark the type and range of geological events corresponding to each data segment. Store the organized geological activity data in the geological database for subsequent analysis, and finally obtain a complete geological data set.

[0121] Based on the collated geological data set, the data is compared with known landslide patterns to identify historical events similar to the current data pattern, and the similarity between the amplitude change and the historical pattern is analyzed to obtain similarity analysis results;

[0122] The geological events contained in the geological data set are classified according to the amplitude and duration, and the landslide pattern characteristics are selected as comparison parameters. The key time points, displacement changes and characteristic amplitudes in the known landslide patterns are extracted and matched one by one with the geological events in the current data set. The similarity of data matching is further analyzed by calculating the difference between the amplitude of the current data and the landslide pattern characteristics, as well as the deviation of the event duration. For cases where direct matching is not possible, matching points are supplemented through time period sliding analysis and continuous data interpolation. Finally, the similarity information between the amplitude of the change and the historical landslide pattern is formed by integrating various matching data.

[0123] like Figure 7 As shown in Figure 2, the steps for obtaining the landslide risk index are as follows:

[0124] Collect real-time surface displacement and soil moisture data, integrate the data, and obtain a real-time updated geological data set;

[0125] The sensor network based on distributed monitoring sites records the surface displacement and soil moisture values ​​of each location in real time. The data is transmitted via wireless communication or wired transmission. The transmitted data is automatically cleaned on the server side to remove outliers and redundant data, and the data is uniformly formatted into standardized time series records. The integrated data is annotated with the collection timestamp and geographic location information to form a multidimensional array, where the surface displacement is recorded in millimeters as one column and the soil moisture is expressed as a percentage in another column. The integrated data set is further calibrated to ensure the synchronization and consistency between different time intervals and different sites, so as to obtain a real-time updated geological data set.

[0126] Based on the real-time updated geological data set, the formula is adopted:

[0127] ;

[0128] Calculate the probability of landslide occurrence , and obtain the landslide risk index, where represents the displacement value of the th data point, represents the soil moisture value of the th monitoring point, and are weight factors used to adjust the key role of displacement value and soil moisture value in calculating the landslide probability, represents the total number of monitoring points;

[0129] There are data of 5 monitoring points, where the displacement values ( ) are [0.2, 0.3, 0.4, 0.5, 0.6] mm respectively, and the soil moisture values ( ) are [20%, 25%, 30%, 35%, 40%], and the weight factors and , the calculation process is as follows:

[0130] ;

[0131] ;

[0132] ;

[0133] ;

[0134] The calculated landslide risk index is 9.28. The value comprehensively considers the influence of surface displacement and soil moisture of each monitoring point. When it is higher than a certain preset threshold, it indicates a relatively high landslide risk, and preventive measures can be taken accordingly.

[0135] As Figure 8 shown, the specific steps to obtain the updated result of the warning status are as follows:

[0136] Based on the landslide risk index, evaluate the relationship between the current warning level and the landslide risk level, analyze whether it is necessary to adjust the warning level, and obtain the preliminary risk warning analysis log;

[0137] Extract the latest landslide risk index data and landslide risk level data. The landslide risk index is calculated after collecting multiple data such as surface displacement and soil moisture through monitoring sensors, and the risk level is a rating value generated by combining historical landslide patterns, geological activity frequencies, and soil characteristics. Then, calculate the difference between the current landslide risk index and the risk level, including calculating the extent to which the index exceeds the level range through differences. If the extent is greater than the set threshold, it is marked as a requirement for warning level adjustment. During the analysis, combine historical landslide warning data and the set safety threshold to evaluate the necessity of adjustment. By comparing the deviation degree between the current warning level and the landslide risk level, record the difference and adjustment requirement data. Finally, generate a preliminary risk warning analysis log for all input and processing details during the analysis process.

[0138] Based on the preliminary risk warning analysis log, adjust the warning level in real-time according to the landslide probability, using the formula:

[0139] ;

[0140] Obtain the updated result of the warning status, where, is the adjusted warning level, represents the current warning level, is the landslide risk index, which quantifies the landslide risk level in the current area, is the threshold for warning changes, which is the benchmark for determining whether the warning level needs to be adjusted, is the adjustment coefficient, used to adjust the sensitivity of the warning level according to the difference between the landslide risk index and the predetermined threshold;

[0141] Current warning level is 3 (in a 5-level warning system), the latest landslide risk index is 4.5, the threshold for warning changes is set to 3.5, and the adjustment coefficient is set to 0.5. Calculate the new warning level:

[0142] ;

[0143] According to the above calculation, the new warning level is 3.5, indicating that the warning level should be slightly increased to reflect the increased landslide risk, ensuring the sensitivity and timeliness of the warning system, so as to be able to more effectively prevent and prepare for landslide events.

[0144] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present invention, and they should all be covered within the scope of the claims and the description of the present invention. In particular, as long as there is no structural conflict, the technical features mentioned in each embodiment can be combined in any way. The present invention is not limited to the specific embodiments disclosed in the text, but includes all technical solutions that fall within the scope of the claims.

Claims

1. Fiber optic sensing landslide dynamic response monitoring system, characterized in that, The system includes: The event monitoring module receives continuous surface displacement and vibration data based on the fiber optic sensor data, identifies abnormal points of the offset amplitude by comparing the offsets of adjacent data points in real time, marks them as geological activity events, and adjusts the sampling frequency to the optimal range according to the event pattern to obtain event detection and location data; The sampling optimization module evaluates the change amplitude and duration of the geological activity based on the event detection and location data, determines whether the sampling frequency matches the current monitoring requirements, and optimizes the current sampling frequency and sampling density to obtain sampling configuration optimization information; The landslide risk analysis module compares the newly monitored geological activity data with the known landslide patterns based on the sampling configuration optimization information, analyzes the similarity of the amplitude change with the historical patterns, and combines the real-time surface displacement and soil moisture data to calculate the probability of landslide occurrence to obtain a landslide risk index; The warning level adjustment module compares the current warning level with the landslide risk level based on the landslide risk index, evaluates the adjustment requirements of the warning level, and adjusts the warning level in real time according to the landslide probability to obtain an updated warning status result.

2. The fiber optic sensing landslide dynamic response monitoring system according to claim 1, characterized in that The specific steps for identifying abnormal points of the offset amplitude are as follows: Receive continuous surface displacement and vibration data based on the fiber optic sensor data. For each pair of adjacent time point data, use the formula: ； Calculate the offset between two time points , where is the ground displacement data at the first time point, is the vibration data at the first time point, is the ground displacement data at the second time point, is the vibration data at the second time point; Based on the offset, set an abnormal threshold, and compare whether the offset of each data point exceeds the abnormal threshold to determine the abnormal points of the offset amplitude.

3. The fiber optic sensing landslide dynamic response monitoring system according to claim 1, characterized in that, The specific steps for obtaining the event detection and location data are as follows: Conduct a spatial analysis of the abnormal points of the offset amplitude, locate each abnormal point on the map according to its geographical coordinates to obtain a preliminary geological activity map; Based on the preliminary geological activity map, adjust the sampling frequency to the optimal range, use the formula: ； Calculate the new sampling frequency , to obtain event detection and location data, where is the current sampling frequency, is the adjustment coefficient, used to adjust the proportion of the sampling frequency according to the change rate or intensity of geological activities, is the attenuation factor, used to control the influence degree of the abnormal point density on the sampling frequency adjustment, is the abnormal point density index, representing the density of abnormal points in the monitoring area, used to adjust the sampling frequency according to the concentration degree of abnormal points.

4. The fiber optic sensing landslide dynamic response monitoring system according to claim 1, characterized in that, The specific steps for evaluating the change amplitude and duration are as follows: Based on the event detection and location data, extract the start and end time points and the maximum change amplitude of each geological activity event to obtain a preliminary geological activity record; Analyze the preliminary geological activity record, use the formula: ； and ； Calculate the duration of geological activities and the maximum amplitude of change , and obtain the duration and amplitude of change data, where refers to the time point when the geological activity ends, refers to the time point when the geological activity starts, are all displacement data recorded during the geological activity; Based on the duration and change amplitude data, determine the average duration and average change amplitude of the geological activity through statistical analysis to obtain geological activity characteristic information.

5. The fiber optic sensing landslide dynamic response monitoring system according to claim 1, characterized in that, The specific steps for obtaining the sampling configuration optimization information are as follows: Evaluate the matching degree of the current sampling frequency with the monitoring requirements, and analyze whether the current sampling frequency can capture geological changes based on the change amplitude and duration to obtain frequency matching evaluation information; Based on the frequency matching evaluation information, use the formula: ； Adjust the sampling frequency and sampling density to obtain optimized sampling configuration information, where is the optimized sampling frequency, represents the current sampling frequency, is the coefficient adjusted according to the sensitivity of the monitoring data, is the average change amplitude, is the average duration, is the current sampling density, is the target sampling density.

6. The fiber optic sensing landslide dynamic response monitoring system according to claim 1, wherein, The specific steps for analyzing the similarity of the amplitude change with the historical patterns are as follows: Based on the sampling configuration optimization information, collect the newly monitored geological activity data, including the change amplitude and duration of the current geological event, to obtain an organized geological data set; Based on the organized geological data set, compare it with the known landslide patterns, identify historical events similar to the current data pattern, and analyze the similarity of the amplitude change with the historical patterns to obtain a similarity analysis result.

7. The fiber optic sensing landslide dynamic response monitoring system according to claim 1, characterized in that, The specific steps for obtaining the landslide risk index are as follows: Collect the real-time surface displacement and soil moisture data, integrate the data, and obtain a real-time updated geological data set; Based on the real-time updated geological data set, use the formula: ； Calculate the probability of landslide occurrence , and obtain the landslide risk index, where represents the displacement value of the -th data point, represents the soil moisture value of the -th monitoring point, and are weight factors used to adjust the key role of displacement value and soil moisture value in calculating the landslide probability, represents the total number of monitoring points.

8. The fiber optic sensing landslide dynamic response monitoring system according to claim 1, characterized in that, The specific steps for obtaining the updated result of the warning state are as follows: Based on the landslide risk index, evaluate the relationship between the current warning level and the landslide risk level, analyze whether it is necessary to adjust the warning level, and obtain a preliminary risk warning analysis log; Based on the preliminary risk warning analysis log, adjust the warning level in real time according to the landslide probability, using the formula: ； Obtain the early warning status update result, where is the adjusted early warning level, represents the current early warning level, is the landslide risk index, is the threshold value of the early warning change, is the adjustment coefficient.

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