Optical fiber sensing landslide dynamic response monitoring system

Abstract

The invention, which relates to the technical field of landslide monitoring, discloses an optical fiber sensing landslide dynamic response monitoring system comprising an event monitoring module, a sampling optimization module, a landslide risk analysis module and an early warning level adjustment module. By comparing the offset of adjacent data points in real time, tiny displacement abnormity can be quickly identified, instant marks are provided for early geological activities, and the sampling frequency is automatically adjusted according to real-time monitoring data, so that data acquisition is finer, meanwhile, storage and processing of irrelevant data are reduced, the operation efficiency of the system is effectively improved, and the working efficiency of the system is improved. Real-time data and a historical landslide mode are finely compared, comprehensive analysis is carried out in combination with key indexes such as earth surface displacement and soil humidity, the system can accurately calculate the probability of occurrence of the landslide, the accuracy and timeliness of early warning are greatly improved, and casualties and property losses caused by landslide disasters are effectively reduced.

Description

Technical Field

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

[0002] Landslide monitoring technology focuses on the use of various sensors and monitoring equipment 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 quasi-real time in order to provide 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 to analyze regional terrain changes, so as to more accurately predict the time and scale of landslides, thereby helping to reduce casualties and property losses.

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

[0004] Data processing in existing technologies faces delays, and sensors are limited in their ability to capture tiny changes, which directly affects the timeliness and accuracy of early warnings. In actual operations, due to the low level of technical integration and sensitivity to environmental interference, effective early warnings cannot be provided at critical moments, especially in complex environments, such as when soil moisture changes dramatically during the rainy season. The response speed and accuracy of traditional technologies are insufficient to support effective disaster prevention. This limitation not only increases monitoring costs, but also leads to significant casualties and property losses due to untimely responses. Summary of the invention

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

[0006] In order to achieve the above object, the present invention adopts the following technical scheme:

[0007] Optical fiber sensing landslide dynamic response monitoring system, the system comprising:

[0008] The event monitoring module receives continuous surface displacement and vibration data based on fiber optic sensor data, identifies abnormal points of the offset amplitude by comparing the offset 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 mode to obtain event detection and positioning data;

[0009] The sampling optimization module evaluates the variation range and duration of geological activities based on the event detection and positioning 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;

[0010] The landslide risk analysis module compares the newly monitored geological activity data with the known landslide pattern based on the sampling configuration optimization information, analyzes the similarity between the amplitude change and the historical pattern, and calculates the probability of landslide occurrence in combination with the real-time surface displacement and soil moisture data to obtain the landslide risk index;

[0011] The warning level adjustment module compares the current warning level with the landslide risk level based on the landslide risk index, evaluates the adjustment demand of the warning level, and adjusts the warning level in real time according to the landslide probability to obtain the warning status update result.

[0012] The present invention is improved in that the step of identifying the abnormal point of the offset amplitude is specifically:

[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 ,in, 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 the offset of each data point is compared to see whether it exceeds the abnormal threshold, so as to determine the abnormal point of the offset amplitude.

[0017] The present invention is improved in that the step of acquiring the event detection positioning data is specifically as follows:

[0018] Performing spatial analysis on the abnormal points of the offset amplitude, locating each abnormal point on the map according to its geographic coordinates, and obtaining a preliminary geological activity map;

[0019] Based on the preliminary geological activity map, the sampling frequency was adjusted to the optimal interval using the formula:

[0020] ;

[0021] Calculate the new sampling frequency , get the event detection and positioning data, where is the current sampling frequency, is the adjustment factor used to adjust the sampling frequency ratio according to the change rate or intensity of geological activities, is the attenuation factor, which is used to control the influence of abnormal point density on the sampling frequency adjustment. It is the outlier density index, which represents the density of outliers in the monitoring area and is used to adjust the sampling frequency according to the concentration of outliers.

[0022] The present invention is improved in that the steps of evaluating the variation range and duration are specifically as follows:

[0023] Based on the event detection and positioning data, the start and end time points and the maximum variation range of each geological activity event are extracted to obtain a preliminary geological activity record;

[0024] The preliminary geological activity records are analyzed using the formula:

[0025] ;

[0026] and

[0027] ;

[0028] Calculating the duration of geological activity and the maximum fluctuation , get the duration and variation data, where Refers to the time point when geological activity ends. Refers to the time when geological activity begins. It is all displacement data recorded during geological activity;

[0029] Based on the duration and variation data, the average duration and average variation of the geological activity are determined through statistical analysis to obtain the characteristic information of the geological activity.

[0030] The present invention is improved in that the step of obtaining the sampling configuration optimization information is 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 the geological changes according to the variation amplitude and duration, and obtain the frequency matching evaluation information;

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

[0033] ;

[0034] The sampling frequency and sampling density are adjusted to obtain the sampling configuration optimization information, where: is the optimized sampling frequency, Indicates the current sampling frequency, is the coefficient adjusted according to the sensitivity of the monitoring data, is the average change, is the average duration, is the current sampling density, is the target sampling density.

[0035] The present invention is improved in that the steps of analyzing the similarity between the amplitude change and the historical pattern are specifically as follows:

[0036] Based on the sampling configuration optimization information, newly monitored geological activity data are collected, including the magnitude and duration of changes in current geological events, to obtain a collated geological data set;

[0037] Based on the collated geological data set, a comparison is made 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 a similarity analysis result.

[0038] The present invention is improved in that the steps of obtaining the landslide risk index are specifically as follows:

[0039] Collecting the real-time surface displacement and soil moisture data, integrating the data, and obtaining a real-time updated geological data set;

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

[0041] ;

[0042] Calculate the probability of a landslide , we get the landslide risk index, where Indicates The displacement value of each data point, Indicates The soil moisture value at each monitoring point. and is a weighting factor used to adjust the criticality of displacement and soil moisture values ​​in calculating landslide probability, Indicates the total number of monitoring points.

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

[0044] Based on the landslide risk index, the relationship between the current warning level and the landslide risk level is evaluated, and whether the warning level needs to be adjusted is analyzed to obtain a preliminary risk warning analysis log;

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

[0046] ;

[0047] Get the warning status update result, where: is the adjusted warning level. Indicates the current warning level. is the landslide risk index, is the threshold for warning changes, is the adjustment factor.

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

[0049] In the present invention, by comparing the offsets of adjacent data points in real time, tiny displacement anomalies can be quickly identified, providing instant markings for early geological activities, and automatically adjusting the sampling frequency according to real-time monitoring data, making data collection more precise, while reducing the storage and processing of irrelevant data, effectively improving the operating efficiency of the system, and by carefully comparing real-time data with historical landslide patterns, and combining key indicators such as surface displacement and soil moisture for comprehensive analysis, the system can accurately calculate the probability of landslide occurrence, greatly improving the accuracy and timeliness of early warning, and effectively reducing 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 briefly introduces the drawings required for describing the embodiments. The drawings are only used to illustrate the implementation methods and are not to be considered as limitations of the present invention.

[0051] Figure 1 is a system flow chart in an embodiment of the present invention;

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

[0053] Figure 3 This is a flow chart of obtaining event detection positioning data in an embodiment of the present invention;

[0054] Figure 4 is a flow chart of evaluating the variation range and duration in an embodiment of the present invention;

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

[0056] Figure 6 is a flow chart of analyzing the similarity between amplitude change and historical pattern in an embodiment of the present invention;

[0057] Figure 7 This is a flow chart of obtaining a landslide risk index in an embodiment of the present invention;

[0058] Figure 8 The following is a flowchart of obtaining the warning status update result in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] The technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all of the embodiments. Based on the embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within 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 technicians in the field of the present invention; the terms used in the present invention are only for the purpose of describing specific embodiments, and are not intended to limit the terms "including" and "having" and any variations thereof in the specification and claims of the present invention and the above-mentioned drawings, and are intended to cover non-exclusive inclusions.

[0061] In the description of the embodiments of the present invention, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present invention, the meaning of "multiple" is more than two, unless otherwise clearly and 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 three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

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

[0064] In the description of the embodiments of the present invention, the technical terms "center", "longitudinal", "lateral", "length", "width", "thickness", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", "clockwise", "counterclockwise", "axial", "radial", "circumferential", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, and do not indicate or imply that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as limiting 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 "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to the specific circumstances.

[0066] Example

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

[0068] The event monitoring module receives continuous surface displacement and vibration data based on fiber optic sensor data, identifies abnormal points of the offset amplitude by comparing the offset 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 mode to obtain event detection and positioning data;

[0069] The sampling optimization module evaluates the magnitude and duration of geological activity based on event detection and positioning 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;

[0070] 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 between the amplitude change and the historical pattern, and calculates the probability of landslide occurrence in combination with the real-time surface displacement and soil moisture data to obtain the landslide risk index;

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

[0072] Event detection and positioning data include geological event type, event frequency, and event intensity. Sampling configuration optimization information includes sampling interval parameters, response time window, and data compression ratio. Landslide risk index includes risk rating, probability interval, and influencing factor analysis results. Warning status update results include update frequency, level change range, and emergency response trigger results.

[0073] like Figure 2 As shown, the steps for 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 is used:

[0075] ;

[0076] Calculate the offset between two time points ,in, 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 ground surface are recorded by optical fiber sensors. The signals are transmitted in digital form. The signals recorded at each time point are time-tagged and arranged in chronological order to form time series data. The time series data is then preprocessed, including filtering out noise signals and cleaning up abnormal points, to ensure the reliability of the input data. The displacement and vibration values ​​at each time point are extracted through point-by-point analysis of the time series data. The recorded values ​​can reflect the movement state of the ground surface at each time point, providing complete basic data for subsequent offset calculations, thus forming a time series of surface displacement and vibration data. At this time point, the displacement and vibration data recorded by the sensor are ,exist The data at the time point is , calculated by substituting into the formula:

[0078] ;

[0079] The results showed that from the time point arrive , the change in surface displacement and vibration is 7.07, indicating that the surface has undergone significant displacement and vibration 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 the offset of each data point is compared to see whether it exceeds the abnormal threshold to determine the abnormal point of the offset amplitude;

[0081] The calculation requires calling the surface displacement and vibration values ​​of two consecutive time points, extracting the offset data between each pair of time points respectively, and judging whether the offset exceeds the abnormal threshold range extracted from the historical data. The setting of the threshold is based on the statistical analysis of historical monitoring data and the extraction of geological activity laws. The result of the offset calculation will be compared with the threshold point by point. The offset exceeding the threshold will be directly marked as abnormal, and the abnormal point of the offset amplitude will be determined.

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

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

[0084] Each abnormal point is located on the map according to its geographic coordinates. Based on the captured abnormal point data set, the sensor record timestamp and geographic location tag information corresponding to each data point are retrieved, and the point data is spatially mapped using the geographic information system. First, the abnormal point data is classified into independent areas or regional clusters according to the timestamp and spatial tag. Then, its spatial coordinates are determined in combination with the geographic distribution pattern of the abnormal point. By calling the rasterization function of the geographic information system, each abnormal point is displayed in the form of spatial coordinates on a two-dimensional or three-dimensional map. Combined with the point density statistics function, a preliminary map of geological activities containing point density and distribution characteristics is generated.

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

[0086] ;

[0087] Calculate the new sampling frequency , get the event detection and positioning data, where is the current sampling frequency, which is the starting frequency set based on previous monitoring data and analysis results. is the adjustment factor used to adjust the sampling frequency ratio according to the change rate or intensity of geological activities, is the attenuation factor, which is used to control the influence of abnormal point density on the sampling frequency adjustment. is the outlier density index, which represents the density of outliers in the monitoring area and is used to adjust the sampling frequency according to the concentration of outliers;

[0088] The following data was collected, the current sampling frequency Hz, adjustment factor , attenuation factor , outlier density index , substituting into the formula:

[0089] ;

[0090] ;

[0091] ;

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

[0093] like Figure 4 As shown in Figure 2, the steps for evaluating the magnitude and duration of changes are as follows:

[0094] Based on the event detection and positioning data, the start and end time points and the maximum change amplitude of each geological activity event are extracted to obtain a preliminary geological activity record;

[0095] Parameter information related to geological activities is extracted from the detection data, including time series, displacement changes and change amplitudes. The start and end time points of geological activities are identified by segmenting the time series data, and the time series is divided into multiple data segments. The change rate of each segment is calculated, and a rate threshold is set. The segments with a change rate lower than the threshold are eliminated, and the segments with significant changes are retained. The change amplitude of the segments is further calculated, and the maximum change point is determined. At the same time, the start and end time of each segment is recorded. In order to ensure the accuracy of the marked time boundary points, the boundary data is interpolated to ensure the continuity of the time points and displacement data. Finally, the start time, end time and maximum change amplitude of each geological activity event are extracted and recorded.

[0096] The preliminary geological activity records were analyzed using the formula:

[0097] ;

[0098] and

[0099] ;

[0100] Calculating the duration of geological activity and the maximum fluctuation , get the duration and variation data, where Refers to the time point when geological activity ends. Refers to the time when geological activity begins. It is all displacement data recorded during geological activity;

[0101] The starting time of a geological activity event is , the end time is , according to the formula , calculate the duration as:

[0102] ;

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

[0104] , according to the formula , calculate the maximum change range as:

[0105] ;

[0106] The results show that the duration of this geological activity event is 5 minutes, with a maximum amplitude of 3.8. The data provide basic input for subsequent statistical analysis.

[0107] Based on the duration and variation data, the average duration and average variation of geological activities are determined through statistical analysis to obtain the characteristic information of geological activities;

[0108] Establish data sets for the duration and variation range of all geological activity events respectively, calculate the average value and data distribution range of the duration and variation range in the set, and conduct statistical analysis on the overall distribution of the data to determine whether its data characteristics conform to a certain regular distribution. If it conforms to the regular distribution, directly record the average value and data distribution range of the duration and variation range as the characteristic description of the geological activity. If it does not conform to the regular distribution, use the median value and data spacing range as the statistical characteristic description, and finally determine the average duration and variation range characteristics of the geological activity events, and record them as part of the geological activity characteristic information.

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

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

[0111] Based on the data of variation amplitude and duration, sampling parameter information is extracted, including time interval, number of sampling points and their coverage. For the data, the time span and variation characteristics of each geological change are calculated, the shortest duration and maximum variation amplitude of each change event are extracted, and the data are analyzed whether they are completely captured at the current sampling frequency. For the time span, by comparing the sampling interval with the shortest duration of the geological change, it is determined whether the sampling has sufficient density to capture the complete geological changes. For the variation amplitude, by extracting the displacement changes recorded at the sampling points and comparing them with the maximum variation amplitude data, it is determined whether there is any information loss. In the above analysis process, it is necessary to conduct a cumulative analysis of the coverage of the data points in the time series to determine whether the sampling frequency covers the entire cycle of geological activities. Finally, the coverage assessment results of the shortest duration and variation amplitude are combined to form an assessment information on the matching of the sampling frequency and monitoring needs.

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

[0113] ;

[0114] The sampling frequency and sampling density are adjusted to obtain the sampling configuration optimization information, where: is the optimized sampling frequency, Indicates the current sampling frequency, that is, the sampling frequency set and used by the system before optimization. is the coefficient adjusted according to the sensitivity of the monitoring data, is the average amplitude of change, which refers to the average value of all amplitudes of change calculated in the analyzed geological activity events. is the average duration, which is the average value of the duration of all events calculated in the analyzed geological activity events. is the current sampling density, which indicates the number of sampling points in the current area. is the target sampling density, which is adjusted according to the monitoring needs and the characteristics of the geological activities;

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

[0116]

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

[0118] like Figure 6 As shown in Figure 2, the analysis steps for the similarity between the amplitude change and the historical pattern are as follows:

[0119] Based on the sampling configuration optimization information, collect the newly monitored geological activity data, including the magnitude and duration of the current geological events, and obtain the collated 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 a landslide , we get the landslide risk index, where Indicates The displacement value of each data point, Indicates The soil moisture value at each monitoring point. and is a weighting factor used to adjust the criticality of displacement and soil moisture values ​​in calculating landslide probability, Indicates the total number of monitoring points;

[0129] There are data from 5 monitoring points, among which the displacement value ( ) are [0.2, 0.3, 0.4, 0.5, 0.6] mm, and the soil moisture value ( ) is [20%, 25%, 30%, 35%, 40%], and the weight factor and , the calculation process is as follows: ; ; ; ;

[0130] The calculated landslide risk index is 9.28, which takes into account the influence of surface displacement and soil moisture at each monitoring point. When it is higher than a preset threshold, it indicates a higher landslide risk, and preventive measures can be taken accordingly.

[0131] like Figure 8 As shown, the specific steps for obtaining the warning status update result are:

[0132] Based on the landslide risk index, the relationship between the current warning level and the landslide risk level is evaluated, and the need to adjust the warning level is analyzed to obtain a preliminary risk warning analysis log;

[0133] The latest landslide risk index data and landslide risk level data are extracted. The landslide risk index is calculated by monitoring sensors to collect multiple data such as surface displacement and soil moisture. The risk level is a level value generated by combining historical landslide patterns, geological activity frequency and soil characteristics. Then, the difference between the current landslide risk index and the risk level is calculated, including the magnitude of the index exceeding the level range through the difference. If the magnitude is greater than the set threshold, it is marked as a warning level adjustment requirement. While analyzing, the necessity of adjustment is evaluated by combining historical landslide warning data and the set safety threshold. By comparing the degree of deviation between the current warning level and the landslide risk level, the difference and adjustment requirement data are recorded. Finally, all input and processing details in the analysis process are used to generate a preliminary risk warning analysis log.

[0134] Based on the preliminary risk warning analysis log, the warning level is adjusted in real time according to the landslide probability, using the formula:

[0135] ;

[0136] Get the warning status update result, where: is the adjusted warning level, Indicates the current warning level. It is the landslide risk index, which quantifies the risk level of landslide in the current area. It is the threshold for warning changes and the benchmark for determining whether the warning level needs to be adjusted. is the adjustment factor used to adjust the sensitivity of the warning level according to the difference between the landslide risk index and the predetermined threshold;

[0137] Current alert level The latest landslide risk index is 3 (out of a 5-level warning system). The threshold for warning changes is 4.5. Set to 3.5, adjust the coefficient Set it to 0.5 and calculate the new warning level:

[0138] ;

[0139] According to the above calculations, the new warning level is 3.5, suggesting that the warning level should be slightly raised to reflect the increased landslide risk and ensure the sensitivity and timeliness of the early warning system, so as to be able to more effectively prevent and prepare for landslide events.

[0140] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, not to limit them; although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention. In particular, as long as there is no structural conflict, the various technical features mentioned in each embodiment can be combined in any way. The present invention is not limited to the specific embodiments disclosed herein, 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 comprises: The event monitoring module receives continuous surface displacement and vibration data based on fiber optic sensor data, identifies abnormal points of the offset amplitude by comparing the offset 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 mode to obtain event detection and positioning data; The sampling optimization module evaluates the variation range and duration of geological activities based on the event detection and positioning 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 pattern based on the sampling configuration optimization information, analyzes the similarity between the amplitude change and the historical pattern, and calculates the probability of landslide occurrence in combination with the real-time surface displacement and soil moisture data to obtain the 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 demand of the warning level, and adjusts the warning level in real time according to the landslide probability to obtain the warning status update result.

2. The optical fiber sensing landslide dynamic response monitoring system according to claim 1 is characterized in that: The step of identifying the abnormal point of the deviation amplitude is specifically as follows: 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: ； Calculate the offset between two time points ,in, 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; Based on the offset, an abnormal threshold is set, and the offset of each data point is compared to see whether it exceeds the abnormal threshold, so as to determine the abnormal point of the offset amplitude.

3. The optical fiber sensing landslide dynamic response monitoring system according to claim 1 is characterized in that: The steps for acquiring the event detection positioning data are specifically as follows: Performing spatial analysis on the abnormal points of the offset amplitude, locating each abnormal point on the map according to its geographic coordinates, and obtaining a preliminary geological activity map; Based on the preliminary geological activity map, the sampling frequency was adjusted to the optimal interval using the formula: ； Calculate the new sampling frequency , get the event detection and positioning data, where is the current sampling frequency, is the adjustment factor used to adjust the sampling frequency ratio according to the change rate or intensity of geological activities, is the attenuation factor, which is used to control the influence of abnormal point density on the sampling frequency adjustment. It is the outlier density index, which represents the density of outliers in the monitoring area and is used to adjust the sampling frequency according to the concentration of outliers.

4. The optical fiber sensing landslide dynamic response monitoring system according to claim 1 is characterized in that: The steps for evaluating the magnitude and duration of the change are as follows: Based on the event detection and positioning data, the start and end time points and the maximum variation range of each geological activity event are extracted to obtain a preliminary geological activity record; The preliminary geological activity records are analyzed using the formula: ； and ； Calculating the duration of geological activity and the maximum fluctuation , get the duration and variation data, where Refers to the time point when geological activity ends. Refers to the time when geological activity begins. It is all displacement data recorded during geological activity; Based on the duration and variation data, the average duration and average variation of the geological activity are determined through statistical analysis to obtain the characteristic information of the geological activity.

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

6. The optical fiber sensing landslide dynamic response monitoring system according to claim 1 is characterized in that: The specific steps for analyzing the similarity between the amplitude change and the historical pattern are: Based on the sampling configuration optimization information, newly monitored geological activity data are collected, including the magnitude and duration of changes in current geological events, to obtain a collated geological data set; Based on the collated geological data set, a comparison is made 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 a similarity analysis result.

7. The optical fiber sensing landslide dynamic response monitoring system according to claim 1 is characterized in that: The steps for obtaining the landslide risk index are specifically as follows: Collecting the real-time surface displacement and soil moisture data, integrating the data, and obtaining a real-time updated geological data set; Based on the real-time updated geological data set, the formula is adopted: ； Calculate the probability of a landslide , we get the landslide risk index, where Indicates The displacement value of each data point, Indicates The soil moisture value at each monitoring point. and is a weighting factor used to adjust the criticality of displacement and soil moisture values ​​in calculating landslide probability, Indicates the total number of monitoring points.

8. The optical fiber sensing landslide dynamic response monitoring system according to claim 1 is characterized in that: The steps for obtaining the warning status update result are specifically as follows: Based on the landslide risk index, the relationship between the current warning level and the landslide risk level is evaluated, and whether the warning level needs to be adjusted is analyzed to obtain a preliminary risk warning analysis log; Based on the preliminary risk warning analysis log, the warning level is adjusted in real time according to the landslide probability, using the formula: ； Get the warning status update result, where: is the adjusted warning level. Indicates the current warning level. is the landslide risk index, is the threshold for warning changes, is the adjustment factor.

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