Geological disaster monitoring method and system based on distributed optical fiber vibration sensing system

Abstract

The technology of the present invention relates to the field of geological disaster monitoring technology, specifically, to a geological disaster monitoring method and system based on a distributed optical fiber vibration sensing system. The problem solved by the present invention is: in optical fiber transformation, how to solve the interference of optical fiber changes themselves and external environmental changes on geological disaster prediction results, thereby improving the prediction accuracy of the distributed optical fiber vibration sensing system. In order to solve the above problem, the present invention provides a geological disaster monitoring method, including: determining the area to be expanded according to monitoring needs and the system distribution path of the original optical fiber; determining the actual distribution path of the newly added optical fiber according to the geomorphological characteristics and geological risk level of the area to be expanded; when the newly added optical fiber is installed, the characteristic data is corrected according to the loss of the original optical fiber and the transmission characteristics of the newly added optical fiber, and a prediction model is constructed according to the corrected data; the prediction model is combined with environmental information to determine the type of disaster occurring in the target area.

Description

Technical Field

[0001] The present invention relates to the technical field of geological disaster monitoring, and in particular to a geological disaster monitoring method and system based on a distributed optical fiber vibration sensing system. Background Art

[0002] Geological disasters, particularly landslides, collapses, and debris flows, pose a serious threat to human life and property. They often occur suddenly and are difficult to predict. Due to their sudden and destructive nature, effective and timely monitoring of geological disasters has become a key research topic in geological disaster prevention, control, and mitigation.

[0003] Distributed fiber-optic sensing technology uses optical fiber as both a signal transmission medium and a sensing unit. By detecting changes in optical signal intensity, phase, polarization state, and other parameters in the fiber under the influence of an external field, it enables continuous distributed measurement of external parameters along the fiber, making it an ideal non-destructive health monitoring technology for large-scale facilities in many fields. In recent years, fiber-optic sensing technology has become a key area of ​​geological hazard monitoring due to its superior performance, including resistance to electromagnetic interference, distributed measurement capabilities, high precision, and high reliability. The existing technology (CN118960857B) provides a geological disaster monitoring method and system based on distributed optical fiber sensors. It uses distributed optical fiber sensors to monitor geological disasters such as landslides and earthquakes, and predicts the location, level and duration of the disaster in real time by analyzing the changes in optical fiber signals. However, when the original optical fiber is modified to expand the monitoring network, there are the following technical problems: the optical fiber itself is "unstable", that is, the addition of new optical fibers during the modification process will trigger the reconstruction of the optical fiber network topology of the original optical fiber, and the coupling distortion of the scattered signal mode will be aggravated, resulting in the original prediction model being unable to accurately capture the disaster characteristics; environmental interference is "difficult to distinguish", that is, the external environment of the optical fiber changes after the modification, and the interference generated by the new environment is easily mixed with the real disaster precursor signal, thereby leading to false alarms or missed alarms.

[0004] Therefore, there is an urgent need for a distributed fiber optic vibration sensing system that can effectively solve the interference of changes in the optical fiber itself and the external environment during optical fiber transformation on the prediction results, and improve the accuracy of geological disaster prediction. Summary of the Invention

[0005] The problem solved by the present invention is: in optical fiber modification, how to solve the interference of optical fiber changes and external environment changes on geological disaster prediction results, thereby improving the prediction accuracy of the distributed optical fiber vibration sensing system.

[0006] To solve the above problems, an embodiment of the present invention provides a geological disaster monitoring method based on a distributed optical fiber vibration sensing system. The geological disaster monitoring method includes: obtaining monitoring needs in the target area, and determining the area to be expanded based on the monitoring needs and the system distribution path of the original optical fiber in the distributed optical fiber vibration sensing system; determining the actual distribution path of the newly added optical fiber based on the geomorphological characteristics and geological risk level of the area to be expanded; performing a virtual disaster scenario test on the system distribution path to obtain characteristic data corresponding to each geological disaster scenario; when the newly added optical fiber is installed, correcting the characteristic data based on the loss of the original optical fiber and the transmission characteristics of the newly added optical fiber, and constructing a prediction model based on the corrected data; combining the prediction model with environmental information to determine the type of disaster occurring in the target area.

[0007] Compared with the existing technology, the technical effects achieved by adopting this technical solution are as follows: the clarification of monitoring requirements and the acquisition of system distribution paths help guide the design of the actual distribution paths of new optical fibers, ensure that the expanded optical fiber vibration sensing system matches the actual needs, avoid redundancy or omissions, accurately identify the areas to be expanded, and focus the new optical fibers on new geological disaster-prone areas and existing monitoring blind spots, thereby improving the integrity of the optical fiber monitoring network. The layout of the new optical fibers is optimized based on the topographic characteristics, so that the actual distribution paths are more consistent with the actual geological conditions. The new optical fibers are configured based on the quantitative results of the geological risk level, and the actual distribution paths are scientifically planned. Disaster signal characteristic data is accumulated through virtual disaster scenario testing to provide a "standard sample" for the prediction model. By comparing real-time signal data with characteristic data, disaster signals can be automatically identified. The influence of optical fiber physical characteristics on vibration signals is effectively eliminated by calculating loss conditions. The characteristic data is corrected by transmission characteristics, which helps to eliminate the vibration signal response deviation of the new optical fibers caused by differences in the laying environment, unify the quantitative benchmark of vibration signals, and the combination of environmental information and prediction models can effectively avoid single signal misjudgment and enhance the environmental adaptability of the prediction system.

[0008] In one embodiment of the present invention, the monitoring requirements in the target area are obtained, and the area to be expanded is determined based on the monitoring requirements and the system distribution path of the original optical fiber in the distributed optical fiber vibration sensing system, specifically including: clarifying the monitoring requirements based on the geological exploration data and historical disaster records of the target area; constructing a three-dimensional monitoring coverage model of the original optical fiber based on the monitoring performance indicators and terrain shielding effects of the original optical fiber; determining new risk areas based on the three-dimensional monitoring coverage model and the monitoring requirements; screening out monitoring blind spots and inefficient areas based on the three-dimensional monitoring coverage model; and determining the area to be expanded based on the new risk areas, monitoring blind spots and inefficient areas.

[0009] Compared with existing technologies, the technical effects achieved by adopting this technical solution are: clarifying the potential disaster types and high-risk areas in the target area through geological exploration and historical disaster data, reasonably defining monitoring needs, dynamically identifying new risk areas and incorporating them into monitoring, reducing response lags when geological disasters occur, and screening and covering monitoring blind spots and inefficient areas, which helps to ensure that the accuracy and reliability of the entire monitoring system meet monitoring requirements and comprehensively improve the integrity and early warning efficiency of the fiber optic monitoring network.

[0010] In one embodiment of the present invention, the actual distribution path of the newly added optical fiber is determined based on the geomorphological characteristics and geological risk level of the area to be expanded, specifically including: determining the distribution density of the newly added optical fiber based on the geological risk level and the frequency of disaster occurrence; determining the theoretical distribution path of the newly added optical fiber based on the geomorphological characteristics and distribution density of the area to be expanded; conducting a virtual disaster scenario test on a three-dimensional model of the theoretical distribution path, adjusting the theoretical distribution path based on the preview results, and determining the actual distribution path.

[0011] Compared with the existing technology, the technical effect achieved by adopting this technical solution is as follows: the distribution density of the newly added optical fiber is determined by the geological risk level and the frequency of disaster occurrence, while ensuring the monitoring accuracy of the area to be expanded and enhancing the timeliness of early warning. The theoretical distribution path is planned in combination with the landform characteristics and distribution density to ensure that the layout of the newly added optical fiber fits the terrain conditions and maximizes the coverage of the potential disaster signal propagation path. By simulating the impact of disasters in the three-dimensional model of the theoretical distribution path, potential problems in the theoretical distribution path of the newly added optical fiber can be identified in advance and optimized and adjusted, further improving the accuracy and adaptability of the optical fiber sensing system.

[0012] In one embodiment of the present invention, after a new optical fiber is installed, the characteristic data is corrected according to the loss of the original optical fiber and the transmission characteristics of the new optical fiber, and a prediction model is constructed based on the corrected data, specifically including: dividing the original optical fiber into multiple loss segments according to the number of turning points; calculating the attenuation coefficient of each loss segment according to the number of turning points and the optical fiber length; calculating the loss coefficient of the original optical fiber according to the attenuation coefficient and service life of each loss segment; correcting the characteristic data according to the loss coefficient and the transmission characteristics of the new optical fiber, and constructing a prediction model based on the corrected data.

[0013] Compared with the existing technology, the technical effect achieved by adopting this technical solution is: accurately dividing the "loss homogeneous section" through turning points, realizing independent analysis of sections with different physical states, avoiding errors caused by global averaging, and obtaining the precise attenuation coefficient of each loss section by calculating the turning degree and fiber length, providing data support for subsequent characteristic data correction, and calculating the loss coefficient through the attenuation coefficient and service life, thereby realizing loss compensation for old optical fibers and improving the monitoring accuracy of the fiber optic sensing system.

[0014] In one embodiment of the present invention, characteristic data is corrected according to the loss coefficient and the transmission characteristics of the newly added optical fiber, and a prediction model is constructed based on the corrected data, specifically including: determining the installation location according to the actual distribution path of the newly added optical fiber, and dividing the sub-installation modules according to the topographical characteristics of the installation location; calculating the actual transmission efficiency of the vibration signal according to the transmission characteristics in the sub-installation modules; obtaining the standard transmission efficiency of the newly added optical fiber under the standard state, and calculating the deviation coefficient of the newly added optical fiber according to the actual transmission efficiency and the standard transmission efficiency; calculating correction data according to the deviation coefficient, loss coefficient and characteristic data, and constructing a prediction model based on the corrected data.

[0015] Compared with the existing technology, the technical effect achieved by adopting this technical solution is as follows: by analyzing the topographic features, the area to be expanded can be divided into multiple homogeneous sub-installation modules, so as to more accurately obtain the transmission characteristics of the newly added optical fiber in each sub-installation module, which is helpful to accurately evaluate the transmission performance of the optical fiber in different modules. By calculating the deviation coefficient, it is possible to identify and compensate for the impact of installation environment factors on the transmission performance of the newly added optical fiber, thereby eliminating the signal transmission deviation between different sub-modules. The prediction model is constructed according to the corrected data, which can improve the adaptability of the prediction model to complex geological environments, thereby improving the accuracy and reliability of the prediction results.

[0016] In one embodiment of the present invention, a prediction model is combined with environmental information to determine the type of disaster occurring in a target area, specifically including: when the environmental information has not changed, the real-time signal data monitored from the newly added optical fiber and the original optical fiber is input into the prediction model to determine the type of geological disaster; when the environmental information changes, the interference signal generated by the change in environmental information is obtained, and the prediction model is adjusted according to the interference signal to obtain an adjusted model; an abnormal parameter set is determined according to the adjusted model and the real-time signal data, and the type of geological disaster is determined according to the abnormal parameter set.

[0017] Compared with the existing technology, the technical effects achieved by adopting this technical solution are: when the environmental parameters have not changed, the real-time signal data can be directly input into the prediction model, avoiding the complex interference identification and model adjustment process, shortening the disaster judgment time, accurately extracting the interference signal according to the environmental changes and dynamically adjusting the prediction model, avoiding the model failure caused by sudden environmental changes, reducing the risk of missed detection of geological disasters, and determining the abnormal parameters by adjusting the model and real-time signal data, which helps to achieve accurate capture and reliable classification of geological disaster signals.

[0018] In one embodiment of the present invention, when environmental information changes, an interference signal generated by the change in environmental information is obtained, and the prediction model is adjusted according to the interference signal to obtain an adjusted model, specifically including: obtaining a monitoring performance indicator affected by the interference signal, and marking the monitoring performance indicator as an indicator to be adjusted; updating the indicator to be adjusted by the signal type and trigger frequency of the interference signal, and recording it as a corrected performance indicator; obtaining a geological disaster scenario affected by the corrected performance indicator as a corrected scenario, and updating the correction data corresponding to the corrected scenario in the prediction model; adjusting the prediction model according to the updated corrected data to obtain an adjusted model.

[0019] Compared with the existing technology, the technical effect achieved by adopting this technical solution is: by extracting the monitoring performance indicators affected by interference, the interference impact is converted into quantifiable indicators to be adjusted, which helps to accurately locate the adjustment target of the prediction model, thereby avoiding blind adjustment of irrelevant parameters and making the optimization direction of the prediction model more targeted. The correction performance indicators are dynamically generated by combining the signal type and trigger frequency of the interference signal, which can effectively capture the impact of external environmental changes on the performance of the prediction model. This precise parameter adjustment based on the characteristics of the interference signal ensures that the prediction model can still maintain the accuracy and timeliness of the prediction in a complex environment. By mapping the correction performance indicators to specific correction scenarios, the correction data of the corresponding scenarios in the prediction model can be updated in a targeted manner, ensuring that the prediction model adapts to the new environment while avoiding the over-correction problem that may be caused by global parameter adjustment, thereby maintaining the stability and adaptability of the prediction model and improving the prediction accuracy.

[0020] In one embodiment of the present invention, an abnormal parameter set is determined based on an adjustment model and real-time signal data, and the disaster type of the geological disaster is determined based on the abnormal parameter set, specifically including: determining the type of disaster that may occur in the target area based on each abnormal data in the abnormal parameter set, and recording it as a theoretical type; obtaining the fluctuation range of the abnormal data when all theoretical types occur from a historical disaster database; when the abnormal data does not exceed the fluctuation range, determining that the disaster type in the target area is a single type, and determining the disaster type based on the degree of fit between the abnormal data and the fluctuation range of different disaster types; when the abnormal data exceeds the fluctuation range, recording the abnormal data that exceeds the fluctuation range as combined data, and matching it with the disaster type corresponding to the combined data to determine the combination method of multiple disaster types.

[0021] Compared with the existing technology, the technical effects achieved by adopting this technical solution are: abnormal data, as the core element for identifying geological disaster risks, can capture characteristic parameters that deviate from the normal state in real-time signal data, provide real-time information for disaster prediction, and combine theoretical types with historical disaster databases to provide historical data support for disaster type prediction, which helps to reduce the prediction error of the adjustment model. The fluctuation range provides a judgment boundary for abnormal data, and the judgment mechanism of single type and degree of fit ensures that the adjustment model maintains high efficiency and accuracy in most conventional scenarios. The determination of combined data and combination method takes into account complex scenarios with multiple disaster types concurrently, so that the adjustment model is more adaptable and can better cope with complex geological disaster situations.

[0022] In one embodiment of the present invention, a geological disaster monitoring system based on a distributed optical fiber vibration sensing system is also provided. The geological disaster monitoring method recorded in the above embodiment is applied to the geological disaster monitoring system. The geological disaster monitoring system includes: a positioning module, the positioning module is used to determine the area to be expanded according to the monitoring requirements and the system distribution path; a testing module, the testing module is used to perform virtual disaster scenario tests on the system distribution path to obtain characteristic data corresponding to each geological disaster scenario; a correction module, the correction module corrects the characteristic data according to the loss of the original optical fiber and the transmission characteristics of the newly added optical fiber; a prediction module, the prediction module is used to predict the type of disaster occurring in the target area. The geological disaster monitoring system has all the technical features of the above-mentioned geological disaster monitoring method, which will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is one of the flow charts of geological disaster monitoring methods;

[0024] Figure 2 This is the second flow chart of the geological disaster monitoring method;

[0025] Figure 3 This is the third flow chart of geological disaster monitoring method;

[0026] Figure 4 This is the fourth flow chart of geological disaster monitoring method;

[0027] Figure 5 This is a system diagram of the geological disaster monitoring system;

[0028] Description of reference numerals:

[0029] 100-Geological disaster monitoring system; 110-Positioning module; 120-Testing module; 130-Correction module; 140-Prediction module. DETAILED DESCRIPTION

[0030] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, specific embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0031] [First embodiment]

[0032] See also Figure 1 In a specific embodiment, the present invention provides a geological disaster monitoring method based on a distributed optical fiber vibration sensing system, the geological disaster monitoring method comprising:

[0033] S100, obtaining monitoring requirements within the target area, and determining the area to be expanded based on the monitoring requirements and the system distribution path of the original optical fiber in the distributed optical fiber vibration sensing system;

[0034] S200, determining the actual distribution path of the newly added optical fibers based on the topographical features and geological risk level of the area to be expanded;

[0035] S300, conducting a virtual disaster scenario test on the system distribution path to obtain characteristic data corresponding to each geological disaster scenario;

[0036] S400, after the new optical fiber is installed, the characteristic data is corrected according to the loss of the original optical fiber and the transmission characteristics of the new optical fiber, and a prediction model is constructed based on the corrected data;

[0037] S500: Combining the prediction model with environmental information to determine the type of disaster occurring in the target area.

[0038] In steps S100 and S200, the target area refers to a specific geographical area where geological disaster monitoring is required, including mountainous areas, mining areas, slopes, and areas around reservoirs. The monitoring requirement is a specific monitoring target formulated based on factors such as the geological disaster type, risk level, and monitoring accuracy of the target area. The existing optical fiber refers to the optical fiber that has been installed and is operating normally in the distributed optical fiber vibration sensing system in the target area. The system distribution path refers to the physical laying route of the existing optical fiber in the target area, including the direction, laying method, and key nodes. The area to be expanded refers to an area where additional optical fiber is required for supplementary monitoring due to changes in monitoring requirements and increased geological disaster risks. The geomorphic characteristics refer to the topographic and geomorphic attributes of the area to be expanded, including slope, vegetation coverage, hydrological conditions, and rock exposure rate. The geological risk level is the geological disaster risk level assessed based on current environmental inducing factors and historical geological disaster data in the area to be expanded. The newly added optical fiber is a monitoring optical fiber newly laid for the area to be expanded based on the existing distributed optical fiber vibration sensing system based on the monitoring requirements and the geological risk level. The actual distribution path is the specific physical laying route of the newly added optical fiber determined based on the geomorphic characteristics and geological risk level of the area to be expanded.

[0039] In step S300 and step S400, the virtual disaster scenario test is to test the response capability of the distributed fiber optic vibration sensing system by using typical geological disaster events generated by numerical simulation. The geological disaster scenario refers to the specific disaster type and its evolution process that may occur in the target area, such as the "creep-acceleration-collapse" process of a landslide. The characteristic data refers to the signal characteristics of the disaster event obtained through the virtual disaster scenario test in the fiber optic sensor, including parameters such as vibration frequency, amplitude, phase change rate and signal propagation speed. The loss condition refers to the degree of attenuation of the optical signal caused by aging, bending, joint defects, etc. of the original optical fiber. The transmission characteristics refer to the propagation law of the vibration signal in the newly added optical fiber, which is usually related to factors such as the laying position, laying method and burial depth of the newly added optical fiber. The correction data is the data after the characteristic data is corrected according to the original optical fiber loss condition and the transmission characteristics of the newly added optical fiber. The prediction model is a machine learning model constructed based on the corrected data, which is used to identify disaster characteristics in real-time signals and predict disaster types.

[0040] For example, when the area to be expanded is a landslide monitoring area, the different burial depths of the newly added optical fiber will affect its transmission characteristics. If the newly added optical fiber is buried at shallow and deep depths on the sliding surface, the shallowly buried optical fiber is more sensitive to the high-frequency vibration signal generated on the surface, and the signal amplitude attenuates with increasing burial depth.

[0041] It should be noted that the deployment and installation of new optical fibers requires a construction period. Before installing the new optical fibers, virtual disaster scenario tests are first carried out on the system distribution paths of the original optical fibers to obtain characteristic data corresponding to various geological disaster scenarios and form an initial signal characteristic library. After the new optical fibers are installed, although virtual disaster scenario tests can be directly carried out on them to obtain more accurate characteristic data, comprehensive repeated testing requires simulating massive disaster scenarios, and the amount of calculation increases exponentially, which may lead to data processing delays and affect the efficiency of real-time warnings. Frequent testing may also cause physical damage to the optical fiber itself. Therefore, by analyzing the loss of the original optical fiber and the transmission characteristics of the new optical fiber and making targeted corrections to the initial characteristic data, the operating efficiency of the expanded optical fiber sensing system can be significantly improved while ensuring monitoring accuracy.

[0042] In step S500, environmental information refers to real-time environmental data in the target area, including meteorological data, human activities, animal activities and geological disaster precursor information, etc. The disaster type is the specific type of geological disaster determined based on the abnormal parameter set, including earthquakes, landslides, collapses, mudslides and ground subsidence karst collapse.

[0043] It should be noted that in the geological disaster monitoring method, environmental information refers to all external conditions in the target area that affect the signal interpretation of the distributed fiber optic vibration sensing system. It includes both disaster activity information directly related to the gestation and occurrence of geological disasters, and non-disaster external interference factors that may interfere with signal analysis. Although these interference factors do not directly cause geological disasters, they may interfere with the normal fluctuations of the vibration signal and affect the accuracy and reliability of the prediction results. Therefore, the prediction model needs to be combined with environmental information.

[0044] Clarifying monitoring requirements and obtaining system distribution paths will help guide the design of the actual distribution paths of new optical fibers, ensure that the expanded optical fiber vibration sensing system matches actual needs, avoid redundancy or omissions, accurately identify the areas to be expanded, and focus the new optical fibers on new geological disaster-prone areas and existing monitoring blind spots, thereby improving the integrity of the optical fiber monitoring network. The layout of the new optical fibers will be optimized in combination with the topographic characteristics to make the actual distribution paths more consistent with the actual geological conditions. The new optical fibers will be configured based on the quantitative results of the geological risk level to achieve scientific planning of the actual distribution paths. Disaster signal characteristic data will be accumulated through virtual disaster scenario testing to provide a "standard sample" for the prediction model. Automatic identification of disaster signals will be achieved by comparing real-time signal data with characteristic data. The influence of optical fiber physical properties on vibration signals will be effectively eliminated by calculating the loss situation. Correcting characteristic data through transmission characteristics will help eliminate the vibration signal response deviation of the new optical fibers caused by differences in the laying environment, unify the quantitative benchmark of vibration signals, and the combination of environmental information and prediction models can effectively avoid single signal misjudgment and enhance the environmental adaptability of the prediction system.

[0045] [Second embodiment]

[0046] See also Figure 1 In a specific embodiment, the monitoring requirements in the target area are obtained, and the area to be expanded is determined based on the monitoring requirements and the system distribution path of the original optical fiber in the distributed optical fiber vibration sensing system, which specifically includes:

[0047] S110. Identify monitoring requirements based on geological exploration data and historical disaster records in the target area;

[0048] S120, constructing a three-dimensional monitoring coverage model of the original optical fiber based on the monitoring performance indicators of the original optical fiber and the terrain shielding effect;

[0049] S130. Determine new risk areas based on the three-dimensional monitoring coverage model and monitoring requirements;

[0050] S140, screening out monitoring blind spots and inefficient areas based on the three-dimensional monitoring coverage model;

[0051] S150. Determine the area to be expanded based on the newly added risk areas, monitoring blind spots, and inefficient areas.

[0052] In steps S110 and S120, geological exploration data refers to basic data such as stratigraphic structure, geotechnical parameters, and hydrogeological characteristics obtained through geophysical exploration, drilling sampling, and remote sensing mapping. Historical disaster records refer to complete records of geological disasters that occurred in the target area within a historical period, including data such as the time of occurrence, location, scale, and inducing factors. Monitoring performance indicators are technical parameters used to describe the performance and capabilities of existing optical fiber monitoring, usually including parameters such as attenuation coefficient, signal-to-noise ratio, spatial resolution, and monitoring node density. Terrain shielding effect refers to the blocking or attenuation effect of the topographic features in the target area on the propagation of vibration signals in the original optical fiber. The three-dimensional monitoring coverage model is a visual three-dimensional model constructed based on the spatial coordinates of the original optical fiber, monitoring performance indicators, and terrain shielding effect.

[0053] In steps S130 to S150, newly added risk areas refer to potential geological disaster areas that were not covered when the original optical fiber was deployed and newly emerged areas in the later period. Monitoring blind spots refer to areas that the original optical fiber cannot effectively monitor. Usually, the signal strength monitored by the original optical fiber in the monitoring blind spot is lower than the monitoring threshold. Inefficient areas refer to areas where the resolution of the original optical fiber is lower than the preset accuracy or the node density is insufficient.

[0054] Through geological exploration and historical disaster data, we can identify the potential disaster types and high-risk areas in the target area, reasonably define monitoring needs, dynamically identify new risk areas and include them in monitoring, reduce response lags when geological disasters occur, and screen and cover monitoring blind spots and inefficient areas. This helps ensure that the accuracy and reliability of the entire monitoring system meet monitoring needs and comprehensively improve the integrity and early warning efficiency of the fiber optic monitoring network.

[0055] [Third embodiment]

[0056] See also Figure 2 In a specific embodiment, the actual distribution path of the newly added optical fibers is determined based on the topographical features and geological risk level of the area to be expanded, specifically including:

[0057] S210. Determine the distribution density of newly added optical fibers based on the geological risk level and the frequency of disasters.

[0058] S220 determines a theoretical distribution path of the newly added optical fiber based on the topographical features and distribution density of the area to be expanded;

[0059] S230 performs a virtual disaster scenario test on the three-dimensional model of the theoretical distribution path, adjusts the theoretical distribution path according to the preview results, and determines the actual distribution path.

[0060] In steps S210 to S230, the frequency of disaster occurrence refers to the number of geological disasters that occur in the area to be expanded per unit time; the distribution density refers to the laying spacing of new optical fibers or the number of monitoring nodes per unit area of ​​the area to be expanded; the theoretical distribution path is the new optical fiber laying route preliminarily planned based on the distribution density and topographic features; the three-dimensional model refers to a three-dimensional digital model that integrates elements such as topographic features, optical fiber distribution density, and optical fiber physical parameters; the preview result refers to the monitoring performance indicators of the new optical fiber output after the three-dimensional model is tested in a virtual disaster scenario, which usually include indicators such as signal-to-noise ratio, spatial resolution, and false alarm probability; the actual distribution path refers to the final deployment plan of the new optical fiber after preview verification and optimization.

[0061] For example, if a hillside in the area to be expanded has historically experienced multiple landslide disasters with high risk levels, the new optical fibers will be densely arranged in valleys or steep slopes where landslides are frequent, thereby determining the theoretical distribution path of the new optical fibers. When simulating landslide disasters based on the three-dimensional model of the theoretical distribution path, if the preview results show that a certain area has caused signal attenuation due to terrain shielding, the path of the new optical fibers will need to be adjusted to avoid terrain with a strong shielding effect, thereby obtaining the actual distribution path of the new optical fibers.

[0062] The distribution density of new optical fibers is determined by the geological risk level and the frequency of disasters. This ensures the monitoring accuracy of the area to be expanded while enhancing the timeliness of early warning. The theoretical distribution path is planned in combination with the topographic characteristics and distribution density to ensure that the layout of the new optical fibers fits the terrain conditions and maximizes the coverage of potential disaster signal propagation paths. By simulating the impact of disasters in a three-dimensional model of the theoretical distribution path, potential problems in the theoretical distribution path of the new optical fibers can be identified in advance and optimized and adjusted, further improving the accuracy and adaptability of the fiber optic sensing system.

[0063] [Fourth embodiment]

[0064] See also Figure 3 In a specific embodiment, after a new optical fiber is installed, the characteristic data is corrected according to the loss of the original optical fiber and the transmission characteristics of the new optical fiber, and a prediction model is constructed based on the corrected data, specifically including:

[0065] S410, dividing the original optical fiber into a plurality of loss sections according to the number of turning points;

[0066] S420, calculating the attenuation coefficient of each loss section according to the turning degree of the loss section and the length of the optical fiber;

[0067] S430, calculating the loss coefficient of the original optical fiber based on the attenuation coefficient of each loss section and the service life;

[0068] S440: Correct the characteristic data according to the loss coefficient and the transmission characteristics of the newly added optical fiber, and construct a prediction model based on the corrected data.

[0069] In step S410 and step S420, the number of turning points refers to the number of locations where the signal transmission characteristics of the original optical fiber change significantly. The turning points are usually located at locations such as terrain changes, joint connections, and equipment access points in the system distribution path. The loss segment refers to the optical fiber segment with relatively uniform signal loss characteristics obtained by dividing the adjacent turning points in the laying direction of the original optical fiber. The turning degree refers to the bending angle of the optical fiber at the turning point. The optical fiber length refers to the actual physical length of each loss segment. The attenuation coefficient refers to the signal power attenuation per unit length of optical fiber in the loss segment. The attenuation coefficient k d The calculation formula is as follows:

[0070] .

[0071] Among them, p i is the input power of the optical signal, p o is the output power of the optical signal in dBm, L is the fiber length in km, β is the bending loss coefficient, usually between 0.001 and 0.005 dB / °, and θ is the turning degree.

[0072] In step S430 and step S440, the service life refers to the accumulated time after the original optical fiber is put into use, and the loss coefficient is a quantitative parameter of the signal loss of the original optical fiber obtained by combining the attenuation coefficient and the aging factor. The loss coefficient k a The calculation formula is as follows:

[0073] k a =k d × (1 + c × t).

[0074] Among them, c is the aging coefficient, which usually ranges from 0.005 to 0.03, and t is the service life.

[0075] It should be noted that due to the complexity of the optical fiber laying method and actual environmental conditions, directly measuring the loss of the entire optical fiber section is costly and difficult to implement. Therefore, the optical fiber loss is usually estimated through simulation calculations or theoretical estimates. However, due to factors such as changes in the external environment of the optical fiber and hidden engineering defects during laying, the estimated results may deviate significantly from the actual loss. Therefore, in order to improve the prediction accuracy of the optical fiber sensing system, the actual loss of the original optical fiber can be measured simultaneously when installing the new optical fiber.

[0076] By accurately dividing the "loss homogeneous sections" through turning points, independent analysis of sections with different physical states can be achieved, avoiding errors caused by global averaging. The precise attenuation coefficient of each loss section is calculated by combining the turning degree and the fiber length, providing data support for subsequent characteristic data correction. The loss coefficient is calculated by combining the attenuation coefficient and the service life, thereby achieving loss compensation for old optical fibers and improving the monitoring accuracy of the fiber optic sensing system.

[0077] [Fifth embodiment]

[0078] See also Figure 3 In a specific embodiment, the characteristic data is corrected according to the loss coefficient and the transmission characteristics of the newly added optical fiber, and a prediction model is constructed based on the corrected data, specifically including:

[0079] S441. Determine the installation location based on the actual distribution path of the newly added optical fiber, and divide the sub-installation modules according to the topographical features of the installation location;

[0080] S442. Calculate the actual transmission efficiency of the vibration signal based on the transmission characteristics of the sub-installation module;

[0081] S443. Obtain the standard transmission efficiency of the newly added optical fiber under a standard state, and calculate the deviation coefficient of the newly added optical fiber based on the actual transmission efficiency and the standard transmission efficiency;

[0082] S444. Calculate correction data based on the deviation coefficient, loss coefficient and characteristic data, and construct a prediction model based on the correction data.

[0083] In step S441 and step S442, the installation location refers to the specific coordinates and laying direction of the newly added optical fiber in the area to be expanded. The sub-installation module is an independent analysis unit divided according to the topographic features of the installation location. The vibration signal is a waveform signal carrying the physical characteristics of the mechanical vibration event monitored by the optical fiber sensing system. The actual transmission efficiency refers to the signal transmission effect of the vibration signal in the sub-installation module. The calculation parameters of the actual transmission efficiency usually include signal-to-noise ratio, polarization stability and spatial resolution.

[0084] For example, when the area to be expanded is plain farmland and new optical fibers are buried to monitor soil subsidence disasters, if the plain area includes clay farmland, sandy irrigation area, and concrete road crossing area, then the sub-installation module can be divided into clay farmland buried section, sandy irrigation buried section, and road pipe section.

[0085] In step S443 and step S444, the standard state refers to the standard installation condition that complies with industry installation specifications, has no significant structural defects, and the environmental parameters are within the design allowable range. Usually, under the standard state, the transmission performance of the optical fiber meets the design expectations. The standard transmission efficiency refers to the theoretical transmission efficiency of the newly added optical fiber under the standard state. The deviation coefficient represents the actual transmission efficiency e a With standard transmission efficiency e s The degree of deviation, the coefficient of deviation k z The calculation formula is as follows:

[0086] k z =(e a -e s )÷e s .

[0087] By analyzing the topographic features, the area to be expanded can be divided into multiple homogeneous sub-installation modules, so that the transmission characteristics of the newly added optical fiber in each sub-installation module can be obtained more accurately, which helps to accurately evaluate the transmission performance of the optical fiber in different modules. By calculating the deviation coefficient, the impact of installation environment factors on the transmission performance of the newly added optical fiber can be identified and compensated, thereby eliminating the signal transmission deviation between different sub-modules. Constructing a prediction model based on the corrected data can improve the adaptability of the prediction model to complex geological environments, thereby improving the accuracy and reliability of the prediction results.

[0088] [Sixth embodiment]

[0089] See also Figure 4 In a specific embodiment, the prediction model is combined with environmental information to determine the type of disaster occurring in the target area, specifically including:

[0090] S510: When the environmental information has not changed, the real-time signal data monitored by the newly added optical fiber and the original optical fiber are input into the prediction model to determine the type of geological disaster;

[0091] S520: When the environmental information changes, obtain an interference signal generated by the environmental information change, and adjust the prediction model according to the interference signal to obtain an adjusted model;

[0092] S530: Determine an abnormal parameter set based on the adjustment model and the real-time signal data, and determine the type of geological disaster based on the abnormal parameter set.

[0093] In step S510 and step S520, the real-time signal data refers to the vibration signal sequence collected from the newly added optical fiber and the original optical fiber sensor, which reflects the current environmental status of the target area. The interference signal refers to the non-disaster vibration signal that will affect the accuracy of the expanded optical fiber sensing system and interfere with disaster warning. The interference signal is usually caused by changes in environmental information, such as human engineering activities. The adjustment model refers to a correction model that can adapt to the changed environment by incorporating the interference signal characteristics into the original prediction model when the environmental information changes.

[0094] It should be noted that environmental changes can cause the characteristic distribution of real-time signal data to shift by changing the physical properties of the geological body or introducing external interference. This shift may manifest as a structural change in the signal components, such as the addition of periodic interference or the disappearance of the original background noise. When there is a long-term stable interference source in the environment, its interference signal will form a specific time-frequency-amplitude distribution pattern. The prediction model has learned and adapted to this normal operating condition containing interference during construction to ensure the accurate extraction of geological disaster signals. However, if the interference source disappears temporarily, the specific frequency component generated by the interference signal will drop sharply, resulting in a significant change in the frequency distribution in the real-time signal. If the prediction model does not synchronously update the characteristic shift caused by this environmental change, it may mistakenly regard the frequency characteristics of the disaster signal as a normal operating condition, thereby missing the opportunity for early warning and resulting in missed detection. Therefore, environmental changes will affect the prediction results of the prediction model by affecting the real-time signal data.

[0095] For example, when there is a high-speed railway section in the target area, there is a regular pulse vibration signal in the 20~80Hz frequency band between 6:00 and 23:00 every day. The prediction model has set the interference tolerance window for this period when it is constructed, allowing periodic signals with an amplitude within 20dB in this frequency band to not trigger an alarm. When the high-speed railway is temporarily shut down for maintenance, the energy in the 10~50Hz frequency band is significantly reduced, but the prediction model still uses the interference tolerance threshold set when the high-speed railway is operating normally. At this time, if the amplitude of the geological disaster signal falls in the 20~80Hz frequency band and the amplitude is less than 20dB, the prediction model will mistakenly judge it as a normal signal within the high-speed railway interference range, thereby ignoring its disaster warning significance, and ultimately leading to missed detection.

[0096] In step S530, the abnormal parameter set refers to a parameter set extracted from the real-time signal that significantly deviates from the normal data pattern. The normal data pattern refers to the normal behavior pattern of the signal data obtained through long-term monitoring when no geological disasters occur in the target area after the environmental information changes.

[0097] When environmental parameters do not change, real-time signal data can be directly input into the prediction model, avoiding the complex interference identification and model adjustment process, shortening the disaster judgment time, accurately extracting interference signals according to environmental changes and dynamically adjusting the prediction model, avoiding model failure due to sudden environmental changes, and reducing the risk of missed detection of geological disasters. By adjusting the model and real-time signal data to determine abnormal parameters, it helps to accurately capture and reliably classify geological disaster signals.

[0098] [Seventh embodiment]

[0099] See also Figure 4 In a specific embodiment, when environmental information changes, an interference signal generated by the environmental information change is obtained, and the prediction model is adjusted according to the interference signal to obtain an adjusted model, which specifically includes:

[0100] S521. Obtain a monitoring performance indicator affected by the interference signal, and mark the monitoring performance indicator as an indicator to be adjusted;

[0101] S522. The signal type and trigger frequency of the interference signal are updated to the index to be adjusted, and recorded as the modified performance index;

[0102] S523, obtaining the geological disaster scenario affected by the revised performance index as a revised scenario, and updating the revised data corresponding to the revised scenario in the prediction model;

[0103] S524: Adjust the prediction model according to the updated correction data to obtain an adjusted model.

[0104] In steps S521 to S524, the indicators to be adjusted refer to those monitoring performance parameters affected by the interference signal, usually including attenuation coefficient, signal-to-noise ratio and spatial resolution, etc. The signal type refers to the physical property classification of the interference signal, the trigger frequency refers to the number of times the interference signal appears per unit time and the time regularity, the corrected performance indicator is the real-time performance parameter after the interfered indicator to be adjusted is updated according to the signal type and trigger frequency, and the correction scenario refers to the specific geological disaster monitoring scenario affected by the corrected performance indicator.

[0105] It should be noted that interference signals are divided into two types: fixed signals and random signals based on their temporal and spatial regularity characteristics. Fixed signals refer to interference with clear periodicity, repetitiveness or stable frequency characteristics, and random signals refer to interference without a fixed period, with random changes in frequency or amplitude. The triggering frequency of fixed signals is usually obtained through historical data, and the triggering frequency of random signals is usually obtained through real-time signal monitoring data. The correction performance indicators reflect the impact of changes in environmental information on specific geological disasters. By analyzing historical disaster data and correction performance indicators, the correction scenarios that need to be adjusted can be determined.

[0106] For example, when there is no high-speed rail in the target area, the signal type of the interference signal after the high-speed rail passes through is a fixed signal, while the interference signal generated by occasional animal group activities in the target area is a random signal.

[0107] By extracting the monitoring performance indicators affected by interference and converting the interference impact into quantifiable indicators to be adjusted, it is helpful to accurately locate the adjustment targets of the prediction model, thereby avoiding blind adjustment of irrelevant parameters and making the optimization direction of the prediction model more targeted. Combining the signal type and trigger frequency of the interference signal to dynamically generate correction performance indicators can effectively capture the impact of external environmental changes on the performance of the prediction model. This precise parameter adjustment based on the characteristics of the interference signal ensures that the prediction model can still maintain the accuracy and timeliness of the prediction in a complex environment. By mapping the correction performance indicators to specific correction scenarios, the correction data of the corresponding scenarios in the prediction model can be updated in a targeted manner, ensuring that the prediction model adapts to the new environment while avoiding the over-correction problem that may be caused by global parameter adjustment, thereby maintaining the stability and adaptability of the prediction model and improving the prediction accuracy.

[0108] [Eighth embodiment]

[0109] See also Figure 4 In a specific embodiment, determining an abnormal parameter set based on the adjustment model and the real-time signal data, and determining the type of geological disaster based on the abnormal parameter set specifically includes:

[0110] S531. Determine the type of disaster that may occur in the target area based on each abnormal data in the abnormal parameter set, and record it as a theoretical type;

[0111] S532. Obtain the fluctuation range of abnormal data when all theoretical types occur from the historical disaster database;

[0112] S533. When the abnormal data does not exceed the fluctuation range, the disaster type in the target area is determined to be a single type, and the disaster type is determined based on the degree of fit between the abnormal data and the fluctuation ranges of different disaster types;

[0113] S534. When the abnormal data exceeds the fluctuation range, the abnormal data exceeding the fluctuation range is recorded as combined data, and the combined data is matched according to the disaster type corresponding to the combined data to determine the combination method of multiple disaster types.

[0114] In steps S531 to S533, abnormal data is the basic unit that constitutes the abnormal parameter set. The theoretical type is the potential disaster type preliminarily inferred based on the abnormal data. The historical disaster database refers to an archive that stores the complete parameters of various geological disaster events in history, usually including parameters such as the disaster's impact radius, signal characteristics, and signal fluctuation range. The fluctuation range refers to the variation range of abnormal data corresponding to each geological disaster in the historical disaster database. A single type indicates that when the abnormal data does not exceed the fluctuation range, the type of disaster that may occur in the target area is unique. The degree of fit refers to the degree of matching between the abnormal data and the fluctuation range of various geological disaster signals.

[0115] It should be noted that in geological disaster monitoring, different disaster types often have corresponding abnormal data. For example, the abnormal data corresponding to earthquakes usually include the amplitude, frequency, duration of seismic waves, and ground displacement when the earthquake occurs. Various abnormal data from past geological disasters are collected to build a historical disaster database. The current abnormal parameter set can be obtained through real-time signal data, and multiple theoretical types of geological disasters can be preliminarily screened out. When the abnormal data corresponding to multiple theoretical types do not exceed the fluctuation range, the disaster type in the target area is determined to be a single type, and the degree of fit between the abnormal data and multiple theoretical types is calculated. The theoretical type with the highest degree of fit is the disaster type finally predicted by the adjusted model.

[0116] In step S534, the combined data refers to abnormal data that exceeds the fluctuation range of a single disaster, and the combined mode refers to the correlation combination pattern between multiple disaster types.

[0117] It should be noted that when the abnormal data exceeds the fluctuation range defined in the historical disaster database, the disaster type in the target area is determined to be a combination of multiple types. The combination of potential disaster types is determined by combining the data, and the degree of fit between the combined data and the combined disaster type is calculated. Based on the fit results, the most likely combination of disaster types and the corresponding combination method are determined.

[0118] For example, when abnormal data exceeds the fluctuation range, combined with the data of amplitude, frequency, ground deformation and soil moisture, it is inferred that the possible disaster types are earthquakes and landslides. The combination of disaster types can be divided into concurrent and induced types. When the degree of fit of the induced type is higher than that of the concurrent type, the disaster type and combination method finally predicted by the adjusted model is determined to be "earthquake-induced landslides."

[0119] Abnormal data, as the core element for identifying geological disaster risks, can capture characteristic parameters that deviate from the normal state in real-time signal data, provide real-time information for disaster prediction, and combine theoretical types with historical disaster databases to provide historical data support for disaster type prediction, which helps to reduce the prediction error of the adjustment model. The fluctuation range provides a judgment boundary for abnormal data, and the judgment mechanism of single type and degree of fit ensures that the adjustment model maintains efficiency and accuracy in most conventional scenarios. The determination of combined data and combination methods takes into account complex scenarios with multiple disaster types concurrently, making the adjustment model more adaptable and better able to cope with complex geological disaster situations.

[0120] Ninth embodiment

[0121] See also Figure 5 In one embodiment of the present invention, a geological disaster monitoring system 100 based on a distributed optical fiber vibration sensing system is also provided. The geological disaster monitoring method recorded in the above embodiment is applied to the geological disaster monitoring system 100. The geological disaster monitoring system 100 includes: a positioning module 110, the positioning module 110 is used to determine the area to be expanded according to the monitoring requirements and the system distribution path; a testing module 120, the testing module 120 is used to perform a virtual disaster scenario test on the system distribution path to obtain characteristic data corresponding to each geological disaster scenario; a correction module 130, the correction module 130 corrects the characteristic data according to the loss of the original optical fiber and the transmission characteristics of the newly added optical fiber; a prediction module 140, the prediction module 140 is used to predict the type of disaster occurring in the target area. The geological disaster monitoring system has all the technical features of the above-mentioned geological disaster monitoring method, which will not be described here one by one.

[0122] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention should be based on the scope defined by the claims.

Claims

1. A geological disaster monitoring method based on a distributed optical fiber vibration sensing system, characterized in that: The geological disaster monitoring method comprises: Obtaining monitoring requirements within a target area, and determining an area to be expanded based on the monitoring requirements and a system distribution path of an original optical fiber in the distributed optical fiber vibration sensing system; Determine the actual distribution path of the newly added optical fibers based on the topographical features and geological risk level of the area to be expanded; Conducting virtual disaster scenario testing on the system distribution path to obtain characteristic data corresponding to each geological disaster scenario; After the newly added optical fiber is installed, the original optical fiber is divided into a plurality of loss sections according to the number of turning points; Calculating the attenuation coefficient of each loss section according to the turning degree of the loss section and the length of the optical fiber; Calculating the loss coefficient of the original optical fiber according to the attenuation coefficient and service life of each loss section; Determine an installation location according to the actual distribution path of the newly added optical fiber, and divide the sub-installation modules according to the topographical features of the installation location; Calculating the actual transmission efficiency of the vibration signal based on the transmission characteristics of the newly added optical fiber in the sub-mounting module; Obtaining a standard transmission efficiency of the newly added optical fiber under a standard state, and calculating a deviation coefficient of the newly added optical fiber according to the actual transmission efficiency and the standard transmission efficiency; Calculating correction data according to the deviation coefficient, the loss coefficient and characteristic data, and constructing a prediction model according to the correction data; The prediction model is combined with environmental information to determine the type of disaster occurring in the target area.

2. The geological disaster monitoring method according to claim 1, characterized in that: The obtaining of monitoring requirements within the target area and determining the area to be expanded based on the monitoring requirements and the system distribution path of the original optical fiber in the distributed optical fiber vibration sensing system specifically includes: Determine the monitoring requirements based on geological exploration data and historical disaster records of the target area; Constructing a three-dimensional monitoring coverage model of the original optical fiber based on the monitoring performance index of the original optical fiber and the terrain shielding effect; Determining new risk areas based on the three-dimensional monitoring coverage model and the monitoring requirements; Screening out monitoring blind spots and inefficient areas according to the three-dimensional monitoring coverage model; The area to be expanded is determined according to the newly added risk area, the monitoring blind area and the inefficient area.

3. The geological disaster monitoring method according to claim 2, characterized in that: Determining the actual distribution path of the newly added optical fibers according to the topographical features and geological risk level of the area to be expanded specifically includes: Determining the distribution density of the newly added optical fibers according to the geological risk level and the frequency of disaster occurrence; Determining a theoretical distribution path of the newly added optical fiber according to the topographical features and the distribution density of the area to be expanded; The three-dimensional model of the theoretical distribution path is subjected to the virtual disaster scenario test, and the theoretical distribution path is adjusted according to the preview result to determine the actual distribution path.

4. The geological disaster monitoring method according to claim 3, characterized in that: Combining the prediction model with environmental information to determine the type of disaster occurring in the target area specifically includes: When the environmental information has not changed, the real-time signal data monitored by the newly added optical fiber and the original optical fiber are input into the prediction model to determine the disaster type of the geological disaster; When the environmental information changes, an interference signal generated by the change in the environmental information is acquired, and the prediction model is adjusted according to the interference signal to obtain an adjusted model; An abnormal parameter set is determined according to the adjustment model and the real-time signal data, and the disaster type of the geological disaster is determined according to the abnormal parameter set.

5. The geological disaster monitoring method according to claim 4, characterized in that: When the environmental information changes, obtaining an interference signal generated by the change in the environmental information, and adjusting the prediction model according to the interference signal to obtain an adjusted model, specifically includes: Obtaining the monitoring performance indicator affected by the interference signal, and marking the monitoring performance indicator as an indicator to be adjusted; Updating the indicator to be adjusted according to the signal type and trigger frequency of the interference signal, and recording it as a modified performance indicator; Obtaining the geological disaster scenario affected by the revised performance index as a revised scenario, and updating the revised data corresponding to the revised scenario in the prediction model; The prediction model is adjusted according to the updated correction data to obtain the adjusted model.

6. The geological disaster monitoring method according to claim 5, characterized in that: Determining an abnormal parameter set according to the adjustment model and the real-time signal data, and determining the disaster type of the geological disaster according to the abnormal parameter set specifically includes: Determine the type of disaster that may occur in the target area according to each abnormal data in the abnormal parameter set, and record it as a theoretical type; Obtaining the fluctuation range of the abnormal data when all the theoretical types occur from the historical disaster database; When the abnormal data does not exceed the fluctuation range, the disaster type in the target area is determined to be a single type, and the disaster type is determined according to the degree of fit between the abnormal data and the fluctuation ranges of different disaster types; When the abnormal data exceeds the fluctuation range, the abnormal data exceeding the fluctuation range is recorded as combined data, and the disaster type corresponding to the combined data is matched to determine a combination method of multiple disaster types.

7. A geological disaster monitoring system based on a distributed optical fiber vibration sensing system, characterized in that: The geological disaster monitoring method according to any one of claims 1 to 6 is applied to the geological disaster monitoring system, wherein the geological disaster monitoring system comprises: A positioning module, configured to determine the area to be expanded based on the monitoring requirements and the system distribution path; A testing module, the testing module is used to perform the virtual disaster scenario test on the system distribution path to obtain the characteristic data corresponding to each geological disaster scenario; a correction module, wherein the correction module corrects the characteristic data according to the loss of the original optical fiber and the transmission characteristics of the newly added optical fiber; A prediction module is used to predict the type of disaster occurring in the target area.

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