A weak grating array sensing landslide state observation system

Abstract

The application discloses a weak light grating array sensing landslide state observation system and belongs to the field of landslide state observation. The method comprises the following steps: a risk division unit is used for dividing the risk levels of landslide disaster areas; an equipment deployment unit is used for deploying multiple weak light grating sensing array monitoring optical cables according to the risk levels of various monitoring areas; a data acquisition unit is used for adaptively adjusting a sampling time interval and acquiring monitoring information of the multiple weak light grating sensing array monitoring optical cables; a threshold criterion unit is used for correcting the threshold criterion of multiple monitoring parameters; a risk research and judgment unit is used for researching and judging the risk of landslide disasters to obtain risk research and judgment indexes of various monitoring points; and a warning and prediction unit is used for determining a warning and prediction level according to the risk research and judgment indexes of various monitoring points and issuing warning and prediction information based on corresponding measures of the warning and prediction level. The application solves the problem that the existing method cannot meet the timeliness and accuracy monitoring of landslides.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of landslide state observation, and particularly relates to a weak grating array sensing landslide state observation system. BACKGROUND

[0002] The previous landslide disaster monitoring method mainly deploys corresponding rainfall, crack, ground displacement and other monitoring sensors on the upper part of the landslide to obtain landslide multi-source sensing data. Limited by on-site sensing data acquisition and analysis processing technology, the timeliness and accuracy of the traditional landslide disaster monitoring and early warning are relatively low, and there is a significant shortcoming in the timeliness of issuing warning information and developing emergency rescue. As a new sensing technology, optical fiber has been widely used in landslide monitoring, especially in the past two years, the weak grating sensing array monitoring technology has developed rapidly. By deploying weak grating sensing array optical cables on the surface of the landslide and inside the borehole, the temperature, seepage pressure, strain, vibration and other multi-field physical quantity parameters of the surface and deep part of the landslide can be effectively captured. The weak grating sensing can timely and accurately perceive the subtle changes of the landslide by virtue of its technical advantages of high sensitivity and high accuracy. The monitoring data captured by the weak grating sensing can discover the geological change phenomenon in the pre-disaster period and the deformation process of the landslide, and effectively improve the monitoring and early warning accuracy of the landslide disaster.

[0003] The prior art discloses a device for real-time measurement of slope deformation based on an ultra-weak optical fiber grating array sensing system. The device mainly comprises a data acquisition and transmission system, an ultra-weak optical fiber grating demodulation system, an ultra-weak optical fiber grating cable and a wire reel. Temperature and strain optical fibers are deployed on the surface of the slope in different ways, and temperature and strain data are obtained by a demodulator to realize long-term monitoring of the surface deformation and diseases of the slope.

[0004] The above method can basically realize the monitoring of the slope, but the above method also has the following disadvantages: first, the weak grating optical cable only covers the surface of the slope, and the monitoring parameters are limited. The landslide is often caused by internal deformation, and the monitoring method has certain limitations. Second, during the pre-disaster period of the landslide, deformation or rock rupture may occur near the sliding zone, and vibration signals may be generated. The above method cannot capture the deep deformation and vibration signals generated during the deformation process, and the monitoring method is not effective. Third, the above method only simply introduces the idea of slope monitoring, and does not realize accurate monitoring and early warning of the landslide by combining with actual monitoring data analysis, and cannot meet the timely and accurate monitoring of the landslide. SUMMARY

[0005] In view of the above deficiencies in the prior art, the weak grating array sensing landslide state observation system provided by the present application solves the problem that the existing method cannot meet the timely and accurate monitoring of the landslide.

[0006] In order to achieve the above-mentioned purpose of the application, the technical scheme adopted by the application is as follows: a weak light grating array sensing landslide state observation system, comprising a risk division unit, an equipment deployment unit, a data acquisition unit, a threshold criterion unit, a risk research and judgment unit and an early warning and prediction unit;

[0007] The risk division unit is used for dividing the risk levels of the landslide disaster areas to obtain the risk levels of each monitoring area of the landslide disaster areas.

[0008] The equipment deployment unit is used for determining the number of monitoring points according to the risk levels of each monitoring area, and deploying a multi-field weak light grating sensing array monitoring optical cable for sensing the changes of multi-field parameters of the landslide surface and deep part at each monitoring point.

[0009] The data acquisition unit is used for adaptively adjusting the sampling time interval according to the actual changes of the landslide, and acquiring the monitoring information of the multi-field weak light grating sensing array monitoring optical cable according to the sampling time interval.

[0010] The threshold criterion unit is used for dynamically correcting the threshold criterion of the multi-field monitoring parameters according to the real-time landslide risk.

[0011] The risk research and judgment unit is used for researching and judging the risk of landslide disaster according to the real-time monitoring information and the corrected threshold criterion of the multi-field monitoring parameters, to obtain the risk research and judgment index of each monitoring point.

[0012] The early warning and prediction unit is used for determining the early warning and prediction level according to the risk research and judgment index of each monitoring point, and issuing early warning and prediction information based on the corresponding measures of the early warning and prediction level.

[0013] The application has the following beneficial effects: the weak light grating sensing array monitoring technology is fully utilized to realize real-time intelligent monitoring of multi-field monitoring parameters such as landslide temperature, osmotic pressure, strain and vibration, which can effectively cover the deformation area of the landslide surface and deep part, especially the deformation displacement and vibration signals generated in the pre-disaster and deformation process of the landslide, the weak light grating sensing array can effectively capture the subtle changes of the landslide rock structure, the adaptive dynamic threshold adjustment can efficiently operate the landslide monitoring system, and the landslide monitoring pertinence can be effectively improved. A landslide disaster risk research and judgment method based on temperature, osmotic pressure, strain and vibration parameters is constructed, especially the vibration parameters are refined into four characteristics of vibration frequency, amplitude, main frequency and power spectral density, the single-point risk index is obtained through the normalized anomaly index and weighted calculation, the regional comprehensive risk index is calculated by combining the spatial weight, and a more comprehensive and scientific risk assessment system is constructed, which effectively improves the timeliness of landslide disaster monitoring and early warning, and provides strong technical support for the landslide monitoring and early warning system.

[0014] Further, the expression of the adaptive adjustment of the sampling time interval is as follows:

[0015]

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023] wherein, is the new acquisition time interval; is the original acquisition time interval; is a frequency adjustment coefficient for controlling the degree of influence of the anomaly degree on the sampling frequency; is a parameter-specific adjustment factor; is the actual change of landslide; is the rate of change of temperature anomaly; is the weight of ; is the rate of change of osmotic pressure anomaly; is the weight of ; is the rate of change of strain anomaly; is the weight of ; is the rate of change of vibration anomaly; is the weight of ; is the current temperature value; is the temperature value at the previous time point; is the historical average temperature; is the current osmotic pressure value; is the osmotic pressure value at the previous time point; is the historical average osmotic pressure; is the current strain value; is the strain value at the previous time point; is the historical average strain; is the number of vibrations within the current time window; is the number of vibrations within the previous time window; is the historical average number of vibrations; is a parameter-specific sensitivity coefficient.

[0024] The beneficial effect of the further scheme is that through real-time monitoring of different monitoring parameters of the landslide, the change condition of different parameters can be dynamically understood, and the sampling time interval of each monitoring parameter can be reasonably adjusted to achieve adaptive adjustment of the sampling time of the landslide monitoring parameter, the monitoring data with changes can be focused on, and the power consumption of the on-site monitoring equipment can be effectively saved, and the pertinence and timeliness of the landslide monitoring can be further improved.

[0025] Further, the expression of the threshold criterion of the modified multi-field monitoring parameter is:

[0026]

[0027]

[0028] wherein, is the threshold criterion of the monitoring parameter after modification; is a reference threshold value set initially; is a threshold adjustment coefficient; is an actual change of the landslide; is a change amount of the monitoring parameter; is a basic threshold value of the monitoring parameter; is a smoothing factor; is a threshold criterion of the monitoring parameter at a previous sampling time; is a current monitoring parameter value; is a monitoring parameter value at a previous time point.

[0029] The beneficial effect of the further scheme is that in order to realize real-time dynamic adjustment of the abnormal discrimination threshold values of the temperature, seepage pressure, strain and vibration four parameters in the landslide monitoring, an adaptive threshold criterion formula is designed, which can dynamically correct the threshold values of the four parameters in combination with the current sampling frequency, abnormal index, parameter change rate and the like, so as to provide more reliable and effective data reference basis for judging the landslide risk level.

[0030] Further, the expression of the risk research index of each monitoring area is:

[0031]

[0032]

[0033]

[0034]

[0035]

[0036] wherein, is the risk research index of the i th monitoring area; Risk assessment index for each monitoring point; This is the weighting coefficient for temperature; This is the weighting coefficient for osmotic pressure; For the weighting coefficients of strain; The weighting coefficient for vibration; For the first Normalized anomaly index of temperature at each monitoring point; For the first Normalized anomaly index of seepage pressure at each monitoring point; For the first Normalized anomaly index of strain at each monitoring point; For the first Normalized anomaly index of vibration at each monitoring point; Weights for the number of vibration events; For the first Normalized anomaly index of the number of vibration events at each monitoring point; The weights for the intensity and energy of the vibration event; For the first Normalized anomaly index of intensity energy of vibration events at each monitoring point; The weight of the dominant frequency of the vibration event; For the first The normalized exponent of the dominant frequency of vibration events at each monitoring point; The weight of the total vibration energy of the vibration event; For the first The normalized index of the total vibration energy of a vibration event at a monitoring point; For the first Normalized anomaly index of monitoring parameters at each monitoring point; for The corresponding threshold criteria after the monitoring parameters are corrected; This serves as a reference average for the monitored parameters; This serves as the reference standard deviation for the monitored parameters.

[0037] The beneficial effects of the above-mentioned further scheme are as follows: adopting a linear weighted form facilitates the understanding and implementation of landslide risk level assessment; by covering multiple fields of information such as temperature, seepage pressure, strain, and vibration, it effectively covers multi-source sensing and multi-parameter monitoring of landslides; and by combining the weights of different monitoring parameters, it effectively focuses on the key monitoring parameters, thereby better realizing landslide risk assessment.

[0038] Furthermore, the expression for the early warning and forecast level is:

[0039]

[0040]

[0041] in, The level of early warning and forecast; This is the sum of the number of monitoring points in each monitoring area; For the first Spatial weights of each monitoring point; For the first Risk assessment index for each monitoring point.

[0042] The beneficial effects of the above-mentioned further scheme are as follows: by classifying landslide monitoring points into different risk categories, it is beneficial to comprehensively understand the risk assessment of landslide disasters in different areas. By utilizing the spatial weights of different monitoring points, the risk index of different landslide areas can be effectively calculated, thereby improving the accuracy and comprehensiveness of landslide risk assessment. The formula system has clear logic and explicit calculation, and is suitable for the comprehensive assessment of landslide risks at multiple monitoring points, which can provide a scientific basis for landslide early warning systems. Attached Figure Description

[0043] Figure 1 This is a system architecture diagram of the present invention. Detailed Implementation

[0044] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0045] like Figure 1 As shown, in one embodiment of the present invention, a weak grating array sensing landslide status observation system includes a risk classification unit, an equipment deployment unit, a data acquisition unit, a threshold criterion unit, a risk assessment unit, and an early warning and forecasting unit.

[0046] The risk classification unit is used to classify the risk level of the landslide disaster area and obtain the risk level of each monitoring area in the landslide disaster area.

[0047] The equipment deployment unit is used to determine the number of monitoring points according to the risk level of each monitoring area, and to deploy a multi-field weak grating sensor array monitoring optical cable at each monitoring point to sense the changes in multiple field parameters on the surface and deep part of the landslide.

[0048] The data acquisition unit is used to adaptively adjust the sampling time interval according to the actual changes of the landslide, and to acquire the monitoring information of the multi-field weak grating sensor array monitoring optical cable according to the sampling time interval.

[0049] The threshold criterion unit is used to dynamically adjust the threshold criteria of multiple monitoring parameters based on real-time landslide risk.

[0050] The risk judgment unit is configured to judge the risk of landslide disaster according to the real-time monitoring information and the threshold criterion of the revised multi-field monitoring parameters, and obtain a risk judgment index of each monitoring point.

[0051] The early warning and prediction unit is configured to determine an early warning and prediction level according to the risk judgment index of each monitoring point, and issue early warning and prediction information based on corresponding measures of the early warning and prediction level.

[0052] In this embodiment, the application makes full use of the weak grating sensing array monitoring technology to realize real-time intelligent monitoring of landslide temperature, osmotic pressure, strain, vibration and other multi-field monitoring parameters, which can effectively cover the surface and deep deformation area of the landslide, especially the deformation displacement and vibration signals generated in the pre-disaster and deformation process of the landslide. The weak grating sensing array can effectively capture the subtle changes of the landslide rock mass structure, and a reasonable and reliable state discrimination unit is designed to effectively evaluate the stability of the landslide, analyze and calculate the deformation development trend of the landslide, further judge the landslide instability damage level using the deformation development trend of the landslide, and timely and reliably issue early warning and prediction information to effectively organize the evacuation of personnel in the threatened area.

[0053] In this embodiment, the weak grating array sensing landslide state observation system mainly includes a risk division unit, a device deployment unit, a data acquisition unit, a threshold criterion unit, a risk judgment unit, and an early warning and prediction unit.

[0054] The risk division unit is configured to divide the deformation area of the landslide disaster according to optical analysis, InSAR analysis and field geological investigation and survey results, and determine the monitoring area where the corresponding weak grating sensing array monitoring optical cable needs to be installed and deployed.

[0055] The device deployment unit is configured to deploy corresponding temperature, osmotic pressure, strain, vibration and other multi-field monitoring optical cables in the monitoring area set by the risk division unit to sense the changes of multi-field parameters on the surface and deep of the landslide.

[0056] The data acquisition unit is configured to acquire monitoring information of the temperature, osmotic pressure, strain, vibration and other multi-field monitoring optical cables deployed on site. The data acquisition unit can adjust the data acquisition time interval adaptively according to the changes of each parameter and the actual changes of the landslide disaster to achieve the best acquisition density of the landslide deformation condition and ensure the timeliness of the data acquisition interval.

[0057] Threshold criterion unit: used to correct and improve the threshold criterion of temperature, osmotic pressure, strain, vibration and other multi-field monitoring parameters. The deformation and development of landslide disaster is a constantly changing process, so the threshold criterion unit can dynamically adjust the threshold criterion according to the actual landslide risk, so as to achieve the dynamic optimization of the threshold criterion of temperature, osmotic pressure, strain, vibration and other threshold criterion, and ensure the accuracy of the threshold criterion.

[0058] Risk research and judgment unit: used to research and judge the risk of landslide disaster according to the multi-field monitoring information of temperature, osmotic pressure, strain, vibration and other fields obtained on site, combined with the geological structure characteristics of landslide, through the design of mathematical calculation model.

[0059] Early warning and prediction unit: used to further research and judge the risk of landslide instability and destruction according to the landslide deformation development trend calculated by the risk research and judgment unit, analyze the landslide state level, and issue early warning and prediction information according to the type and stage of landslide state level, and timely organize personnel evacuation.

[0060] The expression of self-adaptive adjustment of sampling time interval is:

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068]

[0069] wherein, is a new collection time interval; is an original collection time interval; is a frequency adjustment coefficient for controlling the influence degree of abnormal degree on sampling frequency; is a parameter-specific adjustment factor; is a landslide actual change; is a temperature abnormal change rate; is a weight of ; is an osmotic pressure abnormal change rate; is a weight of ; is a strain abnormal change rate; for The weights; The rate of change of vibration anomalies; for The weights; This is the current temperature value; This is the temperature value at the previous time point; The historical average temperature; This is the current osmotic pressure value; This is the seepage pressure value at the previous time point; The historical average seepage pressure; This is the current strain value; This is the strain value at the previous time point; For historical average strain; This represents the number of vibrations within the current time window. This represents the number of vibrations within the previous time window; The historical average number of vibrations; This is the parameter specificity sensitivity coefficient.

[0070] The expression for the threshold criterion for correcting the multi-field monitoring parameters is as follows:

[0071]

[0072]

[0073] in, The threshold criterion is used to monitor the parameters after correction. This is the initially set reference threshold; This is the threshold adjustment coefficient; This represents the actual changes in the landslide. To monitor the changes in parameters; The basic threshold for monitoring parameters; It is a smoothing factor; The threshold criterion for monitoring parameters at the previous sampling time; This is the current monitoring parameter value; This refers to the monitoring parameter value at the previous time point.

[0074] The expression for the risk assessment index of each monitoring area is as follows:

[0075]

[0076]

[0077]

[0078]

[0079]

[0080] wherein, is the risk assessment index of the th monitoring point; is the weight coefficient of temperature; is the weight coefficient of osmotic pressure; is the weight coefficient of strain; is the weight coefficient of vibration; is the normalized anomaly index of temperature of the th monitoring point; is the normalized anomaly index of osmotic pressure of the th monitoring point; is the normalized anomaly index of strain of the th monitoring point; is the normalized anomaly index of vibration of the th monitoring point; is the weight of the number of vibration events; is the normalized anomaly index of the number of vibration events of the th monitoring point; is the weight of the intensity energy of vibration events; is the normalized anomaly index of the intensity energy of vibration events of the th monitoring point; is the weight of the main frequency of vibration events; is the normalized anomaly index of the main frequency of vibration events of the th monitoring point; is the weight of the total energy of vibration events; is the normalized anomaly index of the total energy of vibration events of the th monitoring point; is the normalized anomaly index of the monitoring parameter of the th monitoring point; is the corrected threshold criterion of the corresponding monitoring parameter; is the reference mean value of the monitoring parameter; is the reference standard deviation of the monitoring parameter.

[0081] The expression of the early warning and prediction level is:

[0082]

[0083]

[0084] wherein, is the early warning and prediction level; is the sum of the number of monitoring points of each monitoring area; is the spatial weight of the th monitoring point; Risk assessment index for the first monitoring point.

[0085] In this embodiment, the risk division unit mainly divides the landslide disaster into three different levels of low risk, medium risk and high risk based on the set deformation threshold, and respectively deploys the corresponding weak light barrier sensor array monitoring optical cable in the low risk, medium risk and high risk areas. The deployment density is lower in the low and medium risk areas, and the deployment density is relatively higher in the high risk area. The higher the risk, the higher the deployment density.

[0086] The device deployment unit comprehensively considers the deployment of the sensing optical cable designed by the risk division unit, adopts a deployment method from high to low and from shallow to deep, and respectively deploys the temperature, osmotic pressure, strain and vibration sensing array monitoring optical cable in the landslide surface and deep monitoring area.

[0087] The data acquisition unit comprehensively acquires the sensing array sensing monitoring data of temperature, osmotic pressure, strain and vibration, and adaptively adjusts the data acquisition time interval in combination with the actual change of the landslide.

[0088] On this basis, with the continuous in-depth monitoring of the landslide temperature, osmotic pressure, strain and vibration parameters, it is necessary to appropriately adjust the sampling time interval to achieve dynamic tracking of the landslide deformation by automatically adjusting the collection time interval.

[0089] Threshold criterion unit: considering that the above monitoring parameters will continuously change during the monitoring process, the four parameters can accurately distinguish the state level of the landslide, so a reasonable threshold criterion is needed to continuously meet the actual monitoring needs of the landslide, and therefore the threshold criterion of the temperature, osmotic pressure, strain and vibration monitoring parameters needs to be continuously corrected and improved according to the actual data.

[0090] By combining the abnormal index, parameter change rate and sampling frequency, dynamic adjustment of the landslide monitoring parameter threshold is realized. It not only ensures the sensitivity of the monitoring, but also avoids false positives.

[0091] Risk assessment unit: according to the on-site acquisition of temperature, osmotic pressure, strain and vibration multi-field monitoring information with adaptive threshold, in combination with the geological structure characteristics of the landslide, the acquired temperature, osmotic pressure, strain and vibration multi-field monitoring data are designed in a unified format, and the temperature, osmotic pressure, strain and vibration multi-field monitoring data are marked according to the characteristics of the previous geological survey results. The characteristics corresponding to the temperature are ℃, the characteristics corresponding to the osmotic pressure are kPa, the characteristics corresponding to the strain are , and the characteristics corresponding to the vibration are vibration times , amplitude , main frequency and energy spectrum , and based on the above data, the risk assessment formula of the landslide single monitoring point is calculated.

[0092] Because the rock mass rupture caused by landslide in the process of instability deformation can cause vibration events, the vibration times, amplitude, main frequency and power spectrum density of the vibration parameters are normalized again to determine the abnormal index.

[0093] The above design is used to analyze the risk of disasters in the key deformation area of landslide, and the risk analysis level of a single monitoring point is preliminarily analyzed.

[0094] The early warning and forecasting unit: according to the risk analysis level of a single monitoring point, the risk level of each key monitoring point of landslide is reflected, so the risk analysis level of the above single monitoring point is comprehensively analyzed, and the early warning and forecasting level of the whole landslide is formed.

[0095] Table 1 Risk level threshold value division

[0096]

[0097] In this embodiment, the weak grating sensing array monitoring technology is fully utilized to realize real-time intelligent monitoring of multiple monitoring parameters such as landslide temperature, osmotic pressure, strain and vibration, which can effectively cover the surface and deep deformation area of landslide, especially the deformation displacement and vibration signal generated in the pre-disaster period and deformation process of landslide. The weak grating sensing array can effectively capture the subtle changes of the landslide rock structure, and the adaptive dynamic threshold adjustment can efficiently operate the landslide monitoring system, thereby effectively improving the landslide monitoring pertinence.

[0098] The application constructs a landslide disaster risk analysis method based on temperature, osmotic pressure, strain and vibration parameters, especially the vibration parameters are refined into four characteristics of vibration times, amplitude, main frequency and power spectrum density. Through normalization abnormal index and weighted calculation, a single point risk index is obtained, combined with spatial weight, a regional comprehensive risk index is calculated, a more comprehensive and scientific risk assessment system is constructed, and the timeliness of landslide disaster monitoring and early warning is effectively improved, which can provide strong technical support for landslide monitoring and early warning system.

Claims

1. A weak light grating array sensing landslide state observation system, characterized in that, The risk division unit, the device deployment unit, the data acquisition unit, the threshold criterion unit, the risk research and judgment unit, and the early warning and prediction unit are included. The risk division unit is configured to divide the landslide disaster area into risk levels to obtain risk levels of each monitoring area of the landslide disaster area. The device deployment unit is configured to determine the number of monitoring points according to the risk levels of the monitoring areas, and deploy a multi-field weak grating sensing array monitoring optical cable for sensing changes in multi-field parameters of a landslide surface and deep part at each monitoring point. The data acquisition unit is configured to adaptively adjust a sampling time interval according to actual changes in the landslide, and acquire monitoring information of the multi-field weak grating sensing array monitoring optical cable according to the sampling time interval; the actual changes in the landslide are a weighted sum of a temperature abnormal change rate, a seepage pressure abnormal change rate, a strain abnormal change rate, and a vibration abnormal change rate. The threshold criterion unit is configured to dynamically correct threshold criteria of multi-field monitoring parameters according to real-time landslide risks; and an expression of the corrected threshold criteria of the multi-field monitoring parameters is: wherein, is a threshold criterion after correction of the monitoring parameter; is a reference threshold value set initially; is a threshold adjustment coefficient; is an actual change of the landslide; is a change amount of the monitoring parameter; is a basic threshold value of the monitoring parameter; is a smoothing factor; is a threshold criterion of the monitoring parameter at a previous sampling time; is a current monitoring parameter value; is a monitoring parameter value at a previous time point; The risk research and judgment unit is configured to research and judge risks of landslide disasters according to real-time monitoring information and the corrected threshold criteria of the multi-field monitoring parameters to obtain risk research and judgment indexes of the monitoring points. The early warning and prediction unit is configured to determine early warning and prediction levels according to the risk research and judgment indexes of the monitoring points, and issue early warning and prediction information based on corresponding measures of the early warning and prediction levels.

2. The weak grating array sensing landslide state observation system according to claim 1, wherein, An expression of the adaptively adjusted sampling time interval is: wherein, is a new acquisition time interval; is an original acquisition time interval; is a frequency adjustment coefficient for controlling the degree of influence of the abnormality on the sampling frequency; is a parameter-specific adjustment factor; is a real change in landslide; is a temperature abnormality change rate; is a weight of ; is an osmotic pressure abnormality change rate; is a weight of ; is a strain abnormality change rate; is a weight of ; is a vibration abnormality change rate; is a weight of ; is a current temperature value; is a temperature value at a previous time point; is a historical average temperature; is a current osmotic pressure value; is an osmotic pressure value at a previous time point; is a historical average osmotic pressure; is a current strain value; is a strain value at a previous time point; is a historical average strain; is a number of vibrations within a current time window; is a number of vibrations within a previous time window; is a historical average number of vibrations; is a parameter-specific sensitivity coefficient.

3. The weak grating array sensing landslide state observation system according to claim 1, characterized in that, An expression of the risk research and judgment indexes of the monitoring points is: wherein, is the risk assessment index of the th monitoring point; is the weight coefficient of temperature; is the weight coefficient of osmotic pressure; is the weight coefficient of strain; is the weight coefficient of vibration; is the normalized anomaly index of the temperature of the th monitoring point; is the normalized anomaly index of the osmotic pressure of the th monitoring point; is the normalized anomaly index of the strain of the th monitoring point; is the normalized anomaly index of the vibration of the th monitoring point; is the weight of the number of vibration events; is the normalized anomaly index of the number of vibration events of the th monitoring point; is the weight of the intensity energy of vibration events; is the normalized anomaly index of the intensity energy of vibration events of the th monitoring point; is the weight of the main frequency of vibration events; is the normalized anomaly index of the main frequency of vibration events of the th monitoring point; is the weight of the total energy of vibration events; is the normalized anomaly index of the total energy of vibration events of the th monitoring point; is the normalized anomaly index of the monitoring parameter of the th monitoring point; is the corrected threshold criterion corresponding to the monitoring parameter; is the reference mean value of the monitoring parameter; is the reference standard deviation of the monitoring parameter.

4. The weak grating array sensing landslide state observation system according to claim 1, characterized in that, An expression of the early warning and prediction levels is: wherein, is the early warning forecast level; is the sum of the number of monitoring points of each monitoring area; is the spatial weight of the th monitoring point; is the risk assessment index of the th monitoring point.

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