An early warning method and system for ecological slope instability

Abstract

This invention discloses an early warning method and system for ecological slope instability, relating to the field of ecological slope monitoring technology. The early warning method includes collecting monitoring information from the area where the ecological slope is located; deriving a numerical value reflecting the soil stability based on soil information; and introducing a dynamic decay model of soil mechanical properties over time. This model is constructed based on initial soil state parameters and long-term monitoring decay trends. The ecological slope instability early warning method and system of this invention employ an adaptive weight adjustment method for comprehensive early warning numerical calculation, dynamically optimizing the influence ratio of parameters based on historical early warning accuracy. It also introduces environmental adjustment coefficients and case correction coefficients, enabling the comprehensive early warning numerical value to adapt to different environments and historical experience, enhancing the adaptability and flexibility of the early warning model. Furthermore, the hierarchical dynamic early warning threshold mechanism automatically adjusts according to the slope's geographical environmental sensitivity level and ecological value, making the early warning standards more targeted.

Description

Technical Field

[0001] This invention relates to the field of ecological slope protection monitoring technology, and in particular to an early warning method and system for ecological slope instability. Background Technology

[0002] Ecological slope protection is an important means of preventing slope instability and protecting the ecological environment by combining engineering measures and ecological restoration technology. It is widely used on mountain slopes along highways and railways, as well as on embankments in water conservancy projects. This type of slope protection, through planting vegetation and laying ecological bags, not only stabilizes the slope but also plays an ecological role in conserving soil and water and beautifying the environment. However, in practical applications, ecological slope protection often faces the influence of various natural and human factors, and is prone to instability risks.

[0003] During the rainy summer, continuous rainfall significantly increases soil moisture content, leading to a decline in soil mechanical properties. The scouring effect of strong water flow further erodes the surface of the slope, weakening the stability of the slope protection structure. During the vegetation growth cycle, if attacked by pests and diseases or experiencing extreme drought, the slope stabilization capacity of the vegetation root system will be significantly reduced. At this time, the slope is prone to local landslides under its own weight and external loads. In addition, under the long-term influence of vehicle vibration, the cohesion between soil particles on mountain highway slopes gradually weakens. Coupled with the repeated effects of winter freeze-thaw cycles, cracks are easily generated inside the slope, which in turn leads to overall instability.

[0004] In existing technologies, the monitoring of ecological slope protection often adopts a single-parameter monitoring method, such as monitoring only slope displacement or soil moisture content, which is difficult to fully reflect the actual stability of the slope. At the same time, traditional early warning methods lack the analysis of the synergistic effects between various influencing factors, and often result in delayed or false alarms in complex scenarios. For example, during a rainstorm, a slope of a mountain highway failed to issue an early warning in time because only soil moisture content was monitored without considering the synergistic effects of water erosion and vegetation damage, which ultimately led to slope collapse, causing traffic disruption and economic losses. To address this, we propose an early warning method and system for ecological slope instability. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides an ecological slope instability early warning method and system. This solves the technical problems of existing ecological slope instability early warning systems, which only consider the impact of a single factor on slope instability, resulting in low early warning accuracy; and some systems, although considering multiple factors, do not conduct in-depth analysis of the synergistic effects between these factors, making it difficult to accurately reflect the actual stability state of the slope.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] An early warning method for ecological slope instability, the method comprising:

[0010] The monitoring information of the ecological slope protection area is collected. The monitoring information includes soil-related information, hydrological-related information, vegetation-related information and location movement-related information. The information is collected synchronously in time and space through a variety of monitoring devices with different principles. The monitoring devices include sensors for monitoring soil, devices for monitoring hydrology, devices for observing vegetation, and instruments for tracking location movement.

[0011] Based on soil-related information, numerical values ​​reflecting soil stability are derived. A dynamic decay model of soil mechanical properties over time is introduced in the numerical calculation process. This model is constructed based on soil initial state parameters and long-term monitored decay trends.

[0012] Numerical values ​​reflecting the degree of hydrological impact are derived based on hydrological information. The numerical calculation combines the different weights of the slope effect at different stages of the hydrological cycle, and the different weights are determined based on historical data of regional hydrological characteristics.

[0013] Based on vegetation-related information, numerical values ​​reflecting the slope stabilization effect of vegetation are obtained. The numerical calculation incorporates the dynamic change curve of the root system's slope stabilization capacity during the vegetation growth cycle. This curve is plotted through the correlation analysis between vegetation growth stage and root mechanical properties.

[0014] Based on information related to location movement, numerical values ​​reflecting the degree of danger of location movement are derived. The numerical calculation adopts an analysis method of the temporal and spatial variation patterns of location movement. This method comprehensively considers the correlation and propagation characteristics of location movement at different monitoring points.

[0015] A dynamic correlation analysis was conducted between the numerical values ​​reflecting the soil stability and the numerical values ​​reflecting the degree of hydrological impact to obtain the first synergistic impact coefficient. The dynamic correlation analysis was conducted by establishing an interaction model between the two over time to determine the degree of influence of one change on the other. The model also incorporates a soil response amplification mechanism under extreme hydrological events.

[0016] A dynamic correlation analysis was conducted between the numerical values ​​reflecting the slope stabilization effect of vegetation and the numerical values ​​reflecting the degree of risk of location movement to obtain a second synergistic influence coefficient. The critical value of vegetation damage was included in the analysis process. When the vegetation parameter is lower than the critical value, the influence weight of the location movement parameter is automatically increased.

[0017] By combining the values ​​reflecting the degree of soil stability, the degree of hydrological influence, the value reflecting the effect of vegetation on slope stabilization, the value reflecting the degree of risk of location movement, the first synergistic influence coefficient, and the second synergistic influence coefficient, a comprehensive early warning value is calculated. The calculation process adopts an adaptive weight adjustment method that can dynamically optimize the influence ratio of each parameter based on the accuracy of historical early warnings.

[0018] The system determines whether the comprehensive early warning value exceeds the preset early warning threshold. The early warning threshold adopts a graded dynamic threshold mechanism, which is automatically adjusted according to the sensitivity level and ecological value of the geographical environment where the slope is located.

[0019] If the comprehensive warning value exceeds the pre-set warning threshold, it is determined that the ecological slope may be unstable, and graded warning response measures are initiated. At the same time, the key warning areas are determined based on the first and second synergistic impact coefficients, and a visual warning report containing emergency response priorities and specific prevention and control suggestions is generated for the key areas.

[0020] If the overall warning value does not exceed the pre-set warning threshold, the ecological slope protection is determined to be in a stable state, and the original monitoring time interval is maintained. At the same time, the monitoring data and analysis results are stored in the historical database for model optimization. The model optimization adopts an incremental learning method that only trains the new data to improve the model's adaptability.

[0021] Preferably, the step of deriving a numerical value reflecting the degree of soil stability based on soil-related information includes:

[0022] Extract the current soil moisture content, standard soil moisture content, soil density, and soil organic matter content from soil-related information;

[0023] The difference between the current soil moisture content and the standard soil moisture content is calculated to obtain the change in soil moisture content. At the same time, the change is corrected according to the soil organic matter content. The higher the organic matter content, the smaller the correction coefficient.

[0024] The soil shear strength correction value is determined based on the soil density, and the determination process refers to the nonlinear relationship curve between soil density and shear strength.

[0025] Record the duration of continuous changes in soil moisture content exceeding the first specified value to obtain the duration of soil moisture content anomalies, and set different cumulative weights for the duration of anomalies in different seasons.

[0026] Based on the change in soil moisture content, the correction value of soil shear strength, and the duration of soil moisture content anomalies, combined with the dynamic decay model of soil mechanical properties, a value reflecting the degree of soil stability is obtained.

[0027] Preferably, the step of deriving a numerical value reflecting the degree of hydrological impact based on hydrological information includes:

[0028] Extract current water level, current flow velocity, flow impact force, and water pH from hydrological information.

[0029] Calculate the change in water height per unit time to obtain the rise in water height. At the same time, correct this rise based on the acidity or alkalinity of the water. The greater the deviation of the acidity or alkalinity from neutral, the larger the correction coefficient.

[0030] Calculate the change in current water flow velocity per unit time to obtain the proportion of water flow velocity change;

[0031] The water erosion correction coefficient is determined based on the water flow impact force, and the coefficient is determined by taking into account the matching relationship between the water flow impact force and the erosion resistance of the slope material.

[0032] Record the duration of continuous periods when the rise in water level exceeds the second specified value or the change in water flow velocity exceeds the third specified value, and obtain the duration of hydrological anomalies. The recording process distinguishes the abnormal time weights between the high-water season and the low-water season.

[0033] Based on the rise in water height, the proportion of change in water flow velocity, the water erosion correction coefficient, and the duration of hydrological anomalies, combined with different weights for different stages of the hydrological cycle, a numerical value reflecting the degree of hydrological impact is derived.

[0034] Preferably, the step of deriving a numerical value reflecting the slope stabilization effect of vegetation based on vegetation-related information includes:

[0035] Extract vegetation-related information such as vegetation coverage area ratio, density of plant root distribution, vegetation growth vitality index, and vegetation species diversity;

[0036] The difference between the vegetation cover area ratio and the vegetation cover area ratio in the same period in the past is calculated to obtain the change in the coverage area ratio. The species diversity coefficient is included in the calculation process. The higher the diversity, the smaller the weight adjustment coefficient of the change.

[0037] The coefficients reflecting the strength of the root system are determined based on the density of plant root distribution, vegetation growth vitality index and vegetation species diversity, with the root contribution of different species set according to the actual measurement data of their slope stabilization capacity.

[0038] Based on the change in the proportion of coverage area and the coefficient reflecting the strength of the root system, combined with the dynamic change curve of the root system's ability to fix the slope during the vegetation growth cycle, a value reflecting the stabilizing effect of vegetation on the slope is obtained.

[0039] Preferably, the step of deriving a numerical value reflecting the degree of danger of location movement based on location movement-related information includes:

[0040] Extract the three-dimensional spatial coordinates, corresponding time records, position movement acceleration, and abrupt change frequency of position movement direction at different time points from the position movement-related information;

[0041] The distance moved is calculated based on the three-dimensional spatial coordinates of adjacent time points. The distance calculation takes into account the spatial distribution density of the monitoring points for weighting.

[0042] The speed of position movement is calculated based on the distance moved and the corresponding time interval.

[0043] The path of location movement is plotted based on the distance and time records of location movement at multiple time points. The path analysis introduces fractal dimension calculation to quantify the path complexity.

[0044] The rate of change of position movement direction is determined by the curvature of the position movement path and the frequency of abrupt changes in position movement direction.

[0045] Based on the distance of position movement, the speed of position movement, the rate of change of position movement direction, and the acceleration of position movement, combined with the analysis method of the change law of position movement in time and space, a value reflecting the degree of danger of position movement is obtained.

[0046] Preferably, the step of calculating the comprehensive early warning value by combining values ​​reflecting soil stability, hydrological impact, vegetation's role in slope stabilization, the risk of location movement, a first synergistic influence coefficient, and a second synergistic influence coefficient includes:

[0047] The first importance ratio is determined based on the numerical value reflecting the soil stability and the first synergistic influence coefficient. The fuzzy hierarchical analysis method is used to handle the uncertainty relationship between parameters in the ratio determination process.

[0048] The second importance ratio is determined based on the numerical value reflecting the degree of hydrological impact and the first synergistic impact coefficient;

[0049] The third importance ratio is determined based on the numerical value reflecting the slope stabilization effect of vegetation and the second synergistic influence coefficient.

[0050] The fourth importance ratio is determined based on the numerical value reflecting the degree of danger of location movement and the second synergistic influence coefficient;

[0051] The environmental adjustment coefficient is obtained, which includes the seasonal impact coefficient, the topographic impact coefficient, the climate change impact coefficient, and the human activity impact coefficient. The human activity impact coefficient is determined based on the intensity and frequency of regional engineering activities.

[0052] The system calls up similar case data from the historical database for comparison and correction, and obtains the case correction coefficient. The similar case matching adopts the multi-feature vector cosine similarity calculation method, and sets time decay weights for cases with different time spans.

[0053] The comprehensive early warning value is calculated using an adaptive weighting adjustment method based on the values ​​reflecting soil stability, hydrological impact, vegetation's role in slope stabilization, the risk of location movement, the first synergistic impact coefficient, the second synergistic impact coefficient, the first importance ratio, the second importance ratio, the third importance ratio, the fourth importance ratio, the environmental adjustment coefficient, and the case correction coefficient.

[0054] Preferably, the step of performing dynamic correlation analysis between the numerical values ​​reflecting soil stability and the numerical values ​​reflecting hydrological impact to obtain the first synergistic impact coefficient includes:

[0055] Establish a first series of numerical values ​​reflecting the degree of soil stability over time and a second series of numerical values ​​reflecting the degree of hydrological influence over time;

[0056] Determine the time points and magnitudes of numerical changes in the two sequences;

[0057] The analysis examines the degree of influence of changes in the first sequence values ​​on changes in the second sequence values ​​within the same time interval, and the degree of influence of changes in the second sequence values ​​on changes in the first sequence values.

[0058] An extreme hydrological event identification mechanism is introduced. When an extreme hydrological event is detected, the influence weight of the soil response is amplified according to preset rules.

[0059] Based on the above analysis results, the first synergistic influence coefficient was calculated. The larger the value of this coefficient, the more significant the interaction between the two factors has on slope instability.

[0060] Preferably, the step of performing a dynamic correlation analysis between the numerical value reflecting the slope stabilization effect of vegetation and the numerical value reflecting the degree of risk of location movement to obtain the second synergistic influence coefficient includes:

[0061] Establish a third sequence reflecting the change of values ​​over time in terms of vegetation's effect on slope stabilization, and a fourth sequence reflecting the change of values ​​over time in terms of the degree of risk of location movement.

[0062] A threshold for vegetation damage is set. When the value in the third sequence falls below this threshold, it is marked as a stage of declining vegetation protection capacity.

[0063] During the normal vegetation protection capacity phase, the interaction between the numerical changes of the third sequence and the numerical changes of the fourth sequence was analyzed.

[0064] During the stage of declining vegetation protection capacity, increase the weight of the fourth sequence numerical change in the association analysis;

[0065] The second synergistic influence coefficient is calculated by comprehensively considering the analysis results at different stages. The larger the value of this coefficient, the more significant the interaction between the two factors has on the slope instability.

[0066] An early warning system for ecological slope instability, comprising:

[0067] The information collection section is used to collect monitoring information in the area where the ecological slope protection is located. The monitoring information includes soil-related information, hydrological-related information, vegetation-related information, and location movement-related information. This section also integrates time and space synchronization control modules for various monitoring devices based on different principles, which can realize time calibration and spatial matching of data collected by different devices.

[0068] The analysis and processing section is used to process and analyze the collected monitoring information to obtain values ​​reflecting the degree of soil stability, the degree of hydrological impact, the effect of vegetation on slope stabilization, the degree of risk of location movement, the first synergistic influence coefficient, and the second synergistic influence coefficient. These values ​​are then combined to calculate a comprehensive early warning value.

[0069] The early warning judgment section is used to determine whether the comprehensive early warning value exceeds the preset early warning threshold. Based on the judgment result, it executes corresponding early warning response measures or maintains the original monitoring status, while generating early warning reports or updating the historical database for model optimization.

[0070] Its characteristic is that the analysis and processing section includes:

[0071] The soil information analysis unit is used to derive values ​​reflecting the soil stability based on soil-related information. This unit has a built-in dynamic decay model of soil mechanical properties over time and can automatically call historical data to update model parameters.

[0072] The hydrological information analysis unit is used to derive numerical values ​​reflecting the degree of hydrological impact based on hydrological information. This unit stores historical data on regional hydrological characteristics and can automatically match different weights for the corresponding hydrological stages based on real-time data.

[0073] The vegetation information analysis unit is used to derive values ​​reflecting the slope stabilization effect of vegetation based on vegetation-related information. This unit contains dynamic change curve data of the root system's ability to stabilize the slope during the vegetation growth cycle, and can automatically call up the corresponding curve according to the vegetation growth stage.

[0074] The location movement information analysis unit is used to derive values ​​reflecting the degree of danger of location movement based on location movement-related information. This unit uses an analysis module to analyze the temporal and spatial variation patterns of location movement and can handle the correlation analysis of location movement data from multiple monitoring points.

[0075] The collaborative analysis unit is used to perform dynamic correlation analysis between the numerical values ​​reflecting the degree of soil stability and the numerical values ​​reflecting the degree of hydrological impact to obtain the first collaborative influence coefficient, and to perform dynamic correlation analysis between the numerical values ​​reflecting the effect of vegetation on slope fixation and the numerical values ​​reflecting the degree of risk of location movement to obtain the second collaborative influence coefficient. This unit includes an extreme event response mechanism and a vegetation damage critical value judgment module.

[0076] The comprehensive early warning calculation unit is used to calculate the comprehensive early warning value by combining the above-mentioned numerical values ​​and coefficients. This unit integrates an adaptive weight adjustment method that can dynamically optimize the calculation model based on the accuracy of historical early warnings.

[0077] (III) Beneficial Effects

[0078] 1. Employing multiple monitoring devices based on different principles enables simultaneous time and space data collection, encompassing comprehensive information on soil, hydrology, vegetation, and location movement. Compared to monitoring methods using a single information source, this approach can more comprehensively capture factors affecting the stability of ecological slope protection, providing rich and reliable basic data for subsequent early warning analysis and improving the comprehensiveness of early warning from the source. Secondly, the numerical calculation of soil stability utilizes a dynamic attenuation model of soil mechanical properties, closely reflecting the actual changes in soil over time. The numerical calculation of hydrological impact incorporates differentiated weights for different stages of the hydrological cycle, accurately reflecting the differences in impact at different hydrological stages. The numerical calculation of vegetation slope stabilization incorporates the dynamic change curve of root slope stabilization capacity, accurately reflecting slope stabilization changes within the vegetation growth cycle. The numerical calculation of location movement hazard employs a spatiotemporal evolution analysis method, comprehensively considering the correlation of monitoring points. These measures make the calculation of characteristic values ​​of each factor more closely reflect the actual situation, improving the accuracy of single-factor analysis and laying a solid foundation for subsequent comprehensive early warning.

[0079] 2. By analyzing the synergistic effects of soil and hydrology, and vegetation and location movement, the first and second synergistic effect coefficients were obtained, and the interaction relationships between factors were explored in depth. In particular, the extreme hydrological event response mechanism and vegetation damage threshold adjustment mechanism were embedded in the synergistic analysis, which can effectively deal with the impact of special situations on slope stability. Compared with the early warning method that does not consider synergistic effects, it significantly improves the ability to identify slope instability risks under complex working conditions, and makes the early warning more in line with the complex scenarios of multi-factor interaction in actual engineering.

[0080] 3. The comprehensive early warning numerical calculation adopts an adaptive weight adjustment method, dynamically optimizing the parameter influence ratio based on historical early warning accuracy. It also introduces environmental adjustment coefficients and case correction coefficients, enabling the comprehensive early warning numerical values ​​to adapt to different environments and historical experience, enhancing the adaptability and flexibility of the early warning model. The hierarchical dynamic early warning threshold mechanism automatically adjusts based on the slope's geographical environmental sensitivity level and ecological value, making the early warning standards more targeted and avoiding the potential for delayed or false alarms that may occur with fixed thresholds in different scenarios. In the early warning response phase, key early warning areas are determined based on the synergistic impact coefficient, generating a visual report containing emergency response priorities and specific prevention and control recommendations. This ensures that early warnings not only promptly identify risks but also provide precise guidance for emergency response, improving the efficiency and targeting of risk response. For data in a stable state, an incremental learning method is used to update the model, allowing the system to continuously accumulate experience and optimize its performance. With increasing usage time, the accuracy and adaptability of the early warning system continue to improve. Attached Figure Description

[0081] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, the preferred embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0082] Figure 1 This is an overall flowchart of an embodiment of the present invention;

[0083] Figure 2 This is a flowchart illustrating the system operation process in an embodiment of the present invention.

[0084] Figure 3 This is a system architecture diagram in an embodiment of the present invention. Detailed Implementation

[0085] This application provides an ecological slope instability early warning method and system, addressing the technical problems of existing ecological slope instability early warning systems that only consider the impact of a single factor on slope instability, resulting in low early warning accuracy; and some systems that, while considering multiple factors, fail to deeply analyze the synergistic effects between these factors, making it difficult to accurately reflect the actual stability state of the slope. The method employs a dynamic attenuation model of soil mechanical properties for soil stability calculation, closely reflecting the actual changes in soil over time. The method combines differentiated weights for different stages of the hydrological cycle in the numerical calculation of hydrological impact, accurately reflecting the differences in impact at different hydrological stages. The method incorporates the dynamic change curve of root slope stabilization capacity in the numerical calculation of vegetation slope stabilization effect, accurately reflecting slope stabilization changes during the vegetation growth cycle. The method uses a spatiotemporal evolution law analysis method for the numerical calculation of location movement hazard, comprehensively considering the correlation of monitoring points. These measures make the calculation of characteristic values ​​of each factor more closely reflect the actual situation, improving the accuracy of single-factor analysis and laying a solid foundation for subsequent comprehensive early warning.

[0086] Example: Figure 1 - Figure 3 As shown, the technical solution in this application addresses the problem that existing ecological slope instability early warning systems only consider the impact of a single factor on slope instability, resulting in low early warning accuracy; and that some systems, while considering multiple factors, fail to conduct in-depth analysis of the synergistic effects between these factors, making it difficult to accurately reflect the actual stability state of the slope. The overall approach is as follows:

[0087] Ecological slope protection, as an important means of combining engineering measures and ecological restoration technology, has significant advantages in soil and water conservation, slope stability, and ecological protection. However, during long-term use, ecological slope protection is susceptible to the combined effects of various factors such as changes in soil properties, fluctuations in hydrological conditions, changes in vegetation growth, and slope displacement, posing a risk of instability. Once instability occurs, it will not only damage the local ecological environment but may also trigger geological disasters such as landslides and debris flows, posing a serious threat to the lives and property of surrounding residents. For example, in a certain area, heavy rainfall caused the instability of ecological slope protection, triggering a landslide disaster that resulted in road interruption and damage to some houses.

[0088] To address the problems existing in the prior art, this invention provides an early warning method for ecological slope instability, the details of which are as follows:

[0089] (a) Information collection:

[0090] The monitoring information of the ecological slope protection area is collected, including soil-related information, hydrological-related information, vegetation-related information, and location movement-related information. To ensure the comprehensiveness and accuracy of the information, a variety of monitoring devices based on different principles are used to achieve synchronous collection of information in time and space.

[0091] Sensors used for soil monitoring can include resistive soil moisture sensors and soil compaction meters, which acquire information such as soil moisture content and soil compaction. Among them, resistive soil moisture sensors calculate soil moisture content by measuring soil resistance; soil compaction meters use the impact method to measure and can accurately obtain soil compaction data.

[0092] Devices used for hydrological monitoring include water level gauges, current meters, and pressure sensors, which obtain data such as water height, flow velocity, and flow impact force. Water level gauges utilize ultrasonic principles and have a measurement range of 0-10m. Current meters are electromagnetic current meters with a measurement range of 0.05-10m / s. Pressure sensors are installed on the water-facing side of the slope and can directly measure the flow impact force, with a measurement range of 0-100N / m. 2 ;

[0093] Equipment used for observing vegetation includes drone remote sensing equipment, vegetation coverage measuring instruments, etc., to record vegetation coverage area ratio, vegetation growth vitality index, etc. Drone remote sensing equipment is equipped with high-definition cameras and multispectral sensors, which can acquire high-resolution vegetation images and obtain vegetation coverage area ratio through image analysis. Vegetation coverage measuring instruments calculate growth vitality index by measuring specific wavelengths of light reflected by vegetation, with a measurement range of 0-100.

[0094] Instruments used to track position movement can include total stations, GPS positioning devices, etc., to capture three-dimensional spatial position coordinates and other information; the measurement accuracy of a total station is ±2mm+2ppm, and the GPS positioning device uses differential positioning technology.

[0095] Synchronous data acquisition is achieved by embedding a time synchronization module, also known as a synchronization clock, into each monitoring device. Any module with the same function can be used, and it is not limited to the time synchronization modules given above. The time synchronization module ensures that the time base of all devices is consistent, and the error is controlled within 1 second. At the same time, by pre-setting the spatial coordinates of each monitoring point, spatial matching of data is achieved, ensuring that the data collected by different devices can correspond to the same spatial location. The deployment density of monitoring devices is determined according to the length and complexity of the slope, and generally one monitoring point is set up every 5-10 meters. The installation points are usually installed along the street lights next to the slope.

[0096] Taking an ecological slope protection section beside a mountain highway as an example, this slope is approximately 200 meters long with a 30° slope. The main soil type is loam, and the vegetation consists primarily of common local herbaceous plants and shrubs. A seasonal stream flows at the base of the slope. Monitoring points are set up every 8 meters along this ecological slope protection area, totaling 25 monitoring points. Each monitoring point is equipped with the aforementioned monitoring devices. A time synchronization module ensures that the time reference of each device is consistent, and the latitude, longitude, and altitude information of each monitoring point are pre-entered to achieve spatial matching. Assuming monitoring is conducted on this ecological slope protection section during the summer's high-water season in July, some key data obtained are as follows:

[0097] Soil-related information: The current soil moisture content is 28%, while the standard soil moisture content is 22%, which is the historical average for the same period. The soil density is 1.4 g / cm³. 3 The soil organic matter content is 4%;

[0098] Hydrological information: Current water level is 1.2m, water level 1 hour ago was 0.8m; current flow velocity is 1.5m / s, flow velocity 1 hour ago was 1.2m / s; water flow impact force is 6N / m. 2 The pH value of the water body is 6;

[0099] Vegetation-related information: The vegetation coverage area accounts for 65%, compared to 58% in the same period in the past; the density of plant root distribution is 0.7; the vegetation growth vitality index is 75; and there are 6 plant species.

[0100] Location movement related information: The calculated location movement distance based on the three-dimensional spatial coordinates of adjacent time points is 0.6m, with an interval of 1 hour. The spatial distribution density of monitoring points is high, and the location movement acceleration is 0.08m / s². 2 The frequency of sudden changes in the direction of position movement is 1 time / h.

[0101] (II) Numerical calculation of soil stability:

[0102] Based on the collected soil-related information, a numerical value reflecting the soil stability is obtained. When calculating this value, a dynamic decay model of soil mechanical properties over time is introduced. This model is constructed based on the initial soil state parameters and the decay trend monitored over a long period of time. The initial soil state parameters include initial compaction and initial organic matter content, while the decay trend monitored over a long period of time is obtained by fitting soil parameter monitoring data over several consecutive years, which can better reflect the actual situation of soil changes over time.

[0103] The specific calculation steps are as follows:

[0104] The current soil moisture content, standard soil moisture content, soil density, and soil organic matter content were extracted from soil-related information. The standard soil moisture content was determined by the average value of historical soil moisture data for the same period in the region. Soil-related information was obtained through sampling.

[0105] The difference between the current soil moisture content and the standard soil moisture content is calculated to obtain the change in soil moisture content. This change is then corrected based on the soil organic matter content; the higher the organic matter content, the smaller the correction coefficient. The correction coefficient is obtained by fitting multiple experimental test results. For every 1% increase in organic matter content, the correction coefficient decreases by 0.05. For the example of ecological slope protection along a mountain road, the current soil moisture content is 28%, and the standard soil moisture content is 22%. The change in soil moisture content is 28% - 22% = 6%. If the soil organic matter content is 4%, the correction coefficient is 1 - 4 × 0.05 = 0.8. The corrected change in soil moisture content is 6% × 0.8 = 4.8%. The value 4.8 is taken as the change in soil moisture content.

[0106] Based on soil density, the correction value for soil shear strength is determined by referring to the nonlinear relationship curve between soil density and shear strength; this curve is plotted after indoor tests, for example, when the density is 1.2 g / cm³. 3 At that time, the shear strength was 15 kPa and the density was 1.5 g / cm³. 3At that time, the shear strength was 25 kPa; when the soil density was 1.3 g / cm³, etc. 3 Through curve interpolation, the shear strength is found to be 18 kPa, with a corresponding correction value of 0.8. In this example, referring to the nonlinear relationship curve between soil density and shear strength, the soil density is 1.4 g / cm³. 3 At that time, the corresponding shear strength correction value is 0.85;

[0107] The duration of soil moisture content changes exceeding a first specified value is recorded to obtain the duration of soil moisture content anomalies. Different cumulative weights are assigned to the duration of anomalies in different seasons. The first specified value is determined based on the soil characteristics and historical instability cases in the region; for example, it can be set to 15% for sandy soil. The weight is 1.2 for the rainy season and 0.8 for the dry season. If the duration of soil moisture content changes exceeding 15% for sandy soil in the rainy season is 3 days, then the weighted duration of anomalies is 3 × 1.2 = 3.6 days. In this example, the soil moisture content change of 6% does not exceed the first specified value of 15% for sandy soil, so the duration of soil moisture content anomalies is 0.

[0108] Finally, combining the dynamic attenuation model of soil mechanical properties, the changes in soil moisture content, the correction value of soil shear strength, and the duration of soil moisture content anomalies obtained above are substituted into the model formula to obtain a value reflecting the soil stability. The model formula is: soil stability value = (1 - changes in soil moisture content × 0.02) × correction value of soil shear strength ÷ (1 + duration of soil moisture content anomalies × 0.1). The value calculated by this formula is in the range of 0-1. The closer the value is to 1, the more stable the soil is. Substituting the data obtained in the example above, the soil stability value = (1 - 4.8 × 0.02) × 0.85 ÷ (1 + 0 × 0.1) ≈ (1 - 0.096) × 0.85 ≈ 0.904 × 0.85 ≈ 0.77.

[0109] Among them, the dynamic decay model of soil mechanical properties is a core analytical tool for soil stability. It is mainly used to quantify the changes in soil mechanical properties with time and environmental factors, such as soil shear strength, density, and stability capacity. These are not fixed values ​​and will gradually decrease due to external influences, such as continuous rainfall leading to increased moisture content, vegetation root degradation, and long-term load effects. This process of decreasing capacity is called decay, while dynamic means emphasizing its real-time updating characteristics with time and environmental conditions, such as changes in rainfall intensity, temperature, and vegetation cover.

[0110] The core logic of this model is:

[0111] Inputs include parameters such as initial soil compaction, real-time moisture content, environmental disturbance intensity, and vegetation root support capacity.

[0112] Output: A dynamic value of soil stability over time; the lower the value, the higher the risk of instability.

[0113] Its function is to provide quantitative evidence for single-factor analysis, enabling the system to monitor changes in the soil's own instability resistance in real time, thus laying the foundation for subsequent multi-factor synergistic analysis.

[0114] (III) Numerical calculation of the degree of hydrological impact:

[0115] The numerical values ​​reflecting the degree of hydrological impact are derived based on hydrological information. The calculation process incorporates different weights for the effects of different stages on the slope during the hydrological cycle. These weights are determined based on historical data of regional hydrological characteristics. The stages include precipitation period, runoff period, dry season, etc. Historical hydrological characteristics can be set as needed. Taking hydrological monitoring data from the past 30 years as an example, the differences in the impact of different hydrological stages on the slope can be demonstrated.

[0116] The specific steps are as follows:

[0117] Extract current water height, current water velocity, water flow impact force, and water pH from hydrological information; water flow impact force is measured directly by a pressure sensor or calculated based on flow velocity and cross-sectional area of ​​the flow, and water pH is measured using a pH meter;

[0118] The change in water height per unit time is calculated to obtain the water height rise rate. This rise rate is then corrected based on the water's pH level. The greater the deviation from neutral, the larger the correction factor. Alkalinity has a larger correction factor than acidity of the same deviation. These factors can be preset. For example, if the pH deviation from neutral is 7, the correction factor is 1.3 for pH=5 and 1.4 for pH=9. If the water's pH is 6, a deviation of 1 unit from neutral results in a correction factor of 1.1. The corrected rise rate is 0.4 × 1.1 = 0.44 m / h.

[0119] The change in current water flow velocity per unit time is calculated to obtain the percentage change in water flow velocity. For example, if the current water flow velocity is 2 m / s and it becomes 2.25 m / s after 1 hour, then the percentage change in water flow velocity is (2.25-2) ÷ 1 × 100% = 25% / h.

[0120] Based on the impact force of the water flow, a water erosion correction coefficient is determined considering its matching relationship with the erosion resistance of the slope material. The erosion resistance of the slope material is determined through indoor scour tests. For example, if the erosion resistance of the slope material is determined to be 9 N / m through indoor tests... 2 The water flow impact force is 6 N / m 2 The water erosion correction factor is approximately 0.67, calculated as 6 ÷ 9.

[0121] The duration of any continuous period during which the rise in water level exceeds the second specified value or the change in water flow velocity exceeds the third specified value is recorded to obtain the duration of the hydrological anomaly. The recording process distinguishes between the weighting of abnormal time periods during wet and dry seasons. For example, if the second specified value is 0.5 m / h and the third specified value is 20% / h, the weighting is 1.3 during wet seasons and 0.7 during dry seasons. If the rise in water level is 0.4 m / h, which is less than the second specified value of 0.5 m / h, and the change in water flow velocity is 25% / h, which exceeds the third specified value of 20% / h, the weighting for the abnormal time during wet seasons is 1.3, and the duration of the hydrological anomaly is 1 × 1.3 = 1.3 hours.

[0122] By combining different weights for different stages of the hydrological cycle (e.g., precipitation option weight 0.3, confluence option weight 0.5, and low water option weight 0.2), and substituting these data into the calculation model, a value reflecting the degree of hydrological impact is obtained. The calculation model is: Hydrological impact value = (Water height rise × 0.4 + Water flow velocity change ratio × 0.3 + Water flow erosion correction coefficient × 0.2 + Duration of hydrological anomalies × 0.1) × Hydrological cycle stage weight, with a value range of 0. -1, the closer the value is to 1, the greater the impact of hydrology on the slope; July is the precipitation period and the runoff period, the weight of the hydrological cycle stage is 0.4, which is the average of 0.3 for the precipitation period and 0.5 for the runoff period. The value of the degree of hydrological influence = (0.44×0.4+25%×0.3+0.67×0.2+1.3×0.1)×0.4≈(0.176+0.075+0.134+0.13)×0.4≈0.515×0.4≈0.21.

[0123] (iv) Numerical calculation of the slope stabilization effect of vegetation:

[0124] Based on vegetation-related information, numerical values ​​reflecting the slope-fixing effect of vegetation are derived. The calculation incorporates a dynamic change curve of the slope-fixing capacity of the root system during the vegetation growth cycle. This curve is plotted by analyzing the correlation between vegetation growth stages and root mechanical properties. The vegetation growth stages include the seedling stage, growth stage, maturity stage, and decline stage. The root mechanical properties are measured through root tensile tests, which can accurately reflect the changes in the slope-fixing effect of vegetation roots at different growth stages.

[0125] Specifically, the following steps are included:

[0126] The vegetation coverage percentage, root density, growth vigor index, and species diversity were extracted from vegetation-related information. The vegetation coverage percentage was obtained through analysis of UAV remote sensing images, the root density was measured by soil borehole sampling, the growth vigor index was determined by measuring the chlorophyll content of plant leaves, and the species diversity was determined by quadrat sampling.

[0127] The change in vegetation cover percentage is calculated by comparing it to the vegetation cover percentage in the same period in the past. The calculation incorporates a species diversity coefficient; the higher the diversity, the smaller the adjustment coefficient for the change. For example, the adjustment coefficient is 0.9 for 5 species, 0.85 for 6 species, and 0.7 for 9 species. Specific adjustment coefficients can be preset. If the current vegetation cover percentage is 65%, and it was 58% in the same period in the past, the change in coverage percentage is 10%, and there are 5 species, then the adjusted change in coverage percentage = 65% - 58% = 7%. If there are 6 species and the adjustment coefficient is 0.85, the adjusted change in coverage percentage = 7% × 0.85 = 5.95%. The value 5.95 is taken as the change in coverage percentage.

[0128] The coefficient reflecting the strength of the root system is determined based on the density of plant root distribution, vegetation growth vitality index, and vegetation species diversity. The contribution of different species to the root system is pre-set based on actual measurement data of their slope stabilization capacity. The root system strength coefficient = (density of plant root distribution × 0.3 + vegetation growth vitality index ÷ 100 × 0.4 + vegetation species diversity × 0.3). For example, if the density of plant root distribution is 0.7, the vegetation growth vitality index is 75, and the vegetation species diversity is set to 0.6 when there are 6 species, the root system strength coefficient = 0.7 × 0.3 + 75 ÷ 100 × 0.4 + 0.6 × 0.3 = 0.21 + 0.3 + 0.18 = 0.69, where 0.3, 0.4, and 0.3 are the weights of plant root distribution density, vegetation growth vitality index, and vegetation species diversity, respectively.

[0129] By combining the dynamic change curve of the vegetation's root system's ability to stabilize the slope during the vegetation growth cycle, and based on the change in the proportion of coverage area and the coefficient reflecting the strength of the root system, the numerical value reflecting the vegetation's slope stabilization effect is obtained. The vegetation slope stabilization effect value = (1 - vegetation coverage area proportion ÷ 100) × root system strength coefficient × dynamic change curve coefficient. In the dynamic change curve, the coefficient is 0.3 for the seedling stage, 0.6 for the growth stage, 0.9 for the maturity stage, and 0.4 for the decline stage. In July, the vegetation is in the growth stage, and the dynamic change curve coefficient is 0.6. The vegetation slope stabilization effect value = (1 - 5.95 ÷ 100) × 0.69 × 0.6 ≈ 0.94 × 0.69 × 0.6 ≈ 0.39.

[0130] (v) Calculation of the degree of danger of location movement:

[0131] Based on information related to location movement, numerical values ​​reflecting the degree of danger of location movement are derived. By employing analytical methods that examine the temporal and spatial patterns of location movement, and comprehensively considering the correlation and propagation characteristics of location movement at different monitoring points, the obtained values ​​better reflect the dangerous situation of location movement.

[0132] The specific steps are as follows:

[0133] Extract the three-dimensional spatial coordinates, corresponding time records, position movement acceleration, and abrupt change frequency of position movement direction from the relevant information on position movement at different time points; the position movement acceleration is calculated by the velocity change at adjacent time points, and the abrupt change frequency of position movement direction is the number of times the direction changes per unit time.

[0134] The distance moved is calculated based on the three-dimensional spatial coordinates of adjacent time points. The distance calculation takes into account the spatial distribution density of the monitoring points and performs weighted processing; the area with high density has a weight of 1.1, and the area with low density has a weight of 0.9. For example, if the distance calculated from the three-dimensional spatial coordinates of adjacent time points is 0.6m, and the spatial distribution density of the monitoring points is high, the weighted distance = 0.6 × 1.1 = 0.66m.

[0135] The speed of position movement is calculated based on the distance the position moves and the corresponding time interval. If the distance the position moves is 0.66m and the time interval is 1 hour, then the speed of position movement = 0.66 ÷ 1 = 0.66m / h.

[0136] The path of location movement is plotted based on the distance and time records of location movement at multiple time points. The path analysis introduces fractal dimension calculation to quantify the path complexity; the larger the fractal dimension, the more complex the path. The fractal dimension of the location movement path is calculated to be 1.1.

[0137] The speed of change of position direction is determined based on the curvature of the position movement path and the frequency of sudden changes in position movement direction. If the frequency of sudden changes in position movement direction is 1 time / h, the curvature coefficient is taken as 0.2 here. The greater the curvature, the greater the curvature coefficient. Then the speed of change of direction = 1 × 0.5 + 0.2 = 0.7.

[0138] Combining the analytical method of the temporal and spatial variation patterns of positional movement, the above data is substituted into the calculation model to obtain a numerical value reflecting the degree of danger of positional movement; the calculation model is: Degree of danger of positional movement = (Distance of positional movement × 0.3 + Fractal dimension × 0.2 + Rate of change of positional movement direction × 0.3 + Acceleration of positional movement × 0.2), where the acceleration of positional movement is 0.08 m / s². 2 The numerical value of the danger of location movement is (0.66×0.3+1.1×0.2+0.7×0.3+0.08×0.2)=0.198+0.22+0.21+0.016=0.644≈0.64, with a value range of 0-1. The closer the value is to 1, the higher the danger of location movement.

[0139] (vi) Calculation of synergistic impact coefficient:

[0140] A dynamic correlation analysis was conducted between the numerical values ​​reflecting soil stability and those reflecting hydrological impact to obtain the first synergistic influence coefficient. Specifically, an interaction model was established to determine the degree to which changes in one affects the other. The model incorporates a soil response amplification mechanism under extreme hydrological events to address these extreme conditions. Extreme hydrological events, such as rainstorms and floods, are identified by setting thresholds for rainfall and water level rise; for example, daily rainfall exceeding 50 mm or water level rise exceeding 1 m is considered an extreme hydrological event. When an extreme event occurs, the influence weight of the soil response is amplified by 1.5 times. For example, if the numerical value reflecting soil stability is 0.77 and the numerical value reflecting hydrological impact is 0.21, the interaction coefficient is 0.3 under non-extreme hydrological events, so the first synergistic influence coefficient = 0.77 × 0.21 × 0.3 ≈ 0.048. If an extreme hydrological event occurs, the amplified coefficient = 0.77 × 0.21 × 0.3 × 1.5 ≈ 0.073. A larger coefficient indicates a more significant impact of the interaction between the two on slope instability.

[0141] A dynamic correlation analysis was performed on the values ​​reflecting the slope stabilization effect of vegetation and the value reflecting the degree of risk of location movement to obtain a second synergistic influence coefficient. The analysis included a critical value for vegetation damage. When the vegetation parameter was below this critical value, the influence weight of the location movement parameter was automatically increased to more accurately reflect the synergistic effect of both on slope instability. For example, if the vegetation coverage area was less than 30%, the critical value for vegetation damage was increased to 1.2 times its original value. If the value reflecting the slope stabilization effect of vegetation was 0.39 and the value reflecting the degree of risk of location movement was 0.64, and the vegetation parameters were normal with a coverage area of ​​65% > 30%, the interaction coefficient was 0.2, then the second synergistic influence coefficient = 0.39 × 0.64 × 0.2 ≈ 0.05. If the vegetation parameter was below the critical value, the increased coefficient = 0.39 × 0.64 × 0.2 × 1.2 ≈ 0.06. A larger coefficient indicates a more significant impact of the interaction between the two on slope instability.

[0142] (vii) Calculation of comprehensive early warning numerical values:

[0143] Combining the various numerical values ​​and synergistic impact coefficients obtained above, an adaptive weight adjustment method that can dynamically optimize the impact ratio of each parameter based on the accuracy of historical early warnings is adopted to calculate the comprehensive early warning value.

[0144] Specifically, the following steps are included:

[0145] The first importance ratio is determined based on the numerical value reflecting soil stability and the first synergistic influence coefficient. The determination process employs fuzzy hierarchical analysis (AHP) to handle the uncertainties between parameters. AHP constructs a judgment matrix to transform qualitative parameter relationships into quantitative weight ratios, effectively addressing complex interactions between parameters. Similarly, the second importance ratio is determined by combining the numerical value reflecting hydrological influence and the first synergistic influence coefficient; the third importance ratio is determined by combining the numerical value reflecting the vegetation's role in slope stabilization and the second synergistic influence coefficient; and the fourth importance ratio is determined by combining the numerical value reflecting the risk of location movement and the second synergistic influence coefficient. For the example above, using AHP, combined with the aforementioned numerical values ​​and synergistic influence coefficients, the first importance ratio is 0.2, the second is 0.25, the third is 0.3, and the fourth is 0.25.

[0146] The method for determining the importance ratio is based on fuzzy analytic hierarchy process:

[0147] Fuzzy Hierarchical Analysis (FAHP) transforms the qualitative relationships between parameters into quantitative weights to handle the uncertain correlation between four types of parameters—soil, hydrology, vegetation, and location movement—and their synergistic influence coefficients. The core steps include constructing a hierarchical structure, establishing a fuzzy judgment matrix, calculating weight vectors, and performing consistency checks.

[0148] In the hierarchical structure setting, the target layer is the contribution degree of ecological slope instability risk, the criterion layer consists of four types of parameters: soil stability (A), hydrological influence (B), vegetation slope stabilization effect (C), and location movement risk (D). The correlation factors are synergistic influence coefficients: the first synergistic coefficient K1 correlates A and B, and the second synergistic coefficient K2 correlates C and D. A triangular fuzzy number (l, m, u) is used to represent the relative importance between parameters, where l is the minimum value, m is the most likely value, and u is the maximum value. The importance scale is as follows: equal importance is (1, 1, 1), slightly important is (1 / 2, 1, 3 / 2), and significantly important is (1, 3 / 2, 2). Based on the example data, A = 0.77, B = 0.21, C = 0.39, D = 0.64; K1 = 0.048, K2 = 0.052. The judgment matrix is ​​constructed as follows:

[0149]

[0150] Note: Matrix elements are judged based on a combination of parameter values ​​and synergy coefficients. For example, if soil stability is higher in A and K1 is smaller, A has a weaker impact on B, so A is significantly more important than B.

[0151] The weight calculation steps are as follows: First, perform fuzzy synthesis and calculate the fuzzy weight vector for each parameter. The formula is:

[0152] In the formula, Mi Let be the fuzzy weight vector of the i-th criterion layer parameter (A, B, C, D), which is a triangular fuzzy number (l, m, u) used to represent the fuzzy proportion of the parameter in the risk contribution. This involves summing the four triangular fuzzy numbers (l, m, u) in the i-th row of the judgment matrix; where l ij m ij u ij Let l be the minimum, most likely, and maximum values ​​of the triangular fuzzy number in the i-th row and j-th column, respectively. For example, in row A and column B, (3 / 2, 2, 5 / 2), l AB =3 / 2, m AB =2, u AB =5 / 2; This is a multiplication operation for fuzzy numbers, used to multiply the sum of the numerators by the inverse operation of the denominators to obtain the fuzzy weight vector. First, a double summation is performed on all 16 triangular fuzzy numbers (l, m, u) in the judgment matrix. Then, the inverse operation of the fuzzy numbers is performed, i.e., taking the reciprocal, which is used as the denominator for normalization to ensure that the sum of weights conforms to the logical range. The subscripts i, j, and k are used to iterate through the elements in the judgment matrix. i represents the criterion layer parameters, A=1, B=2, C=3, D=4, and j and k are matrix column indices, both ranging from 1 to 4. The fuzzy weights of A are calculated as (0.28, 0.35, 0.42), B as (0.12, 0.18, 0.24), C as (0.25, 0.32, 0.39), and D as (0.15, 0.21, 0.27). Next, the centroid method is used for defuzzification, with the formula: w i =(l i +2m i +u i ) / 4; where w i The precise weight value for the i-th criterion layer parameter is the result of defuzzifying the fuzzy weight vector (l,m,u), and is a specific numerical value used for subsequent weight correction; l i m i u i Let l represent the minimum, most likely, and maximum values ​​in the fuzzy weight vector for the i-th parameter, respectively. For example, in the fuzzy weights (0.28, 0.35, 0.42) of parameter A, l A =0.28, m A =0.35, u A =0.42; coefficients 2 and 4 are due to the most likely value m i It has higher representativeness among fuzzy numbers, so it is assigned a weight of 2. The sum of the numerators divided by 4 is used to obtain the average value, which is the result of the calculation of the precise weight. The calculation result is w. A =0.35, w B =0.18, wC =0.32, w D =0.21; Finally, a synergy coefficient is introduced to fine-tune the weights, the formula is: In the formula, The adjusted importance ratio of the i-th parameter is ultimately used in the comprehensive early warning calculation; w i K represents the precise weight values ​​obtained through the deblurring formula. 关联 The coefficient of synergistic influence is (1+K). 关联 () is a co-regulation term, which amplifies or reduces the weights to reflect the interaction between parameters; The sum of (weights × adjustment terms) of the associated parameters is used for normalization to ensure... + + + =1, where the association between A and B is K1=0.048, after correction =0.20, =0.25; C and D correlation K2=0.052, after correction =0.30, =0.25.

[0153] Obtain the environmental adjustment coefficient, which includes the seasonal influence coefficient, topographic influence coefficient, climate change abruptness influence coefficient, and human activity influence coefficient. The seasonal influence coefficient is set according to the differences in the impact of different seasons on slope stability, such as 0.9 in winter and 1.1 in summer. The topographic influence coefficient is determined based on the slope gradient and topographic complexity, with 1.2 for steep slopes and 0.8 for gentle slopes. The climate change abruptness influence coefficient is set for extreme weather events, including cold waves and typhoons, with 1.3 during cold waves. The human activity influence coefficient is determined based on the intensity and frequency of regional engineering activities, such as 1.2 when there are more than 3 engineering activities per day. Environmental adjustment coefficient = seasonal influence coefficient × topographic influence coefficient × climate change abruptness influence coefficient × human activity influence coefficient: Currently, it is summer (1.1), slope is 30°, steep slope is 1.2, no extreme weather events (1.0), and engineering activities once a day (1.0), then the environmental adjustment coefficient = 1.1 × 1.2 × 1.0 × 1.0 = 1.32.

[0154] The system retrieves similar case data from the historical database for comparison and correction, resulting in a case correction coefficient. Similar case matching employs a multi-feature vector cosine similarity calculation method. By calculating the cosine similarity between the feature vector of the current monitoring data and the feature vector of historical cases, cases with high similarity are selected. The case correction coefficient is calculated as follows: (Cosine similarity of the past year × Case weight of the past year + Cosine similarity of the past three years × Case weight of the past three years) ÷ (Case weight of the past year + Case weight of the past three years). Simultaneously, a time decay weight is applied to cases with different time spans, selecting two similar cases: one from the past year with a similarity of 0.75, and one from the past three years with a similarity of 0.65. The case correction coefficient is calculated as follows: (0.75 × 1.0 + 0.65 × 0.8) ÷ (1.0 + 0.8) ≈ (0.75 + 0.52) ÷ 1.8 ≈ 0.706 ≈ 0.71, highlighting the reference value of recent cases.

[0155] The comprehensive early warning value is calculated based on the aforementioned data using an adaptive weighting adjustment method. This method periodically calculates the early warning accuracy rate based on historical early warning data and dynamically adjusts the influence weights of each parameter accordingly. Historical early warning data includes the number of successful early warnings, false alarms, and missed warnings. For example, when soil-related parameters are found to contribute less to the early warning, their weight is automatically reduced, while the weights of other more critical parameters are increased. The formula for calculating the comprehensive early warning value is: Comprehensive Early Warning Value = (First Importance Ratio × Value Reflecting Soil Stability + Second Importance Ratio × Value Reflecting Hydrological Impact + Third Importance Ratio × Value Reflecting Vegetation's Slope Fixation Effect + Fourth Importance Ratio) The formula is: (degree ratio × value reflecting the degree of danger of location movement) × environmental adjustment coefficient × case correction coefficient × ED, with a value range of 0-100. The larger the value, the higher the risk of ecological slope instability. ED is a correction factor. Since the calculated value is small, 100 is used to correct the calculated result here. In this example, the comprehensive warning value = (0.2×0.77+0.25×0.21+0.3×0.39+0.25×0.64)×1.32×0.71≈(0.154+0.053+0.117+0.16)×1.32×0.71×100≈0.49×1.32×0.71×100≈0.49×0.937×100≈0.46×100≈46.

[0156] (viii) Early warning judgment and response:

[0157] The system determines whether the comprehensive warning value exceeds the preset warning threshold. This warning threshold adopts a graded dynamic threshold mechanism, which will be automatically adjusted according to the sensitivity level and ecological value of the geographical environment where the slope is located. The sensitivity level of the geographical environment is divided as follows: if it is close to a residential area, it is a high sensitivity level with a threshold set at 80; if it is far from a residential area, it is a low sensitivity level with a threshold set at 60. In terms of ecological value, the threshold is set at 85 for ecological protection areas and 70 for ordinary areas.

[0158] If the comprehensive warning value exceeds the warning threshold, it is determined that the ecological slope is likely to be unstable, and graded warning response measures are initiated. For a comprehensive warning value ≥ 90: a Level 1 warning is issued, an emergency evacuation plan is activated, and professional personnel are organized for on-site rescue. For a comprehensive warning value ≤ 90: a Level 2 warning is issued, monitoring frequency is increased to once per hour, and emergency supplies are prepared. For a warning threshold ≤ comprehensive warning value < 80: a Level 3 warning is issued, patrols are increased, and slope changes are closely monitored. Simultaneously, based on the first and second synergistic influence coefficients, key warning areas are determined, with areas having higher coefficients being designated as key areas. Visualized warning reports containing emergency response priorities and specific prevention and control recommendations are generated for these key areas. Emergency response priorities are ranked from highest to lowest according to the synergistic influence coefficient, and specific prevention and control recommendations include priority reinforcement, installation of drainage facilities, and replanting vegetation.

[0159] If the comprehensive warning value does not exceed the warning limit, the ecological slope protection is determined to be in a stable state. The original monitoring interval is maintained, and monitoring is conducted once a day. The monitoring data and analysis results are stored in the historical database. An incremental learning method is used to improve the model's adaptability by training only on new data. The incremental learning method can update the model parameters with new data without retraining the entire model, thereby improving the model's adaptability to new environments and situations.

[0160] For the example of ecological slope protection along the mountain road mentioned above, the ecological slope protection is located in an ordinary area and far from residential areas, so the warning threshold value is set at 60; the comprehensive warning value is 46, which does not exceed the warning threshold value, so the ecological slope protection is determined to be in a stable state; the monitoring frequency is maintained once a day, and the monitoring data and analysis results are stored in the historical database. The incremental learning method is used to optimize the model in order to improve the accuracy and adaptability of subsequent warnings.

[0161] The weights mentioned above are model parameters obtained by performing multiple linear regression analysis on historical monitoring data or by training with machine learning algorithms (such as gradient boosting trees) to reflect the contribution of each parameter to soil stability. They can also be preset.

[0162] Finally, it should be noted that the above embodiments are merely examples for clearly illustrating the present invention and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. An ecological slope protection instability early warning method, characterized in that, The early warning method comprises: Collecting monitoring information of the area where the ecological slope protection is located, wherein the monitoring information comprises soil information, hydrological information, vegetation information and location movement information; According to the soil information, a value reflecting the soil stability degree is obtained, and a dynamic attenuation model of soil mechanical properties with time is introduced to quantify the changes of soil mechanical properties with time and environmental factors, which is constructed based on the initial state parameters of the soil and the long-term monitored attenuation trend; According to the hydrological information, a value reflecting the hydrological influence degree is obtained; According to the vegetation information, a value reflecting the fixing effect of the vegetation on the slope is obtained; According to the location movement information, a value reflecting the location movement danger degree is obtained; The value reflecting the soil stability degree and the value reflecting the hydrological influence degree are dynamically correlated and analyzed to obtain a first synergistic influence coefficient; The value reflecting the fixing effect of the vegetation on the slope and the value reflecting the location movement danger degree are dynamically correlated and analyzed to obtain a second synergistic influence coefficient; The value reflecting the soil stability degree, the value reflecting the hydrological influence degree, the value reflecting the fixing effect of the vegetation on the slope, the value reflecting the location movement danger degree, the first and second synergistic influence coefficients are combined to calculate a comprehensive early warning value, and it is judged whether the comprehensive early warning value exceeds a pre-set early warning limit value. 2.The ecological slope protection instability early warning method according to claim 1, characterized in that, The step of obtaining the value reflecting the soil stability degree according to the soil information comprises: Extracting the current soil water content ratio, the standard soil water content ratio, the soil density and the soil organic matter content in the soil information; Calculating the difference between the current soil water content ratio and the standard soil water content ratio to obtain a soil water content ratio change amount; determining a soil shear strength correction value according to the soil density; recording the continuous duration that the soil water content ratio change amount exceeds a first specified value to obtain the duration of soil water content ratio anomaly; and obtaining the value reflecting the soil stability degree by using the soil water content ratio change amount, the soil shear strength correction value and the duration of soil water content ratio anomaly in combination with the dynamic attenuation model of soil mechanical properties.

3. The ecological slope protection instability early warning method according to claim 1, characterized in that, The step of obtaining the value reflecting the hydrological influence degree according to the hydrological information comprises: Extracting the current water body height, the current water flow speed, the water flow impact force and the water body pH value in the hydrological information; Calculating the change amount of the current water body height per unit time to obtain a water body height rising amplitude; calculating the change amount of the current water flow speed per unit time to obtain a water flow speed change ratio; determining a water flow erosion correction coefficient according to the matching relationship between the water flow impact force and the erosion resistance of the slope material; recording the continuous duration that the water body height rising amplitude exceeds a second specified value or the water flow speed change ratio exceeds a third specified value to obtain the duration of hydrological anomaly, and recording the abnormal time weight of the process distinguishing between the wet period and the dry period; and obtaining the value reflecting the hydrological influence degree by using the water body height rising amplitude, the water flow speed change ratio, the water flow erosion correction coefficient and the duration of hydrological anomaly in combination with the different weights of the hydrological cycle stages.

4. The ecological slope protection instability early warning method according to claim 1, characterized in that, The step of obtaining the value reflecting the fixing effect of the vegetation on the slope according to the vegetation information comprises: extracting vegetation coverage ratio, plant root distribution density, vegetation growth vigor index and vegetation species diversity in the vegetation information; calculating the difference between the vegetation coverage ratio and the vegetation coverage ratio in the same period in the past to obtain the coverage ratio change amount; determining the root firmness coefficient according to the plant root distribution density, the vegetation growth vigor index and the vegetation species diversity; and obtaining the value reflecting the slope fixation effect of the vegetation by combining the coverage ratio change amount and the root firmness coefficient with the vegetation growth cycle root fixation slope ability dynamic change curve.

5. The ecological slope protection instability early warning method of claim 1, wherein, The step of obtaining the value reflecting the position movement danger degree according to the position movement information comprises: extracting three-dimensional space position coordinates, corresponding time records, position movement acceleration and position movement direction mutation frequency at different time points in the position movement information; calculating the position movement distance according to the three-dimensional space position coordinates of adjacent time points, and the distance calculation considers the spatial distribution density of the monitoring points for weighted processing; then calculating the position movement speed according to the position movement distance and the corresponding time interval; drawing the position movement path by using the position movement distance and the time records at multiple time points; determining the position movement direction change speed by the bending situation of the position movement path and the position movement direction mutation frequency; and obtaining the value reflecting the position movement danger degree according to the position movement distance, the position movement speed, the position movement direction change speed and the position movement acceleration.

6. The ecological slope protection instability early warning method of claim 1, wherein, The step of synthesizing the early warning value comprises: determining a first importance degree proportion by the value reflecting the soil firmness degree and the first synergistic influence coefficient; determining a second importance degree proportion according to the value reflecting the hydrological influence degree and the first synergistic influence coefficient; determining a third importance degree proportion by the value reflecting the slope fixation effect of the vegetation and the second synergistic influence coefficient; and determining a fourth importance degree proportion according to the value reflecting the position movement danger degree and the second synergistic influence coefficient; obtaining an environmental adjustment coefficient; calling similar case data in the historical database for comparison and correction to obtain a case correction coefficient, and the similar case matching adopts a multi-feature vector cosine similarity calculation method, and time decay weights are set for cases of different time spans; synthesizing the early warning value by the value reflecting the soil firmness degree, the value reflecting the hydrological influence degree, the value reflecting the slope fixation effect of the vegetation, the value reflecting the position movement danger degree, the first and second synergistic influence coefficients, the first to fourth importance degree proportions, the environmental adjustment coefficient and the case correction coefficient after adaptive weight adjustment.

7. The ecological slope protection instability early warning method of claim 1, wherein, The step of obtaining the first synergistic influence coefficient by dynamically correlating the value reflecting the soil firmness degree and the value reflecting the hydrological influence degree comprises: establishing a first sequence of values reflecting the degree of soil stability changing over time and a second sequence of values reflecting the degree of hydrological influence changing over time; determining time nodes and variation amplitudes of the values in the two sequences; analyzing the influence degree of the value variation in the first sequence on the value variation in the second sequence and the influence degree of the value variation in the second sequence on the value variation in the first sequence within the same time interval; introducing an extreme hydrological event identification mechanism, and amplifying the influence weight of soil response according to a preset rule when an extreme hydrological event is monitored; and calculating the first synergistic influence coefficient according to the analysis results. 8.The ecological slope protection instability early warning method of claim 1, wherein, The step of obtaining the second synergistic influence coefficient by dynamically correlating the value reflecting the fixing effect of vegetation on the slope and the value reflecting the degree of position movement danger comprises: establishing a third sequence of values reflecting the fixing effect of vegetation on the slope changing over time and a fourth sequence of values reflecting the degree of position movement danger changing over time; setting a critical value of vegetation damage, and marking a vegetation protection capability decline stage when the value in the third sequence is lower than the critical value; analyzing the mutual influence relationship between the value variation in the third sequence and the value variation in the fourth sequence in a vegetation protection capability normal stage; increasing the weight of the value variation in the fourth sequence in the correlation analysis in a vegetation protection capability decline stage; and comprehensively calculating the second synergistic influence coefficient according to the analysis results in different stages.

9. An ecological slope protection instability early warning system, characterized in that, comprise: an information collection part for collecting monitoring information of an area where the ecological slope protection is located, wherein the monitoring information comprises soil information, hydrological information, vegetation information and position movement information; an analysis and processing part for processing and analyzing the collected monitoring information to obtain values reflecting the degree of soil stability, the degree of hydrological influence, the fixing effect of vegetation on the slope, the degree of position movement danger, the first and second synergistic influence coefficients, and calculating a comprehensive early warning value in combination with the values; an early warning judgment part for judging whether the comprehensive early warning value exceeds a pre-set early warning limit value, and executing corresponding early warning response measures or maintaining the original monitoring state according to the judgment result, and generating an early warning report or updating a historical database for model optimization; wherein the step of obtaining the first synergistic influence coefficient comprises: establishing a first sequence of values reflecting the degree of soil stability changing over time and a second sequence of values reflecting the degree of hydrological influence changing over time; determining time nodes and variation amplitudes of the values in the two sequences; analyzing the influence degree of the value variation in the first sequence on the value variation in the second sequence and the influence degree of the value variation in the second sequence on the value variation in the first sequence within the same time interval; introducing an extreme hydrological event identification mechanism, and amplifying the influence weight of soil response according to a preset rule when an extreme hydrological event is monitored; and calculating the first synergistic influence coefficient according to the analysis results. the step of obtaining the second synergistic influence coefficient comprises: A third sequence of values reflecting the fixing effect of the vegetation on the slope over time and a fourth sequence of values reflecting the degree of danger of position movement over time are established; a critical value of vegetation damage is set, and when the value in the third sequence is lower than the critical value, the vegetation protection capability is marked as being in a decline phase; in the normal phase of the vegetation protection capability, the mutual influence relationship between the value change in the third sequence and the value change in the fourth sequence is analyzed; in the decline phase of the vegetation protection capability, the weight of the value change in the fourth sequence in the correlation analysis is increased; and the second synergistic influence coefficient is obtained by comprehensively calculating the analysis results in different phases.

10. The ecological slope protection instability early warning system of claim 9, wherein, The analysis processing part comprises: a soil information analysis unit for deriving a value reflecting the soil stability degree according to soil information, which is provided with a dynamic attenuation model of soil mechanical properties over time and can automatically call historical data to update the model parameters; a hydrological information analysis unit for deriving a value reflecting the hydrological influence degree according to hydrological information; a vegetation information analysis unit for deriving a value reflecting the fixing effect of the vegetation on the slope according to vegetation information; a position movement information analysis unit for deriving a value reflecting the degree of danger of position movement according to position movement information; a synergistic analysis unit for performing dynamic correlation analysis on the value reflecting the soil stability degree and the value reflecting the hydrological influence degree to obtain a first synergistic influence coefficient, and performing dynamic correlation analysis on the value reflecting the fixing effect of the vegetation on the slope and the value reflecting the degree of danger of position movement to obtain a second synergistic influence coefficient; a comprehensive early warning calculation unit for calculating a comprehensive early warning value by combining the above values and coefficients, which is integrated with an adaptive weight adjustment method capable of dynamically optimizing the calculation model according to the accuracy of historical early warnings.

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