A system and method for assessing slope deformation with reduced influence of vegetation growth

Abstract

The present application relates to the technical field of slope deformation monitoring, and particularly relates to a slope deformation evaluation system and method for reducing the influence of vegetation growth, comprising the following steps: identifying the distribution range of slope vegetation, and dividing the vegetation into regions according to growth conditions; determining the characteristics of slope vegetation growth, and constructing a growth time model according to the growth rules of vegetation in the region; identifying slope deformation data, including displacement, settlement and deformation rate; and constructing a vegetation interference correction function based on the time model. Compared with the prior art, the present application has the beneficial effect that: by constructing a time model, the present application determines the growth rules of slope vegetation, and obtains slope deformation data under reduced vegetation interference. By comparing actual slope deformation data, the present application obtains abnormal slope deformation data, thereby determining slope deformation under reduced vegetation interference.

Description

Technical Field

[0001] This invention relates to the field of slope deformation monitoring technology, specifically to a slope deformation assessment system and method that reduces the impact of vegetation development. Background Technology

[0002] When a highway crosses a mountain, valley, depression, or other geological structure, it is necessary to excavate or raise the roadbed, which will form slopes on both sides. The tunnel entrance is a stable mountain above the tunnel, which also requires a slope structure. The stability of the slopes is directly related to the safety, stability, and operation of the highway and tunnel structures.

[0003] In the field of slope monitoring, radar and machine vision technologies are widely used due to their high precision and real-time performance. However, in densely vegetated areas, vegetation growth and movement can significantly interfere with monitoring data, leading to false alarms or missed alarms. Traditional slope monitoring methods often ignore the impact of vegetation development, making it difficult to distinguish between actual slope deformation and vegetation disturbance, thus reducing the accuracy and reliability of monitoring.

[0004] Therefore, developing a new slope deformation assessment system and method to reduce the impact of vegetation development is not only of urgent research value, but also has good economic benefits and industrial application potential. This is the driving force and basis for the completion of this invention. Summary of the Invention

[0005] In order to overcome the deficiencies of the prior art as mentioned above, the inventors conducted in-depth research and, after a great deal of creative work, completed this invention.

[0006] Specifically, the technical problem to be solved by the present invention is to provide a slope deformation assessment system and method that reduces the impact of vegetation development, so as to solve the problem that the influence of vegetation cannot be excluded in existing slope monitoring.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] A method for assessing slope deformation to reduce the impact of vegetation development includes the following steps:

[0009] Identify the distribution range of slope vegetation and divide the vegetation into regions based on growth and development conditions;

[0010] Clearly define the characteristics of slope vegetation development and construct a development time history model based on the regional vegetation growth patterns.

[0011] Identify slope deformation data, including displacement, settlement, and deformation rate;

[0012] A vegetation disturbance correction function is constructed based on the time history model. The slope deformation data is dynamically filtered to correct the vegetation influence and obtain the corrected slope deformation data under the time history conditions.

[0013] When the difference between the actual slope deformation data and the corrected slope deformation data exceeds a threshold, an early warning message is issued.

[0014] In this invention, as an improvement, the vegetation is divided into regions according to growth conditions, including dividing the regions according to the light, water and density of vegetation growth. The vegetation divided into two different regions has a difference in growth conditions exceeding a threshold. Among the growth conditions, light is limited by the total daytime light duration and water is limited by the annual rainfall.

[0015] In this invention, as an improvement, constructing a developmental timeline model includes:

[0016] Fit the growth patterns of vegetation and construct the growth curve of a single plant;

[0017] Based on vegetation growth conditions, the competitive relationships in vegetation growth are clarified, and a competition coefficient is introduced to correct the upper limit of growth of a single plant.

[0018] The total vegetation growth under competitive conditions is calculated based on the revised upper limit of single plant growth.

[0019] Considering density-dependent conditions, the core form of total vegetation growth within the region is identified as the basis for the time-history model.

[0020] In this invention, as an improvement, the growth curve of a single plant is fitted with the logistic growth equation to represent the plant growth pattern, and the plant growth varies over time. B i ( t The calculation is as follows:

[0021]

[0022] in, r The intrinsic growth rate is determined by the vegetation species and the basic environment. K This represents the maximum biomass potential of a single plant in the absence of competition. This represents the rate of change of a single plant per unit time.

[0023] In this invention, as an improvement, a competition coefficient is introduced. α Adjust the upper limit of single plant growth K Corrected maximum biomass potential per plant K eff for:

[0024]

[0025] In the formula, N represents the number of plants per unit area. α This is the competition intensity coefficient;

[0026] The dynamic correction calculation for total biomass of the population considering competition among plants is as follows:

[0027]

[0028] In the formula, B i • N This represents the current total biomass. This represents the rate of change in total biomass per unit time.

[0029] In this invention, as an improvement, the density-dependent total vegetation biomass convergence calculation is as follows:

[0030]

[0031] The core form of a developmental time-course model considering density-dependent conditions is:

[0032]

[0033] In the formula, e This serves as the base for the exponential decay of resources.

[0034] In this invention, as an improvement, the slope deformation data is calculated as follows:

[0035] R corrected ( t )= R ( t )−δ· B * total

[0036] Where δ is the vegetation disturbance coefficient. R ( t () represents slope deformation monitoring data.

[0037] In this invention, as an improvement, the steps for determining the vegetation disturbance coefficient are as follows:

[0038] We screened for strong interference factors affecting slope vegetation development, including environmental factors, matrix factors, and hydrological factors.

[0039] The dimensionless value of the factor is calculated using the following formula:

[0040]

[0041] in, X i These are measured values. X min / X max This is the threshold value for the factor under extreme conditions;

[0042] The weights of each factor are determined using methods such as analytic hierarchy process (AHP), principal component analysis (PCA), or expert review. W i ;

[0043] The vegetation disturbance coefficient is calculated, and the disturbance coefficient δ is retrieved from the residual inversion of vegetation cover area deformation monitoring on a quarterly basis to update the weights. W i .

[0044] In this invention, as an improvement, the vegetation disturbance coefficient... δ The calculation formula is as follows:

[0045]

[0046] In the formula, G i This is the normalization coefficient for calculating the factor.

[0047] A slope deformation assessment system that reduces the impact of vegetation development includes:

[0048] The monitoring module uses radar and machine vision to monitor slope deformation and vegetation growth data, and feeds the data back to the processing system.

[0049] The processing system receives data from the monitoring module, analyzes the data, clarifies the vegetation division areas, constructs a vegetation development time history model based on vegetation development characteristics, corrects slope deformation data, and issues early warning information based on the comparison of the corrected data.

[0050] The early warning module receives and processes early warning information sent by the system and then sends it back to the user interface.

[0051] The signal receiving and transmitting module is used to process data and signal transmission between the system and its various modules.

[0052] Compared with the prior art, the beneficial effects of the present invention are:

[0053] (1) This invention clarifies the slope vegetation development pattern by constructing a time history model and obtains slope deformation data under reduced vegetation disturbance. By comparing the actual slope deformation data, abnormal slope deformation data is obtained, thereby clarifying slope deformation under reduced vegetation disturbance.

[0054] (2) This invention distinguishes the vegetation on slopes based on their special locations and topographic structures, and improves the prediction accuracy by using the interference coefficient to make the model approximate the actual growth curve of the vegetation. Attached Figure Description

[0055] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0056] Figure 1 This is a thermal diagram of vegetation density distribution in Embodiment 1 of the present invention;

[0057] Figure 2 This is a schematic diagram comparing the monitoring data of the radar-visual fusion device with the vegetation development pattern in Embodiment 1 of the present invention. Detailed Implementation

[0058] The embodiments of the technical solution of the present invention will now be described in detail with reference to the accompanying drawings. These embodiments are merely illustrative of the technical solution of the present invention and are therefore intended to limit the scope of protection of the present invention.

[0059] A slope deformation assessment system for reducing the impact of vegetation development is disclosed. The system includes a monitoring module, an early warning module, a signal receiving and transmitting module, and a processing system. The processing system is the center of the entire assessment system, used for data processing and controlling the operation of other modules. The monitoring module controls relevant monitoring components to monitor slope deformation and vegetation growth data. The monitoring components are machine vision monitoring devices such as radar or drones. The monitoring module receives control signals sent by the processing system, allocates monitoring components to observe the slope and vegetation in the area, and feeds back the monitoring data to the processing system.

[0060] The early warning module receives early warning signals sent by the processing system and displays the early warning information to the user interface through sound or images. The user interface includes monitors and portable display devices.

[0061] The processing system receives feedback data from the monitoring module, analyzes the vegetation growth data, divides the vegetation under different growth conditions into regions, constructs a vegetation development time history model based on the vegetation development characteristics, corrects the slope deformation data, and issues early warning information based on the comparison of the corrected data, which is then sent to the early warning module.

[0062] Each module and processing system includes a signal receiving and transmitting module for data and signal transmission between the processing system and various modules.

[0063] The method for assessing slope deformation under the influence of low vegetation development using the above-mentioned assessment system includes the following steps:

[0064] Identify the distribution range of vegetation on the slope and divide it into different monitoring areas based on the different growth conditions of the vegetation within the distribution range;

[0065] The characteristics of slope vegetation development were clarified, and a development time history model was constructed based on the vegetation growth pattern in the region. Among the characteristics of vegetation development, density is one of the factors affecting vegetation development. In slope vegetation, density has a significant impact on development due to natural conditions.

[0066] Identify slope deformation data, including local slope displacement, settlement, and deformation rate;

[0067] The factors affecting vegetation development were identified, the influencing factors were calculated, and a vegetation interference correction function was constructed based on the time history model. The slope deformation data was dynamically filtered to remove the influencing factors and correct the vegetation influence, thus obtaining the corrected slope deformation data under the time history conditions.

[0068] When the difference between the actual slope deformation data and the corrected slope deformation data exceeds a threshold, an early warning message is issued.

[0069] Vegetation is divided into regions based on the growth conditions of vegetation in different areas. For example, the light conditions of vegetation on the sunny side and the shady side are different, so their growth time is different. Similarly, the growth time of vegetation in areas with more rainfall is different from that in areas with less rainfall. Vegetation is divided into regions based on the light, water and density of vegetation growth. Among them, the difference in vegetation growth conditions between two different regions exceeds the threshold. Light is limited by the total daytime light duration, water is limited by the annual rainfall, and the threshold for poor growth conditions is defined by plant classification standards combined with the growth characteristics of slope vegetation.

[0070] The construction of a time-history model of vegetation includes the following steps:

[0071] By observing vegetation growth and development data, fitting vegetation growth patterns, and constructing individual plant growth curves, the logistic growth equation was used to fit the plant growth patterns, and the plant growth amount changed over time. B i ( t The calculation is as follows:

[0072]

[0073] in, r The intrinsic growth rate is determined by the vegetation species and the basic environment. K This represents the maximum biomass potential of a single plant in the absence of competition. This represents the rate of change of a single plant per unit time.

[0074] Based on density conditions during vegetation growth, the competitive relationships in vegetation growth are clarified, and a competition coefficient is introduced. α Adjust the upper limit of single plant growth K ;

[0075] Revised maximum biomass potential per plant Keff for:

[0076]

[0077] In the formula, N represents the number of plants per unit area. α This is the competition intensity coefficient.

[0078] The total vegetation growth considering competition conditions is calculated based on the corrected upper limit of single plant growth, as follows:

[0079]

[0080] In the formula, B i • N This represents the current total biomass. This represents the rate of change in total biomass per unit time.

[0081] Considering density dependence, the core form of total vegetation growth within the region is identified as the basis for the time-history model. The density-dependent total vegetation biomass convergence calculation is as follows:

[0082]

[0083] The core form of a developmental time-course model considering density-dependent conditions is:

[0084]

[0085] In the formula, e This serves as the base for the exponential decay of resources.

[0086] Based on the model, the monitored environmental conditions for vegetation growth and development, such as light and precipitation conditions, can be adjusted to construct a developmental time-series model that conforms to the characteristics of vegetation growth.

[0087] After establishing the basic time history model, it is also necessary to consider the impact of specific conditions.

[0088] In certain geographical locations, vegetation on slopes is affected by specific conditions. These include: tunnel structures, natural wind fields at the tunnel entrance and the shading effect of tunnel support facilities; the impact of blasting disturbance during tunnel construction on soil and rock masses; the impact of tunnel drainage on hydrological conditions; and the impact of vehicle exhaust on vegetation. In special geological areas, soil conditions such as soil texture and salinity, topographical factors such as slope and altitude, and biological factors also play a role. These are all disturbance factors in vegetation development. Based on the monitoring data of these disturbance factors, a vegetation disturbance coefficient is calculated. δ .

[0089] The specific calculation process for the interference coefficient is as follows:

[0090] Dynamic factors strongly correlated with vegetation disturbance on tunnel slopes were selected, including environmental factors, matrix factors, and hydrological factors. The selection criteria were based on the location of the vegetation and the above-mentioned influencing factors, and the data of each dynamic factor were monitored.

[0091] Factor normalization is performed to convert factors of different dimensions into dimensionless values ​​between 0 and 1. The calculation formula is as follows:

[0092]

[0093] in, X i These are measured values. X min / X max This is the threshold value for the factor under extreme conditions;

[0094] The weights of each factor are determined using methods such as analytic hierarchy process (AHP), principal component analysis (PCA), or expert review. W i , satisfying ∑ W i =1;

[0095] The vegetation disturbance coefficient is calculated, and the disturbance coefficient δ is retrieved quarterly from the residual inversion of vegetation cover area deformation monitoring, and the weights are updated accordingly. W i .

[0096] The formula for calculating the interference coefficient δ is as follows:

[0097]

[0098] In the formula, G i This is the normalization coefficient for calculating the factor.

[0099] The corrected slope deformation data are as follows:

[0100] R corrected ( t )= R ( t )−δ· B * total

[0101] The corrected slope deformation data is compared with the actual slope deformation data. The difference between the two must be greater than the safety threshold σ. R ( t )− R corrected ( t )>σ, where the safety threshold σ is set based on the slope safety standard.

[0102] Example 1:

[0103] Deformation monitoring was conducted on a slope located at the tunnel entrance. The soil was alkaline with a pH value of 10.0. The area contaminated by concrete was 40%, the area affected by through-drafts at the tunnel entrance was 40%, the effective soil layer thickness was 8 cm, and the slope scour intensity was 5000 t / km².

[0104] (1) Slope images were acquired using LiDAR and machine vision equipment, and vegetation cover areas were identified through a deep learning model. The vegetation density distribution map is shown below. Figure 1 As shown.

[0105] (2) Fit the vegetation development time history function by monitoring vegetation growth data and density distribution. ;

[0106] (3) Monitor slope deformation data using radar and machine vision equipment;

[0107] (4) Dynamic calibration of vegetation disturbance coefficient: After normalizing the factors affecting vegetation disturbance, the following results were obtained:

[0108] project Normalized values strong wind speed 0.7 Concrete contamination area 0.4 Soil pH 0.75 Slope erosion intensity 0.6 Effective soil layer thickness 0.8

[0109] The weight W of the strong wind impact factor was determined based on the expert scoring method. i =0.25, Concrete Contamination Area Weight W i =0.3, weight of soil pH influencing factor W i =0.2, slope erosion intensity W i =0.15, Weight W of Effective Soil Layer Thickness Influence Factor i =0.1, satisfying ∑W i =1;

[0110] (5) Interference coefficient calculation δ=(0.25×0.7)+(0.30×0.4)+(0.20×0.75)+(0.15×0.6)+(0.10×0.8)=0.615;

[0111] (6) During the vegetation growth period t≤60 days, the radar data and the vegetation development model are periodically consistent, indicating that the slope is normal. At t=70 days, [the following occurs]. R ( t )− R corrected ( t If ) > σ, an early warning mechanism is triggered, such as Figure 2 As shown.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for assessing slope deformation to reduce the impact of vegetation development, characterized in that, Includes the following steps: Identify the distribution range of slope vegetation and divide the vegetation into regions based on growth and development conditions; Clearly define the characteristics of slope vegetation development and construct a development time history model based on the regional vegetation growth patterns. Identify slope deformation data, including displacement, settlement, and deformation rate; A vegetation disturbance correction function is constructed based on the time history model. The slope deformation data is dynamically filtered to correct the vegetation influence and obtain the corrected slope deformation data under the time history conditions. When the difference between the actual slope deformation data and the corrected slope deformation data exceeds a threshold, an early warning message is issued. Constructing a developmental timeline model includes: Fit the growth patterns of vegetation and construct the growth curve of a single plant; Based on vegetation growth conditions, the competitive relationships in vegetation growth are clarified, and a competition coefficient is introduced to correct the upper limit of growth of a single plant. The total vegetation growth under competitive conditions is calculated based on the revised upper limit of single plant growth. Considering density-dependent conditions, the core form of total vegetation growth within the region is identified as the basis for the time-history model. Add vegetation growth environment data and construct a developmental time-series model that conforms to the characteristics of vegetation growth; Introducing a competition coefficient α Adjust the upper limit of single plant growth K Corrected maximum biomass potential per plant K eff for: In the formula, N The number of plants per unit area. α This is the competition intensity coefficient; The dynamic correction calculation for total biomass of the population considering competition among plants is as follows: In the formula, B i · N This represents the current total biomass. The rate of change of total biomass per unit time. r Intrinsic growth rate; The density-dependent total vegetation biomass convergence calculation is as follows: The core form of a developmental time-course model considering density-dependent conditions is: In the formula, e As the base for the exponential decay of resources, t This refers to the growth period.

2. The slope deformation assessment method for reducing the impact of vegetation development according to claim 1, characterized in that: The vegetation is divided into regions based on growth conditions, including the light, water and density of vegetation growth. Among them, the vegetation in two different regions has a difference in growth conditions exceeding a threshold. In terms of growth conditions, light is limited by the total daytime light duration and water is limited by the annual rainfall.

3. The slope deformation assessment method for reducing the impact of vegetation development according to claim 1, characterized in that: The growth curve of a single plant was fitted using the logistic growth equation to represent the plant's growth pattern over time. B i ( t The calculation is as follows: in, r The intrinsic growth rate is determined by the vegetation species and the basic environment. K This represents the maximum biomass potential of a single plant in the absence of competition. This represents the rate of change of a single plant per unit time.

4. The slope deformation assessment method for reducing the impact of vegetation development according to claim 1, characterized in that: The corrected slope deformation data are calculated as follows: R corrected ( t )= R ( t )−δ; B * total Where δ is the vegetation disturbance coefficient. R ( t () represents slope deformation monitoring data.

5. The slope deformation assessment method for reducing the impact of vegetation development according to claim 4, characterized in that, The steps for determining the vegetation disturbance coefficient are as follows: We screened for strong interference factors affecting slope vegetation development, including environmental factors, matrix factors, and hydrological factors. The dimensionless value of the factor is calculated using the following formula: in, X i These are measured values. X min / X max This is the threshold value for the factor under extreme conditions; The weights of each factor are determined using methods such as analytic hierarchy process (AHP), principal component analysis (PCA), or expert review. W i ; The vegetation disturbance coefficient is calculated, and the disturbance coefficient δ is retrieved from the residual inversion of vegetation cover area deformation monitoring on a quarterly basis to update the weights. W i .

6. The slope deformation assessment method for reducing the impact of vegetation development according to claim 4, characterized in that, Vegetation disturbance coefficient δ The calculation formula is as follows: In the formula, G i This is the normalization coefficient for calculating the factor.

7. A slope deformation assessment system for reducing the impact of vegetation development using the assessment method described in claim 1, characterized in that, include: The monitoring module uses radar and machine vision to monitor slope deformation and vegetation growth data, and feeds the data back to the processing system. The processing system receives data from the monitoring module, analyzes the data, clarifies the vegetation division areas, constructs a vegetation development time history model based on vegetation development characteristics, corrects slope deformation data, and issues early warning information based on the comparison of the corrected data. The early warning module receives and processes early warning information sent by the system and then sends it back to the user interface. The signal receiving and transmitting module is used to process data and signal transmission between the system and its various modules.

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