Comprehensive analysis method for anti-sliding stability of flood discharge slope under multiple working conditions

Abstract

The invention provides a comprehensive analysis method for the anti-skid stability of a flood discharge slope under multiple working conditions. The method comprises the steps that soil mass, hydraulic power and structural parameters are collected; performing dynamic grouping and nonlinear transformation on the soil body parameters; calculating a multi-source coupling stability index based on the fuzzy logic weight model; constructing a three-state logic judgment unit, and comparing the real-time stability index with a dynamic safety threshold value; a parameter membership compensation mechanism is started when early warning is triggered; performing time-frequency domain reconstruction on the compensation parameters through a mutation factor demodulator; based on the reconstruction parameters, anti-sliding stability comprehensive evaluation is executed, and a three-dimensional security situation map is generated. According to the method, the accuracy and reliability of slope anti-sliding stability analysis can be improved, and a scientific basis is provided for making slope safety protection and reinforcement measures.

Description

Technical Field

[0001] The present invention relates to the technical field of water conservancy projects and slope stability analysis. More specifically, the present invention relates to a comprehensive analysis method for the anti-sliding stability of flood-discharging slopes under multiple working conditions. Background Art

[0002] In water conservancy projects, the stability of flood-discharging slopes is one of the key factors to ensure the safe operation of dams. Existing slope stability analysis methods are mostly based on single working condition or static parameter evaluation, and it is difficult to comprehensively reflect the dynamic change characteristics of slopes under multiple working conditions. Traditional methods usually rely on empirical formulas or simplified models, and insufficiently consider comprehensive factors such as slope soil parameters, hydraulic actions, and structural deformations, resulting in limited accuracy and reliability of analysis results. In addition, existing technologies have deficiencies in real-time monitoring and early warning, unable to capture the subtle characteristics of slope stability changes in a timely manner, and it is difficult to effectively prevent sudden instability events.

[0003] In the process of implementing the embodiments of the present invention, the inventors found that there are at least the following problems or defects in the prior art: unable to adapt to dynamic changes under multiple working conditions, the evaluation of slope stability under complex working conditions is not accurate enough, and there is a lack of effective real-time early warning and dynamic compensation mechanisms. Summary of the Invention

[0004] The present invention provides a comprehensive analysis method for the anti-sliding stability of flood-discharging slopes under multiple working conditions, including:

[0005] S1. Collect the soil parameter set, hydraulic parameter set, and structural parameter set of the flood-discharging slope;

[0006] S2. Perform dynamic parameter grouping and non-linear transformation on the soil parameter set;

[0007] S3. Calculate the multi-source coupling stability index based on the fuzzy logic weight model;

[0008] S4. Construct a three-state logic judgment unit to compare the real-time stability index with the dynamic safety threshold;

[0009] S5. When the judgment result triggers the early warning condition, start the parameter membership compensation mechanism;

[0010] S6. Perform time-frequency domain reconstruction on the compensation parameters through the mutation factor demodulator;

[0011] S7. Perform a comprehensive evaluation of the anti-sliding stability based on the reconstructed parameters and generate a three-dimensional safety situation map.

[0012] Further, the S1 includes:

[0013] S101. Use a distributed sensor array to collect the soil permeability coefficient K s , the internal friction angle Cohesion c;

[0014] S102. Obtain the flood discharge flow velocity distribution V(x, y) through an ultrasonic velocity meter, where x and y are planar coordinates;

[0015] S103. Use a strain monitoring device to measure the displacement ΔD of the slope structure, where ΔD represents the displacement change;

[0016] S104. Simultaneously record the reservoir water level change rate dH / dt and the rainfall intensity parameter R t , where H is the water level height and t is the time.

[0017] Furthermore, the S2 includes:

[0018] S201. Establish dynamic grouping according to the parameter mutation frequency: When the absolute value of the parameter change rate |ΔP / Δt| > θ, it is classified into the high-frequency mutation group; otherwise, it is classified into the low-frequency slow-varying group, where ΔP is the parameter change amount, Δt is the time interval, and θ is a preset threshold;

[0019] S202. Perform a non-linear transformation on the parameters in the high-frequency mutation group:

[0020] P′ = ln(1 + e k·ΔP )

[0021] where k is a preset transformation gain coefficient and ΔP is the parameter change amount;

[0022] S203. Construct a parameter correlation tensor

[0023] T ijk = σ(P i , P j , P k )

[0024] where σ is a preset three-parameter coupling function and i, j, k are parameter indices.

[0025] Furthermore, the calculation formula of the fuzzy logic weight model in S3 is:

[0026] W total = ∑(μ i · ω i ) + λ · maxΔP m

[0027] where μ i represents the membership degree of the i-th parameter, ω i is the reference weight of the i-th parameter, λ is the mutation factor coefficient, and ΔP m is the mutation amount of the m-th parameter;

[0028] The parameter membership degree μi The calculation formula is:

[0029]

[0030] In the formula, a is the membership gradient factor, b is the parameter critical value, and P i is the current value of the i-th parameter.

[0031] Furthermore, the calculation of the mutation factor coefficient λ includes:

[0032] S501. Real-time monitor the number of parameter mutations N t , where N t is the number of times the parameter change exceeds the threshold within a unit time;

[0033] S502. When N t > the critical number N c , activate dynamic adjustment:

[0034] λ = λ0·[1 + 0.5·tanh(0.1·(N t - N c ))]

[0035] where λ0 is the initial coefficient value;

[0036] S503. Set the maximum constraint condition: λ max = 1.2λ0.

[0037] Furthermore, the three-state logic judgment unit of S4 executes:

[0038] S401. Set the dynamic safety threshold

[0039]

[0040] where α is the reference threshold coefficient, T0 is the initial safety threshold, and β is the differential coupling coefficient;

[0041] S402. When the real-time stability index S satisfies T d ≤ S < 1.5T d , trigger a yellow warning;

[0042] S403. When 0.8T d ≤ S < T d , trigger an orange warning and activate the first-level compensation mechanism;

[0043] S404. When S < 0.8T d , trigger a red warning and activate the second-level compensation mechanism.

[0044] Furthermore, the parameter membership compensation mechanism includes:

[0045] S701. Adjust the membership function according to the warning level:

[0046] When an orange warning is triggered, adjust the membership gradient factor a to 1.2 times the original value, and at the same time adjust the parameter critical value b to 0.9 times the original value;

[0047] When a red warning is triggered, adjust the membership gradient factor a to 1.5 times the original value, and at the same time adjust the parameter critical value b to 0.8 times the original value;

[0048] S702. Implement exponential decay compensation for the mutation factor:

[0049]

[0050] where t w is the warning duration;

[0051] S703. Reconstruct the coupling stability index

[0052] S ′ = f(W total , λ ′ )

[0053] where f is a preset reconstruction function.

[0054] Furthermore, the decay compensation of S702 satisfies:

[0055] When t w > the critical time t c , activate periodic anti-compensation:

[0056]

[0057] where t p is the flood discharge period parameter, and π is the pi.

[0058] Furthermore, the mutation factor demodulator of S6 executes:

[0059] S601. Construct the parameter time-frequency matrix

[0060] Ψ = [P i (t)·e -jωt

[0061] where j is the imaginary unit, ω is the angular frequency, and t is the time variable;

[0062] S602. Calculate the mutation energy density

[0063] E = ∫|Ψ(t,ω)| 2 dω

[0064] Among them, the integration range is the entire frequency domain;

[0065] S603. When E > the energy threshold E c , trigger parameter reconstruction:

[0066]

[0067] Among them, the summation range is the frequency bands exceeding the threshold.

[0068] Furthermore, the said S7 includes:

[0069] S701. Input the reconstructed parameters into the spatio-temporal coupling analysis model;

[0070] S702. Calculate the safety factor of each node according to where τ

[0071] is the anti-sliding force, τ f is the static shear stress, τ m is the dynamic hydraulic shear stress, and η is the coupling coefficient; d S703. Generate a three-dimensional safety situation map based on the improved moving least squares method;

[0072] S704. Mark the area where the safety factor F

[0073] < 1.15 as the critical instability area, and output a multi-level reinforcement plan. s <000-0043>

[0074] According to the above embodiments of the present invention, it has at least the following beneficial effects: The method proposed by the present invention can realize the comprehensive dynamic stability evaluation of the flood discharge slope under multiple working conditions. By collecting multi-source parameters and performing dynamic grouping and non-linear transformation, it can accurately capture the mutation characteristics of soil parameters. Combining with the fuzzy logic weight model, it can effectively quantify the influence of multi-source coupling factors on the slope stability, thereby improving the accuracy and reliability of the stability analysis. At the same time, the constructed three-state logic judgment unit can compare the stability index with the dynamic safety threshold in real time, quickly trigger the early warning mechanism, and provide a timely decision-making basis for slope safety management.

[0075] In addition, the present invention can also dynamically adjust and perform time-frequency domain reconstruction on the parameters after early warning through the parameter membership compensation mechanism and the mutation factor demodulator, further optimizing the stability evaluation results. The comprehensive anti-sliding stability evaluation based on the reconstructed parameters can generate a three-dimensional safety situation map, intuitively display the safety factor distribution of each area of the slope, and accurately mark the critical instability area, providing scientific support for the formulation of targeted reinforcement measures. This method can not only improve the refinement level of slope stability analysis, but also realize the whole-process management from static evaluation to dynamic early warning and real-time compensation, enhancing the safety and reliability of the flood discharge slope. Description of the Drawings

[0076] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present invention will become readily understandable. In the drawings, several embodiments of the present invention are shown in an exemplary rather than restrictive manner, wherein:

[0077] Figure 1 It is a schematic flowchart of a comprehensive analysis method for the anti-sliding stability of a flood-discharging slope under multiple working conditions provided in an embodiment of the present invention. Specific Embodiments

[0078] The principles and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are given only to enable those skilled in the art to better understand and then implement the present invention, rather than to limit the scope of the present invention in any way. On the contrary, these embodiments are provided to make the present invention more thorough and complete, and to be able to convey the scope of the present invention fully to those skilled in the art.

[0079] Those skilled in the art know that the embodiments of the present invention can be implemented as a system, device, equipment, method, or computer program product. Therefore, the present invention can be specifically implemented in the following forms: completely hardware, completely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0080] It should be noted that any number of elements in the drawings is for illustration rather than limitation, and any naming is only for distinction and does not have any limiting meaning.

[0081] The following references Figure 1 , Figure 1 It is a schematic flowchart of a comprehensive analysis method for the anti-sliding stability of a flood-discharging slope under multiple working conditions provided in an embodiment of the present invention. As Figure 1 shown, a comprehensive analysis method 100 for the anti-sliding stability of a flood-discharging slope under multiple working conditions includes:

[0082] S1. Collect the soil parameter set, hydraulic parameter set, and structural parameter set of the flood-discharging slope;

[0083] S2. Perform dynamic parameter grouping and non-linear transformation on the soil parameter set;

[0084] S3. Calculate the multi-source coupling stability index based on the fuzzy logic weight model;

[0085] S4. Construct a three-state logic judgment unit to compare the real-time stability index with the dynamic safety threshold;

[0086] S5. When the judgment result triggers the warning condition, start the parameter membership compensation mechanism;

[0087] S6. Reconstruct the compensation parameters in the time-frequency domain through a mutation factor demodulator;

[0088] S7. Perform a comprehensive assessment of anti-slip stability based on the reconstructed parameters to generate a three-dimensional safety situation map.

[0089] It should be noted that the core of this method lies in providing a scientific basis for slope safety management by comprehensively analyzing the stability of the flood discharge slope under multiple working conditions. In the first step, a set of soil parameters, hydraulic parameters, and structural parameters of the flood discharge slope are collected, which are the basis for subsequent analysis. The set of soil parameters includes the physical and mechanical properties of the soil, such as the permeability coefficient, internal friction angle, and cohesion, which reflect the mechanical behavior of the soil under different working conditions; the set of hydraulic parameters involves information such as the velocity distribution of the flood discharge flow, which reflects the impact of the water flow on the slope stability; the set of structural parameters includes the displacement of the slope structure, etc., which is used to evaluate the deformation of the slope. By collecting these parameters, the dynamic characteristics of the slope under multiple working conditions can be comprehensively grasped.

[0090] Specifically, the collection of soil parameters can adopt a distributed sensor array, which can monitor parameters such as the permeability coefficient, internal friction angle, and cohesion of the soil in real time. For example, the permeability coefficient reflects the water penetration ability of the soil, and the internal friction angle and cohesion determine the shear strength of the soil. The collection of hydraulic parameters can be achieved through an ultrasonic velocimeter, which can obtain the velocity distribution of the flood discharge flow at different positions, where the planar coordinates (x, y) are used to locate the measurement points of the water flow velocity. The collection of structural parameters is completed through a strain monitoring device, which is used to measure the displacement and its change rate of the slope structure. In addition, the change rate of the reservoir water level and the rainfall intensity parameters also need to be synchronously recorded, which are crucial for analyzing the stability of the slope under different hydraulic conditions.

[0091] Preferably, when collecting parameters, the layout of the sensors can be optimized to improve the accuracy and representativeness of the data. For example, the distributed sensor array can be arranged in a grid pattern or along the key parts of the slope to ensure that the key areas of the slope can be fully covered. For the collection of hydraulic parameters, the measurement points of the ultrasonic velocimeter can be adjusted according to the geometric shape and water flow characteristics of the flood discharge channel to obtain more accurate velocity distribution data. When processing the parameters, the collected data can be preprocessed, such as filtering or denoising, to improve the reliability of subsequent analysis. In addition, for the measurement of structural parameters, a high-precision strain monitoring device can be used, combined with real-time data transmission technology, to ensure the timely update and analysis of the slope displacement data.

[0092] In some embodiments, S1 includes:

[0093] S101. Collect the soil permeability coefficient K using a distributed sensor arrays , the angle of internal friction cohesion c;

[0094] S102. Obtain the flood discharge flow velocity distribution V(x, y) through an ultrasonic velocity meter, where x and y are planar coordinates;

[0095] S103. Use a strain monitoring device to measure the displacement ΔD of the slope structure, where ΔD represents the displacement change;

[0096] S104. Synchronously record the rate of change of the reservoir water level dH / dt and the rainfall intensity parameter R t , where H is the water level height and t is the time.

[0097] It should be noted that the second step of this method is to process the soil parameter set through dynamic parameter grouping and non - linear transformation. Dynamic parameter grouping divides the parameters into a high - frequency mutation group and a low - frequency slow - change group according to the change frequency of the parameters, so as to better identify and process the dynamic change characteristics of the parameters. Non - linear transformation adjusts the parameters of the high - frequency mutation group to enhance their influence in stability analysis. The construction of the parameter correlation degree tensor is to quantify the mutual relationship between different parameters and provide more comprehensive coupling information for subsequent stability analysis. The purpose of these steps is to improve the accuracy and reliability of slope stability assessment through dynamic processing and coupling analysis of parameters.

[0098] Specifically, dynamic parameter grouping is based on the absolute value of the parameter change rate to judge its change frequency. When the absolute value of the parameter change rate exceeds a preset threshold, the parameter is classified into the high - frequency mutation group; otherwise, it is classified into the low - frequency slow - change group. Here, the parameter change rate is determined by calculating the ratio of the change amount of the parameter within a certain time interval to the time interval. Non - linear transformation adjusts the parameters of the high - frequency mutation group by introducing a preset transformation gain coefficient to enhance the weight of these parameters in subsequent analysis. The construction of the parameter correlation degree tensor is realized through a three - parameter coupling function, which quantifies the relationship between different parameters into a tensor for subsequent multi - source coupling stability analysis. This processing method can more accurately reflect the interaction between parameters and their influence on slope stability.

[0099] Preferably, when performing dynamic parameter grouping, the preset threshold can be adjusted according to the actual engineering requirements to more accurately divide high - frequency and low - frequency parameters. For example, for areas with complex geological conditions or poor slope stability, the threshold can be appropriately reduced to more sensitively capture parameter changes. In non - linear transformation, the transformation gain coefficient can be adjusted according to the importance of the parameters and the specific working conditions of the slope. For example, for parameters such as the angle of internal friction that have a greater impact on slope stability, the gain coefficient can be appropriately increased.

[0100] Furthermore, different coupling functions can be selected according to actual needs for the construction of the parameter correlation degree tensor to better reflect the mutual relationship between parameters. For example, a coupling function obtained based on experience or numerical simulation can be adopted to improve the accuracy and adaptability of the analysis.

[0101] In some embodiments, S2 includes:

[0102] S201. Establish dynamic grouping according to the parameter mutation frequency: When the absolute value of the parameter change rate |ΔP / Δt| > θ, it is classified into the high-frequency mutation group; otherwise, it is classified into the low-frequency slow-varying group, where ΔP is the parameter change amount, Δt is the time interval, and θ is a preset threshold;

[0103] S202. Perform a non-linear transformation on the parameters in the high-frequency mutation group:

[0104] P′ = ln(1 + e k·ΔP )

[0105] where k is a preset transformation gain coefficient and ΔP is the parameter change amount;

[0106] S203. Construct the parameter correlation degree tensor

[0107] T ijk = σ(P i , P j , P k )

[0108] where σ is a preset three-parameter coupling function, and i, j, k are parameter indices.

[0109] It should be noted that the third step of this method is to calculate the multi-source coupling stability index based on the fuzzy logic weight model. The fuzzy logic weight model is an analysis method that combines fuzzy mathematics theory. By introducing the concepts of membership degree and weight, it can effectively handle the uncertainty and complexity of parameters. Among them, the membership degree is used to measure the relationship between the actual value of a parameter and its critical value, while the weight reflects the importance of the parameter in the stability analysis. The mutation factor coefficient is used to dynamically adjust the weight of the parameter to adapt to the impact of parameter changes on stability. Through this model, the coupling effect of multi-source parameters can be comprehensively considered, thereby more accurately evaluating the stability of the slope.

[0110] Specifically, the membership degree in the fuzzy logic weight model is calculated through a membership degree function, which reflects the degree of difference between the current value of the parameter and the critical value. The membership degree steepness factor is used to control the shape of the membership degree function and determines the change rate of the membership degree when the parameter value approaches the critical value. The reference weight is preset according to the importance of the parameter in slope stability analysis and reflects the influence degree of different parameters on stability. The mutation factor coefficient is dynamically adjusted according to the mutation amount of the parameter and is used to enhance the influence of the mutation parameter in stability analysis. By setting these parameters, the slope stability problems under different working conditions can be handled more flexibly.

[0111] Preferably, in the setting of the membership degree function, the membership degree steepness factor and the critical value can be adjusted according to the specific geological conditions and working conditions of the slope. For example, in areas with poor geological conditions or weak slope stability, the membership degree steepness factor can be appropriately increased to improve the sensitivity to parameter changes. For the dynamic adjustment of the mutation factor coefficient, it can be optimized according to the actually monitored number of mutations and the magnitude of the mutation amount. For example, when the number of mutations exceeds a certain critical value, the mutation factor coefficient can be further increased to more accurately reflect the influence of the mutation parameter on stability. In addition, different forms of membership degree functions can be selected according to actual needs, such as using S-shaped or Z-shaped functions, to better adapt to the distribution characteristics of different parameters.

[0112] In some embodiments, the calculation formula of the fuzzy logic weight model of S3 is:

[0113] W total =∑(μ i ·ω i )+λ·maxΔP m

[0114] Wherein, μ i represents the membership degree of the i-th parameter, ω i is the reference weight of the i-th parameter, λ is the mutation factor coefficient, and ΔP m is the mutation amount of the m-th parameter;

[0115] The calculation formula of the parameter membership degree μ i is:

[0116]

[0117] In the formula, a is the membership degree steepness factor, b is the parameter critical value, and P i is the current value of the i-th parameter.

[0118] It should be noted that in the fourth step of this method, the mutation factor coefficient is dynamically adjusted by monitoring the number of parameter mutations in real time. The mutation factor coefficient is used to enhance the weight of the mutation parameter in the stability analysis, so as to more accurately reflect the impact of parameter changes on slope stability. Specifically, when the change of a parameter within a unit time exceeds a preset threshold, the counting of the number of mutations will be triggered. When the number of mutations exceeds the critical number, the mutation factor coefficient will be updated according to a preset dynamic adjustment mechanism to adapt to the stability changes of the slope under complex working conditions. In addition, a maximum constraint condition is set to prevent the excessive growth of the mutation factor coefficient and ensure the stability and reliability of the analysis.

[0119] Specifically, the adjustment of the mutation factor coefficient is based on the number of mutations monitored in real time. The number of mutations refers to the number of times the parameter change exceeds the preset threshold within a unit time, reflecting the frequency and intensity of the parameter change. When the number of mutations exceeds the critical number, it indicates that the stability of the slope may be significantly affected, and at this time, the mutation factor coefficient needs to be dynamically adjusted. In the adjustment formula, the initial coefficient value is a preset reference value used to reflect the weight of the parameter under normal working conditions. A logarithmic function is introduced in the adjustment function to dynamically adjust the size of the mutation factor coefficient according to the change of the number of mutations. At the same time, the maximum constraint condition limits the maximum value of the mutation factor coefficient to avoid deviation of the analysis results caused by excessive adjustment.

[0120] Preferably, the monitoring of the number of mutations can be realized by high-precision sensors. For example, strain sensors or displacement sensors are installed at key parts of the slope to monitor the changes of soil parameters or structural parameters in real time. The setting of the critical number can be optimized according to the specific geological conditions and historical data of the slope. For example, in areas with complex geological conditions or poor slope stability, the critical number can be appropriately reduced to more sensitively capture parameter changes.

[0121] Furthermore, the logarithmic function in the adjustment function can also be adjusted according to actual needs. For example, an exponential function or other non-linear functions can be used to better adapt to the mutation characteristics under different working conditions. The maximum constraint condition can be fine-tuned according to the actual analysis results to ensure that the adjustment of the mutation factor coefficient can reflect the impact of parameter changes and will not lead to the instability of the analysis results.

[0122] In some embodiments, the calculation of the mutation factor coefficient λ includes:

[0123] S501. Real-time monitoring of the number of parameter mutations N t , N t is the number of times the parameter change exceeds the threshold within a unit time;

[0124] S502. When N t > critical number N cWhen activated, perform dynamic adjustment:

[0125] λ = λ0·[1 + 0.5·tanh(0.1·(N t -N c ))]

[0126] where λ0 is the initial coefficient value;

[0127] S503. Set the maximum constraint condition: λ max = 1.2λ0.

[0128] It should be noted that the fifth step of this method is to dynamically evaluate and warn the slope stability through a three - state logic judgment unit. The three - state logic judgment unit is an evaluation mechanism based on dynamic safety thresholds. By comparing the real - time stability index with the preset safety thresholds, it judges the stability state of the slope and triggers corresponding warning signals. The dynamic safety thresholds are calculated based on the reference threshold coefficient, the initial safety threshold, and the differential coupling coefficient, and can adapt to the stability changes under different working conditions. By setting different warning levels and triggering conditions, slope stability problems can be detected in time and corresponding measures can be taken.

[0129] Specifically, the dynamic safety thresholds of the three - state logic judgment unit are calculated based on the reference threshold coefficient, the initial safety threshold, and the differential coupling coefficient. The reference threshold coefficient is a constant preset according to the slope geological conditions and historical data, used to adjust the size of the initial safety threshold; the initial safety threshold is the stability reference value of the slope under normal working conditions; the differential coupling coefficient is used to consider the influence of the parameter change rate on stability. When the real - time stability index is compared with the dynamic safety thresholds, yellow, orange, or red warnings are triggered according to different ranges. For example, when the real - time stability index is lower than the dynamic safety thresholds within a certain range, a yellow warning is triggered, indicating that the slope stability may be affected to a certain extent; when it further decreases, an orange or red warning is triggered, indicating that the slope stability problem is more serious and measures need to be taken.

[0130] Preferably, the reference threshold coefficient can be adjusted according to the specific geological conditions and historical data of the slope. For example, in areas with complex geological conditions or poor slope stability, the reference threshold coefficient can be appropriately reduced to improve the sensitivity of the warning. The differential coupling coefficient can be optimized according to the sensitivity of the parameter change rate. For example, in areas with a high slope displacement change rate, the differential coupling coefficient can be appropriately increased to more accurately reflect the stability changes. In addition, the triggering conditions of the warning levels can be refined according to the actual engineering requirements. For example, more warning levels can be introduced, or different warning thresholds can be set according to the specific location and importance of the slope.

[0131] In some embodiments, the three - state logic judgment unit of S4 performs:

[0132] S401. Set the dynamic safety threshold

[0133]

[0134] where ɑ is the reference threshold coefficient, T0 is the initial safety threshold, and β is the differential coupling coefficient;

[0135] S402. When the real-time stability index S satisfies T d ≤S<1.5T d a yellow warning is triggered;

[0136] S403. When 0.8T d ≤S<T d an orange warning is triggered and the first-level compensation mechanism is activated;

[0137] S404. When S<0.8T d a red warning is triggered and the second-level compensation mechanism is activated.

[0138] It should be noted that the sixth step of this method is to adjust the parameters after the warning through the parameter membership compensation mechanism to optimize the results of slope stability analysis. The parameter membership compensation mechanism is a dynamic adjustment method. When the slope stability triggers a warning, by adjusting the parameters of the membership function (such as the membership steepness factor and the critical value), and implementing attenuation compensation for the mutation factor, the slope stability is re-evaluated. This method can flexibly adjust the weight of the parameters according to the different warning levels, so as to more accurately reflect the stability changes of the slope under different working conditions.

[0139] Specifically, when the orange warning is triggered, the membership steepness factor will be adjusted to 1.2 times its original value, and at the same time, the parameter critical value will be adjusted to 0.9 times its original value; when the red warning is triggered, the membership steepness factor is adjusted to 1.5 times its original value, and the critical value is adjusted to 0.8 times its original value. This adjustment method can enhance the sensitivity to parameter changes and make the stability analysis more stringent. For the attenuation compensation of the mutation factor, by introducing the attenuation factor and the warning duration, the mutation factor is dynamically adjusted. When the warning duration is long, the influence of the mutation factor will gradually weaken, so as to avoid the excessive influence of short-term mutations on the stability analysis results.

[0140] Preferably, according to the specific geological conditions and working conditions of the slope, the adjustment strategy of the membership function can be further refined. For example, in areas with poor geological conditions, the adjustment range of the membership steepness factor can be appropriately increased to more strictly evaluate the stability. For the attenuation compensation of the mutation factor, according to the actual operation data of the slope, the size of the attenuation factor can be adjusted to make it more in line with the dynamic change characteristics of the slope.

[0141] Furthermore, a periodic anti-compensation mechanism can be introduced. When the early warning duration exceeds a certain critical value, the attenuated mutation factor is periodically adjusted to adapt to the stability change of the slope within the flood discharge period.

[0142] In some embodiments, the parameter membership compensation mechanism includes:

[0143] S701. Adjust the membership function according to the early warning level:

[0144] When an orange early warning is triggered, the membership gradient factor a is adjusted to 1.2 times the original value, and at the same time, the parameter critical value b is adjusted to 0.9 times the original value;

[0145] When a red early warning is triggered, the membership gradient factor a is adjusted to 1.5 times the original value, and at the same time, the parameter critical value b is adjusted to 0.8 times the original value;

[0146] S702. Implement exponential decay compensation for the mutation factor:

[0147]

[0148] where t w is the early warning duration;

[0149] S703. Reconstruct the coupling stability index

[0150] S ′ = f(W total , λ ′ )

[0151] where f is a preset reconstruction function.

[0152] It should be noted that the seventh step of this method is to perform time-frequency domain reconstruction on the compensated parameters through a mutation factor demodulator to further optimize the accuracy of slope stability analysis. The mutation factor demodulator is a signal processing tool used to analyze the change characteristics of parameters in the time domain and frequency domain. By constructing the time-frequency matrix of the parameters, calculating the mutation energy density, and triggering parameter reconstruction according to the energy threshold, the mutation characteristics in the parameters and their impact on slope stability can be more accurately identified. This process can effectively improve the dynamic adaptability and reliability of slope stability analysis.

[0153] Specifically, the mutation factor demodulator first constructs a time-frequency matrix of parameters. By introducing the imaginary unit, angular frequency, and time variable, the change characteristics of the parameters are extended from the time domain to the frequency domain. The time-frequency matrix can reflect the changes of parameters at different frequencies and provides a basis for subsequent energy density calculation. The mutation energy density is obtained by integrating the squared modulus of the time-frequency matrix over the entire frequency domain, which quantifies the energy distribution of parameter mutations. When the energy density exceeds the preset energy threshold, parameter reconstruction is triggered. By selecting the frequency bands exceeding the threshold for reconstruction, the mutation information in the parameters can be captured more accurately, thus providing more accurate input for stability analysis.

[0154] Preferably, the construction method of the time-frequency matrix can be optimized. For example, according to the specific working conditions of the slope and the parameter characteristics, an appropriate angular frequency range and time resolution are selected to improve the accuracy and representativeness of the time-frequency matrix. In the energy density calculation, by introducing a weighting factor, the energy density at different frequencies can be weighted, so as to more prominently reflect the influence of high-frequency or low-frequency mutation characteristics.

[0155] Furthermore, for the setting of the energy threshold, it can be dynamically adjusted according to historical data and the actual operation of the slope. For example, in areas with poor slope stability, the energy threshold can be appropriately reduced to more sensitively capture parameter mutations.

[0156] In some embodiments, the attenuation compensation of S702 satisfies:

[0157] When t w > critical time t c a periodic anti-compensation is activated:

[0158]

[0159] where t p is the flood discharge period parameter, and π is the pi.

[0160] It should be noted that the eighth step of this method is to optimize the attenuation compensation in the mutation factor demodulator to adapt to the dynamic changes of slope stability analysis. The attenuation compensation is a dynamic adjustment process of the mutation factor during the warning duration. By introducing an exponential decay function, the long-term impact of the mutation factor on stability analysis can be effectively reduced. When the warning duration exceeds the critical time, a periodic anti-compensation mechanism is further introduced. By periodically adjusting the mutation factor, the dynamic change characteristics of the slope during the flood discharge period can be better adapted, thereby improving the accuracy and reliability of stability analysis.

[0161] Specifically, the attenuation compensation is achieved through an exponential decay function that associates the mutation factor with the warning duration, causing the mutation factor to gradually weaken over time. This attenuation mechanism can avoid the excessive influence of the mutation factor on the stability analysis results, especially when the warning duration is long. The periodic anti-compensation mechanism is activated after the warning duration exceeds the critical time. It introduces the flood discharge cycle parameter and pi to periodically adjust the attenuated mutation factor. This adjustment can better reflect the dynamic changes of the slope during the flood discharge cycle and avoid analysis biases caused by a single attenuation mechanism.

[0162] Preferably, the parameters of the attenuation compensation can be optimized according to the specific working conditions and historical data of the slope. For example, in areas with poor slope stability or complex geological conditions, the attenuation coefficient can be appropriately adjusted to decay more rapidly, reducing the long-term impact of the mutation factor. For the periodic anti-compensation mechanism, the cycle parameter can be adjusted according to the actual flood discharge cycle of the slope to more accurately reflect the dynamic changes of the slope.

[0163] Furthermore, an adaptive adjustment mechanism can be introduced to dynamically adjust the parameters of the attenuation compensation and the periodic anti-compensation according to real-time monitoring data, thereby further improving the adaptability and accuracy of the stability analysis.

[0164] In some embodiments, the mutation factor demodulator in S6 performs:

[0165] S601. Construct a parameter time-frequency matrix

[0166] Ψ = [P i (t)·e -jωt

[0167] where j is the imaginary unit, ω is the angular frequency, and t is the time variable;

[0168] S602. Calculate the mutation energy density

[0169] E = ∫|Ψ(t,ω)| 2 dω

[0170] where the integration range is the full frequency domain;

[0171] S603. When E > the energy threshold E c trigger parameter reconstruction:

[0172]

[0173] where the summation range is the frequency bands exceeding the threshold.

[0174] It should be noted that the ninth step of this method is to reconstruct the parameters in the time-frequency domain through a mutation factor demodulator to further optimize the accuracy of slope stability analysis. Time-frequency domain reconstruction is a process of analyzing the time-frequency matrix of parameters, calculating the mutation energy density, and reconstructing the parameters according to the energy threshold. This process can effectively identify the mutation characteristics in the parameters and their impact on slope stability, thereby providing more accurate inputs for subsequent stability assessment.

[0175] Specifically, the time-frequency matrix is constructed by extending the change characteristics of the parameters from the time domain to the frequency domain, where the imaginary unit, angular frequency, and time variable are introduced. The time-frequency matrix can reflect the changes of the parameters at different frequencies and provide a basis for subsequent energy density calculation. The mutation energy density is obtained by integrating the squared modulus of the time-frequency matrix over the entire frequency domain and is used to quantify the energy distribution of parameter mutations. When the energy density exceeds the preset energy threshold, parameter reconstruction is triggered. By selecting the frequency bands exceeding the threshold for reconstruction, the mutation information in the parameters can be captured more accurately. This process can effectively improve the dynamic adaptability and reliability of slope stability analysis.

[0176] Preferably, the construction of the time-frequency matrix can be optimized by selecting appropriate window functions and time resolutions to improve the ability to capture the mutation characteristics of the parameters. For example, the Hanning window or Gaussian window can be used to reduce spectral leakage, and the time resolution can be adjusted according to the actual working conditions of the slope to better reflect the dynamic changes of the parameters. For the setting of the energy threshold, it can be dynamically adjusted according to the historical data and actual operation conditions of the slope. For example, in areas with poor slope stability, the energy threshold can be appropriately reduced to more sensitively capture parameter mutations. In addition, an adaptive energy threshold mechanism can be introduced to dynamically adjust the threshold according to real-time monitoring data, thereby further improving the adaptability and accuracy of stability analysis.

[0177] In some embodiments, the S7 includes:

[0178] S701. Input the reconstructed parameters into the spatio-temporal coupling analysis model;

[0179] S702. Calculate the safety factor of each node according to where τ

[0180] is the anti-sliding force, τ f is the static shear stress, τ m is the dynamic hydraulic shear stress, and η is the coupling coefficient; d is the dynamic hydraulic shear stress, and η is the coupling coefficient;

[0181] S703. Generate a three-dimensional safety situation map based on the improved moving least squares method;

[0182] S704. Mark the safety factor F sThe area < 1.15 is the critical instability area, and a multi-level reinforcement plan is output.

[0183] It should be noted that the tenth step of this method is to perform a comprehensive evaluation of anti-slide stability based on the reconstructed parameters and generate a three-dimensional safety situation map. This process involves inputting the reconstructed parameters into a spatio-temporal coupling analysis model, calculating the safety factors of each node, and generating a three-dimensional safety situation map based on the improved moving least squares method. This map can visually display the distribution of safety factors in each area of the slope, mark the critical instability area, and provide a scientific basis for formulating the slope reinforcement plan. This step realizes a complete closed-loop from parameter reconstruction to stability evaluation, improving the visualization and refinement level of slope stability analysis.

[0184] Specifically, the spatio-temporal coupling analysis model is an analysis tool that comprehensively considers time and space factors and is used to evaluate the anti-slide stability of slopes under different working conditions. The anti-slide force, static shear stress, and dynamic hydraulic shear stress involved in the model are key parameters for slope stability analysis, which respectively reflect the anti-slide ability of the soil mass, the internal shear force, and the impact of water flow on slope stability. The coupling coefficient is used to quantify the interaction between these parameters. By calculating the safety factors of each node, the stability state of the slope at different positions can be evaluated. The improved moving least squares method is a numerical method for generating a three-dimensional safety situation map, which can generate intuitive visualization results based on the spatial distribution of safety factors to help engineers quickly identify potential instability areas.

[0185] Preferably, more parameters related to slope stability, such as the pore water pressure of the soil mass or the slope gradient of the slope, can be introduced into the spatio-temporal coupling analysis model to further improve the accuracy and adaptability of the model. For the calculation of safety factors, the weights of the anti-slide force, static shear stress, and dynamic hydraulic shear stress can be adjusted according to the specific geological conditions of the slope to better reflect the influence of different factors on stability. When generating a three-dimensional safety situation map, the geographical information system (GIS) technology can be combined to integrate the map with actual terrain data, so as to more intuitively display the stability distribution of the slope. In addition, for the marking of the critical instability area, the threshold of the safety factor can be adjusted according to the actual engineering requirements. For example, the threshold can be appropriately reduced in high-risk areas to early warn of potential instability risks.

[0186] The above-mentioned various embodiments of the present invention have the following beneficial effects: By collecting multi-source parameters and performing dynamic grouping and non-linear transformation, and combining with a fuzzy logic weight model to calculate the multi-source coupling stability index, the mutation characteristics of soil parameters can be accurately captured, and the influence of multi-source coupling factors on slope stability can be quantified, thereby improving the accuracy and reliability of stability analysis. At the same time, the constructed three-state logic judgment unit can compare the stability index with the dynamic safety threshold in real time, quickly trigger the early warning mechanism, and provide a timely decision-making basis for slope safety management. In addition, the parameter membership compensation mechanism and the mutation factor demodulator can dynamically adjust the parameters and perform time-frequency domain reconstruction after early warning, further optimizing the stability evaluation results, generating a three-dimensional safety situation map, intuitively displaying the safety factor distribution of each area of the slope, and accurately marking the critical instability area, providing scientific support for the formulation of targeted reinforcement measures.

[0187] This method can realize the whole-process management from static evaluation to dynamic early warning and real-time compensation, effectively enhancing the safety and reliability of the flood discharge slope. Through dynamic parameter grouping and non-linear transformation, it can meet the requirements of slope stability analysis under complex working conditions; the dynamic adjustment mechanism of the fuzzy logic weight model and the mutation factor coefficient can further improve the analysis accuracy; and the comprehensive evaluation of anti-sliding stability based on the reconstructed parameters can provide a scientific basis for the formulation of slope reinforcement plans, realizing the refinement and intelligentization of slope stability analysis.

[0188] Furthermore, the storage medium of the embodiment of the present application stores program instructions that can implement all the above methods. Among them, the program instructions can be stored in the above storage medium in the form of a software product, including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor to execute all or part of the steps of the methods described in various embodiments of the present application. And the aforementioned storage medium includes: various media that can store program codes such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs, or terminal devices such as computers, servers, mobile phones, and tablets.

[0189] The above description is only some preferred embodiments of the present invention and an explanation of the applied technical principles. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present invention is not limited to the technical solutions formed by the specific combination of the above technical features, but also covers other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the above inventive concept. For example, the technical solutions formed by mutually replacing the above features with the (but not limited to) technical features with similar functions disclosed in the embodiments of the present invention.

Claims

1. A comprehensive analysis method for the anti-sliding stability of flood-discharging slopes under multiple working conditions, characterized in that It includes the following steps: S1. Collect the soil parameter set, hydraulic parameter set, and structural parameter set of the flood discharge slope; S2. Perform dynamic parameter grouping and non-linear transformation on the soil parameter set; S3. Calculate the multi-source coupling stability index based on the fuzzy logic weight model; S4. Construct a three-state logic judgment unit to compare the real-time stability index with the dynamic safety threshold; S5. When the judgment result triggers the warning condition, start the parameter membership compensation mechanism; S6. Perform time-frequency domain reconstruction on the compensation parameters through the mutation factor demodulator; S7. Perform a comprehensive evaluation of the anti-sliding stability based on the reconstructed parameters and generate a three-dimensional safety situation map.

2. The method according to claim 1, wherein The S1 includes: S101. Use a distributed sensor array to collect the soil permeability coefficient K s , the internal friction angle cohesion c; S102. Obtain the flood discharge flow velocity distribution V(x,y) through an ultrasonic velocity meter, where x and y are plane coordinates; S103. Measure the slope structure displacement ΔD using a strain monitoring device, and ΔD represents the displacement change; S104. Synchronously record the change rate of the reservoir water level dH / dt and the rainfall intensity parameter R t , where H is the water level height and t is the time.

3. The method according to claim 2, wherein The S2 includes: S201. Establish dynamic grouping according to the parameter mutation frequency: When the absolute value of the parameter change rate |ΔP / Δt| > θ, it is classified into the high-frequency mutation group, otherwise it is classified into the low-frequency slow change group, where ΔP is the parameter change amount, Δt is the time interval, and θ is the preset threshold; S202. Perform non-linear transformation on the parameters in the high-frequency mutation group: P′ = ln(1 + e k·ΔP ) where k is the preset transformation gain coefficient and ΔP is the parameter change amount; S203. Construct the parameter correlation tensor T ijk = σ(P i , P j , P k ) where σ is the preset three-parameter coupling function and i, j, k are parameter indices.

4. The method according to claim 3, wherein The calculation formula of the fuzzy logic weight model in S3 is: W total = ∑(μ i · ω i ) + λ · maxΔP m Among them, μ i represents the membership degree of the i-th parameter, ω i is the reference weight of the i-th parameter, λ is the mutation factor coefficient, and ΔP m is the mutation amount of the m-th parameter; The membership degree μ of the parameter i is calculated by the following formula: where a is the membership degree steepness factor, b is the parameter critical value, and P i is the current value of the i-th parameter.

5. The method according to claim 4, wherein The calculation of the mutation factor coefficient λ includes: S501. Real-time monitoring of the number of parameter mutations N t , N t is the number of times the parameter changes exceed the threshold within a unit time; S502. When N t > the critical number of times N c , activate dynamic adjustment: λ = λ0·[1 + 0.5·tanh(0.1·(N t - N c ))] where λ0 is the initial coefficient value; S503. Set the maximum constraint condition: λ max = 1.2λ0.

6. The method according to claim 5, characterized in that, The three-state logic judgment unit in S4 performs: S401. Set the dynamic safety threshold where α is the reference threshold coefficient, T0 is the initial safety threshold, and β is the differential coupling coefficient; S402. When the real-time stability index S satisfies T d ≤S<1.5T d a yellow warning is triggered; S403. When 0.8T d ≤ S < T d a orange warning is triggered and the first-level compensation mechanism is activated; S404. When S < 0.8T d a red warning is triggered and a secondary compensation mechanism is activated.

7. The method according to claim 1, wherein The parameter membership compensation mechanism includes: S701. Adjust the membership function according to the warning level: When the orange warning is triggered, adjust the membership gradient factor a to 1.2 times the original value, and at the same time adjust the parameter critical value b to 0.9 times the original value; When the red warning is triggered, adjust the membership gradient factor a to 1.5 times the original value, and at the same time adjust the parameter critical value b to 0.8 times the original value; S702. Implement exponential decay compensation for the mutation factor; Among them, t w is the early warning duration, λ′ is the compensated mutation factor coefficient, and λ is the mutation factor coefficient before compensation; S703. Reconstruct the coupling stability index S′ = f(W total , λ′) where f is a preset reconstruction function, and W total is the fuzzy logic weight.

8. The method according to claim 7, characterized in that, The decay compensation in S702 satisfies: When t w > the critical time t c , activate the periodic anti-compensation: Among them, t p is the flood discharge cycle parameter.

9. The method according to claim 1, wherein The mutation factor demodulator in S6 performs: S601. Construct the parameter time-frequency matrix, and the parameter time-frequency matrix is as shown in the following formula; Ψ = [P i (t)·e -jωt ​ where j is the imaginary unit, ω is the angular frequency, and t is the time variable; S602. Calculate the mutation energy density, as shown in the following formula; E = ∫|Ψ(t, ω)| 2 dω S603. When E > energy threshold E c a parameter reconstruction is triggered to generate reconstruction parameters, as shown in the following formula; Among them, P rec (t) is a reconstruction parameter.

10. The method according to claim 9, wherein The S7 includes: S701. Input the reconstructed parameters into the spatio-temporal coupling analysis model; S702. Press Calculate the safety factor of each node Among them, τ f is the anti-sliding force, τ m is the static shear stress, τ d is the dynamic hydraulic shear stress, and η is the coupling coefficient; S703. Generate a three-dimensional safety situation map based on the improved moving least squares method; S704. Mark the safety factor F s The area smaller than the preset safety factor is the critical instability area, and a multi-level reinforcement plan is output.

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