Multi-source monitoring data fusion construction period slope dynamic stability analysis method

Abstract

The invention discloses a construction period slope dynamic stability analysis method based on multi-source monitoring data fusion, and relates to the field of intelligent monitoring analysis prevention and control of a construction period slope of water conservancy and hydropower engineering, and the method comprises the following steps: carrying out construction period slope digital twinborn modeling, collecting real-time monitoring data of the construction period slope, and according to the construction progress, determining the construction period slope dynamic stability according to the real-time monitoring data of the construction period slope; dynamically updating the slope numerical model structure; according to the dynamically updated slope numerical model structure, numerical simulation is carried out after slope material parameters are inverted, and the stability of the slope before and after the corresponding construction stage in the construction period is analyzed. According to the method, real-time monitoring and intelligent parameter inversion technologies are fused, dynamic updating of the slope model and parameters in the construction period is achieved, the timeliness and accuracy of safety monitoring and analysis are remarkably improved, and an efficient and intelligent decision support tool is provided for safety management of slope construction.

Description

Technical Field

[0001] The present invention relates to the field of intelligent monitoring, analysis, prevention and control of slopes during the construction period of water conservancy and hydropower projects. Specifically, it relates to a method for dynamic stability analysis of slopes during the construction period by fusing multi-source monitoring data. Background Art

[0002] The analysis of slope stability is a key link in the prevention of geological disasters and the construction safety management of water conservancy and hydropower projects. During the construction period, slopes are often in a state of dynamic change due to construction activities such as excavation and support installation. There are often differences between the construction plan and the actual on-site situation. The timing of support in local areas, the continuous exploration of the slope stratum structure, and the deterioration of the lithology of the excavation surface due to unloading and weathering effects may all lead to changes in slope stability during the construction process.

[0003] Traditional static analysis methods mainly rely on numerical simulation tools such as the finite element method, the finite difference method, and the discrete element method. They require complex processes such as model establishment, mesh generation, numerical calculation, and parameter inversion. The data processing cycle is long and the real-time performance is insufficient. It is difficult to accurately reflect this dynamic change in a short time and cannot meet the requirements of construction safety monitoring and emergency response.

[0004] In recent years, the development of multi-source monitoring technologies (such as slope deformation monitoring, excavation progress monitoring, etc.) has made it possible to obtain real-time slope state data. However, how to organically combine these real-time monitoring data with dynamic numerical simulation technologies, realize the real-time update of the slope model structure and parameters, and timely and accurately evaluate the slope stability during the construction period through means such as intelligent parameter inversion is still a technical problem to be solved urgently.

[0005] In summary, there is an urgent need to develop a new method for dynamic stability analysis of slopes during the construction period. This method can dynamically update the slope numerical model based on real-time monitoring information, and use intelligent algorithms to realize parameter inversion and uncertainty evaluation, so as to improve the timeliness and accuracy of slope safety monitoring and early warning, and provide reliable decision-making support for project construction safety. Summary of the Invention

[0006] In order to solve the problems of insufficient real-time performance, long data processing cycle, and difficulty in efficiently fusing with on-site real-time monitoring data in the existing slope safety monitoring and dynamic stability analysis during the construction period, the purpose of the present invention is to provide a method for dynamic stability analysis of slopes during the construction period by fusing multi-source monitoring data. The aim is to realize the dynamic update of the slope numerical model structure and material parameters by fusing multi-source real-time monitoring data and intelligent parameter inversion technology. At the same time, an appropriate numerical simulation method is used to calculate the stability of the slope at different construction stages and output the safety analysis results, providing reliable technical support for construction safety monitoring, emergency early warning and prevention and control.

[0007] To achieve the above technical objectives, the present application provides a method for analyzing the dynamic stability of a slope during the construction period by fusing multi-source monitoring data, including the following steps:

[0008] Conduct digital twin modeling of the slope during the construction period, simultaneously collect real-time monitoring data of the slope during the construction period, and dynamically update the structure of the slope numerical model according to the construction progress;

[0009] Based on the dynamically updated structure of the slope numerical model, after inverse analysis of the slope material parameters, conduct numerical simulation to analyze the stability of the slope during the construction period before and after the corresponding construction stage.

[0010] Preferably, when conducting digital twin modeling of the slope during the construction period, use the initial slope excavation design data and construction plan to conduct digital twin modeling of the slope. Among them, excavate layer by layer according to the construction plan, and refine each layer of excavation according to the minimum construction volume.

[0011] Preferably, when collecting real-time monitoring data, collect the real-time data of the slope during the construction period, including excavation progress, support status, and slope deformation information, and perform noise filtering, calibration, and normalization processing on the collected data to generate real-time monitoring data.

[0012] Preferably, when dynamically updating the structure of the slope numerical model, based on the construction progress and real-time monitoring data, conduct a refined simulation of the process of layer-by-layer excavation and support of the model, and dynamically update the structure of the slope numerical model.

[0013] Preferably, when conducting inverse analysis of the slope material parameters, use the obtained slope deformation data as feedback information, and through a method combining the particle swarm algorithm or genetic algorithm or simulated annealing algorithm with the backpropagation neural network, conduct inverse analysis of the slope material parameters to obtain the inverse analysis results of the slope material parameters for the current excavation.

[0014] Preferably, when conducting inverse analysis of the slope material parameters, use Gaussian process-based uncertainty assessment to optimize the trained backpropagation neural network.

[0015] Preferably, when conducting numerical simulation, based on the updated structure of the slope numerical model and the inverse analysis results of the material parameters, use the finite element method or finite difference method or discrete element method to conduct numerical simulation to calculate the stability of the slope during the construction period before and after the corresponding construction stage.

[0016] The present invention also discloses a system for analyzing the dynamic stability of a slope during the construction period by fusing multi-source monitoring data, which is used to implement the method for analyzing the dynamic stability of a slope during the construction period, including:

[0017] Digital modeling module: used for conducting digital twin modeling of the slope during the construction period;

[0018] Monitoring data acquisition module: Collect real-time monitoring data and construction progress of the slope during the construction period;

[0019] Model structure update module: Dynamically update the slope numerical model structure according to the real-time monitoring data and construction progress of the slope during the construction period;

[0020] Model parameter update module: Update the material parameters of the model according to the results of the inversion of the monitoring data parameters;

[0021] BPNN optimization module: Used to optimize the BPNN by methods such as incremental learning, full-scale retraining, and uncertainty evaluation based on Gaussian processes;

[0022] Stability analysis module: Used to perform numerical simulation and analyze the stability of the slope during the construction period before and after the corresponding construction stage by inverting the slope material parameters based on the dynamically updated slope numerical model.

[0023] The present invention discloses the following technical effects:

[0024] 1. By the organic combination of real-time monitoring data and intelligent parameter inversion technology, the present invention realizes the dynamic update of the slope numerical model and material parameters, thereby greatly improving the timeliness and accuracy of slope stability analysis during the construction period.

[0025] 2. The present invention realizes the full-process automatic processing from multi-source data acquisition, model dynamic update to numerical simulation, significantly reducing the time cost and error risk brought by manual modeling, calculation and long-time data processing in the traditional method.

[0026] 3. The present invention provides an efficient, intelligent and real-time analysis means for slope safety monitoring and emergency response during the construction period, providing an important basis for real-time monitoring and dynamic prevention and control of slopes during the construction period. Description of the drawings

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required to be used in the embodiments. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0028] Figure 1 It is the schematic diagram of the method flow described in the present invention;

[0029] Figure 2 It is the sample diagram of the layered excavation and support of the slope during the construction period described in the present invention;

[0030] Figure 3It is the contour map of the displacement along the slope direction of the numerical simulation result after the slope excavation during the construction period described in the present invention;

[0031] Figure 4 It is the schematic diagram of the usage process described in the present invention. Detailed implementation manners

[0032] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Usually, the components of the embodiments of the present application described and illustrated herein can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the accompanying drawings is not intended to limit the scope of the present application to be protected, but only represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without creative efforts shall fall within the scope of protection of the present application.

[0033] As Figures 1-4 shown, the present invention provides a method for dynamic stability analysis of a slope during the construction period by fusing multi-source monitoring data, including the following processes:

[0034] Digital twin modeling of the slope during the construction period, using the initial slope excavation design data and construction plan to carry out the digital twin modeling work of the slope. It is required to excavate layer by layer according to the construction plan, and refine each layer of excavation according to the minimum construction volume to realize the real-time follow-up of the subsequent construction progress.

[0035] Multi-source monitoring data collection and preprocessing, using the monitoring system to collect the real-time data of the slope during the construction period, including information such as excavation progress, support status, and slope deformation; filtering noise, calibrating, and normalizing the collected data to ensure the accuracy and consistency of the data;

[0036] Dynamic update of the slope model structure, dynamically updating the slope numerical model structure based on the construction progress and real-time monitoring data; carrying out refined simulation of the process of layer-by-layer excavation and support of the model to truly reproduce the actual situation of the construction site;

[0037] Dynamic update of the slope model parameters, through parameter inversion and BPNN optimization, using the slope deformation monitoring data, inversing the material parameters in the slope model by the particle swarm algorithm or genetic algorithm combined with the backpropagation neural network (BPNN); at the same time, using methods such as incremental learning, full-scale retraining, and uncertainty evaluation based on Gaussian processes to optimize the BPNN to ensure the accuracy of the next inversion;

[0038] Numerical simulation after model update: Based on the completion of relevant updates to the structure and parameters, methods such as the finite element method, finite difference method, and discrete element method are used for numerical simulation to calculate the stability of the slope during the construction period before and after corresponding construction stages, obtain real-time stability analysis results, and then continuously repeat the above update and calculation processes until the slope construction is completed;

[0039] Methods for obtaining real-time multi-source monitoring data include but are not limited to three-dimensional laser scanning, drone scanning, surface displacement gauges, etc.

[0040] Contents of real-time multi-source monitoring data include but are not limited to construction progress data, support status data, slope deformation monitoring data, etc.

[0041] Methods for dynamically updating the slope numerical model structure include but are not limited to layer-by-layer simulation of excavation according to the construction plan and refinement of each layer of excavation based on the minimum construction volume to achieve real-time follow-up of the construction progress.

[0042] Methods used in the parameter inversion steps for real-time updating of slope material parameters include but are not limited to particle swarm optimization, genetic algorithm, and simulated annealing algorithm, etc.

[0043] Optimization methods for backpropagation neural networks include but are not limited to incremental learning, full-scale retraining, and uncertainty assessment based on Gaussian processes, etc.

[0044] Numerical simulation methods for analyzing and calculating the stability of the slope during the construction period include but are not limited to the finite element method, finite difference method, discrete element method, etc.

[0045] Embodiment: The present invention provides a method for dynamic stability analysis of a slope during the construction period by fusing multi-source monitoring data, including the following steps:

[0046] Step 1, digital twin modeling of the slope during the construction period;

[0047] Carry out digital twin modeling of the slope according to the initial slope excavation design data and construction plan. In the embodiment of the present invention, excavation is simulated layer by layer according to the construction plan, and each layer of excavation is refined based on the minimum construction volume to achieve real-time follow-up of the subsequent construction progress. In the embodiment of the present invention, commercial software is used for digital twin modeling of the slope during the construction period and imported into the finite difference analysis software for subsequent model structure update and numerical simulation. See Figure 2 , which is the effect diagram of digital twin modeling for simulating excavation and support.

[0048] Step 2, multi-source monitoring data collection and preprocessing;

[0049] Collect real-time data of the slope during the construction period using a monitoring system, including information such as excavation progress, support status, and slope deformation. Then, perform noise filtering, calibration, and normalization on the collected data to ensure the accuracy and consistency of the data.

[0050] Collect multi-source real-time data of the slope during the construction period through various monitoring devices. The specific content includes excavation progress, support status, slope deformation, etc. Common monitoring technologies include, but are not limited to: laser scanning, UAV imagery, satellite remote sensing data, ground sensors, etc. The data collection method in the embodiments of the present invention is: use laser point cloud and settlement displacement meters combined with the real-time uploaded construction logs to obtain the three-dimensional morphological changes, settlement displacement, and support actual situation of the slope under real-time construction.

[0051] Data preprocessing: Complete and denoise the collected data. And in combination with Step 1, layer by layer simulate the refined model according to the construction plan, and adjust the data to adapt to it.

[0052] Step 3, dynamically update the slope model structure;

[0053] Based on the construction progress and real-time monitoring data, dynamically update the slope numerical model structure; perform refined simulation on the process of layer-by-layer excavation and support of the model to truly reproduce the actual situation of the construction site. In the embodiments of the present invention, in the finite difference analysis software, by automatically reading the parameterized construction progress and real-time monitoring data information, simulate the excavation and support conditions under the real construction progress.

[0054] Step 4, dynamically update the slope model parameters;

[0055] Invert the slope material parameters by combining the particle swarm optimization algorithm (PSO) or genetic algorithm (GA) with the backpropagation neural network (BPNN). The slope deformation data monitored in real time is used as feedback information to continuously optimize the accuracy of the numerical model.

[0056] In the embodiments of the present invention, normalize the measured displacement changes of the measuring points measured by the surface displacement meter and the point cloud differences of the laser point cloud scanning results. Through the optimization method of the genetic algorithm, perform Python language programming, train the backpropagation neural network (BPNN), and obtain the inversion results of the slope material parameters for the current excavation. Combine the inversion results with the normalized monitoring information, perform Python language programming, and use the uncertainty evaluation based on the Gaussian process to optimize the trained backpropagation neural network (BPNN) to ensure the timeliness and reliability of the next parameter inversion.

[0057] Step 5, numerical simulation after model update:

[0058] On the basis of the above-mentioned relevant updates of the structure and parameters, numerical simulations are carried out by methods such as the finite element method, the finite difference method, and the discrete element method to calculate the stability of the slope during the construction period before and after the corresponding construction stages, and obtain the real-time stability analysis results. Then, the above update and calculation processes are continuously repeated until the slope construction is completed.

[0059] In the embodiment of the present invention, the updated digital twin model is numerically simulated by the finite difference method in the finite difference analysis software to obtain the slope stability analysis results under the latest construction conditions. And with further construction, steps 3-5 are repeated until the slope construction is completed. See Figure 3 , which is the numerical simulation result diagram during the construction process.

[0060] See Figure 4 , which is the module operation diagram of the method: the digital modeling module is used for digital twin modeling of the slope during the construction period; the data acquisition module is used for collecting slope excavation design data, construction plans, real-time monitoring data of the slope during the construction period, etc.; the model structure update module is used for constructing the initial model and updating the construction and support dynamics of the slope model in real time based on the construction plan and real-time monitoring data; the model parameter update module is used for inversely calculating the slope model parameters by combining the particle swarm algorithm or the genetic algorithm with the backpropagation neural network by using the slope deformation monitoring data; the BPNN optimization module is used for optimizing the BPNN by methods such as incremental learning, full-scale retraining, and uncertainty evaluation based on the Gaussian process; the numerical simulation module is used for calculating and analyzing the stability of the updated slope model.

[0061] In the embodiment of the present invention, the commercial software used for digital twin modeling of the slope during the construction period and subsequent model structure update and numerical simulation includes but is not limited to finite element modeling analysis software, finite difference modeling analysis software, etc.

[0062] The real-time multi-source monitoring data used in the embodiment of the present invention includes but is not limited to laser point cloud, settlement displacement meter, and construction logs uploaded in real time, etc.

[0063] In the embodiment of the present invention, the method for dynamically updating the slope numerical model structure includes but is not limited to simulating the excavation layer by layer according to the construction plan and refining each layer of excavation according to the minimum construction volume to realize real-time follow-up of the construction progress.

[0064] In the embodiment of the present invention, the method adopted in the parameter inversion step for realizing real-time update of the slope material parameters includes but is not limited to the genetic algorithm.

[0065] In the embodiment of the present invention, the optimization method of the backpropagation neural network includes but is not limited to the uncertainty evaluation method based on the Gaussian process.

[0066] In the embodiments of the present invention, the commercial software for analyzing and calculating the stability of the slope during the construction period includes, but is not limited to, finite difference analysis software.

[0067] In the embodiments of the present invention, the numerical simulation methods for analyzing and calculating the stability of the slope during the construction period include, but are not limited to, the finite difference method.

[0068] In summary, the present invention conducts digital twin modeling based on the slope during the construction period, updates the model structure and parameters by obtaining real-time multi-source monitoring information, and responds in real time to the excavation, support conditions at the construction site and the changes in rock mass parameters. And quickly analyzes the stability of the slope. It real-timely simulates the state changes during the construction excavation and support process of the slope during the construction period. It provides a real-time and intelligent means for the real-time monitoring and dynamic prevention and control of major slopes in water conservancy and hydropower projects.

[0069] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, and the combination of processes and / or blocks in the flowchart and / or block diagram can be realized by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for realizing the functions specified in one Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0070] In the description of the present invention, it should be understood that the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0071] Obviously, those skilled in the art can make various modifications and variations to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for analyzing the dynamic stability of slopes during the construction period by fusing multi-source monitoring data, characterized in that, It includes the following steps: Carry out digital twin modeling of the slope during the construction period, simultaneously collect real-time monitoring data of the slope during the construction period, and dynamically update the structure of the slope numerical model according to the construction progress; Based on the dynamically updated structure of the slope numerical model, after inverse analysis of the slope material parameters, conduct numerical simulation to analyze the stability of the slope during the construction period before and after the corresponding construction stage.

2. The method for dynamically analyzing the stability of a slope during the construction period by fusing multi-source monitoring data according to claim 1, characterized in that: When carrying out digital twin modeling of the slope during the construction period, use the initial slope excavation design data and construction plan to carry out digital twin modeling of the slope. Among them, excavate layer by layer according to the construction plan, and refine each layer of excavation according to the minimum construction volume.

3. The method for dynamically analyzing the stability of a slope during the construction period by fusing multi-source monitoring data according to claim 2, characterized in that: When collecting real-time monitoring data, collect the real-time data of the slope during the construction period, including excavation progress, support status and slope deformation information, and perform noise filtering, calibration and normalization processing on the collected data to generate the real-time monitoring data.

4. The method for dynamically analyzing the stability of a slope during the construction period by fusing multi-source monitoring data according to claim 3, characterized in that: When dynamically updating the structure of the slope numerical model, based on the construction progress and the real-time monitoring data, conduct a refined simulation of the process of layer-by-layer excavation and support of the model, and dynamically update the structure of the slope numerical model.

5. The method for dynamically analyzing the stability of a slope during the construction period by fusing multi-source monitoring data according to claim 4, characterized in that: When performing inverse analysis of the slope material parameters, use the obtained slope deformation data as feedback information, and through the particle swarm optimization algorithm or genetic algorithm or simulated annealing algorithm, combined with the backpropagation neural network, perform inverse analysis of the slope material parameters to obtain the inverse analysis result of the slope material parameters for the current excavation.

6. The method for dynamically analyzing the stability of a slope during the construction period by fusing multi-source monitoring data according to claim 5, characterized in that: When performing inverse analysis of the slope material parameters, use the uncertainty evaluation based on the Gaussian process to optimize the trained backpropagation neural network.

7. The method for dynamically analyzing the stability of a slope during the construction period by fusing multi-source monitoring data according to claim 6, characterized in that: When conducting numerical simulation, based on the updated structure of the slope numerical model and the inverse analysis result of the material parameters, use the finite element method or finite difference method or discrete element method to conduct numerical simulation to calculate the stability of the slope during the construction period before and after the corresponding construction stage.

8. The method for dynamically analyzing the stability of a slope during the construction period by fusing multi-source monitoring data according to any one of claims 1-7, characterized in that: The dynamic stability analysis system of the slope during the construction period for realizing the method for dynamically analyzing the stability of the slope during the construction period includes: Digital modeling module: used for carrying out digital twin modeling of the slope during the construction period; Monitoring data acquisition module: collecting the real-time monitoring data and construction progress of the slope during the construction period; Model structure update module: Dynamically update the slope numerical model structure according to the real-time monitoring data and construction progress of the slope during the construction period; Model parameter update module: Update the material parameters of the model according to the results of the inversion of the monitoring data parameters; BPNN optimization module: Used to optimize the BPNN by methods such as incremental learning, full-scale retraining, and uncertainty evaluation based on Gaussian processes; Stability analysis module: Used to perform numerical simulation and analyze the stability of the slope during the construction period before and after the corresponding construction stage by inverting the slope material parameters based on the dynamically updated slope numerical model.

Images