Slope disaster early warning method and system based on multi-domain perception analysis

Abstract

The present invention discloses a slope disaster early warning method and system based on multivariate and multi-domain perception analysis, which relates to the field of data processing technology, including generating the influence coefficient of traffic flow on the tunnel slope system according to the traffic data in the tunnel and the relevant multivariate data of the tunnel slope, and describing the influence degree of the traffic situation in the tunnel on the stability of the tunnel slope system. The analysis reflects the relevant data to determine the key position information and non-key position information in the three-dimensional structural position model of the tunnel slope system, providing a basis for the establishment of subsequent numerical models. The numerical model of the tunnel slope system is constructed according to the key position information, non-key position information and the influence coefficient of traffic flow on the tunnel slope system of the three-dimensional structural position model of the tunnel slope system, and the early warning value of the tunnel slope system disaster is analyzed, so as to establish a more accurate numerical model of the tunnel slope system, improve the timeliness and adaptability of the slope disaster early warning, and ensure the safety of the tunnel slope system.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a slope disaster early warning method and system based on multivariate and multi-domain perception analysis. Background Art

[0002] The tunnel slope system refers to a complex geological structure system composed of tunnel engineering and slope engineering. In this system, the tunnel is usually an underground passage formed by excavating a mountain or underground rock, and the slope is the inclined surface of the mountain or rock. The tunnel and the slope interact and influence each other, forming the tunnel slope system.

[0003] Traditional slope monitoring methods have problems such as limited monitoring range, low frequency, and high maintenance costs, which make it difficult to meet the safety needs of modern engineering projects. Therefore, it is particularly urgent to develop an intelligent monitoring and early warning system that can monitor slope changes in real time, automatically, and remotely, and warn of slope disasters in advance. This solution integrates sensor technology, communication technology, data processing technology, and artificial intelligence technology to achieve continuous and accurate monitoring of parameters such as slope displacement, cracks, and groundwater levels. It also evaluates the situation of the tunnel slope system through multi-domain perception analysis, provides timely and accurate early warning information, and provides strong technical support for disaster prevention and mitigation of slope projects.

[0004] In the prior art, tunnel slope disasters are monitored through vibration data. However, when a disaster occurs, the vibration data will be abnormal, resulting in poor timeliness and adaptability of slope disaster warning, and the safe operation of the tunnel slope cannot be guaranteed.

[0005] Therefore, how to improve the timeliness and adaptability of slope disaster warning is a technical problem that needs to be solved. Summary of the invention

[0006] The purpose of the present invention is to solve the problems of poor timeliness and low adaptability of slope disaster warning in the prior art, and propose a slope disaster warning method based on multivariate and multi-domain perception analysis, which is applied to the geological structure system analysis of tunnel slopes, and includes:

[0007] Obtaining the traffic flow data in the tunnel and the relevant multivariate data of the tunnel slope under the tunnel slope system, and generating the influence coefficient of the traffic flow on the tunnel slope system according to the traffic flow data in the tunnel and the relevant multivariate data of the tunnel slope;

[0008] The geographical location related data and the reflection related data are screened out from the relevant multivariate data of the tunnel slope, and a three-dimensional structural position model of the tunnel slope system is established through the geographical location related data. The reflection related data are analyzed to determine the key position information and non-key position information in the three-dimensional structural position model of the tunnel slope system, and the key position information is marked on the three-dimensional structural position model of the tunnel slope system.

[0009] On the basis of the three-dimensional structural position model of the tunnel slope system, the numerical model of the tunnel slope system is constructed according to the key position information, non-key position information and the influence coefficient of the traffic flow on the tunnel slope system of the three-dimensional structural position model of the tunnel slope system.

[0010] The tunnel slope system's numerical model is used to analyze the early warning value of the tunnel slope system's disaster, and the disaster early warning value is used to realize the slope disaster early warning of the tunnel slope system;

[0011] Among them, the relevant data include stress data, structural data, geological data and slope deformation data.

[0012] In some embodiments of the present application, the influence coefficient of the traffic flow on the tunnel slope system is generated based on the traffic flow data in the tunnel and the relevant multivariate data of the tunnel slope, including:

[0013] The traffic data in the tunnel includes traffic volume, vehicle load, tunnel vibration, vehicle driving information and vehicle information;

[0014] According to the relevant multivariate data of vehicle flow, vehicle load, tunnel vibration, vehicle driving information and vehicle information, as well as tunnel slope, the influence of dynamic load on tunnel slope system, the influence of tunnel vibration effect on tunnel slope system and the influence of drainage system pressure on tunnel slope system are calculated respectively;

[0015] The influence coefficient of traffic flow on the tunnel slope system is generated based on the influence of dynamic load on the tunnel slope system, the influence of tunnel vibration effect on the tunnel slope system, and the influence of drainage system pressure on the tunnel slope system.

[0016] ;

[0017] in, is the influence coefficient of traffic flow on the tunnel slope system, is the conversion constant, , , are the weights of the influence of dynamic load on tunnel slope system, the influence of tunnel vibration effect on tunnel slope system, and the influence of drainage system pressure on tunnel slope system. , , They are the influence values ​​of dynamic load on tunnel slope system, the influence values ​​of tunnel vibration effect on tunnel slope system, and the influence values ​​of drainage system pressure on tunnel slope system.

[0018] In some embodiments of the present application, a three-dimensional structural position model of a tunnel slope system is established by using geographic location related data, including:

[0019] Perform data cleaning, data format conversion, and data difference preprocessing on geographic location related data;

[0020] The preprocessed geographic location related data is imported into the 3D modeling software, and the terrain surface layer and geological layer are created respectively. The terrain features are added to the terrain surface layer, the geological features are added to the geological layer, the tunnel slope is created and the support structure is added, so as to generate a visual 3D structural position model of the tunnel slope system.

[0021] In some embodiments of the present application, the analysis reflects the relevant data to determine the key position information and non-key position information in the three-dimensional structural position model of the tunnel slope system, including:

[0022] The volume of the tunnel slope system is calculated by using the structural data and the three-dimensional structural position model of the tunnel slope system, and the three-dimensional structural position model is divided into multiple slope areas according to the volume of the tunnel slope system;

[0023] The slope area is divided into a slope area with known stress data and a slope area with unknown stress data according to the presence or absence of stress data in each slope area, the geographical features of each slope area are extracted, and the slope areas with known stress data similar to the slope areas with unknown stress data are screened according to the geographical features, and recorded as similar slope areas with known stress data, and the slope areas with known stress data within a preset distance near the slope areas with unknown stress data are recorded as adjacent slope areas with known stress data;

[0024] If, for a slope region with the same unknown stress data, the corresponding slope region with similar known stress data and the adjacent slope region with known stress data are the same slope region, stress data interpolation processing is performed on the slope region with the unknown stress data according to the stress data of the slope region;

[0025] Otherwise, a first stress weight is assigned to the slope region of the unknown stress data by the distance between the slope region of the unknown stress data and the corresponding adjacent slope region of the known stress data, a second stress weight is assigned to the slope region of the similar known stress data by the similarity of the geographical features between the slope region of the unknown stress data and the corresponding similar slope region of the known stress data, and the stress data of the slope region of the unknown stress data is determined by combining the first stress weight, the second stress weight, the stress data of the slope region of the similar known stress data and the stress data of the adjacent slope region of the known stress data;

[0026] The stress data, geological data and slope deformation data of each slope area in the three-dimensional structural position model of the tunnel slope system are analyzed to determine the key position information and non-key position information.

[0027] In some embodiments of the present application, stress data, geological data and slope deformation data of each slope area in the three-dimensional structural position model of the tunnel slope system are analyzed to determine key position information and non-key position information, including:

[0028] According to the geological data and slope deformation data of each slope area, the potential sliding surface area and the geological layer junction area are identified respectively, and the thresholds of the stress characteristics corresponding to the potential sliding surface area and the geological layer junction area are set. The stress characteristics of each slope area are extracted based on the stress data, and each stress characteristic is normalized.

[0029] Generate a first stress reflection index according to each stress characteristic of the potential sliding surface area or the geological layer boundary area;

[0030] ;

[0031] in, It is the first stress reflection indicator in the potential sliding surface area or the boundary area of ​​geological layers. is the number of stress characteristics, For the The reflection weight of each stress feature, , Respectively The actual value and threshold of each stress feature, Indicates is a function of the difference between the actual value of a stress feature and the threshold value, is the maximum value of the function of the difference between the actual value and the threshold value in the stress feature, is the minimum value of the function of the difference between the actual value and the threshold value in the stress feature, is a preset constant;

[0032] The slope areas other than the potential sliding surface area and the boundary area of ​​the geological layer are recorded as other slope areas, and the second stress reflection index of each other slope area is calculated. Some slope areas are screened out as key positions in the other slope areas through the second stress reflection index, and the boundary area of ​​the potential sliding surface area and the geological layer is taken as the key position;

[0033] The geographical coordinates, the first stress reflection index, and the second stress reflection index of the key position are used as key position information;

[0034] The geographical coordinates and second stress reflection indexes of other slope areas outside the key positions are regarded as non-key position information.

[0035] In some embodiments of the present application, a numerical model of a tunnel slope system is constructed based on key position information, non-key position information and the influence coefficient of vehicle flow on the tunnel slope system of a three-dimensional structural position model of the tunnel slope system, including:

[0036] The numerical modeling process of the tunnel slope system includes two parts: meshing and solver parameter setting;

[0037] The grid division is completed through the key position information and non-key position information, and the solver parameter setting is completed by combining the key position information and the influence coefficient of the vehicle flow on the tunnel slope system, so as to construct a numerical model of the tunnel slope system.

[0038] In some embodiments of the present application, grid division is completed through key position information, including:

[0039] The key position information and the non-key position information are matched with the first corresponding relationship and the second corresponding relationship respectively, and the grid density of each key position and non-key position is determined by the first stress reflection index, the second stress reflection index, the first corresponding relationship and the second corresponding relationship. Each slope area on the three-dimensional structural position model of the tunnel slope system is gridded through the grid density.

[0040] In some embodiments of the present application, the solver parameter setting is completed by combining key position information and the influence coefficient of vehicle flow on the tunnel slope system, including:

[0041] The model complexity level is determined according to the first stress reflection index, the second stress reflection index and the influence coefficient of traffic flow on the tunnel slope system at the key position;

[0042] ;

[0043] in, is the model complexity level, is the influence coefficient of traffic flow on the tunnel slope system, , are the conversion coefficients corresponding to the first stress reflection index and the second stress reflection index, , are the quantities corresponding to the first stress reflection index and the second stress reflection index, respectively. , are the weights corresponding to the first stress reflection index and the second stress reflection index, respectively. , They are the first stress reflection index and the second stress reflection index respectively. is a preset constant, [] is a rounding symbol;

[0044] Different model complexity levels correspond to different solver parameter interval values.

[0045] Correspondingly, the present application also provides a slope disaster early warning system based on multivariate and multi-domain perception analysis, which is applied to the geological structure system analysis of tunnel slopes, including:

[0046] An evaluation module is used to obtain the traffic flow data in the tunnel and the relevant multivariate data of the tunnel slope under the tunnel slope system, and generate the influence coefficient of the traffic flow on the tunnel slope system according to the traffic flow data in the tunnel and the relevant multivariate data of the tunnel slope;

[0047] A distinguishing module is used to select geographical location related data and reflection related data from the relevant multivariate data of the tunnel slope, establish a three-dimensional structural position model of the tunnel slope system through the geographical location related data, analyze the reflection related data to determine the key position information and non-key position information in the three-dimensional structural position model of the tunnel slope system, and mark the key position information on the three-dimensional structural position model of the tunnel slope system;

[0048] A construction module is used to construct a numerical model of the tunnel slope system based on the three-dimensional structural position model of the tunnel slope system and according to key position information, non-key position information and the influence coefficient of vehicle flow on the tunnel slope system of the three-dimensional structural position model of the tunnel slope system;

[0049] The early warning module is used to analyze the early warning value of the disaster of the tunnel slope system through the numerical model of the tunnel slope system, and to realize the slope disaster early warning of the tunnel slope system through the early warning value of the disaster.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] 1. The influence coefficient of traffic flow on the tunnel slope system is generated based on the traffic flow data in the tunnel and the relevant multivariate data of the tunnel slope, which describes the influence of the traffic flow in the tunnel on the stability of the tunnel slope system. The analysis reflects the relevant data to determine the key position information and non-key position information in the three-dimensional structural position model of the tunnel slope system, providing a basis for the establishment of subsequent numerical models and improving the reliability of modeling.

[0052] 2. Construct a numerical model of the tunnel slope system based on the key position information, non-key position information and the influence coefficient of traffic flow on the tunnel slope system of the three-dimensional structural position model of the tunnel slope system, analyze the warning value of the tunnel slope system disaster, and establish a more accurate numerical model of the tunnel slope system to improve the timeliness and adaptability of the slope disaster warning and ensure the safety of the tunnel slope system. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 It is a schematic diagram of the process of the slope disaster early warning method based on multivariate and multi-domain perception analysis proposed by the present invention;

[0054] Figure 2 This is a schematic diagram of the structure of the slope disaster early warning system based on multi-element and multi-domain perception analysis proposed in the present invention. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0056] Reference Figure 1 , a slope disaster early warning method based on multivariate and multi-domain perception analysis is applied to the geological structure system analysis of tunnel slopes (for slope terrain with tunnels). The tunnel slope system refers to a complex geological structure system composed of tunnel engineering and slope engineering. In this system, tunnels are usually underground passages formed by excavating mountains or underground rocks, while slopes are the inclined surfaces of mountains or rocks. Tunnels and slopes interact and influence each other, forming a tunnel slope system. The stability of the tunnel slope system is a dynamic process. Over time, geological conditions, hydrological conditions, engineering structures and other factors may change, thus affecting the stability of the tunnel slope system.

[0057] The program includes the following steps,

[0058] Step S101, obtaining the traffic flow data in the tunnel and the related multivariate data of the tunnel slope under the tunnel slope system, and generating the influence coefficient of the traffic flow on the tunnel slope system according to the traffic flow data in the tunnel and the related multivariate data of the tunnel slope.

[0059] In this embodiment, the traffic data in the tunnel mainly includes information such as traffic volume, vehicle speed, and vehicle weight. These data can be collected in real time through the traffic monitoring system in the tunnel. The traffic monitoring system usually includes equipment such as cameras, radars, and geomagnetic sensors, which can monitor and record the traffic conditions in the tunnel in real time. The relevant multivariate data of the tunnel slope include geological conditions, topography, rainfall, seismic activity, slope deformation monitoring data, etc.

[0060] In some embodiments of the present application, the influence coefficient of the traffic flow on the tunnel slope system is generated based on the traffic flow data in the tunnel and the relevant multivariate data of the tunnel slope, including:

[0061] The traffic data in the tunnel includes traffic volume, vehicle load, tunnel vibration, vehicle driving information and vehicle information;

[0062] According to the relevant multivariate data of vehicle flow, vehicle load, tunnel vibration, vehicle driving information and vehicle information, as well as tunnel slope, the influence of dynamic load on tunnel slope system, the influence of tunnel vibration effect on tunnel slope system and the influence of drainage system pressure on tunnel slope system are calculated respectively;

[0063] The influence coefficient of traffic flow on the tunnel slope system is generated based on the influence of dynamic load on the tunnel slope system, the influence of tunnel vibration effect on the tunnel slope system, and the influence of drainage system pressure on the tunnel slope system.

[0064] ;

[0065] in, is the influence coefficient of traffic flow on the tunnel slope system, is the conversion constant, , , are the weights of the influence of dynamic load on tunnel slope system, the influence of tunnel vibration effect on tunnel slope system, and the influence of drainage system pressure on tunnel slope system. , , They are the influence values ​​of dynamic load on tunnel slope system, the influence values ​​of tunnel vibration effect on tunnel slope system, and the influence values ​​of drainage system pressure on tunnel slope system.

[0066] In this embodiment, the impact of traffic on the tunnel slope system is mainly reflected in dynamic load, vibration effect, drainage system pressure, etc. Dynamic load refers to the impact of dynamic load generated when vehicles pass through the tunnel on the stability of the slope; vibration effect refers to the disturbance of the rock and soil of the slope caused by the vibration generated during the driving of the vehicle; drainage system pressure refers to the increase in traffic flow leading to an increase in the load of the tunnel drainage system, which in turn affects the stability of the slope.

[0067] In this embodiment, the size of the dynamic load is calculated using a dynamic model or an empirical formula according to the vehicle type, weight, speed, and geotechnical properties of the tunnel slope. Then, the direct impact of the dynamic load on the slope stability is evaluated by methods such as finite element analysis or limit equilibrium analysis. Vibration sensors are installed to monitor the vibration data generated when the vehicle passes, including vibration frequency, amplitude, etc. The impact of vibration data on the physical properties (such as strength, stiffness) and structural characteristics (such as cracks and joints) of the slope rock and soil is analyzed. The impact of vibration effects on slope stability is quantified using vibration engineering principles or experimental data. The flow, pressure and other data of the tunnel drainage system are monitored to analyze the impact of increased traffic on the drainage system load. Considering factors such as water level rise and increased seepage that may be caused by the increase in the drainage system load, the potential threats of these factors to slope stability are evaluated. Using relevant knowledge such as hydrology and soil mechanics, the impact of drainage system pressure on slope stability is quantified.

[0068] Step S102, screen out geographical location related data and reflection related data from the relevant multivariate data of the tunnel slope, establish a three-dimensional structural position model of the tunnel slope system through the geographical location related data, analyze the reflection related data to determine the key position information and non-key position information in the three-dimensional structural position model of the tunnel slope system, and mark the key position information on the three-dimensional structural position model of the tunnel slope system.

[0069] In this embodiment, geographical location related data, such as longitude and latitude, elevation, topography, etc., are screened out from the relevant multivariate data of the tunnel slope. These data are the basis for establishing a three-dimensional structural position model. Reflection related data refers to data that can reflect the stability of the tunnel slope system (stress data, structural data, geological data, slope deformation data, etc.). Critical positions and non-critical positions are screened out, and critical positions refer to positions where natural instability factors exist, such as sliding surfaces or junctions of multiple geological layers. Among them, reflection related data include stress data, structural data, geological data, and slope deformation data.

[0070] In some embodiments of the present application, a three-dimensional structural position model of a tunnel slope system is established by using geographic location related data, including:

[0071] Perform data cleaning, data format conversion, and data difference preprocessing on geographic location related data;

[0072] The preprocessed geographic location related data is imported into the 3D modeling software, and the terrain surface layer and geological layer are created respectively. The terrain features are added to the terrain surface layer, the geological features are added to the geological layer, the tunnel slope is created and the support structure is added, so as to generate a visual 3D structural position model of the tunnel slope system.

[0073] In this embodiment, a 3D modeling software suitable for modeling the tunnel slope system is selected, such as ArcGIS, AutoCAD Civil 3D, Bentley MicroStation, etc. These softwares usually have powerful terrain processing, 3D modeling and visualization functions.

[0074] In some embodiments of the present application, the analysis reflects the relevant data to determine the key position information and non-key position information in the three-dimensional structural position model of the tunnel slope system, including:

[0075] The volume of the tunnel slope system is calculated by using the structural data and the three-dimensional structural position model of the tunnel slope system, and the three-dimensional structural position model is divided into multiple slope areas according to the volume of the tunnel slope system;

[0076] The slope area is divided into a slope area with known stress data and a slope area with unknown stress data according to the presence or absence of stress data in each slope area, the geographical features of each slope area are extracted, and the slope areas with known stress data similar to the slope areas with unknown stress data are screened according to the geographical features, and recorded as similar slope areas with known stress data, and the slope areas with known stress data within a preset distance near the slope areas with unknown stress data are recorded as adjacent slope areas with known stress data;

[0077] If, for a slope region with the same unknown stress data, the corresponding slope region with similar known stress data and the adjacent slope region with known stress data are the same slope region, stress data interpolation processing is performed on the slope region with the unknown stress data according to the stress data of the slope region;

[0078] Otherwise, a first stress weight is assigned to the slope region of the unknown stress data by the distance between the slope region of the unknown stress data and the corresponding adjacent slope region of the known stress data, a second stress weight is assigned to the slope region of the similar known stress data by the similarity of the geographical features between the slope region of the unknown stress data and the corresponding similar slope region of the known stress data, and the stress data of the slope region of the unknown stress data is determined by combining the first stress weight, the second stress weight, the stress data of the slope region of the similar known stress data and the stress data of the adjacent slope region of the known stress data;

[0079] The stress data, geological data and slope deformation data of each slope area in the three-dimensional structural position model of the tunnel slope system are analyzed to determine the key position information and non-key position information.

[0080] In this embodiment, in the tunnel slope system, the stress data of some areas are inconvenient to measure directly. Here, the slope area with unknown stress data is interpolated (stress data filling) according to the slope area with known stress data. And the slope area with known stress data similar to the slope area with unknown stress data is screened by geographical features (topography, geological structure, geological layer intersection, etc.), and the similarity can be described by a similar Pearson correlation coefficient.

[0081] In this embodiment, the interpolation processing is divided into two cases. One is that the similar and adjacent regions are the same, and the nearest neighbor interpolation processing is performed according to the stress data of the region. The other is that the similar and adjacent regions are not the same, and the first stress weight and the second stress weight are respectively assigned according to the similarity and the distance, and the weighted interpolation processing is performed by the two weights.

[0082] In some embodiments of the present application, stress data, geological data and slope deformation data of each slope area in the three-dimensional structural position model of the tunnel slope system are analyzed to determine key position information and non-key position information, including:

[0083] According to the geological data and slope deformation data of each slope area, the potential sliding surface area and the geological layer junction area are identified respectively, and the thresholds of the stress characteristics corresponding to the potential sliding surface area and the geological layer junction area are set. The stress characteristics of each slope area are extracted based on the stress data, and each stress characteristic is normalized.

[0084] Generate a first stress reflection index according to each stress characteristic of the potential sliding surface area or the geological layer boundary area;

[0085] ;

[0086] in, It is the first stress reflection indicator in the potential sliding surface area or the boundary area of ​​geological layers. is the number of stress characteristics, For the The reflection weight of each stress feature, , Respectively The actual value and threshold of each stress feature, Indicates is a function of the difference between the actual value of a stress feature and the threshold value, is the maximum value of the function of the difference between the actual value and the threshold value in the stress feature, is the minimum value of the function of the difference between the actual value and the threshold value in the stress feature, is a preset constant;

[0087] The slope areas other than the potential sliding surface area and the boundary area of ​​the geological layer are recorded as other slope areas, and the second stress reflection index of each other slope area is calculated. Some slope areas are screened out as key positions in the other slope areas through the second stress reflection index, and the boundary area of ​​the potential sliding surface area and the geological layer is taken as the key position;

[0088] The geographical coordinates, the first stress reflection index, and the second stress reflection index of the key position are used as key position information;

[0089] The geographical coordinates and second stress reflection indexes of other slope areas outside the key positions are regarded as non-key position information.

[0090] In this embodiment, slope deformation monitoring data: deformation monitoring data such as displacement, settlement, and crack expansion of the slope are collected. These data can be obtained through monitoring equipment such as GPS, total station, and inclinometer. And set the thresholds of the stress characteristics corresponding to the potential sliding surface area and the geological layer junction area. Here, the threshold of the stress characteristics is set according to the specific conditions of the sliding surface area and the specific conditions of the geological layer junction area. The sliding surface is an interface formed between the landslide body and the immovable body (parent body) when it moves, and slides down along it. This interface is called a sliding surface, or sliding surface for short. The sliding surface is usually steep at the top and gentle at the bottom, approximately in the shape of an arc, and is one of the important components of the landslide landform. Carefully analyze the geological exploration report (geological data) to identify the distribution and junction of different geological layers. Pay special attention to the junction between the weak layer and the hard layer, as these locations are often the key parts of slope instability.

[0091] In this embodiment, stress characteristics include stress magnitude, stress angle, stress concentration degree and stress change, etc. Indicates The function of the difference between the actual value of a stress characteristic and the threshold value is to convert the difference between the two into an impact on stability. It represents the correction of the average value of the maximum difference and the minimum difference on the integrated influence. The second stress reflection index is similar, not determined by the difference, but by the size of each stress feature.

[0092] Step S103, based on the three-dimensional structural position model of the tunnel slope system, a numerical model of the tunnel slope system is constructed according to key position information, non-key position information and the influence coefficient of vehicle flow on the tunnel slope system of the three-dimensional structural position model of the tunnel slope system.

[0093] In this embodiment, the key position information and non-key position information are used to help realize the numerical modeling process. The complete process of the numerical model of the tunnel slope system includes several steps such as geometric modeling (three-dimensional structure position model), meshing, material property definition, boundary condition limitation and solver parameter (such as time step, number of iterations, etc.) setting.

[0094] The quality of meshing has a great influence on the accuracy of simulation results. Usually, adaptive meshing technology is needed to automatically adjust the mesh density according to the stress situation to improve the calculation efficiency and accuracy.

[0095] In some embodiments of the present application, a numerical model of a tunnel slope system is constructed based on key position information, non-key position information and the influence coefficient of vehicle flow on the tunnel slope system of a three-dimensional structural position model of the tunnel slope system, including:

[0096] The numerical modeling process of the tunnel slope system includes two parts: meshing and solver parameter setting;

[0097] The grid division is completed through the key position information and non-key position information, and the solver parameter setting is completed by combining the key position information and the influence coefficient of the vehicle flow on the tunnel slope system, so as to construct a numerical model of the tunnel slope system.

[0098] In some embodiments of the present application, grid division is completed through key position information, including:

[0099] The key position information and the non-key position information are matched with the first corresponding relationship and the second corresponding relationship respectively, and the grid density of each key position and non-key position is determined by the first stress reflection index, the second stress reflection index, the first corresponding relationship and the second corresponding relationship. Each slope area on the three-dimensional structural position model of the tunnel slope system is gridded through the grid density.

[0100] In this embodiment, in the first corresponding relationship and the second corresponding relationship, different first stress reflection indicators and second stress reflection indicators correspond to different grid densities, and the density of the grid should be based on the stress conditions in the tunnel slope system. In areas with higher stress reflection indicators, the grid density should be appropriately increased to improve the calculation accuracy; while in areas with lower stress reflection indicators, the grid density can be appropriately reduced to improve the calculation efficiency.

[0101] In some embodiments of the present application, the solver parameter setting is completed by combining key position information and the influence coefficient of vehicle flow on the tunnel slope system, including:

[0102] The model complexity level is determined according to the first stress reflection index, the second stress reflection index and the influence coefficient of traffic flow on the tunnel slope system at the key position;

[0103] ;

[0104] in, is the model complexity level, is the influence coefficient of traffic flow on the tunnel slope system, , are the conversion coefficients corresponding to the first stress reflection index and the second stress reflection index, , are the quantities corresponding to the first stress reflection index and the second stress reflection index, respectively. , are the weights corresponding to the first stress reflection index and the second stress reflection index, respectively. , They are the first stress reflection index and the second stress reflection index respectively. is a preset constant, [] is a rounding symbol;

[0105] Different model complexity levels correspond to different solver parameter interval values.

[0106] In this embodiment, the stress response index reflects the complexity of the calculation, and the model complexity level is determined by combining the influence coefficient of the traffic flow on the tunnel slope system.

[0107] Step S104, analyzing the early warning value of the disaster of the tunnel slope system through the numerical model of the tunnel slope system, and realizing the early warning of the slope disaster of the tunnel slope system through the early warning value of the disaster.

[0108] In this embodiment, the evolution mechanism of disasters in the tunnel slope system, such as the occurrence and development process of disasters such as landslides and collapses, can be studied in depth through the numerical model. This helps to identify the precursory characteristics of disasters and provide a scientific basis for early warning. Based on the analysis results of the numerical model, a disaster early warning system for the tunnel slope system can be constructed. The system can set early warning indicators and thresholds. When the monitoring data reaches or exceeds the threshold, the system automatically sends out an early warning signal to remind relevant personnel to take countermeasures.

[0109] Compared with the prior art, the present invention has the following beneficial effects:

[0110] 1. The influence coefficient of traffic flow on the tunnel slope system is generated based on the traffic flow data in the tunnel and the relevant multivariate data of the tunnel slope, which describes the influence of the traffic flow in the tunnel on the stability of the tunnel slope system. The analysis reflects the relevant data to determine the key position information and non-key position information in the three-dimensional structural position model of the tunnel slope system, providing a basis for the establishment of subsequent numerical models and improving the reliability of modeling.

[0111] 2. Construct a numerical model of the tunnel slope system based on the key position information, non-key position information and the influence coefficient of traffic flow on the tunnel slope system of the three-dimensional structural position model of the tunnel slope system, analyze the warning value of the tunnel slope system disaster, and establish a more accurate numerical model of the tunnel slope system to improve the timeliness and adaptability of the slope disaster warning and ensure the safety of the tunnel slope system.

[0112] Through the description of the above implementation methods, those skilled in the art can clearly understand that the present invention can be implemented by hardware, or by software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.), including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each implementation scenario of the present invention.

[0113] Correspondingly, the present application also provides a slope disaster early warning system based on multi-domain perception analysis, which is applied to the geological structure system analysis of tunnel slopes, such as Figure 2 As shown, including,

[0114] An evaluation module is used to obtain the traffic flow data in the tunnel and the relevant multivariate data of the tunnel slope under the tunnel slope system, and generate the influence coefficient of the traffic flow on the tunnel slope system according to the traffic flow data in the tunnel and the relevant multivariate data of the tunnel slope;

[0115] A distinguishing module is used to select geographical location related data and reflection related data from the relevant multivariate data of the tunnel slope, establish a three-dimensional structural position model of the tunnel slope system through the geographical location related data, analyze the reflection related data to determine the key position information and non-key position information in the three-dimensional structural position model of the tunnel slope system, and mark the key position information on the three-dimensional structural position model of the tunnel slope system;

[0116] A construction module is used to construct a numerical model of the tunnel slope system based on the three-dimensional structural position model of the tunnel slope system and according to key position information, non-key position information and the influence coefficient of vehicle flow on the tunnel slope system of the three-dimensional structural position model of the tunnel slope system;

[0117] The early warning module is used to analyze the early warning value of the disaster of the tunnel slope system through the numerical model of the tunnel slope system, and to realize the slope disaster early warning of the tunnel slope system through the early warning value of the disaster.

[0118] Those skilled in the art will appreciate that the accompanying drawings are merely schematic diagrams of a preferred implementation scenario, and the modules or processes in the accompanying drawings are not necessarily required for implementing the present invention.

[0119] Those skilled in the art will appreciate that the modules in the system in the implementation scenario can be distributed in the system of the implementation scenario according to the implementation scenario description, or can be changed accordingly and located in one or more systems different from the implementation scenario. The modules in the above implementation scenario can be combined into one module, or can be further split into multiple submodules.

[0120] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A slope disaster early warning method based on multivariate and multi-domain perception analysis is applied to the geological structure system analysis of tunnel slopes, which is characterized by: include, Obtaining the traffic flow data in the tunnel and the relevant multivariate data of the tunnel slope under the tunnel slope system, and generating the influence coefficient of the traffic flow on the tunnel slope system according to the traffic flow data in the tunnel and the relevant multivariate data of the tunnel slope; The geographical location related data and the reflection related data are screened out from the relevant multivariate data of the tunnel slope, and a three-dimensional structural position model of the tunnel slope system is established through the geographical location related data. The reflection related data are analyzed to determine the key position information and non-key position information in the three-dimensional structural position model of the tunnel slope system, and the key position information is marked on the three-dimensional structural position model of the tunnel slope system. On the basis of the three-dimensional structural position model of the tunnel slope system, the numerical model of the tunnel slope system is constructed according to the key position information, non-key position information and the influence coefficient of the traffic flow on the tunnel slope system of the three-dimensional structural position model of the tunnel slope system. The tunnel slope system's numerical model is used to analyze the early warning value of the tunnel slope system's disaster, and the disaster early warning value is used to realize the slope disaster early warning of the tunnel slope system; Among them, the relevant data include stress data, structural data, geological data and slope deformation data; in, Analyze and reflect relevant data to determine the key position information and non-key position information in the three-dimensional structural position model of the tunnel slope system, including: The volume of the tunnel slope system is calculated by using the structural data and the three-dimensional structural position model of the tunnel slope system, and the three-dimensional structural position model is divided into multiple slope areas according to the volume of the tunnel slope system; The slope area is divided into a slope area with known stress data and a slope area with unknown stress data according to the presence or absence of stress data in each slope area, the geographical features of each slope area are extracted, and the slope areas with known stress data similar to the slope areas with unknown stress data are screened according to the geographical features, and recorded as similar slope areas with known stress data, and the slope areas with known stress data within a preset distance near the slope areas with unknown stress data are recorded as adjacent slope areas with known stress data; If, for a slope region with the same unknown stress data, the corresponding slope region with similar known stress data and the adjacent slope region with known stress data are the same slope region, stress data interpolation processing is performed on the slope region with the unknown stress data according to the stress data of the slope region; Otherwise, a first stress weight is assigned to the slope region of the unknown stress data by the distance between the slope region of the unknown stress data and the corresponding adjacent slope region of the known stress data, a second stress weight is assigned to the slope region of the similar known stress data by the similarity of the geographical features between the slope region of the unknown stress data and the corresponding similar slope region of the known stress data, and the stress data of the slope region of the unknown stress data is determined by combining the first stress weight, the second stress weight, the stress data of the slope region of the similar known stress data and the stress data of the adjacent slope region of the known stress data; Analyze the stress data, geological data and slope deformation data of each slope area in the three-dimensional structural position model of the tunnel slope system to determine the key position information and non-key position information; Analyze the stress data, geological data and slope deformation data of each slope area in the three-dimensional structural position model of the tunnel slope system to determine the key position information and non-key position information, including, According to the geological data and slope deformation data of each slope area, the potential sliding surface area and the geological layer junction area are identified respectively, and the thresholds of the stress characteristics corresponding to the potential sliding surface area and the geological layer junction area are set. The stress characteristics of each slope area are extracted based on the stress data, and each stress characteristic is normalized. Generate a first stress reflection index according to each stress characteristic of the potential sliding surface area or the geological layer boundary area; ; in, It is the first stress reflection indicator in the potential sliding surface area or the boundary area of ​​geological layers. is the number of stress characteristics, For the The reflection weight of each stress feature, , Respectively The actual value and threshold of each stress feature, Indicates is a function of the difference between the actual value of a stress feature and the threshold value, is the maximum value of the function of the difference between the actual value and the threshold value in the stress feature, is the minimum value of the function of the difference between the actual value and the threshold value in the stress feature, is a preset constant; The slope areas other than the potential sliding surface area and the boundary area of ​​the geological layer are recorded as other slope areas, and the second stress reflection index of each other slope area is calculated. Some slope areas are screened out as key positions in the other slope areas through the second stress reflection index, and the boundary area of ​​the potential sliding surface area and the geological layer is taken as the key position; The geographical coordinates, the first stress reflection index, and the second stress reflection index of the key position are used as key position information; The geographical coordinates and second stress reflection indexes of other slope areas outside the key positions are regarded as non-key position information.

2. The slope disaster early warning method based on multivariate and multi-domain perception analysis according to claim 1 is characterized in that: The influence coefficient of traffic flow on the tunnel slope system is generated based on the traffic flow data in the tunnel and the relevant multivariate data of the tunnel slope. include, The traffic data in the tunnel includes traffic volume, vehicle load, tunnel vibration, vehicle driving information and vehicle information; According to the relevant multivariate data of vehicle flow, vehicle load, tunnel vibration, vehicle driving information and vehicle information, as well as tunnel slope, the influence of dynamic load on tunnel slope system, the influence of tunnel vibration effect on tunnel slope system and the influence of drainage system pressure on tunnel slope system are calculated respectively; The influence coefficient of traffic flow on the tunnel slope system is generated based on the influence of dynamic load on the tunnel slope system, the influence of tunnel vibration effect on the tunnel slope system, and the influence of drainage system pressure on the tunnel slope system. ; in, is the influence coefficient of traffic flow on the tunnel slope system, is the conversion constant, are the weights of the influence of dynamic load on tunnel slope system, the influence of tunnel vibration effect on tunnel slope system, and the influence of drainage system pressure on tunnel slope system. They are the influence values ​​of dynamic load on tunnel slope system, the influence values ​​of tunnel vibration effect on tunnel slope system, and the influence values ​​of drainage system pressure on tunnel slope system.

3. The slope disaster early warning method based on multivariate and multi-domain perception analysis according to claim 1 is characterized in that: The three-dimensional structural location model of the tunnel slope system is established through the geographical location related data, including: Perform data cleaning, data format conversion, and data difference preprocessing on geographic location related data; The preprocessed geographic location related data is imported into the 3D modeling software, and the terrain surface layer and geological layer are created respectively. The terrain features are added to the terrain surface layer, the geological features are added to the geological layer, the tunnel slope is created and the support structure is added, so as to generate a visual 3D structural position model of the tunnel slope system.

4. The slope disaster early warning method based on multivariate and multi-domain perception analysis according to claim 1 is characterized in that: The numerical model of the tunnel slope system is constructed based on the key position information, non-key position information and the influence coefficient of the traffic flow on the tunnel slope system of the three-dimensional structural position model of the tunnel slope system, including: The numerical modeling process of the tunnel slope system includes two parts: meshing and solver parameter setting; The grid division is completed through the key position information and non-key position information, and the solver parameter setting is completed by combining the key position information and the influence coefficient of the vehicle flow on the tunnel slope system, so as to construct a numerical model of the tunnel slope system.

5. The slope disaster early warning method based on multivariate and multi-domain perception analysis according to claim 4 is characterized in that: Meshing is done with key location information, including, The key position information and the non-key position information are matched with the first corresponding relationship and the second corresponding relationship respectively, and the grid density of each key position and non-key position is determined by the first stress reflection index, the second stress reflection index, the first corresponding relationship and the second corresponding relationship. Each slope area on the three-dimensional structural position model of the tunnel slope system is gridded through the grid density.

6. The slope disaster early warning method based on multivariate and multi-domain perception analysis according to claim 4 is characterized in that: Combine key location information and the influence coefficient of traffic flow on the tunnel slope system to complete the solver parameter setting, including: The model complexity level is determined according to the first stress reflection index, the second stress reflection index and the influence coefficient of traffic flow on the tunnel slope system at the key position; ; in, is the model complexity level, is the influence coefficient of traffic flow on the tunnel slope system, are the conversion coefficients corresponding to the first stress reflection index and the second stress reflection index, respectively. are the quantities corresponding to the first stress reflection index and the second stress reflection index, respectively. are the weights corresponding to the first stress reflection index and the second stress reflection index, respectively. They are the first stress reflection index and the second stress reflection index respectively. is the preset constant, is the rounding symbol; Different model complexity levels correspond to different solver parameter interval values.

7. The slope disaster early warning system based on multi-domain perception analysis is applied to the geological structure system analysis of tunnel slopes, which is characterized by: The system is used to implement the slope disaster early warning method based on multivariate and multi-domain perception analysis as described in any one of claims 1 to 6, wherein the system comprises: An evaluation module is used to obtain the traffic flow data in the tunnel and the relevant multivariate data of the tunnel slope under the tunnel slope system, and generate the influence coefficient of the traffic flow on the tunnel slope system according to the traffic flow data in the tunnel and the relevant multivariate data of the tunnel slope; A distinguishing module is used to select geographical location related data and reflection related data from the relevant multivariate data of the tunnel slope, establish a three-dimensional structural position model of the tunnel slope system through the geographical location related data, analyze the reflection related data to determine the key position information and non-key position information in the three-dimensional structural position model of the tunnel slope system, and mark the key position information on the three-dimensional structural position model of the tunnel slope system; A construction module is used to construct a numerical model of the tunnel slope system based on the three-dimensional structural position model of the tunnel slope system and according to key position information, non-key position information and the influence coefficient of vehicle flow on the tunnel slope system of the three-dimensional structural position model of the tunnel slope system; The early warning module is used to analyze the early warning value of the tunnel slope system disaster through the numerical model of the tunnel slope system, and to realize the slope disaster early warning of the tunnel slope system through the early warning value of the disaster.

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